Brookings Papers on Economic Activity: Spring 2009

David H. Romer and Justin Wolfers, Editors

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SWAGEL on the Treasury and the Financial Crisis CAMPBELL, SHILLER, and VICEIRA on Inflation-Indexed Bond Markets ROMER and ROMER on the Starve-the-Beast Hypothesis HAMILTON on Oil Prices and the Recession DI TELLA and MACCULLOCH on Popular Support for Capitalism

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Editors’ Summary

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PHILLIP SWAGEL

The Financial Crisis: An Inside View

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Comments by René M. Stulz and Luigi Zingales 64 General Discussion 75 JOHN Y. CAMPBELL, ROBERT J. SHILLER, and LUIS M. VICEIRA

Understanding Inflation-Indexed Bond Markets

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Comments by Frederic S. Mishkin and Jonathan H. Wright 121 General Discussion 135 CHRISTINA D. ROMER and DAVID H. ROMER

Do Tax Cuts Starve the Beast? The Effect of Tax Changes on Government Spending

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Comments by Steven J. Davis and Jeffrey A. Miron 201 General Discussion 211 JAMES D. HAMILTON

Causes and Consequences of the Oil Shock of 2007–08

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Comments by Alan S. Blinder and Lutz Kilian 262 General Discussion 278 RAFAEL DI TELLA and ROBERT MACCULLOCH

Why Doesn’t Capitalism Flow to Poor Countries? Comments by George A. Akerlof and Peter J. Klenow 322 General Discussion 330

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The Brookings Papers on Economic Activity publishes research in macroeconomics, broadly defined, with an emphasis on analysis that is empirical, focuses on real-world events and institutions, and is relevant to economic policy. Papers are presented and discussed at conferences twice each year, and the papers and discussant remarks are published in the journal several months later. The intended audience includes analysts from universities, research institutions, governments, and business. The subject matter encompasses all fields of economic inquiry relevant to macroeconomics, including business cycles; development and long-term growth; the distribution of income, wealth, and opportunities; financial markets; international capital and foreign exchange markets; fiscal and monetary policy; international trade; labor markets; the public sector and the welfare state; energy; the environment; political economy; regulation and industrial organization; education; health; and demography. The conference and the journal are based upon the work partially supported by the National Science Foundation under Grant No. 0752779 and the Alfred P. Sloan Foundation. The papers and discussant remarks reflect the views of the authors and not necessarily the views of the funding organizations or the staff members, officers, or trustees of the Brookings Institution. PURPOSE

Although most papers that appear in the Brookings Papers are solicited by the editors, the editors welcome submitted proposals. Editorial decisions are generally made about nine months in advance of each conference. Therefore, proposals should be received by December 1 for the following fall conference and by June 1 for the following spring conference. Proposals should be no more than five double-spaced pages and should be sent to [email protected]. CALL FOR PAPERS

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EDITORS, PANEL ADVISERS, AND STAFF FOR THE EIGHTY-SEVENTH CONFERENCE

Daron Acemoglu Massachusetts Institute of Technology George A. Akerlof University of California, Berkeley Alan S. Blinder Princeton University Leah Platt Boustan University of California, Los Angeles John Y. Campbell Harvard University Steven J. Davis University of Chicago Rafael Di Tella Harvard University William G. Gale Brookings Institution Robert E. Hall Stanford University James D. Hamilton University of California, San Diego Lutz Kilian University of Michigan Peter J. Klenow Stanford University Robert MacCulloch Imperial College, London Jeffrey A. Miron Harvard University Frederic S. Mishkin Columbia University Kevin M. Murphy University of Chicago George L. Perry Brookings Institution Christina D. Romer University of California, Berkeley David H. Romer University of California, Berkeley Robert J. Shiller Yale University René M. Stulz Ohio State University Phillip Swagel Georgetown University Luis M. Viceira Harvard University Justin Wolfers University of Pennsylvania Michael Woodford Columbia University Jonathan H. Wright Johns Hopkins University Luigi Zingales University of Chicago Jean-Marie Callan Brookings Institution Rebecca N. Mintz Brookings Institution Michael Treadway Brookings Institution Lindsey B. Wilson Brookings Institution

GUESTS WHOSE WRITINGS OR COMMENTS APPEAR IN THIS ISSUE

Henry J. Aaron Brookings Institution Philippe Aghion Harvard University Richard N. Cooper Harvard University Janice C. Eberly Northwestern University Benjamin M. Friedman Harvard University Robert J. Gordon Northwestern University Carol Graham Brookings Institution and University of Maryland Caroline M. Hoxby Stanford University N. Gregory Mankiw Harvard University Ricardo A. M. R. Reis Columbia University Charles L. Schultze Brookings Institution Matthew D. Shapiro University of Michigan Betsey Stevenson University of Pennsylvania

Editors’ Summary THE BROOKINGS PANEL ON ECONOMIC ACTIVITY held its eightyseventh conference in Washington, D.C., on April 2 and 3, 2009. The conference occurred barely six months after the collapse of the investment bank Lehman Brothers, an event often used to date the transition from a largely conventional cyclical downturn, characterized by a strained financial system and mild recession, to a full-blown financial and economic crisis. In keeping with the Brookings Papers’ tradition of providing timely analysis of current economic events, three of the papers in this volume address the role of various factors in the initial downturn and the ensuing crisis, including the response of policymakers, the behavior of bond markets, and the role played by the oil market. The two remaining papers examine the impact of tax cuts on government spending, and the role of corruption in undermining popular support for market-oriented policies. In the first paper in this issue, Phillip Swagel provides an insider’s account of the policy debates as they unfolded in real time during his tenure as assistant secretary of the Treasury for economic policy in the last two years of the Bush administration. Swagel’s account is both a blow-byblow history of the policy response to the crisis and a lesson in economic realpolitik. He documents that the Treasury under Secretary Henry Paulson was quite aware of the fragility of the financial system as early as 2006, and indeed, interagency work was well under way to develop a strategy for dealing with a crisis should one occur. Swagel’s narrative provides insight into the constraints on the policy process that were not immediately apparent to many commentators. In particular, policymakers at the Treasury and at the Federal Reserve were unable to pursue a number of useful policy proposals simply because they and other government agencies lacked appropriate legal authority. Although Congress could have granted that authority, this raised the even vii

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larger concern of political constraints, which were particularly important in the context of a profound lack of trust between the executive and legislative branches. A recurrent theme of the paper is the sheer difficulty of getting Congress to respond under anything less than crisis conditions— and possibly even then—which delayed and diluted the eventual policy response. Swagel also highlights a third, more practical constraint: time. Policymakers had to make decisions rapidly, often with too little information, as financial markets collapsed around them. He reserves some constructive criticism for the role played by many academic macroeconomists throughout the ensuing public debate: their editorializing, in his view, appeared largely uninformed by the various constraints, rendering their advocacy often unhelpful and occasionally even counterproductive. At the same time, however, he faults the Treasury for doing a poor job of making its case to a skeptical public. In the second paper, John Campbell, Robert Shiller, and Luis Viceira present a thorough accounting of what has been learned from the first quarter-century of experience with inflation-indexed bonds in the United Kingdom and the first decade of experience in the United States. Yields on these bonds indicate a substantial and puzzling decline in long-term real interest rates from the 1990s through 2008. The volatility of these real rates was likewise unexpected, given that a key determinant, the marginal product of capital, can reasonably be presumed to be stable over time. Over the same period, movements in the prices of inflation-indexed bonds have come to be negatively correlated with movements in stock prices. The authors also find that seemingly very similar bonds can bear surprisingly different yields, with real U.S. and U.K. yields diverging at times by over 2 percentage points. Having documented these facts, the authors set out to explain them. They begin with the expectations theory of the term structure—the view that long-term real yields reflect current and expected future short-term real interest rates. As expected short-term real rates vary, so too does this long-run expectation. Using a simple econometric model to proxy for expectations about current and future short-term rates, the authors succeed in replicating some of the observed changes in long-term inflation-indexed bond yields. Even so, the actual yields are higher and more volatile than suggested by these expectations. This leads the authors to explore whether the yields include a positive risk premium, and if so, how it has varied through time. However, the fact that inflation-indexed bond prices are negatively correlated with equity prices suggests that the risk premium should be negative, reflecting the value of these bonds in portfolio diversification.

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Moreover, changes over time in this correlation can explain very little of the changes over time in real bond yields. Finally, the authors pay special attention both to the high yields on inflation-indexed bonds in the years following their introduction, and to the extraordinary volatility in these yields during the financial crisis in 2008. They conclude that institutional factors played an important role in both phenomena. Such “technical” factors are typically invoked as a euphemism for changes that cannot be otherwise explained, but the authors probe more deeply, finding that the current episode is “highly abnormal” and likely due to illiquidity, as financial institutions were forced to unwind large positions quickly and few of the usual buyers were able to absorb these large shifts. Despite the apparent pricing anomalies that they document, however, the authors conclude that inflation-indexed bonds provide both a useful investment instrument for many investors and a valuable financing tool for governments. In the third paper, Christina Romer and David Romer examine the “starve the beast” hypothesis. (In the interest of full disclosure, we note that this paper was commissioned before David Romer accepted the editorship of this journal, and that the editorial duties were handled by his coeditor.) This hypothesis, most closely identified with President Ronald Reagan and his advisers, holds that tax cuts today impel future reductions in government spending, and hence in the size of government. Some previous studies have found evidence tending to support the hypothesis, but Romer and Romer point to two inherent problems in testing it. One is that the observed correlation between tax cuts and changes in spending might reflect reverse causality, with changes (typically increases) in government spending priorities causing changes in taxation. The other is that some third factor, such as a slowing economy, might affect both taxes and spending, producing a spurious association between the two. To try to isolate the effect of tax changes on government spending, the authors rely on a narrative approach, poring over presidential speeches, congressional reports, and other documents to identify those legislated tax changes not tied to either spending changes or the state of the economy. This yields a set of tax changes that, they argue, can appropriately be used to test the starve-the-beast hypothesis. The most important of these are the 1948 tax cut that passed despite President Harry Truman’s veto, the KennedyJohnson tax cut in the mid-1960s, the Reagan tax cut in the early 1980s, and two tax cuts passed during President George W. Bush’s first term. In an exhaustive analysis of this more restricted set of tax changes, Romer and Romer find remarkably little evidence in favor of the starve-

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the-beast hypothesis. The tax cuts they identify are not followed by any systematic decrease in government spending relative to its previously expected path; indeed, there is some evidence of a tendency for spending to increase. Moreover, based on the documentary evidence, policymakers in the major episodes appeared largely unconcerned about the sufficiency of revenue when making their spending decisions. Instead, the subsequent budgetary adjustments in these episodes generally consisted of a combination of legislated tax increases and nonlegislated increases in revenue. The popular view that tax cuts are “sticky” and not easily undone, and thus, that they create strong pressures for reductions in spending, appears false. The fourth paper, by James Hamilton, returns to a theme to which he has already made major contributions: the macroeconomic consequences of oil price shocks. The price of oil has recently been spectacularly volatile, rising from $60 a barrel in mid-2007 to a high of $145 a barrel in mid-2008 before collapsing to $30 a barrel by the end of 2008. Hamilton notes that this oil shock differed sharply from previous disruptions. Whereas those could typically be traced to geopolitical developments, this time the rise in oil prices reflected the working of the price mechanism to reconcile stagnating worldwide production with rising demand, particularly from China. Because the price elasticity of demand for oil is low, it took a large price rise to bring the quantity demanded back into line with the quantity supplied. In Hamilton’s account, perhaps the most important “shock” during this period was to the received ideas of market participants, who learned that short-run demand is even more price inelastic than they had thought. This shock helps explain both why oil production did not rise in response to the growing demand and why oil inventories declined. The popular competing story—that speculators bid up the price of oil futures, driving up the spot price—faces the key difficulty that it suggests, counterfactually, that inventories should have been accumulating. Hamilton argues further that the subsequent collapse in oil prices in 2008 was too large to be explained by the global recession. Instead, it likely reflects forces similar to those that accounted for its rise, but in the opposite direction, as market players underestimated the medium-run price elasticity of oil demand. Hamilton shows that the oil price rise had quantitatively important effects in reducing both consumption spending—particularly on motor vehicles, and especially on domestically produced SUVs—and consumer sentiment in the United States. Marshalling an array of macroeconometric models, he shows that the oil shock explains a large part of the weak macroeconomic performance in late 2007 and much of 2008, and indeed

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that in the absence of the shock, this period would have been one of slow growth rather than outright recession. The final paper of this issue, by Rafael Di Tella and Robert MacCulloch, begins by documenting an important and previously overlooked stylized fact, namely, that capitalism is surprisingly uncommon around the world. Outside the major industrialized nations, heavy business regulation, leftist rhetoric, and pro-intervention beliefs are the norm. As the authors stress, this pattern is puzzling, given the strong evidence of the positive effects of market-oriented policies on economic growth and the enormous potential gains for poor countries, in particular, from adopting such policies. The authors propose an intriguing explanation of this seemingly perverse bias against capitalism in poor countries: that it reflects the response of their citizens to a corrupt business sector. Corruption, the authors argue, is seen in these countries as disproportionately the fault of business leaders who seek favors from government rather than the bureaucrats who grant them. Hence corruption undermines the legitimacy of business, leading voters to favor interventionist and anticapitalist policies as “punishment” against the business owners, who are broadly seen as having benefited unfairly from their favored position in society. The authors document that within a country, those individuals who perceive corruption to be more widespread tend to have more-interventionist beliefs. They also show that sharp increases in corruption within a country lead to a rise in left-wing voting. Anger is also found to be a more prevalent emotion in corrupt countries than elsewhere, and it is lessened by stricter regulation of business. We conclude this summary by noting the recent changes in the editorship of the Brookings Papers. Although this is the first volume with our names on the cover, the papers were selected by the previous editorial team of Douglas W. Elmendorf, N. Gregory Mankiw, and Lawrence H. Summers; William G. Gale handled much of the work during the transition to the new team. All four have our thanks for their work on this volume and throughout their tenures. We hope to build upon the outstanding foundation built not only by these editors but also by their predecessors, William C. Brainard, Arthur M. Okun, and George L. Perry. We will strive to ensure that the Brookings Papers continues to be a key source for original, timely, and substantive analysis of a wide array of pressing economic issues.

PHILLIP SWAGEL Georgetown University

The Financial Crisis: An Inside View ABSTRACT This paper reviews the policy response to the 2007–09 financial crisis from the perspective of a senior Treasury official at the time. Government agencies faced severe constraints in addressing the crisis: lack of legal authority for potentially helpful financial stabilization measures, a Congress reluctant to grant such authority, and the need to act quickly in the midst of a market panic. Treasury officials recognized the dangers arising from mounting foreclosures and worked to facilitate limited mortgage modifications, but going further was politically unacceptable because public funds would have gone to some irresponsible borrowers. The suddenness of Bear Stearns’ collapse in March 2008 made rescue necessary and led to preparation of emergency options should conditions worsen. The Treasury saw Fannie Mae and Freddie Mac’s rescue that summer as necessary to calm markets, despite the moral hazard created. After Lehman Brothers failed in September, the Treasury genuinely intended to buy illiquid securities from troubled institutions but turned to capital injections as the crisis deepened.

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his paper reviews the events associated with the credit market disruption that began in August 2007 and developed into a full-blown crisis in the fall of 2008. This is necessarily an incomplete history: events continued to unfold as I was writing it, in the months immediately after I left the Treasury, where I served as assistant secretary for economic policy from December 2006 to the end of the George W. Bush administration on January 20, 2009. It is also necessarily a selective one: the focus is on key decisions made at the Treasury with respect to housing and financial markets policies, and on the constraints faced by decisionmakers at the Treasury and other agencies over this period. I examine broad policy matters and economic decisions but do not go into the financial details of specific transactions, such as those involving the government-sponsored enterprises (GSEs) and the rescue of the American International Group (AIG) insurance company. I first explain some constraints on the policy process—legal, political, and otherwise—that were perhaps not readily apparent to outsiders such as 1

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academic economists or financial market participants. These constraints ruled out several policy approaches that might have appeared attractive in principle, such as forcing lenders to troubled firms to swap their bonds for equity. I then proceed with a chronological discussion, starting with preparations taken at the Treasury in 2006 and moving on to policy proposals considered in the wake of the August 2007 lockup of the asset-backed commercial paper market. The main development following the events of August was a new focus on housing and in particular on foreclosure prevention, embodied in the Hope Now Alliance. The Treasury sought to have mortgage servicers (the firms that collect monthly payments on behalf of lenders) make economic decisions with respect to loan modifications—to modify loans when this was less costly than foreclosure. This approach involved no expenditure of public money, and it focused on borrowers who could avoid foreclosure through a moderate reduction in their monthly mortgage payment. People whose mortgage balance far exceeded the value of their home—so-called deeply underwater borrowers—would still have an incentive to walk away and allow their lender to foreclose. But political constraints bound tightly in addressing this situation, since there was little appetite in Congress for a program that would transparently reward “irresponsible” borrowers who had purchased homes they could never have hoped to afford. Even after the October 2008 enactment of the Emergency Economic Stabilization Act of 2008 (EESA) gave the Treasury the resources and authority to put public money into foreclosure avoidance, the need to husband limited resources against worsening financial sector problems ruled out undertaking a foreclosure avoidance program at the necessary scale until after the change in administrations in January 2009. The foreclosure avoidance initiative eventually implemented by the Obama administration in March 2009, which took the form of an interest rate subsidy, was a refinement of a proposal developed at the Treasury in October 2007. Returning to events on Wall Street, the paper picks up the chronology with the failure of Bear Stearns in early 2008, the rescue of the governmentsponsored enterprises (GSEs) Fannie Mae and Freddie Mac that summer, and the failures of the investment bank Lehman Brothers and AIG the week of September 14, 2008. The run on money market mutual funds in the wake of Lehman’s collapse led to a lockup of the commercial paper market and spurred the Treasury to seek from Congress a $700 billion fund—the Troubled Assets Relief Program (TARP)—with which to purchase illiquid assets from banks in order to alleviate uncertainty about

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financial institutions’ viability and restore market confidence. However, as market conditions continued to deteriorate even after the early-October enactment of EESA, the Treasury shifted from asset purchases to capital injections directly into banks, including the surviving large investment banks that had either become bank holding companies or merged with other banks. The capital injections, together with a Federal Deposit Insurance Corporation (FDIC) program to guarantee bank debt, eventually helped foster financial sector stability. Even in late 2008, however, continued market doubts about the financial condition of Citigroup and Bank of America led the Treasury and the Fed to jointly provide additional capital and “ring fence” insurance for some of the assets on these firms’ balance sheets. In effect, providing insurance through nonrecourse financing from the Fed meant that taxpayers owned much of the downside of these firms’ illiquid assets. The paper concludes with a brief discussion of several key lessons of the events of the fall of 2008. An essential insight regarding the policies undertaken throughout the fall is that providing insurance through nonrecourse financing is economically similar to buying assets—indeed, underpricing insurance is akin to overpaying for assets. But insurance is much less transparent than either asset purchases or capital injections, and therefore politically preferable as a means of providing subsidies to financial market participants. A second lesson is that maintaining public support is essential to allowing these transfers to take place. These two lessons appear to have informed the policies put into place in the first part of 2009.

I. Constraints on the Policy Process Legal constraints were omnipresent throughout the crisis, since the Treasury and other government agencies such as the Fed necessarily operate within existing legal authorities. Given these constraints, some steps that are attractive in principle turn out to be impractical in reality, two key examples being the notion of forcing investors to enter into debt-for-equity swaps to address debt overhangs, and that of forcing banks to accept government capital. These both run hard afoul of the constraint that there is no legal mechanism to make them happen. A lesson for academics is that any time they use the verb “force” (as in “The policy should be to force banks to do X or Y”), the next sentence should set forth the section of the U.S. legal code that allows that course of action. Otherwise the policy suggestion is of theoretical but not practical interest. Legal constraints bound in other ways as well, including with respect to modifications of loans.

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New legal authorities can be obtained through legislative action, but this runs hard into the political constraint: getting a bill through Congress is much easier said than done. This difficulty was especially salient in 2007 and 2008, the first two years after both chambers of Congress switched from Republican to Democratic leadership. A distrustful relationship between the congressional leadership and President Bush and his White House staff made 2007 an unconstructive year from the perspective of economic policy, although, ironically, it had the effect of making possible the rapid enactment of the early-2008 stimulus: Democratic leaders by then appeared to be eager to demonstrate that they could govern effectively. More legislative actions were taken in 2008 as the credit crisis worsened and the economy slowed, but political constraints remained a constant factor in the administration’s deliberations. Political constraints were an important factor in the reluctance at the Treasury to put forward proposals to address the credit crisis early in 2008. The options that later turned into the TARP were first written down at the Treasury in March 2008: buy assets, insure them, inject capital into financial institutions, or massively expand federally guaranteed mortgage refinance programs to improve asset performance from the bottom up. But we at the Treasury saw little prospect of getting legislative approval for any of these steps, including a massive program to avoid foreclosures. Legislative action would be possible only when Treasury Secretary Henry Paulson and Federal Reserve Chairman Ben Bernanke could go to Congress and attest that the crisis was at the doorstep, even though by then it could well be too late to head it off. Political constraints also affected the types of legislative authorities that could be requested in the first place, notably with regard to the initial conception of the TARP. Secretary Paulson truly meant to acquire troubled assets in order to stabilize the financial system when he and Chairman Bernanke met with congressional leaders on Thursday, September 18, 2008, to request a $700 billion fund for that purpose. One criticism of the initial “Paulson plan” is that it would have been better to inject capital into the system in the first place, since the banking system was undercapitalized, and asset purchases inject capital only to the extent that too high a price is paid. But Congress would never have approved a proposal to inject capital. House Republicans would have balked at voting to allow the government to buy a large chunk of the banking system, and Democrats would not have voted for such an unpopular bill without a reasonable number of Republican votes to provide political cover.

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A final constraint was simply time. Decisions had to be made rapidly in the context of a continuous cascade of market events. Certainly this was the case by the week of September 14, 2008, when Lehman Brothers and AIG both failed; a major money market mutual fund, the Reserve Fund, “broke the buck,” allowing its value per share to fall below the $1 par level; a panicked flight from money market mutual funds ensued; and then the commercial paper market locked up, with major industrial companies that relied on commercial paper issuance telling the Treasury that they faced imminent liquidity problems. This was the situation in which the TARP was proposed, and the decisions and actions surrounding its creation must be understood in the context of the events of that week. Time constraints meant that sometimes blunt actions were taken, notably the guarantees on the liabilities of AIG, of money market mutual funds, and several weeks later of banks’ qualified new senior debt issues. A blanket guarantee is certainly not a preferred policy approach, but in the face of broad runs on the financial system, guarantees were needed to deal with the problems in real time. Other impediments to decisionmaking were self-imposed hurdles rather than external constraints. Notable among these was chronic disorganization within the Treasury itself, a broadly haphazard policy process within the administration, and sometimes strained relations between the Treasury and White House staff that made it difficult to harness the full energies of the administration in a common direction. To many observers, the Treasury also lacked an appreciation that the rationales behind its actions and decisions were not being explained in sufficient detail; without understanding the motivation for each decision, outside observers found it difficult to anticipate what further steps would be taken as events unfolded. Part of the problem was simply the difficulty of keeping up, of providing adequate explanation in real time as decisions were being made rapidly, while another part was a lack of trust in the Treasury and the administration. Many journalists and other observers did not believe simple (and truthful) explanations for actions. For example, the switch from asset purchases to capital injections really was a response to market developments. It was too easy—and wrong—to believe that Secretary Paulson was looking out for the interests of Wall Street, or even of a particular firm, rather than the interests of the nation as he saw them. Whatever the reason, such communication gaps led to natural skepticism as the Treasury’s approach to the crisis evolved in the fall. There were valid reasons behind the initial plan to purchase assets (even if many people found them inadequate), and valid

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reasons for the switch to capital injections. But the insufficient explanations of these moves led to skepticism and growing hostility in Congress and beyond to the rescue plan as a whole. Notwithstanding these criticisms with regard to the Treasury, a paper such as this will inevitably be seen as defensive, if not outright self-serving. Since this is unavoidable, I simply acknowledge it up front. Other accounts of the credit crisis will come out in due course and can be correlated with the discussion here.

II. On the Verge of Crisis Secretary Paulson, on his arrival at the Treasury in summer 2006, told Treasury staff that it was time to prepare for a financial system challenge. As he put it, credit market conditions had been so easy for so long that many market participants were not prepared for a financial shock with systemic implications. His frame of reference was the market dislocations of 1998 following the Russian debt default and the collapse of the hedge fund Long Term Capital Management (LTCM). Starting that summer, Treasury staff worked to identify potential financial market challenges and policy responses, both in the near term and over the horizon. The longer-range policy discussions eventually turned into the March 2008 Treasury Blueprint for a Modernized Financial Regulatory Structure. Possible near-term situations that were considered included sudden exogenous crises such as terror attacks, natural disasters, or massive power blackouts; marketdriven events such as the failure of a major financial institution, a large sovereign default, or huge losses at hedge funds; as well as slower-moving macroeconomic developments such as an energy price shock, a prolonged economic downturn that sparked wholesale corporate bankruptcies, or a large and disorderly movement in the exchange value of the dollar. These problems were not seen as imminent in mid- to late 2006. The focus at the Treasury was on risk mitigation beforehand and on preparing broad outlines of appropriate responses in the event that a crisis did develop, always recognizing that the details would vary with the situation. To help ensure smooth teamwork in the event of a problem, Secretary Paulson reinvigorated the President’s Working Group on Financial Markets (PWG), which had been formed after the October 1987 stock market crash. The PWG brought together senior officials from the Treasury, the Fed, the Securities and Exchange Commission (SEC), and the Commodities Futures Trading Commission (CFTC) to discuss financial and economic developments and potential problems. The heads of these agencies

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met regularly to discuss market developments, and interaction at the staff level was both frequent and routine. Secretary Paulson also talked regularly in both public and private settings about the need for financial institutions to prepare for an end to abnormally loose financial conditions. Treasury staff recognized that changes in financial markets since 1998 would affect the contours of any new financial crisis and the policy response. These developments generally had positive impacts in that they contributed to increased financial market efficiency, but they often increased complexity as well. Such developments included —Deeper international capital market integration. Tighter linkages between financial markets in different countries lowered financing costs for U.S. borrowers, given the low national saving rate and the need to import capital to fund spending. But under some views of the international financial architecture, capital market integration also contributed to the housing bubble that helped precipitate the crisis. —The rise of securitization. Financial assets of all types, including credit card debt, auto loans, and residential and commercial mortgages, were increasingly being packaged into ever more complex securities. Securitization reduced finance costs and contributed to stronger aggregate demand; it also allowed the risks of lending to be diversified more widely across market participants than if the loans had remained on bank balance sheets. These benefits, however, came with the downsides of increased complexity and diminished transparency. When problems with mortgage performance did emerge, the bundling of mortgages into securities made it difficult to gauge the distribution and magnitude of credit losses. —The growth of private pools of capital. Hedge funds and private equity firms were becoming increasingly important players. The rise of these nontraditional asset managers should in general increase the efficiency of financial markets: the presence in the market of asset management approaches that include both long and short positions rather than just long would be expected to improve liquidity and efficiency. But these funds tend to be nontransparent; indeed, calls for increased disclosure of their trading positions are at odds with the hedge fund business model. Particularly in Europe, hedge funds were seen as the source of the next financial markets crisis. In the event, many hedge funds suffered massive losses in 2007 and 2008, and their deleveraging certainly contributed to the downward spiral in asset markets. But hedge funds do not appear to have been the fundamental source of the problem. —The growth of financial derivatives. New financial instruments such as credit default swaps increased financial market efficiency by allowing mar-

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ket participants to better hedge the risks of underlying assets such as commodities, bonds, equities, or currencies. But these derivatives added complexity and reduced transparency and further facilitated increased leverage. By September 2008, worsening performance of securitized housing assets such as mortgage-backed securities (MBSs) led to rapid and massive deterioration of the balance sheets of firms such as AIG and Lehman. Derivatives also led to increased interconnectedness of markets, as the over-the-counter nature of credit default swaps and many repo (repurchase agreement) transactions meant that difficulties at financial institutions such as Bear Stearns, Lehman, and AIG could have broad impacts through their role as counterparties to these transactions. These considerations were to play important roles in decisions made throughout 2008 regarding the deployment of public funds to “bail out” particular institutions. Broadly speaking, these financial innovations were viewed at Treasury as fundamentally a good thing in that they added to the liquidity and efficiency of capital markets and made it easier for firms and investors to lay off risk. Even so, the concern was that it was not clear how the evolving financial system would perform under stress. Under Secretary of the Treasury for Domestic Finance Robert Steel talked to Treasury staff about the challenge of trying to figure out in advance how correlations between asset classes would change in a crisis. He pointed out that before the terror attacks of September 11, 2001, a reasonable way to diversify a real estate portfolio would have been to invest in high-rise office buildings in different cities, but that the returns on these investments suddenly became correlated in the wake of the attacks. The same would be likely to happen in a time of financial crisis: financial structures that had worked before would break down in unexpected ways. Finally, Secretary Paulson and Under Secretary Steel tried hard in the fall of 2006, but did not succeed, in getting a reform bill through Congress that would give the GSEs’ regulator, the Office of Federal Housing Enterprise Oversight (OFHEO), more power to limit the activities of the two major GSEs, Fannie Mae and Freddie Mac. The push on this issue came over opposition from some White House staff, who took the reasonable position that no deal on GSEs was better than one that appeared to strengthen these firms’ implicit government backing without fully empowering their regulator. My own introduction to the building credit bubble came at a talk I gave early in 2007 to a group of financial industry participants in commercial real estate—the firms that build, fund, and invest in office buildings, factories, shopping centers, and apartments. Participants told me that there was

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such incredible liquidity in the market that any project that could be dreamed up could be financed. Although this talk was alarming, economic indicators seemed to back it up: GDP growth had slowed in the second half of 2006 but looked to be strong again in 2007 (as it proved to be in the middle two quarters of the year), and the labor market upswing that had taken hold in mid-2003 remained in force. Indeed, Secretary Paulson’s public message was that growth had been unsustainably strong and that it would be no surprise to have a period of slower growth as the economy settled into a more normal pattern. By early 2007 we at the Treasury were well aware of the looming problems in housing, especially among subprime borrowers as foreclosure rates increased and subprime mortgage originators such as New Century went out of business. Under Secretary Steel took the lead in organizing a series of interagency meetings to discuss the situation. As part of this, he asked for forward-looking analysis on housing prices, home sales and starts, and foreclosure rates—how bad would it get and what would be the economic implications? Economists at the Treasury and the Fed separately did empirical work relating foreclosures to economic conditions such as the unemployment rate, housing prices, and past foreclosure rates. (The Fed work looked at a panel of pooled state data; the Treasury’s approach was a time-series model, looking at the nation as a whole and at key states with high or rising foreclosure rates: the Midwest, the Gulf Coast, and bubble states such as California and Florida.) At the Treasury, we then used Blue Chip forecasts for future economic data and ran a dynamic forecast of future foreclosures. The prediction we made at an interagency meeting in May 2007 was that foreclosure starts would remain elevated and the inventory of foreclosed homes would continue to build throughout 2007, but that the foreclosure problem would subside after a peak in 2008. What we at the Treasury missed was that our regressions did not use information on the quality of the underwriting of subprime mortgages in 2005, 2006, and 2007. This was something pointed out by staff from the FDIC, who had already (correctly) reported that the situation in housing was bad and getting worse and would have important implications for the banking system and the broader economy. As shown in figure 1, which is from the Fed’s July 2008 Monetary Policy Report to the Congress, default rates on subprime adjustable-rate mortgages (ARMs) originated in 2005, 2006, and early 2007 were substantially higher than in previous years, and the defaults were coming quickly, within months of origination. The problems were baked into the mortgage at origination in a way that they had not been before 2005; they were not a

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Figure 1. Cumulative Defaults on Subprime 2/28 Loans, by Year of Origination, 2001–07a Percent of total

2006

25

2005 20 2007 Average, 2001–04

15 10 5

6

12

24 18 30 Months since origination

36

42

48

Sources: Federal Reserve, Monetary Policy Report to the Congress, July 2008, p. 5. a. Monthly data; data for 2005–07 are incomplete. A 2/28 loan is a 30-year loan with a low fixed rate for the first 2 years and an adjustable rate for the remaining 28.

function of the cooling economy, except in the sense that the end of easy mortgage terms and the reversal of home price gains removed the possibility of refinancing for subprime borrowers. It is interesting to note as well that the default rates in figure 1 do not have an inflection point upward at the 24-month mark, when the interest rate typically adjusts upward in a “reset.” There was, however, a marked propensity for borrowers to refinance at the reset date. These facts further indicated that the problem in the 2005–07 loans was the initial underwriting, not the interest rate reset. It was not that these borrowers could not afford the higher interest rate after the reset—the rapid defaults suggested that borrowers could not afford the initial home payment, or perhaps (rationally) did not want to keep paying the monthly bill once the value of their home had declined below their mortgage balance.

III. August 2007: The Vacation of the Blackberry The initial moment for an urgent Treasury-wide response came in August 2007, when asset-backed commercial paper markets seized up as investors grew skeptical about the business model of banks’ off-balance-sheet struc-

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tured investment vehicles (SIVs), which relied on short-term funding to finance longer-term assets. Many Treasury officials, including myself, were not in Washington when this crisis broke. I was in Rehoboth Beach, Delaware, where up and down the boardwalk one could see as many Blackberrys being toted around as tubes of suntan lotion. Many papers have since examined the economic and financial factors that led to the crisis; Markus Brunnermeier (2009) provides a discussion. Within the Treasury, the financial market disruption was seen as the aftermath of twin credit and housing bubbles, with repricing of risk across asset classes and consequent deleveraging across financial institutions coming about as information on the poor underwriting quality in the past several years became more widely understood (this is discussed in detail by Gorton 2008), and as several financial institutions announced dismal results reflecting losses from subprime lending. Two main policy proposals aimed at calming the financial markets emerged from the August episode: the so-called Master Liquidity Enhancement Conduit (MLEC), or “Super SIV,” a common vehicle in which banks would hold their illiquid assets, and a mortgage information database that would provide individual loan-level information on the quality of underwriting and subsequent performance of mortgages, and thereby facilitate analysis of complex MBSs and their derivatives. Neither of these efforts came to fruition, although the American Securitization Forum (ASF) independently began to work on a mortgage database under the rubric of their “Project Restart.” A byproduct of the August credit meltdown that did come to fruition was the formation of the Hope Now Alliance aimed at reducing foreclosures. This is discussed further below. The idea behind the mortgage information database was to directly address the lack of transparency and information behind the August lockup of the markets for asset-backed securities. A database could be organized to provide market participants with loan-level information on mortgage origination and ongoing performance. The data would be anonymously tagged with an identification number akin to a CUSIP on a security. This could be done on a forward-looking basis for new mortgages as they were securitized into MBSs, or on a backward-looking basis for existing MBSs. The latter would be much more difficult: servicers were already overwhelmed by the volume of loan modification requests and did not want to be diverted by a backward-looking project. Investors could use the information in the database to analyze the performance of MBSs and collateralized debt obligations (CDOs) containing the mortgages, allowing analysis to pierce the complexity of these arrangements (as I put it in a speech in February

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2008). Ultimately, the database would allow investors to assess the performance of mortgages originated by particular firms or even particular loan officers. This would create a “reputational tail” so that originators would have a connection to the future performance of mortgages even after they had been offloaded from their books through securitization. This reputational tail could be a less intrusive alternative to the suggestion that lenders be required to keep a piece of any loans they originate—that they have “skin in the game.” A database could also help overcome the informational problem posed by second liens, which are often not visible to the servicer of the first mortgage and pose an obstacle to loan modification. What was surprising was that this database did not exist already—that investors in MBSs had not demanded the information from the beginning. With the freeze of the asset-backed commercial paper market leaving assets stuck in banks’ SIVs, officials in the Treasury’s Office of Domestic Finance developed the MLEC plan as a temporary “bridge” structure to give participating institutions time to reprice and reassess risk. The idea was that the value of the complex securities held by bank SIVs was not well understood and that it would be useful for institutions to hold their illiquid assets in a common pool until there was more clarity on performance. An orderly disposal of the illiquid assets, it was thought, would avoid banks having to sell off assets into a thin market at fire-sale prices. Under the MLEC proposal, banks would have agreed on a multilateral pricing mechanism for the illiquid assets and taken pro rata shares of a common pool, which would have then turned into something close to a buy-and-hold investment vehicle, with the intent being to unwind the portfolio as markets stabilized. The MLEC concept implicitly rested on the assumption that trading had ground to a halt because uncertainty about asset performance gave rise to a liquidity premium. The metaphor of choice was “mad cow disease”: investors could not tell which asset-backed securities were toxic, so they chose not to touch any of them. MLEC would have provided a breathing space under which conditions would return to some new “normal” (not a new bubble), and bid-ask spreads would have narrowed and trading naturally resumed. Of course, such a pause is of little use if the problem is fundamentally one of insufficient capital, not liquidity— as turned out to be the case. Officials in the Office of Domestic Finance brought together market participants at a Sunday meeting at the Treasury to discuss MLEC. The meeting and the whole MLEC concept were something of a mystery to many Treasury senior staff—including me. MLEC was seen within the Treasury and portrayed to the world as a private sector solution. Some doubtful

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banks, however, saw it as something being forced on them; indeed, a number of economists at investment banks wondered if the supposed utility of the idea in the first place rested upon a violation of the Modigliani-Miller theorem (meaning that they did not see the utility). MLEC never got off the ground; in the end, banks preferred to take the SIV assets back onto their balance sheets—thus demonstrating the tenuous nature of the off-balancesheet treatment in the first place. When the banks in the end chose to deal with the problem on their own, the MLEC episode looked to the world, and to many within the Treasury, like a basketball player going up in the air to pass without an open teammate in mind—a rough and awkward situation. Ironically, the Treasury bank rescue plan unveiled by the Obama administration in late March 2009 had elements of MLEC in that institutions are supposed to partner with the federal government to purchase pools of assets. That version, however, has the (huge) advantage of being able to fund the purchases through low-cost government financing, with taxpayers assuming much of the downside risk.

IV. Housing Policy and Foreclosure Avoidance Throughout 2007, staff at the Treasury and other government agencies prepared numerous analyses and memos on the situation in housing. There was a keen awareness of the serious problems facing households with subprime mortgages, and a rising concern that households with prime mortgages would soon exhibit a similar pattern of rising delinquencies and foreclosures. It was also clear that there were two types of housing problems. In some states in the Midwest and along the Gulf Coast, high delinquency and foreclosure rates reflected weak economies or the continued aftermath of the 2005 hurricanes. This was a traditional problem, in which the causality ran from the economy to housing. The other problem was found in states that were on the downside of housing bubbles, notably Arizona, California, Florida, and Nevada. In these areas, foreclosures reflected the steep declines in home prices and limited availability of credit for marginal buyers, which together put at risk subprime borrowers who had bought homes in 2004 to early 2007 in the expectation that rising home prices would give them equity with which to refinance out of their subprime adjustable-rate mortgages (ARMs). The end of the bubble had closed off this option and left borrowers in danger. Rising foreclosure rates among subprime borrowers led to pressures— both political and economic—for the Treasury and the administration to do something to assist families at risk of foreclosure. The chairman of the

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FDIC, Sheila Bair, correctly identified the rising foreclosure problem early on and pushed for the administration to take action.

IV.A. Initial Measures Housing policy was seen as involving two main dimensions: a “forwardlooking” one relating to measures that would boost demand for housing, including through housing-specific policies such as a tax credit for homebuyers (possibly first-time buyers only) or as part of an economy-wide stimulus, and “backward-looking” policies to help existing homeowners at risk of foreclosure. The administration’s response to the housing crisis as of September 2007 included three main proposals, all requiring congressional action. All three might have been worthwhile—indeed, all eventually were enacted in one form or another—but they were dissatisfying in their limited scope. The first proposal was a set of changes to the legislation governing the Federal Housing Authority (FHA) that would allow additional low- and moderate-income homeowners to refinance into FHA-guaranteed loans.1 This was on top of a program known as FHASecure, which allowed refinancing by borrowers who had become delinquent because the interest rate on their ARM had increased. All together, the proposals involving the FHA were seen as helping perhaps 500,000 families. The FHA had gained substantial market share as private sector subprime lending disappeared in 2007, and there were concerns that the agency was near its capacity, not least because Congress had not approved funding requested by the administration to update its computer systems. The second proposal was a change to the tax code, eventually enacted, that forgave the tax due from a borrower whose debt is canceled by the lender, for example when a borrower walks away from a home without paying off the mortgage. The existing tax law treated this reduction in debt as income to the borrower. This change did not boost housing demand or prevent foreclosures but was seen as avoiding an unfair tax bill for people who had just lost their home. The third proposal was the long-standing effort by the administration to improve the regulation of Fannie Mae and Freddie Mac. The idea was that a strong and independent regulator could better ensure the safety and 1. The FHA package included lowering required down payments, raising loan limits, and allowing for risk-based pricing of insurance premiums so that the FHA could insure loans to yet riskier borrowers by charging them higher premiums. Such risk-based pricing was a political red line for many in Congress, who saw it as unfair to charge more to the people in the worst financial condition and thus in the greatest need of assistance.

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soundness of the two companies, thereby helping ensure that they had the financial wherewithal to provide continued financing to mortgage markets. GSE reform was finally enacted as part of the summer 2008 housing bill, by which time it was too late to avert insolvency at the two firms. The initial focus of housing policy was on the difficulties faced by homeowners in subprime ARMs who were facing an interest rate reset, typically two years (but sometimes three years or more) after origination. The concern by mid-2007 was that many families would not be able to afford the resulting higher payments. (The term “payment shock” was used, although this is a misnomer of sorts, since the interest rate hike was not a surprise but instead the central feature of the mortgage.) FDIC Chairman Bair, for example, argued that up to 1.75 million homeowners could benefit from keeping the interest rate on these subprime mortgages unchanged rather than allowing the rate to reset. Although we at the Treasury agreed that about 1.8 million subprime ARMs would face resets in 2008 to 2010, our assessment was that the driver of foreclosures was the original underwriting, not the reset. Too many borrowers were in the wrong house, not the wrong mortgage. Moreover, as the Fed cut interest rates in late 2007, the rates to which mortgage resets were tied came down as well, reducing or even eliminating the payment shock for many subprime borrowers. This meant that preventing interest rate resets was not likely by itself to avert many foreclosures.

IV.B. Hope Now To better identify avenues for effective solutions, the Treasury convened meetings in the fall of 2007 with groups of housing industry participants, including lenders, servicers, nonprofit housing counselors, and organizations representing investors in MBSs. What became apparent through this dialogue was that frictions and communication gaps between housing industry participants meant that some homeowners faced foreclosure unnecessarily. The Hope Now Alliance was formed to address these issues. The Hope Now Alliance was launched by the Department of Housing and Urban Development (HUD) and the Treasury on October 10, 2007. As the organization puts it on its website, Hope Now is an alliance among HUD-approved counseling agents, mortgage companies, investors, and other mortgage market participants that provides free foreclosure prevention assistance. The Treasury saw an important part of the initial work done through Hope Now as basic “blocking and tackling” (football was a preferred source of metaphors in the Paulson Treasury) in getting industry participants to work together and with borrowers more smoothly. The first

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step in avoiding a foreclosure is for the servicer and borrower to talk to one another, but this was not happening in a surprisingly high proportion of instances: some estimates were that half of foreclosures started without any contact between the borrower and the lender or servicer. Failures of outreach were observed in all directions: servicers were frustrated at the low response rate of borrowers to their letters and phone calls, while many borrowers who did reach out on their own found it difficult to get to the right person for help at their servicer or lender. In some cases they could not get help until they were already substantially delinquent, even if they had seen the problem coming. Nonprofit housing counselors had a valuable role to play, since they were often seen by borrowers as a neutral party, and tended to report higher response rates from at-risk borrowers. But counseling was something of a patchwork, with uncertain funding and unclear relationships between counselors and lenders. Counselors would tell the Treasury that they worked well with some lenders and servicers but could not get in the door at others; servicers had similar issues with uneven relationships in the other direction. For their part, servicers were still hesitantly exploring the legal room they had to modify loans, and they faced resource constraints in that their contracts did not envision the need for large-scale modification efforts to avoid foreclosures.2 Hope Now brought together the leading subprime servicers, national counseling agencies (including the highly regarded NeighborWorks organization), and industry and investor trade associations such as the Mortgage Bankers Association, the Financial Services Roundtable, the Securities Industry and Financial Markets Association, and the ASF. The inclusion of industry associations was helpful, providing a channel through which to bring together firms across the housing ecosystem. Getting the servicers involved was essential, since they were the point of contact between the industry and individual borrowers. From the outset, servicers accounting for about half of subprime mortgages participated in Hope Now; this grew to cover better than 90 percent of subprime and 70 percent of all loans by mid-2008. (The potential coverage is limited because some banks service their own mortgages.) This effort was backstopped by intense involvement of Treasury staff (particularly Neel Kashkari, then a senior adviser to Secretary Paulson who had come up with the idea) and substantial personal involvement by Paulson himself. Participants in Hope Now committed to creating a unified plan to reach homeowners and help them avoid foreclosure. 2. Cordell and others (2008) discuss issues regarding servicers in detail.

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Hope Now initially focused on outreach—the blocking and tackling— with the goal of reaching troubled borrowers early enough so that a loan modification could at least be contemplated. A national foreclosure counseling hotline (888-995-HOPE) was set up, along with a publicity campaign to advertise it, featuring public service announcements and public events with government officials, including President Bush. Hope Now arranged for servicers to provide funding for the nonprofit counselors (who had previously relied on government and foundation resources), standardized communication protocols between counselors and servicers, and collected systematic data on the number of people helped and the modifications made. Participants in Hope Now agreed to provide subprime borrowers with information about their reset four months in advance, and to send high-visibility letters to all borrowers who became 60 days delinquent, urging them to call the Hope Now hotline. This kind of outreach sounds basic, but it was unprecedented for the industry. Hope Now reported that the call volume on its hotline surged in late 2007 and into 2008. The next step was to follow up these activities with a systematic approach to help at-risk borrowers refinance or obtain a loan modification that would avoid a foreclosure. The fundamental goal was to “avoid preventable foreclosures.” As Secretary Paulson and others were to say repeatedly, this meant that the Treasury was looking for ways to help homeowners who were struggling with their mortgage payments but both wanted to stay in their home and had the basic financial wherewithal to do so. “Wanting to stay” meant that the homeowner would not walk away from a home as long as he or she could afford the monthly payment. “Basic financial wherewithal” meant that the Treasury’s efforts were aimed at getting mortgage servicers to modify loans for homeowners with subprime ARMs who could afford their payments at the initial (pre-reset) interest rate, where the cost to the beneficial owner of the mortgage of modifying the loan was less than the loss that would be suffered in a foreclosure. Not every foreclosure could be prevented through a modification—after all, over 600,000 foreclosures occur in a “normal” year. But we at the Treasury wanted to make sure that no borrowers got foreclosed on who could afford to stay in their home under the set of circumstances above. The loan modifications were part of the solution and would complement other efforts to enable homeowners to refinance into fixed-rate loans, whether through the FHA or through a private lender. Through Hope Now, the Treasury pushed lenders and servicers to undertake a calculation that balanced the cost (in net present value) of a modification that would keep a family in their home against the loss to the

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mortgage owner that would be suffered in a foreclosure—from legal fees, possible damage to the home, the resale consequences for a bank trying to sell a foreclosed home in a declining market, and the fact that putting the house up for sale would further depress home prices. When this net present value calculation indicated that it made sense to modify the loan, the Treasury—and Secretary Paulson personally—expected lenders to do so to avoid foreclosure. The Treasury also pushed servicers to ensure that loan modifications were of a long enough duration to give borrowers a chance for the income growth and home price appreciation that would allow them to refinance permanently into a conforming fixed-rate loan. Although these modifications were in everyone’s best interest, they did not appear to be taking place on the scale that would be expected. The impact of second liens was one reason, since these make it difficult for the servicer of the first lien to get agreement on a modification—and in the case of piggyback second loans, it meant that the borrower was in much worse financial shape than would be indicated by the first lien alone and thus less likely to be able to sustain even a modified first mortgage. Addressing the frictions in the modification process turned out to be an ongoing project at the Treasury. The goal, again, was a modification that would lower the monthly payment to an amount that the borrower could afford. Some borrowers might still walk away from their homes because they were deeply underwater, while others would have such a severe mismatch between mortgage and income that it made more sense from the point of view of the mortgage owner to foreclose. Servicers would structure loan modifications to lower an at-risk borrower’s monthly payment in the way that imposed the least cost on the beneficial owner of the mortgage. Given simple bond mathematics, this meant that servicers would first reduce the monthly payment by extending the loan term out to 30 or 40 years; then, if necessary, lower the payment further by cutting the interest rate; and only as a last resort lower the principal (and then only if the contract governing the servicer allowed for a principal reduction, which was not always the case). If a homeowner could not sustain payments at the initial interest rate, the view at the Treasury was that this person was probably in the wrong home. The Treasury asked lenders to look at each situation, but we recognized that, as Secretary Paulson put it, many such homeowners would become renters. The loan modification approach thus focused on people with payment and income problems, not on underwater borrowers. Since mortgages in many states do not allow the lender recourse to claim a borrower’s assets

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beyond the home collateralizing the mortgage, this meant that many underwater borrowers might walk away and allow foreclosure even if they could afford their monthly payment. Not everyone would do so: a household with a mortgage equal to 105 or 110 percent of their home value might well stay if they could afford the monthly payment—they might like their neighborhood or the local school, for example, or hope to see prices rebound. But it was quite rational for a person who got into a home with little or no equity and then suffered a 40 or 50 percent price decline to walk away. Being underwater thus made a foreclosure more likely but was not a sufficient condition. The Treasury did not expect banks to modify loans where borrowers could afford the payment but were balking at paying because they were underwater—quite the opposite: Secretary Paulson’s view was that a homeowner who could afford the mortgage but chose to walk away was a speculator. As a practical matter, servicers told us, reputational considerations meant that they did not write down principal on a loan when the borrower had the resources to pay—never. They would rather take the loss in foreclosure when an underwater borrower walked away than set a precedent for writing down principal, and then have to take multiple losses when entire neighborhoods of homeowners asked for similar writedowns. We also realized that the prospect of assistance could lead borrowers who were not in difficulty to stop making payments in order to qualify for easier terms. Such moral hazard is unavoidable, but one can choose the screens and hurdles that borrowers must pass to qualify for a modification. The trade-off is that steps to limit moral hazard also limit take-up.

IV.C. The Debate over Subsidizing Foreclosure Avoidance The Treasury expected lenders to go up to the line, making modifications wherever the net present value calculation favored it. But there was no public money on the table to get them to go further.3 Even though we realized that there was no appetite in Washington for crossing the line, Treasury economists in October 2007 developed plans for two types of policies to put public resources into foreclosure prevention. The first policy focused on underwater borrowers, with the federal government in effect writing checks in cases where lenders were willing to take a write-down. The lender had to take a loss on the principal, after 3. A possible exception was that FHA-guaranteed loans involved a public subsidy to the extent that the FHA unintentionally underpriced its insurance, as one might expect from a government insurer.

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which the federal government would subsidize the cost of a guarantee on the modified loan—this could in effect be a substantial subsidy because these would be loans to borrowers who were still quite risky. The borrower would be required to pay part of the annual premium for the federal guarantee. This arrangement was broadly similar to the Hope for Homeowners program later developed jointly by the Fed and congressional staff, but with more realistic parameters for servicers and without the pretense that no federal spending was involved. The plan was known at the Treasury as the “GHA,” a reference both to its operation through a dramatic expansion of the FHA in putting guarantees on mortgages to risky borrowers, and to one of the main authors of the idea, Deputy Assistant Secretary for Microeconomic Analysis Ted Gayer, who was at the Treasury for a year on leave from Georgetown University’s Public Policy Institute. The second type of policy focused on affordability and involved a matching federal subsidy to lenders willing to lower interest rates in order to reduce the monthly payments for at-risk borrowers.4 The approach was based on the bond math above that the most cost-affordable way to lower monthly payments, after extending the term, was to cut the interest rate, and on the straightforward notion that the government should pay lenders and servicers to do what it wanted them to do. In this case the federal government wanted them to lower interest rates to avoid foreclosures on at-risk borrowers, and therefore it would give them a financial incentive to do so and no financial incentive to put people into foreclosure. Lenders would have to fund the first 50 basis points of the interest rate reduction, to give them an incentive to screen out marginal cases where they should just modify the loan without any subsidy, after which the federal government would pay half the cost of lowering the interest rate up to a total of 450 basis points; thus, the lender would fund a maximum of 250 basis points and the federal government 200 basis points. Lenders could reduce interest rates further on their own without an additional subsidy, but the presumption was that a borrower who needed more than a 450-basis-point reduction was in the wrong home. If a borrower defaulted after the modification, the federal subsidy would end—the government would pay for success, not for failure. The subsidy would end after five years, long enough of a breathing space for borrowers to have income growth and home price appreciation and thus be in a position to refinance into a fixed-rate loan. The trade-off involved in setting this time limit is clear: a longer subsidy 4. Credit for this idea goes to Ted Gayer and John Worth, director of the Office of Microeconomic Analysis. Their idea was adopted by the Obama administration in 2009.

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than five years gives people more time to ensure that they can afford the home after the subsidy ends, but it means a more expensive modification for the lender and thus less uptake—fewer people would get into the program, but more of those who did would be saved. We saw five years as striking the right balance, and our analysis showed that several million homeowners could avoid foreclosure with this interest rate subsidy. The initial reaction to the proposed interest rate subsidy among Fed staff responsible for analysis of housing policy in October 2007 was disinterest, because the plan did not address the problem of underwater borrowers on which they were focused (as shown by the Hope for Homeowners approach the Fed helped to develop). We agreed that the subsidy would not be enough of an incentive to dissuade a deeply underwater borrower— say, one with a loan of 150 percent of home value—from walking away. But our view was that there was a government budget constraint (even if many outside critics charged that the Bush administration did not act like it), and it was not a wise use of public resources to write huge checks to people who could afford their homes but might then choose not to stay in them. This view, unlike the secretary’s assertion that a person who would walk away was a speculator, was based on practical, not moral, grounds: it would be better at the margin to use taxpayer dollars to hire more preschool teachers, say, than to subsidize deeply underwater borrowers. While the Fed staff was focused on underwater borrowers, within the administration—among White House staff in particular, but also within the Treasury—many were unwilling to put public money on the line to prevent additional foreclosures, because any such program would inevitably involve a bailout of some “irresponsible” homeowners. Put more cynically, spending public money on foreclosure avoidance would be asking responsible taxpayers to subsidize people living in McMansions they could not afford, with flat-screen televisions paid for out of their home equity line of credit. The policy rationale to spend public money is clear in that there is a negative externality from foreclosures to home inventories and thus prices. But the public opposition to such bailouts appeared to be intense— ironically, many people were already angry at the Treasury for supposedly bailing out irresponsible homeowners through Hope Now, even though this did not involve explicit public spending. Congress appeared to heed this opposition as well: there were constant calls for the Treasury and the administration to do more on foreclosure prevention, but this was just rhetoric. Until the FDIC came out with a proposal late in 2008, there was no legislative support to spend public money to actually prevent foreclosures—the congressional proposal discussed below

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ostensibly did not use public funds. And as discussed below in relation to the TARP, even in the fall of 2008, Congress’ desire was for the Treasury to spend TARP money for foreclosure avoidance. Members of Congress did not want to have to vote specifically to spend money on this, suggesting that they understood the poor optics of having the government write checks when some would find their way into the hands of “irresponsible homeowners.” In 2007 and through the middle of 2008, the focus of legislative energies was on the so-called Frank-Dodd legislation, which became law on July 30, 2008, as part of the Housing and Economic Recovery Act of 2008 (which included provisions to reform the GSEs). This proposal, named for its main sponsors Congressman Barney Frank (D-MA) and Senator Christopher Dodd (D-CT), involved FHA-guaranteed refinances of mortgages for which lenders were willing to write down the loan principal to 87 percent of the current market value. This was a great deal for the homeowner, who would face lower payments and gain substantial equity (while having to share some of these gains with the federal government on a future sale), but a huge write-down for the lender, actually exceeding 13 percent in instances where home prices had declined since origination. And there was ostensibly no government money involved, as the legislation required the GSEs to cover any costs—again demonstrating the reluctance of policymakers to be seen as writing checks to irresponsible homeowners. The Congressional Budget Office (CBO) estimated that the Frank-Dodd approach would help some 400,000 homeowners. Having heard directly from lenders about their reluctance to reduce loan principals, we saw the CBO estimate as optimistic by 400,000. Because the bill included legislation to strengthen the regulation of the GSEs, however, President Bush signed it into law. Staff from the FHA, HUD, the Fed, the Treasury, and the FDIC made an immense effort to implement the new Hope for Homeowners program— and then unfortunately the Treasury’s estimate of participation turned out to be correct, with few loans refinanced through the middle of 2009. As before, avoiding more foreclosures required someone—either the government or lenders—to write a check. The attraction of the so-called bankruptcy cramdown proposal, under which bankruptcy courts could retroactively change mortgage contracts by reducing the loan principal, was that it appeared to be “free”—which it was to the government—but only because the cramdown would be a forced transfer from lenders to homeowners. The Treasury opposed the cramdown proposal out of concern that abrogating contracts in this way would have undesirable consequences for the future availability of credit, especially to low-income borrowers. Some

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current borrowers would benefit from having their mortgage balance reduced, but future borrowers would find it more difficult to obtain a loan.

IV.D. The ASF Fast-Track Framework With subsidies still off the table, what was done with respect to foreclosure avoidance in late 2007 and into 2008 was that the Treasury and Hope Now worked with the ASF to make modifications happen faster and more frequently. This turned into the Streamlined Foreclosure and Loss Avoidance Framework announced on December 6, 2007. This initiative focused on approximately 1.8 million subprime ARMs with initial teaser rates set to reset in 2008 and 2009. Servicers agreed to carry out a fast-track process to help borrowers refinance into a fixed-rate loan (the first choice for borrowers with adequate income and credit history), or, failing this, to provide a five-year extension of the initial rate for borrowers who could afford their monthly payment at that rate. This would give borrowers time to experience income gains and home appreciation that would put them in a position to refinance into a fixed-rate loan in the future. A longer modification than five years would be more costly to a lender, and thus fewer modifications would pass the cost test. And even a five-year horizon would be a change from industry practice, which was geared to “repayment plans”—shortterm modifications appropriate for a borrower with a temporary income problem of a few months. Industry participants estimated that about onethird of the 1.8 million potential borrowers in the program could not afford their starter rate, and another one-third could clearly receive either a refinancing or a rate freeze. The aim was to save as many as possible of the remaining 600,000, so as to come close to helping 1.2 million homeowners. The ASF fast-track framework provided servicers with a set of best practices to implement modifications. The streamlined framework was formally launched in early 2008, but some servicers began to use it in late 2007. Hope Now reported a dramatic increase in the number of homeowners receiving help in the form of a refinancing or a loan modification, from about 300,000 per quarter in the first half of 2007 to over 500,000 per quarter in mid-2008 and nearly 700,000 in the last three months of 2008. The increase was especially noticeable for subprime borrowers, where the number of long-term modifications rose from fewer than 50,000 per quarter in the first nine months of 2007 to over 200,000 in the last quarter of 2008 alone. By the end of 2008, nearly half of homeowners receiving help got long-term modifications rather than shortterm repayment plans, compared with fewer than 20 percent previously. Hope Now was not solving the foreclosure problem, but it was performing

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as designed. Moreover, other homeowners refinanced without the involvement of Hope Now. The Treasury and Hope Now nonetheless faced continuing criticism that these efforts were inadequate and that servicers were not doing enough loan modifications. The Center for Responsible Lending (CRL), for example, put out a widely cited report on January 30, 2008, claiming that the “Paulson plan” for voluntary loan modifications would help only 3 percent of at-risk homes. What was not reported, however, was that the 3 percent figure was calculated using several unusual assumptions. First, the denominator, the number of at-risk homes, included not just owneroccupied homes but also investor properties, even though the ostensible goal was to save homeowners, not investors. Second, the numerator—the measure of success—included the loan modifications but not the refinancings into fixed-rate mortgages, which were usually better than a modification. Treasury economists who redid the analysis correcting these questionable assumptions calculated that at least 30 percent, and possibly more than half, of eligible homeowners would be helped by the Hope Now framework. The CRL did not correct their analysis when we quietly pointed out to them the flaws (which their researchers acknowledged), but neither did the Treasury go out proactively to the media to dispel the misconception. As criticisms continued that not enough was being done to prevent foreclosures, the focus at Treasury turned to coming up with additional actions through Hope Now that would show that more was being done. Out of this came the February 12 announcement of “Project Lifeline,” under which severely delinquent borrowers would be granted a 30-day pause on foreclosure proceedings, as a last-ditch breathing space to allow borrowers to work with their lender or servicer to find a modification that made sense for both sides. Some hurdles to modifications were difficult to address. Servicers had varying abilities to deal with the large number of modification requests. Also, as already noted, the presence of a second lien, such as a home equity line of credit or a piggyback mortgage, could present a challenge to a modification on the primary mortgage, since owners of second liens had an incentive to hold up the process unless they received a payoff—this even though a second lien on a troubled borrower was worth only pennies on the dollar, since the primary mortgage holder would have the first right to the proceeds of a foreclosure sale. Legal and accounting issues constituted two further hurdles to loan modifications. Servicers were unclear as to their legal ability to modify

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loans within securitization trusts, and they worried that undertaking too many modifications would lead to an adverse change in the accounting treatment of the MBSs containing the loans. Financial Accounting Standards Board statement number 140 provides guidance on whether a transfer of assets to a securitization trust can receive off-balance-sheet treatment. The concern was that if too many loans were modified, this would make the trust no longer a passive structure and therefore ineligible for offbalance-sheet treatment. SEC Chairman Christopher Cox indicated that having loans in an MBS trust receive the five-year rate freeze did not preclude continued off-balance-sheet treatment so long as it was “reasonably foreseeable” that the loans being modified were otherwise headed for default. Treasury economists worked with FDIC staff to analyze loan-level data on subprime mortgages. The results showed that for subprime borrowers in the years covered by the Hope Now streamlined approach, it was sadly straightforward to conclude that a default was reasonably foreseeable. These results went into a letter from the Treasury to the SEC that was meant to provide backing for Chairman Cox. The view at the Treasury was then that servicers had the legal authority they needed to modify loans, and that there was no need for congressional proposals to enact a “safe harbor” that would explicitly provide such cover. Although we realized that the safe harbor provision might have avoided some lawsuits against servicers who modified loans, our concern was that it was a retroactive change to contracts—not as obviously harmful as the mortgage cramdown proposal, but harmful nonetheless in suggesting to lenders that they should henceforth worry about retroactive changes to contracts. It turned out that the original motivation for the Hope Now streamlined modification protocol was incorrect, in that interest rate resets by themselves were not the fundamental driver of rising foreclosures—a point documented by Mark Schweitzer and Guhan Venkatu (2009). This can be inferred from figure 1, since the foreclosure rate does not have an upward kink at the typical reset point at month 24. Many subprime ARMs started at an initial rate of 8 to 9 percent for two years and then were scheduled to reset to 600 basis points above the six-month LIBOR (the London interbank offered rate). By early 2008, however, LIBOR had fallen to 3 percent or less, so that the step-up in the interest rates and thus the payment shock were fairly modest. We nonetheless saw the ASF streamlined modification framework as useful, since it would be ready in case interest rates rose in the future, and it was driving modifications for loans even before resets.

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IV.E. Housing Policy in 2008 Treasury housing policy by early 2008 had four goals: —avoiding preventable foreclosures as discussed above; —ensuring the continued flow of capital into housing markets, both through efforts to enact reform of the GSEs and by resisting proposals, such as the bankruptcy cramdown, that would have reduced the availability of capital for housing finance; —enabling the necessary housing correction to proceed, which meant warding off proposals for long-lasting foreclosure moratoriums, which we saw as simply prolonging the difficulty without providing lasting help for at-risk homeowners; and —supporting the broad economy, such as through the January 2008 stimulus. With little desire on anyone’s part to put public money on the table, housing policy was to remain largely focused around the debate over modifications achieved through Hope Now, and over the Frank-Dodd legislation. A recurring theme of policy proposals from outside the Treasury was that the Treasury should promote shared-appreciation mortgages, in which homeowners would get a loan modification or financing concessions in exchange for giving up part of the home’s future appreciation to the lender. We studied this proposal, which amounted to a debt-for-equity swap, but concluded that this type of mortgage was not already common because there was little demand for it. The one truly new proposal we heard in early 2008 was that of Martin Feldstein, who in a March 7 op-ed in the Wall Street Journal (“How to Stop the Mortgage Crisis,” p. A15) and in subsequent writings proposed stabilizing the housing market by offering all homeowners a government loan that would be used to reduce the principal on first-lien mortgages. Such a loan would make it less likely that homeowners would have negative mortgage equity and thereby reduce future defaults in the face of continued home price declines. Participating homeowners would not be able to walk away from the government loan, because it would be a tax lien that could not be escaped in bankruptcy. The Feldstein proposal would not help borrowers already facing foreclosure, but that was not the point—it was meant to arrest the impact of future potential underwater borrowers walking away from their homes and adding to inventories, thus intensifying the downward momentum of home prices. Intrigued, we analyzed the potential impacts, including looking at the Internal Revenue Service’s record in collecting on tax liens to get a sense of the budget cost. In the end, however, with little political support for spending money on risky

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homeowners, there was even less prospect of a massive housing program aimed at the better-off homeowners who were not in imminent danger. Housing policy was to stay essentially static until later in 2008, when the $700 billion TARP fund became available and calls grew to spend part of it on foreclosure prevention. In the fall of 2008, the FDIC developed two initiatives aimed at foreclosure avoidance. The first was a roadmap for servicers to follow in modifying loans—a “mod in a box” as they called it—detailing the calculations needed to implement the net present value calculation comparing the costs of foreclosure with those of loan modification. This was based on the FDIC’s experience with IndyMac, the Los Angeles-area savings and loan that the agency had taken over on July 11. The IndyMac protocol involved steps to bring a borrower’s monthly payment on his or her first mortgage down to 38 percent of pre-tax income (a figure that the FDIC changed to 31 percent when it found that many borrowers could not stay current at the 38 percent level). The steps were familiar from the bond math above: there was no principal writedown but instead a term extension, interest rate cuts, and principal forbearance, all aimed at lowering monthly payments. The FDIC approach looked only at the monthly payment as a share of the first mortgage—the so-called front end ratio—and not at total loan payments (the so-called back-end ratio) including a second lien, if present, and any auto loans and credit card bills. This focus on the front end was done for speed; the idea was to allow for rapid modification of loans, accepting that some might well go bad, since a borrower with loaded-up credit cards might ultimately still default even if the interest rate on the home loan was reduced. This approach to modifications was a natural extension of the streamlined protocol developed in late 2007 through the auspices of Hope Now, although the media did not make this connection and the Treasury did not press it (that is, the Treasury did not pro-actively note that the Hope Now activities that so many people had criticized had provided the groundwork for the widely acclaimed FDIC approach). The GSEs later adopted much of the approach of the IndyMac protocol in putting out their own streamlined approach to modifications on November 11, 2008. The second FDIC proposal for foreclosure avoidance was a loss-sharing insurance plan, under which the federal government would make good on half of the loss suffered by a lender that modified a loan according to the IndyMac protocol but later saw the loan go into default and foreclosure. This was an innovative margin on which to push: there was a great deal of anecdotal evidence, later confirmed by statistical evidence from the Office of the Comptroller of the Currency, that many loans were going bad even

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after they had been modified to reduce the payment. The FDIC plan provided some comfort to a lender for making the modification, since the lender would be reimbursed for half of the loss if the borrower eventually defaulted. Housing activist groups such as the Center for Responsible Lending endorsed the FDIC plan, as did Elizabeth Warren, the Harvard law professor appointed by Congress to chair an oversight panel for the TARP. The proposal received a good deal of coverage in the press, some of which confused the loss-sharing insurance proposal with the IndyMac protocol, even though the latter involved no government resources. At the Treasury, we noted that the FDIC plan gave rise to new forms of both adverse selection and moral hazard in ways that made it mainly a windfall for the beneficial owners of mortgages rather than a benefit for homeowners. In other words, American taxpayers would be providing a subsidy to banks, hedge funds, and other owners of MBSs (including foreign banks and foreign hedge funds) rather than to American families. Under the FDIC proposal, if a servicer modified a loan and the borrower was able to stay in the home as a result, the owner of the mortgage got nothing from the government.5 If, however, a loan was modified according to the FDIC’s protocol and it went bad, the government would write a large check to the mortgage owner. Moreover, there was no deductible on this loss-sharing insurance coverage, so in the case of an underwater borrower, the government would have in effect been providing fire insurance on an entire house when several of the rooms were already engulfed in flames. At the Treasury, we viewed the loss-sharing insurance proposal as a nontransparent way to funnel money to institutions that had made bad lending decisions and to investors who had bought the loans—a hidden bailout. Ironically, however, the New York Times on November 1, 2008, published an article by columnist Joe Nocera asserting that the Treasury opposed the FDIC proposal because “aid is going to homeowners, not giant financial institutions.”6 The confusion in the New York Times column might have reflected a common difficulty in understanding the impacts of insurance proposals, since the costs are implicit at the start whereas the payouts are yet to be realized, and thus the subsidy is somewhat obscured. In this case, big checks 5. The proposal would have paid a flat $1,000 for modifying a loan, but this went to the servicer, not to the owner of the loan; this payment could have put the servicer at odds with its fiduciary obligation to make modifications that were only for the benefit of the owners of the mortgage. 6. Joe Nocera, “A Rescue Hindered by Politics,” New York Times, November 1, 2008, p. B1.

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would get written to banks and hedge funds, but only six months or more down the road as the modified loans defaulted. In contrast, the interest rate subsidy puts the government resources to avoid foreclosure in clear daylight—it looks exactly like what it is, which is writing checks to people who are in homes they cannot afford. The cost per incremental foreclosure avoided, however, is much less with the interest rate subsidy. In short, this proposal is more efficient but suffers from its transparency. In evaluating the FDIC proposal, Treasury economists suggested that a way to remove some of the unwanted windfall for lenders was to have the insurance payout reflect any decline in the area home price index after the loan modification, rather than the lender’s loss from foreclosure. Setting the payout in this way would cover the valid concern that declining home prices gave servicers an incentive to foreclose sooner rather than give a risky borrower another chance. Although the FDIC declined to incorporate this suggestion, the Obama administration eventually made it part of its February 2009 foreclosure avoidance proposal. A related proposal by Treasury economist Steven Sharpe (a Fed staffer who came to Treasury for several months to help with capital markets and housing proposals) was for the federal government to sell insurance against price declines to home purchasers. At closing, buyers could pay a fee and receive insurance that compensated them five years later for any decline in overall home prices in their area—homeowners would receive the payout, if any, without having to sell their home. The idea was to boost housing demand going forward by removing the fear among potential homebuyers of “catching a falling knife”—that is, buying a home that would continue to lose value and leave them underwater. The adverse selection in the FDIC loss-sharing proposal came about because lenders would naturally want to put into the program those loans that were most likely to default, so that the government would cover half of any loss. At the suggestion of the Fed, the FDIC included a six-month waiting period, which meant that the lender would have to bear the cost of modifying the loan for six months. The moral hazard came about because the lender would have a financial incentive to foreclose immediately after the six-month waiting period. Under the FDIC proposal, lenders would qualify for this loss-sharing insurance coverage only if they agreed to apply the IndyMac modification protocol to all loans in their portfolio— lenders could not choose to include, for example, only the loans of borrowers that they knew had huge credit card debts. But this did not change the fundamental incentives; it just meant that lenders would participate in the program only if the expected value of the insurance windfall they

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received to cover losses exceeded the total cost of the modifications they would be required to fund. Both the interest rate subsidy developed at the Treasury and the FDIC’s loss-sharing insurance proposal focused on affordability rather than on underwater borrowers; we saw this as entirely appropriate from the point of view of the allocation of government resources. But the incentive effects of the two proposals were clearly different, since the interest rate subsidy would be paid only when foreclosure was avoided, whereas the loss-sharing insurance by its nature would pay out when foreclosure occurred. Even Elizabeth Warren conceded to Treasury staff that she understood that banks rather than homeowners would benefit more from the FDIC plan. She was evidently supporting the FDIC proposal in public because she thought something had to be done about foreclosures, and the FDIC plan seemed to be the only one on the table. The American Bankers Association endorsed the FDIC plan as well; presumably this reflected their understanding of its impact.

V. The Stimulus of 2008 By October 2007 there were increasing signs that the economy would remain weak into 2008 and that there was considerable downside risk from the housing and financial markets. Work began in earnest on fiscal policy options to support growth. The idea that such action might be needed was buttressed by public calls for it by prominent economists, notably Lawrence Summers and Martin Feldstein. Throughout November and December, the administration’s economic team—the Treasury, the Council of Economic Advisers (CEA), the Office of Management and Budget, and the National Economic Council—considered various approaches, focusing on tax cuts for households and businesses. In the end, the Economic Stimulus Act of 2008 contained mainly tax cuts, along with an extension of unemployment insurance benefits. The form of the tax cuts was remarkably similar to what CEA Chairman Edward Lazear had sketched out as an initial proposal: rebate checks implemented as a reduction of the lowest individual income tax rate, and thus mainly an inframarginal tax cut, along with additional expensing and bonus depreciation for businesses. Sending a one-time check to households was not the administration’s first choice—the view was that a longer-lasting policy would have more impact. But there was no political prospect of a permanent tax cut or extending the administration’s 2001 and 2003 tax cuts. This was about tactics—supporting the broad economy while housing and credit

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markets continued to adjust—not a strategic approach to increasing longterm growth. The stimulus was proposed in early January and signed into law in midFebruary, speeded by the administration’s stipulation that it would not fight for “Bush-style” tax policy and what seemed to be a determination by the congressional leadership to get this done quickly after an initial year in power with only modest accomplishments. The details of the tax provisions were agreed with the House leadership late one evening, when only that very morning the Treasury legislative affairs staff had reported that it could be weeks before a compromise was reached. The Internal Revenue Service and the Financial Management Service within the Treasury worked wonders to push out nearly $100 billion in rebate checks and electronic payments, with most of the cash going out the door from April 28 to July 11, 2008. We at the Treasury, at least, expected the main impact of the stimulus to come from the rebate checks rather than the expensing provision; with the economy weakening, it was hard to see much stimulus to business investment from a tax incentive that amounted to the time value of money. Our expectation was that about 30 percent would be spent in the second and third quarters, rising to 40 percent by the end of 2008. Assuming a modest second-round multiplier, we tallied up a boost of $50 billion to aggregate demand. With each job created or preserved corresponding to about $100,000 of income in the national accounts, a back-of-the-envelope calculation suggested a boost of 500,000 jobs. Simulations using the private sector Macroeconomic Advisers model suggested roughly the same impact on employment. In retrospect, the stimulus appears to have been the right thing for the wrong reason in that the rebate checks effectively served to offset the drag from higher energy prices. Looking back in January 2009, we calculated that higher energy prices in mid-2008 had meant an unexpected hit to U.S. consumers of about $40 billion—close to the additional spending we had expected from the stimulus. Others have disagreed, claiming that the stimulus was simply ineffective. This remains an important topic for future research. The higher energy prices hit at precisely the wrong time, causing a downdraft to spending just as the labor market was finally feeling the impact of slower-than-potential GDP growth in the latter part of 2007.

VI. Bear Stearns and Plans to Break the Glass The collapse of Bear Stearns over the weekend of March 14, 2008, was a watershed event for the Treasury. Until that point the Treasury had urged

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financial institutions to raise capital to provide a buffer against possible losses but had not contemplated fiscal actions aimed directly at the financial sector. Instead, the main policy levers were seen as being the purview of the Fed, which had cut interest rates and developed new lending facilities in the face of events. From the Treasury side, the deliberations of that weekend were handled directly by Secretary Paulson working the phones from his home; meanwhile the Fed provided J. P. Morgan with financing to purchase Bear Stearns. Moral hazard was a huge concern, but the feeling at the Treasury was that even when the Bear Stearns transaction was renegotiated up from $2 per share to $10, the loss of wealth was still large enough to give pause to market participants and thus mitigate the moral hazard. Of course, moral hazard derived more broadly from the fact that Bear Stearns’ bondholders and counterparties avoided a loss. But the Treasury and the Fed saw little alternative to rescuing the firm at that time (or at least cushioning its fall), simply because the speed of its collapse left markets unprepared. A number of lessons of that weekend have received extensive discussion in the financial press and in the academic literature, including the role of liquidity (as discussed by Allen and Carletti 2008), the fragilities arising from counterparty risks embedded in the three-party repo system and the over-the-counter derivative markets, and the need for a resolution mechanism for troubled nonbank financial institutions. At the Treasury, two additional lessons were learned: first, we had better get to work on contingency plans in case things got worse, and second, many in Washington did not understand the implications of nonrecourse lending from the Fed. The second lesson was somewhat fortuitous, in that it took some time before the political class realized that the Fed had not just lent J. P. Morgan money to buy Bear Stearns, but in effect now owned the downside of a portfolio of $29 billion of possibly dodgy assets. This discovery of the lack of transparency of nonrecourse lending by the Fed was to figure prominently in later financial rescue plans. The Fed’s March 17 announcement that it would provide loans to brokerdealers through the new Primary Dealer Credit Facility seemed to us and many Wall Street economists to remove the risk of another large financial institution suffering a sudden and catastrophic collapse as a result of a liquidity crisis. This provided some time to plan for further events. Part of the planning was for the long term. On March 31, 2008, the Treasury released its Blueprint for a Modernized Financial Regulatory Structure, with a vision for a long-term reshaping of financial sector regulation. This plan had long been in the works; indeed, Treasury had requested pub-

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lic comments on the topic in October 2007. However, the timing of the Blueprint’s release led to press reports that this was the Treasury’s “response” to the crisis. More near term in vision was work being done on so-called break-theglass options—what to do in case of another major emergency. This work evolved from a recurring theme of input from market participants, which was that the solution to the financial crisis was for the Treasury to buy up the toxic assets on bank balance sheets. Eventually Neel Kashkari and I wrote a memo listing options for dealing with a financial sector crisis arising from an undercapitalized system. The memo went through more than a dozen iterations in discussions around the Treasury and with Fed headquarters and the New York Federal Reserve Bank between March and April. The options were fourfold: buy the toxic assets, turn the Treasury into a monoline insurer and insure the assets, directly buy stakes in banks to inject capital, or refinance risky mortgages into government-guaranteed loans and thus improve asset performance and firms’ capital positions from the bottom up. With estimates such as that of David Greenlaw and others (2008) in mind that U.S. financial institutions would suffer $250 billion of losses from mortgage securities, we envisioned a government fund of $500 billion. A mix of asset purchases, capital injections, and additional private capital raising by banks would allow this amount to roughly offset the expected losses. These options would move the focus of financial markets policy back from the Fed to the Treasury, which would be appropriate in that the problem reflected inadequate capital rather than insufficient liquidity. But these actions all required congressional action, and there was no prospect of getting approval for any of this. With economic growth positive and the stimulus rebates only just beginning to go out in late April, it was unimaginable that Congress would give the Treasury secretary such a fund. And it was doubly unimaginable that the fund could be enacted without immediately being put to use. Such a massive intervention in financial markets could be proposed only if Secretary Paulson and Chairman Bernanke went to Congress and announced that the financial system and the economy were on the verge of collapse. By then it could well be too late. For several months in the second quarter of 2008, things seemed to be improving. The housing adjustment appeared to be proceeding. Prices continued to fall and construction and sales were still in decline, but the rate of descent appeared to be slowing, and our view was that by the end of 2008 housing would no longer be subtracting from GDP. The second half of 2008 looked to be difficult, but we expected the rebate checks to support

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consumption until the drags from housing and the credit disruption eased and growth rebounded in 2009.

VII. Rescuing the GSEs The relative quiet was to hold until early summer, when the effects of the housing collapse manifested themselves in the collapse of IndyMac and severe pressures on the GSEs, in the form of declining stock prices and widening spreads on Fannie and Freddie securities, and thus on mortgage interest rates for potential homebuyers. The FDIC took over IndyMac and turned the firm into a laboratory for its foreclosure prevention ideas, but the problems of the GSEs fell squarely in the Treasury’s court. The Treasury was in a difficult position. GSE debt and MBSs with GSE guarantees were held throughout the financial system, and a failure of the firms would have meant chaos in financial markets. As commentators such as Peter Wallison of the American Enterprise Institute had long warned, (see, for example, Wallison, Stanton, and Ely 2004), the GSEs were holding the financial system and taxpayers hostage—and in mid-July 2008 it seemed they would win the standoff. The options were all unpleasant, and all required congressional action: to provide the GSEs with more liquidity by raising their line of credit with the Treasury from $2.25 billion each to something much larger; to inject capital; or to ask Congress to put the two firms into conservatorship, with the government running the companies on behalf of their shareholders (which would eventually be mainly the government). This last option could be done under existing legislative authority but still required congressional approval, and the GSEs could have fought this and might well have won, since their regulator had said as recently as July that the two firms were adequately capitalized. (This statement referred to statutory definitions of capital, which included tax assets that could only be monetized in the future when the firms became profitable again, but it nonetheless carried weight.) Moreover, even putting the GSEs into conservatorship raised questions about whether their $5 trillion in liabilities would be added to the public balance sheet. This did not seem to Treasury economists to be a meaningful issue, since the liabilities had always been implicitly on the balance sheet—and in any case were matched by about the same amount of assets. But the prospect that rating agencies might respond by downgrading U.S. sovereign debt was unappealing. A fourth option, receivership, would involve liquidating the companies and was deemed off the table because it would have required winding down the GSE portfolios.

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These portfolios were the source of the systemic risk arising from the GSEs’ activities, but the GSEs’ purchases of MBSs were important for ensuring the availability of financing to potential homebuyers. Addressing the portfolios would have to wait for a longer-term reform. In the end, Secretary Paulson went to the steps of the Treasury building on Sunday, July 13, and proposed “all of the above”: the power to give the GSEs both liquidity and capital in amounts that would make clear to market participants that the U.S. government stood behind the obligations of these companies. He asked Congress to raise the GSEs’ lines of credit; to authorize unlimited (subject to the statutory debt ceiling) direct Treasury purchases of GSE securities, including both their MBSs and their common stock, through the end of 2009, to ensure that the firms could fulfill their missions with respect to housing markets; and to give their regulator, OFHEO, the power of conservatorship and other authorities that the administration had long sought. The Treasury would insist on terms and conditions to protect the taxpayer if public money were ever put into the firms. These powers were requested with the idea that the firms’ liquidity crunch reflected a lack of market confidence that a show of Treasury support could assuage—that standing behind the firms would calm market fears and avoid the need for a bailout. (The secretary’s unfortunate phrasing, at a July 15 congressional hearing, about having a “bazooka” in terms of the financial ability to stand behind the firms was to be repeated constantly in the media in the months to come.) The Fed authorized bridge lending to Fannie and Freddie while Congress worked on the legislation, which was enacted on July 30, 2008 (and which included the Hope for Homeowners program). Some market participants complained that the rescue did not distinguish between senior and subordinated debt but instead made both of them whole, whereas many participants had expected the subordinated debt not to be included within the rubric of a guarantee. However, the view at the Treasury was that simplicity and clarity were paramount (although, of course, clarity is sometimes in the eye of the beholder). This effective hardening of the heretofore-implicit guarantee of the GSEs left mixed feelings among Treasury staff. A crisis had been forestalled with a flurry of weekend activity (soon to become a regular part of the Treasury workweek), but the outcome seemed to cement in place the awkward status of the GSEs and their ability to privatize gains and socialize risk by borrowing at advantageous terms under the shelter of a now-explicit government guarantee. Past Treasury departments across administrations had sought to remove the implicit guarantee, not to harden

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it. At a dinner in Cambridge, Massachusetts, on Thursday, July 24, 2008, to honor Martin Feldstein, outgoing president of the National Bureau of Economic Research, many people expressed to me directly their misgivings about what looked like a bailout, in which GSE bondholders and shareholders won and taxpayers lost. It was hard to disagree. It turned out that Secretary Paulson had the same misgivings. The following Monday, July 28, he instructed Treasury staff to analyze the capital situations of the GSEs. To protect taxpayers in the case that an actual investment was needed in the future, he wanted to know first if these firms were solvent. The Treasury’s Office of Domestic Finance engaged a topnotch team from Morgan Stanley to dig into Fannie and Freddie’s books and assess their financial condition. While this was happening, it became apparent that the July 13 announcement and subsequent legislation had left markets uncertain about the status of the enterprises. The GSEs had access to private sector debt funding, although with increased costs, as the spreads on five-year Fannie benchmark agency debt above Treasuries rose from about 65 basis points in early June to 94 basis points on September 5, just before the firms were put into conservatorship. But the common stocks of the two firms continued to decline. Market participants were in effect saying that they (mostly) believed that the government stood behind the debt and guarantees on the MBSs, but were not confident that the firms were solvent. This was not Secretary Paulson’s intent—he did not deliberately set up the GSEs to fail and get them into conservatorship. The weeks in July and August were tense ones within the Treasury, as markets deteriorated while waiting for more clarity on Fannie and Freddie. It looked to market participants as if there was no guidance, but this was because we were busy working—and Secretary Paulson was willing to suffer for a few weeks in order to have his next step come out right. The Morgan Stanley team came back several weeks later in August with a bleak analysis: both Fannie and Freddie looked to be deeply insolvent, with Freddie the worse of the two. In light of the firms’ well-publicized accounting irregularities of previous years, Treasury staff were especially amazed that the GSEs appeared to have made accounting decisions that obscured their problems. With receivership still an undesirable outcome because it would imply prematurely winding down the retained portfolio, the Treasury worked with the GSEs’ regulator (formerly OFHEO, the July legislation having merged it with the Federal Housing Finance Board to create the Federal Housing Finance Agency, or FHFA) to set out an airtight case of insolvency that warranted putting the firms into conservatorship. The July legislation allowed FHFA to do this without consulting

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Congress, although no one had contemplated actually using that power so rapidly. Even though the analysis from Morgan Stanley was clear, it took some time to bring the FHFA examiners on board—it seemed difficult for them to acknowledge that the firms they had long overseen had gone so wrong, and it would have been awkward for the head of FHFA to decide on the conservatorship over the objection of his senior career staff. It was also necessary to convince the management of Fannie and Freddie to acquiesce without a legal fight. There was no expectation of a problem with Freddie’s management—the CEO had publicly expressed his fatigue with the whole situation—but Fannie appeared then to be in somewhat better financial shape and might reasonably have expected to be treated differently than Freddie. Ultimately, Secretary Paulson had a trump card: he could say in public that he could not in good conscience invest taxpayer money in these firms, and that would doubtless spark their demise. But in the end he did not have to play this card. In well-publicized meetings with Secretary Paulson, Chairman Bernanke, and FHFA Director James Lockhart, both firms acceded to conservatorship, which was announced on Sunday, September 7, 2008. The Treasury announced three measures jointly with the conservatorship decision: so-called keepwells, under which the Treasury committed to inject up to $100 billion of capital each into Fannie and Freddie as needed to ensure their positive net worth; a Treasury lending facility if needed; and a program under which the Treasury would purchase the GSEs’ MBSs in the open market. This last program was mainly symbolic—a demonstration by the Treasury that the obligations of the GSEs were “good enough for us” and should be seen as secure by the rest of the world. The U.S. government ended up as 79.9 percent owner of the GSEs, receiving preferred stock on terms that essentially crushed the existing shareholders. (The precise level of ownership was chosen in light of accounting rules that would have brought GSE assets and liabilities onto the government balance sheet at 80 percent ownership.) The real action here was the two $100 billion keepwells, which were meant to effectuate the now-explicit guarantee of GSE debt and MBS coverage—they would provide just-in-time capital injections as losses were realized and ensure that Fannie and Freddie had the financial ability to service their debt and insurance obligations. The Treasury could not by law make GSE debts full-faith-and-credit obligations of the U.S. government— this could only happen through an act of Congress that changed the GSE charters. Unfortunately, the keepwells were not well explained by the Treasury, and it took some time for market participants to understand that they

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were the explicit guarantee—and even then, some observers questioned whether $100 billion was enough to cover possible losses at either firm. As with many decisions made quickly at the Treasury in this period, the figure of $100 billion did not receive considered discussion across the building and was eventually revised upward by the Obama administration. The conservatorship arrangement left unanswered the question of the long-term status of Fannie and Freddie. This was by necessity, since any such decision required congressional action to amend the firms’ charters. An unfortunate consequence, however, was that borrowing costs for the GSEs remained above those for Treasury debt. Even though the public balance sheet was effectively behind the firms, this could change in the future, and the spread over Treasuries seemed to reflect this uncertainty. The confusion over what the Treasury could and could not do was evident in the writings of outside observers. In his blog on November 25, 2008, for example, New York Times columnist Paul Krugman wrote, “the Bush administration, weirdly, has refused to declare that GSE debt is backed by the full faith and credit of the US government.” Krugman wondered whether this reflected politics. No politics were involved: the Treasury did not do this because it was not legal. Although the criticism of the Bush administration was off target, the Treasury had not explained the situation clearly. The long-term status of the GSEs remains at this writing to be decided by Congress. Each of the GSEs before conservatorship could be thought of as two related entities under one roof: a securitizer and monoline insurer that packaged and guaranteed mortgages with relatively good underwriting standards, and a hedge fund that leveraged the funding advantage from its implicit guarantee. Their retained portfolios were the embodiment of this positive carry and the source of the systemic risk, since scaling up the balance sheet with MBS purchases had driven the GSEs’ massive borrowing. It was clear that the desired long-term outcome for the GSEs was to wind down the portfolios. Indeed, the agreements struck at the time of the conservatorship explicitly committed the firms to do so over time, starting in 2010. In the meantime, however, the portfolios were a tool with which to support the housing market, and the Treasury wanted there to be upward room for more MBS purchases so that homebuyers would not face higher interest rates. As a result, Treasury officials, including the secretary, did not talk directly about winding down the portfolios, out of fear that this would fluster markets and cause a spike in interest rates paid by the GSEs. This tension was not resolved until later in the year, with the November 25, 2008, announcement by the Fed that it would fund the GSEs directly by purchasing their debt and MBSs.

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Treasury staff did draw up sketches of long-run plans for the GSEs, and Secretary Paulson spoke publicly on this topic in early January 2009. He favored turning the GSEs into a utility-like company, with private shareholders but government regulation. This preference seemed to be driven by a view that there would be substantial waste from the duplication involved with multiple GSEs, which was an approach favored by some at the Fed. A possible alternative would combine the two, with one or two GSEs running the automated networks by which banks originating mortgages sold conforming loans to the GSEs, and then a multitude of financial institutions competing with each other to securitize those loans into MBSs that would receive a government-backed guarantee. Such a restructuring would be along the lines of the present credit card market, which consists of a few large networks such as Visa and MasterCard but many credit card issuers in fierce competition. The agreements struck with the GSEs took one small step in the direction of fostering future competition, in that the companies would have to pay a fee to the government for the explicit backing of the securities they issued starting in 2009. The details remain to be determined, but one could imagine over time allowing banks to pay such a fee and receive government backing on their securitizations of conforming loans. This would allow entry, which, one hopes, would drive innovation for the benefit of American homebuyers. Eventually the GSEs could become boutique financial firms rather than behemoths, or they might even one day acquire banks and become normal financial services firms. All of this, however, is for the future.

VIII. “Free Market Day”: Lehman Brothers and AIG The way Congressman Barney Frank put it at a hearing at which I testified on Wednesday, September 17, was that we should celebrate the previous Monday, September 15, as “Free Market Day,” because on that day Lehman Brothers was allowed to fail and the free market to work. On the 16th, however, AIG had been bailed out, so, Chairman Frank continued, “the national commitment to the free market lasted one day,” but we should celebrate that day.7 The decision not to save Lehman Brothers is perhaps the most hotly debated decision of the entire crisis. Secretary Paulson and Chairman 7. As quoted by the Wall Street Journal on blogs.wsj.com/economics/2008/09/17/ barney-frank-celebrates-free-market-day/.

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Bernanke have made the point that with the firm evidently insolvent, they had no authority to save it: the Treasury had no such authority whatsoever, whereas the Fed could provide liquidity but not capital. The Fed can, however, lend against collateral to its satisfaction, and so in principle it could have lent against Lehman’s unencumbered assets—essentially what it did with AIG. This would not have saved Lehman—indeed, it would have concentrated losses on the rest of the firm—but it might have provided time for a more orderly dissolution. Indeed, there are estimates that the disorderly bankruptcy reduced the recovery value of the firm by billions of dollars. The view at Treasury, however, was that Lehman’s management had been given abundant time to resolve their situation by raising additional capital or selling off the firm, and market participants were aware of this and had time to prepare. In the end there was no one prepared to buy Lehman with any realistic amount of government assistance as had been the case with Bear Stearns. On Monday, September 15, it did not look like the outcome of Lehman’s bankruptcy would be the start of the third and most difficult phase of the crisis (the first being from August 2007 to the collapse of Bear Stearns). What we did not realize would occur next were two things: the breaking of the buck by the Reserve Fund, and the reaction of foreign investors to the failure of Lehman. It is hard to see how the Treasury could have anticipated that the Reserve Fund money market mutual fund would incur such heavy losses from Lehman commercial paper and medium-term notes that it would break the buck, with its net asset value slipping below par. We might have better anticipated, however, that foreign investors were not prepared for Lehman to collapse—after all, there is an evident gulf in the understanding of policy actions even in moving from Washington to New York or Boston; this deficit of clarity grows only more severe across borders and oceans. Together these events led to a run on money market mutual funds, which in turn caused commercial paper markets to freeze up. If left unstopped, this would have led issuers of commercial paper to turn to their backup lines of credit—meaning that banks would have needed to massively fund these lines simultaneously under circumstances they had never contemplated, and then hoard capital against those lines. As discussed by Victoria Ivashina and David Scharfstein (2008), banks in the fall of 2008 did fund these lines as companies drew on them as a precautionary measure, but this played out over time rather than all at once. From the Treasury’s perspective, all this looked like a broad run on the financial system. The panic in the money market mutual funds led investors to pull out roughly $200 billion net from these vehicles from September 5

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to 19—more than 7 percent of assets in the funds. In the face of these largescale redemptions, money market mutual fund companies began to hoard cash rather than invest in wholesale funding instruments such as commercial paper, repo agreements, and certificates of deposit. As the wholesale funding market dried up, broker-dealers began cutting their credit lines to clients such as hedge funds and other counterparties. This in turn threatened to lead to fire sales of assets and a disorderly deleveraging, with potentially catastrophic consequences across the entire financial system. The focus at the Treasury and the Fed was on the commercial paper market. As the three-month Treasury rate fell nearly to zero, the rate on overnight asset-backed commercial paper jumped from 2.4 percent on Friday, September 12, to 5.7 percent on Wednesday, September 17. Firms were reporting to the Treasury, however, that they could not obtain funds at all. It is hard to know how to evaluate this; economists instinctively believe that there is some interest rate at which lenders will lend, on a highly secured basis, to blue chip industrial companies, provided the latter are willing to pay. Other companies said they could issue commercial paper only at very short maturities: issuance of term commercial paper (80+ days), for example, fell from $13.7 billion on Friday, September 12, to $2.4 billion on Friday, September 19, and over 70 percent of commercial paper issued by financial institutions was at one- to four-day maturities, compared with only about 50 percent previously. One possibility is that there are transition costs in asset allocation decisions: once the money market mutual funds stopped buying commercial paper, there was simply no ready buyer to take their place—it would take time for other potential investors to observe rising yields, evaluate particular assets, and then buy. In the meantime, companies calling the Treasury worried about whether they would have the liquidity to make payroll. Meanwhile in this chaotic week, AIG failed on Tuesday, September 16, and was kept afloat by emergency lending from the Fed. Treasury staff were sent to the New York Fed for weeks to negotiate the terms of the support package for AIG that was eventually announced on October 8. If Monday, September 15, felt like a good day at the Treasury in that the market was allowed to work (and it was too soon to know the full adverse ramifications), Tuesday, September 16, when AIG was not allowed to fail, felt much the opposite. Saving AIG was not what anyone wanted, but at the time it seemed the only possible course of action. The belief at the Treasury and the Fed was that bankruptcy at AIG would have far-reaching and disruptive effects on the financial system and on American families, as failure of the parent firm disrupted the operating companies that provide

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insurance in the United States and around the world. AIG had $1 trillion in assets at the time of its crisis; the firm was one of the world’s largest insurance companies, the largest property and casualty insurer in the United States, and a leading provider of insurance and annuity products and retirement services. Individual 401(k) retirement plans would have been at risk, because AIG insured the returns of large mutual funds. Nonfinancial businesses would also have come under pressure because AIG provided credit guarantees to bank loans, and thus its failure would have forced banks to raise capital. Moreover, money markets had even more exposure to AIG than to Lehman. In sum, AIG was larger, more interconnected, and more “consumer facing” than Lehman. There was little time to prepare for anything but pumping in money—and at the time only the Fed had the ability to do so for AIG. Eventually the AIG deal was restructured, with TARP funds replacing Fed lending, to give AIG a more sustainable capital structure and avoid a rating downgrade that would have triggered collateral calls. As time went on, it became clear that AIG was a black hole for taxpayer money, and perhaps a retrospective analysis will demonstrate that the cost-benefit analysis of the action to save AIG came out on the other side. But this was not apparent at the time.

IX. Launching the TARP With markets in disarray, Secretary Paulson on Wednesday, September 17, set out three principles for Treasury staff in how to deal with the crisis: 1. Simplicity. Any policies adopted should be readily understood by markets. 2. Actions should be decisive and overwhelming. This was a lesson from the experience with the GSEs, where the initial July announcement left the situation unresolved. 3. Actions must have the explicit endorsement of Congress. The secretary made clear that a large-scale intervention would be undertaken as fiscal policy; he would not ask or expect the Fed to take on a massive bank rescue, and he would not look for a statutory loophole through which to commit massive amounts of public funds (for example, by reinterpreting the July housing bill to tap into the $300 billion that had been authorized but not used for the Hope for Homeowners program since the program was not yet in operation). Treasury staff had worked late into the night on Wednesday, September 17, on a series of calls with staff from Fed headquarters and the New York Fed, to come up with options that included ways to add liquidity to the par-

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ticular markets under stress and approaches to shore up the financial system broadly. That day already, the Treasury had announced the Supplementary Financing Program under which the Treasury would borrow, through special bill issues, to soak up cash on behalf of the Fed (a program that became redundant once the Fed was given the authority to pay interest on deposits), and the SEC had put into effect an emergency ban on short selling of stock of financial companies. Opinions about this latter action at the Treasury and other government agencies differed sharply: economists were skeptical that reducing liquidity in markets would be helpful, whereas those with market backgrounds thought it was important to short-circuit “predatory” behavior in the markets. Liquidity options focused on money market mutual funds and the commercial paper market. After rapid consultations with industry participants, the Treasury announced on Friday morning, September 19, in a pre-market conference call, a temporary guarantee program for money market mutual funds to directly stem the panicked withdrawals. At the same time the Fed announced its Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility, to provide money market funds with liquidity so that they could avoid fire sales of their assets in the face of redemption pressures. Fund managers were quite positive about the Treasury guarantee until they realized they would have to pay for it; most funds eventually participated, but not happily (and with no subsequent failures, the guarantee will be a moneymaker for taxpayers). There was incoming fire at the same time from banks, who (reasonably) complained that the guarantee put them at a competitive disadvantage against the money market funds. This became a familiar story: nearly every Treasury action had some side effect or consequence that we had not expected or had foreseen only imperfectly. Other options included action by the SEC to reinstate the so-called uptick rule, which prohibits short selling of a stock when the price has just declined from one trade to the next, or to require disclosure on short positions, having the Fed allow investment banks to convert rapidly into bank holding companies (which Goldman Sachs and Morgan Stanley did the next weekend), or changes in accounting or tax rules to foster bank consolidation. Guidance on a related tax issue—the so-called section 382 rule on the use of tax credits from net operating losses of acquisitions—was released by the Treasury to some controversy later in September. The controversy arose because of reports that this action played a role in the acquisition of Wachovia by Wells Fargo; the guidance was repealed in the February 2009 stimulus bill. Everyone was aware that this was not the time to propose fundamental changes in the regulatory structure of the financial

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system, but it was important to ensure that any steps taken not conflict directly with long-term goals such as had been set out in the Blueprint. The actions taken with respect to money market mutual funds and commercial paper seemed useful but incremental—it was a sign of the times that so drastic a step as using the Treasury’s main source of emergency funding to put a blanket guarantee on heretofore-unguaranteed assets seemed incremental. What was still needed was action to get ahead of the downward market dynamic and broadly stabilize the financial system. The options were familiar from the “break the glass” work back in March and April: buy stakes in banks directly, buy the toxic assets, or dramatically expand the FHA and Hope for Homeowners programs to refinance loans and improve asset performance from the bottom up. Buying stakes in banks would constitute a “high powered” capital injection, whereas buying assets would add liquidity but also inject a wedge of capital to the extent that the price paid after the announcement of the program was higher than the price ex ante (because simply announcing an asset purchase program would boost asset prices). Secretary Paulson and Chairman Bernanke went to Capitol Hill Thursday night, September 18, to tell congressional leaders that the problems in financial markets posed a severe threat to the economy, and that they wanted authority to buy the illiquid assets that were creating uncertainty about the viability of firms at the core of the financial system. Equity markets had rallied strongly that day even before the meeting, evidently sparked by afternoon comments from Senator Charles Schumer (D-NY) that the Treasury and the Fed were working on a “comprehensive solution” to the financial market difficulties. Senator Schumer had it exactly right— but no one at the Treasury could figure out what he actually knew when he spoke. On Saturday, September 20, the Treasury sent Congress a “Legislative Proposal for Treasury Authority to Purchase Mortgage-Related Assets”— a three-page request for a $700 billion fund to be used over the following two years. The proposal sought maximum flexibility, allowing the secretary to determine the terms and conditions for purchases of “mortgagerelated assets from any financial institution having its headquarters in the United States.” In doing so, section 3 of the proposal instructed the secretary to “take into consideration means for (1) providing stability or preventing disruption to the financial markets or banking system; and (2) protecting the taxpayer,” while section 4 required reports to Congress. Section 8 was to raise immense controversy, with its assertion that “Decisions by the Secretary pursuant to the authority of this Act are nonreview-

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able and committed to agency discretion, and may not be reviewed by any court of law or any administrative agency.” The legislation eventually enacted—the Emergency Economic Stabilization Act of 2008—showed if anything that there had been a counterreaction, as it provided abundant layers of oversight, including by the Government Accountability Office, a new inspector general specially for the TARP, and a congressional oversight panel. Treasury staff were soon to venture that there would be more people working on TARP oversight than on the TARP itself. The initial proposal was meant purely as a starting point, not as a demand. In retrospect, however, the sparseness of those three pages was a communications mistake that foreshadowed later recriminations. Eventually the lengthier EESA was negotiated with Congress, but the core was the same: the Treasury would have broad authority to purchase $700 billion of assets through the TARP, with the money split into two equal tranches (technically the Treasury had access to only an initial $250 billion, but an additional $100 billion could be obtained without a further role for Congress). Most of the negotiation was over issues relating to executive compensation and warrants. Members of Congress eventually settled for fairly modest restrictions on compensation (their main focus), but congressional staff insisted that the government should receive warrants in the firm selling assets to the government rather than warrants relating to the future performance of the specific assets purchased. Congressional staff also insisted on a provision to guard against “unjust enrichment,” which was defined as the Treasury buying an MBS for more than the seller had paid for it. This effectively made it impossible for, say, a hedge fund to buy assets from a bank before the TARP got up and running and later sell those assets to the Treasury. This was counterproductive; it ran precisely counter to the goal of using the TARP to get illiquid MBSs off bank balance sheets. But this obvious point fell on deaf ears on the Hill. The TARP proposal was voted down in the House of Representatives on September 29, and an amended bill was then enacted on October 3. President Bush signed the bill on arrival and then came over to the Treasury to give a pep talk to staff assembled in the department’s Diplomatic Reception Room. Always gracious, the president had warm words for the Treasury team, including recognition of the Treasury dining room staff, who had become part of the weekend efforts. While Congress debated the legislation, markets got worse—the S&P 500 index fell almost 9 percent the day the House rejected the bill—and conditions continued to deteriorate after EESA was enacted. One-month and three-month LIBOR rates rose another 100 basis points after EESA was

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approved, and stock market volatility as measured by the VIX went up from about 30 percent on September 19 to 45 percent on October 3 and 70 percent on October 10. After EESA was approved, the amount of outstanding commercial paper fell by another $160 billion, or nearly 10 percent, and financial institutions were issuing nearly 90 percent of their commercial paper on a one- to four-day basis. The Dow Jones Industrial Average fell 18 percent, or almost 1,900 points, the week after EESA was approved. It is hard to remember from the vantage point of mid-2009, when the United States and other nations are in the midst of a severe economic downturn, but in late September and early October of 2008 it was a challenge to explain to people that what was happening in credit markets mattered for the broad economy—that it would affect the proverbial Main Street, not just lower Manhattan. By mid-October, however, everyone understood that the crisis was real. Families stopped spending, while firms stopped hiring and put investment projects on hold. The economy had been deteriorating since July after having been in a sideways grind for the first half of 2008. Activity pitched slightly downward by some economic measures, but GDP growth remained positive in the first and second quarters, even though growth was not strong enough to maintain positive job growth or prevent rising unemployment. In October and beyond, everyone got the message to pull back on spending all at once—and the economy plunged. For some time within the Treasury, we had been analyzing statistical relationships between financial markets and the real economy. Back in February we had predicted in internal analysis that the unemployment rate, which had been only 4.9 percent in January, would reach 5.5 to 6 percent by year’s end as the economy slowed, but would hit 6.5 percent or more if the problems in financial markets became worse than expected. That was the limit of the ability of our (linear) models to predict the worst, although we acknowledged and explained this limitation in the prose of the accompanying memos. In fact, the unemployment rate reached 7.2 percent in December 2008, en route to 8.1 percent by February 2009, with yet-higher rates to come. Many factors were at work to dampen consumer and business spending, including the weak and deteriorating job market and huge wealth losses in both housing and equity markets. Yet the way in which the TARP was proposed and eventually enacted must have contributed to the lockup in spending. Having long known that the Treasury could not obtain the authorities to act until both Secretary Paulson and Chairman Bernanke could honestly state that the economic and financial world seemed to be

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ending, they went up and said just that, first in a private meeting with congressional leaders and then several days later in testimony to Congress on September 23 and 24. Americans might not have understood the precise channels by which credit markets would affect the real economy, but they finally realized that it was happening. And whether or not they agreed with the proposed response of buying assets with the TARP, they could plainly see that the U.S. political system appeared insufficient to the task of a considered response to the crisis. Surely these circumstances contributed to the economic downturn, although the extent to which they did remains for future study. A counterfactual to consider is that the Treasury and the Fed could have acted incrementally, with backstops and a flood of liquidity focused on money markets and commercial paper, but without the TARP. With financial institutions beyond Lehman weakening as asset performance deteriorated, it seems likely that the lockup would have taken place anyway, and perhaps sooner than it did. The proposal to buy assets was met with substantial criticism from academic economists, with a leading source of skepticism being faculty at the University of Chicago’s Booth School of Business (where, ironically, I taught a course on money and banking to MBA candidates in the spring of 2009 after leaving the Treasury). There was little public defense of the proposal—instead, the Treasury’s efforts were aimed mainly at the 535 members of Congress whose votes were needed. These were difficult issues to explain to the vast majority of Americans who had not yet felt the direct impact of the credit market disruption in their daily lives, yet it strikes me as a fair criticism that the Treasury did not try hard enough. So far as I know, I provided the only detailed public defense of the Paulson plan at the time that addressed criticisms from both academic economists and market participants. In a September 25, 2008, posting on Harvard economics professor Gregory Mankiw’s blog, I addressed three common concerns about the Treasury’s proposal to buy assets.8 The first criticism was that the only way the Treasury plan could work was if the Treasury intentionally overpaid for assets. Implicit in this criticism was either that the Treasury would not overpay, and thus the plan would not work, or that the Treasury intended to bail out financial institutions (starting, the cynics inevitably said, with the secretary’s former firm, Goldman Sachs). This is simply wrong in both directions. At the Treasury, we were already working hard to set up reverse auctions with which to buy 8. “A Defense of the Paulson Plan,” Greg Mankiw’s Blog, September 25, 2008. greg mankiw.blogspot.com/2008/09/defense-of-paulson-plan.html.

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structured financial products such as MBSs, focusing on mechanisms to elicit market prices. On this we received a huge amount of help from auction experts in academia—an outpouring of support that to us represented the economics profession at its finest. There was no plan to overpay. The announcement of the proposal (or rather, Senator Schumer’s announcement) had lifted asset prices by itself. If the Treasury got the asset prices exactly right in the reverse auctions, those prices would be higher than the prices that would have obtained before the program was announced. That difference means that by paying the correct price, Treasury would be injecting capital relative to the situation ex ante. And the taxpayer could still see gains—say, if the announcement and enactment of the TARP removed some uncertainty about the economy and asset performance, but not all. Then prices could rise further over time. But the main point is that it is not necessary to overpay to add capital. The second criticism of the plan to buy assets was that in order to safeguard the taxpayer’s interests, the warrants in the plan needed to give the government additional protection (that is, it should pay a lower price ex post) if the assets being purchased turned out to perform markedly worse than was contemplated at the time of the transaction. This would have been a valid point had the warrants in question been specific to the assets being purchased. But this was not the case—as already noted, congressional staff had insisted instead that the warrants be on the firms selling the assets, not on the assets themselves. Thus, the point being made by academic and other critics was a non sequitur. Instead, warrants proved to be a huge hassle for the auctions in that they diluted the price signal and thereby confused the bidding. This was a straightforward application of the Modigliani-Miller theorem. Rather than bid to sell assets such as MBSs at a particular price to the Treasury, firms would have to bid to jointly sell both MBSs and stakes in the selling firm. If the warrants and assets were identical across sellers, the price of the assets would simply adjust to net out the value of the warrants. Modigliani-Miller implies that the price of the asset (assuming the auction gets it right) will adjust to offset the value of any warrants the Treasury receives. In this case of a reverse auction, imagine that the price of an asset is set at $10. If the Treasury instead demands warrants for future gains of some sort, then the price will rise by the expected value of the warrants. If that value is, say, $2, the Treasury will pay $12 total for the asset and the warrants. Working with academic experts, we came up with a reverse auction mechanism that would go a long way to make for apples-to-apples com-

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parisons across different MBSs. The auction would not be perfect—we knew that it was possible only to minimize adverse selection, not to eliminate it. Firm-specific warrants confounded this, since even if the MBSs being offered by seller A were identical to those offered by seller B, the warrants on firms A and B would not be identical. (We considered using penny warrants—essentially common stock—to get around the problem but concluded that this was contrary to congressional intent.) All of this resulted from the insistence in Congress on this type of warrant. Ironically, critics of the September blog posting asserted that the Treasury did not understand the Modigliani-Miller theorem, when in fact it was the critics who did not understand the nature of the warrants specified by Congress. The third criticism of the original plan to purchase assets was that it would be better to inject capital into banks—to buy parts of institutions instead of the assets they held. Capital injections were allowed even in the initial three-page proposal, under which Treasury could purchase any mortgage-related assets, including shares of companies that originate mortgages. The problem with this criticism is that Secretary Paulson never would have gotten legislative authority if he had proposed from the start to inject capital into banks. The secretary truly intended to buy assets—this was absolutely the plan; the TARP focused on asset purchases and was not a bait-and-switch maneuver to inject capital. But Secretary Paulson would have gotten zero votes from Republican members of the House of Representatives for a proposal that would have been portrayed as nationalizing the banking system. And Democratic House members would not have voted for the proposal without the bipartisan cover of votes from Republicans. This was simply a political reality—and a binding constraint on the Treasury. The calls from academics to inject capital were helpful, however, in lending support for the eventual switch to capital injections (even though at times the vitriolic criticism was frustrating in that it was so politically oblivious). A similar calculus applies to suggestions that holders of bank debt should have been compelled to accept a debt-for-equity swap. As Luigi Zingales (2008, p. 4) notes, debt-for-equity swaps could “immediately make banks solid, by providing a large equity buffer.” All that would be required, according to Zingales, was a change in the bankruptcy code. A major change to the bankruptcy law had previously been enacted (for better or for worse, depending on one’s point of view) with the Bankruptcy Abuse Prevention and Consumer Protection Act of 2005, but this was the culmination of years of legislative debate. Thus, the idea of a further instantaneous change in the bankruptcy code was unrealistic. Indeed, efforts to

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make such changes in the middle of the crisis would have reopened the debate over the 2005 act, along with controversial provisions such as the mortgage cramdown. The simple truth is that it was not feasible to force a debt-for-equity swap or to rapidly enact the laws necessary to make this feasible. To academics who made this suggestion to me directly, my response was to gently suggest that they spend more time in Washington.

X. From Asset Purchases to Capital Injections Secretary Paulson’s intent to use the TARP to purchase assets reflected a philosophical concern with having the government buy equity stakes in banks: he saw it as fundamentally a bad idea to have the government involved in bank ownership. From the vantage point of early September, it still looked like buying $700 billion of assets would be enough to settle the markets: there were about $1 trillion each of whole loans and structured products such as MBSs and CDOs on U.S. firms’ balance sheets, so that $700 billion would have been sufficient to add liquidity, improve price discovery by closing bid-ask spreads, and inject some measure of capital relative to the situation ex ante. As markets continued to deteriorate after the enactment of EESA, however, Secretary Paulson switched gears and came to favor injecting capital, since he well understood that directly adding capital to the banking system provided greater leverage in terms of providing a buffer to ensure the viability of banks against further losses from their rapidly souring assets. Confidence in the banking system continued to deteriorate, with the one-month LIBOR-OIS spread (the difference between LIBOR and the overnight index swap rate), for example, rising from around 250 basis points when EESA was enacted to nearly 350 basis points in the first full week of October, just before the three-day Columbus Day weekend (figure 2). With confidence rapidly ebbing in the banking system, the secretary, in consultation with the Fed chairman and New York Fed President Timothy Geithner, instructed the “deal team” at the Treasury to prepare term sheets that spelled out the financial arrangements under which capital would be injected into banks. Discussions began as well with the FDIC around the middle of the week of October 5 about guarantees on bank debt—an idea that we were hearing about from Wall Street economists and which had some support at both the Treasury and the Fed. As Pietro Veronesi and Zingales (2009) have shown, these guarantees involved a huge benefit for market participants—most of the “gift” calculated by Veronesi and Zingales arises from the guarantees.

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Figure 2. One-Month LIBOR-OIS Spread, April 2007–March 2009 Basis points CPP and TLGPa 350 EESA passed 300 Wachovia

250

WAMU 200

Legislation drafted

150 100

Bear Stearns

Beginning of credit crisis

Lehman, AIG GSEs IndyMac

50

Jul

Nov

Feb

May

2007

Aug 2008

Dec

Mar 2009

Source: Kashkari (2009). a. TLGP, Temporary Liquidity Guarantee Program (FDIC).

No one at the Treasury or the Fed was happy about the prospect of giving blanket guarantees, but in the midst of what appeared to be a renewed run on the banking system, this blunt instrument was seen as essential to stopping the run. This highlights the constraint that the policymaking process must be done in real time even while the rush of events continues. Treasury staff had been working on plans for capital injections for some time, focusing on matching programs under which the Treasury would invest on terms similar to what private investors received in exchange for equal investments in banks. In early October, however, banks were neither able nor willing to raise private capital on the terms available from private investors—if any were to be found. Warren Buffett had extracted a premium for investing in Goldman Sachs, but other firms did not have even that possibility available to them. In the face of these circumstances, the Treasury instead worked with bank regulators and outside counsel to develop term sheets for a stand-alone investment by the Treasury; this work went from not far past the starting line to completion in just four days, from Thursday, October 9, to Monday, October 13. It was on that Monday that the CEOs of the nine largest American banks came to the

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Treasury to meet with Secretary Paulson, Chairman Bernanke, SEC Chairman Christopher Cox, and others to be told about and ultimately accept capital injections from the TARP in the form of preferred stock purchases. This was called the Capital Purchase Program, or CPP. An important consideration with regard to the terms of the capital injections was that the U.S. executive branch has no authority to force a private institution to accept government capital. This is a hard legal constraint. The government can take over a failing institution, but this is done on a one-by-one basis, not en masse, and is not the same as injecting capital into an institution that is healthy in order to guard against future asset problems. Therefore, to ensure that the capital injection was widely and rapidly accepted, the terms had to be attractive, not punitive. In a sense, this had to be the opposite of the “Sopranos” or the “Godfather”—not an attempt to intimidate banks, but instead a deal so attractive that banks would be unwise to refuse it. The terms of the capital injections were later to lead to reports that the Treasury had “overpaid” for its stakes in banks, which is true relative to the terms received by Warren Buffett. But this was for a policy purpose: to ensure broad and rapid take-up. The terms of the CPP—the TARP’s program to put capital into “good banks”—allowed banks to sell preferred stock to the Treasury in an amount equal to up to 3 percent of their risk-weighted assets. The annual interest rate on the preferred shares was 5 percent for five years and then increased to 9 percent, meaning that banks would have a substantial incentive to pay back the money at that point. This made the funds more of a five-year bridge loan than high-quality capital. EESA was about distressed assets, which might have seemed at odds with the notion of the CPP as a “good bank” program, but the idea was that the low level of confidence among banks, as indicated by the soaring LIBOR-OIS spread, meant that the whole financial system was under stress. Capital injections would foster stability in banks in particular, and thus in the financial system as a whole, initially by ensuring that banks had the capacity to lend against a sufficient capital buffer and would not have to hunker down and hoard capital. The ultimate goal was to improve confidence in the system so that over time private capital would again invest in the banking system. Other terms were similarly aimed at ensuring broad uptake: the Treasury wanted no part of running banks, so the preferred shares would be nonvoting except when an issue affected an entire class of investors in a way that would adversely affect the taxpayer’s interest. The Treasury received warrants with a 10-year maturity that could be exercised at any time, with an aggregate market value equal to 15 percent of the amount of the pre-

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ferred stock; the strike price on the warrants equaled the previous-20-dayaverage stock price for each institution on the day of preliminary approval of the investment. Warrants in this context made sense in that they allowed the taxpayer to participate in any upside from increased stability in the financial system. Banks were allowed to continue to pay dividends (but not to increase them); this provision in particular drew criticism, but it, too, was aimed at ensuring broad take-up of the capital. The capital injections included rules meant to address not just the letter but also the spirit of EESA, which required participating financial institutions to meet “appropriate standards for executive compensation and corporate governance,” while avoiding such burdensome restrictions that banks would not participate or would find it difficult to attract and retain key personnel. It is worth spelling out these restrictions in some detail to make clear that the TARP from the start had reasonable provisions in place to protect taxpayers—this might not have seemed the case to someone landing in Washington in March 2009 and observing the president of the United States competing with members of Congress over who could most angrily denounce the compensation agreements at AIG (which, it should be noted, were outside the CPP). Each bank’s compensation committee would be required to review incentive compensation features each year with the CEO, the CFO, and the three highest-paid executives to ensure that contracts did not encourage unnecessary and excessive risk, and to certify annually that this had been done. Incentive payments for senior executives could be taken back after the fact if it was found that they had been made on the basis of materially inaccurate statements of earnings or gains or performance criteria. These rules applied to more executives than section 304 of the Sarbanes-Oxley Act (the provision that required executives to return bonuses in the event of an accounting error) and would not be limited to financial restatements. Banks could not provide senior executive officers with golden parachute payments; severance payments were capped at three times base salary, calculated as a moving average of each officer’s taxable compensation over the previous five years. And recipients of TARP capital would have to agree to limit the income tax deduction of compensation paid to each senior executive to $500,000 instead of $1 million for as long as the Treasury held a capital stake in the bank. This was not a tax rule but instead a bilateral contract between the Treasury and the firm. In sum, the TARP did not involve the Treasury in the details of setting pay, nor did it outright ban bonuses or severance pay, but it did include a number of provisions aimed at ensuring that

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taxpayer investments were not squandered through excessive executive compensation. As has been widely reported, most (but not all) of the nine CEOs needed little persuasion to accept the capital investments on Monday, October 13. Nearly 8,500 banks were eligible to receive TARP funds through the CPP, but these nine alone accounted for close to half of both the more than $8 trillion of deposits and the more than $13 trillion of assets in the U.S. banking system. In contrast, the bottom 70 percent of banks all together accounted for only about 5 percent of both total assets and total deposits. It would take time for the Treasury to inject capital into these thousands of banks. The combined actions of that Monday—the FDIC guarantee and the injections into the top nine banks—stabilized the financial sector, as demonstrated by the LIBOR-OIS spread falling back to 100 basis points (figure 2). Although this seemed like progress, it was still twice the spread that had prevailed before Lehman’s failure, suggesting that market participants were still not reassured about the soundness of financial institutions. Subsequent events were to prove their doubts correct. EESA had created a new Office of Financial Stability within the Treasury, which Neel Kashkari was appointed to head as interim assistant secretary (he had been confirmed by the Senate earlier in 2008 to be an assistant secretary for international affairs). The office borrowed personnel from across the government and brought in experts from the private sector to help get the CPP up and running. The details of the process are beyond the scope of this paper, but suffice it to say that TARP staff, working in concert with the federal bank regulators, worked diligently and effectively: a January 27 press release from the Treasury noted that the CPP team had made capital injections of $194.2 billion in 317 institutions in 43 states and Puerto Rico since Columbus Day. President Obama was to tell Congress on February 24, 2009, that he was “infuriated by the mismanagement and the results” of the assistance for struggling banks. His actions, however, belied the words on the teleprompter—the Office of Financial Stability was kept essentially whole through the presidential transition and beyond.

XI. The Decision to Call Off Asset Purchases Of the first $350 billion of the TARP, $250 billion was allocated to the CPP, which was enough for all banks that might potentially apply to get capital equal to up to 3 percent of their risk-weighted assets. It was already clear that part of the TARP would be needed to restructure the federal rescue of

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AIG, since the company needed capital rather than liquidity, and this implied that with the TARP now available, the Treasury should take this operation over from the Fed (which was done on November 10, 2008). Financial market conditions had improved since the launch of the CPP and the announcement of other actions including additional Fed facilities aimed at money markets and commercial paper issuance. But credit markets were still disrupted—and the implosion of business and household demand as output fell and the labor market sagged would make things worse. Treasury staff turned to the task of figuring out how to allocate the remainder of the TARP, a process that ultimately led Secretary Paulson to announce, on November 12, 2008, that he would not use the TARP for its original purpose of purchasing assets. This decision ultimately came down to the fact that the TARP’s $700 billion looked insufficient to buy assets on a scale large enough to make a difference while at the same time holding in reserve enough resources for additional capital programs that might be needed. What we did not fully see in late October and early November was that the Federal Reserve’s balance sheet could be used to extend the TARP. This was done in late 2008 and early 2009, with ring fence insurance applied to assets held by Citigroup and Bank of America, and then on a larger scale with the Term Asset-Backed Securities Loan Facility (TALF) announced in late 2008 and the Public-Private Investment Funds announced in 2009. We had reverse auctions to buy MBSs essentially ready to go by late October 2008—including a pricing mechanism—but faced a decision as to whether we had the resources left in the TARP to implement them. We figured that at least $200 billion was needed for the program to make a difference. With credit markets still in worse shape than before the TARP had been proposed, it seemed more important to reserve TARP resources for future capital injections, including the wherewithal to act in the face of further AIG-like situations. Secretary Paulson therefore decided to cancel the auctions. Another factor in this decision was simply time: the first reverse auction to buy MBSs might have taken place in early December but would have been small—perhaps a few hundred million dollars—while we became comfortable with the systems. The auctions would have ramped up in size but still would likely have remained at $5 billion or $10 billion a month, meaning that it could have taken two or more years to deploy the TARP resources in this way. A concern of many at the Treasury was that the reverse auctions would indicate prices for MBSs so low as to make other companies appear to be

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insolvent if their balance sheets were revalued to the auction results.9 This could easily be handled within the reverse auction framework, however: many of the individual securities are owned by only a small number of entities, so Treasury would not have purchased all of the outstanding issues of any security such as an MBS. The fraction to be purchased thus represented a demand shift—we could experiment with the share of each security to bid on; the more we purchased, the higher, presumably, would be the price that resulted. But this was yet another reason why the auctions would take time—and why to some at the Treasury the whole auction setup looked like a big science project. Further delaying the auctions was a procurement process that left us with an outside vendor that was supposed to run the auctions but whose staff did not seem to understand that the form of the auction mattered crucially, given the complexity of the MBSs and the ultimate goal of protecting the taxpayer (although, to be fair, the vendor was receiving mixed signals from within the Treasury as well). Warrants and executive compensation restrictions played havoc with setting up the auctions. For executive compensation, the administrative systems had to be able to detect, for each of the many firms (which often had many subsidiaries), when the total securities purchased crossed the congressionally determined dollar amounts at which the restrictions kicked in. And finally, the firm-specific warrants complicated the auctions, since as noted above, they confounded the effort made in the reverse auctions to ensure a level playing field across assets being offered for sale by different firms. Despite all this, by the last weekend of October, the auction team returned from a day of meetings in New York on Sunday, October 26, feeling that the asset purchases could be done, first for MBSs and then later for whole loans (for which the idea was to create “artificial MBSs” out of a random selection of the whole loans offered by banks). We would have tried two auction approaches, one static and one dynamic—the latter approach is discussed by Lawrence Ausubel and Peter Cramton (2008), who were among the academic experts providing enormous help to the Treasury in developing the reverse auctions. Meeting at the Treasury on Sunday evening, October 26, Treasury senior staff and the secretary focused on the key question of whether to proceed with asset purchases or instead to put that work on hold and focus on additional programs to inject capital and on the nascent securitization project 9. In contrast to the TALF, and to the Public-Private Investment Program announced in March 2009, the reverse auctions involved did not provide financial institutions with lowcost financing or downside risk protection, both of which effectively constitute a subsidy.

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that would use TARP money to boost key credit markets directly (and which eventually turned into the TALF). At another meeting the following Sunday, November 2, senior staff and the secretary went through the options about the uses of the remaining money in the first part of the TARP and the $350 billion in the second tranche. By the time of this second meeting, the economy had deteriorated and the tide of public opinion had begun to turn against the TARP, so much so that there were real doubts as to whether Congress would release the second stage of TARP funds. We knew that to have a chance, there had to be a well-developed set of programs to account for the money. The Treasury had to be able to explain what it was doing and how the programs fit together— never our strength. There could not be another instance of asking for money to do one thing and then using it for another as had happened with the first part of the TARP. The objectives that the TARP needed to accomplish were, in broad strokes, to continue to stabilize the financial system and avoid systemic meltdown; to improve credit markets and facilitate stronger demand by consumers and businesses; to protect taxpayers; and to help homeowners. To meet these objectives, there were several possible uses of TARP funds in late October and early November: —More capital for banks and nonbanks, including one-time situations such as systemically significant failing institutions and nonfailing financial firms other than banks. With regard to nonbanks, proposals were on the table to inject capital into the broader financial sector, including life insurers, municipal bond insurers, and private mortgage insurers. Resources for further capital would also constitute “dry powder” in case of unforeseen situations. —Asset purchases to buy illiquid MBSs and whole loans. —Foreclosure prevention or forward-looking actions to lower mortgage rates and thereby boost housing demand. This category included ideas such as directly funding the GSEs to buy down interest rates for homebuyers, something that the Fed eventually put into effect with its purchases of GSE debt and MBSs. —Direct assistance to unplug securitization channels, which had been locked up since August 2007 but had previously provided financing for auto loans, credit cards, student loans, commercial real estate, and jumbo mortgages. This nascent “securitization project” eventually grew into the TALF—a centerpiece of the programs in effect in 2009. The TARP was looking undersized against these competing alternatives, particularly as the slowing economy began to have a noticeable

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second-round impact on the financial system. Internal estimates by the New York Fed of bank losses and capital raised suggested that banks faced a capital hole above and beyond the initial $250 billion CPP of perhaps as much as $100 billion in the case of a moderate recession and perhaps another $250 billion or more in a severe recession. These would be in addition to hundreds of billions of dollars in losses among U.S. nonbank financial firms such as hedge funds and insurance companies. The decision to cancel the asset purchases was made on October 26 with this in mind. Instead, the focus was to be on developing the securitization project and a second capital program with a private match. There were some continued discussions of possible whole loan purchase programs. At even a modest scale, this activity would have allowed the secretary to say that he was fulfilling his initial promise to buy toxic assets—the bad mortgages—directly and then to address foreclosures by modifying the loans. Again, however, the decision was made that it was more important to husband the resources. With the work on asset purchases set aside, Treasury staff worked intensely during the week between October 26 and November 2 to flesh out proposals for the remaining uses of the TARP: more capital, assistance for securitization, and foreclosure prevention. To unlock the second $350 billion of the TARP, we realized that $50 billion of it would have to be used for a foreclosure prevention effort. Helping homeowners had been part of the TARP’s original mandate. Section 109(a) of EESA specified that: To the extent that the Secretary acquires mortgages, mortgage backed securities, and other assets secured by residential real estate . . . the Secretary shall implement a plan that seeks to maximize assistance for homeowners and use the authority of the Secretary to encourage the servicers of the underlying mortgages, considering net present value to the taxpayer, to take advantage of the HOPE for Homeowners Program under section 257 of the National Housing Act or other available programs to minimize foreclosures. In addition, the Secretary may use loan guarantees and credit enhancements to facilitate loan modifications to prevent avoidable foreclosures.

This language made sense in the context of buying whole loans and MBSs: the Treasury could modify the whole loans it purchased or encourage servicers to modify loans for mortgages in securitizations where the Treasury owned a large share of the MBS structure. But the EESA language never contemplated direct spending to subsidize modifications such as were occurring under the FDIC insurance loss-sharing proposal and the interest rate

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subsidy. Under EESA, the TARP was to be used to purchase or guarantee troubled assets. Implementing a foreclosure avoidance plan under the law would require the Treasury to intentionally purchase a loss-making asset, where the loss was then structured, using financial engineering, to turn into the subsidies to the parties for taking the desired actions to avoid foreclosure (as either insurance payouts or interest rate subsidies). This was hugely ironic, since at the same time that the Treasury was being pushed to use TARP resources for foreclosure avoidance, we were being criticized for having overpaid for the preferred shares in banks. From the secretary’s point of view, it was essential to husband the TARP resources to use to shore up the financial sector. By this time he was less adamant against crossing the line and using public money for foreclosure avoidance, but he did not want it to be done with TARP money. As discussed previously, however, Congress did not appear eager to record a vote that transparently spent money on foreclosure avoidance: members wanted the outcome but not any potential blame for a bailout of “irresponsible” homeowners (a reasonable concern in light of the political backlash that ensued when the Obama administration announced that it would implement the interest rate subsidy proposal). By early November it was becoming increasingly clear that what we were saving the “dry powder” for would include addressing the crisis at the automobile companies. A group of Treasury staff had worked with the Commerce Department on auto industry issues from Columbus Day on. Indeed, I went over to the Commerce Department building that Monday with a group from Treasury to meet with General Motors management. Walking out the south side of the Treasury building around noon, we strode past the television cameras that had assembled to get shots of the nine bank CEOs, whose pending arrival at Treasury had by then become known to the press. At a November 12 speech to the assembled press, Secretary Paulson formally announced that he would not be using the TARP to buy assets. The secretary fully understood that canceling the auctions would make it seem as if he was switching course yet again—first in changing from asset purchases to capital injections, and then in canceling the asset purchases altogether. He was willing to take the criticism, however, as he viewed it as essential to keep the resources available for more capital injections. The problem was that the capital program that was slated to form the core of the second wave of TARP programs was never developed. Instead, events again overtook the Treasury as problems at Citigroup and the U.S. auto companies demanded attention.

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XII. Ring Fence Insurance Schemes Two weeks later, on November 23, 2008, the Treasury, the Fed, and the FDIC jointly announced that Citigroup was being given another $20 billion of TARP capital (on less generous terms than the CPP but not as onerous as those faced by AIG before the TARP was available), and that the three federal agencies would provide guarantees against losses on a $306 billion pool of Citi assets. The Treasury put up a modest amount of TARP money as a second loss position, the FDIC took the next set of losses, and the Fed then took the rest of the downside. This position of the Treasury reflected the language of section 102 of EESA, which counted each dollar of gross assets insured by the TARP as a dollar against the $700 billion allotment. This meant that it was most efficient from a TARP perspective for the Treasury to take an early loss position and provide coverage of a narrow band in the asset structure with a high probability of loss. The Fed could then use its balance sheet to take on the rest of the risk. The crucial new development in this use of TARP resources was the use of the Fed’s balance sheet to effectively extend the TARP beyond $700 billion; the Fed decided that having the Treasury ahead of it in a sufficient loss position provided the credit enhancement for it to take further downside risk. As had been the case with the Bear Stearns transaction, it took some time for the arrangement to be understood in Washington. The transaction, it turned out, did not appear to stabilize Citigroup. This could have reflected a number of reasons, including that the pool of covered assets was still modest compared with a balance sheet of nearly $2 trillion, that the Treasury did not provide details of the assets within the ring fence, and perhaps that many market participants saw the firm as deeply insolvent. A key insight, however, is that underpricing insurance coverage is economically similar to overpaying for assets—but turns out to be far less transparent. This insight underpins both the TALF and the bank rescue programs announced by the Obama administration in March 2009. The federal government is effectively providing potential buyers of assets in either program with a two-part subsidy of both low-cost financing and low-cost insurance. This federal contribution then helps to close the bidask spread and restore functioning in illiquid markets. From the perspective of the Treasury in November 2008, the second Citi transaction meant that we had fallen behind the market and were back into reactive mode. Moreover, the downside insurance appeared to give rise to moral hazard, as Citi announced its support for the mortgage cramdown proposal. Many within the Treasury viewed this as an artifact of the

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transfer of risk to the public balance sheet inherent in the nonrecourse financing behind the ring fence insurance—Citi could make this politically popular offer because taxpayers ultimately were on the hook for the losses. A feeling of resignation likewise marked the work by Treasury staff on a similar ring fence insurance scheme and additional TARP capital promised to Bank of America late in 2008. Treasury staff nonetheless worked intensely until the transaction was formalized on January 16, 2009, the last business day of the Bush administration. In contrast, the use of the TARP to support the auto companies was straightforwardly political: Congress did not appear to want to take on the burden of writing these checks, and President Bush did not want his administration to end with the firms’ bankruptcies. A concern in the administration was that the rapid collapse of the automakers would have severe adverse consequences for an economy that was already staggering. With the incoming administration refusing to coordinate policy with regard to automakers, TARP funds were used to provide the firms with enough breathing space to give the next team a chance to address the situation. Using TARP to support unsustainable firms is akin to burning public money while industry stakeholders arrive at a sustainable long-term arrangement. This appears to be the American approach to systemically significant “zombie” firms—to use public resources to cushion their dissolution and restructuring.

XIII. Evaluation and Conclusion There is something of a playbook (to again use a football metaphor) for dealing with a banking crisis. The steps are familiar from previous crises, such as the Swedish bank crisis in the early 1990s: —Winnow the banking system by putting insolvent institutions out of business (including through nationalization where a buyer is not at hand). The key is to avoid supporting zombie firms that squander resources and clog credit channels. This was done to a modest degree with the decisions made by the federal bank regulators and the Treasury regarding which institutions would receive money from the TARP under the CPP. The denial of funds to National City Bank and its acquisition by PNC Bank, however, set off a firestorm of criticism that banks were using their TARP funds for mergers rather than to support lending. This criticism is misguided; it is fundamentally good for everyone when a strong bank that is in a position to boost lending and serve its community takes over a weak

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one that is not in that position. But this point was lost in October and onward. In any case, the furor revealed that there was no prospect of putting out of business a large number of banks. —Recapitalize the surviving banks to ensure that they have a buffer against further losses. The TARP was able to do this in a broad and rapid way. Notwithstanding President Obama’s assertion to the contrary, the CPP appears as of this writing to be a salient success of the TARP. —Resolve uncertainty about the viability of surviving banks by either taking away or “disinfecting” their toxic assets, for example through ring fence insurance. The near-term goal is to avoid having banks hunker down and ride out the uncertainty, but instead to give them the confidence to put capital to work. Over time, the goal is to bring about conditions under which private capital flows back into the banking system. I would add a fourth play, which is to ensure continued public support for the difficult decisions involved in plays one to three. An honest appraisal is that the Treasury in 2007 and 2008 took important and difficult steps to stabilize the financial system but did not succeed in explaining them to a skeptical public. An alternative approach to this challenging necessity is to use populist rhetoric and symbolic actions to create the political space within which the implicit subsidies involved in resolving the uncertainty of legacy assets can be undertaken. It remains to be seen whether this approach will be successful in 2009.

ACKNOWLEDGMENTS I am grateful to Ted Gayer of Georgetown University for helpful comments. The opinions expressed in this paper are solely those of the author and should not be ascribed to anyone else.

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References Allen, Franklin, and Elena Carletti. 2008. “The Role of Liquidity in Financial Crises.” In Maintaining Stability in a Changing Financial System: A Symposium Sponsored by the Federal Reserve Bank of Kansas City. Kansas City, Mo. Ausubel, Lawrence, and Peter Cramton. 2008. “A Troubled Asset Reverse Auction.” University of Maryland. www.cramton.umd.edu/papers2005–2009/ ausubel-cramton-troubled-asset-reverse-auction.pdf. Brunnermeier, Markus. 2009. “Deciphering the Liquidity and Credit Crunch 2007–08.” Journal of Economic Perspectives. 23, no. 1: 77–100. Center for Responsible Lending. 2008. “Voluntary Loan Modifications Fall Far Short: Foreclosure Crisis Will Continue Unabated without Court-Supervised Modifications.” VRL Issue Brief. Washington. Cordell, Larry, Karen Dynan, Andreas Lehnert, Nellie Liang, and Eileen Mauskopf. 2008. “The Incentives of Mortgage Servicers: Myths and Realities.” Finance and Economics Discussion Series Paper 2008-46. Washington: Federal Reserve Board (September). Gorton, Gary. 2008. “The Panic of 2007.” In Maintaining Stability in a Changing Financial System: A Symposium Sponsored by the Federal Reserve Bank of Kansas City. Kansas City, Mo. Greenlaw, David, Jan Hatzius, Anil K Kashyap, and Hyun Song Shin, eds. 2008. Leveraged Losses: Lessons from the Mortgage Market Meltdown: Proceedings of the U.S. Monetary Policy Forum. Rosenberg Institute, Brandeis International Business School, and Initiative on Global Markets, University of Chicago Graduate School of Business. Ivashina, Victoria, and David S. Scharfstein. 2008. “Bank Lending during the Financial Crisis of 2008” (December 15, 2008). Harvard Business School. Kashkari, Neel. 2009. “Testimony before the House Committee on Oversight and Government Reform, Subcommittee on Domestic Policy” (March 11). domesticpolicy.oversight.house.gov/documents/20090311101942.pdf Schweitzer, Mark, and Guhan Venkatu. 2009. “Adjustable-Rate Mortgages and the Libor Surprise.” Economic Commentary. Federal Reserve Bank of Cleveland (January 21). www.clevelandfed.org/research/commentary/2009/012109.cfm. Veronesi, Pietro, and Luigi Zingales. 2009. “Paulson’s Gift.” University of Chicago Booth School of Business. www.aeaweb.org/annual_mtg_papers/2009/. Wallison, Peter J., Thomas H. Stanton, and Bert Ely. 2004. Privatizing Fannie Mae, Freddie Mac, and the Federal Home Loan Banks: Why and How. Washington: American Enterprise Institute for Public Policy Research. Zingales, Luigi. 2008. “Plan B.” The Economist’s Voice 5, no. 6, article 4.

Comments and Discussion COMMENT BY

RENÉ M. STULZ This paper by Phillip Swagel is an extremely useful one. The author deserves credit for providing an explanation of what the Treasury was up to from the start of the credit crisis to the end of 2008 and why. As a result of this paper, generations of future economists will have a better understanding of the thinking behind various actions by the Treasury. No doubt this is also a very brave paper, because it would be surprising if future generations thought that 2008 was a time during which the Treasury addressed the financial market crisis with sufficient wisdom, skill, and foresight. In this discussion I will not question a key theme of the Swagel paper, namely, that many actions that the Treasury might have wanted to undertake were simply not feasible politically, because Congress would not have approved them. I have no expertise on this issue. I am also willing to give Swagel and the Treasury much of the benefit of the doubt when it comes to actions taken in the midst of the market panic after Lehman Brothers’ bankruptcy. Had I been in the position of Treasury officials at that time, I would have tried to do my best in very difficult circumstances to help the financial markets. But it would be presumptuous to argue that I would have done better than they did. I do not question that these officials were trying to do their best at that time. Where these officials should not receive the benefit of the doubt is for what happened before the fall of Lehman. One could argue that the difficulties at mortgage banks early in 2007, as well as the sharp decline of various ABX indices at that time, should have been a wake-up call for regulators and the Treasury that there were problems in the housing market. The next wake-up call was one that could not possibly have escaped them. It was what John Taylor and John Williams (2008) call the “black 64

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swan” in the money markets in early August 2007. From then on it was clear that the financial markets and financial institutions were in unknown territory. In short order this black swan was followed by massive downgrades of collateralized debt obligations and securitization tranches, by a dramatic reduction in asset-backed commercial paper outstanding, and by a freeze in the markets for asset-backed securities. In the language of Frank Knight, investors seemed to go from the world of risk, where probabilities can be assigned to foreseeable outcomes, to the world of uncertainty, where they thought they had little clue about what the possible outcomes were and how to assign probabilities to them. From then until the fall of Lehman, there was ample time for regulators and the Treasury to have taken action. The road to Lehman’s bankruptcy was marked with still more wake-up calls. The fall of Bear Stearns in the spring of 2008 made it transparently clear that runs on investment banks could take place, because these banks were funded with large amounts of overnight repos. After Bear Stearns’ failure, one had to know that investment banks were fragile and that what to do if a run took place was a key question that had to be addressed. Yet even today a resolution mechanism for such situations is lacking. Although Swagel focuses on poor underwriting and fraud in the subprime market as a serious issue, by the time of Bear Stearns’ failure there was not much more to learn on that issue, if there ever was. The Treasury had been receiving reports on mortgage fraud all along (the so-called SAR reports received by Treasury have a mortgage fraud component) and had seen that it was increasing before the move in the ABX indices in February 2007 (Pendley, Costello, and Kelsch 2007). The fundamental fact about subprime mortgages was always that they were much less risky as long as home prices were rising. An observer transported from the summer of 2007 to today would find it hard to believe that the crisis could have caused as much damage as it did. Many have called it a subprime crisis. At the end of the second quarter of that year, subprime securitized debt amounted to $1.3 trillion.1 At least another $300 billion of subprime loans was held on banks’ books (International Monetary Fund 2008, p. 51). An extreme scenario at that time would have been that half of all subprime mortgages would go into default, generating losses of 50 percent: losses expected by Moody’s were well below this level in 2007 (Moody’s Investor Service 2008a). In such a scenario, one would have expected a loss of $400 billion on subprime 1. Steve Schifferes, “Carnage on Wall Street as Loans Go Bad,” BBC News, November 13, 2007.

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mortgages due to default—less than half of what was lost during the stock market crash of 1987, which did not lead to a massive recession. Moreover, this loss in the subprime market would not have happened all at once, but rather would have been spread over time. Why then did the problems in the subprime market lead to such serious difficulties for the financial system? The consensus explanation is that banks and broker-dealers had large positions in securities backed by these mortgages. Only a fraction, perhaps one-third, of the securitized subprime mortgages were held by banks. Had these mortgages been held as individual loans on banks’ books, the losses from their default would have been recognized slowly over time, as borrowers stopped paying interest and principal. Banks would have been adversely affected earlier through required increases in loan provisions. Much of this impact could have been absorbed by the banks out of their current income. Instead, however, banks held interests in these mortgages through securities that had to be marked to market. Marking to market meant that the increase in the probability of default of these subprime mortgages affected banks’ regulatory capital immediately. This impact was made worse by the increase in liquidity premiums charged by investors, which would not have affected mortgages kept on the banks’ books as individual loans rather than as securities. By reducing the value of the securities through marking to market, the increase in liquidity premiums adversely affected banks’ regulatory capital. Marking to market therefore dramatically accelerated the impact of the worsening prospects of subprime mortgages. As the Bank of England (2008, pp. 18–20) has pointed out, the drop in the dollar value of triple-A tranches from subprime securitizations was extremely large. However, most of that drop appears to have been caused by an increase in liquidity premiums. In fact, to this day a default on a tranche rated triple-A at issuance of a subprime securitization has yet to occur.2 Banks were suffering mark-to-market losses in plain sight in the fall of 2007. It was also clear then that banks would have to take back on their books securities held by off-balance-sheet vehicles, lowering their regulatory capital ratio even more. By then any market participant could tell that the market for many securities with subprime collateral was not functioning properly and that, as a result, banks would have to suffer costs from marking to market that had not been anticipated when the accounting rules were put in place. 2. Moody’s Investors Service (2008b) shows no impairments for triple-A tranches of subprime securitizations in 2007. Collateralized debt obligations with an initial triple-A rating backed by subprime asset-backed securities have, however, defaulted.

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In the fall of 2008, counterparty risks were a major factor in the freezing up of financial markets. It was difficult to know which financial institutions were solvent and which were not. Whereas before Lehman’s failure the markets thought that default by a major counterparty was highly unlikely, they could no longer believe that in the immediate aftermath of that debacle. The freeze of money markets that occurred at the same time heightened concerns about fire sales of assets even further. Yet counterparty risk was already a concern much earlier. In fact, regulators were well aware of it. A report by senior supervisors of the most financially developed countries, published in April 2008, reported discussions with banks in which they raised concerns about counterparty risk to guarantors. The report then added, “Subsequent to our meetings, these concerns have become more widespread and pronounced across the industry, with many firms’ exposures continuing to grow through year-end 2007” (Senior Supervisors Group 2008, p. 19) Thus, the counterparty risk problem did not come out of nowhere in September 2008. There was ample warning of it. For the markets and for most observers, August 2007 was an unexpected lightning strike. Even those who at times get credit for having forecast the crisis did not predict the events of that month. The dramatic move in LIBOR that occurred then had never before been seen, nor had many of the other events that transpired. To be sure, many of the regulators of financial institutions had gone on a long vacation. The Treasury appeared focused on reducing regulation. A commission of academics, aptly named for the secretary of the Treasury and with his apparent blessing, was pushing for deregulation in the financial industry. Even after the start of the crisis, the focus of the Treasury was still on deregulation. But in August 2007 the regulatory vacation should have been over. The focus should have been on making sure that worst-case scenarios could be handled effectively and that contingency plans were in place. It is quite clear that there was much concern about moral hazard in 2008. Yet not much can be done about moral hazard in the midst of a crisis. If the objective of letting Lehman go under was to reduce moral hazard in the future, by showing that the Treasury was willing to let large financial institutions go bankrupt, this was a complete failure. Instead, letting Lehman fail put moral hazard on steroids, and it is not clear how moral hazard will ever be restored to where it was before Lehman’s demise. Controlling moral hazard is critical when the taxpayers are the insurers of banks. It cannot be done without regulation and robust enforcement. However, the regulators also have to be provided with the right incentives to do their job. They did not have these incentives

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when the Treasury was focused on deregulation. Instead, the regulatory regime was at times too intrusive and at other times nonexistent. Unfortunately, trying to install regulation “lite” may have had the unintended effect of creating a world in which much more intrusive regulation, which may hurt economic growth in the United States, is likely. There has been much criticism of banks’ risk management practices. However, the official sector has a clear risk management task: to avoid, plan for, and resolve systemic events. No bank has failed at risk management as badly as the official sector has. Banks are not responsible for systemic risk. They face complicated trade-offs between risk and return. The task for the Treasury and the rest of the official sector was to focus on events that could endanger the financial system and be ready for them. At this they failed. REFERENCES FOR THE STULZ COMMENT

Bank of England. 2008. Financial Stability Report, no. 23. London (April). International Monetary Fund. 2008. Global Financial Stability Report: Containing Systemic Risks and Restoring Financial Soundness. Washington (April). Moody’s Investors Service. 2008a. “RMBS Investor Briefing.” New York (May 13). ———. 2008b. “Default and Loss Rates of Structured Finance Securities: 1993– 2007.” New York (July). Pendley, M. Diane, Glenn Costello, and Mary Kelsch. 2007. “The Impact of Poor Underwriting Practices and Fraud in Subprime RMBS Performance.” New York: Fitch Ratings (November 28). Senior Supervisors Group. 2008. “Observations on Risk Management Practices during the Recent Market Turbulence.” New York (March 6). Taylor, John B., and John C. Williams. 2008. “A Black Swan in the Money Market.” American Economic Journal: Macroeconomics 1, no. 1: 58–83.

COMMENT BY

LUIGI ZINGALES The end of an administration is no time to have a financial crisis, and the end of the George W. Bush administration was an especially inopportune time. With its senior staff substantially reduced and the remaining political appointees potentially distracted by concerns about their next job, the most powerful Treasury in the world found itself in late 2007 and 2008 without the human capital needed to plan for and deal with the worst financial crisis in three generations. Worse still, by that time the administration had lost the trust of Congress over the alleged weapons of mass destruction in Iraq, and the unpopular president was all but missing from the scene. This paper by Phillip Swagel provides some 60 pages of detailed description of the events of this period, yet President Bush appears

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only once, thanking the Treasury’s cafeteria staff as well as the policy team for their efforts. In this context, Swagel should be thrice commended. First, he should be commended for serving his country with passion and dedication until the end of his appointment. Although I disagree with many of the choices made, I have complete faith that Swagel worked with only with the interest of the country at heart. Second, he should be commended for consenting to serve as the public voice and face of an unpopular administration. Last but not least, he should be commended for the candor with which he has written this account of his extraordinary time at the Treasury. I think historians will long use his chronicle as the best description of what was going on at those critical moments. It is precisely this candor that makes my role as a discussant easy, perhaps unfairly so. Although I write without the full benefit of hindsight— the crisis has yet to run its course, making it too early to draw final lessons—I certainly benefit from more information and more time to process it than Swagel and the other key players had at the time. Most important, criticizing other people’s choices is much easier than improving upon them. With all these caveats, however, my role as discussant is to point out the contradictions and limitations in Swagel’s account. Only by reviewing and criticizing the decisions made in this crisis can the economic policy community train itself and prepare for the next one. Just as the analysis of the policy mistakes at the onset of the Great Depression proved useful in informing the decisions made at the onset of the current crisis, so, too, one may hope, analysis of the mistakes made at the onset of this crisis will help tomorrow’s policymakers cope with or even avoid the next one. Let me first point out the elements of Swagel’s narrative most likely to suffer from the naturally “self-serving” bias he honestly admits to. The first regards the role played by the lack of legal authority, which, according to Swagel, prevented the Treasury from taking all the actions it deemed appropriate to deal with the crisis. Obviously, the United States is a country of law, and an administration cannot intervene in financial markets without legal authority. But this limitation is not so clear cut as Swagel makes it out to be. In March 2008 the Federal Reserve had dubious legal authority to lend to Bear Stearns, yet it found a mechanism (lending to J.P. Morgan to purchase Bear Stearns) by which to do so. It had no legal authority to buy toxic assets from Bear Stearns, yet, as Swagel describes, it found a trick to make it happen. The Treasury had no authority to force the major banks to take TARP money, but by exercising moral suasion it was

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able to bring them on board. Indeed, so strong is the power of moral suasion wielded by U.S. bank regulators that bankers often joke that when the regulators tell them to jump, they can only ask, “How high?” When one considers these various capacities in their totality—the Fed’s control over whom to lend to, the FDIC’s authority to take over bank subsidiaries (but not bank holding companies) that pose a systemic risk, the Treasury’s ability to exercise moral suasion—it is clear there was some power to intervene, had there been the political will. For example, the Fed could have mandated a very large bank recapitalization, with the Treasury offering to provide the capital in case the market was unwilling to do so. Thus, the real problem was the lack of political will, and the real question is why it was lacking. Was it because the Treasury experts really thought that buying toxic assets was the right solution, or because the lobbying pressure to do so was overwhelming? Unfortunately, it is here that Swagel’s account is uncharacteristically lacking in detail: the paper contains no mention of any lobbying pressure from the financial industry. Is it possible that an industry that in 2008 spent $422 million in lobbying expenses played no role in shaping a policy so crucial for its survival? Why is the paper silent about these pressures? The second potentially self-serving bias in Swagel’s account is the emphasis on the limits imposed by Congress, which the paper amply blames as the source of all the administration’s woes. Much of the paper suggests or implies that if only Treasury officials could have made Congress do what they wanted, the world today would be a better place. To be sure, Congress has imposed and still imposes limits on what an administration can do. But this is not necessarily a bad thing; in fact, this country was founded on the premise that there should be no taxation without representation and that each branch of government should exercise a check over the others. Decisions that impose a fiscal burden on U.S. taxpayers are and should be subject to the approval of their elected representatives. Of course, Congress does not always perform this job perfectly, and often individual representatives in powerful positions pursue their own agendas rather than the interest of the American people. But that does not justify the implicit call, which percolates through Swagel’s account, for freeing the administration from congressional oversight. In fact, more useful than a blunt attack on Congress as a body would have been a detailed account of the self-serving constraints that individual members may have put on the path to a superior solution. Yet the only constraints that Swagel outlines in some detail appear to have been imposed not by self-serving minority interests, but by the lack of political (and popular) consensus on the proposed policies— which in a democracy should be a constraint.

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Beyond that, Congress’ unwillingness to appear to be rewarding people who overextended themselves financially to buy a house was not only a legitimate democratic constraint, but also good economic policy. And with the right amount of ingenuity, the negative home equity problem could have been (and still could be) resolved without violating this constraint (see, for example, Zingales 2008a and Posner and Zingales 2009). Similarly, when Congress balked at the prospect of handing out billions of dollars to the banks through the TARP, that was not a manifestation of congressional myopia, but rather an indictment of a Treasury secretary more used to strong-arming corporate boards than to eliciting popular consensus. The September 29, 2008, House vote against the TARP, far from being the short-sighted response of a hopelessly politicized Congress, was in fact a high point of American democracy. Undaunted by the dramatic headlines and the catastrophic forecasts issued by Secretary Paulson and Fed Chairman Bernanke, Congress realized the dangers involved in issuing a $700 billion blank check—and voted no. In fact, if there are grounds for criticizing Congress’s performance in this episode, it is for later reversing its vote under the enticement of a heavy dose of pork-barrel add-ons. This view of Congress as an obstacle to the Treasury’s enlightened leadership, rather than as an equal player exercising proper constitutional balance, is what leads Swagel to congratulate the Treasury and the Fed for engaging in various financial engineering maneuvers aimed at imposing a fiscal burden on taxpayers without Congress’ approval. One example is the nonrecourse loans offered by the Fed to Bear Stearns. Another is the guarantees offered to Citigroup and Bank of America. It is sad to learn that Swagel regrets that the Paulson Treasury took too long to fully appreciate the power of these tricks, leaving to the Obama administration the rare privilege of actually implementing the most deceptive ones. Having written against the use of these interventions by Paulson’s successor, Tim Geithner (Veronesi and Zingales 2009), I believe I can criticize their creation by the Paulson team without fear of being accused of bias. It is precisely these types of tricks that feed the mistrust that Congress and the American people have toward the administration. As Swagel aptly describes, congressional mistrust toward the Treasury had very negative consequences during the crisis. But Swagel’s own account provides the justification for that mistrust. We know from microeconomics that any choice can be represented as the optimal one, depending on how one characterizes the constraints that apply. Swagel’s description of the Bush Treasury’s political constraints seems calculated in exactly this manner, as a means of relieving the administration of any responsibility for making the wrong decision: if the chosen strategy was the only feasible one, it must also have been the opti-

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mal one. Ironically, Swagel persists in maintaining this fiction of an absence of alternatives even after the Treasury’s policy changed course dramatically in a matter of weeks. Whence the change in policy? The constraints had changed! This Manichean view of an enlightened elite fighting against the neutering constraints imposed by Congress prevents Swagel from discussing the other feasible options in greater detail. Since the approval of the TARP, academics have produced detailed analyses of the costs and benefits of several such alternatives: from asset purchases to debt guarantees, from equity infusions to long-term put options to a spinoff of toxic assets into a “bad bank” (Philippon and Schnabl 2009; Caballero and Kurlat 2009; Landier and Ueda 2009; Veronesi and Zingales 2008; Zingales 2009). Similar discussions should have taken place inside the Treasury and the Fed before any decision was made. Yet Swagel’s account provides no evidence that the costs and benefits were seriously debated. As he correctly points out, the turning point was the Bear Stearns crisis. Up to that point the administration could cultivate the illusion that the crisis would remain contained; afterward there was no excuse. Indeed, as Swagel recounts, it was after Bear Stearns that the Treasury started thinking about what he calls the “break the glass” policy—what to do in the event of a systemwide collapse. From the Bear Stearns rescue to the Lehman collapse, six months went by. What was the Treasury able to produce in that time? By Swagel’s own admission, only the three pages of draft legislation that Paulson presented to Congress on September 20 and that led to the TARP. There is no mention of any intellectual discussion, no mention of any internal disagreement, no mention of any assessment of costs and benefits. This deafening silence in Swagel’s account does nothing to dispel the pervasive (and, one hopes, wrong) view that the TARP was just a welfare plan for needy bankers pushed by Wall Street upon their friends in the government. Even if the TARP had been the right break-the-glass plan—which it was not, as I wrote at the time (Zingales 2008b) and as Paulson himself later admitted—the fact that the plan required two full months to become implementable (as Swagel clearly details) validates the accusation of incompetence raised against the Paulson Treasury. What would one say about a hurricane emergency plan that took two months after the calamity to start working? Why were the details of a plan that had been conceived by at least March not fully worked out by September? If lack of staff is the reason, then the Obama administration is right to make the creation of a more permanent research department at the Treasury a priority.

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Swagel’s account is extremely interesting not only for what it says, but also for what it does not say. There is no mention of any economic principles guiding the Paulson Treasury. All its actions seem to have been guided entirely by legal and political constraints, without any overarching aim. Even if one accepts the idea that these constraints were rigidly binding, a well-justified strategy would have been helpful not only in selling the plan to Congress and the country, but also in avoiding confusion in the markets. After all, the government’s actions during the course of the crisis were all over the map—from bailing out creditors but not shareholders in Bear Stearns, Fannie Mae, and Freddie Mac, to wiping out both in Lehman Brothers and Washington Mutual, to bailing out both in AIG, Citigroup, and since. In the words of the legendary Yale endowment manager David Swensen, “they’ve [acted] with an extraordinary degree of inconsistency. You almost have to be trying to do things in an incoherent and inconsistent way to have ended up with the huge range of ways that they have come up with to address these problems.”1 Nor has this inconsistency escaped the notice of ordinary Americans. In a representative survey of more than 1,000 American households conducted in December 2008, 80 percent declared that they felt less confident about investing in financial markets as a result of the type of government intervention undertaken in the last three months of 2008 (Sapienza and Zingales 2009b). This outcome did not stem from an ideological bias against government involvement; on the contrary, a majority of respondents expressed the belief that the government must regulate financial markets. What they objected to was the specifics. It is hard to estimate the real damage created by this inconsistency. What is known is that it had major negative effects on the level of trust that Americans have in the stock market (Sapienza and Zingales 2009a), leading them to shun investing in equities (Guiso, Sapienza, and Zingales 2008). In his conclusion, Swagel nicely summarizes the four key dimensions along which a rescue plan should be evaluated: shutting down the zombie banks, adequately recapitalizing the solvent ones, eliminating uncertainty about the surviving institutions, and maintaining consensus on all these actions. Swagel admits failure on the first and last counts—the Paulson Treasury was unable to be selective in the allocation of TARP money and unable to maintain political consensus—but he claims victory on the other two. A final judgment is certainly premature. 1. FOXBusiness, “Yale’s Swensen: Pols Missing the Point,” January 6, 2009 (www. foxbusiness.com/search-results/m/21735678/yale-s-swensen-pols-missing-the-point.htm).

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In 1998, after its first bank recapitalization, the Japanese government declared victory only to discover later that it would have to go through four more recapitalizations (Hoshi and Kashyap 2008). But even at this early date, Swagel’s claim of victory seems hollow. As the recent bank stress tests have shown, the capital injections under the CPP program were insufficient to make troubled institutions fully viable, but sufficient to allow insolvent ones to keep limping along. The very fact that additional interventions had to be undertaken to support Citigroup and Bank of America after the first CPP injection suggests that the second and third goals had not been reached. Although Swagel is right in pointing out that after the CPP program the tension in financial markets subsided, it is unclear whether most of the credit goes to the capital injection or to the FDIC debt guarantee. And even if the Treasury is given full credit for stopping the panic in October 2008, one cannot ignore the fact that the Treasury shares much of the blame for creating that panic to begin with. REFERENCES FOR THE ZINGALES COMMENT

Caballero, Ricardo J., and Pablo Kurlat. 2009. “Public-Private Partnerships for Liquidity Provision.” Massachusetts Institute of Technology (March 4). Guiso, Luigi, Paola Sapienza, and Luigi Zingales. 2008. “Trusting the Stock Market.” Journal of Finance 63, no. 6: 2557–2600. Hoshi, Takeo, and Anil Kashyap. 2008. “Will the U.S. Bank Recapitaliztion Succeed? Lessons from Japan.” Working Paper 14401. Cambridge, Mass.: National Bureau of Economic Research (December). Landier, Augustin, and Kenichi Ueda. 2009. “The Economics of Bank Restructuring: Understanding the Options.” Staff Position Note 2009/12. Washington: International Monetary Fund. Philippon, Thomas, and Philipp Schnabl. 2009. “Cost-Efficient Mechanisms against Debt Overhang.” Working paper. New York University. Posner, Eric A., and Luigi Zingales. 2009. “The Housing Crisis and Bankruptcy Reform: The Prepackaged Chapter 13 Approach.” Working paper. London: Centre for Economic Policy Research. Sapienza, Paola, and Luigi Zingales. 2009a. “Anti-Trust America: A Trust Deficit Is Driving Our Economy Down.” City Journal, February 27 (www.cityjournal.org/2009/eon0227pslz.html). ———. 2009b. “The Results: Wave 1.” Chicago Booth/Kellogg School. www.financialtrustindex.org/resultswave1.htm. Veronesi, Pietro, and Luigi Zingales. 2008. “Paulson’s Gift.” Paper presented at the Annual Meeting of the American Economic Association, San Francisco, January 5. ———. 2009. “Geithner’s AIG Strategy.” City Journal, February 18 (www. city-journal.org/2009/eon0218pvlz.html). Zingales, Luigi. 2008a. “Plan B.” The Economists’ Voice 5, no. 6, article 4.

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———. 2008b. “Why Paulson Is Wrong.” The Economists’ Voice 5, no. 5, article 2. ———. 2009. “Yes We Can, Secretary Geithner.” The Economists’ Voice 6, no. 2, article 3.

GENERAL DISCUSSION Several panelists praised Swagel for sharing his insider perspective and for his frank description and assessment of the events he had witnessed. Alan Blinder summarized the paper’s main point as follows: critics, particularly academics, pay too little attention to the legal and political constraints faced by the Treasury. And there are times when Treasury officials would like to take certain actions but refrain from seeking the authority because they are convinced Congress will not grant it. Blinder framed the rest of his comment by citing the motto of Montagu Norman, former head of the Bank of England: “Never explain, never apologize.” Although the Paulson Treasury never enunciated it, in Blinder’s view it operated under this motto. That said, Blinder did not believe the Paulson Treasury should be excused for its actions or its inactions. There are two ways to get around a legal constraint: either go to Congress to have the constraint relaxed, as the Paulson Treasury attempted with Fannie Mae and Freddie Mac, or get clever and find ways to get around the constraint within the law, as was done with the nationalization of AIG. Blinder disputed Swagel’s point that, in the law-based society of the United States, banks cannot be forced to accept capital. He noted that Paulson did force some banks to take capital against their will, under the pretext of preventing stigmatization of other banks, and those banks made it very clear to the press that they did not want the money. Blinder also pointed out that although the TARP legislation mentioned foreclosure avoidance 12 to 15 times, no TARP dollars were used for that purpose during the Bush administration. Daron Acemoglu noted that thinking about constraints is important, but that it is also important to consider the process, in particular its gradual nature. He described the Bush administration’s approach as taking whatever action sufficed to keep an institution alive and then waiting to see what happened next. This strategy is correct, he argued, in a singleplayer decision problem, but when dealing with markets, gradual action may be counterproductive because there is a specter of something bad, like bankruptcy, nationalization, or other types of asset sales, happening at the end. This uncertainty breeds inaction and might make the problem much worse. Instead, Acemoglu suggested a different way of thinking

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about these situations, one in which making mistakes is acceptable, and making a decision and sticking to it is better than letting events unfold gradually. Robert Hall brought to the Panel’s attention a factor that had been underdiscussed but was, in his view, responsible for some of the economic distress in the commercial paper market following the Lehman Brothers bankruptcy, namely, the failure of money market mutual funds to behave like true mutual funds. One problem is that the “penny rounding rule” of the Securities and Exchange Commission allows money market funds to pay a withdrawal as if the fund’s net asset value per share were $1 as long as the actual net asset value is between $0.995 to $1.005. This rule by itself provides a strong incentive for investors to withdraw when net asset value drops below $1. In addition, it appears that some funds did not write down their net asset value by enough when Lehman went bankrupt, further increasing the incentive for a run. Hall claimed that these two factors effectively turned money market funds into depository institutions and made them susceptible to runs. The Lehman bankruptcy might have gone more smoothly, he asserted, had the Reserve Fund, a large money market fund, immediately lowered its net asset value to a realistic level, so that early withdrawals did not have an advantage over later withdrawals. The run on money market funds caused the commercial paper market to fall apart, because these funds were among the main buyers. Hall argued that the central problem was that money market mutual funds wanted to be banks and not mutual funds, and that the SEC had failed to insist that they behave like mutual funds. Justin Wolfers complimented Swagel on a compelling paper, from which he and others had learned a great deal about the political and legal constraints on policymakers. He was distressed that economists as a group know so little about how policy is really made, and he faulted the White House and the Treasury for not better communicating those constraints, as well as the economics profession for not taking those constraints seriously. He took issue with the veneration of political naïveté expressed in Luigi Zingales’s comment, arguing instead that to take public positions on important policy issues without knowledge of the political process is a big mistake. Zingales countered that economists should say what they think are the proper actions to take, regardless of the political constraints. Constraints are endogenous, and economists can help bring pressure to bear to modify those constraints. Paulson himself, for example, changed his ideas on capital injection as a result of pressure from the economics profession. Zin-

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gales did concede that although economists need not internalize the political constraints on policymakers, they should not criticize policymakers when constraints prevent them from taking recommended actions. Caroline Hoxby questioned the asymmetry in the Treasury’s use of TARP funds. People were concerned initially about the exact value of the assets to be bought and argued over whether it was, say, 76 or 81 cents on the dollar. In contrast, no such calculations were made for the multitude of other ways the stimulus money was spent. She wondered why the Treasury did not say up front that it might inadvertently buy assets at the wrong price, thus ending up throwing money away, but that even if they bought at 76 cents something that later proved to be worth 38 cents, the amount of money thrown away was at least eventually known. She observed that today money is being thrown away in many directions, whereas the earlier troubled assets likely would have recovered their value. It concerned her that the Treasury was too worried about getting the prices right, which ended up making matters worse. Frederic Mishkin expressed concern about the Treasury’s capital injections into banks, particularly the absence of conditions placed on taking the money in order to get all the banks to take it and avoid stigma. He noted that in MBA ethics courses, students are taught to maximize shareholder value. Under this principle, capital given to a business with a lot of debt should be used to pay the shareholders and other stakeholders, and potentially to give bonuses to management. He agreed that “getting clever,” as Blinder put it, is important in tough times, and he acknowledged that the Federal Reserve had reasonably acted at the limits of its legal authority. Mishkin raised the issue of the importance of the AIG bankruptcy in comparison to Lehman Brothers. He felt that Lehman’s downturn had been expected and therefore did not shake up the markets as badly as the surprising collapse of AIG, which revealed the rot in the entire financial system, causing a systemwide blowup. David Romer asked why, if the Treasury had been working on the break-the-glass plan since March 2008, the bill that emerged in September 2008 was so minimal. He also wondered whether Swagel agreed with the claims of Paul Krugman and others that regulators interact so much with the financial markets that it causes them to give greater weight to the interests of Wall Street than to the general public welfare. John Campbell noted that during the boom years credit ratings had been extended to new types of instruments carrying systemic risk, and that this had allowed investors to buy assets within a credit rating constraint yet still load up on systemic risk. The credit rating agencies had moved outside

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their sphere of competence, and the information in their ratings had become corrupted. He wondered at what point the Treasury had become aware of this problem and whether anything could have been done early on to mitigate its effects. Benjamin Friedman expressed reservations about the use of the central bank to conduct what amounts to a shadow fiscal policy, which it does when it assumes credit risk. He noted that there is a reason why fiscal actions should go through Congress, namely, that such actions use taxpayer money, and he expressed concern that the end result, should the Federal Reserve take losses on the amounts it has advanced in private credit during this episode, would be to compromise central bank independence. He also underlined a distinction, which he felt had been overlooked, between two types of losses. The first involves a genuine loss to the economy, as in the case of a decline in the price of a home. Someone bears the loss, whether it be the homeowner, the lender, an investor who bought the securitized loan, or the taxpayer if the Treasury steps in to bail out the investor. The second involves a zero-sum loss, in which one party gains exactly what the other loses. Although the distinction might not matter to an individual institution that loses money, for the system as a whole, which is the ultimate concern of public policy, the two types of losses are very different. Charles Schultze interpreted René Stulz’s comment to say that underwriting standards were not an important factor in causing the financial crisis. But, he pointed out, between mid-September 2008 and mid-March 2009, the ABX price index for the second-half 2005 vintage of PENAAA subprime mortgage-backed securities fell from 96 to 83, whereas for the first-half 2007 vintage, the index declined from 57 to 26. Schultze argued that the large erosion of underwriting standards, combined with market uncertainty about how it had affected the quality of the portfolios of individual financial institutions, accounted for this pattern. He also thought worth mentioning, even though of little relevance to the paper, the role that annual bonuses had played in the decline of underwriting standards. In response, Stulz explained that he did not mean to say that underwriting was unimportant, but that there had been no visible change in standards consistent with the facts mentioned in the paper. He noted that fraud was very important at the end of the timeframe discussed, but he did not view it as a driving factor.

JOHN Y. CAMPBELL Harvard University

ROBERT J. SHILLER Yale University

LUIS M. VICEIRA Harvard University

Understanding Inflation-Indexed Bond Markets ABSTRACT This paper explores the history of inflation-indexed bond markets in the United States and the United Kingdom. It documents a massive decline in long-term real interest rates from the 1990s until 2008, followed by a sudden spike during the financial crisis of 2008. Breakeven inflation rates, calculated from inflation-indexed and nominal government bond yields, were stable from 2003 until the fall of 2008, when they showed dramatic declines. The paper asks to what extent short-term real interest rates, bond risks, and liquidity explain the trends before 2008 and the unusual developments that followed. Low yields and high short-term volatility of returns do not invalidate the basic case for inflation-indexed bonds, which is that they provide a safe asset for long-term investors. Governments should expect inflation-indexed bonds to be a relatively cheap form of debt financing in the future, even though they have offered high returns over the past decade.

I

n recent years government-issued inflation-indexed bonds have become available in a number of countries and have provided a fundamentally new instrument for use in retirement saving. Because expected inflation varies over time, conventional, nonindexed (nominal) Treasury bonds are not safe in real terms; and because short-term real interest rates vary over time, Treasury bills are not safe assets for long-term investors. Inflationindexed bonds fill this gap by offering a truly riskless long-term investment (Campbell and Shiller 1997; Campbell and Viceira 2001, 2002; Brennan and Xia 2002; Campbell, Chan, and Viceira 2003; Wachter 2003). 79

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The U.K. government first issued inflation-indexed bonds in the early 1980s, and the U.S. government followed suit by introducing Treasury inflation-protected securities (TIPS) in 1997. Inflation-indexed government bonds are also available in many other countries, including Canada, France, and Japan. These bonds are now widely accepted financial instruments. However, their history creates some new puzzles that deserve investigation. First, given that the real interest rate is determined in the long run by the marginal product of capital, one might expect inflation-indexed bond yields to be extremely stable over time. But whereas 10-year annual yields on U.K. inflation-indexed bonds averaged about 3.5 percent during the 1990s (Barr and Campbell 1997), and those on U.S. TIPS exceeded 4 percent around the turn of the millennium, by the mid-2000s yields on both countries’ bonds averaged below 2 percent, bottoming out at around 1 percent in early 2008 before spiking to near 3 percent in late 2008. The massive decline in long-term real interest rates from the 1990s to the 2000s is one puzzle, and the instability in 2008 is another. Second, in recent years inflation-indexed bond prices have tended to move opposite to stock prices, so that these bonds have a negative “beta” with the stock market and can be used to hedge equity risk. This has been even more true of prices on nominal government bonds, although these bonds behaved very differently in the 1970s and 1980s (Campbell, Sunderam, and Viceira 2009). The reason for the negative beta on inflationindexed bonds is not well understood. Third, given integrated world capital markets, one might expect that inflation-indexed bond yields would be similar around the world. But this is not always the case. During the first half of 2000, the yield gap between U.S. and U.K. inflation-indexed bonds was over 2 percentage points, although yields have since converged. In January 2008, 10-year yields were similar in the United States and the United Kingdom, but elsewhere yields ranged from 1.1 percent in Japan to almost 2.0 percent in France (according to Bloomberg data). Yield differentials were even larger at long maturities, with U.K. yields well below 1 percent and French yields well above 2 percent. To understand these phenomena, it is useful to distinguish three major influences on inflation-indexed bond yields: current and expected future short-term real interest rates; differences in expected returns on long-term and short-term inflation-indexed bonds caused by risk premiums (which can be negative if these bonds are valuable hedges); and differences in expected returns on long-term and short-term bonds caused by liquidity premiums or technical factors that segment the bond markets. The expecta-

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tions hypothesis of the term structure, applied to real interest rates, states that only the first influence is time-varying whereas the other two are constant. However, there is considerable evidence against this hypothesis for nominal Treasury bonds, so it is important to allow for the possibility that risk and liquidity premiums are time-varying. The path of real interest rates is undoubtedly a major influence on inflation-indexed bond yields. Indeed, before TIPS were issued, Campbell and Shiller (1997) argued that one could anticipate how their yields would behave by applying the expectations hypothesis of the term structure to real interest rates. A first goal of this paper is to compare the history of inflationindexed bond yields with the implications of the expectations hypothesis, and to explain how shocks to short-term real interest rates are transmitted along the real yield curve. Risk premiums on inflation-indexed bonds can be analyzed by applying theoretical models of risk and return. Two leading paradigms deliver useful insights. The consumption-based paradigm implies that risk premiums on inflation-indexed bonds over short-term debt are negative if returns on these bonds covary negatively with consumption, which will be the case if consumption growth rates are persistent (Backus and Zin 1994; Campbell 1986; Gollier 2007; Piazzesi and Schneider 2007; Wachter 2006). The capital asset pricing model (CAPM) implies that risk premiums on inflationindexed bonds will be negative if their prices covary negatively with stock prices. The second paradigm has the advantage that it is easy to track the covariance of inflation-indexed bonds and stocks using high-frequency data on their prices, in the manner of Viceira and Mitsui (2007) and Campbell, Adi Sunderam, and Viceira (2009). Finally, it is important to take seriously the effects of institutional factors on inflation-indexed bond yields. Plausibly, the high TIPS yields in the first few years after their introduction were due to the slow development of TIPS mutual funds and other indirect investment vehicles. Currently, long-term inflation-indexed yields in the United Kingdom may be depressed by strong demand from U.K. pension funds. The volatility of TIPS yields in the fall of 2008 appears to have resulted in part from the unwinding of large institutional positions after the failure of the investment bank Lehman Brothers in September. These institutional influences on yields can alternatively be described as liquidity, market segmentation, or demand and supply effects (Greenwood and Vayanos 2008). This paper is organized as follows. Section I presents a graphical history of the inflation-indexed bond markets in the United States and the United Kingdom, discussing bond supplies, the levels of yields, and the

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volatility and covariances with stocks of high-frequency movements in yields. Section II asks what portion of the TIPS yield history can be explained by movements in short-term real interest rates, together with the expectations hypothesis of the term structure. This section revisits the vector autoregression (VAR) analysis of Campbell and Shiller (1997). Section III discusses the risk characteristics of TIPS and estimates a model of TIPS pricing with time-varying systematic risk, a variant of the model in Campbell, Sunderam, and Viceira (2009), to see how much of the yield history can be explained by changes in risk. Section IV discusses the unusual market conditions that prevailed in the fall of 2008 and the channels through which they might have influenced inflation-indexed bond yields. Section V draws implications for investors and policymakers. An appendix available online presents technical details of our bond pricing model and of data construction.1

I. The History of Inflation-Indexed Bond Markets The top panel of figure 1 shows the growth of the outstanding supply of TIPS during the past 10 years. From modest beginnings in 1997, TIPS grew to around 10 percent of the marketable debt of the U.S. Treasury, and more than 3.5 percent of U.S. GDP, in 2008. This growth has been fairly smooth, with a minor slowdown in 2001–02. The bottom panel shows a comparable history for U.K. inflation-indexed gilts (government bonds). From equally modest beginnings in 1982, the stock of these bonds has grown rapidly and accounted for almost 30 percent of the British public debt in 2008, equivalent to about 10 percent of GDP. Growth in the inflationindexed share of the public debt slowed in 1990–97 and reversed in 2004–05 but otherwise proceeded at a rapid rate. The top panel of figure 2 plots yields on 10-year nominal and inflationindexed U.S. Treasury bonds from January 1998, a year after their introduction, through March 2009.2 The figure shows a considerable decline in both nominal and real long-term interest rates since TIPS yields peaked early in 2000. Through 2007 the decline was roughly parallel, as inflationindexed bond yields fell from slightly over 4 percent to slightly over 1. The online appendix can be found at kuznets.fas.harvard.edu/∼campbell/papers.html. 2. We calculate the yield for the longest-maturity inflation-indexed bond outstanding at each point in time whose original maturity at issue was 10 years. This is the on-the-run TIPS issue. We obtain constant-maturity 10-year yields for nominal Treasury bonds from the Center for Research in Security Prices (CRSP) database. Details of data construction are reported in the online appendix.

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Figure 1. Stocks of Inflation-Indexed Government Bonds Outstanding United States Percent 10 8

As share of all government debt

6 4

As percent of GDP

2 1998

2000

2002

2004

2006

2008

United Kingdom Percent 25 As share of all government debt

20 15 10

As percent of GDP

5 1985

1990

1995

2000

2005

Sources: Treasury Bulletin, various issues, table FD-2; Heriot-Watt/Faculty and Institute of Actuaries Gilt Database (www.ma.hw.ac.uk/~andrewc/gilts/, file BGSAmounts.xls).

1 percent, while yields on nominal government bonds fell from around 7 percent to 4 percent. Thus, this was a period in which both nominal and inflation-indexed Treasury bond yields were driven down by a large decline in long-term real interest rates. In 2008, in contrast, nominal Treasury yields continued to decline, while TIPS yields spiked above 3 percent toward the end of the year. The bottom panel of figure 2 shows a comparable history for the United Kingdom since the early 1990s. To facilitate comparison of the two plots, the beginning of the U.S. sample period is marked with a vertical line. The downward trend in inflation-indexed yields is even more dramatic over this longer period. U.K. inflation-indexed gilts also experienced a dramatic yield spike in the fall of 2008.

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Figure 2. Yields on Ten-Year Nominal and Inflation-Indexed Government Bonds, 1991–2009a United States Percent a year TIPS introduced

10 8

Nominal

6 4 2

Inflation-indexed 1992

1994

1996

1998

2000

2002

2004

2006

2008

2004

2006

2008

United Kingdom Percent a year 10

Nominal

8 6 4 2

Inflation-indexed 1992

1994

1996

1998

2000

2002

Source: Authorsí calc ulations using data from Bloomberg and Heriot-Watt/Faculty and Institute of Actuaries Gilt Database; see the online appendix (kuznets.fas.harvard.edu/~campbell/papers.html) for details. a. Yields are calculated from spliced yields and price data of individual issuances.

The top panel of figure 3 plots the 10-year breakeven inflation rate, the difference between 10-year nominal and inflation-indexed Treasury bond yields. The breakeven inflation rate was fairly volatile in the first few years of the TIPS market; it then stabilized between 1.5 and 2.0 percent a year in the early years of this decade before creeping up to about 2.5 percent from 2004 through 2007. In 2008 the breakeven inflation rate collapsed, reaching almost zero at the end of the year. The figure also shows, for the early years of the sample, the subsequently realized 3-year inflation rate. After the first

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Figure 3. Breakeven Inflation Rates Implied by Ten-Year Nominal Inflation-Indexed Bond Yields, and Actual Three-Year Inflation, 1991–2009a United States Percent a year TIPS introduced

5 4

3-year actual inflationb

3 2 1

10-year breakeven inflationc 1992

1994

1996

1998

2000

2002

2004

2006

2008

United Kingdom Percent a year 5 3-year actual inflationb

4 3 2

10-year breakeven inflationc

1 1992

1994

1996

1998

2000

2002

2004

2006

2008

Source: Authors’ calculations from Bloomberg and Bureau of Labor Statistics data; see the online appendix for details. a. Bond yields are computed from spliced yields and price data of individual issuances. b. Annualized percent change in the consumer price index over the preceding 3 years. c. Difference between 10-year yields of nominal and inflation-indexed bonds; monthly data.

couple of years, in which there is little relationship between breakeven and subsequently realized inflation, a slight decrease in breakeven inflation between 2000 and 2002, followed by a slow increase from 2002 to 2006, is matched by similar gradual changes in realized inflation. Although this is not a rigorous test of the rationality of the TIPS market—apart from anything else, the bonds are forecasting inflation over 10 years, not 3 years— it does suggest that inflation forecasts influence the relative pricing of TIPS

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and nominal Treasury bonds. We explore this issue in greater detail in the next section. The bottom panel of figure 3 depicts the breakeven inflation history for the United Kingdom. It shows a strong decline in the late 1990s, probably associated with the granting of independence to the Bank of England by the newly elected Labour government in 1997, and a steady upward creep from 2003 to early 2008, followed by a collapse in 2008 comparable to that in the United States. Realized inflation in the United Kingdom also fell in the 1990s, albeit less dramatically than breakeven inflation, and rose in the mid-2000s. The top panel of figure 4 examines the short-run volatility of TIPS returns. Using daily government bond prices, with the appropriate correction for coupon payments, we calculate daily nominal return series for the on-the-run 10-year TIPS. This graph plots the annualized standard deviation of this series within a centered moving one-year window. For comparison, it also shows the corresponding annualized standard deviation for 10-year nominal Treasury bond returns, calculated from Bloomberg yield data on the assumption that the nominal bonds trade at par. The striking message of this graph is that TIPS returns have become far more volatile in recent years. In the early years, until 2002, the short-run volatility of 10-year TIPS was only about half that of 10-year nominal Treasury bonds, but the two standard deviations converged between 2002 and 2004 and have been extremely similar since then. The annualized standard deviations of both bonds ranged between 5 and 8 percent between 2004 and 2008 and then increased dramatically to almost 14 percent. Mechanically, two variables drive the volatility of TIPS returns. The more important of these is the volatility of TIPS yields, which has increased over time; in recent years it has been very similar to the volatility of nominal Treasury bond yields as breakeven inflation has stabilized. A second, amplifying factor is the duration of TIPS, which has increased as TIPS yields have declined.3 The same two variables determine the very similar volatility patterns shown in the bottom panel of figure 4 for the United Kingdom.

3. The duration of a bond is the average time to payment of its cash flows, weighted by the present values of those cash flows. Duration also equals the elasticity of a bond’s price with respect to its gross yield (one plus its yield in natural units). A coupon bond has duration less than its maturity, and its duration increases as its yield falls. Since TIPS yields are lower than nominal bond yields, TIPS have greater duration for the same maturity, and hence a greater volatility of returns for the same yield volatility, but the differences in volatility explained by duration are quite small.

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Figure 4. Volatility of Ten-Year Nominal and Inflation-Indexed Government Bond Returns, 1992–2009a United States Standard deviationb (percent) TIPS introduced

14 12

Nominal

10 8 6 4

Inflation-indexed

2 1992

1994

1996

1998

2000

2002

2004

2006

2008

2006

2008

United Kingdom Standard deviation (percent) 14 12 10 Nominal

8 6 4

Inflation-indexed

2 1992

1994

1996

1998

2000

2002

2004

Source: Authors’ calculations from Bloomberg data; see the online appendix for details. a. Bond yields are computed from spliced yields and price data of individual issuances. b. Standard deviation of daily returns on government bonds with 10 years to maturity, over a one-year centered moving window.

The top panel of figure 5 plots the annualized standard deviation of 10-year breakeven inflation (measured in terms of the value of a bond position long a 10-year nominal Treasury bond and short a 10-year TIPS). This standard deviation trended downward from 7 percent in 1998 to about 1 percent in 2007 before spiking above 13 percent in 2008. To the extent that breakeven inflation represents the long-term inflation expectations of market participants, these expectations stabilized during most of the sample period but moved dramatically in 2008. Such a destabilization of

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Figure 5. Volatility of Ten-Year Breakeven Inflation and Correlation of Nominal and Inflation-Indexed Government Bond Returns, 1992–2009a United States Standard deviation (percent)

Correlation coefficient TIPS introduced

14 12 10 8 6

1.0

Correlation of returnsc (right scale)

0.8

Volatility of breakeven inflationb (left scale)

0.6

4

0.4

2

0.2 1992

1994

1996

1998

2000

2002

2004

2006

2008

United Kingdom Correlation coefficient

Standard deviation (percent) 14 12 10

Volatility of breakeven inflation (left scale)

1.0

Correlation of returns (right scale)

8

0.8

6

0.6

4

0.4

2

0.2 1992

1994

1996

1998

2000

2002

2004

2006

2008

Source: Authors’ calculations from Bloomberg data; see the online appendix for details. a. Bond yields are computed from spliced yields and price data of individual issuances. b. Standard deviation of the daily 10-year breakeven inflation rate, measured in terms of the value of a position long a 10-year nominal government bond and short a 10-year inflation-indexed bond, over a one-year moving window. c. Correlation of daily inflation-indexed and nominal bond returns within a one-year moving window.

inflation expectations should be a matter of serious concern to the Federal Reserve, although, as we discuss in section IV, institutional factors may have contributed to the movements in breakeven inflation during the market disruption of late 2008. The bottom panel of figure 5 suggests that the Bank of England should be equally concerned by the recent destabilization of the yield spread between nominal and inflation-indexed gilts. Figure 5 also plots the correlations of daily inflation-indexed and nominal government bond returns within a one-year moving window. Early in

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the period, the correlation for U.S. bonds was quite low at about 0.2, but it increased to almost 0.9 by the middle of 2003 and stayed there until 2008. In the mid-2000s TIPS behaved like nominal Treasuries and did not exhibit independent return variation. This coupling of TIPS and nominal Treasuries ended in 2008. The same patterns are visible in the U.K. data. Although TIPS have been volatile assets, this does not necessarily imply that they should command large risk premiums. According to rational asset pricing theory, the risk premium on an asset should be driven by the covariance of its returns with the marginal utility of consumption rather than by the variance of returns. One common proxy for marginal utility, used in the CAPM, is the return on an aggregate equity index. Figure 6 plots the correlations of daily inflation-indexed bond returns, nominal government bond returns, and breakeven inflation returns with daily returns on aggregate U.S. and U.K. stock indexes, again within a centered moving one-year window. Figure 7 repeats this exercise for betas (regression coefficients of daily bond returns and breakeven inflation on the same stock indexes). All these figures tell a similar story. During the 2000s there has been considerable instability in both countries in the correlations between government bonds of both types and stock returns, but these correlations have been predominantly negative, implying that government bonds can be used to hedge equity risk. To the extent that the CAPM describes risk premiums across asset classes, government bonds should have predominantly negative rather than positive risk premiums. The negative correlation is particularly strong for nominal government bonds, because breakeven inflation has been positively correlated with stock returns, especially during 2002–03 and 2007–08. Campbell, Sunderam, and Viceira (2009) build a model in which a changing correlation between inflation and stock returns drives changes in the risk properties of nominal Treasury bonds. That model assumes a constant equity market correlation for TIPS and thus cannot explain the correlation movements shown for TIPS in figures 6 and 7. In section III we explore the determination of TIPS risk premiums in greater detail.

II. Inflation-Indexed Bond Yields and the Dynamics of Short-Term Real Interest Rates To understand the movements of inflation-indexed bond yields, it is essential first to understand how changes in short-term real interest rates propagate along the real term structure. Declining yields for inflation-indexed bonds in the 2000s may not be particularly surprising given that short-term real interest rates have also been low in this decade.

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Figure 6. Correlations of Ten-Year Government Bond Returns and Breakeven Inflation Rates with Equity Returns, 1992–2009a United States Correlation coefficient TIPS introduced

0.6

Breakeven inflation

0.4 0.2 0 –0.2

Inflation-indexed bonds

–0.4 –0.6

Nominal bonds 1992

1994

1996

1998

2000

2002

2004

2006

2008

United Kingdom Correlation coefficient 0.6

Inflation-indexed bonds

0.4 0.2 0 –0.2 –0.4 –0.6

Breakeven inflation 1992

1994

1996

1998

Nominal bonds 2000

2002

2004

2006

2008

Source: Authors’ calculations from Bloomberg and Center for Research in Security Prices data; see the online appendix for details. a. Correlations between nominal returns on the stock index of the indicated country (CRSP Value-Weighted Index for the United States, FTSE-100 for the United Kingdom) and either nominal 10-year returns on the indicated bond type (computed from spliced yields and price data of individual issuances) or returns in the breakeven inflation rate (the difference between nominal bond returns and inflation-indexed bond returns).

Before TIPS were introduced in 1997, Campbell and Shiller (1997) used a time-series model for the short-term real interest rate to create a hypothetical TIPS yield series under the assumption that the expectations theory of the term structure in logarithmic form, with zero log risk premiums, describes inflation-indexed bond yields. (This does not require the assumption that the expectations theory describes nominal bond yields, a model that

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JOHN Y. CAMPBELL, ROBERT J. SHILLER, and LUIS M. VICEIRA

Figure 7. Betas of Ten-Year Government Bond Returns and Breakeven Inflation Rates with Equity Returns, 1992–2009a United States Beta TIPS introduced

0.4 0.2

Breakeven inflation

0 –0.2 Inflation-indexed bonds

–0.4 1992

1994

1996

1998

2000

2002

Nominal bonds

2004

2006

2008

United Kingdom Beta Nominal bonds 0.4 Inflation-indexed bonds

0.2 0 –0.2 –0.4

Breakeven inflation 1992

1994

1996

1998

2000

2002

2004

2006

2008

Source: Authors’ calculations from Bloomberg and Center for Research in Security Prices data; see the online appendix for details. a. Coefficients from a regression of either nominal 10-year returns on the indicated bond type (computed from spliced yields and price data of individual issuances) or the breakeven inflation rate (the difference between nominal bond returns and inflation-indexed bond returns) on nominal returns on the stock index of the indicated country (CRSP Value-Weighted Index for the United States, FTSE-100 for the United Kingdom).

has often been rejected in U.S. data.) In this section we update Campbell and Shiller’s analysis and ask how well the simple expectations theory describes the 12-year history of TIPS yields. Campbell and Shiller (1997) estimated a VAR model on quarterly U.S. data over 1953–94. Their basic VAR included the ex post real return on a 3-month nominal Treasury bill, the nominal bill yield, and the once-

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Table 1. Results of VAR Estimation and Observed and Hypothetical Moments of Ten-Year Inflation-Indexed Bond Yields, United Statesa Dependent variable Independent variable Inflation-indexed bill return Nominal bill yield Inflation Constant R2 Moments of 10-year inflationindexed bond yields Mean Standard deviation Correlation

Inflation-indexed bill return −0.06 (0.10) 0.62 (0.17) 0.09 (0.08) −0.005 (0.002) 0.26

Nominal bill yield

Inflationb

0.01 (0.02) 0.95 (0.04) −0.04 (0.02) 0.001 (0.0005) 0.91

−0.21 (0.10) 0.57 (0.16) 0.58 (0.08) 0.007 (0.002) 0.63

Observed

Hypothetical

2.66 0.95

1.04 0.39 0.71

Source: Authors’ regressions. Independent variables are lagged one period. a. Numbers in parentheses are standard errors. b. Non–seasonally adjusted all-urban-consumer price index (NSA CPI-U).

lagged one-year inflation rate. They solved the VAR forward to create forecasts of future quarterly real interest rates at all horizons, and then aggregated the forecasts to generate the implied long-term inflation-indexed bond yield. Table 1 repeats this analysis for 1982–2008. The top panel reports the estimated VAR coefficients, and the bottom panel reports selected sample moments of the hypothetical VAR-implied 10-year TIPS yields, and for comparison the same moments of observed TIPS yields, over the period since TIPS were introduced. The table delivers several interesting results. First, the hypothetical yields are considerably lower on average than the observed yields, with a mean of 1.04 percent compared with 2.66 percent. This implies that on average, investors demand a risk or liquidity premium for holding TIPS rather than nominal Treasuries. Second, hypothetical yields are more stable than observed yields, with a standard deviation of 0.39 percent as opposed to 0.95 percent. This reflects the fact that observed yields have declined more dramatically since 1997 than have hypothetical yields. Third, hypothetical and observed yields have a relatively high correlation of 0.71, even though no TIPS data were used to construct the hypothetical

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Figure 8. Hypothetical and Actual Yields on Ten-Year Inflation-Indexed Bonds United States Percent a year 4 3 2

Actuala

1 0 Hypotheticalb

–1 –2 –3

Fitted real 3-month Treasury bill rateb 1992

1994

1996

1998

2000

2002

2004

2006

2008

United Kingdom Percent a year 5 Fitted real 3-month interest rate

4 3 2

Hypothetical

1

Actual 1992

1994

1996

1998

2000

2002

2004

2006

2008

Source: Authors’ calculations from Bloomberg, Center for Research in Security Prices, and Bureau of Labor Statistics data; see the online appendix for details. a. Quarterly averages of 10-year TIPS yields (from the top panel of figure 2). b. Extracted from an estimated VAR(1) model in quarterly U.S. data over 1953–94 on the ex post real return on a 3-month nominal Treasury bill, the nominal bill yield, and the lagged one-year inflation rate.

yields. Real interest rate movements do have an important effect on the TIPS market, and the VAR system is able to capture much of this effect. The top panel of figure 8 shows these results in graphical form, plotting the history of the observed TIPS yield, the hypothetical VAR-implied TIPS yield, and the VAR estimate of the ex ante short-term real interest rate. The sharp decline in the real interest rate in 2001 and 2002 drives down the hypothetical TIPS yield, but the observed TIPS yield is more volatile and declines more strongly. The gap between the observed TIPS yield and the

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Table 2. Results of VAR Estimation and Observed and Hypothetical Moments of Ten-Year Inflation-Indexed Bond Yields, United Kingdoma Dependent variable Independent variable Inflation-indexed bill return Nominal bill yield Inflation Constant R2 Moments of 10-year inflationindexed bond yields Mean Standard deviation Correlation

Inflation-indexed bill return 0.09 (0.09) 0.42 (0.19) 0.02 (0.07) 0.0001 (0.0019) 0.22

Nominal bill yield

Inflationb

−0.04 (0.03) 1.07 (0.05) −0.03 (0.02) 0.0002 (0.0005) 0.93

−0.39 (0.09) 0.82 (0.18) 0.66 (0.07) 0.0007 (0.0018) 0.87

Observed

Hypothetical

2.64 1.00

2.49 0.61 0.77

Source: Authors’ regressions. Independent variables are lagged one period. a. Numbers in parentheses are standard errors. b. Retail price index.

hypothetical yield shrinks fairly steadily over the sample period until the very end, when the 2008 spike in the observed yield widens the gap again. These results suggest that when they were first issued, TIPS commanded a high risk or liquidity premium, which then declined until 2008. Table 2 and the bottom panel of figure 8 repeat these exercises for the United Kingdom. Here the hypothetical and observed yields have similar means (2.64 and 2.49 percent, respectively), but again the standard deviation is lower for the hypothetical yield, at 0.61 percent, than for the observed yield, at 1.00 percent. The two yields have a high correlation of 0.77. The graph shows that the VAR model captures much of the decline in inflation-indexed gilt yields since the early 1990s. It is able to do this because the estimated process for the U.K. ex ante real interest rate is highly persistent, so that the decline in the real rate over the sample period translates almost one for one into a declining yield on long-term inflation-indexed gilts. However, for the same reason the model cannot account for variations in the spread between the short-term expected real interest rate and the longterm inflation-indexed gilt yield. It is notable that the expectations hypothesis of the real term structure does not explain the low average level of inflation-indexed gilt yields since 2005.

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A new U.K. accounting standard introduced in 2000, FRS17, may account for this. As Viceira and Mitsui (2003) and Dimitri Vayanos and Jean-Luc Vila (2007) explain, FRS17 requires U.K. pension funds to mark their liabilities to market, using discount rates derived from government bonds. The standard was implemented, after some delay, in 2005, and it greatly increased the demand for inflation-indexed gilts from pension funds seeking to hedge their inflation-indexed liabilities.

III. The Systematic Risks of Inflation-Indexed Bonds The yield history and VAR analysis presented in the previous two sections suggest that U.S. and U.K. inflation-indexed bonds had low risk premiums in the mid-2000s, but the former, at least, had higher risk premiums when they were first issued. In this section we use asset pricing theory to ask what fundamental properties of the macroeconomy might lead to high or low risk premiums on inflation-indexed bonds. We first use the consumption-based asset pricing framework and then present a less structured empirical analysis that relates bond risk premiums to changing covariances of bonds with stocks.

III.A. Consumption-Based Pricing of Inflation-Indexed Bonds A standard paradigm for consumption-based asset pricing assumes that a representative investor has Epstein-Zin (1989, 1991) preferences. This preference specification, a generalization of power utility, allows the coefficient of relative risk aversion γ and the elasticity of intertemporal substitution (EIS) ψ to be separate free parameters, whereas power utility restricts one to be the reciprocal of the other. Under the additional assumption that asset returns and consumption are jointly log normal and homoskedastic, the Epstein-Zin Euler equation implies that the risk premium RP on any asset i over the short-term safe asset is (1)

RPi ≡ Et [ ri,t +1 ] − rf ,t +1 +

σ i2 σ = θ ic + (1 − θ ) σ iw . 2 ψ

In words, the risk premium is defined to be the expected excess log return on the asset over the risk-free log return rf , plus one-half its variance to convert from a geometric average to an arithmetic average, that is, to correct for Jensen’s inequality. The preference parameter θ ≡ (1 − γ)/[1 − (1/ψ)]; in the power utility case, γ = 1/ψ, so that θ = 1. According to this formula, the risk premium on any asset is a weighted average of two conditional covariances,

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the consumption covariance σic (scaled by the reciprocal of the EIS), which gets full weight in the power utility case, and the wealth covariance σiw. The risk premium is constant over time by the assumption of homoskedasticity. It is tempting to treat the consumption covariance and the wealth covariance as two separate quantities, but this ignores the fact that consumption and wealth are linked by the intertemporal budget constraint and by a timeseries Euler equation. By using these additional equations, one can substitute either consumption (Campbell 1993) or wealth (Restoy and Weil 1998) out of the formula for the risk premium. The first approach explains the risk premium using covariances with the current market return and with news about future market returns; this might be called “CAPM+,” as it generalizes the insight about risk that was first formalized in the CAPM. Campbell (1996) and Campbell and Tuomo Vuolteenaho (2004) pursue this approach, which can also be regarded as an empirical version of Robert Merton’s (1973) intertemporal CAPM. The second approach explains the risk premium using covariances with current consumption growth and with news about future consumption growth; this might be called “CCAPM+,” as it generalizes the insight about risk that is embodied in the consumption-based CAPM with power utility. This approach has generated a large asset pricing literature in recent years (for example, Bansal and Yaron 2004; Bansal, Khatchatrian, and Yaron 2005; Piazzesi and Schneider 2007; Bansal, Kiku, and Yaron 2007; Bansal, Dittmar, and Kiku 2009; Hansen, Heaton, and Li 2008). Some of this recent work adds heteroskedasticity to the simple homoskedastic model discussed here. The CAPM+ approach delivers an approximate formula for the risk premium on any asset as RPi = γσ iw − ( γ − 1) σ i ,TIPS , where σiw is the covariance of the unexpected return on asset i with the return on the aggregate wealth portfolio, and σi,TIPS is the covariance with the return on an inflation-indexed perpetuity. The intuition, which dates back to Merton (1973), is that conservative long-term investors value assets that deliver high returns at times when investment opportunities are poor. Such assets hedge investors against variation in the sustainable income stream that is delivered by a given amount of wealth. In a homoskedastic model, risk premiums are constant, and the relevant measure of long-run investment opportunities is the yield on an inflation-indexed bond. Thus, the covariance with the return on an inflation-

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indexed perpetuity captures the intertemporal hedging properties of an asset. In equilibrium, an asset that covaries strongly with an inflationindexed perpetuity will offer a low return as the price of the desirable insurance it offers. Applying this formula to the inflation-indexed perpetuity itself, we find that 2 RPTIPS = γσ TIPS,w − ( γ − 1) σ TIPS .

In words, the risk premium on a long-term inflation-indexed bond is increasing in its covariance with the wealth portfolio, as in the traditional CAPM, but decreasing in the variance of the bond return whenever the risk aversion of the representative agent is greater than 1. Paradoxically, the insurance value of inflation-indexed bonds is higher when these bonds have high short-term volatility, because in this case they hedge important variability in investment opportunities. In a traditional model with a constant real interest rate, inflation-indexed bonds have constant yields; but in this case there is no intertemporal hedging to be done, and the traditional CAPM can be used to price all assets, including inflation-indexed bonds. The CCAPM+ approach can be written as (2)

1⎞ ⎛ RPi = γσ ic + ⎜ γ − ⎟ σ ig , ⎝ ψ⎠

where σig is the covariance of the unexpected return on asset i with revisions in expected future consumption growth g~t+1, defined by ∞

(3)

gt +1 ≡ ( Et +1 − Et ) ∑ ρ j Δct +1+ j . j =1

In equation 2 the risk premium on any asset is the coefficient of risk aversion γ times the covariance of that asset with consumption growth, plus (γ − 1/ψ) times the covariance of the asset with revisions in expected future consumption growth, discounted at a constant rate ρ. The second term is zero if γ = 1/ψ, the power utility case, or if consumption growth is unpredictable so that there are no revisions in expected future consumption growth. Evidence on the equity premium and the time-series behavior of real interest rates suggests that γ > 1/ψ. This implies that controlling for assets’ contemporaneous consumption covariance, investors require a risk premium to hold assets that pay off when expected future consumption

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growth increases. Ravi Bansal and Amir Yaron (2004) use the phrase “risks for the long run” to emphasize this property of the model. What does this model imply about the pricing of an inflation-indexed perpetuity? When expected real consumption growth increases by 1 percentage point, the equilibrium real interest rate increases by 1/ψ percentage points, and thus the return on the inflation-indexed perpetuity is given by4 (4)

rTIPS,t +1 = −

1 g . ψ t +1

Combining equation 2 with equation 4, one can solve for the risk premium on the inflation-indexed perpetuity: (5)

1⎞⎛ 1⎞ ⎛ 1⎞ ⎛ RPTIPS = γ ⎜ − ⎟ σ cg + ⎜ γ − ⎟ ⎜ − ⎟ σ 2g . ⎝ ψ⎠ ⎝ ψ⎠⎝ ψ⎠

With power utility, only the first term in equation 5 is nonzero. This case is described by Campbell (1986). In a consumption-based asset pricing model with power utility, assets are risky if their returns covary positively with consumption growth. Since bond prices rise when interest rates fall, bonds are risky assets if interest rates fall in response to consumption growth. Because equilibrium real interest rates are positively related to expected future consumption growth, this is possible only if positive consumption shocks drive expected future consumption growth downward, that is, if consumption growth is negatively autocorrelated. In an economy with temporary downturns in consumption, equilibrium real interest rates rise and TIPS prices fall in recessions, and therefore investors require a risk premium to hold TIPS. In the presence of persistent shocks to consumption growth, by contrast, consumption growth is positively autocorrelated. In this case recessions not only drive down current consumption but also lead to prolonged periods of slow growth, driving down real interest rates. In such an economy the prices of long-term inflation-indexed bonds rise in recessions, making them desirable hedging assets with negative risk premiums. This paradigm suggests that the risk premium on TIPS will fall if investors become less concerned about temporary business-cycle shocks, and more concerned about shocks to the long-term consumption growth rate. 4. A more careful derivation of this expression can be found in Campbell (2003, p. 841), equation 41.

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It is possible that such a shift in investor beliefs did take place during the late 1990s and 2000s, as the Great Moderation mitigated concerns about business-cycle risk (Bernanke 2004; Blanchard and Simon 2001; Kim and Nelson 1999; McConnell and Perez-Quiros 2000; Stock and Watson 2003) while long-term uncertainties about technological progress and climate change became more salient. Of course, the events of 2007–08 have brought business-cycle risk to the fore again. The movements of inflation-indexed bond yields have been broadly consistent with changing risk perceptions of this sort. The second term in equation 5 is also negative under the plausible assumption that γ > 1/ψ, and its sign does not depend on the persistence of the consumption process. However, its magnitude does depend on the volatility of shocks to long-run expected consumption growth. Thus, increasing uncertainty about long-run growth drives down inflation-indexed bond premiums through this channel as well. Overall, the Epstein-Zin paradigm suggests that inflation-indexed bonds should have low or even negative risk premiums relative to short-term safe assets, consistent with the intuition that these bonds are the safe asset for long-term investors.

III.B. Bond Risk Premiums and the Bond-Stock Covariance The consumption-based analysis of the previous section delivers insights but also has weaknesses. The model assumes constant second moments and thus implies constant risk premiums; it cannot be used to track changing variances, covariances, or risk premiums in the inflation-indexed bond market. Although one could generalize the model to allow time-varying second moments, as in the long-run risks model of Bansal and Yaron (2004), the low frequency of consumption measurement makes it difficult to implement the model empirically. In this section we follow a different approach, writing down a model of the stochastic discount factor (SDF) that allows us to relate the risk premiums on inflation-indexed bonds to the covariance of these bonds with stock returns. To capture the time-varying correlation of returns on inflation-indexed bonds with stock returns, we propose a highly stylized term structure model in which the real interest rate is subject to conditionally heteroskedastic shocks. Conditional heteroskedasticity is driven by a state variable that captures time variation in aggregate macroeconomic uncertainty. We build our model in the spirit of Campbell, Sunderam, and Viceira (2009), who emphasize the importance of changing macroeconomic conditions for an understanding of time variation in systematic risk and in the correlations of

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returns on fundamental asset classes. Our model modifies their quadratic term structure model to allow for heteroskedastic shocks to the real rate. We assume that the log of the real SDF, mt+1 = log Mt+1, can be described by (6)

− mt + 1 = x t +

1 2 σ + ε m , t + 1, 2 m

where xt follows a conditionally heteroskedastic AR(1) process, (7)

x t +1 = μ x (1 − ϕ x ) + ϕ x x t + vt ε x ,t +1 + ε ′x ,t +1,

and vt follows a standard AR(1) process, (8)

vt +1 = μ v (1 − ϕ v ) + ϕ v vt + ε v ,t +1.

The shocks εm,t+1, εx,t+1, ε′x,t+1, and εv,t+1 have zero means and are jointly normally distributed with a constant variance-covariance matrix. We assume that ε′x,t+1 and εv,t+1 are orthogonal to each other and to the other shocks in the model. We adopt the notation σi2 to describe the variance of shock εi, and σij to describe the covariance between shock εi and shock εj. The conditional volatility of the log SDF (σm) describes the price of aggregate market risk, or the maximum Sharpe ratio in the economy, which we assume to be constant.5 The online appendix to this paper (see footnote 1) shows how to solve this model for the real term structure of interest rates. The state variable xt is equal to the log short-term real interest rate, which follows an AR(1) process whose conditional variance is driven by the state variable vt. In a standard consumption-based power utility model of the sort discussed in the previous subsection, vt would capture time variation in the dynamics of consumption growth. When vt is close to zero, shocks to the real interest rate are uncorrelated with the SDF; in a power utility model, this would imply that shocks to future consumption growth are uncorrelated with shocks to the current level of consumption. As vt moves away from zero, the volatility of the real interest rate increases and its covariance with the SDF becomes more positive or more negative. In a power utility model,

5. Campbell, Sunderam, and Viceira (2009) consider a much richer term structure model in which σ 2m is time varying. They note that in that case the process for the log real SDF admits an interpretation as a reduced form of structural models such as those of Bekaert, Engstrom, and Grenadier (2006) and Campbell and Cochrane (1999) in which aggregate risk aversion is time varying. Campbell, Sunderam, and Viceira find that timevarying risk aversion plays only a limited role in explaining the observed variation in bond risk premiums. For simplicity, we set σ2m constant.

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this corresponds to a covariance between consumption shocks and future consumption growth that is either positive or negative, reflecting either momentum or mean reversion in consumption. Broadly speaking, one can interpret vt as a measure of aggregate uncertainty about long-run growth in the economy. At times when that uncertainty increases, real interest rates become more volatile. Solving the model for the real term structure of interest rates, we find that the log price of an n-period inflation-indexed bond is linear in the shortterm real interest rate xt, with coefficient Bx,n, and quadratic in aggregate economic uncertainty vt, with linear coefficient Bv,n and quadratic coefficient Cv,n. An important property of this model is that bond risk premiums are time varying. They are approximately linear in vt , where the coefficient on vt is proportional to σ 2m. A time-varying conditional covariance between the SDF and the real interest rate implies that the conditional covariance between inflationindexed bonds and risky assets such as equities should also vary over time as a function of vt. To see this, we now introduce equities into the model. To keep things simple, we assume that the unexpected log return on equities is given by (9)

re , t +1 − Et re , t +1 = β em ε m , t +1.

This implies that the equity premium equals βemσ m2 , the conditional standard deviation of stock returns is βemσm, and the Sharpe ratio on equities is σm. Equities deliver the maximum Sharpe ratio because they are perfectly correlated with the SDF. Thus, we are imposing the restrictions of the traditional CAPM, ignoring the intertemporal hedging arguments stated in the previous subsection. The covariance between stocks and inflation-indexed bonds is given by (10)

cov t ( re ,t +1 , rn ,t +1 ) = Bx ,n −1β em σ mx vt ,

which is proportional to vt. This proportionality is also a reason why we consider two independent shocks to xt. In the absence of a homoskedastic shock ε′x,t to xt, our model would imply that the conditional volatility of the short-term real interest rate would be proportional to the conditional covariance of stock returns with returns on inflation-indexed bonds. However, although the two conditional moments appear to be correlated in the data, they are not perfectly correlated, still less proportional to one another. We estimate this term structure model by applying the nonlinear Kalman filter procedure described in Campbell, Sunderam, and Viceira (2009) to

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data on zero-coupon inflation-indexed bond yields, from Refet Gürkaynak, Brian Sack, and Jonathan Wright (2008) for the period 1999–2008, and total returns on the value-weighted U.S. stock market portfolio, from CRSP data.6 Because the U.S. Treasury does not issue TIPS with short maturities, and there are no continuous observations of yields on near-to-maturity TIPS, this dataset does not include short-term zero-coupon TIPS yields. To approximate the short-term real interest rate, we use the ex ante short-term real interest rate implied by our VAR approach described in section II. Our estimation makes several identifying and simplifying assumptions. First, we identify σm using the long-run average Sharpe ratio for U.S. equities, which we set to 0.23 on a quarterly basis (equivalent to 0.46 on an annual basis). Second, we identify βem as the sample standard deviation of equity returns in our sample period (0.094 per quarter, or 18.9 percent per year) divided by σm, for a value of 0.41. Third, we exactly identify xt with the ex ante short-term real interest rate estimated from the VAR model of the previous section, which we treat as observed, adjusted by a constant. That is, we give the Kalman filter a measurement equation that equates the VAR-estimated short-term real interest rate to xt with a free constant term but no measurement error. The inclusion of the constant term is intended to capture liquidity effects that lower the yields on Treasury bills relative to the longer-term real yield curve. Fourth, because the shock εx,t+1 is always premultiplied by vt, we normalize σx to 1. Fifth, we assume that there is perfect correlation between the shock εx,t+1 and the shock εm,t+1 to the SDF; equivalently, we set σmx equal to 0.23. This delivers the largest possible time variation in inflationindexed bond risk premiums and thus maximizes the effect of changing risk on the TIPS yield curve. Sixth, we treat equation 10 as a measurement equation with no measurement error, where we replace the covariance on the left-hand side of the equation with the realized monthly covariance of returns on 10-year zero-coupon TIPS with returns on stocks. We estimate the monthly realized covariance using daily observations on stock returns and on TIPS returns from the Gürkaynak-Sack-Wright dataset. Since βem and σmx have been already exactly identified, this is equivalent to identifying the process vt with a scaled version of the covariance of returns on TIPS and stocks. 6. The CRSP (Center for Research in Security Prices) data cover all three major U.S. stock exchanges. Gürkaynak, Sack, and Wright estimate zero-coupon TIPS yields by fitting a flexible functional form, a generalization of Nelson and Siegel (1987) suggested by Svensson (1994), to the instantaneous forward rates implied by off-the-run TIPS yields. From fitted forward rates it is straightforward to obtain zero-coupon yields.

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Table 3. Parameter Estimates for Alternative Risk Models Restricted models Parameter ϕx µx ϕv µv σm σx σmx σx′ σv βem σyield σcov Premium

Full model

Constant-covariance model

Persistent-risk model

0.94 0.0028 0.77 −2.01 × 10−5 Set to 0.23 Set to 1 0.23 0.0048 0.0003 Set to 0.41 1.16 × 10−6 4.74 × 10−4 0.0157

0.93 0.0104 NAa NA Set to 0.23 0.0031 7.23 × 10−4 NA NA NA 1.12 × 10−4 NA 0.0016

0.95 0.0034 Set to 1 0.0010 Set to 0.23 Set to 1 0.23 0.0031 0.0004 Set to 0.41 9.14 × 10−6 5 × 10−4 0.00160

Source: Authors’ calculations. a. NA, not applicable. See the text for descriptions of the models.

We include one final measurement equation for the 10-year zero-coupon TIPS yield using the model’s solution for this yield and allowing for measurement error. The identifying assumptions we have made imply that we are exactly identifying xt with the ex ante short-term real interest rate, vt with the realized covariance of returns on TIPS and stocks, and the log SDF with stock returns. Thus, our estimation procedure in effect generates hypothetical TIPS yields from these processes and compares them with observed TIPS yields. Table 3 reports the parameter estimates from our full model and two restricted models. The first of these two models, reported in the second column, drops the measurement equation for the realized stock-bond covariance and assumes that the stock-bond covariance is constant, and hence that TIPS have a constant risk premium, as in the VAR model of section II. The second restricted model, reported in the last column, generates the largest possible effects of time-varying risk premiums on TIPS yields by increasing the persistence of the covariance state variable vt from the freely estimated value of 0.77, which implies an eight-month half-life for covariance movements, to the largest permissible value of 1. Figure 9 shows how these three variants of our basic model fit the history of the 10-year TIPS yield. The yields predicted by the freely estimated model of changing risk and by the restricted model with a constant bondstock covariance are almost on top of one another, diverging only slightly

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Figure 9. Real Ten-Year Inflation-Indexed Bond Yields Implied by Alternative Risk Models, United States, 1998–2009a Percent a year Observed yield

4.0 3.5

Fitted yield

3.0 2.5 2.0 1.5

Constant-covariance yield

1.0 0.5

Persistent-risk yield

0 1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Source: Authors’ calculations based on data for yields from Gürkaynak, Sack, and Wright (2008) and for stock returns fom the Center for Research in Security Prices. a. See the text for descriptions of the models.

in periods such as 2003 and 2008 when the realized bond-stock covariance was unusually negative. This indicates that changing TIPS risk is not persistent enough to have a large effect on TIPS yields. Only when we impose a unit root on the process for the bond-stock covariance do we obtain large effects of changing risk. This model implies that TIPS yields should have fallen more dramatically than they did in 2002–03, and again in 2007, when the covariance of TIPS with stocks turned negative. The persistent-risk model does capture observed TIPS movements in the first half of 2008, but it dramatically fails to capture the spike in TIPS yields in the second half of 2008. Over all, this exploration of changing risk, as captured by the changing realized covariance of TIPS returns and aggregate stock returns, suggests that variations in risk play only a supporting role in the determination of TIPS yields. The major problem with a risk-based explanation for movements in the inflation-indexed yield curve is that the covariance of TIPS and stocks has moved in a transitory fashion, and thus should not have had a large effect on TIPS yields unless investors were expecting more persistent variation and were surprised by an unusual sequence of temporary changes in risk. These results contrast with those reported by Campbell, Sunderam, and Viceira (2009), who find that persistent movements in the covariance between inflation and stock returns have had a powerful influence on the

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nominal U.S. Treasury yield curve. They find that U.S. inflation was negatively correlated with stock returns in the late 1970s and early 1980s, when the major downside risk for investors was stagflation; it has been positively correlated with stock returns in the 2000s, when investors have been more concerned about deflation.7 As a result, Campbell, Sunderam, and Viceira argue that the inflation risk premium was positive in the 1970s and 1980s but has been negative in the 2000s, implying even lower expected returns on nominal Treasury bonds than on TIPS. The movements in inflation risk identified by Campbell, Sunderam, and Viceira are persistent enough to have important effects on the shape of the nominal U.S. Treasury yield curve, reducing its slope and concavity relative to what was typical in the 1970s and 1980s.

IV. The Crisis of 2008 and Institutional Influences on TIPS Yields In 2008, as the subprime crisis intensified, the TIPS yield became highly volatile and appeared to become suddenly disconnected from the yield on nominal Treasuries. At the beginning of 2008, the 30-year TIPS yield as reported by the Federal Reserve Bank of St. Louis fell to extremely low levels, as low as 1.66 percent on January 23, 2008. Shorter-maturity TIPS showed even lower yields, and in the spring and again in the summer of 2008 some of these yields became negative, falling below −0.5 percent, reminding market participants that zero is not the lower bound for inflationindexed bond yields. The fall of 2008 then witnessed an unprecedented and short-lived spike in TIPS yields, peaking at the end of October 2008 when the 30-year TIPS yield reached 3.44 percent. These extraordinary short-run movements in TIPS yields are mirrored in the 10-year TIPS yield shown in figure 2. The extremely low TIPS yield in early 2008 was given a convenient explanation by some market observers, namely, that investors were panicked by the apparently heightened risks in financial markets due to the subprime crisis and sought safety at just about any price. But if this is the correct explanation, the massive surge in the TIPS yield later in that year remains a mystery. This leap upward was puzzling, since it was not observed in nominal bond yields and so marked a massive drop in the breakeven inflation rate, as seen in figure 3. The U.K. market behaved in similar fashion. 7. The top panel of figure 6 illustrates the positive correlation of U.S. inflation and stock returns during the 2000s, and the bottom panel shows that this correlation has changed sign in the United Kingdom since the early 1990s.

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The anomalous sudden jump in inflation-indexed bond yields came as a total surprise to market participants. Indeed, just as the jump was occurring in October 2008, some observers were saying that because inflation expectations had become extremely stable, TIPS and nominal Treasury bonds were virtually interchangeable. For example, Marie Brière and Ombretta Signori concluded, in a paper published in March 2009 (p. 279), “Although diversification was a valuable reason for introducing IL [inflation-linked] bonds in a global portfolio before 2003, this is no longer the case.” The extent of this surprise suggests that the rise in the TIPS yield, and its decoupling from nominal Treasury yields, had something to do with the systemic nature of the crisis that beset U.S. financial institutions in 2008. Indeed, the sharp peak in the TIPS yield and the accompanying steep drop in the breakeven inflation rate occurred shortly after an event that some observers blame for the anomalous behavior of TIPS yields. This was the bankruptcy of the investment bank Lehman Brothers, announced on September 15, 2008. The unfolding of the Lehman bankruptcy proceedings also took place over the same interval of time during which the inflationindexed bond yield made its spectacular leap upward. Lehman’s bankruptcy was an important event, the first bankruptcy of a major investment bank since that of Drexel Burnham Lambert in 1990. That is not to say that other investment banks did not also get into trouble in the meantime, especially during the subprime crisis. But the federal government had always stepped in to allay fears. Bear Stearns was sold to the commercial bank J.P. Morgan in March 2008 in a deal arranged and financed by the government. Bank of America announced its purchase of Merrill Lynch on September 14, 2008, again with government financial support. Yet the government decided to let Lehman fail, and investors may have interpreted this event as indicative of future government policy that might spell major changes in the economy. One conceivable interpretation of the events that followed the Lehman bankruptcy announcement is that the market viewed the bankruptcy as a macroeconomic indicator, a sign that the economy would be suddenly weaker. This could have implied a deterioration in the government’s fiscal position, justifying an increase in expected future real interest rates and therefore in the long-term real yield on Treasury debt, as well as a decline in inflation expectations, thus explaining the drop in breakeven inflation. However, many observers doubt that the perceived macroeconomic impact of just this one bankruptcy could bring about such a radical change in expectations about real interest rates and inflation. At one point in 2008

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the breakeven seven-year inflation rate reached −1.6 percent. According to Gang Hu and Mihir Worah (2009, p. 1), bond traders at PIMCO, “The market did not believe that it was possible to realize that kind of real rate or sustained deflation.” Another interpretation is that there was a shift in the risk premium for inflation-indexed bonds. In terms of our analysis above, this could be a change in the covariance of TIPS returns with consumption or wealth. But such a view sounds even less plausible than the view that the Lehman effect worked through inflation expectations. We have shown that the observed fluctuations in the covariances of TIPS returns with other variables are hard to rationalize even after the fact, and so it is hard to see why the market would have made a major adjustment in this covariance. Hu and Worah (2009, pp. 1, 3) conclude instead that, “the extremes in valuation were due to a potent combination of technical factors. . . . Lehman owned Tips as part of repo trades or posted Tips as counterparty collateral. Once Lehman declared bankruptcy, both the court and its counterparty needed to sell these Tips for cash.” The traders at PIMCO saw then a flood of TIPS on the market, for which there appeared to be few buyers. Distressed market makers were not willing to risk taking positions in these TIPS; their distress was marked by a crisis-induced sudden and catastrophic widening, by October 2008, in TIPS bid-asked spreads. Making the situation worse was the fact that some institutional investors in TIPS had adopted commodity overlay strategies that forced them to sell TIPS because of the fall at that time in commodity prices. Moreover, institutional money managers had to confront a sudden loss of client interest in relative value trades. Such trades, which take advantage of unusual price differences between securities with related fundamentals, might otherwise have exploited the abnormally low breakeven inflation. An important clue about the events of fall 2008 is provided by the diverging behavior of breakeven inflation rates in the TIPS cash market and breakeven inflation rates implied by zero-coupon inflation swaps during the months following the Lehman bankruptcy. Zero-coupon inflation swaps are derivatives contracts in which one party pays the other cumulative CPI (consumer price index) inflation over the term of the contract at maturity, in exchange for a predetermined fixed rate. This rate is known as the “synthetic” breakeven inflation rate, because if inflation grew at this fixed rate over the life of the contract, the net payment on the contract at maturity would be zero. As with the “cash” breakeven inflation rate implied by TIPS and nominal Treasury bonds, this rate reflects both expected inflation over the relevant period and an inflation risk premium.

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Figure 10. Breakeven Inflation Rates and Asset Swap Spreads on TIPS, July 2007–April 2009 Percent a year

Basis points a

Synthetic breakeven inflation (left scale) 3.0

120

2.5

100

2.0

80 60

Cash breakeven inflationb (left scale)

1.5

40

1.0 0.5 0

20 0

TIPS asset swap spread (right scale)

–20

–0.5

–40 Aug

Oct 2007

Dec

Feb

Apr

Jun Aug 2008

Oct

Dec

Feb Apr 2009

Source: Authors’ calculations based on data from Barclays Capital. a. Synthetic breakeven inflation rate derived from interest rates on zero-coupon inflation swaps. b. Breakeven inflation rate derived from differences in yields on nominal government bonds and TIPS.

Figure 10 plots the cash breakeven inflation rate implied by off-the-run (as opposed to newly issued, or on-the-run) TIPS and nominal Treasury bonds maturing in July 2017, and the synthetic breakeven inflation rate for the 10-year zero-coupon inflation swap, from July 2007 through April 2009. The figure also plots the TIPS asset swap spread, explained below. The two breakeven rates track each other very closely until mid-September 2008, with the synthetic breakeven inflation rate about 35 to 40 basis points above the cash breakeven inflation rate on average. This difference in breakeven rates is typical under normal market conditions. According to analysts, it reflects among other things the cost of manufacturing pure inflation protection in the United States. Most market participants supplying inflation protection in the U.S. inflation swap market are leveraged investors such as hedge funds and banks’ proprietary trading desks. These investors typically hedge their inflation swap positions by simultaneously taking long positions in TIPS and short positions in nominal Treasuries in the asset swap market. A buying position in an asset swap is functionally similar to a leveraged position in a bond. In an asset swap, one party pays the cash flows on a specific bond and receives in exchange interest at the London interbank offer rate (LIBOR) plus a spread known as the asset swap spread. Typically this spread is negative and larger in absolute magnitude for nominal Treasuries than for TIPS. Thus, leveraged investors

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selling inflation protection in an inflation swap face a positive financing cost derived from their long-TIPS, short-nominal Treasuries position. Figure 10 shows that starting in mid-September 2008, cash breakeven inflation rates fell dramatically while synthetic rates did not fall nearly as much; at the same time TIPS asset swap spreads increased from their normal level of about −35 basis points to about +100 basis points. Although not shown in the figure, nominal Treasury asset swap spreads remained at their usual levels. That is, financing long positions in TIPS became extremely expensive relative to historical levels just as their cash price fell abruptly. There is no reason why declining inflation expectations should directly affect the cost of financing long positions in TIPS relative to nominal Treasuries. The scenario that these two simultaneous changes suggest instead is one of intense selling in the cash market and insufficient demand to absorb those sales—as described by Hu and Worah—and simultaneously another shortage of capital to finance leveraged positions in markets other than that for nominal Treasuries; that is, the bond market events of the fall of 2008 may have been a “liquidity” episode. Under this interpretation, the synthetic breakeven inflation rate was at the time a better proxy for inflation expectations in the marketplace than the cash breakeven inflation rate, despite the fact that in normal times the inflation swap market is considerably less liquid than the cash TIPS market. The synthetic breakeven inflation rate declined from about 3 percent a year to about 1.5 percent at the trough. This long-run inflation expectation is perhaps more plausible than the 10-year expectation of zero inflation reflected in the cash market for off-the-run bonds maturing in 2017. Interestingly, cash breakeven inflation rates also diverged between on-the-run and off-the-run TIPS with similar maturities during this period. The online appendix shows that breakeven rates based on on-the-run TIPS were lower than those based on off-the-run TIPS. This divergence reflected another feature of TIPS that causes cash breakeven inflation rates calculated from on-the-run TIPS to be poor proxies for inflation expectations in the face of deflation risk. Contractually, TIPS holders have the right to redeem their bonds at maturity for the greater of either par value at issuance or that value plus accrued inflation during the life of the bond. Thus, when there is a risk of deflation after a period of inflation, new TIPS issues offer better deflation protection than older ones. Accordingly, on-the-run TIPS should be more expensive than off-the-run TIPS, and thus their real yields should be lower. Breakeven inflation rates derived from on-the-run TIPS must be adjusted upward for this deflation protection premium to arrive at a measure of inflation expectations.

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We view the experience with TIPS yields after the Lehman bankruptcy as reflecting a highly abnormal market situation, where liquidity problems suddenly created severe financial anomalies. This may seem to imply that one can regard the recent episode as unrepresentative and ignore the observations from these dates. However, investors in TIPS who would like to regard them as the safest long-term investment must consider the extraordinary short-term volatility that such events have given their yields.

V. The Uses of Inflation-Indexed Bonds We conclude by drawing out some implications of the recent experience with inflation-indexed bonds for both investors and policymakers.

V.A. Implications for Investors The basic case for investing in inflation-indexed bonds, stated by Campbell and Shiller (1997) and further developed by Michael Brennan and Yihong Xia (2002), Campbell and Viceira (2001, 2002), Campbell, Yeung Lewis Chan, and Viceira (2003), and Jessica Wachter (2003), is that these bonds are the safe asset for long-term investors. An inflationindexed perpetuity delivers a known stream of real spending power to an infinite-lived investor, and a zero-coupon inflation-indexed bond delivers a known real payment in the distant future to an investor who values wealth at that single horizon. This argument makes no assumption about the timeseries variation in yields, and so it is not invalidated by the gradual longterm decline in inflation-indexed bond yields since the 1990s, the mysterious medium-run variations in TIPS yields relative to short-term real interest rates, the spike in yields in the fall of 2008, or the high daily volatility of TIPS returns. There are, however, two circumstances in which other assets can substitute for inflation-indexed bonds to provide long-term safe returns. First, if the breakeven inflation rate is constant, as will be the case when the central bank achieves perfect anti-inflationary credibility, then nominal bonds are perfect substitutes for inflation-indexed bonds, and conventional government bonds will suit the preferences of conservative long-term investors. For a time in the mid-2000s, it looked as if this nirvana of central bankers was imminent, but the events of 2008 dramatically destabilized inflation expectations and reaffirmed the distinction between inflation-indexed and nominal bonds. Second, if the ex ante real interest rate is constant, as Eugene Fama (1975) famously asserted, then long-term investors can roll over short-term

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Treasury bills to achieve almost perfectly certain long-term real returns. Because inflation uncertainty is minimal over a month or a quarter, Treasury bills expose investors to minimal inflation risk. In general, they do expose investors to the risk of persistent variation in the real interest rate, but this risk is absent if the real interest rate is constant over time. Investors can tell whether this happy circumstance prevails by forecasting realized real returns on Treasury bills and measuring the movements of their forecasts, as we did in figure 8, or more simply by measuring the volatility of inflation-indexed bond returns. If inflation-indexed bonds have yields that are almost constant and returns with almost no volatility, then Treasury bills are likely to be good substitutes.8 Seen from this point of view, the high daily volatility of inflation-indexed bond returns illustrated in figure 4, far from being a drawback, demonstrates the value of inflationindexed bonds for conservative long-term investors. A simple quantitative measure of the usefulness of inflation-indexed bonds is the reduction in the long-run standard deviation of a portfolio that these bonds permit. One can estimate this reduction by calculating the long-run standard deviation of a portfolio of other assets chosen to minimize long-run risk (what we call the global minimum variance, or GMV, portfolio). This is the smallest risk that long-run investors can achieve if inflation-indexed bonds are unavailable. Once inflation-indexed bonds become available, the minimum long-run risk portfolio consists entirely of these bonds and has zero long-run risk. Thus, the difference between the minimized long-run standard deviation of the GMV portfolio and zero measures the risk reduction that inflation-indexed bonds make possible.9 We constructed a 10-year GMV portfolio consisting of U.S. stocks, nominal 5-year Treasury bonds, and 3-month Treasury bills. To derive the composition of this portfolio and its volatility at each horizon, we used the long-horizon mean-variance approach described in Campbell and Viceira (2005) and its companion technical guide (Campbell and Viceira 2004). We estimated a VAR(1) system for the ex post real return on Treasury bills 8. Strictly speaking, this argument assumes that real yields are described by the expectations hypothesis of the term structure, so that constant short-term real interest rates imply constant long-term real yields. Volatile risk or liquidity premiums on inflation-indexed bonds could make their yields volatile even if short-term real interest rates are constant. However, it is quite unlikely that time variation in risk or liquidity premiums would stabilize the yields on inflation-indexed bonds in an environment of time-varying real interest rates. 9. As an alternative approach, Campbell, Chan, and Viceira (2003) calculate the utility of an infinite-lived investor who has access to stocks, nominal bonds, and bills, and the utility gain when this investor can also hold an inflation-indexed perpetuity. We do not update this more complex calculation here.

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Figure 11. Volatility of Returns on the Global Minimum Variance Portfolio and on Inflation-Indexed Government Bonds Standard deviation (percent)

Standard deviation (percent)

14

5

12 10 8

b

TIPS returns (left scale) 10-year GMV portfolio (right scale)

6 4 2

4

a

3 2

U.K. inflation-indexed bond returnsb (left scale)

1

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Source: Authors’ calculations from Bloomberg and Center for Research in Security Prices data. a. Annualized 10-year standard deviation of the 10-year global minimum variance portfolio of U.S. stocks, nominal 5-year Treasury bonds, and 3-month Treasury bills, computed from a VAR model as described in the text. b. Annualized standard deviation of daily returns.

and the excess log return on stocks and nominal bonds. The system also includes variables known to forecast bond and equity risk premiums: the log dividend-price ratio, the yield on Treasury bills, and the spread between that yield and the 5-year Treasury bond yield. From this system we extracted the conditional variance-covariance of 10-year returns using the formulas in Campbell and Viceira (2004) and found the portfolio that minimizes this variance. Instead of estimating a single VAR system for our entire quarterly sample, 1953Q1–2008Q4, we estimated two VAR systems, one for 1953Q1– 1972Q4 and another for 1973Q1–2008Q4. We split the sample this way because we are concerned that the process for inflation and the real interest rate might have changed during the period as a whole. The conditional long-horizon moments of returns also depend on the quarterly variancecovariance matrix of innovations, which we estimated using 3-year windows of quarterly data. Within each window and VAR sample period, we combined the variance-covariance matrix with the full-sample estimate of the slope coefficients to compute the 10-year GMV portfolio and its annualized volatility. Figure 11 compares the estimated standard deviation of the GMV portfolio with the annualized daily standard deviations of TIPS and inflation-

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Figure 12. Volatility of Returns on the Global Minimum Variance Portfolio and of Hypothetical Quarterly TIPS Returns Standard deviation (percent) 7

Hypothetical TIPSa

6 5 10-year GMV portfoliob

4 3 2 1 1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Source: Authors’ calculations from Bloomberg and Center for Research in Security Prices data. a. Annualized standard deviation of quarterly returns. b. Annualized 10-year standard deviation of the 10-year global minimum variance portfolio of U.S. stocks, nominal 5-year Treasury bonds, and 3-month Treasury bills, computed from a VAR model as described in the text.

indexed gilts over the period where these bonds exist. Figure 12 compares the same GMV standard deviation with the estimated standard deviation of hypothetical TIPS returns, constructed from the VAR system using the method of Campbell and Shiller (1997) and section II of this paper, which assumes the log expectations hypothesis for inflation-indexed bonds. The annualized 10-year standard deviation of the 10-year GMV portfolio is fairly low in the 1960s, at around 1 percent a year. This is the period that led Fama (1975) to assert that the ex ante real interest rate is constant over time. Starting in the 1970s, however, persistent movements in the real interest rate cause the standard deviation to rise rapidly to about 4 percent a year. The standard deviation drops back to about 2 percent in the mid-1990s, but by 2008 it is once again at a historical high of 4 percent. These numbers imply that inflation-indexed bonds substantially reduce risk for long-term investors. Both comparisons show that, historically, the minimum long-run risk that can be achieved using other assets has been high when short-term TIPS returns have been volatile. In other words, inflation-indexed bonds are particularly good at reducing long-run risk whenever their short-run risk is high. Such a result may seem paradoxical, but it follows directly from the

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fact that the need for inflation-indexed bonds for long-term safety is greater when real interest rates vary persistently over time.10 Inflation-indexed bonds also play an important role for institutional investors who need to hedge long-term real liabilities. Pension funds and insurance companies with multiyear commitments should use inflationindexed bonds to neutralize the swings in the present value of their longdated liabilities due to changes in long-term real interest rates. Of course, these swings become apparent to institutional investors only when they discount real liabilities using market real interest rates, as the United Kingdom has required in recent years. The resulting institutional demand for inflationindexed gilts seems to have been an important factor driving down their yields (Viceira and Mitsui 2003; Vayanos and Vila 2007). The total demand of long-term investors for inflation-indexed bonds will depend not only on their risk properties, but also on their expected returns relative to other available investments and on the risk tolerance of the investors. An aggressive long-term investor might wish to short inflation-indexed bonds and invest the proceeds in equities, since stocks have only very rarely underperformed bonds over three or more decades in U.S. and U.K. data. In 2008 it was reported that Clare College, University of Cambridge, was planning to undertake such a strategy.11 However, Campbell, Chan, and Viceira (2003) estimated positive long-term demand for inflation-indexed bonds by long-term investors who also have the ability to borrow short term or to issue long-term nominal bonds. Long-term inflation-indexed bonds may be of interest to some shortterm investors. Given their high short-run volatility, however, short-term investors will wish to hold these bonds only if they expect to receive high excess returns over Treasury bills (as might reasonably have been the case in 1999–2000 or during the yield spike of the fall of 2008), or if they hold other assets, such as stocks, whose returns can be hedged by an inflationindexed bond position. We have shown evidence that TIPS and inflationindexed gilts did hedge stock returns during the downturns of the early 2000s and the late 2000s, and this should make them attractive to short-term equity investors.

10. This point is related to the asset pricing result discussed in section III.A, namely, that when one controls for the stock market covariance of inflation-indexed bonds, the equilibrium risk premium on these bonds for a conservative, infinite-lived, representative investor is declining in their variance. 11. David Turner, “College to Invest 15m Loan in Shares,” Financial Times, October 27, 2008.

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The illiquidity of inflation-indexed bonds is often mentioned as a disadvantage. But in developed countries these bonds are illiquid only relative to the same countries’ nominal government bonds, which, along with foreign exchange, are the most liquid financial assets. Compared with almost any other long-term investment vehicle, inflation-indexed government bonds are extremely cheap to trade. In addition, long-term buy-and-hold investors should care very little about transactions costs since they will rarely need to turn over their bond positions.

V.B. Implications for Policymakers In managing the public debt, the Treasury seeks to minimize the average cost of debt issuance while paying due regard to risk, including refinancing risk. It is commonly thought that short-term Treasury bills are less expensive than long-term debt but that exclusive reliance on bills would impose an unacceptable refinancing risk, as bills must frequently be rolled over. In the period since TIPS were introduced in 1997, they have proved to be an expensive form of debt ex post, because of the unexpected decline in real interest rates from the 1990s through early 2008. However, our analysis implies that the cost of TIPS should be lower than that of Treasury bills ex ante, because TIPS offer investors desirable insurance against future variation in real interest rates. This is the relevant consideration going forward, as Jennifer Roush, William Dudley, and Michelle Steinberg Ezer (2008) emphasize, and therefore governments should not be deterred from issuing inflation-indexed bonds by the high realized returns on their past issues. In the current environment, with inflation positively correlated with stock prices, the inflation risk premium in nominal Treasury bonds is likely negative. This implies that long-term nominal debt should be even cheaper for the Treasury than TIPS. However, the correlation between inflation and stock prices has changed sign in the past (Campbell, Sunderam, and Viceira 2009), and it may easily do so again in the future. Several other considerations also suggest that inflation-indexed bonds are a valuable form of public debt. First, to the extent that particular forms of debt have different investment clienteles, all with downward-sloping demand curves for bonds, it is desirable to diversify across different forms so as to tap the largest possible market for government debt (Greenwood and Vayanos 2008; Vayanos and Vila 2007). Second, inflation-indexed bonds can be used to draw inferences about bond investors’ inflation expectations, and such information is extremely

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valuable for monetary policymakers.12 It is true that market disruptions, such as those that occurred in the fall of 2008, complicate the measurement of inflation expectations, but our analysis shows that it is possible to derive meaningful information even in these extreme conditions. Finally, inflation-indexed bonds provide a safe real asset for long-term investors and promote public understanding of inflation. Fiscal authorities should take these public benefits into account as part of their broader mission to improve the functioning of their economies.

ACKNOWLEDGMENTS Campbell and Viceira’s research was supported by the Division of Research at the Harvard Business School, and by the U.S. Social Security Administration (SSA) through grant #10-M-98363-1-01 to the National Bureau of Economic Research (NBER) as part of the SSA Retirement Research Consortium. The findings and conclusions expressed are solely those of the authors and do not represent the views of SSA, any agency of the federal government, or the NBER. We are grateful to Carolin Pflueger for exceptionally able research assistance, to Mihir Worah and Gang Hu of PIMCO, Derek Kaufman of Citadel, and Albert Brondolo, Michael Pond, and Ralph Segreti of Barclays Capital for their help in understanding TIPS and inflation derivatives markets and the unusual market conditions of the fall of 2008, and to Barclays Capital for providing data. We acknowledge the helpful comments of Brookings Panel participants and our discussants Frederic Mishkin and Jonathan Wright.

12. Recent papers extracting information from the inflation-indexed yield curve include Beechey and Wright (2008), Christensen, Lopez, and Rudebusch (2009), D’Amico, Kim, and Wei (2008), Grishchenko and Huang (2008), and Haubrich, Pennacchi, and Ritchken (2008).

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References Backus, David K., and Stanley E. Zin. 1994. “Reverse Engineering the Yield Curve.” Working Paper 4676. Cambridge, Mass.: National Bureau of Economic Research. Bansal, Ravi, and Amir Yaron. 2004. “Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles.” Journal of Finance 59, no. 4: 1481–1509. Bansal, Ravi, Robert Dittmar, and Dana Kiku. 2009. “Cointegration and Consumption Risks in Asset Returns.” Review of Financial Studies 22, no. 3: 1343–75. Bansal, Ravi, Varoujan Khatchatrian, and Amir Yaron. 2005. “Interpretable Asset Markets?” European Economic Review 49, no. 3: 531–60. Bansal, Ravi, Dana Kiku, and Amir Yaron. 2007. “Risks for the Long Run: Estimation and Inference.” Duke University and University of Pennsylvania. Barr, David G., and John Y. Campbell. 1997. “Inflation, Real Interest Rates, and the Bond Market: A Study of UK Nominal and Index-Linked Government Bond Prices.” Journal of Monetary Economics 39, no. 3: 361–83. Beechey, Meredith J., and Jonathan H. Wright. 2008. “The High-Frequency Impact of News on Long-Term Yields and Forward Rates: Is It Real?” FEDS Working Paper 2008-39. Washington: Board of Governors of the Federal Reserve. Bekaert, Geert, Eric Engstrom, and Steven R. Grenadier. 2006. “Stock and Bond Returns with Moody Investors.” Working Paper 12247. Cambridge, Mass.: National Bureau of Economic Research. Bernanke, Ben. 2004. “The Great Moderation.” Address to the Eastern Economic Association, Washington, February 20. (www.federalreserve.gov/BOARD DOCS/SPEECHES/2004/20040220/default.htm). Blanchard, Olivier, and John Simon. 2001. “The Long and Large Decline in U.S. Output Volatility.” BPEA, no. 1: 135–64. Brennan, Michael J., and Yihong Xia. 2002. “Dynamic Asset Allocation under Inflation.” Journal of Finance 57, no. 3: 1201–38. Brière, Marie, and Ombretta Signori. 2009. “Do Inflation-Linked Bonds Still Diversify?” European Financial Management 15, no. 2: 279–97. Campbell, John Y. 1986. “Bond and Stock Returns in a Simple Exchange Model.” Quarterly Journal of Economics 101, no. 4: 785–804. ———. 1993. “Intertemporal Asset Pricing without Consumption Data.” American Economic Review 83, no. 3: 487–512. ———. 1996. “Understanding Risk and Return.” Journal of Political Economy 104, no. 2: 298–345. ———. 2003. “Consumption-Based Asset Pricing.” Chapter 13 in Handbook of the Economics of Finance, vol. IB, edited by George M. Constantinides, Milton Harris, and René Stulz. Amsterdam: North-Holland. Campbell, John Y., and John H. Cochrane. 1999. “By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior.” Journal of Political Economy 107, no. 2: 205–51.

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Campbell, John Y., and Robert J. Shiller. 1991. “Yield Spreads and Interest Rate Movements: A Bird’s Eye View.” Review of Economic Studies 58, no. 3: 495–514. ———. 1997. “A Scorecard for Indexed Government Debt.” NBER Macroeconomics Annual 1996, vol. 11, pp. 155–97. Campbell, John Y., and Luis M. Viceira. 2001. “Who Should Buy Long-Term Bonds?” American Economic Review 91, no. 1: 99–127. ———. 2002. Strategic Asset Allocation: Portfolio Choice for Long-Term Investors. New York: Oxford University Press. ———. 2004. “Long-Horizon Mean-Variance Analysis: A User Guide.” Harvard University. kuznets.fas.harvard.edu/∼campbell/papers/FAJ_CV_userguide.pdf. ———. 2005. “The Term Structure of the Risk-Return Trade-off.” Financial Analysts Journal 61, no. 1: 34–44. Campbell, John Y., and Tuomo Vuolteenaho. 2004. “Bad Beta, Good Beta.” American Economic Review 94, no. 5: 1249–75. Campbell, John Y., Yeung Lewis Chan, and Luis M. Viceira. 2003. “A Multivariate Model of Strategic Asset Allocation.” Journal of Financial Economics 67, no. 1: 41–80. Campbell, John Y., Adi Sunderam, and Luis M. Viceira. 2009. “Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds.” Working Paper 14701. Cambridge, Mass.: National Bureau of Economic Research. Christensen, Jens H. E., Jose A. Lopez, and Glenn D. Rudebusch. 2009. “Inflation Expectations and Risk Premiums in an Arbitrage-Free Model of Nominal and Real Bond Yields.” Financial Institutions Center Paper 09-02. Wharton School, University of Pennsylvania. D’Amico, Stefania, Don H. Kim, and Min Wei. 2008. “Tips from TIPS: The Informational Content of Treasury Inflation-Protected Security Prices.” FEDS Working Paper 2008-30. Washington: Board of Governors of the Federal Reserve. Epstein, Larry G., and Stanley Zin. 1989. “Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework.” Econometrica 57, no. 4: 937–69. ———. 1991. “Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: An Empirical Investigation.” Journal of Political Economy 99, no. 2: 263–86. Fama, Eugene F. 1975. “Short-Term Interest Rates as Predictors of Inflation.” American Economic Review 65, no. 3: 269–82. Fleming, Michael J., and Neel Krishnan. 2009. “The Microstructure of the TIPS Market.” Federal Reserve Bank of New York and Option Arbitrage Fund. Gollier, Christian. 2007. “The Consumption-Based Determinants of the Term Structure of Discount Rates.” Mathematics and Financial Economics 1, no. 2: 81–101. Greenwood, Robin, and Dimitri Vayanos. 2008. “Bond Supply and Excess Bond Returns.” Working Paper 13806. Cambridge, Mass.: National Bureau of Economic Research.

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Grishchenko, Olesya V., and Jing-zhi Huang. 2008. “Inflation Risk Premium: Evidence from the TIPS Market.” Pennsylvania State University. Gürkaynak, Refet S., Brian Sack, and Jonathan H. Wright. 2008. “The TIPS Yield Curve and Inflation Compensation.” FEDS Working Paper 2008-05. Washington: Board of Governors of the Federal Reserve. Hansen, Lars Peter, John C. Heaton, and Nan Li. 2008. “Consumption Strikes Back? Measuring Long-Run Risk.” Journal of Political Economy 116, no. 2: 260–302. Haubrich, Joseph, George Pennacchi, and Peter Ritchken. 2008. “Estimating Real and Nominal Term Structures Using Treasury Yields, Inflation, Inflation Forecasts, and Inflation Swap Rates.” Working Paper 08-10. Federal Reserve Bank of Cleveland. Hu, Gang, and Mihir Worah. 2009. “Why Tips Real Yields Moved Significantly Higher after the Lehman Bankruptcy.” PIMCO, Newport Beach, Calif. (available on request from the authors). Kim, C.-J., and Charles R. Nelson. 1999. “Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle.” Review of Economics and Statistics 81, no. 4: 608–16. McConnell, Margaret M., and Gabriel Perez-Quiros. 2000. “Output Fluctuations in the United States: What Has Changed Since the Early 1980’s?” American Economic Review 90, no. 5: 1464–76. McCulloch, J. Huston, and Heon-Chul Kwon. 1993. “US Term Structure Data, 1947–1991.” Working Paper 93-6. Ohio State University. Merton, Robert C. 1973. “An Intertemporal Capital Asset Pricing Model.” Econometrica 41, no. 5: 867–87. Nelson, Charles R., and Andrew F. Siegel. 1987. “Parsimonious Modeling of Yield Curves.” Journal of Business 60, no. 4: 473–89. Piazzesi, Monika, and Martin Schneider. 2007. “Equilibrium Yield Curves.” NBER Macroeconomics Annual 2006, pp. 389–472. Restoy, Fernando, and Philippe Weil. 1998. “Approximate Equilibrium Asset Prices.” Working Paper 6611. Cambridge, Mass.: National Bureau of Economic Research. Roush, Jennifer, William Dudley, and Michelle Steinberg Ezer. 2008. “The Case for TIPS: An Examination of the Costs and Benefits.” Staff Report 353. Federal Reserve Bank of New York. Stock, James, and Mark Watson. 2003. “Has the Business Cycle Changed and Why?” NBER Macroeconomics Annual 2002, pp. 159–218. Svensson, Lars E. O. 1994. “Estimating and Interpreting Forward Interest Rates: Sweden 1992–1994.” Working Paper 4871. Cambridge, Mass.: National Bureau of Economic Research. Vayanos, Dimitri, and Jean-Luc Vila. 2007. “A Preferred-Habitat Model of the Term Structure of Interest Rates.” London School of Economics and Merrill Lynch. Viceira, Luis M., and Akiko M. Mitsui. 2003. “Pension Policy at the Boots Company PLC.” Case 203105. Harvard Business School.

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———. 2007. “Bond Risk, Bond Return Volatility, and the Term Structure of Interest Rates.” Working Paper 07-082. Harvard Business School. Wachter, Jessica A. 2003. “Risk Aversion and Allocation to Long-Term Bonds.” Journal of Economic Theory 112, no. 2: 325–33. ———. 2006. “A Consumption-Based Model of the Term Structure of Interest Rates.” Journal of Financial Economics 79, no. 2: 365–99.

Comments and Discussion COMMENT BY

FREDERIC S. MISHKIN This paper by John Campbell, Robert Shiller, and Luis Viceira is excellent. Indeed, I would have titled it, “Everything You Always Wanted to Know about Inflation-Indexed Bond Markets, But Were Afraid to Ask.”1 The paper documents many key facts and puzzles about this market, including the following: —the decline in long-term real yields on inflation-indexed bonds from the 1990s; —the instability of real yields and returns on these bonds during the recent financial crisis; —the negative correlation of returns on these bonds with those on stock prices, indicating that these bonds can be used to hedge equity risk; —the fact that real yields on these bonds differ in different countries; —the fact that the expectations hypothesis view that long-term real yields are driven by expectations of short-term real interest rates is supported by the data; —but also the fact that risk and liquidity premiums on these bonds are very important and are volatile, suggesting that institutional factors matter a lot to their yields; —the fact that long-term inflation-indexed bonds have high short-term risk; —but also the fact that this is fully consistent with their being good long-term risk reducers.

1. For readers too young to remember, this is a takeoff on the title of a popular book and a Woody Allen movie from the 1970s.

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The paper focuses on inflation-indexed bonds from the perspective of the investor. Given my comparative advantage as a former governor of the Federal Reserve, I will instead provide a different perspective by discussing why their analysis is so important for policymakers. One of the most important issues for monetary policymakers is whether they can keep long-run inflation expectations anchored. Such anchoring is key to successful monetary policy for several reasons, and this is one of the reasons that I and many other monetary economists have argued strongly for some form of inflation targeting. First, anchoring long-run inflation expectations leads to more stable inflation outcomes. As I discussed in Mishkin (2007), long-run expectations of inflation on the part of households and firms are a key factor in determining the actual behavior of inflation. If these expectations are unstable, so, too, will be inflation. Moreover, the commitment that inflation targeting provides can play an important role in minimizing the risk of what Marvin Goodfriend (1993) has called “inflation scares,” that is, episodes in which longer-term inflation expectations jump sharply in response to specific macroeconomic developments or monetary policy actions. Second, anchoring long-run inflation expectations can help stabilize output and employment. Specifically, to counter a contractionary demand shock, the monetary authorities need to reduce the short-run nominal interest rate; however, the effectiveness of such a policy action may be hindered if long-run inflation expectations are not firmly anchored. For example, if the private sector becomes less certain about the longer-run inflation outlook, the resulting increase in the inflation risk premium could boost longer-term interest rates by more than the increase in expected inflation. The higher premium would in turn place upward pressure on the real cost of long-term financing for households and businesses (whose debt contracts are almost always expressed in nominal terms) and hence might partly offset any direct monetary stimulus. Thus, firmly anchoring inflation expectations can make an important contribution to the effectiveness of the central bank’s actions aimed at stabilizing economic activity in the face of adverse demand shocks. Third, anchoring long-run inflation expectations provides the central bank with greater flexibility to respond decisively to adverse demand shocks. Well-anchored expectations help ensure that an aggressive policy easing is not misinterpreted as signaling a shift in the central bank’s inflation objective; they thereby minimize the possibility that long-run inflation expectations could move upward in response to the easing and lead to a rise in actual

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inflation. Well-anchored expectations are especially valuable in periods of financial market stress; at such times, prompt and decisive policy action may be required to prevent a severe contraction in economic activity that could further exacerbate the uncertainty and the stress, leading to a further deterioration in macroeconomic activity, and so on. Thus, by providing the central bank with greater flexibility in mitigating the risk of such an adverse feedback loop, well-anchored long-run inflation expectations play an important role in promoting financial stability as well as the stability of economic activity and inflation. Fourth, well-anchored long-run inflation expectations can help prevent deflation from setting in—a particularly relevant consideration today. Deflation can severely weaken economic activity by triggering debt-deflation of the type described by Irving Fisher (1933), in which the falling price level increases the real indebtedness of firms, undermining their balance sheets. Fifth, well-anchored long-run inflation expectations can help minimize the effects of an adverse cost shock such as a persistent rise in the price of energy. Generally speaking, such shocks tend to result in weaker economic activity as well as higher inflation. However, when long-run inflation expectations are firmly anchored, these shocks are likely to have only transitory effects on actual inflation, thus obviating the need to raise interest rates aggressively to keep inflation from rising. Thus, well-anchored long-run inflation expectations can help reduce output and employment fluctuations that impose unnecessary hardship on workers and on the economy more broadly. The bottom line is that anchoring long-run inflation expectations is so important to successful monetary policy that the monetary authorities need to know what is happening to these expectations at all times. Indeed, when I was on the Federal Reserve Board, we spent a lot of time and effort trying to assess where long-run inflation expectations were heading, and we looked at several measures of these expectations. Surveys of households, such as the University of Michigan Inflation Expectation Survey, are one important source of information, but they have an important drawback. Research in the field of behavioral economics suggests that biases due to framing are likely to make survey measures of long-run inflation expectations unreliable. The problem is that when survey measures of shortrun inflation expectations change, survey measures of long-run inflation expectations are likely to move with them, even if long-run expectations have not changed. This might happen because questions about both are

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asked at the same time, and the answer to the first question influences (“frames”) the response to the second, resulting in a spurious co-movement between the two. Indeed, this is exactly what has happened recently. When oil prices rose, driving up inflation in terms of the consumer price index (CPI), not only did one-year inflation expectations move up in the Michigan survey, which makes sense, but so did measures of 5-to-10-year inflation expectations. Then, when CPI inflation and one-year survey expectations came back down, so, too, did the 5-to-10-year survey expectations. These temporary fluctuations in the 5-to-10-year survey measure were almost surely illusory. A second measure of long-run inflation expectations comes from the Survey of Professional Forecasters (SPF). In recent years this measure has been rock steady. Of course, this may indicate that inflation expectations are firmly anchored, but it may instead be that the measure is failing to capture long-run inflation expectations that are in fact moving around. Skepticism about survey measures is one reason why many economists, including myself, are more willing to trust expectations measures that are derived from financial markets data. After all, people buying or selling securities are putting their money where their mouth is—they thus have a strong incentive to base their decisions on their true forecasts. Here the inflation-indexed bond market provides exactly the information desired. The difference between interest rates on nominal government bonds and those on inflation-indexed bonds, or what the paper calls “breakeven inflation” and the Federal Reserve Board calls “inflation compensation,” serves as a measure of inflation expectations. Such measures can be used as the canary in the coal mine to let monetary policymakers know if inflation expectations are becoming unanchored. Indeed, when I was at Board meetings, I would always ask Jonathan Wright, the other discussant of this paper, what he thought long-run breakeven measures of inflation were telling us about long-run inflation expectations. As the paper points out, however, there is one big problem with using breakeven inflation measures from inflation-indexed bonds to assess whether long-run inflation expectations are becoming unanchored, namely, the presence of risk and liquidity premiums. The paper demonstrates that these premiums are substantial and seem to vary a lot. Sorting out what drives these premiums is thus key to helping policymakers evaluate what is happening to inflation expectations, and the paper attempts to do that. The results in the paper raise three issues, however. First, the standard risk premium theories do not seem to explain much of the actual movements in inflation-indexed bond yields. Second, these theories suggest that

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inflation-indexed bonds should be good hedges against both consumption risk and equity risk, in which case inflation-indexed bonds should have a negative risk premium. Yet, to the contrary, they seem to have a positive risk premium. Both of these findings suggest that the existing theories do not tell us much about why liquidity and risk premiums vary. Third, it appears that a lot of the fluctuation in real yields on inflation-indexed bonds is due to institutional factors. This became very apparent during the recent period of financial market stress, when there were huge swings in these yields. However, as the paper points out, how these institutional factors affect real yields on these bonds is not well understood. The paper’s bottom line is that financial economists do not yet understand what causes the risk and liquidity premiums on inflation-indexed bonds to move around. This means that extracting information from these bonds about expected inflation is not easy. A striking example of this problem was occurring at the time of this conference. As the paper shows, long-run breakeven inflation as measured by the difference in bond yields declined precipitously as the economy went into a tailspin. Does this mean that long-run inflation expectations became unanchored in the downward direction? If so, the situation was dangerous indeed, because it meant that deflation was more likely to set in, and aggressive monetary policy to prevent this unanchoring of inflation expectations was called for. Yet because one could not be sure what was happening to the risk and liquidity premiums on inflation-indexed bonds, neither could one be sure that this decline in breakeven inflation really meant that long-run inflation expectations had fallen. Even though there was still some uncertainty about what inflationindexed bonds were saying about long-run inflation expectations, I do think the sharp fall in breakeven inflation was cause for worry—that the dangers of deflation were real. To me this suggests that it is even more imperative that the Federal Reserve take steps to anchor inflation expectations better. This is why I have argued, both when I was a governor of the Federal Reserve and afterward,2 that if ever there was a time for the Federal Reserve to announce an explicit, numerical inflation objective, that time is now.

2. Mishkin (2008); Frederic S. Mishkin, “In Praise of an Explicit Number for Inflation,” Financial Times, January 12, 2009, p. 7.

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REFERENCES FOR THE MISHKIN COMMENT

Fisher, Irving. 1933. “The Debt-Deflation Theory of Great Depressions.” Econometrica 1, no. 4: 337–57. Goodfriend, Marvin. 1993. “Interest Rate Policy and the Inflation Scare Problem: 1979–1992.” Federal Reserve Bank of Richmond Economic Quarterly 79, no. 1: 1–24. Mishkin, Frederic S. 2007. “Inflation Dynamics,” International Finance 10, no. 3: 317–34. _________. 2008. “Whither Federal Reserve Communications.” Speech at the Peterson Institute for International Economics, Washington, July 28, 2008 (www.federalreserve.gov/newsevents/speech/mishkin20080728a.htm).

COMMENT BY

JONATHAN H. WRIGHT It is now just over a decade since the United States began issuing inflation-linked Treasury bonds. This paper by John Campbell, Robert Shiller, and Luis Viceira is a timely and excellent analysis of what has been learned from the pricing of these new securities and their counterparts in other countries. TIPS yields have been more volatile than might have been anticipated. Campbell, Shiller, and Viceira discuss the reasons why this is so before turning to the most topical issue, namely, explaining the behavior of TIPS in the recent financial crisis. ARE RISK PREMIUMS ON INFLATION-INDEXED BONDS POSITIVE OR NEGATIVE?

Abstracting for the moment from issues of liquidity, the yield on an inflation-linked bond is the sum of the average expected real short-term interest rate over the life of the bond and a risk premium. Campbell, Shiller, and Viceira use both a consumption-based model of asset pricing and a capital asset pricing model to argue that the risk premium on TIPS ought to be low or even negative. That would make them an ideal instrument for a Treasury seeking to minimize expected debt-servicing costs. Some simple pieces of empirical evidence can be brought to bear on the question of the typical sign of the risk premium on such bonds. The average 5-to-10-year-forward TIPS yield from January 2003 to August 2008 was 21⁄2 percent. If the risk premium on TIPS is zero or negative, this means that the expectation of r*, the equilibrium real short-term interest rate, must be at least 21⁄2 percent (abstracting from any liquidity premium, but this was a time when TIPS liquidity was generally good). This seems a rather high number. Expectations of real short-term interest rates 5 to 10 years hence, computed from the twice-yearly Blue Chip survey of economic forecasters, are volatile but were around 2 percent over this period. This reasoning suggests that risk premiums on TIPS are positive.

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Table 1. Average Slopes of Forward Yield Curves on Nominal and Inflation-Linked Government Bondsa Basis points Bond Nominal Inflation-linked

United Kingdom

United States

0.5 −6.5

28.2 13.7

Sources: Bank of England data; Federal Reserve research data (Gürkaynak, Sack, and Wright 2007, forthcoming). a. Spread of six-year-ahead over five-year-ahead continuously compounded instantaneous forward rates for U.K. and U.S. yield curves; the spread is averaged over all days from the start of January 2003 to the end of August 2008.

Another simple calculation uses the slope of the yield curve for inflationlinked bonds. In normal circumstances one might suppose that expectations of real short-term interest rates 5 to 10 years hence are fairly flat. If the forward TIPS yield curve at those horizons slopes up, that would suggest that term premiums are positive, and if the curve slopes down, it would suggest that they are negative. Table 1 shows the average slopes of the forward (five to six years out) yield curves on nominal and inflationlinked bonds in the United States and in the United Kingdom over the period from January 2003 to August 2008.1 In the United Kingdom the yield curve for nominal bonds slopes up whereas the yield curve for inflation-linked gilts slopes down—evidence for the view expressed in the paper. In the United States the evidence is not so clear: the inflation-linked curve is flatter than the nominal one, but both slope up. Taken together, this simple evidence does not seem to me to support the view that risk premiums on TIPS have typically been negative, although I agree that they are much lower than their nominal counterparts. THE TIPS MARKET AND THE FINANCIAL CRISIS. Since the collapse of Lehman Brothers in September 2008, yields on inflation-linked and nominal bonds have decoupled and have been exceptionally volatile. The yields on some inflation-linked bonds rose above their nominal counterparts, making the breakeven inflation rate negative. This could represent either a fear of deflation or special demand for the comparative liquidity of nominal securities. Knowing which it is matters a lot. Indeed, it is surely the most important thing to understand from the TIPS market right now. It is a hard question to answer, but there are some clues.

1. Piazzesi and Schneider (2007) did a similar comparison for an earlier sample period.

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Figure 1. Yields on Two TIPS of Comparable Maturity but Differing Issue Dates, 2008–09 Percent 4.0

July 2013 TIPS (issued 2003)

3.5 3.0 2.5 2.0 1.5

April 2013 TIPS (issued 2008)

1.0 May

Jul

Sep 2008

Nov

Jan

Mar 2009

May

Source: Bloomberg data.

TIPS bonds have the feature that the principal repayment cannot be less than the face value of the bond, even if the price level falls over the life of the bond. This gives TIPS an option-like feature in which the “strike price” is the reference CPI (that is, the price level at the time that the bond is issued). For a newly issued bond, any deflation will result in this option being in the money. For a bond issued, say, five years ago, however, deflation has to be very severe—enough to unwind all the cumulative inflation over the past five years—before this deflation option has any value. This means that one can obtain information on the perceived probability of deflation by comparing the real yields on pairs of TIPS with comparable maturity dates but different reference CPIs. Figure 1 plots the real yields on the April 2013 and July 2013 TIPS. These were issued in 2008 and 2003, and the reference CPIs are 211.37 and 183.66, respectively. Before September 2008, the real yields on these two bonds were comparable, as the deflation option was perceived to be too far out of the money to matter. But subsequently the spread soared to 2 percentage points. The natural interpretation is that investors started to put substantial odds on deflation taking hold, increasing the relative attractiveness of the more recently issued TIPS.

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By comparing the yields on these two TIPS, one can calculate a lower bound on the implied probability of deflation over the period until 2013. This requires a number of strong assumptions, including risk neutrality.2 But the calculation is based on comparing two TIPS yields, not a TIPS yield with a nominal yield, and so the technical factors that Campbell, Shiller, and Viceira cite as pushing down TIPS prices in the fall of 2008 should not distort this calculation, unless they affected one TIPS issue more than the other. Figure 2 shows how this implied probability of deflation evolved over time. From around zero before September 2008, it soared to over 60 percent before falling back to about 10 percent early in 2009. Again, the calculation embeds many strong assumptions, but it is only a lower bound, and so it seems reasonable to think that fear of deflation explains a significant part of the unusual behavior of TIPS last fall. That fear is now much reduced but has not entirely gone away. Fear of deflation was surely not the only influence on inflation-linked bonds over this period; issues that come under the broad heading of liquidity were important, too. Campbell, Shiller, and Viceira make a compelling case that TIPS prices were depressed last fall as leveraged investors were

2. Here are the mechanics of the calculation. Pretend that the April 2013 and July 2013 TIPS are both zero-coupon bonds maturing June 1, 2013, and are identical apart from their reference CPIs. Let m denote the remaining time to maturity in years. Let x denote the CPI at the maturity date, and f(x) and F(x) the probability density and cumulative distribution functions of x, respectively. Assume that agents are risk-neutral. The reference CPIs are xu = 211.37 and xl = 183.66 for the April 2013 and the July 2013 bond, respectively, so that their principal repayments per dollar of face value are max(1, x/xu) and max (1, x/xl), respectively. Under these assumptions, the difference between the July 2013 and the April 2013 continuously compounded TIPS yields is r = which means that r ≤

⎫ 1 ⎧ xu x ⎛ xu ⎞ ⎨ln F ( xl ) +∫ u ln⎜ ⎟ f ( x )dx ⎬, xl ⎝ x ⎠ m ⎩ xl ⎭

⎫ 1 ⎧ ⎛ xu ⎞ 1 ⎛ xu ⎞ x ⎛x ⎞ F ( xl ) + ∫ u ln⎜ u ⎟ f ( x )dx ⎬ = ln ⎜ ⎟ F x u . So the ⎨ln xl ⎝ xl ⎠ m ⎩ ⎜⎝ xl ⎟⎠ ⎭ m ⎝ xl ⎠

( )

risk-neutral probability of deflation (that is, of the price index in 2013 being below xu = 211.37, rm . which is also approximately its current level) is bounded below as F x u ≥ ln x u x l

( )

(

)

This is the probability shown in figure 2. The assumptions made are strong, and it is possible that part of the spread between the two TIPS represents instead a premium for the greater liquidity of the on-the-run issue, the April 2013 TIPS. However, there has never been much evidence of an on-the-run premium in the TIPS market, and qualitatively similar spreads between other pairs of TIPS issues with close maturity dates but different reference CPIs can also be observed since early fall 2008.

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Figure 2. Probability of Deflation as Calculated from TIPS of Differing Issue Dates, 2008–09 Probability (p)

0.6 0.5 0.4 0.3 0.2 0.1 0 May

Jul

Sep 2008

Nov

Jan

Mar 2009

May

Source: Authorís calc ulations.

forced to unwind large TIPS positions quickly.3 Refet Gürkaynak, Brian Sack, and I (forthcoming) estimate that worsening liquidity pushed up five-year TIPS yields by more than a percentage point in the fall of 2008. The issue of liquidity can be seen starkly by comparing the yield on the April 2013 TIPS with the yield curve on nominal Treasury bonds. Because this TIPS was issued in 2008 (when the CPI was around its current level), and because the inflation adjustment to the TIPS principal cannot be negative, this particular TIPS effectively becomes a nominal security in the event of deflation,4 while of course it pays off more than a nominal security in the event of inflation. Thus, the payoff on this security stochastically dominates the payoff on a nominal Treasury bond of corresponding 3. As Campbell, Shiller, and Viceira point out, the divergence between TIPS breakeven rates and rates quoted on inflation swaps is strongly suggestive of distressed TIPS sales. However, the inflation swaps market in the United States is tiny, with a trading volume roughly 1 percent of that in TIPS. One might be hesitant to read too much into prices from such a small and illiquid market. 4. This neglects the inflation adjustment to the coupon, which can be negative. The coupon rate on the April 2013 TIPS is tiny (five-eighths of a percentage point), and so even a sizable deflation should have only a small effect on the pricing of the security through coupon indexation.

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Figure 3. Yield Spread between Nominal Treasury Bonds and the Most Recently Issued Five-Year TIPSa Percentage points 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 May

Jul

Sep 2008

Nov

Jan

Mar 2009

May

Source: Bloomberg data and author’s calculations using the Federal Reserve Board’s smoothed yield curve. a. Yield on nominal Treasury securities minus the yield on April 2013 TIPS (both securities of comparable maturity).

maturity. Figure 3 shows that the yield spread between the April 2013 TIPS and comparable-maturity nominal Treasury bonds went negative for an extended period in late 2008 and early 2009, and it was large and negative at times. This makes no sense from a standard asset pricing perspective, as it means that investors were leaving an arbitrage opportunity on the table. And even though the spread is now positive once again, it remains remarkably low given that there are surely sizable odds in favor of a pickup in inflation between now and 2013. Lawrence Summers (1985) once quipped that financial economics entailed simply checking that two-quart bottles of ketchup sold for twice as much as one-quart bottles. Alas, it is not so any more—there have recently been many examples of investors seemingly leaving arbitrage opportunities unexploited. The comparison between the April 2013 TIPS yield and the nominal yield curve is one example. A second is the fact that the yield on old 30-year Treasury bonds is systematically higher than the yield on off-the-run 10-year notes of the same maturity. Another is that the yields on Resolution Funding Corporation (Refcorp) bonds, which are guaranteed by

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the Treasury,5 are nonetheless substantially higher than yields on ordinary Treasury securities of comparable maturity. All these Treasury market anomalies are conventionally treated as the effects of a “liquidity premium.” For example, the cheapness of TIPS could be thought of as the compensation that investors demand for the poor liquidity of these instruments relative to nominal bonds. But TIPS are mainly bought by buy-and-hold investors, and bid-ask spreads on these securities are tiny. The cheapness of TIPS thus cannot really be rationalized as simply amortizing the transactions costs of a long-term investor. Moreover, as figure 4 shows, trading volume in TIPS (from the New York Federal Reserve Bank’s survey of primary dealers) has declined but is still around its level in 2003. All this indicates to me that the TIPS liquidity premium has to have some explanation beyond just transactions costs. As Campbell, Shiller, and Viceira indicate, this explanation might be along the lines of a segmented market with arbitrageurs who rationally pass up hold-tomaturity arbitrage opportunities at times of market stress (Greenwood and Vayanos 2008; Shleifer and Vishny 1997).6 CENTRAL BANK PURCHASES OF TIPS. In standard equilibrium asset pricing models, a decision by the Federal Reserve to purchase bonds should do nothing to their price, unless expectations of future short-term interest rates are thereby affected (Eggertsson and Woodford 2003). Sufficiently large purchases would result in a corner solution in which the Federal Reserve owned all of the particular security being purchased, but the price would still be unaffected. However, if markets are segmented and highly illiquid, this story may break down. The reaction to the announcement following the March 2009 Federal Open Market Committee (FOMC) meeting is a telling “event study” of the effects of central bank purchases. On that occasion the FOMC surprised market participants by announcing that the Federal Reserve would buy $300 billion in Treasury securities. The yield curves for both nominal and inflation-linked securities right before and after this announcement are shown in figure 5. Both moved down sharply, but the TIPS yield curve moved even more, especially at shorter maturities. The magnitude of this 5. This is not just the implicit guarantee that could be thought to apply to agency securities in general. Rather, Refcorp bonds have principal payments that are fully collateralized by nonmarketable Treasury securities and coupon payments that are guaranteed by the Treasury under the Financial Institutions Reform, Recovery, and Enforcement Act. 6. One way to improve TIPS market functioning might be to encourage the formation of a TIPS futures market. Such a market would make hedging cheaper and easier while improving liquidity in the cash market as well.

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Figure 4. Trading Volume in TIPS, 2002–09a Billions of dollars a day

10 8 6 4 2

2002

2003

2004

2005

2006

2007

2008

2009

Source: Federal Reserve Bank of New York FR 2004 survey. a. Eight-week moving average of interdealer volume in TIPS.

decline was far more than is consistent with what investors could have learned from the announcement about the expected path of future shortterm interest rates. Other announcements of this sort by the Federal Reserve and by foreign central banks have had comparable effects. This indicates that central banks can indeed drive down longer-term interest rates by direct purchases of securities, at least at times of market stress. Of course, aggregate demand is more sensitive to the long-term interest rates paid by households and businesses than to Treasury yields. But lower Treasury rates could nonetheless spill over into private sector borrowing costs. More important, if changing asset supply affects prices in the Treasury market, then the same should be true in the markets for corporate bonds and mortgage-backed securities, meaning that the Federal Reserve could improve financial conditions by buying assets in these markets, too. CONCLUSIONS. TIPS contain valuable information for economists and policymakers. In normal times they can be used to infer expectations of inflation and real short-term interest rates. They still can, but in the financial crisis that began last year, the most important information these securities provide is of how dysfunctional asset markets were and, to a large extent, still are. I emphasize two conclusions. First, in a financial crisis, markets are segmented and illiquid, and changes in effective asset supply brought about by Federal Reserve purchases can and evidently do have large effects on

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Figure 5. Nominal and TIPS Yield Curves before and after the March 2009 FOMC Announcementa Nominal Percent Before

3 2

After

1 2

3

4

5 6 Years to maturity

7

8

9

8

9

TIPS Percent 3

Before

2 After

1 2

3

4

5 6 Years to maturity

7

Source: Federal Reserve Board estimates. a. Data are as of the late afternoon of March 17 (before) and 18 (after).

prices. Second, policymakers and the press are often obsessed with finding the “market price” of extraordinarily opaque securities. TIPS are extremely simple securities. If, for whatever reason, the market cannot price TIPS coherently, then any faith in the ability of the market to come up with the textbook valuation of esoteric financial instruments seems quite misplaced. REFERENCES FOR THE WRIGHT COMMENT

Eggertsson, Gauti B., and Michael Woodford. 2003. “The Zero Bound on Interest Rates and Optimal Monetary Policy.” BPEA, no. 1: 139–211. Greenwood, Robin, and Dimitri Vayanos. 2008. “Bond Supply and Excess Bond Returns.” Working Paper 13806. Cambridge, Mass.: National Bureau of Economic Research. Gürkaynak, Refet S., Brian Sack, and Jonathan H. Wright. 2007. “The U.S. Treasury Yield Curve: 1961 to the Present.” Journal of Monetary Economics 54, no. 8: 2291–2304. _________. Forthcoming. “The TIPS Yield Curve and Inflation Compensation.” American Economic Journal: Macroeconomics.

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Piazzesi, Monika, and Martin Schneider. 2007. “Equilibrium Yield Curves.” NBER Macroeconomics Annual 2006, pp. 389–472. Shleifer, Andrei, and Robert W. Vishny. 1997. “The Limits of Arbitrage.” Journal of Finance 52, no. 1: 35–55. Summers, Lawrence H. 1985. “On Economics and Finance.” Journal of Finance 40, no. 3: 633–35.

GENERAL DISCUSSION Matthew Shapiro agreed that market segmentation likely accounted for the spike in the TIPS yield in November. He suggested that hedge funds and other institutions were desperate for liquidity at that time. TIPS were among the few assets that were holding their value reasonably well, and so they were among the assets that got dumped on the market, thus revealing substantial segmentation between the market for indexed and that for nonindexed Treasury securities. Shapiro also suggested that with the breakdown of the barrier between fiscal and monetary policy observed in the response to the financial crisis, TIPS were an increasingly important tool for jointly disciplining fiscal and monetary policy. He speculated, however, that in the event of a hyperinflation, Congress might impose a windfall profits tax on the inflation indexation component of TIPS returns. Ricardo Reis noticed that both expected inflation and the differential between TIPS and nominal bond yields had remained stable until around 2006, when the relationship started to break down. He compared this to the movement in oil prices shown in James Hamilton’s paper in this volume. Oil prices went up and then came down by a lot, which, Reis felt, could have changed perceptions of what was happening to oil prices even at a 10-year horizon. He proposed that expectations of movements in the price of oil might account for part of the risk and liquidity premiums observed in TIPS prices, given that the Federal Reserve targets core inflation, which excludes oil, whereas TIPS are indexed to overall inflation. Reis also suggested that much of CPI inflation is actually relative price inflation, which would impact TIPS’ hedging potential. His own research with Mark Watson found that 75 percent of annual variation, and 85 percent of quarterly variation, in the CPI is due to relative price changes. The results diminish over longer time horizons but are still in the range of 5 to 40 percent at a 10-year horizon. He suggested that relative price changes may also capture changes in the relative productivity of different sectors, providing a possible hedging opportunity in expected inflation based on relative productivity changes between sectors.

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Alan Blinder observed that traditional monetary policy theory says that the central bank can manipulate nominal things but cannot manipulate real things, including real interest rates, and especially long-term real rates. He interpreted the evidence in the paper as showing that this theory is not just slightly wrong but very wrong. The paper’s findings, in his view, are relevant to formulas such as the Taylor rule, where the real interest rate is usually assumed to be constant at 2 percent and it is the other factors that change. As a long-time advocate of inflation-linked bonds, Blinder had been excited when Campbell and Shiller’s 1996 paper put an actual number on the likely interest rate savings to the Treasury. That paper, he recalled, said that TIPS should be cheaper for the Treasury because they were less risky to bondholders and would therefore pay a lower rate of return. In reality, they have not paid a lower rate, which, Blinder reasoned, was due to their lesser liquidity compared with nominal bonds. He wondered whether the main message of the paper was that economists have been focusing too much on risk and not enough on liquidity. James Hamilton asked whether TIPS served equally well as nominal Treasuries as collateral for credit default swaps. John Campbell answered that he did not believe so but was unsure whether the difference was large and how much of the yield spread it would explain. He noted that there are other costs to using TIPS, such as larger “haircuts,” which make their use as collateral less standard. Benjamin Friedman expressed surprise that both the paper and the discussion thus far had proceeded entirely on a pre-tax basis. He suggested that differential taxation might impact TIPS’ hedging properties, especially now that tax rates for individuals are lower on qualified dividends. Michael Woodford commented on whether recent TIPS behavior indicated market segmentation. He felt this to be the most obvious explanation, but he disagreed with Jonathan Wright’s hypothesis that market segmentation implies that Federal Reserve purchases of Treasury securities should be an effective way of stimulating aggregate demand. He instead proposed that as a result of market segmentation, a policy designed to lower TIPS yields (or other long-term Treasury yields) may change only the relationship of those yields to other real interest rates; the desired effect of such a policy, that of affecting the terms on which others can borrow, need not occur. Justin Wolfers included himself among those economists who have always been hopeful that prices contain a lot of embedded information. Looking at the prices reported in the paper, however, he was glad that he was not a macrofinance economist looking for structural interpretations of price movements, because the conclusion he felt drawn to was that market

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prices are informative except when they are not. He recommended that the authors try to provide some guidance on determining under what circumstances TIPS prices will be uninformative. Steven Davis was struck by the evidence for a market segmentation interpretation of TIPS behavior and said he would have liked to see a more thorough explanation of the extent, nature, and importance of that segmentation. He suggested that the authors conduct additional exercises that would help pinpoint where the segmentation occurs: is it between TIPS and nominal Treasuries, across different vintages and payoff horizons of TIPS themselves, or in markets that are thinly traded versus those that are not? Understanding this would be useful, he believed, in determining when drawing inferences from these securities about expectations and inflation might be more problematic. He also wanted to know whether the observed asset pricing anomalies occurred only in a very thinly traded, less important part of the market or were endemic to the system as a whole. David Romer thought that segmentation was perhaps too easy an explanation and proposed instead that certain features of the market may dissuade people from arbitraging TIPS. It would be worth asking professional investors why TIPS do not provide a riskless opportunity or whether some sort of agency problem inhibits their purchase. Gregory Mankiw addressed Alan Blinder’s comment that a major argument for the creation of TIPS had been their lower cost of financing for the Treasury. He wondered whether that argument had been the primary one, and, if it had and now turned out to be wrong, whether Blinder felt that TIPS had been a mistake and should be phased out. Blinder responded that it had been the primary argument and that TIPS were a mistake from that perspective, but that TIPS should not therefore disappear, because they still provide a low-risk investment vehicle for investors, albeit at a cost to taxpayers. Jonathan Wright addressed the question of whether purchases of large quantities of Treasuries would affect corporate borrowing and mortgage interest rates. The Federal Reserve’s announcement of Treasury purchases had had some impact on these rates, but it was small. He suggested that the apparent market segmentation meant that the Federal Reserve could lower the interest rates paid by households and businesses more substantially, but only by buying assets that are riskier than Treasury securities, including securities with ratings below triple-A. Janice Eberly remarked, in response to David Romer’s comment, that a great deal of research is being conducted on markets for bonds similar to Treasuries that are trading at much higher premiums. For example, student

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loans, which are 97 percent guaranteed by the Treasury, trade at prices 200 basis points higher than Treasuries with the same maturity. The research she described is attempting to determine whether certain features of TIPS, like the deflation option, explain some of the difference, or whether characteristics of the other securities explain it, or whether market segmentation is the explanation. Luigi Zingales further addressed David Romer’s question by sharing answers given by a University of Chicago faculty member turned bond trader. The trader’s explanation relied primarily on liquidity. After the Lehman Brothers collapse, the lenders who had to repossess the securities offered as collateral by Lehman discovered that they had to suffer losses when they liquidated a large amount of these relatively illiquid bonds. The differentiation in corporate bonds issued by the same entity makes the market for these securities segmented and thus less liquid. When many lenders dumped bonds on the market at the same time, they could not get full price because there were too few buyers. Without collateralized lending, it was more difficult to exploit arbitrage opportunities. As a result, many arbitrage opportunities became available. When many violations of arbitrage are occurring at the same time, Zingales thought it likely that traders with limited resources would focus on the low-hanging fruit, acting on the easiest and most profitable opportunities while ignoring others.

CHRISTINA D. ROMER University of California, Berkeley

DAVID H. ROMER University of California, Berkeley

Do Tax Cuts Starve the Beast? The Effect of Tax Changes on Government Spending ABSTRACT The hypothesis that decreases in taxes reduce future government spending is often cited as a reason for cutting taxes. However, because taxes change for many reasons, examinations of the relationship between overall measures of taxation and subsequent spending are plagued by problems of reverse causation and omitted variable bias. To derive more reliable estimates, this paper examines the behavior of government expenditure following legislated tax changes that narrative sources suggest are largely uncorrelated with other factors affecting spending. The results provide no support for the hypothesis that tax cuts restrain government spending; indeed, the point estimates suggest that tax cuts increase spending. The results also indicate that the main effect of tax cuts on the government budget is to induce subsequent legislated tax increases. Examination of four episodes of major tax cuts reinforces these conclusions.

I

n a speech urging passage of the 1981 tax cuts, President Ronald Reagan made the following argument: Over the past decades we’ve talked of curtailing government spending so that we can then lower the tax burden. Sometimes we’ve even taken a run at doing that. But there were always those who told us that taxes couldn’t be cut until spending was reduced. Well, you know, we can lecture our children about extravagance until we run out of voice and breath. Or we can cure their extravagance by simply reducing their allowance.1

1. “Address to the Nation on the Economy,” February 5, 1981, p. 2. Quotations from presidential speeches are from John T. Woolley and Gerhard Peters, The American Presidency Project (www.presidency.ucsb.edu), an online database of presidential documents.

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This idea that cutting taxes will lead to a reduction in government spending is often referred to as the “starve the beast” hypothesis: the most effective way to shrink the size of government is to reduce the revenue that feeds it. This view has been embraced not just by politicians but also by distinguished economists from Milton Friedman to Robert Barro.2 Of course, the starve-the-beast hypothesis is not the only view of how tax cuts affect expenditure. Another possibility is that government spending is determined with little or no regard to taxes, and thus does not respond to tax cuts. A third possibility is that tax cuts actually lead to increases in expenditure. One way this could occur is through the “fiscal illusion” effect proposed by James Buchanan and Richard Wagner (1977) and by William Niskanen (1978): a tax cut without an associated spending cut weakens the link in voters’ minds between spending and taxes, and so leads them to demand greater spending. Another possible mechanism is “shared fiscal irresponsibility”: if supporters of tax reduction are acting without concern for the deficit, supporters of higher spending may do the same (see, for example, Gale and Orszag 2004). The question of how tax cuts affect government spending is clearly an empirical one. And, indeed, there have been attempts to investigate the aggregate relationship between revenue and spending. However, such examinations of correlations cannot settle the issue. Changes in revenue occur for a variety of reasons. Many changes are legislated, but many others occur automatically in response to changes in the economy. And legislated tax changes themselves are motivated by numerous factors. Some, such as many increases in payroll taxes, are driven by increases in current or planned spending. Others, such as tax cuts motivated by a belief in the importance of incentives, are designed to raise long-run economic growth. The relationship between revenue and spending is surely not independent of the causes of changes in revenue. For example, if spending-driven tax changes are common, a regression of spending on revenue will almost certainly show a positive correlation. But this relationship does not show that tax changes cause spending changes; causation, in fact, runs in the opposite direction. To give another example, if automatic and legislated countercyclical tax changes are common, one might expect to see expenditure rising

2. See, for example, Milton Friedman, “Fiscal Responsibility,” Newsweek, August 7, 1967, p. 68; Robert J. Barro, “There’s a Lot to Like about Bush’s Tax Plan,” Business Week, February 24, 2003, p. 28; Gary S. Becker, Edward P. Lazear, and Kevin M. Murphy, “The Double Benefit of Tax Cuts,” Wall Street Journal, October 7, 2003, p. A20.

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after declines in revenue, because spending on unemployment insurance and other relief measures typically rises in bad economic times. In this case, both revenue and spending are being driven by an omitted variable: the state of the economy. These examples suggest that looking at the aggregate relationship between revenue and spending without accounting for the causes of revenue changes may lead to biased estimates of the effect of revenue changes on spending. This paper therefore proposes a test of the starve-the-beast hypothesis that accounts for the motivations for tax changes. In previous work (Romer and Romer 2009), we identified all significant legislated tax changes in the United States over the period 1945–2007. We then used the narrative record—presidential speeches, executive branch documents, congressional reports, and records of congressional debates—to identify the key motivation and the expected revenue effects of each action. In this paper we use our classification of motivations to isolate those tax changes that can legitimately be used to examine the effect of revenue changes on spending from those that are likely to give biased estimates. In particular, we focus on the behavior of spending following tax changes enacted for long-run purposes. These are changes in taxes that are explicitly not tied to current spending changes or the current state of the economy. They are, instead, intended to promote various long-run objectives, such as spurring productivity growth, improving efficiency, or, as in the case of the 1981 Reagan tax cut, shrinking the size of government. Examining the behavior of government spending following these long-run tax changes should provide a relatively unbiased test of the starve-the-beast hypothesis. We examine the relationship between real government expenditure and our measure of long-run tax changes in a variety of specifications. We find no support for the hypothesis that a relatively exogenous decline in taxes lowers future government spending. In our baseline specification, the estimates in fact suggest a substantial and marginally significant positive impact of tax cuts on government spending. The finding of a lack of support for the starve-the-beast hypothesis is highly robust. The evidence of an oppositesigned effect, in contrast, is not particularly strong or robust. The result that spending does not fall following a tax cut raises an obvious question: how then does the government budget adjust in response to the cut? One possibility is that what gives is not spending but the tax cut itself. To investigate this possibility, we examine the response of both tax revenue and tax legislation to long-run tax cuts. We find that revenue falls in response to a long-run tax cut in the short run but then recovers after about two years. Most of this recovery is due to the fact that a large part

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of a long-run tax cut is typically counteracted by legislated tax increases within the next several years. As we discuss, the fact that policymakers are able to adjust on the tax side helps to explain why they do not adjust on the spending side. Although there have been numerous long-run tax changes spread fairly uniformly over the postwar era, four stand out as the largest and best known: the tax cut passed over President Harry Truman’s veto in the Revenue Act of 1948; the Kennedy-Johnson tax cut legislated in the Revenue Act of 1964; the Reagan tax cut contained in the Economic Recovery Tax Act of 1981; and the tax cuts passed (along with some countercyclical actions) under President George W. Bush in 2001 and 2003. As a check on our analysis, we examine these four episodes in detail. We find that the behavior of spending and taxes in these extreme episodes is consistent with the aggregate regressions. Perhaps more important, we find that policymakers often did not even talk as if their spending decisions were influenced by revenue developments. They did, however, often invoke the tax cuts as a motivation for later tax increases. Finally, we find that concurrent developments, namely wars, account for some of the rise in spending in these episodes. But other concurrent developments caused measured spending changes to understate the effects of the spending decisions made in these episodes. In particular, three of the four episodes featured decisions to expand entitlement programs that had only modest short-term effects on spending but very large long-term effects. As a result, it appears unlikely that the failure of total expenditure to fall after these tax cuts was due to chance or unobserved factors. As mentioned above, ours is not the first study to investigate the starve-the-beast hypothesis. The most common approach is some variation of a regression of spending on lagged revenue; examples include the studies by William Anderson, Myles Wallace, and John Warner (1986) and by Rati Ram (1988). More sophisticated versions of this methodology are pursued by Henning Bohn (1991) and Alan Auerbach (2000, 2003). Bohn, focusing on a long sample period dominated by wartime budgetary changes, examines the interrelationships between revenue and spending in a vector autoregression (VAR) framework that allows for cointegration between the two variables (see also von Furstenberg, Green, and Jeong 1986 and Miller and Russek 1990). Auerbach, focusing on recent decades, studies the relationship between policy-driven changes in spending (rather than all changes in spending) and past deficits or projections of what future deficits would be if policy did not change (see also Calomiris and Hassett 2002).

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The results of these studies are mixed, but for the most part they suggest that tax cuts are followed by reductions in spending. None of these studies, however, consider the different reasons for changes in revenue, and thus none isolate the impact of independent tax changes on future spending. Indeed, our results point to a potentially important source of bias in studies using aggregate data. We find that the only type of legislated tax changes that are systematically followed by spending changes in the same direction are ones motivated by decisions to change spending. Since causation in these cases clearly does not run from the tax changes to the spending changes, this relationship is not informative about the starve-the-beast hypothesis. We also find that this type of tax change is sufficiently common to make the overall relationship between tax changes and subsequent spending changes substantially positive.3 The rest of the paper is organized as follows. Section I discusses the different motivations for tax changes and identifies the types of tax actions best suited for testing the starve-the-beast hypothesis. Section II analyzes the relationship between tax changes and government expenditure and includes a plethora of robustness checks. Section III examines how changes in taxes affect future tax revenue and tax legislation. Section IV discusses spending and taxes in the four key episodes. Section V presents our conclusions and discusses the limitations of our analysis.

I. The Motivations for Legislated Tax Changes and Tests of the Starve-the-Beast Hypothesis Legislated tax changes classified by motivation are a key input into our tests of the starve-the-beast hypothesis. Therefore, it is important to describe our classification of motivations and to discuss which types of tax changes are likely to yield informative estimates of the effects of tax changes on government spending. We also provide a brief overview of our identification 3. One can also test the starve-the-beast hypothesis indirectly. Perhaps the best-known study of this type is Becker and Mulligan (2003). They show that under appropriate assumptions, the same forces that would give rise to a starve-the-beast effect would cause a reduction in the efficiency of the tax system to reduce government spending. They therefore examine the cross-country relationship between the efficiency of the tax system and the share of government spending in GDP. Although they find a strong positive relationship, the correlation between efficiency and spending, like that between taxes and spending, may reflect reverse causation or omitted variables. That is, countries may invest in efficient tax systems because they desire high government spending, or a third factor, such as tolerance of intrusive government or less emphasis on individualism, may lead both to a broader, more comprehensive tax system and to higher government spending.

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of the motivations for tax changes and of our findings about the patterns of legislated tax changes in the postwar era.

I.A. Classification of Motivations Our classification and identification of the motivations for postwar legislated tax changes are described in detail in Romer and Romer (2009). That paper shows that the motivations for almost all tax changes have fallen into four broad categories. One type of tax change consists of those motivated by contemporaneous changes in spending. Often, policymakers will introduce a new program or social benefit and raise taxes at about the same time to pay for it. This was true, for example, in the mid-1950s when the interstate highway system was started, and in the mid-1960s when Medicare was introduced. The key feature of these changes is that the spending change is the impetus for the tax change. Typically, such changes are tax increases, but spending-driven tax cuts are not unheard of. A second type of tax change encompasses those made because policymakers believe that economic growth in the near term will be above or below its normal, sustainable level. A classic example of such a countercyclical action is the 1975 tax cut. Taxes were reduced because the economy was in a severe recession and growth was predicted to remain substantially below normal. Countercyclical actions can be either tax cuts or tax increases, depending on whether they are designed to counteract unusually slow or unusually rapid expected growth. A third type of tax change consists of those made to reduce an inherited budget deficit. By definition, these changes are all increases. A classic example is the 1993 tax increase under President Bill Clinton. This increase was undertaken not to finance a contemporaneous rise in spending, but to reduce a persistent deficit caused by past developments. The fourth type consists of tax changes intended to raise long-run economic growth. This is a broad category that includes changes motivated by a range of factors. What unites these changes is that they are all designed to improve the long-term functioning of the economy. The most common motivation is a belief that lower tax rates will improve incentives and thereby spur long-run growth. Another motivation is a belief in the benefits of small government and a desire to return the people’s money to them; a third is a desire to improve the efficiency and equity of the tax system. Many of the most famous tax cuts, such as the 1964 KennedyJohnson tax cut and the Reagan tax cuts of the early 1980s, fall under the general heading of tax changes aimed at raising long-run growth.

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Most of these changes are cuts, but some of the tax reforms included in this category increased revenue.

I.B. Which Tax Changes Are Useful for Testing the Starve-the-Beast Hypothesis? This description of the different motivations for legislated tax changes makes it clear that some changes are much more appropriate for testing the starve-the-beast hypothesis than others. What are needed are tax changes that are not systematically correlated with other factors influencing government spending. An obvious implication is that spending-driven tax changes are not appropriate observations to use. Causation in these episodes runs from the desired change in spending to the change in taxes. There is an omitted influence on spending—the prior decision to change spending— that is strongly correlated with these tax changes. Thus, if we have classified spending-driven tax changes correctly, there will be a positive correlation between these changes and spending changes by construction. Including spending-driven tax changes in a regression of spending changes on tax changes would therefore bias the results toward finding a starve-thebeast effect. Similar reasoning suggests that examining spending changes following countercyclical and deficit-driven tax changes could also be problematic. In these cases, however, the likely bias is against the starve-the-beast hypothesis. In both cases there may be spending changes that are negatively correlated with the tax changes but not caused by them. Rather, both the tax and the spending changes may be caused by a third factor. In the case of countercyclical actions, the third factor is the state of the economy. In bad economic times, policymakers may cut taxes and increase spending as a way of raising aggregate demand. Also, some types of spending, such as unemployment compensation and public assistance, increase automatically in recessions. Thus, the relationship between taxes and spending in these episodes may reflect discretionary and automatic responses to the state of the economy, not a behavioral link between tax revenue and spending decisions. In the case of deficit-driven tax changes, the unobserved third factor is a general switch to fiscal responsibility. Tax increases to reduce inherited budget deficits are often passed as parts of packages that include spending reductions. The spending reductions are not caused by the tax increases; rather, both are driven by a desire to eliminate the deficit. Inclusion of such packages in a regression of spending changes on tax changes will tend to bias the results away from supporting the starve-the-beast hypothesis.

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This concern may be more important in theory than in reality, however. Our narrative analysis of tax changes documents the spending reductions agreed to in conjunction with deficit-driven tax changes. In almost every case, the spending cuts were small relative to the tax increases. Therefore, although one may want to treat the behavior of spending following deficit-driven tax changes with caution, it may in fact yield relatively unbiased estimates. The tax changes that are surely the most appropriate for testing the starvethe-beast hypothesis are those taken to spur long-run growth. As described above, these tax changes are not made in response to current macroeconomic conditions or in conjunction with spending changes. As a result, they are exactly the kind of changes that proponents of the starve-the-beast hypothesis believe are likely to alter government spending. To the degree that focusing on this type of tax change may lead to bias, it is likely to be in the direction of finding a positive effect of taxes on spending. The ideal experiment for testing the starve-the-beast hypothesis would be a tax change resulting from factors that have no direct impact on spending. Our long-run tax changes, however, include tax cuts for which the possible induced reduction in future spending is sometimes cited as a motivation. As a result, there is a potential correlation between spending and tax changes in these episodes driven by a third factor: a desire for smaller government. Policymakers, in addition to cutting taxes to starve the beast, may take other actions to achieve this goal. Because this possible omitted variable bias works in the direction of supporting the starvethe-beast hypothesis, a finding of a positive relationship between taxes and spending would have to be treated with caution. Since we in fact find a negative relationship, there is less cause for concern. Also, our narrative analysis suggests that this potential bias is likely to be small. The desire for smaller government is rarely the primary motivation for long-run tax changes; a belief in the incentive effects of lower taxes is considerably more common, for example.

I.C. Overview of the Narrative Analysis The implementation and results of our narrative analysis of postwar tax changes are described in Romer and Romer (2009). We use a detailed examination of a wide range of policy documents to identify all significant legislated tax changes over the period 1945–2007. We then identify the motivations policymakers gave for each action. We find that policymakers were usually both quite explicit and remarkably unanimous in their stated reasons for undertaking tax actions. Only infrequently do they emphasize

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multiple motivations. In these cases we divide the tax changes into pieces reflecting the different motivations. We also use the narrative sources to estimate the revenue impacts of the actions. Specifically, we determine how policymakers expected the actions to affect tax liabilities. Very often, tax bills change taxes in a number of steps. In these cases our baseline revenue estimates show changes in each of the quarters the various provisions took effect.4 Figure 1 shows legislated postwar tax changes classified by motivation, measured by their expected revenue effects as a percent of nominal GDP.5 The top panel shows the long-run changes, which are the key actions for our purposes. The graph makes clear that the vast majority of long-run tax actions are cuts. It also makes clear that long-run tax changes have been fairly evenly distributed over the postwar era. The largest were the 1948 tax cut, the Kennedy-Johnson tax cut in the mid-1960s, the Reagan tax cut in the early 1980s, and the two Bush tax cuts in the early 2000s. The bottom panel shows the other types of tax changes. Although the first half of the postwar era saw a number of small, deficit-driven tax increases, the vast majority took place in the 1980s and early 1990s. Most of the deficit-driven increases were passed to deal with the long-run solvency of the Social Security and Medicare systems. Spending-driven changes are typically tax increases, and these were both frequent and relatively large in the first half of the postwar era. By far the largest were those in the Revenue Act of 1945 following the end of World War II, and those in the early 1950s to pay for the Korean War. Many of the other changes in this category were related to expansions of Social Security. Finally, explicitly countercyclical tax changes were confined to the fairly short period 1966–75 until they were resurrected as the reason for portions of the tax cuts in 2001 and 2002.

4. Tax actions are often retroactive for a quarter or two. Such changes have a much larger effect on liabilities in the initial quarter than in subsequent ones. In terms of differences, this results in a large movement in one direction in the initial quarter and a partially offsetting movement in the next quarter. For this study, which examines the longer-run responses of spending and future taxes, the short-run volatility caused by these changes may unnecessarily complicate the analysis. We therefore ignore the retroactive changes in forming our baseline estimates. Including the retroactive changes has almost no impact on any of the results, however. 5. The nominal GDP data are from the National Income and Product Accounts, table 1.1.5 (downloaded February 17, 2008). Quarterly nominal GDP data are available only after 1947. We therefore normalize the one tax change in 1946 using the annual nominal GDP figure for that year.

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Figure 1. Legislated Tax Changes Classified by Motivation, 1945–2007 Long-run tax changes Percent of GDP

2 1 0 –1 –2

1950

1960

1970

1980

1990

2000

Other tax changes Percent of GDP

2

Spending-driven

1

Deficit-driven

0 –1 –2

Countercyclical

1950

1960

1970

1980

1990

2000

Source: Romer and Romer (2009).

II. The Effect of Tax Changes on Expenditure The previous section describes our identification of legislated tax changes motivated by concern about long-run growth. This section investigates the relationship between these relatively exogenous tax changes and subsequent changes in government spending. It includes a detailed analysis

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of the robustness of the results. We also investigate the behavior of spending following other types of tax changes to see if there is evidence of bias when these changes are included.

II.A. Specification and Data To estimate the effects of tax changes on government spending, we begin by estimating, using quarterly data, a simple reduced-form regression of the form N

(1)

Δ Et = a + ∑ bi Δ Tt − i + et , i=0

where ΔE is the change in the logarithm of real government expenditure and ΔT is our measure of long-run tax changes (specifically, the expected revenue effects, as a percent of nominal GDP, of the tax changes we identify as motivated by long-run considerations). The key feature of long-run tax changes as we have defined them is that they are based on actions motivated by considerations largely unrelated to current spending, current macroeconomic conditions, or an inherited budget deficit. Our discussion above of why such long-run changes provide the best test of the starve-the-beast hypothesis suggests that they are unlikely to have a substantial systematic correlation with other factors affecting spending. It is for this reason that our baseline specification includes no control variables. However, it is certainly possible that there are correlations in small samples, or that the dynamics of the relationship between tax changes and spending are more complicated than is expressed in equation 1. We therefore also consider a wide range of control variables and a variety of more complicated specifications. We include a number of lags of the tax variable to allow for the possibility that the response of spending to tax changes is quite delayed or gradual. In our baseline specification we set the number of lags to 20, and so look at the response of spending over a five-year horizon. Because the starve-thebeast hypothesis does not make predictions about the exact timing of the spending response, we focus on the cumulative effect at various horizons. We summarize the regression results by reporting the implied impact of a tax cut of 1 percent of GDP on the path of expenditure (in logarithms). For our baseline specification, the cumulative impact after n quarters is just the negative of the sum of the coefficients on the contemporaneous value and first n lags of the tax variable. The starve-the-beast hypothesis predicts that tax cuts reduce spending. Therefore, the estimated cumulative impact of a tax cut on expenditure should be negative if the hypothesis is correct.

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We use quarterly data on government expenditure from the National Income and Product Accounts (NIPA). Our series on long-run tax changes refers only to federal legislation. Therefore, we consider only the behavior of federal expenditure. What the Bureau of Economic Analysis (BEA) calls “total expenditures,” however, includes two components that are not appropriate to include in considering the response of spending to tax changes. One is a deduction for the consumption of fixed capital (that is, depreciation). This largely reflects spending decisions in the distant past and so almost surely cannot show a starve-the-beast response. Thus, we do not subtract depreciation. The other component is interest payments on government debt. For a given interest rate, interest payments rise with the amount of debt. As a result, any tax cut that increases the deficit will almost certainly increase interest payments. We therefore exclude this type of spending. The resulting aggregate that we consider is thus total gross expenditure less interest. For simplicity, we refer to this as total expenditure in what follows.6 The NIPA expenditure data are expressed in nominal terms. Deflators exist for some components, such as defense and nondefense purchases, but not for others, especially those involving transfers. We therefore deflate total gross expenditure less interest by the price index for GDP (NIPA table 1.1.4, downloaded February 22, 2008). Our data on tax changes begin in 1945Q1, and the data on expenditure in 1947Q1. Therefore, in the baseline specification, where we include 20 lags of the tax variable, the earliest starting date for the regression is 1950Q1. However, previous work has found some evidence that the behavior and effects of fiscal policy were unusual in the Korean War period (see, for example, Blanchard and Perotti 2002 and Romer and Romer forthcoming). We therefore also report estimates for regressions starting in 1957Q1. In both cases we carry the regressions through 2007Q4.

II.B. The Effect of Long-Run Tax Changes on Total Expenditure Table 1 shows the results of estimating equation 1 for total expenditure using 20 lags of the long-run tax variable over the full sample. The coefficient estimates for the individual lags fluctuate between positive and 6. Data on total expenditures, consumption of fixed capital, and interest payments are from NIPA table 3.2 (downloaded February 17, 2008). Because the BEA does not have data on “net purchases of nonproduced assets” (which are normally a trivial component of total expenditures) until 1959Q3, before then we estimate total gross expenditure less interest as the sum of current expenditure, gross government investment, and capital transfer payments, minus interest payments.

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Table 1. Estimated Impact of Tax Changes on Total Expenditurea Variable

Coefficient

Constant Tax change: Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10 Lag 11 Lag 12 Lag 13 Lag 14 Lag 15 Lag 16 Lag 17 Lag 18 Lag 19 Lag 20

0.72 (0.25)

R2 Durbin-Watson statistic Standard error of the estimate

0.24 (0.85) 0.40 (0.85) −0.11 (0.85) −0.28 (0.83) −0.92 (0.87) −1.50 (0.87) 0.31 (0.87) −1.42 (0.75) 2.63 (0.75) 2.52 (0.75) −0.98 (0.75) −1.53 (0.74) −2.19 (0.76) −2.13 (0.76) −1.11 (0.76) 0.47 (0.76) 0.02 (0.76) −0.11 (0.74) 0.51 (0.78) 0.86 (0.78) 0.20 (0.78) 0.20 1.90 2.72

Source: Authors’ regression. a. The table reports estimates of equation 1 in the text using data for long-run tax changes only and defining expenditure as total gross expenditure less interest payments. The sample period is 1950Q1–2007Q4. Numbers in parentheses are standard errors.

negative. As one would expect, few of the individual coefficients are statistically significant. The overall fit of the regression is modest (R2 = 0.20). Figure 2 summarizes the results by showing the implied response of total expenditure to a long-run tax cut of 1 percent of GDP, together with 1-standard-error bands. There is no evidence of a starve-the-beast effect. The cumulative effect is negative in the quarter of the tax cut and the subsequent three quarters, as the starve-the-beast hypothesis predicts, but very small, and the t statistics do not rise above 0.6 in absolute value. After that, the estimated cumulative effect is positive at every horizon except quarters 9 and 10, suggesting fiscal illusion or shared fiscal irresponsibility. The estimated positive impact of the tax cut on spending is often substantial. Since federal government spending averages roughly 20 percent of GDP in our sample, a tax cut of 1 percent of GDP is equal to about 5 percent

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Figure 2. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Baseline Specificationa Percent 10 8 6 4 2 0 –2 –4 2

4

6

8 10 12 Quarters after tax change

14

16

18

Source: Authorsí re gressions. a. Based on an ordinary least squares (OLS) regression of the quarterly change in the logarithm of real total gross federal expenditure less interest payments on the contemporaneous value and 20 quarterly lags of the measure of long-run tax changes; the sample period is 1950Q1–2007Q4. Dashed lines indicate 1-standard-error bands.

of government spending. The point estimates suggest that a tax cut of that magnitude raises spending by 4 percent or more in quarters 13 through 20. That is, they suggest that spending eventually rises by almost the amount of the tax cut. However, the estimates are not very precise. The t statistics for the cumulative impact of the tax cut on spending at horizons of more than three years are generally between 1.5 and 2, exceeding 2 for only one horizon (quarter 14, for which the t statistic is 2.21).

II.C. Richer Dynamics Our baseline results suggest that there is no discernable starve-the-beast effect, and some evidence of shared fiscal irresponsibility, over a five-year horizon. But perhaps the main effects of tax changes occur with longer lags. Here we consider several approaches to allowing for more delayed effects. ADDITIONAL LAGS. The most straightforward approach to examining whether tax changes have important effects at longer horizons is to include additional lags in equation 1. Of course, including more lags requires shortening the sample period and estimating additional parameters. The top panel of figure 3 shows the results of including 40 lags of the tax variable in

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Figure 3. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Estimates over Longer Horizons OLS estimatesa Percent 10 8 6 4 2 0 –2 –4 4

8

12

16 20 24 Quarters after tax change

28

32

36

28

32

36

Two-variable VAR estimatesb Percent 10 8 6 4 2 0 –2 –4 4

8

12

16 20 24 Quarters after tax change

Source: Authors’ estimates. a. Regression is specified as in figure 2 but with 40 lags of the measure of long-run tax changes; the sample period is 1955Q1–2007Q4. b. Impulse response function from a vector autoregression (VAR) using the logarithm of total expenditure as defined in figure 2 and the measure of long-run tax changes; there are 12 lags, and the tax measure is ordered first.

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equation 1 and estimating the regression over the longest feasible sample (1955Q1–2007Q4). For horizons beyond five years, the estimated cumulative impact of a tax cut of 1 percent of GDP on total expenditure is always small, fluctuates between positive and negative, and is never remotely close to statistically significant. Thus, this specification provides no evidence that tax cuts reduce government spending, but also fails to support the hypothesis that they increase it. A TWO-VARIABLE VECTOR AUTOREGRESSION. Our second approach to allowing for more complicated and potentially longer-lasting dynamics is to estimate a VAR with our series for long-run tax changes and total expenditure. This approach allows spending to depend on its own lags as well as on the tax changes, and so allows for dynamics beyond the number of lags of the tax variable that are included. For consistency with the earlier regressions, we put the tax changes first and expenditure second, so that tax changes can affect spending within the quarter. We enter expenditure in logarithms; given the availability of the data, we can include 12 lags while still using our baseline sample. The bottom panel of figure 3 shows that the estimated response of spending to an innovation of −1 percent of GDP to our series on long-run tax changes is similar to that for a long-run tax cut of 1 percent of GDP in the baseline specification.7 The point estimates suggest that the tax cut reduces spending in the short run but then raises it, with a fairly large positive long-run effect. None of the estimated effects are statistically significant, however. Thus, again there is no support for the starve-the-beast hypothesis. Another finding from the VAR is that the estimated response of the tax series to an innovation to government spending is very small and highly insignificant at all horizons. This indicates that the actions we classify as long-run tax changes are not responses to spending developments.8

7. Note that this experiment is slightly different from that considered in summarizing the results from the baseline specification. There we consider a one-time tax cut of 1 percent of GDP with no further tax changes. Here, following the innovation to our tax measure in the VAR, there are on average additional long-run tax cuts of about one-fifth of a percent of GDP over the next several years. We compute the standard errors by taking 10,000 draws of the vector of coefficient estimates from a multivariate normal distribution with mean and variance-covariance matrix given by the point estimates and variance-covariance matrix of the coefficient estimates, and then finding the standard deviation of the implied responses at each horizon. 8. We also estimated the bivariate VAR with 20 lags for the period 1952Q1–2007Q4. The estimated effects of a tax cut on spending in this specification are even more consistently positive and are marginally significant. The maximum effect is an increase of 3.97 percent after 18 quarters (t = 1.93).

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Another way that a starve-the-beast effect could occur at longer horizons is if tax cuts affect other variables that in turn affect government spending. We therefore consider VARs with additional variables. This, however, requires either estimating more parameters in each equation or including fewer lags. Thus, rather than just include a long list of variables that might be relevant, we consider various combinations of variables. One way that tax cuts could create pressures for reduced government spending is by increasing government debt. Thus, our first multivariable VAR uses three variables: our series on long-run tax changes, log real spending, and log real debt.9 We also consider two four-variable VARs. In one, we add the log of real federal total receipts as the fourth variable, so that the system includes both the spending and the revenue sides of the government budget. In the other, the fourth variable is log real GDP. Our reason for including this variable is that tax cuts have large short-run effects on output (Romer and Romer forthcoming), which could in turn affect the dynamics of spending in response to a tax cut.10 Finally, the nominal interest rate and inflation also affect the government budget constraint. Our last system is therefore a VAR with seven variables: our long-run tax series, log real spending, log real debt, log real revenue, log real GDP, the three-month Treasury bill rate, and the log of the price index for GDP.11 In all of the VARs we put the tax series first, so that it can affect the other variables within the quarter. We include 12 lags and use the full sample (1950Q1–2007Q4). LARGER SYSTEMS.

9. From 1970Q1 to the end of the sample, we use quarterly data on the stock of federal debt held by the public. From the beginning of the sample to 1969Q4, we use the available series on gross federal debt held by the public for the second quarter of each year, and we interpolate linearly between the annual observations. Both series are taken from the St. Louis Federal Reserve Bank’s FRED database, series FYGFDPUN and FYGFDPUB (www.stls.frb.org, downloaded March 24, 2008). We ratio-splice the two series in 1970Q2 and deflate the resulting series by the price index for GDP. Note that since it is likely to be the level of debt, rather than the change, that affects spending, the errors caused by the interpolation in the first part of the sample should have only minor effects on the estimates. 10. For receipts we use the federal total receipts series from NIPA table 3.2 (downloaded April 6, 2009), deflated by the price index for GDP from NIPA table 1.1.4. Our real GDP series is the quantity index for GDP from NIPA table 1.1.3 (downloaded February 17, 2008). 11. Data on the three-month Treasury bill rate are from the Board of Governors, series H15/H15/RIFSGFSM03_N.M (monthly data for secondary market rates on a discount basis, downloaded February 15, 2008).

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Figure 4. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Multivariate VAR Estimates Three-variable VARa Percent 8 4 0 –4 4

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Figure 4 displays the response of government spending to an innovation of −1 percent of GDP to our series on long-run tax changes in each of the VARs.12 The results consistently fail to support the starve-the-beast hypothesis. In every specification, the estimated effect of a tax cut on spending is negative at only a few horizons. And in every case, those estimates are small and insignificant: at no horizon is the t statistic for the spending response negative and greater than 1 in absolute value. Adding debt to the baseline VAR (first panel) in fact moves the estimates further in the direction of suggesting fiscal illusion. The estimated maximum effect of the tax cut is an increase in spending of 5.75 percent (t = 2.12) after 17 quarters, and the estimated effect after 10 years is an increase of 3.93 percent (t = 1.70). In the four-variable and seven-variable systems, the point estimates suggest a slightly weaker fiscal illusion effect, although it is more precisely 12. In each of the VARs, following the innovation to the tax series, there are modest additional long-run tax cuts over the next year that are largely offset over the following few years. There is never an important response of the tax variable to the other variables.

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Figure 4. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Multivariate VAR Estimates (Continued) Four-variable VAR including GDPc Percent 8 4 0 –4 4

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estimated than in the two-variable VAR. In all three of those systems, the estimated maximum effect is an increase in spending of between 3.6 and 3.9 percent after about four years (except for a spike to 4.6 percent after seven quarters in the seven-variable system). In the four-variable VAR with receipts (second panel of figure 4), the effect is not significant (t = 1.73), but in the other two it is: the t statistic for the maximum effect is 2.51 in the four-variable VAR with GDP (third panel) and 2.49 in the seven-variable VAR (fourth panel). Finally, in all three of these specifications, the estimated effect after 10 years is in the direction predicted by fiscal illusion but is small and not significant.

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II.D. Other Robustness Checks The next step is to examine the robustness of the findings along other dimensions. The most important of these checks are summarized in figure 5, which shows the implied response of total expenditure to a long-run tax cut of 1 percent of GDP for a number of variants of the baseline regression (equation 1). For comparison, panel A of the figure repeats the baseline estimates from figure 2. SAMPLE PERIOD AND OUTLIERS. One obvious concern is the possible importance of the sample period and of outliers. As described above, fiscal policy was very unusual in the Korean War period. Panel B of figure 5 shows that considering only the post–Korean War sample weakens the evidence for a perverse effect of tax cuts on spending, but still yields no evidence of a starve-the-beast effect. The change in the sample makes the initial negative impact even smaller and more insignificant. The response in quarters 3 through 20 is always positive, but con siderably smaller than for the full sample and not even marginally significant. To check more generally for the possible influence of outliers, we consider the effects of excluding each of the four large long-run tax cuts discussed in the case studies in section IV.13 In all four cases the estimated effect of a tax cut on spending remains mainly positive and is never close to significantly negative at any horizon. Dropping the 1948 tax cut, however, renders the positive effect of tax cuts on spending small and insignificant.14 MILITARY ACTIONS. A second concern is the role of military actions in driving spending. As discussed in the case studies, many of the largest long-run tax cuts were followed by wars. The wars could have caused federal spending to rise after the tax change just by chance, thus obscuring any starve-the-beast effect. To test for this possibility, we consider two alternative specifications of our baseline regression. The first adds an indicator of military actions to equation 1. Valerie Ramey (2008) suggests an updated list of the exogenous military actions identified by Ramey and Matthew Shapiro (1998) from narrative sources. This list dates military actions as beginning in 1950Q3 (Korean War),

13. To exclude a tax cut, we set our series for long-run tax changes to zero from the first to the last quarter in which the bill changed taxes. We treat the 2001 and 2003 cuts as a single measure; thus, in this case we set our series to zero from 2002Q1 to 2005Q1. 14. In a related exercise along these lines, we split the sample in 1980Q4. For the period 1950Q1–1980Q4, the estimates suggest a large and statistically significant positive effect of tax cuts on spending. For the period 1981Q1–2007Q4, the estimated effects are again virtually always positive, but consistently small and far from significant.

Figure 5. Cumulative Spending Impact of a Tax Cut of 1 Percent of GDP, Alternative Specifications A. Baseline specificationa Percent 8 4 0 –4 2

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Figure 5. Cumulative Spending Impact of a Tax Cut of 1 Percent of GDP, Alternative Specifications (Continued) E. Including dummy variable for Democratic administrationse Percent 8 4 0 –4 2

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Figure 5. Cumulative Spending Impact of a Tax Cut of 1 Percent of GDP, Alternative Specifications (Continued) I. Using expenditure as share of trend GDPi Percent of GDP 1.5 1.0 0.5 0 –0.5 –1.0 2

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Source: Authors’ regressions. a. Repeated from figure 2. The other panels differ from this specification only as noted below. b. Regression omits observations from the beginning of the sample period through 1956Q4. c. Regression adds the contemporaneous value and 20 lags of a dummy variable set equal to 1 in each of the following quarters: 1950Q3, 1965Q1, 1980Q1, and 2001Q3. d. Regression replaces the change in the logarithm of real total expenditure with the change in the logarithm of real total expenditure less national defense purchases. e. Regression includes a dummy variable set equal to 1 in quarters when a Democrat is president. f. Regression replaces the measure of tax changes based on the quarters in which liabilities changed with the present discounted value of all revenue changes called for by a given piece of legislation, dated as occurring in the quarter it was passed. g. Regression replaces the NIPA measure of total expenditure with official budget data. h. Regression replaces the NIPA total expenditure measure with discretionary spending only, from official budget data. i. Regression uses as the expenditure measure the change in the ratio of NIPA real total expenditure to trend real GDP, calculated by fitting a Hodrick-Prescott filter (λ = 1600) to real GDP for the full sample period (1947Q1–2007Q4). j. Regression uses as the expenditure measure the change in the ratio of real total expenditure to actual real GDP.

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1965Q1 (Vietnam War), 1980Q1 (the Carter-Reagan military buildup in response to the Soviet invasion of Afghanistan), and 2001Q3 (the wars in Afghanistan and Iraq following the September 11 terrorist attacks). We expand the baseline regression to include the contemporaneous value and 20 lags of a dummy variable set equal to 1 in each of these four quarters. This specification shows the effect of tax cuts on total expenditure allowing for the possibility that wars have a separate effect on spending. Panel C of figure 5 shows the cumulative impact of a tax cut of 1 percent of GDP in this specification. The estimates are very similar to those in the baseline specification. The effect of tax cuts on total spending controlling for military actions is largely positive, although not statistically significant. Thus, accounting for military actions does not reveal a starve-the-beast relationship. This is true even though wars exert a strong independent upward force on spending: the maximum cumulative impact of a military action on total expenditure is an increase of 15.83 percent (t = 2.77). The lack of a relationship between taxes and spending in this alternative specification is equally strong in the post-1957 sample. The second alternative specification looks only at the response of nondefense spending to long-run tax cuts. In place of the log difference in total federal expenditure in equation 1, we use the log difference in total expenditure less national defense purchases (from NIPA table 3.9.5, downloaded March 25, 2008), deflated by the price index for GDP. This test almost surely biases the results in favor of the starve-the-beast hypothesis, for two reasons. First, the case studies show some correlation in our sample between support for tax cuts and support for shifting spending toward defense. Most notably, Ronald Reagan, who presided over the largest long-run tax cut in the postwar period, strongly advocated such a reallocation. Thus, nondefense spending could fall in the wake of long-run tax cuts not because of the effects of the cuts themselves but because of other actions. Second, to the degree that defense spending rises following a tax cut because of war, nondefense spending may decline for the same reason. Wartime tends naturally to lead policymakers to reallocate spending away from other purposes and toward defense. Therefore, chance correlation between wars and long-run tax cuts could cause the regression to find a starve-the-beast effect for nondefense spending when none exists. Panel D of figure 5 shows the results of this exercise. (Note that the vertical scale differs from that in most of the other panels.) The point estimates are now generally negative, consistent with the starve-the-beast hypothesis. The effects are not statistically significant, however: the t statistics for the cumulative impact are almost always less than −1 and never greater than −1.3.

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More important, the estimates are small and not robust. Total expenditure less defense accounts, on average, for about 10 percent of GDP over our sample. Therefore, for a tax cut of 1 percent of GDP to reduce nondefense spending by the same amount, spending would need to decline by roughly 10 percent. The estimated effect, however, is almost always a fall of less than 4 percent (or a rise). And dropping the Reagan tax cut (where, as described above, an important omitted factor seems to have acted directly to reduce nondefense spending) yields estimates that fluctuate irregularly around zero; similarly, either excluding the Korean War period or including the contemporaneous value and 20 lags of the dummy variable for military actions weakens the estimated effect considerably. Thus, there is little evidence that tax cuts have a noticeable negative effect even on nondefense spending. POLITICAL VARIABLES. A third robustness issue concerns the role of political variables. It is certainly possible that the party of the president or the existence of unified government (that is, the same party controlling both houses of Congress and the presidency) has an influence on government spending. If such variables are correlated with our tax measure, the baseline regression could suffer from omitted variable bias. For this reason, we try adding a variety of political variables to our baseline specification. To give one example, panel E of figure 5 shows the effect of a tax cut on spending when a dummy variable for Democratic administrations is included in the regression. This regression asks whether tax cuts lower spending, taking into account that Democratic presidents may consistently spend more or less than their Republican counterparts. Adding this variable has very little effect on the estimates, although it strengthens the evidence for fiscal illusion or shared fiscal irresponsibility slightly: both the estimated positive effects of tax cuts on spending and their statistical significance increase modestly. We also consider specifications including a dummy variable for unified government, and including separate dummies for the first quarter of a new Republican or a new Democratic administration.15 Both specifications change the estimates only trivially, and neither provides support for the starve-the-beast hypothesis. ALTERNATIVE TAX VARIABLE. A fourth concern involves the specification of our tax variable. Our baseline series dates revenue changes in the quarter in which liabilities actually change. An alternative measure, which emphasizes expectational effects, calculates the present discounted value of all 15. For the latter specification, we include both the contemporaneous value and 15 lags of the new Republican and new Democratic dummy variables.

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revenue changes called for by a given piece of legislation and dates the revenue change in the quarter the law was passed.16 Panel F of figure 5 shows that the starve-the-beast hypothesis fares even worse when this alternative tax measure is used: the estimated impact of a tax cut on spending is generally in the opposite direction from the prediction of the hypothesis, often large, and sometimes marginally significant. ALTERNATIVE SPENDING CONCEPTS. Our baseline specification uses a NIPA measure of total spending on the grounds that it is available quarterly and is likely to correspond most closely with economic concepts of government spending. A natural alternative is to use the official budget numbers, which may be more closely tied to policymakers’ intentions. To do this, we aggregate our quarterly measure of long-run tax changes to construct a fiscal-year measure, and then reestimate equation 1 using the change in the logarithm of the budget-based real expenditure measure and the contemporaneous value and five annual lags of our tax measure. For there to be a substantial starve-the-beast effect, tax cuts would almost certainly have to reduce not just discretionary spending, but also spending on entitlement programs. At the same time, because policymakers can change discretionary spending more quickly, it is interesting to ask whether there is a starve-the-beast effect for this type of spending. We therefore also examine the response of discretionary spending to long-run tax cuts, again using annual budget data and five annual lags of our tax measure.17 Panels G and H of figure 5 show the results. Once again, there is no support for the starve-the-beast hypothesis. The response of overall spending using the official budget measure (panel G) is quite similar to that using the NIPA measure in panel A. And discretionary spending (panel H, again on a different scale) rises even more than overall spending follow-

16. See Romer and Romer (2009) for a detailed description of how we calculate the present value of revenue changes. 17. The budget data are from Budget of the United States Government: Historical Tables Fiscal Year 2009 (www.gpoaccess.gov/usbudget/fy09/hist.html, tables 3.1 and 8.1, downloaded March 16, 2009). We measure overall spending as total federal spending minus net interest. Discretionary spending figures are available only beginning in 1962. For the years up through 1962, we estimate the growth rate of discretionary spending as the change in the log of total spending minus the sum of Social Security, income security, veterans benefits and services, agriculture, commerce and housing credit, net interest, and undistributed offsetting receipts. The estimates constructed in this way track the official estimates for the years immediately after 1962 quite well. In aggregating our measure of long-run tax changes to fiscalyear values, we omit the transition quarter (1976Q3). We deflate both the overall spending measure and the discretionary measure by the price index for GDP.

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ing a tax cut, with a maximum increase of 11.01 percent after four years (t = 2.23). ALTERNATIVE SPECIFICATIONS OF THE SPENDING VARIABLE. A final robustness issue involves the appropriate way to enter the spending variable. In all of the specifications discussed so far, we examine the response of the growth rate of real government expenditure to long-run tax changes. The cumulative impact therefore shows the effect of a tax change on the level of real expenditure. We feel this is the appropriate measure for testing the starve-the-beast hypothesis: does a tax cut change the spending decisions of policymakers? However, an alternative form of the hypothesis could be that a tax cut reduces expenditure as a percent of GDP. In this view, a tax cut could lower the share of spending in GDP not by changing policymakers’ spending decisions, but by changing output growth. To test this alternative version, we reestimate equation 1 using two different specifications of the dependent variable. The more sensible of the two expresses real total expenditure as a percent of trend real GDP (where trend real GDP is calculated using a conventional Hodrick-Prescott filter), and then uses the change in this variable as the dependent variable in equation 1.18 Detrending real GDP is reasonable because, to the extent that a tax cut causes a temporary boom, it will inherently tend to reduce real expenditure as a percent of actual GDP in the short run. We do not believe that this is the mechanism proponents of even the alternative form of the starve-thebeast hypothesis have in mind. However, as a further robustness check, we also reestimate equation 1 using the change in the ratio of total real expenditure to actual real GDP. Panels I and J of figure 5 show the results of these two exercises. (These two panels are on a different scale than the others in figure 5 because the dependent variable is now a percent of GDP, not a percent of total expenditure.) Panel I shows that the results using the change in spending as a share of trend GDP are very similar to the results using the percentage change in spending. A tax cut of 1 percent of GDP generally raises the share of spending in GDP. The estimated maximum effect is large (0.94 percent of GDP) but only marginally significant (t = 1.92). Thus, the results again fail to support the starve-the-beast hypothesis, and provide moderate support for the alternative view of fiscal illusion or shared fiscal irresponsibility.

18. We again calculate real expenditure by dividing nominal expenditure by the price index for GDP. Real GDP is constructed by dividing nominal GDP by the same price index. We fit a Hodrick-Prescott filter (λ = 1600) to log real GDP for the full sample (1947Q1–2007Q4).

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Panel J shows that a tax cut does not even reduce spending as a share of actual GDP. The estimated effects fluctuate irregularly around zero. The estimates suggest a marginally significant starve-the-beast effect in a single quarter (quarter 9), but they are more often positive than negative, and the estimated long-run effect is positive, small, and very far from significant. That this second specification fails to support the starve-the-beast hypothesis is quite surprising. As discussed in Romer and Romer (forthcoming), the short-run stimulatory effects of tax cuts on output are very strong. Yet even this rapid growth of output is not enough to generate a systematic fall in expenditure as a share of GDP. The robustness checks in this section yield two conclusions. First, and more important, the lack of support for the starve-the-beast hypothesis is very robust: with the possible exception of the examination of nondefense spending, which appears to be biased in favor of the starve-the-beast hypothesis and for which the results are mixed, none of the specifications we consider provide evidence that tax cuts reduce government expenditure. Second, although we find evidence for the alternative view of fiscal illusion or shared fiscal irresponsibility, it is only modest. The point estimates consistently suggest that tax cuts raise government expenditure, but they are only occasionally significantly different from zero, and then usually only marginally so.

II.E. The Relationship between Other Types of Tax Changes and Total Expenditure As discussed above, we focus on the response of government spending to long-run tax changes because this is likely to provide the least biased test of the starve-the-beast hypothesis. Nevertheless, it is interesting to look at the behavior of spending following the other types of tax changes we have identified: deficit-driven, countercyclical, and spending-driven. This analysis can reveal whether the feared biases from using these other types of tax changes to estimate the response of spending appear to be present. It can also provide an indirect check on our classification procedures. For example, if we have classified spending-driven tax changes correctly, they should be positively correlated with spending changes. For this exercise we reestimate equation 1 using the contemporaneous value and 20 lags of a particular type of tax change as the independent variable. We estimate a separate regression for each type of tax change, using data from the full postwar sample period. The results are again summarized by calculating the implied cumulative response of spending to a tax cut (of a given type) of 1 percent of GDP. Figure 6 presents the results for each type

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of tax action.19 To facilitate comparisons, the first panel repeats our baseline results for long-run tax actions from figure 2. DEFICIT-DRIVEN TAX CHANGES. Of the three additional types of tax changes, those driven by deficits are likely to be the most informative about the starve-the-beast hypothesis. Like the long-run changes, these actions are not taken in response to current or prospective short-run macroeconomic conditions or because spending is moving in the same direction. The reason for excluding these changes from the baseline regression was that deficitdriven tax increases are often parts of deficit reduction packages that include spending reductions. These observations might therefore bias the results against the starve-the-beast hypothesis. The estimated impact of deficitdriven tax changes on total expenditure (second panel of figure 6) shows this fear is somewhat justified. In the quarter of a deficit-driven tax cut and the subsequent two quarters, spending rises substantially. Or, to put it in terms of the realistic case, following a deficit-driven tax increase, spending falls substantially. This is exactly the sort of inverse relationship one would expect if deficit reduction packages were common. The effects, although large, are not precisely estimated. The t statistic for the maximum impact is 1.98. After the first few quarters, the estimated effects of a deficit-driven tax cut turn negative for several years but return to positive at distant horizons. None of these estimates are close to statistically significant, however. These results suggest that any spending cuts agreed to at the time of a deficitdriven tax increase disappear within the first year. The lack of a consistent pattern to the estimates at longer horizons suggests little ultimate impact of tax changes on expenditure. In this way, the results for deficit-driven tax changes echo those for long-run actions and do not support the starve-thebeast hypothesis. COUNTERCYCLICAL TAX CHANGES. The third panel of figure 6 shows the implied impact on spending of a countercyclical tax cut. We exclude such tax changes from our baseline regression because the state of the economy could tend to influence spending and taxes in opposite directions, and so again bias the estimates against the starve-the-beast hypothesis. The results suggest that this is somewhat the case. A countercyclical tax cut is associated with a persistent rise in spending. However, the standard errors are quite large, so it is impossible to reject the hypothesis of no relationship. 19. This way of summarizing the estimates is slightly less intuitive for deficit-driven and spending-driven tax changes than for our baseline case of long-run changes, because deficitand spending-driven tax changes are almost always tax increases. Nevertheless, the interpretation is the same as before: a negative response of spending to a tax cut is supportive of the starve-the-beast hypothesis; a positive response or no response is not.

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Figure 6. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, by Type of Tax Cut Long-run tax changesa Percent 12 6 0 –6 –12 2

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Figure 6. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, by Type of Tax Cut (Continued) Spending-driven tax changes Percent 12 6 0 –6 –12 2

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The fourth panel of figure 6 shows the behavior of government spending following a spending-driven tax cut. In this case the relationship is negative, large in absolute terms, and highly statistically significant.20 This is exactly the result one would expect: if we have classified spending-driven tax changes correctly, there should be a positive correlation between them and spending. That the relationship persists is consistent with the spending changes associated with these spending-driven actions being permanent. The findings for spending-driven tax changes both confirm our classification and illustrate the importance of controlling for motivation when testing the starve-the-beast hypothesis. Including spending-driven actions would clearly bias the results toward finding a positive correlation between spending changes and tax changes. ALL LEGISLATED TAX CHANGES. One way to see how much bias would result from including spending-driven tax changes in our analysis is to define a tax variable that sums all four types of legislated tax changes and then use this as the explanatory variable in equation 1. The fifth panel of figure 6 shows the implied impact on total expenditure of a legislated tax cut of any motivation of 1 percent of GDP. The estimated response is strongly negative, and often statistically significant, for the first three years after a tax cut. The point estimate for the maximum cumulative effect is −3.82 percent (t = −2.41). Since none of the other types of tax changes show a consistent negative response, this implied negative effect of the aggregate tax variable must reflect the influence of the spending-driven tax changes. To test this proposition more directly, we define a second composite tax variable that includes all legislated tax changes other than those motivated by spending changes. The last panel of figure 6 shows the cumulative response of total expenditure to a non-spending-driven legislated tax cut of 1 percent of GDP. The effects are consistently positive, suggesting that, if anything, tax cuts appear to be followed by increases in government spending, not decreases as the starve-the-beast hypothesis predicts. And, for horizons beyond three years, these positive effects are significantly different from zero. THE CHANGE IN CYCLICALLY ADJUSTED REVENUE. These results suggest that the inclusion of spending-driven tax changes in the sample may explain why much of the previous literature has found evidence for the starve-the-beast hypothesis. This possibility can be investigated further by considering a SPENDING-DRIVEN TAX CHANGES.

20. These findings are somewhat sensitive to the sample period. Some of the largest spending-driven tax changes occurred during the Korean War. When the post-1957 sample period is used, the maximum impact of a spending-driven tax cut of 1 percent of GDP is large (−6.65 percent) but not statistically significant (t = −1.60).

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Figure 7. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Estimates Using Cyclically Adjusted Revenuea All changes in cyclically adjusted revenue Percent 12 6 0 –6 –12 1

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more standard measure of tax changes. A typical test of the starve-the-beast hypothesis uses the change in cyclically adjusted revenue, which includes all changes in revenue not related to short-run fluctuations in income, as the measure of tax changes. Data on the change in cyclically adjusted revenue are available beginning in 1947Q2. We therefore investigate the effects of using the contemporaneous value and 11 lags of this variable as the tax measure for the period 1950Q1–2007Q4.21 When we use this conventional tax variable, the results indeed seem to support the starve-the-beast hypothesis. The top panel of figure 7 shows that the estimated cumulative effect 21. For comparability with our tax measure, we use the change in real cyclically adjusted revenue as a percent of real GDP. See Romer and Romer (forthcoming) for a more detailed discussion of the sources and derivation of this measure.

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of a decline in real cyclically adjusted revenue of 1 percent of GDP starts out positive but then turns negative. The maximum impact is a change in government expenditure of −2.94 percent (t = −2.04). If spending-driven tax changes are driving this result, subtracting these changes from the change in cyclically adjusted revenue should cause the effect to disappear.22 Indeed, the results using such a series (bottom panel of figure 7) are dramatically different from those using the total change in cyclically adjusted revenue. The estimated impact of a 1-percent-of-GDP decline in cyclically adjusted revenue less spending-driven changes is strongly positive in the short run: the maximum impact is 3.63 percent (t = 4.56). It then gradually declines toward zero, but it never turns negative over the 11-quarter horizon we consider. Thus, the results provide no support for the starve-the-beast hypothesis and, indeed, are somewhat supportive of shared fiscal irresponsibility. This supports the view that the inclusion of spending-driven changes in conventional revenue measures is an important source of the finding that government spending moves in the same direction as tax revenue.23

III. Effects of Long-Run Tax Changes on Future Taxes Our analysis finds no evidence that tax cuts lead to reductions in government spending. This finding naturally raises another question: how then does the government budget adjust to the cuts? An obvious possibility is that the adjustment occurs on the tax side rather than on the expenditure side. To explore this possibility, we examine the response of both tax revenue and tax legislation to long-run tax changes.24 22. Since both series are expressed as a percent of GDP, the spending-driven tax changes can be subtracted without further adjustment. 23. The importance of spending-driven tax changes in biasing the results toward finding a starve-the-beast effect is sensitive to the sample period used. Spending-driven changes were largest during the Korean War and tend to cause substantial bias in samples that include this period. In later sample periods, spending-driven changes are smaller and so are a less important source of bias. This may explain why studies such as Ram (1988), Miller and Russek (1990), and Bohn (1991), which use data from the Korean War period and before, find support for the starve-the-beast hypothesis, whereas those such as von Furstenberg, Green, and Jeong (1986), which use data starting in 1954, do not. 24. Bohn (1991) also examines the degree to which deficits caused by falls in revenue are eliminated by subsequent tax increases. But because he does not account for the sources of changes in revenue, his estimates may suffer from important omitted variable bias. This is particularly true because many of the most important revenue changes in his sample are associated with wars.

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III.A. Response of Tax Revenue To investigate how revenue responds to long-run tax changes, we first reestimate equation 1 using a measure of the change in real tax revenue as the dependent variable. That is, we regress the percentage change in real revenue on a constant and on the contemporaneous value and 20 lags of our measure of long-run tax actions. As in the VARs in section II, we measure revenue using NIPA federal total receipts, deflated by the price index for GDP. We estimate the revenue response over both the full postwar sample period (1950Q1–2007Q4) and the post–Korean War sample (1957Q1–2007Q4). The top and middle panels of figure 8 show the implied cumulative response of total receipts to a long-run tax cut of 1 percent of GDP in each sample period. Tax receipts decline strongly in the short run in response to a tax cut. The contemporaneous effect is a change in receipts of −1.90 percent in the full sample (t = −2.00) and −2.06 percent in the post–Korean War sample (t = −2.33). Total receipts remain substantially below their pre–tax cut path for the next year and a half. In both samples, receipts then recover substantially. For the full sample, the rise in revenue two years after the tax cut is dramatic and marginally significant. This finding is largely driven by the Korean War. As described in section IV, the large 1948 tax cut was followed roughly two years later by the outbreak of the war. Three major tax increases were passed during the war, and the war was accompanied by rapid output growth. For this reason the results for the full sample almost surely overstate the true tendency of revenue to rebound. For the post–Korean War sample, receipts rise above their pre–tax cut path seven quarters after the tax cut, but the effect is modest and the standard errors are large (the t statistic for the positive effect does not rise above 1). To further investigate the response of receipts to tax shocks, we also estimate a bivariate VAR using our measure of long-run tax changes and the log of real total receipts. We include 12 lags of each series, which allows us to use our baseline sample period of 1950Q1–2007Q4. The bottom panel of figure 8 shows the response of real receipts to a long-run tax cut of 1 percent of GDP in this specification. Receipts fall markedly following a long-run tax cut, and the effects are significant, or nearly so, for the first year and a half. Receipts then turn positive nine quarters after the shock. However, even though this specification uses the full sample,

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Figure 8. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Receiptsa OLS estimates, full sampleb Percent 8 4 0 –4 2

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Source: Authors’ estimates. a. Based on regressions of the percentage change in real revenue (NIPA federal total receipts deflated by the price index for GDP) on a constant and the contemporaneous value and 20 lags of the measure of long-run tax changes. b. Regression is estimated for the full postwar sample period (1950Q1–2007Q4). c. Regression is estimated for the post–Korean War sample period (1957Q1–2007Q4). d. Impulse response function from a VAR using the logarithm of real total receipts and the measure of long-run tax changes estimated for the full postwar sample period; there are 12 lags, and the tax measure is ordered first.

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the positive effects are extremely small in absolute terms and not statistically significant.25

III.B. Response of Tax Legislation To understand the behavior of revenue following a long-run tax cut, it is important to investigate the behavior of subsequent tax legislation. Does tax revenue recover because of unusually rapid growth in the economy, or because policymakers legislate tax increases? Given that we have constructed measures of the revenue impact of legislated tax changes classified by motivation, this is an issue we can investigate. In our single-equation analyses of spending and revenue, we consider the experiment of a tax cut intended to spur long-run growth that is not followed by any additional tax changes based on long-run considerations. Therefore, it does not make sense to ask how long-run tax changes respond to this experiment. But it is reasonable to ask how other types of legislated tax changes respond to a long-run tax cut. Long-run tax cuts that do not lower spending, and so increase the deficit, might lead to tax increases designed to reduce an inherited budget deficit. Likewise, a long-run tax cut that gives rise to a short-run boom could lead to a countercyclical tax increase. A long-run tax cut could also lead policymakers to switch to a “pay-as-you-go” policy: a budget deficit resulting from a long-run tax cut may make policymakers unwilling to increase spending without increasing taxes. Therefore, one could also see an increase in spending-driven tax increases following long-run tax cuts. Our basic empirical framework is again identical to that in equation 1, except that the dependent variable is now a measure of legislated tax changes. That is, we regress legislated tax changes of some motivation on a constant and on the contemporaneous and lagged values of our measure of long-run tax changes. In our baseline specification we again use 20 lags, but we also

25. The response of total receipts to a long-run tax cut is even more negative when the bivariate VAR includes 20 lags of each variable and is estimated over the shorter sample period 1952Q2–2007Q4. For this specification, tax revenue does not turn consistently positive until four years after the tax cut. The results for the behavior of revenue using the multivariate VARs described in section II are broadly similar to those from the bivariate VAR. For example, in the four-variable VAR that includes our measure of long-run tax changes, government expenditure, debt, and tax receipts, the effect of a long-run tax change of 1 percent of GDP on receipts is negative for the contemporaneous quarter and the six quarters after the shock and then turns positive. The positive effects are somewhat larger than in the bivariate VAR, but still small in absolute terms and not significant.

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Figure 9. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Subsequent Tax Changes, by Typea On deficit-driven tax changes Percent of GDP 1.2 0.8 0.4 0 2

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experiment with longer lags. We estimate the responses over both the full postwar sample and the post–Korean War sample. As before, we summarize the results by examining the cumulative impact of a long-run tax cut of 1 percent of GDP. A positive impact implies that subsequent tax actions counteracted the long-run tax cut. Because the other tax variables are also expressed as a percent of nominal GDP, the cumulative impact can be interpreted as the fraction of the long-run tax cut that is undone over the horizon considered. Figure 9 shows the estimated impacts of a long-run tax cut of 1 percent of GDP on tax changes of various types. The first panel shows that the impact on deficit-driven tax actions is positive and highly statistically significant, suggesting that long-run tax cuts tend to be followed by deficit-driven tax increases. The cumulative impact is 0.23 percentage point (t = 3.06) after

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Figure 9. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Subsequent Tax Changes, by Typea (Continued) On spending-driven tax changes Percent 1.2 0.8 0.4 0 2

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8 quarters and 0.24 percentage point (t = 2.39) after 16 quarters.26 This suggests that about a fifth of a long-run tax cut is undone by deficit-driven tax increases within a few years. These results are highly robust. Starting 26. The contemporaneous impact is substantial (0.11 percentage point; t = 3.73). The most important observation behind this estimate is 1983Q1. A large part of the tax cuts in the Economic Recovery Tax Act of 1981 were scheduled to go into effect in that quarter. Concern about current and prospective deficits, however, led to passage of the Tax Equity and Fiscal Responsibility Act of 1982, which raised revenue mainly by modifying some features of the 1981 act that had already taken effect (Romer and Romer 2009). Thus, although the long-run tax cut and the deficit-driven tax increase occurred simultaneously, there is a clear sense in which the deficit-driven increase was a response to the long-run cut.

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the sample in 1957 has virtually no impact, and increasing the number of lags to 40 and carrying out the simulations for 10 years strengthen the results. Ten years after the long-run tax action, 44 percent of the action has been undone by deficit-driven tax increases (t = 2.53). The second panel in figure 9 shows the impact of a long-run tax cut on countercyclical tax actions. The estimated impact is moderate but not close to significantly different from zero. After 20 quarters, countercyclical tax actions have counteracted 18 percent of a long-run tax cut (t = 0.57). Starting the sample in 1957 has virtually no impact, because there were no countercyclical tax actions in the early 1950s. Including more lags suggests that the response diminishes at longer horizons. The estimated effect after 10 years is 0.11 percentage point (t = 0.21).27 The third panel of figure 9 shows the impact of a long-run tax cut on spending-driven tax changes. In this case the effects are virtually zero for the first nine quarters and then turn strongly positive. The maximum cumulative impact is 0.47 percentage point (t = 2.53) after 14 quarters. The impact after 20 quarters is 0.36 percentage point (t = 1.58). This suggests that spending-driven tax increases occur after a long-run tax cut and that they counteract close to half of the initial cut. Thus, long-run tax cuts may indeed tend to give rise to pay-as-you-go policies. More than with the other tax changes, there is reason to be concerned that the results for spending-driven actions are influenced by the observations from the Korean War. Starting the sample in 1957 does indeed weaken the link substantially. The strongest impact of a long-run tax cut is now a rise in spending-driven taxes of 0.14 percentage point after eight quarters (t = 2.03). Likewise, including 40 lags reduces the impact substantially for the full sample, but this effect is due entirely to the required shortening of the sample period. The last panel of figure 9 shows the effect of a long-run tax cut on the other three types of legislated tax changes combined. The effect is positive, large, and significant: 0.61 percentage point (t = 2.08) after 12 quarters, 0.81 percentage point (t = 2.34) after 16, and 0.74 percentage point (t = 1.92) after 20. This suggests that roughly three-quarters of a long-run tax cut is typically undone by legislated tax increases of various sorts within five years.

27. We also experiment with leaving out the 1975 tax rebate, which is a huge outlier among countercyclical actions, because it mainly cut taxes dramatically in one quarter and then raised them dramatically in the next. Zeroing out this action reduces the response at medium horizons but has almost no effect on the longer-run response. The main effect is to cut the standard errors by more than half.

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Figure 10 reports the results of three robustness checks for the effect of a long-run tax cut on this composite of other tax changes. The top panel shows the impact of starting the sample in 1957. Both the maximum impact and the statistical significance are somewhat reduced by this change. The impact now peaks at 0.60 percentage point (t = 1.66) after 19 quarters. The middle panel shows the effect of including 40 lags of long-run tax changes. The required shortening of the sample reduces the estimated response over the first 20 quarters somewhat. Thereafter it moves irregularly upward. The response after 40 quarters is large (0.77 percentage point) but not precisely estimated (t = 1.39). Although they weaken the evidence slightly, these two robustness checks tend to confirm that a large fraction of a long-run tax cut is typically reversed by legislated tax increases within the next few years. Our final robustness check allows for more complicated dynamics. We estimate a bivariate VAR that includes both our measure of long-run tax changes and the composite measure of the three other types of legislated tax changes. We include 12 lags of each series and estimate the VAR over our baseline sample period of 1950Q1–2007Q4.28 The bottom panel of figure 10 shows the response of other legislated tax changes to a long-run tax cut of 1 percent of GDP in this specification. The results are again very similar to those from the single-equation specification. The response of other tax changes is strongly positive: the maximum effect is 0.78 percentage point (t = 2.22) 18 quarters after the shock. The effect diminishes slightly thereafter but levels off at around 0.65 percentage point. Thus, the VAR specification confirms that long-run tax cuts tend to be substantially counteracted by other types of tax increases over the next several years.

III.C. Discussion The fact that policymakers have been able to largely reverse tax cuts helps to explain why the cuts have not reduced spending.29 To see this connection, note that a tax cut could reduce future spending in either of two ways. The first is through debt: by bequeathing greater debt to future policymakers, 28. The experiment we can consider in this framework is again slightly different from that in the single-equation specification. When we look at the effect of an innovation to longrun tax changes in the VAR specification, we are no longer assuming that the tax change is not followed by other long-run tax changes. Rather, we let the data say how long-run tax changes respond to the innovation. The cumulative response of long-run tax changes to a long-run tax cut of 1 percent of GDP levels off at around −1.2 percentage points. This suggests that a long-run tax change is typically followed by subsequent long-run tax changes in the same direction. This is consistent with the fact that many long-run tax changes are legislated to take effect in a series of steps. 29. We are grateful to our discussant Steven Davis for this point.

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Figure 10. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Subsequent Tax Changes, Alternative Specificationsa Sample excluding Korean War Percent 1.2 0.8 0.4 0 2

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Including 40 lags of the tax change variable Percent 1.2 0.8 0.4 0 4

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Two-variable VAR estimatesb Percent 1.2 0.8 0.4 0 4

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Source: Authors’ estimates. a. Based on regressions of the measure of legislated tax changes of all types other than long-run tax changes on a constant and the contemporaneous and lagged values of the measure of long-run tax changes. Regressions include 20 lags of the tax variable and are estimated for the full postwar sample except where indicated otherwise. b. Impulse response function from a VAR using the measure of long-run tax changes and the composite measure of the three other types of legislated tax changes; there are 12 lags, and the long-run tax measure is ordered first.

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current policymakers restrict future policymakers’ choice set, which is likely to lead to some combination of higher taxes and lower spending. This is the mechanism emphasized in standard models of strategic budget deficits (for example, Tabellini and Alesina 1990 and Persson and Svensson 1989). The second is by leaving future policymakers with less tax revenue. If increasing taxes is costly, this further reduces spending. This mechanism appears important in informal discussions of the starve-the-beast effect (see, for example, the quotation from Ronald Reagan at the beginning of the paper, which seems to suggest a permanent reduction in government revenue). If the costs of reversing a tax cut are small relative to the costs of cutting spending, then only the first channel is relevant. And that channel is likely to be quantitatively small. Suppose, for example, that a policymaker cuts taxes by 2 percent of GDP for five years. The result will be a deficit that is larger than it otherwise would have been by about 2 percent of GDP for five years, and thus a stock of debt that is larger by about 10 percent of GDP after five years. If the difference between the real interest rate and the economy’s growth rate is 2 percentage points, then the interest costs associated with maintaining the debt-to-GDP ratio at its higher level are about 0.2 percent of GDP (2 percent times 10 percent). Thus, policymakers can keep the tax cut from raising the debt-to-GDP ratio further by first undoing the tax cut and then enacting a permanent spending reduction of 0.1 percent of GDP and an additional permanent tax increase of 0.1 percent of GDP. Since spending is about 20 percent of GDP, this corresponds to a spending reduction of about 0.5 percent—a quite small starvethe-beast effect. If, however, undoing the tax cut is difficult, the effect is much stronger. In the extreme case where none of the tax cut can be reversed, satisfying the government budget constraint requires a spending cut equal to the tax cut (or by even more if there is a delay between the tax cut and the spending reduction, so that the amount of debt increases before the spending reduction). The result is a spending reduction of about 10 percent. Our results concerning the behavior of tax legislation following tax cuts suggest that the truth is closer to the first case than to the second. This suggests a critical reason for our failure to find a substantial starve-the-beast effect: adjustment on the tax side, although presumably not costless, appears feasible, making large adjustments on the spending side unnecessary. We also find that the overall rebound in revenue exceeds the portion due to legislated changes. The key source of the nonlegislated change in revenue is almost certainly the effect of the tax cut on economic activity.

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In Romer and Romer (forthcoming), we find that a tax cut of 1 percent of GDP increases real output by approximately 3 percent over the next three years. Since revenue is a function of income, this growth raises revenue. There is, however, an important caveat to this finding that tax cuts partly pay for themselves through more rapid growth: some of the output response is almost surely a transitory departure of output from normal, not a permanent change in the economy’s normal level of output. To the extent that this is the case, some of the rebound in revenue is also temporary. As a result, without further legislated changes, there may be some long-run budgetary shortfall in the wake of the tax cut. Because of these complications, our results do not allow us to describe with complete confidence how the government budget constraint adjusts following a tax cut. What we can say is that we find no evidence of adjustment on the spending side, and considerable evidence of substantial adjustment on the tax side.

IV. Spending and Taxes in Four Key Episodes In this section we examine the four episodes in our sample that stand out as having the largest long-run tax cuts. This examination serves several purposes. The first is to see whether the narrative record suggests that the tax cuts affected spending decisions. We examine the reasoning that policymakers gave for their spending behavior, and so check whether tax cuts appear to have had an important effect on the decisionmaking process. To keep the narrative analysis manageable, we focus primarily on presidential documents and statements.30 However, in cases where congressional views appear to be central, or at odds with those of the executive branch, we also examine congressional documents. The second purpose is to check whether our regression results reflect consistent patterns in the data. Specifically, we look at the behavior of overall spending and its two broad components, defense purchases and nondefense spending, in each episode. This allows us to investigate whether the relationships shown by the regressions appear in the key episodes. Our third purpose is to examine whether any omitted variables or idiosyncratic shocks account for the failure of spending to fall after a tax cut.

30. The key presidential documents that we use are the Budget of the United States Government (abbreviated as Budget in citations) and the Economic Report of the President (abbreviated as Economic Report). Presidential speeches are identified by their title and date as given in Woolley and Peters, The American Presidency Project (www.presidency.ucsb.edu).

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We ask whether any unusual developments in the episodes had important impacts on spending. This analysis can suggest whether the regression results overstate (or understate) the evidence against the starve-the-beast hypothesis. The final purpose is to address a similar set of issues concerning the tax side of the episodes. We look at what tax actions were taken following the tax cuts, and thus again check whether the regression results reflect consistent patterns. Perhaps more important, we examine the reasons policymakers gave for those actions to see to what extent they appear to have been responses to the cuts. As with spending, we also check whether idiosyncratic factors were an important determinant of tax changes in each episode.

IV.A. The Revenue Act of 1948 The Revenue Act of 1948 was passed over President Harry Truman’s veto in April 1948. The bill was projected to reduce revenue by 1.9 percent of GDP beginning in 1948Q2. The primary motivation for the cut was a desire to improve economic efficiency by reducing marginal tax rates.31 The tax cut was followed by a substantial reduction in revenue. Truman’s view, however, was that government spending should be determined by considerations other than the level of revenue and that tax policy should be adjusted accordingly. The 1950 Economic Report provides a clear statement of this belief: In fields such as resource development, education, health, and social security, Government programs are essential elements of our economic strength. If we cut these programs below the requirements of an expanding economy, we should be weakening some of the most important factors which promote that expansion. Furthermore, we must maintain our programs for national security and international peace. . . . Government revenue policy should take into account both the needs of sound Government finance and the needs of an expanding economy. (p. 8)

Consistent with this view, Truman’s main response to the tax cut was to propose a counteracting tax increase. He argued, “In a period of high prosperity it is not sound public policy for the Government to operate at a deficit. . . . I am, therefore, recommending new tax legislation to raise revenues by 4 billion dollars” (1950 Budget, p. M5). This increase would have offset 80 percent of the 1948 cut.

31. Our descriptions in this section of the motivations for tax changes and our figures for their revenue effects are based on Romer and Romer (2009). The revenue estimates exclude the effects of retroactive features of the bills.

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Nonetheless, the fall in revenue appears to have had a marginal effect on Truman’s spending policies. In the 1949 Midyear Economic Report of the President, he explained, “When I submitted my budget for the fiscal year 1950 last January, the programs of expenditure that I then recommended were held to a minimum consistent with our basic needs in view of the inflationary strain upon materials and manpower then prevailing” (p. 7). Since Truman viewed the budget deficit as contributing to inflationary pressures (see, for example, his Annual Message to the Congress on the State of the Union, January 5, 1949, p. 3), this points to at least some effect of the tax cut on spending decisions. After North Korea invaded South Korea on June 25, 1950, taxes and the deficit essentially disappeared from Truman’s discussions of spending. Even more than it had been in peacetime, his view was that spending should be determined by the country’s needs, and taxes adjusted accordingly. For example, in his budget message of January 1951, Truman described the spending side of the budget and then stated, “I shall shortly recommend an increase in tax revenues in the conviction that we must attain a balanced budget to provide a sound financial basis for what may be an extended period of very high defense expenditures” (1952 Budget, p. M6). Finally, although Congress’s view of the tax cut was obviously very different from Truman’s, Congress does not appear to have sought lower spending than the president. For example, in August 1948 Truman reported that although Congress had not appropriated the full amount he had requested for fiscal 1948 and 1949, this shortfall was offset by two factors: some spending had been authorized but not yet appropriated, and several pieces of legislation had been enacted that would require higher spending, but no spending had yet been authorized. As a result, he expected spending in fiscal 1949 to be significantly higher than what he had requested in January (“Statement by the President: The Midyear Review of the Budget,” August 15, 1948, p. 3). Thus, there is no evidence of a starve-the-beast effect operating through congressional actions in this episode. The first panel of figure 11 shows the behavior of real government spending in this episode. It plots, in logarithms, both our measure of total expenditure and the two categories of spending, national defense purchases and nondefense spending. As in section II, we define nondefense spending as the difference between our measure of total expenditure and national defense purchases; the two main components of this measure are nondefense purchases and current transfer payments. The vertical line indicates the quarter in which the tax cut took effect. Several things are apparent. First and most important, there was no discernable slowdown in overall

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spending or in either of the two categories of spending. Indeed, growth in overall spending increased after the tax cut. Total expenditure, which had been essentially flat before the tax cut, rose by 16 percent (calculated using the change in logarithms) in the two years between the cut and the start of the war. Second, there was a substantial one-time spike in nondefense spending in 1950Q1, reflecting a one-time dividend payment from the trust fund for National Service Life Insurance (the government insurance program for military personnel). These payments were the result of a large accumulation of assets in the trust fund, which could not be used for other purposes (Hines 1943; Survey of Current Business, March 1950, pp. 1–3, and August 1950, p. 7). Third, both defense and overall spending rose sharply after the outbreak of the war. Both the National Service Life Insurance dividend payment and the increased military spending after the start of the war clearly reflected unusual developments, not just the normal response of spending to tax cuts. Thus, they tend to cause our regressions to overstate the impact of tax cuts on subsequent spending increases. Another important unusual development operated in the opposite direction. The Social Security Amendments of 1950 almost doubled Social Security benefits starting in September 1950 and substantially increased the coverage of the system beginning in January 1951 (Social Security Bulletin, October 1950, pp. 3–14). Because Social Security benefits were initially small, these changes had little immediate impact on overall spending. Nonetheless, the rise in the benefit base and the expansion of coverage contributed significantly to the growth of spending over time. The fact that these delayed spending effects are not captured by our regressions tends to make them understate the impact of tax cuts on later spending increases. On the tax side, the 1948 tax cut was followed by a series of tax increases that were largely spending driven. The first, and least important, was an increase in Social Security taxes of 0.3 percent of GDP in 1950Q1, which had been legislated before the tax cut was passed. Larger tax actions followed. The Social Security Amendments of 1950 increased the base of the payroll tax from $3,000 to $3,600, effective at the beginning of 1951, and called for a gradual increase in the combined (employer plus employee) Social Security tax rate from 3 percent to 61⁄2 percent over the next two decades (Social Security Bulletin, October 1950, pp. 3–14). And three bills in 1950 and 1951 to finance the Korean War increased taxes by a combined 4.1 percent of GDP.32 32. We measure the effect of a series of tax changes by finding the share of each one in nominal GDP in the quarter in which it took place, and then summing the shares.

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Figure 11. Real Expenditure Following Major Long-Run Tax Cuts Revenue Act of 1948 Logarithms Tax cut takes effect 2.0 Total expenditures

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The move to spending-driven tax increases in the early 1950s was clearly a policy decision. In the case of Social Security, policymakers were grappling with how to finance the system. A special congressional commission and the Social Security Administration both recommended that Social Security taxes be limited and that the system move toward increasing reliance on general revenue. Instead, however, the 1950 amendments repealed the provision of the Social Security Act that permitted financing from general revenue, and made the system entirely self-financing (Social Security Bulletin, May 1948, pp. 21–28; February 1949, pp. 3–9; October 1950, pp. 3–14). However, we have found no direct evidence that the 1948 tax cut played a causal role in this decision. The extent of the government’s reliance on contemporaneous tax increases to finance the Korean War is remarkable: total government expenditure rose by 6.0 percent of GDP from 1950Q2 to its peak in 1952Q3, only moderately more than the expected revenue effects of the tax increases to finance the war. Moreover, Truman explicitly cited the deficit as a reason for

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Figure 11. Real Expenditure Following Major Long-Run Tax Cuts (Continued) Economic Recovery Tax Act of 1981 Logarithms Tax cut takes effect 2.5 2.0 1.5 1.0 1980

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Source: Authors’ calculations using National Income and Product Accounts data. a. The first vertical line marks the date of the Economic Growth and Tax Relief Reconciliation Act of 2001, and the second that of the Jobs and Growth Tax Relief Reconciliation Act of 2003.

this heavy reliance on tax finance. Soon after the war began, he wrote to congressional leaders: We embark on these enlarged expenditures at a time when the Federal budget is already out of balance. This makes it imperative that we increase tax revenues promptly lest a growing deficit create new inflationary forces detrimental to our defense effort. We must make every effort to finance the greatest possible amount of needed expenditures by taxation. (“Letter to the Chairman, Senate Committee on Finance, on the Need for an Increase in Taxes,” July 25, 1950, p. 1)

Thus, the Korean War tax increases were in part a response to the 1948 tax cut.

IV.B. The Revenue Act of 1964 In February 1964 President Lyndon Johnson signed the Revenue Act of 1964. The act reduced revenue by 1.3 percent of GDP in 1964Q2 and by

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another 0.6 percent in 1965Q1. The key motivation for the tax cut was a desire to increase long-run growth. Because economic growth following the tax cut was very rapid, revenue recovered quickly, and budget deficits that could have triggered a starve-the-beast response did not emerge immediately. Nevertheless, policymakers’ statements and behavior provide some evidence concerning this mechanism. At almost the same time that he signed the tax bill, Johnson began to propose drastic increases in spending. In February 1964 he gave a speech proposing federal hospital insurance for the elderly and other health initiatives (“Special Message to the Congress on the Nation’s Health,” February 10, 1964). His “Great Society” speech followed in May 1964, calling for the elimination of poverty, urban renewal, pollution reduction, and expansion of education (“Remarks at the University of Michigan,” May 22, 1964). Over the next year, a number of spending increases directed at achieving these goals were passed. The most significant were the dramatic expansion of benefits and the introduction of Medicare contained in the Social Security Amendments of 1965. The Johnson administration believed that spending should be determined by necessity and efficiency. For example, the 1967 Economic Report stated, “most economists now agree that the selection of appropriate expenditure levels . . . should be made in light of the relative merits of alternative programs, and of the benefits of added public expenditures, compared with private ones, at the margin. . . . It is preferable to emphasize changes in tax rates (suitably coordinated with changes in monetary policy) for stabilization purposes” (p. 68). The narrative record in this episode is striking in the degree to which revenue was not mentioned as a determinant of expenditure. Defense spending increased substantially starting in mid-1965 because of the escalation of the war in Vietnam. Johnson argued forcefully against allowing budgetary concerns to stop the rise in nondefense spending, stating: There are men who cry out: We must sacrifice. Well, let us rather ask them: Who will they sacrifice? Are they going to sacrifice the children who seek the learning, or the sick who need medical care, or the families who dwell in squalor now brightened by the hope of home? . . . I believe that we can continue the Great Society while we fight in Vietnam. (“Annual Message to the Congress on the State of the Union,” January 12, 1966, p. 2)

Congress went along with his calls for increased spending. For example, the Social Security Amendments of 1967 brought about another substan-

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tial increase in benefits and a significant increase in coverage. Thus, the rise in spending following the tax cut was not just the consequence of the war. Beginning in early 1966, policymakers began to worry that the economy was overheating, and by late that year the budget deficit had increased substantially. Nevertheless, the administration did not call for substantial spending reductions. Federal expenditure was expected to rise by $15 billion in 1968 (1968 Economic Report, p. 54). Instead, the administration concluded that “the cost of meeting our most pressing defense and civilian requirements cannot be responsibly financed without a temporary tax increase” (1969 Budget, p. 8). Over the president’s objection, Congress included a $6 billion spending reduction (relative to projections) in the 1968 bill imposing a 10 percent temporary tax surcharge. Congress pressed for the spending cuts not because revenue had declined, but because members felt it was unfair to take all of the needed macroeconomic restraint in the form of higher taxes. A number of senators expressed sentiments similar to those of Senator Robert Byrd of West Virginia, who stated, “Before any new tax burden . . . is placed upon the American taxpayer, the executive branch and the legislative branch should reduce, and eliminate where possible, all nonessential expenditures” (Congressional Record, 90th Congress, 2d session, volume 114, part 7, April 2, 1968, p. 8561). The tax cut was surely one factor contributing to the overheating that motivated the tax surcharge. Therefore, although policymakers did not explicitly draw a direct link between the tax cut and the spending reduction, the reduction is the one development in this episode that could suggest some connection between tax cuts and subsequent spending decisions. The actual behavior of spending following the 1964 tax cut is completely consistent with policymakers’ stated positions. The second panel of figure 11 shows that total expenditure was basically constant during the first year after the tax cut but then rose strongly. Total expenditure increased by 27 percent in the five years after the tax cut, noticeably more than the 18 percent in the five years before the cut.33 The rise in defense purchases was one source of the increase, but nondefense spending, fueled by a large increase in transfer payments, increased even more rapidly. Special factors clearly played a role in the behavior of spending. Much of the rise in defense expenditure was related to the Vietnam War. To the extent 33. These changes are computed as the change (in logarithms) of our measure of real total gross expenditure less interest over the periods 1959Q2–1964Q2 and 1964Q2–1969Q2. The other figures for spending growth reported in this section are computed similarly.

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that defense spending truly was nondiscretionary, some of the rise in spending reflects this exogenous shock rather than a failure of the starve-the-beast phenomenon. At the same time, the immediate increase in spending called for by the Social Security Amendments of 1965 and 1967 understates in a fundamental way the true rise in spending. The creation of the Medicare program and the increases in Social Security benefits and coverage put in place an enormous stream of future spending. Thus, in present value terms, the increase in spending passed in the wake of the 1964 tax cut was unquestionably huge. Policymakers’ statements and actions on taxes in this episode are striking. In 1965 the Johnson administration proposed (and succeeded in passing) two significant tax actions. One was the Excise Tax Reduction Act of 1965, passed in January of that year. The administration viewed this tax cut as a continuation of the 1964 action. In this case, then, the serial correlation of tax changes reflects continuity in views about appropriate policy. The second was the Social Security Amendments of 1965, which included a substantial increase in payroll taxes to help pay for a large increase in benefits, including hospital insurance for the elderly. This tax increase appears to have had little to do with the 1964 tax cut. Policymakers paid for the desired expansion of benefits by raising taxes, because the decision had been made in 1950 that the Social Security system should be self-financing.34 The overheating of the economy beginning in 1966 led policymakers to advocate tax increases. The Tax Adjustment Act of 1966 (enacted in March) rescinded the excise tax reduction of the previous January, and Public Law 89-800 (enacted in November) suspended the investment tax credit. Together these two tax increases were expected to raise revenue by 0.3 percent of GDP.35 By far the largest tax increase in the immediate post-1964 period was the 1968 surcharge. The administration first proposed a 6 percent surcharge in January 1967. In August 1967 Johnson stated, “If left untended, this deficit could cause . . . a spiral of ruinous inflation” and “brutally higher interest rates” (“Special Message to the Congress: The State of the Budget 34. The Social Security Amendments of 1967, enacted in January 1968, also raised taxes substantially to pay for another increase in benefits and coverage. 35. Public Law 90-26, enacted in June 1967, restored the investment tax credit. As discussed in Romer and Romer (2009), the motivation for this change involved the conditions in a particular sector (the capital goods market) and concern about longer-run incentives for investment. It does not appear to have been motivated by the 1964 tax cut or by short-run macroeconomic conditions.

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and the Economy,” August 3, 1967, p. 1). He requested that the surcharge be increased to 10 percent, the level ultimately included in the Revenue and Expenditure Control Act of 1968. The act increased taxes by 0.9 percent of GDP in 1968Q3 and by another 0.2 percent in 1969Q1. Johnson was explicit in saying that the surcharge was undoing part of the 1964 tax cut. In his signing statement he said, “This temporary surcharge will return to the Treasury about half the tax cuts I signed into law in 1964 and 1965” (June 28, 1968, p. 1). This action, combined with the continued rise in expenditure, is a vivid example that what typically gives in response to a tax cut is not spending but the tax cut itself.

IV.C. The Economic Recovery Tax Act of 1981 A very large long-run tax cut was enacted in August 1981, shortly after President Ronald Reagan took office. The cut lowered taxes by a combined 4.5 percent of GDP in a series of steps. Reagan was a strong advocate of spending reductions throughout his presidency. For example, in a speech presenting his economic program, he identified “reducing the growth in government spending and taxing” as a central goal, and he argued that “spending by government must be limited to those functions which are the proper province of government” (“Address before a Joint Session of the Congress on the Program for Economic Recovery,” February 18, 1981, pp. 1, 5). Similarly, in his first budget message, in February 1982, he listed “reducing the growth of overall Federal spending by eliminating Federal activities that overstep the proper sphere of Federal Government responsibilities” as one of his fundamental economic goals (1983 Budget, p. M4). The 1981 tax cut was followed by a substantial fall in revenue and a sharp rise in the deficit. As the deficit increased, Reagan often cited it as a further reason for restraining spending. For example, in his February 1986 budget message, he said, “there is a major threat looming on the horizon: the Federal deficit” (1987 Budget, p. M-4). He went on to say, “Spending is the problem—not taxes—and spending must be cut. The program of spending cuts and other reforms contained in my budget will lead to a balanced budget at the end of five years” (p. M-5). Similarly, his February 1988 budget message stated: Last year, members of my Administration worked with the Leaders of Congress to develop a 2-year plan of deficit reduction—the Bipartisan Budget Agreement. . . . The Bipartisan Budget Agreement reflects give and take on all sides. I agreed to some $29 billion in additional revenues and $13 billion less than

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I had requested in defense funding over 2 years. However, because of a willingness of all sides to compromise, an agreement was reached that pared $30 billion from the deficit projected for 1988 and $46 billion from that projected for 1989. (1989 Budget, p. 1-6)

Thus, the narrative record from this episode provides some evidence that the decline in revenue due to the 1981 tax cut affected later spending decisions. The third panel of figure 11 plots government spending before and after the 1981 tax cut. The vertical line is drawn at 1981Q3, the date of the first of the series of cuts. Despite what the narrative evidence suggests, growth in overall spending did not slow but actually quickened. In the five years following the tax cut, total expenditure grew by 23 percent, substantially above the 14 percent growth in the five years before the cut. This acceleration in overall spending reflects a combination of a large rise in the growth of defense spending and a more moderate rise in the growth of nondefense spending. Two important unusual spending developments marked this episode. First, the tax cuts coincided with a shift in political power toward supporters of lower spending. Reagan’s goal of restraining government spending was not shared by his predecessor. For example, in his final budget message, President Jimmy Carter, while advocating “budget restraint,” stated, “The growth of budget outlays is puzzling to many Americans, but it arises from valid social and national security concerns” (1982 Budget, pp. M4–M5). The balance of political power in Congress also swung sharply toward advocates of spending restraint at the time of Reagan’s election. Thus, there was clearly an omitted variable acting to reduce spending in this episode.36 Second, the heightening of the cold war prompted policymakers to increase defense spending. Ramey and Shapiro (1998), for example, identify the Soviet invasion of Afghanistan at the end of 1979 as an exogenous positive shock to defense spending. This factor operated in the opposite direction of the political shift toward supporters of lower spending. The tax cuts were followed by two types of tax increases. First, the Social Security Amendments of 1983 called for a series of payroll tax increases from 1984 to 1990 to improve the solvency of the Social Security system. These increases appear to have been largely a continuing consequence of the 1950 decision to make the Social Security program self-financing. 36. Although Reagan supported spending reduction in general, he favored higher defense spending. He had campaigned on a need to rebuild the military and identified “strengthening the Nation’s defenses” as one of his key goals (1983 Budget, p. M4).

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Second, a series of income tax increases were explicitly motivated by a desire to reduce the budget deficits that emerged following the tax cuts. These included the Tax Equity and Fiscal Responsibility Act of 1982, which undid some of the provisions of the 1981 act; the Deficit Reduction Act of 1984; the Omnibus Budget Reconciliation Act of 1987; and the Omnibus Budget Reconciliation Act of 1990. For example, in a national address on the 1982 act, Reagan stated that it reflected a choice to “reduce deficits and interest rates by raising revenue from those who are not now paying their fair share,” rather than to “accept bigger budget deficits, higher interest rates, and higher unemployment” (“Address to the Nation on Federal Tax and Budget Reconciliation Legislation,” August 16, 1982, p. 4). Similarly, the 1989 Budget reported that the 1987 act was enacted “in conformance with the Bipartisan Budget Agreement” (p. 4-5), which, as described above, was motivated by concern about the deficit. The 1982 and 1984 actions alone increased taxes by 1.0 percent of GDP. Thus, these tax increases were a fairly direct response to the earlier tax cut.

IV.D. The Tax Cuts of 2001 and 2003 Two long-run tax cuts were passed early in the administration of President George W. Bush. The Economic Growth and Tax Relief Reconciliation Act of 2001, enacted in June, included a long-run tax cut of 0.8 percent of GDP in 2002Q1, as well as a large countercyclical tax cut in 2001Q3. The Jobs and Growth Tax Relief Reconciliation Act of 2003, enacted in May, included a long-run cut of 1.1 percent of GDP in 2003Q3. These tax cuts do not appear to have had any substantial impact on the administration’s view of appropriate spending. Throughout the episode, both spending restraint and either preserving the surplus or reducing the deficit received some attention. But discussions of spending did not change appreciably in response either to the tax cuts or to the subsequent deterioration of the budget situation. The administration’s first budget proposals, which predated the tax cuts, put some emphasis on spending restraint and on paying down the national debt. The president’s first budget document, for example, stated that the budget would “Moderate Growth in Government and Fund National Priorities” and achieve “Debt Reduction” (“A Blueprint for New Beginnings: A Responsible Budget for America’s Priorities,” February 28, 2001, p. 7).37 37. This document was not part of the president’s formal 2002 budget, which was not submitted until April 2001. However, it is included with the other 2002 budget documents on the Government Printing Office website. See www.gpoaccess.gov/usbudget/fy02/index.html.

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It also said that “the President’s Budget commits to using today’s surpluses to reduce the Federal Government’s publicly held debt so that future generations are not shackled with the responsibility of paying for the current generation’s overspending” (p. 22), and that “we must ensure that we rein in excessive Government spending” (p. 23). In the immediate aftermath of the terrorist attacks of September 11, 2001, discussions of budget policy placed less emphasis on spending restraint (see, for example, Bush’s “Address before a Joint Session of the Congress on the State of the Union,” January 29, 2002, pp. 3–4). Later presidential statements, however, returned to calls for spending restraint similar to those in 2001. For example, in his 2004 State of the Union Address, Bush stated, “I will send you a budget that funds the war, protects the homeland, and meets important domestic needs while limiting the growth in discretionary spending. . . . By doing so, we can cut the deficit in half over the next 5 years” (“Address before a Joint Session of the Congress on the State of the Union,” January 20, 2004, p. 4). Similarly, in his 2007 State of the Union Address, Bush said, “What we need is spending discipline. . . . I will submit a budget that eliminates the Federal deficit within the next 5 years” (“Address before a Joint Session of the Congress on the State of the Union,” January 23, 2007, p. 1). Although these statements were very similar to those Bush had made before the tax cuts, actual budget conditions had changed substantially: revenue had fallen and the overall budget had shifted from surplus to deficit. The similarity in the rhetoric despite the large changes in the deficit suggests that there was little link between the level of revenue and the perceived need for spending restraint. The last panel of figure 11 plots the major categories of spending in this episode. The two vertical lines show the dates that the two tax cuts first took effect. As in the other episodes, overall spending growth did not slow. In the five years following the first cut in 2001Q3, spending grew by 22 percent, substantially more than the 14 percent in the five years before the cut. The growth in spending following the tax cut was greatest in defense: national defense purchases rose by 33 percent in the five years after the tax cut, while nondefense spending rose by 19 percent. The events of September 11, 2001, were clearly an important outside influence on spending. Some of the behavior of total expenditure surely reflects the impact of this development rather than the effect of the tax cuts. On the other hand, one important spending action is not well reflected in our spending measures. The addition of prescription drug coverage to

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Medicare, enacted in December 2003, was expected to have only a modest short-run effect on spending but to raise its path substantially over time. Thus, although the change was enacted soon after the tax cuts, most of its impact on spending will almost surely come after the period considered in our regressions. One notable feature of this episode is that the tax cuts were not soon followed by counteracting tax increases. A modest countercyclical tax cut was enacted in March 2002, in the wake of the September 11 attacks. The only important tax increase was that the bonus depreciation provisions included in the 2002 bill, and then expanded and slightly extended as part of the 2003 tax bill, were allowed to expire at the end of 2004. Thus, the issue of how the government will eventually deal with the loss of revenue from the 2001 and 2003 tax cuts remains open.

IV.E. Assessment Examination of these four episodes of major long-run tax cuts reinforces the findings from our statistical analysis: there is little evidence of a starvethe-beast effect. The one aspect of the episodes that is at times consistent with the hypothesis that tax cuts reduce government spending is the narrative record of the budget process. Although the presidents in two of the episodes (Johnson and Bush) appear to have paid little attention to the impact of the tax cuts on revenue in formulating their budget policies, the presidents in the other two (Truman and Reagan) cited the level of revenue as a consideration in budget policy. Even in these cases, however, other factors were clearly much more important, and to a considerable extent the concern over revenue led not to advocacy of spending reductions, but to support for (or acceptance of) tax increases. The actual behavior of spending in all four episodes provides no support for the starve-the-beast hypothesis. In no episode was there a discernible slowdown in spending following the tax cut. Indeed, all of the episodes saw an acceleration of spending. This is similar to the overall statistical finding of a positive (although only marginally significant) effect of tax cuts on spending, and it suggests that the regression results reflect a consistent pattern in the data rather than the effects of outliers. Examination of other influences on spending in the episodes does not change these conclusions. On the one hand, there was an important external development in each episode that acted to raise defense spending. By itself, this pattern would suggest that the regressions might overestimate the positive effects of tax cuts on spending. Two considerations, however,

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point in the opposite direction.38 First, the largest of the tax cuts (that of 1981) coincided with the election of a president who had a strong commitment to reducing the size of government. This suggests that the positive impact of tax cuts on spending might be even larger than implied by the regressions. Second, significant actions were taken in three of the four episodes to increase spending that had important effects after the five-year window considered in our baseline regressions. For example, in two of the episodes (1964 and 2001–03), the government enacted major changes in the provision of medical care for the elderly that had very large implications for the long-term path of government spending. Since our regressions miss much of the effects of these actions, this too suggests that the regressions may underestimate the extent to which tax cuts increase spending. Thus, examination of other factors affecting spending in the four episodes suggests that, on net, the regressions do not overstate the evidence against the starve-the-beast hypothesis. Tax policy in these episodes is also consistent with the regression results. In three of the four episodes, substantial tax increases followed the initial tax cut within five years, offsetting a substantial fraction of it. Perhaps more striking is what policymakers said about the tax increases. In all three cases they referred directly to the need to raise taxes to counter the macroeconomic and budgetary effects of the original tax cuts. And in two cases (1948 and 1964), the president said explicitly that raising taxes was preferable to cutting spending.

V. Conclusions The starve-the-beast hypothesis—the idea that tax cuts restrain government spending—is a central argument for tax reduction. Despite its importance, however, the hypothesis has been subject to few tests, and the tests that have been done have important limitations. This paper tests the starve-the-beast hypothesis by examining the behavior of government spending following tax changes motivated by long-run considerations. Because these tax changes were not motivated by factors that are likely to have an important direct effect on government spending, they are the most appropriate for testing the theory. The results provide no evidence of a starve-the-beast effect: following long-run tax cuts, govern38. In addition, recall that our statistical results are robust to controlling for a measure of exogenous shocks to defense spending, and that even excluding defense spending entirely provides little evidence for the starve-the-beast hypothesis.

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ment spending does not fall. Indeed, if anything, spending rises, providing some support for the alternative view of fiscal illusion or shared fiscal irresponsibility. The lack of support for a starve-the-beast effect is highly robust. Detailed examination of the four largest postwar episodes of long-run tax cuts reinforces the statistical findings. We also identify a potentially powerful source of bias in tests of the starve-the-beast hypothesis that use data on overall revenue and spending. Some tax changes are explicitly motivated by contemporaneous or planned changes in spending. Not surprisingly, these tax changes are followed by large spending changes in the same direction. Causation in these cases, however, runs from the decision to raise spending to the tax change. For the full postwar sample, this type of tax change is sufficiently common that it causes the overall relationship between tax revenue and spending to be significantly positive. Excluding these spending-driven changes makes the relationship negative and marginally significant. The fact that tax cuts do not lead to spending reductions raises the question of how the government budget constraint is ultimately satisfied. We find that long-run tax cuts are offset by legislated and nonlegislated tax increases over the next several years. The fact that policymakers are able to make changes on the tax side helps to explain why they do not appear to make large changes on the spending side. Of course, failing to find support for the starve-the-beast hypothesis is not the same as definitively refuting it. There are several ways in which our results are not inconsistent with the presence of at least some starvethe-beast effect. First, our failure to find such an effect for the postwar U.S. federal government does not mean it is not important in other times and places. Second, the case that focusing on tax changes taken for long-run purposes yields unbiased estimates is not airtight. As we explain, however, the most likely direction of bias is in favor of the starve-the-beast hypothesis, not against it. Third, because our estimates are not highly precise, the hypothesis that tax cuts exert some restraining influence on spending usually cannot be rejected. Fourth, some of our evidence (the statistical examination of nondefense spending and the narrative evidence for the 1948 and 1981 episodes) provides some hints of support for a small starve-the-beast effect. Finally, although we find that the fall in revenue caused by a tax cut disappears after a few years, some of this disappearance is most likely the result of a temporary output boom. Thus, we do not completely resolve the issue of how the government restores long-run budget balance. Since the government’s long-run budgetary situation deteriorated substantially over the

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period we consider, to some extent this limitation is inherent: not all of the offsetting actions have yet occurred. It is possible that some of the remaining adjustment will take place on the spending side. Taken together, these caveats imply that one cannot necessarily conclude that tax cuts do not restrain government spending at all. But it remains the case that, over the period we consider, there is virtually no evidence of such an effect. The finding that tax cuts do not appear to substantially restrain government spending could obviously have implications for policy. At the very least, policymakers should be aware that the historical experience suggests that tax cuts tend to lead to tax increases rather than to spending cuts. The finding also has implications for models that assume the existence of a starve-the-beast effect. For example, Bohn (1992) argues that one reason for Ricardian equivalence to fail is that a tax cut implies that government spending will be lower; as a result, a tax cut leads households to reduce their estimates of the present value of their present and future liabilities, and so to increase their consumption. Similarly, a restraining effect of tax cuts on government spending plays a central role in the theories of strategic debt accumulation of Torsten Persson and Lars Svensson (1989), Guido Tabellini and Alberto Alesina (1990), and others. If decisionmakers understand that tax cuts do not in fact lead to substantial reductions in government spending, these mechanisms are much less important. Thus, better estimates of the effects of tax cuts on spending may require changes to the modeling of a wide range of issues.

ACKNOWLEDGMENTS We are grateful to Alan Auerbach, Raj Chetty, Steven Davis, Barry Eichengreen, William Gale, Jeffrey Miron, and Ivo Welch for helpful comments and suggestions, and to the National Science Foundation for financial support.

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References Anderson, William, Myles S. Wallace, and John T. Warner. 1986. “Government Spending and Taxation: What Causes What?” Southern Economic Journal 52, no. 3: 630–39. Auerbach, Alan J. 2000. “Formation of Fiscal Policy: The Experience of the Past Twenty-Five Years.” Federal Reserve Bank of New York Economic Policy Review 6, no. 1: 9–23. ———. 2003. “Fiscal Policy, Past and Present.” BPEA, no. 1: 75–122. Becker, Gary S., and Casey B. Mulligan. 2003. “Deadweight Costs and the Size of Government.” Journal of Law and Economics 46, no. 2: 293–340. Blanchard, Olivier, and Roberto Perotti. 2002. “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output.” Quarterly Journal of Economics 117, no. 4: 1329–68. Bohn, Henning. 1991. “Budget Balance through Revenue or Spending Adjustments? Some Historical Evidence for the United States.” Journal of Monetary Economics 27, no. 3: 333–59. ———. 1992. “Endogenous Government Spending and Ricardian Equivalence.” Economic Journal 102, no. 412: 588–97. Buchanan, James M., and Richard E. Wagner. 1977. Democracy in Deficit: The Political Legacy of Lord Keynes. New York: Academic Press. Calomiris, Charles W., and Kevin A. Hassett. 2002. “Marginal Tax Rate Cuts and the Public Tax Debate.” National Tax Journal 55, no. 1: 119–31. Gale, William G., and Peter R. Orszag. 2004. “Bush Administration Tax Policy: Starving the Beast?” Tax Notes 105, no. 8: 999–1002. Hines, Frank T. 1943. “National Service Life Insurance.” Annals of the American Academy of Political and Social Science 227: 83–93. Miller, Stephen M., and Frank S. Russek. 1990. “Co-Integration and Error-Correction Models: The Temporal Causality between Government Taxes and Spending.” Southern Economic Journal 57, no. 1: 221–29. Niskanen, William A. 1978. “Deficits, Government Spending, and Inflation: What Is the Evidence?” Journal of Monetary Economics 4, no. 3: 591–602. Persson, Torsten, and Lars E. O. Svensson. 1989. “Why a Stubborn Conservative Would Run a Deficit: Policy with Time-Inconsistent Preferences.” Quarterly Journal of Economics 104, no. 2: 325–45. Ram, Rati. 1988. “Additional Evidence on Causality between Government Revenue and Government Expenditure.” Southern Economic Journal 54, no. 3: 763–69. Ramey, Valerie A. 2008. “Identifying Government Spending Shocks: It’s All in the Timing.” University of California, San Diego (June). Ramey, Valerie A., and Matthew D. Shapiro. 1998. “Costly Capital Reallocation and the Effects of Government Spending.” Carnegie-Rochester Conference Series on Public Policy 48: 145–94. Romer, Christina D., and David H. Romer. 2009. “A Narrative Analysis of Postwar Tax Changes.” University of California, Berkeley (June).

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———. Forthcoming. “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks.” American Economic Review. Tabellini, Guido, and Alberto Alesina. 1990. “Voting on the Budget Deficit.” American Economic Review 80, no. 1: 37–49. Von Furstenberg, George M., R. Jeffery Green, and Jin-Ho Jeong. 1986. “Tax and Spend, or Spend and Tax?” Review of Economics and Statistics 68, no. 2: 179–88.

Comments and Discussion COMMENT BY

STEVEN J. DAVIS In this paper Christina Romer and David Romer investigate the hypothesis that tax cuts curtail government spending. To do so, they study the experience of the federal government since 1945. They stress, quite rightly, that the empirical relationship between tax changes and spending changes depends greatly on why the changes occurred. Some tax change episodes are potentially informative about the hypothesis, and others are not. This observation underlies their two-step empirical strategy. First, Romer and Romer use contemporaneous narrative sources to determine the motives for legislated tax changes. The goal is to identify tax changes that aim to spur productivity growth or promote other long-run objectives. They argue that such tax changes are less likely to be correlated with other factors that drive government spending and, hence, are more informative about the effect of tax changes on government spending. In the second step, they examine the response of government spending to these informative tax change episodes. They consider a variety of statistical specifications, and they supplement the statistical analysis with a detailed examination of four large tax changes. The authors execute this empirical strategy with considerable care and skill.1 They conclude that the results provide “virtually no evidence” that tax cuts restrain government spending. Instead, the results suggest that tax cuts motivated by long-run objectives are largely offset in the ensuing years by tax increases. They provide a balanced summary of these and other results in their concluding section. 1. I encourage the reader to consult their closely related paper (Romer and Romer 2009) to gain a fuller appreciation for the care and skill that they bring to the first step of their empirical strategy.

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In my view, legislated tax cuts have done little to restrain U.S. government spending in the postwar era. I reach this view based mainly on the arguments sketched in Romer and Romer’s section III.C. These arguments rely on economic reasoning about the force of the mechanisms that link current tax cuts to future government spending. I place less weight on the results of the two-step empirical strategy outlined above. The strategy is a sensible one, but it does not yield sharp inferences in a sample focused on the postwar U.S. experience. This fact shows up as large standard errors for the estimated spending responses to tax cuts. In addition, and despite the authors’ careful effort, it is hard to fully dispel concerns about the classification of tax change episodes and concurrent developments that influence the estimates. Section III.C describes two mechanisms whereby tax cuts might curtail future government spending. One mechanism works through the link between current tax cuts and future debt-servicing costs. In particular, a deficit-financed tax cut today means higher debt-servicing costs in the future, leading future policymakers to choose a lower level of noninterest government spending than otherwise. A second mechanism rests on the political and economic costs of reversing a tax cut. To assess the force of the first mechanism, assume linear marginal schedules for the costs and benefits of government spending: MC = 1 + cg, c > 0, MB = m − bg, m > 0 and b ≥ 0; where g is the ratio of government spending to GDP, and c, b, and m are parameters. Treating output as exogenous and equating benefits and costs at the margin, the policymaker chooses g* = (m – 1)/(b + c) for the size of government. This outcome need not be optimal from the perspective of the median voter or a utilitarian social welfare criterion. It simply reflects the policymaker’s preferred outcome in light of budgetary and political pressures. When a policymaker implements a deficit-financed tax cut, this raises the MC schedule facing future policymakers. In the example offered in section III.C, the policymaker cuts taxes by 2 percent of GDP for five years, raising the debt-to-GDP ratio by about 10 percentage points. Given a real interest rate that exceeds the output growth rate by 2 percentage points a year, the implied rise in debt-servicing costs amounts to about 0.2 percent of GDP and 1.0 percent of government spending. Accounting for this upward

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shift in the MC schedule, the effect is to lower future government spending by c/(c + b) multiplied by 0.2 percent of GDP, that is, by at most 0.2 percent of GDP. This is a very small starve-the-beast effect. Relaxing the assumption of exogenous output and allowing for tax cuts to stimulate growth yields an even smaller restraint on government spending. Since the example is similar in size to the largest tax cut episodes in the postwar U.S. experience, this analysis implies that tax cuts have not, through their effects on debt-servicing costs, significantly restrained government spending. It also implies that the mechanism is much too weak to be detected in a sample of postwar U.S. tax changes. Of course, the mechanism operates with greater force when there is a bigger rise in the debt-toGDP ratio or the government faces a higher real interest rate. In the postwar U.S. setting, however, the first mechanism has little force. Now consider the second mechanism. If tax cuts are hard to reverse for political or economic reasons, it is easy to see that they exercise more restraint on future government spending. Building on the previous example, if it takes 5 years for a new policymaker to reverse a previous tax cut, so that it remains in effect for 10 years rather than 5, the starve-thebeast effect roughly doubles. In the extreme case where tax cuts cannot be reversed, government spending cuts must eventually absorb the entire adjustment. Clearly, then, tax cuts can produce large starve-the-beast effects if they are sufficiently sticky. Thus, the force of the second mechanism depends on the difficulty of reversing tax cuts in practice. Romer and Romer address this issue in their section III.B. Figures 9 and 10 provide strong evidence that tax hikes usually follow in the wake of tax cuts motivated by long-run concerns. The final panel of figure 9 suggests that about three-quarters of the tax cut is reversed within five years, and it provides little evidence against the hypothesis of full reversal. This evidence, coupled with the analysis above, indicates that tax cuts of the sort that dominate the postwar U.S. experience are not sticky enough to generate large starve-the-beast effects. In short, neither mechanism operates with much force under the conditions that have prevailed in the postwar United States. This conclusion has important implications for economic policymaking and for models of fiscal behavior, as the authors discuss. However, the conclusion also has limited scope. In particular, it does not apply to tax changes or other fiscal policy actions that are hard to reverse. My remaining remarks develop this point. Most developed economies rely on a national value added tax (VAT) as a major source of government revenue. The United States is a large outlier

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in this respect. Many, perhaps most, economists look on the VAT with favor because of its broad tax base, ease of administration, and pro-saving incentive effects. These observations motivate many proposals to introduce a national VAT or other broad-based consumption tax in the United States. In contrast, Gary Becker and Casey Mulligan (2003), among others, question the desirability of introducing a broad-based consumption tax, which in their view would lead to substantial increases in federal spending. I share this view, and I see it as fully consistent with the evidence produced by Romer and Romer’s two-part empirical strategy and with my analysis of the mechanisms whereby tax cuts restrain government spending. Two observations are important in this regard. First, I expect that a new national consumption tax, once introduced, would be hard to reverse. In all likelihood, it would become a permanent feature of the U.S. fiscal landscape. In this respect, U.S. experience with “routine” tax changes in the postwar era is not a good guide to the reversibility of a new national consumption tax. Second, I agree with most other economists that the VAT and other broad-based consumption taxes rank highly on standard economic efficiency criteria. In addition, the VAT is less visible and less salient to taxpayers than the personal income tax and hence less likely to generate political pressure for lower taxes. For this reason, as well, the VAT generates lower marginal costs of government revenue as perceived by the policymaker. To parameterize the effects of introducing a broad-based consumption tax, rewrite the marginal cost schedule for government revenues as MC′ = 1 + (1 − γ ) cg. The new parameter γ captures the effect of introducing the VAT on the marginal cost of funds, again as perceived by the policymaker. Comparing outcomes under MC and MC′, it is easy to show that the introduction of a VAT increases the size of government by Δg b+c . = g b + (1 − γ ) c As an example, suppose γ = 0.2, which corresponds to a reduction in the marginal cost of funds from 1.5 to 1.4 with c = 0.5. Using the formula above and γ = 0.2, the introduction of a VAT causes government spending to rise by 25 percent when b = 0, and by 11 percent when b = c. Obviously, these are large effects on the size of government.

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There is certainly room to improve and deepen this analysis by embedding it in a fuller model and by grounding the choice of parameter values. The analysis is sufficient, however, to support two conclusions. First, there are good reasons to anticipate that the introduction of a national consumption tax would lead to a large expansion in the size of government. Second, this first conclusion is fully consistent with the evidence in this paper and with my analysis of the mechanisms that link current tax cuts to future government spending. As a final remark, it should be clear that a similar analysis applies to other new sources of government revenue that lower the marginal cost of government revenue from the perspective of policymakers. Cap-and-trade proposals to limit carbon emissions and other pollutants are a good case in point. These proposals have the potential to raise large amounts of government revenue in ways that are opaque to most taxpayers and that will make it easy for politicians to deflect the blame for higher energy costs onto energy producers, electric utilities, and others. These features of capand-trade proposals are likely to lower the marginal cost of government revenue from the perspective of policymakers and to lead to higher government spending as a result. REFERENCES FOR THE DAVIS COMMENT

Becker, Gary S., and Casey B. Mulligan. 2003. “Deadweight Costs and the Size of Government.” Journal of Law and Economics 46, no. 2 (October): 293–340. Romer, Christina D., and David H. Romer. 2009. “A Narrative Analysis of Postwar Tax Changes.” University of California, Berkeley (June).

COMMENT BY

JEFFREY A. MIRON I was delighted to be asked to discuss this paper, in part because I enjoy reading anything by Christina Romer and David Romer, and in part because I believe this is an important topic. Although I had not spent a significant amount of time thinking about the starve-thebeast hypothesis before taking up the paper, my hunch had always been that the standard version was probably correct. I think my gut instinct, however, came from thinking about the hypothesis in terms that are the reverse of the way Romer and Romer state it: that is, my guess was that if some event provides policymakers with additional tax revenue, they will spend it, not save it. If one assumes that the effect is symmetric, then the standard starve-the-beast conclusion follows. So, implicitly assuming symmetry, I took the hypothesis as at least plausible.

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The paper thus initially presented me with a dilemma, since I am hard pressed to think of a paper by either or both of these authors that I did not find convincing. In particular, I liked the precursor to this paper (Romer and Romer 2009), for two reasons. On the one hand, that paper made a solid case for their approach to identifying the effects of tax cuts. On the other, that paper’s result was consistent with my prior, which is that tax cuts should increase output because, on average, tax cuts mean lower tax rates, and that means improved incentives. My goal in reviewing the current paper, therefore, is to determine whether some aspect of their interpretation might not be the whole story, or whether instead my instincts about the starve-the-beast hypothesis were just wrong. In the end, my conclusion merges a bit of both possibilities. I will explain this by first discussing the aspects of the paper that I do not wish to dispute, and then by presenting a modified interpretation of certain key results that I think can reconcile their results and my priors. OVERALL EVALUATION. The first aspect of the paper that I do not wish to challenge is the authors’ strategy for identifying the effects of tax cuts. This is not to say that I regard that strategy as beyond all possible quibbling. For example, policymakers’ stated reasons for a particular tax change might differ from their actual reasons, and even their stated intentions might be ambiguous in some cases. Nevertheless, no approach to identification is beyond reproach. On the whole, I find the authors’ strategy far more convincing than most of those commonly used. The second aspect of their paper that I find myself unable to challenge is the thoroughness of their empirical investigation. That is, I have not identified ways in which some aspect of that analysis seems inappropriate or incomplete. On the contrary, every time I thought I had discovered a possible weakness, such as some alternative specification that might yield a different answer, I discovered a page or two later that they had already addressed the issue and that it did not make much difference to their overall results. One such issue might be worth mentioning, however, since I actually missed their treatment of it the first time through and therefore spent some effort, courtesy of their data, examining it on my own. I have long had the hunch that divided government (gridlock) might be a significant factor in slowing expenditure, reducing the deficit, and even improving output growth. I thought the authors’ failure to find a starve-the-beast effect might be due to omission of this factor. In fact, I could not find any gridlock effect, and the authors had in fact tested this hypothesis themselves and come to the same conclusion.

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So, given this assessment, it might seem that reconciliation of their results with my priors requires me to update my priors. That will be part of the resolution, but not the whole story. To show this, I will examine two specific results in more detail. INTERPRETING THE RESULTS ON LONG-RUN TAX CHANGES. The first of the authors’ results that I think bears additional scrutiny is their baseline result, reported in their table 1 and figure 2, which indicates that exogenous tax cuts (what they call long-run tax changes) do not appear to lead to reductions in expenditure. Indeed, the authors find mild evidence that these tax cuts lead to increased expenditure over the 5-year horizon, although this effect seems to disappear over the 10-year horizon (see their figure 3). A possibly relevant objection, however, is that virtually all the exogenous changes in taxes in their data are tax cuts, not tax increases. The top panel of their figure 1, which plots the exogenous tax variable, shows mainly decreases in taxes throughout the sample period, with only a few examples of increases. This makes sense, since Romer and Romer identify exogenous tax changes as those motivated by a desire to shrink government or improve incentives, and it is not obvious why these motivations would favor tax increases. One can confirm that their main result is dominated by the exogenous tax cuts rather than the exogenous tax increases by rerunning their baseline regression using only those tax changes that are decreases. Figure 1 below, which is virtually the same as their figure 2, shows the results. Tax cuts do not appear to starve the beast and may even feed it. So, given that their results are dominated by episodes of tax cuts, it is clear that they do not necessarily address my prior that a windfall tax increase might cause expenditure to increase. One could assume that the relationship is symmetric, in which case the latter proposition follows from the former, but there is no a priori reason why the effect has to be symmetric. Given sufficient observations on exogenous tax increases, one could examine the possibility of asymmetry directly. It seems unlikely that such an exercise would be fruitful in their dataset, however, because there are so few exogenous increases in their sample period. More generally, given the classification system they have used, it seems unlikely that one could ever examine this asymmetry, since it is not obvious that policymakers would ever announce that their intention is to make incentives worse. The bottom line on this first result is therefore the following: I take the authors’ result as convincing when stated as they state it, that is, that exogenous tax cuts do not starve the beast. The results are silent, however, on whether exogenous tax increases feed the beast.

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Figure 1. Cumulative Impact of a Tax Cut of 1 Percent of GDP on Total Expenditure, Sample Excluding Tax Increasesa Percent 8 6 4 2 0 –2 –4 –6 2

4

6

8 10 12 Quarters after tax change

14

16

18

Source: Author’s regression using data from Romer and Romer, this volume. a. The sample includes only those long-term tax changes identified by Romer and Romer that are tax decreases.

INTERPRETING THE RESULTS ON SPENDING-DRIVEN TAX CHANGES. The second result I want to consider in more detail is the finding that spending-driven tax cuts are followed by noticeable reductions in expenditure (see the panel labeled “Spending-driven tax changes” in the authors’ figure 6). Romer and Romer argue that this should not be taken as evidence in favor of the starve-the-beast hypothesis, because the correlation confounds a missing, unmeasured variable, namely, prior decisions to change spending. Such decisions plausibly move spending and taxes in the same direction, independent of any causal impact of taxes on spending. The authors’ argument for not regarding this as evidence for the starvethe-beast hypothesis is appropriate given the way that its advocates have typically stated the hypothesis, arguing that any tax cut is good because it helps shrink government. This view suggests an independent effect of tax cuts, but one can only estimate that effect by controlling for other factors, like antigovernment sentiment, that might also reduce spending. Again, however, it is useful to examine this result a bit more carefully, and to pose the question as the reverse of the way the authors present it. In their sample, most spending-driven tax changes are increases, not decreases

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Figure 2. Cumulative Impact of a Spending-Driven Tax Increase of 1 Percent of GDP on Total Expenditurea Percent

12 10 8 6 4 2 2

4

6

8 10 12 Quarters after tax change

14

16

18

Source: Author’s regression using data from Romer and Romer, this volume. a. Figure inverts the fourth panel in figure 6 in Romer and Romer, this volume, so that it depicts the effect of an increase rather than a decrease in taxes by 1 percent of GDP.

(again see their figure 1). Hence, their result is mainly saying that when taxes increase because policymakers want to increase spending, expenditure in fact goes up. Figure 2 above shows this explicitly simply by presenting the mirror image of the analogous graph in the paper. Even more important, this figure shows that for an expenditure-driven tax increase, expenditure increases by well more than one for one. Specifically, a tax cut of 1 percent of GDP equals about 5 percent of government spending, and the estimates suggest that even 20 quarters out, a spendingdriven tax increase of that magnitude raises government expenditure by 10 percent. Thus, the long-term increase in spending is about twice the initial increase in taxes. Why might this occur? The obvious explanation is that initial estimates of program costs are systematically below the eventual costs. Congress, for example, might systematically underestimate costs in order to get programs adopted, or political forces might lead to the expansion of programs once they have been adopted, whether or not the initial costs were fair estimates of the future costs. As a result, if the size of the tax increase was chosen to match the initial estimate of program costs, the actual costs incurred will

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far exceed the tax increase. Whatever the mechanism, the implication is that spending-driven tax increases feed the beast, or at least allow the beast to feed itself. Thus, my interpretation of these results is more nuanced than the authors’ interpretation. I agree with their assessment that exogenous tax cuts do not starve the beast. Their evidence would still appear to be consistent, however, with my prior and with the broader concern of small-government advocates, which is that when policymakers have ready access to tax revenue, they spend it. A simple story to account for this combination of results goes as follows. Politicians want to spend money because that helps them get reelected. The kind of spending they seek differs from politician to politician according to the political preferences of their districts, but logrolling and earmarking allow everyone to be happy when money is free and easy. Thus, if politicians are flush with cash, the temptation to spend is huge. If instead politicians are pushed to reduce spending, they resist, because they usually get more benefit from higher spending than from tax cuts, and so they find ways to raise taxes back up when they can. This simple “model” does not validate the claim that all tax cuts are good tax cuts because they starve the beast, but it does suggest that concerns over letting children play with matches—that is, giving politicians access to increased tax revenue— are valid. Thus, advocates of small government would seem to have good reason to oppose tax increases. HOW SHOULD ADVOCATES OF SMALL GOVERNMENT RESPOND TO THESE RESULTS?

One final issue is whether advocates of small government should be unhappy or happy with the authors’ results, taking them as correct. The fact that attempts to shrink government through tax cuts do not seem to work might at first blush strike small-government types as frustrating. Much of the citizenry has some interest in tax cuts, and politicians are sometimes interested in running on a tax-cutting platform, so this might appear an easy way to accomplish the goal of shrinking government, if the starve-the-beast hypothesis were correct. Further reflection, however, should make advocates of small government fully comfortable with these results. The cut-taxes-first approach is at some level dishonest; it tries to shrink government while avoiding discussion of the fact that lower taxes mean less government. Advocates of small government should pride themselves on being honest about their intentions and have confidence that their criticisms of government are sufficiently convincing to carry the day without resort to trickery. That means reducing government by debating specific policies and programs on their merits.

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The result that tax cuts are not sufficient to reduce government is also consistent with the view that institutional “tricks” are rarely successful at producing substantial and sustained changes in the way governments operate. Balanced-budget amendments are one such trick, but they founder on the fact that governments have access to innumerable accounting gimmicks for appearing to balance a budget while not really doing so (for example, by providing off-budget subsidies to Fannie Mae and Freddie Mac). Similarly, laws that allegedly establish central bank independence do not seem to bind in practice (Campillo and Miron 1997). This is not to say that institutions are irrelevant or to deny that having institutions that nudge in the right direction might help generate better outcomes. Institutions and tricks nevertheless do not seem to fundamentally change outcomes by themselves. Finally, advocates of small government need not shed their view that tax cuts are desirable. After all, the very same methodology that invalidates the starve-the-beast hypothesis also suggests that tax cuts stimulate output substantially. What advocates of tax cuts presumably should do, however, is focus their attention not on any and all tax cuts, independent of their merit, but instead on those tax cuts that make sense from an efficiency perspective. At the same time, they need to refocus their efforts on convincing the populace that government spending is too high. If they can do that, lowering taxes should be easy. REFERENCES FOR THE MIRON COMMENT

Campillo, Marta, and Jeffrey A. Miron. 1997. “Why Does Inflation Differ across Countries?” In Reducing Inflation: Motivation and Strategy, edited by Christina D. Romer and David H. Romer. University of Chicago Press. Romer, Christina D., and David H. Romer. 2009. “A Narrative Analysis of Postwar Tax Changes.” University of California, Berkeley (June).

GENERAL DISCUSSION George Perry suggested that the introduction of inflation indexing of income tax brackets about halfway through the authors’ sample period should have had a noticeable effect on spending if starve-the-beast effects were in fact important. In the years before indexing, politicians had the luxury of deciding what to do with the “fiscal dividend” that gradually arose. In the early 1960s, it provided fiscal room for a major tax cut without the need to restrain spending, whereas in the early 1970s it permitted an outrageous enhancement of Social Security benefits. Once tax brackets were indexed—a feature not captured by the authors’ tax cut measure—discretionary tax cuts or spending increases should have been more constrained, and if starve-the-beast effects were significant, they should

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have been more evident in this period. That they were not strengthens the authors’ results. Robert Shiller questioned the paper’s implicit assumption that the starve-the-beast impulse takes the same form in all periods, suggesting instead that it was a Reagan invention. He noted that the largest long-run tax cut other than Reagan’s during the sample period came in 1948 and could be attributed to postwar demobilization. The subsequent increase in spending could be explained by the Korean War. Both factors might offset the paper’s results. Robert Hall argued for analyzing the relationship between spending and taxation in the context of the level of U.S. national debt. Unlike some European countries whose debt is large enough to be in danger of falling below investment grade, the United States has maintained a persistently low debt-to-GDP ratio and a credit rating well above triple-A. Spending could indeed be much higher than it is, given the fiscal headroom provided by a small national debt. He suggested that a factor that contributes to keeping spending low in the United States but not in European countries is the former’s racial and ethnic diversity, which may discourage spending on social programs if such spending tends to favor one group over another. Benjamin Friedman agreed with Hall and with Steven Davis that the level of the national debt should be included in the analysis, and he proposed another, related factor to consider, namely, the relationship between the interest rate on the debt and the growth rate of the economy. Although the theoretical literature assumes that the real interest rate will exceed the real growth rate, the opposite was true during most of the authors’ sample period. If the economy grows at a rate above the real interest rate, the ratio of the national debt to GDP will decline over time, weakening the tax burden argument that underlies the supposed starve-the-beast mechanism. Caroline Hoxby noted that reducing the standard errors on the paper’s main findings would be challenging given that there are essentially only four observations of the long-run tax cut variable. She also observed that testing the starve-the-beast hypothesis becomes nearly impossible if reductions in top marginal tax rates increase the rate of economic growth. Increased growth brings increased tax revenue without an increase in tax rates. For example, during the Reagan years marginal tax rates fell yet revenue increased significantly. Matthew Shapiro stated that even though he found the paper’s narrative believable, it did not match his understanding of the stylized facts. Around 1980 the U.S. political economy changed from one in which the debt-toGDP ratio was steadily declining to one where, except during the Clinton

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administration, the debt-to-GDP ratio has been generally increasing. The fiscal restraint of the first six years of the Clinton administration clearly arose in part because of concern about inherited deficits. He wondered why the authors’ regressions did not pick up these broad trends. Two possible reasons were, first, that the lags used are too short, and second, the difficulty in inferring effects from time series that consist of only a small number of very persistent policy episodes. Ricardo Reis remarked that although he appreciated the virtue of focusing on long-run tax cuts, given their exogenous nature, he worried that they are not representative of tax cuts in general. He suggested looking at the substance of tax cuts, in addition to their motivation, to determine whether the long-run cuts are really representative. Reis also noted that long-run tax cuts have only long-run benefits and therefore tend not to create shortrun political advocates. As a result, these cuts are prone to reversal after a short while, with a change in administration or in the dominant ideology. Large, immediate cuts could avoid this problem and thus allow a starvethe-beast strategy a chance to force a correction of the resulting deficit through spending. Gregory Mankiw credited Robert Reich and Henning Bohn with making him sympathetic toward the starve-the-beast hypothesis. Reich’s book Locked in the Cabinet documents that the Clinton administration had had great spending plans but was prevented by the inherited Reagan-Bush budget deficits from carrying them out. However, the events in the book occurred roughly 12 years (48 quarters) after the Reagan tax cuts, a lag much longer than used in the paper and possibly beyond the capability of any econometric study. Henning Bohn’s 1991 paper in the Journal of Monetary Economics also comes to a very different conclusion than the authors, and Mankiw suggested that the authors address that paper directly and explain why they believe Bohn was wrong. Luigi Zingales agreed with Caroline Hoxby on the limitations imposed by using, in effect, only four observations. To get around this problem, he suggested looking at data from other countries with different levels of debt and different political constraints to determine whether a starve-the-beast strategy worked. Additionally, he noted that in corporate finance there is an analogy to the starve-the-beast hypothesis, namely, the free cash flow theory, which can be tested on micro rather than macro data and has found a lot of empirical support. Steven Davis agreed with Zingales but observed that extending the data internationally would entail a large amount of additional work. He also remarked that a desire to starve the beast could motivate many tax changes yet not significantly restrain spending. For example,

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a current policymaker might implement a deficit-financing tax cut to undo the strategic beast-starving efforts of its predecessor. If political power changes hands every few years, then strategic tax cuts with a starve-thebeast motive can be both frequent and largely ineffective. William Gale noted that the real-world experience in the United States since 1980 has been the opposite of what the starve-the-beast hypothesis predicts, unless a very long term story is told. The effect, if any, of tax changes on spending appears to be inverse: Ronald Reagan cut taxes and increased spending, Bill Clinton raised taxes and lowered spending, and George W. Bush cut taxes and raised spending again. Gale also cited a study he did with Brennan Kelly (published in Tax Notes in 2004) of the voting behavior of members of Congress who had signed the “no new taxes” pledge. That study found that among those who had signed the pledge, nearly all voted for the 2001 and 2003 tax cuts, 86 percent voted for Medicare Part D (the most expensive new federal entitlement in decades), and 90 percent voted for the pork-laden 2005 highway bill. Essentially, those who insisted that taxes must not be raised were the very people most willing to raise spending—evidence against the starve-the-beast hypothesis. Lastly, Gale suggested looking further into which tax features change in tax cuts and in subsequent tax increases. If the changes occur via marginal tax rates, which are cut first but end up rising later, that is inconsistent with optimal public finance theory, which shows that it is more efficient to keep tax rates constant than to shift them up and down. Henry Aaron cited several established facts of political economy that, in addition to the inflation indexing of tax brackets mentioned by Perry, would make it difficult to find any statistically significant effects from four relatively small events. The first is that government spending as a share of GDP has been nearly flat for the past 50 years. Thus, the data likely contain too little variation to allow any strong effect to emerge. Second, the composition of spending has, in contrast, changed drastically, and these changes would likely mask the effect of modest fiscal policy changes. For example, defense spending declined from over 10 percent of GDP at the time of the Korean War to only 3 percent at its lowest point in the late 1990s. Nondefense discretionary spending declined significantly during the Reagan administration and has continued to decline since then. These spending changes imply large shifts in the political consensus on government spending over time and make it unlikely that any real impact of small tax changes on total spending at different points in time could be detected.

JAMES D. HAMILTON University of California, San Diego

Causes and Consequences of the Oil Shock of 2007–08 ABSTRACT This paper explores similarities and differences between the run-up of oil prices in 2007–08 and earlier oil price shocks, looking at what caused these price increases and what effects they had on the economy. Whereas previous oil price shocks were primarily caused by physical disruptions of supply, the price run-up of 2007–08 was caused by strong demand confronting stagnating world production. Although the causes were different, the consequences for the economy appear to have been similar to those observed in earlier episodes, with significant effects on consumption spending and purchases of domestic automobiles in particular. Absent those declines, it is unlikely that the period 2007Q4–2008Q3 would have been characterized as one of recession for the United States. This episode should thus be added to the list of U.S. recessions to which oil prices appear to have made a material contribution.

T

he price of oil has been anything but stable over the last four decades (figure 1). A series of dramatic events in the 1970s sent the price of crude oil over $40 a barrel by the end of that decade, which would be over $100 a barrel at current prices. The price remained very volatile after the collapse in the 1980s but was still as low as $20 a barrel at the end of 2001. The next six years saw a steady increase that tripled the real price by the middle of 2007. Later that year the path of oil prices steepened sharply, sending the nominal price to an all-time high of $145 a barrel on July 3, 2008, only to be followed by an even more spectacular price collapse.1 What caused this remarkable behavior of oil prices, and what were the effects on the economy? To answer these questions, I begin by reviewing, in section I, some important characteristics of the demand for petroleum. Section II then 1. Crude oil prices quoted in this paper refer to the spot price of West Texas Intermediate (WTI) except where stated otherwise.

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Figure 1. Real Crude Oil Prices, January 1947 to December 2008a Constant dollars per barrel

120 100 80 60 40 20 1955

1965

1975

1985

1995

2005

Sources: Energy Information Administration (EIA) data; Bureau of Labor Statistics data. a. Monthly average price of West Texas Intermediate crude in dollars of November 2008.

explores the causes of several of the principal oil shocks of the late 20th century. I then turn, in section III, to an analysis of what happened to produce the dramatic price moves in 2007 and 2008. Next, section IV reviews some of the evidence on how the economy seemed to respond to earlier oil price shocks, and section V investigates the effects on the economy of the oil shock of 2007–08. Section VI briefly notes some implications for policy.

I. Some Observations on Petroleum Demand The single most important fact for understanding short-run changes in the price of oil is that income rather than price is the key determinant of the quantity demanded. One quick way to become convinced of this fact is to examine figure 2, which is essentially a scatterplot of U.S. petroleum consumption against GDP over the last 60 years, with successive years connected by a smoothed curve. Tracing this curve from lower left to upper right thus tracks the realized combinations of real GDP and petroleum consumption as they changed over time. As the figure shows, despite the huge fluctuations in the relative price of oil over this period, petroleum consumption followed income growth remarkably steadily, with an elasticity of about 1 through 1973. There was some downward adjustment in oil use

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Figure 2. Real GDP and Oil Consumption, United States, 1949–2007a Log of oil consumption relative to 1949 1.2 1.0 0.8

1961–73: slope = 1.04

1985–97: slope = 0.47

0.6 0.4

1949–61: slope = 1.2

0.2

0.25

0.50

0.75 1.00 1.25 Log of real GDP relative to 1949

1.50

1.75

Source: Hamilton (2009), using data from Bureau of Economic Analysis, National Income and Product Accounts, table 1.1.6, “Real Gross Domestic Product, Chained Dollars,” and EIA, “Petroleum Overview, 1949–2007,” table 5.1 (www.eia.doe.gov/emeu/aer/txt/ptb0501.html). a. Each observation plots for a single year the cumulative change in the logarithm of total petroleum products supplied to the U.S. market since 1949 against the cumulative change in the logarithm of real GDP since 1949.

between 1978 and 1982, although achieving that 20 percent (logarithmic) drop required an 80 percent (logarithmic) increase in the relative price of oil and two recessions in a three-year period (1980–82). The slope of this path flattens after the early 1970s to reflect an income elasticity nearer 0.5, a phenomenon that some might attribute to the delayed consequences of increased energy conservation following the 1970s oil shocks. However, this flatter slope persists long after the price has fallen quite dramatically, and it seems more likely to be due to a tendency for the income elasticity of oil consumption to decline as a country becomes more developed. One sees a similar pattern of slowing growth of petroleum use in other developed countries as they become richer, and post1990 data for several newly industrialized countries are still quite supportive of an income elasticity near unity (Hamilton 2009; Gately and Huntington 2002). Table 1 summarizes the estimated price elasticities for gasoline and crude oil demand from a half-dozen meta-analyses or literature reviews. Since crude oil represents about half the retail cost of gasoline (Energy Information Administration 2006), one would expect that a 10 percent

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Table 1. Literature Estimates of the Short-Run Price Elasticity of Demand for Gasoline and Crude Oil Study

Product

Method

Elasticity

Dahl and Sterner (1991) Espey (1998) Graham and Glaister (2004) Brons and others (2008) Dahl (1993) Cooper (2003)

Gasoline Gasoline Gasoline Gasoline Crude oil Crude oil

Literature survey Literature survey Literature survey Literature survey Literature surveya Annual time-series regression

0.26 0.26 0.25 0.34 0.07 0.05b

Source: Hamilton (2009). a. Survey covers developing countries only. b. Average of 23 countries.

increase in the price of crude would be associated with a 5 percent increase in the price of gasoline,2 in which case the price elasticity of demand for crude oil should be about half that for retail gasoline. Most of the studies summarized in these reviews reported low estimates of the price elasticity of gasoline demand and significantly smaller elasticities for crude. The price elasticity of petroleum demand has always been small, and it is hard to avoid any conclusion other than that it became even smaller in the United States in the 2000s. One can barely detect any downward deviation from the trend in petroleum consumption in figure 2 for that period despite the enormous price increase through 2007. Jonathan Hughes, Christopher Knittel, and Daniel Sperling (2008) estimate that the short-run price elasticity of gasoline demand was (in absolute value) in the range of 0.21 to 0.34 over 1975–80, but between only 0.034 and 0.077 for 2001–06. Another key parameter for determining the consequences of an energy price increase is the value share of energy purchases in total expenditure. The fact that the U.S. income elasticity of demand has been substantially below unity over the last quarter century induces a downward trend in that share: for a given relative price, if the percentage growth in energy use is less than the percentage growth in income, total dollar expenditure on energy will decline as a percentage of income. On the other hand, the very low short-run price elasticity of demand causes the value share to move in

2. The regression coefficient relating the logarithm of the nominal U.S. gasoline retail price to the log of the nominal WTI price in a monthly cointegrating regression estimated over April 1993–August 2008 is 0.62. Data from Energy Information Administration (EIA), “Spot Prices for Crude Oil and Petroleum Products” (tonto.eia.doe.gov/dnav/pet/pet_pri_ spt_s1_m.htm).

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Figure 3. Consumer Energy Expenditure as a Share of Total Personal Consumption Expenditure, United States, January 1959 to September 2008 Percent

7.5

5.0

2.5

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Source: Bureau of Economic Analysis, National Economic Accounts, table 2.3.5U, “Personal Consumption Expenditures by Major Type of Product.”

the same direction as the relative price: if the percentage increase in price is greater than the percentage decrease in quantity demanded, dollar spending as a share of income will rise when the price of energy goes up. Figure 3 displays the net effect of these two factors on spending by consumers on energy goods and services, measured here as a percentage of total consumption spending. The income elasticity effect imparts a chronic downward trend, so that by 2002 energy purchases had fallen to a little over 4 percent of a typical consumer’s total budget. However, subsequent energy price increases produced a dramatic reversal of this trend, with the share in 2008 rising to almost twice the 2002 value. Figure 3 also serves as a reminder that a price elasticity cannot remain below unity in all circumstances. A consumer who fails to reduce the quantity purchased of an item by as much in percentage terms as its price goes up will find that the item comes to consume an ever-larger fraction of her budget. If her price elasticity were constant at less than unity, an arbitrarily large price increase would ultimately bring her to a point where 100 percent of her budget was going to energy, in which case ignoring the price would no longer be possible. The low energy expenditure share in the early part of this decade may be part of the explanation for why Americans largely ignored the early price increases: we didn’t change our behavior much because most of us could afford not to. By 2007–08, however, the

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situation had changed, as energy once again returned to an importance in the typical budget that had not been seen since the 1970s.

II. Causes of Past Oil Supply Disruptions Figure 4 plots monthly oil production for three Middle Eastern countries that have recurrently appeared in the news over the last 35 years. Three events over this period—the Iranian revolution in the fall of 1978, Iraq’s invasion of Iran in September 1980, and Iraq’s invasion of Kuwait in August 1990—resulted in dramatic and immediate disruption of the flow of oil from key global producers. Another episode, not evident in figure 4 but that I will nevertheless include in the set of historical oil shocks discussed, began with the Yom Kippur War on October 6, 1973. Although that conflict did not directly prevent any significant shipments of oil, the Organization of Arab Petroleum Exporting Countries (OAPEC) announced on October 17 that it would cut production by 5 percent until Israeli forces “are completely evacuated from all the Arab territories occupied in the June 1967 war, and the legitimate rights of the Palestinian people are restored.”3 In a previous paper (Hamilton 2003), I included the Suez crisis of 1956 as a fifth significant oil shock. However, the price increase in that episode was much more modest, and data for the kinds of comparisons performed below are not readily available for that period, so this paper will use the above four episodes only. Each row of graphs in figure 5 focuses on one of these four episodes. The four left-hand panels record the drop in oil production during each episode, for OAPEC as a group in the 1973–74 episode (top left panel) and for the producing country or countries most affected in the others. In each case the production shortfall is expressed as a percentage of total global production just before the shock.4 As the figure shows, each of these events removed between 6 and 9 percent of world supply. In each episode, increased production in other countries partially mitigated the consequences. To indicate the net consequences of the disruptions for global production, the left-hand panels of figure 5 also show the percentage decline in world oil production following each event. Production increases from other countries were rather minor in 1973–74 but quite substantial in 1990–91. 3. OAPEC ministers’ press release, as quoted in Al-Sowayegh (1984, p. 129). 4. These numbers differ slightly from those reported in table 4 of Hamilton (2003) because of small differences in the estimates of total global oil production used, and because here the Iranian shortfall is dated as beginning in October rather than September 1978.

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Figure 4. Oil Production of Selected Middle Eastern Countries, January 1973 to June 2007 Iran Millions of barrels a daya 6 5 4 3 2 1 1973

1977

1981

1985

1989

1993

1997

2001

2005

1993

1997

2001

2005

1997

2001

2005

Iraq 3.5 3.0 2.5 2.0 1.5 1.0 0.5 1973

1977

1981

1985

1989

Kuwait 3.5 3.0 2.5 2.0 1.5 1.0 0.5 1973

1977

1981

1985

1989

1993

Source: EIA, Monthly Energy Review, table 11.1a, “World Crude Oil Production: OPEC Members” (tonto.eia. doe.gov/merquery/mer_data.asp?table=T11.01a). a. Monthly averages, including lease condensate.

Figure 5. Cumulative Changes in World Oil Production, Prices, and Inventories after Four Major Disruptions Percent 1973–74 episode Change in world production 0 Global

–2 –4 –6

Due to OAPEC

–8

Change in pricea 60 50 40 30 20 10

0 2 4 6 8 10 12 Months after September 1973

Change in U.S. inventoriesb 0 –2 –4 –6

0 2 4 6 8 10 12 Months after September 1973

0 2 4 6 8 10 12 Months after September 1973

1978–79 episode Change in world production Global

0 –2 –4 –6

Due to Iran

–8 0

Change in pricea 60 50 40 30 20 10

2 4 6 8 10 12 Months after October 1978

Change in U.S. inventories 0 –2 –4 –6

0

2 4 6 8 10 12 Months after October 1978

0

2 4 6 8 10 12 Months after October 1978

1980–81 episode Change in world production Global

0 –2 –4

Due to Iran and Iraq

–6 –8

Change in pricea 60 50 40 30 20 10

0 2 4 6 8 10 12 Months after September 1980

Change in U.S. inventories 0 –2 –4 –6

0 2 4 6 8 10 12 Months after September 1980

0 2 4 6 8 10 12 Months after September 1980

1990–91 episode Change in world production Global

0 –2 –4

Due to Iraq and Kuwait

–6 –8 0

2 4 6 8 10 12 Months after July 1990

Change in pricea 60 50 40 30 20 10

Change in U.S. inventories 0 –2 –4 –6

0

2 4 6 8 10 12 Months after July 1990

0

2 4 6 8 10 12 Months after July 1990

Sources: EIA, Monthly Energy Review, tables 11.1a (tonto.eia.doe.gov/merquery/mer_data.asp?table= T11.01a) and 11.1b (tonto.eia.doe.gov/merquery/mer_data.asp?table=T11.01b); EIA, “Refiner Acquisition Cost of Crude Oil” (tonto.eia.doe.gov/dnav/pet/pet_pri_rac2_dcu_nus_m.htm); EIA, “Total Stocks” (tonto.eia.doe.gov/dnav/pet/pet_stoc_wstk_dcu_nus_m.htm); Bureau of Labor Statistics data; author’s calculations. a. For the 1973–74 episode, change relative to the indicated starting month in 100 × the log of the seasonally unadjusted producer price index for crude petroleum; for other episodes, cumulative change in 100 × the log of the monthly refiner acquisition cost for crude petroleum. b. In all episodes, monthly change in end-of-month U.S. stocks of crude oil and petroleum products, as a share of world production.

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Table 2. Quantity and Price Changes in Past Oil Shocks Episode October 1973–March 1974 November 1978–July 1979 October 1980–March 1981 August 1990–October 1990

Supply reduction (percent)a

Increase in price (percent)b

Implied price elasticity of demandc

4.0 1.3 1.2 2.9

41.3 38.7 25.8 71.6

0.10 0.03 0.05 0.04

Sources: EIA, Monthly Energy Review, table 11.1a (tonto.eia.doe.gov/FTPROOT/multifuel/mer/ 00350807.pdf); EIA, “Refiner Acquisition Cost of Crude Oil” (tonto.eia.doe.gov/dnav/pet/pet_pri_rac2_ dcu_nus_m.htm); Bureau of Labor Statistics data; author’s calculations. a. Average monthly shortfall of global production of crude petroleum over the episode as a share of global production in the month before the episode. b. Peak value during the episode of the cumulative change in price, calculated as 100 times the logarithm of the ratio of the current monthly refiner acquisition cost to the cost at the beginning of the episode. c. Ratio of the supply reduction to the change in price.

The contemporaneous path of oil prices is depicted in the middle column of panels in figure 5. Each episode was associated with a significant increase in the price of oil, for example by 25 percent (logarithmically) in 1980 and 70 percent in 1990. However, price controls in effect during the first three episodes spread the consequences over time. Lutz Kilian downplays the contribution of these supply disruptions to the price movements portrayed in figure 5, instead attributing much of the historical fluctuation in the price of oil to what he describes as “precautionary demand associated with market concerns about the availability of future oil supplies” (Kilian 2009, p. 1053). He identifies precautionary demand as any movement in the real price of oil that cannot be explained statistically by his measures of shocks to supply and aggregate demand. One might also try to measure the contribution of precautionary demand by looking at changes in inventories. The four right-hand panels of figure 5 record the monthly change in U.S. inventories of crude oil and petroleum products in each episode, again measured as a percentage of total global production. In each of these episodes, inventories were going down, not up, at the time of the sharpest price movements, suggesting that inventory changes were serving to mitigate rather than aggravate the price shocks. Positive inventory investment typically came much later, as firms sought to restock inventories that had been earlier drawn down. One can also explore whether the supply disruptions alone suffice to explain the observed price movements on the basis of plausible elasticities. Table 2 compares the average decline in global oil production during each of the four episodes with the observed price change and calculates the implied price elasticity of demand assuming zero shift in demand from

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growing income and no other influences on prices during the episode. These elasticities are a bit smaller than might have been expected from the consensus estimates in table 1, but none of them render it implausible to attribute most of the price change to the supply shortfall itself. Kilian (2009) also argues that the declines in individual-country (or OAPEC) production in the left-hand panels of figure 5 overstate the magnitude of the supply disruptions in these four episodes. He observes, for example, that Iraq increased production significantly in anticipation of both its war with Iran in 1980 and its invasion of Kuwait in 1990, so that measuring the decline in Iraqi production relative to levels just before the conflict overstates the shock (see the middle panel of figure 4). Note, however, that this is not a factor in the global production figures used in figure 5 or the calculations in table 2, which are based on the observed global decline after the indicated date. Moreover, despite high Iraqi production in the months before the 1980 war, global production in September 1980 was 2.9 percent below its level three months earlier and 5.4 percent below that of six months earlier. Likewise, global production in July 1990 was down 2.1 percent and 0.7 percent from its values three and six months earlier, respectively. Hence, a comparison of global production in these episodes with values earlier than the September 1980 and July 1990 reference dates used would have made the imputed quantity reductions in table 2 even more significant. Kilian (2009) and Robert Barsky and Kilian (2002) argue, quite correctly in my view, that demand pressures also contributed to higher oil prices in several of these episodes. In particular, it would be irresponsible to claim that the nominal oil price increase in 1973–74 had nothing to do with the general inflation and the boom in the prices of other commodities also observed at that time. Nevertheless, I share Alan Blinder and Jeremy Rudd’s (2008) doubts about whether inflationary pressures are the primary explanation for why OAPEC chose to reduce its oil output by 5 percent within weeks of the onset of the Yom Kippur War. My overall conclusion thus supports the conventional interpretation: the oil price shocks of past decades were primarily caused by significant disruptions in crude oil production brought about by largely exogenous geopolitical events.

III. Causes of the Oil Shock of 2007–08 Figure 6 plots five different measures of energy prices during the last quarter of 2007 and the first half of 2008. By any measure this episode qualifies as one of the biggest shocks to oil prices on record. However, the

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Figure 6. Five Measures of the Change in Energy Prices, August 2007 to June 2008 Percenta

60 50 PPI-oil 40

RAC

30

CPI-gasoline WTI price

20

PCE energy deflator

10

August

October 2007

December

February

April

June

2008

Sources: Bureau of Labor Statistics, series PW561 and PZU471, via Webstract; FRED, Federal Reserve Bank of St. Louis, series OILPRICE; EIA, “Refiner Acquisition Cost of Crude Oil” (tonto.eia.doe.gov/ dnav/pet/pet_pri_rac2_dcu_nus_m.htm); Bureau of Economic Analysis, series PINRGG3, via EconStats. a. Cumulative change in price, calculated as 100 × the logarithm of the ratio of the current value to the value in August 2007. PPI, producer price index for crude petroleum; WTI price, monthly average price of West Texas Intermediate crude oil; RAC, refiner’s acquisition cost for crude petroleum; CPI-gasoline, consumer price index for gasoline; PCE energy deflator, implicit price deflator for personal consumption expenditure on energy goods and services.

causes were quite different from those associated with the four episodes examined above.

III.A. Supply Despite occasional dramatic news, such as hurricanes in the Gulf of Mexico in September 2005, turmoil in Nigeria in 2006–08, and ongoing strife in Iraq, global oil production in the 2000s has been remarkably stable (figure 7). The big story has been not a dramatic reduction in supply of the kind summarized in figure 5, but rather a failure of production to increase between 2005 and 2007. Why did global production stagnate? In any given producing field, pressure eventually falls and daily output begins to decline. Increasing global production requires moving on to new producing areas. The United States’ endowment of crude oil has been extensively explored and developed, and total U.S. production is now about half what it was in 1971 (top panel of

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Figure 7. World Crude Oil Production, January 2003 to October 2008 Millions of barrels a daya

85

Monthly production 12-month moving averageb

80 75 70 65

2004

2005

2006

2007

2008

Source: EIA, “Total Oil Supply,” table 1.4 (www.eia.doe.gov/emeu/ipsr/t14.xls). a. Includes lease condensate, natural gas plant liquids, other liquids, and refinery processing gain. b. Average is centered at the indicated date, with end-of-sample values representing the average of {xt-6, . . . , xt+s} for feasible s.

figure 8). World production has nevertheless increased substantially since then as new fields have been developed. On the other hand, figure 8 also shows that several of the new fields are now in significant decline, including the North Sea (which accounted for 8 percent of world production in 2001) and Mexico’s Cantarell field (formerly the world’s second-largest producing field). Production declines caused former OPEC member Indonesia to become an oil importer; it dropped out of OPEC in 2008. The world’s most important oil exporter has for many years been Saudi Arabia, whose monthly production is plotted in the bottom panel of figure 8. Saudi oil output has historically been quite volatile, not because of depletion but because the Saudis followed a deliberate strategy of adjusting production in an effort to stabilize prices. For example, the kingdom’s decision to increase production sharply in late 1990 was a reason why the oil price shock of 1990 was so short-lived: increased Saudi output accounts for much of the early rebound in the bottom-left panel of figure 5. Because the Saudis had historically used their excess capacity to offset short-run supply shortfalls elsewhere, many analysts assumed that they would continue to do so in response to the longer-run pressure of growing world demand, and so most forecasts called for continuing increases

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Figure 8. Crude Oil Production from Selected Countries and Fields Millions of barrels a daya United States 8 6 4 2 1925

1935

1945

1955

1965

1975

1985

1995

2005

North Seab 5 4 3 2 1 1975

1980

1985

1990

1995

2000

2005

Cantarell field, Mexicoc 2.0 1.8 1.6 1.4 1.2 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Saudi Arabia 10 8 6 4 1975

1980

1985

1990

1995

2000

2005

Sources: Adapted from figures 11, 13, and 14 in Hamilton (2009), using data from EIA, Monthly Energy Review, tables 11.1a and 11.1b; Pemex, Statistical Yearbook 2007, “Oil Production by Region”; Green Car Congress, “Mexico’s Cantarell Continues Steep Decline in Oil Production in 2007” (www.greencarcongress.com/2008/01/mexicos-cantare.html). a. Monthly data except where noted otherwise. b. Moving average of the preceding 12 months of production by the United Kingdom and Norway. c. Annual data.

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in Saudi production over time. For example, the International Energy Agency’s World Energy Outlook 2007 was projecting that the Saudis would be pumping 12 million barrels a day (mbd) by 2010. In the event, Saudi production for 2007 was about 850,000 barrels a day lower than it had been for 2005. It is a matter of conjecture whether the decline in Saudi production should be attributed to depletion of the country’s Ghawar oilfield (the world’s largest), to a deliberate policy decision in response to a perceived decline in the price elasticity of demand, or to the long-run considerations discussed below. Whatever its cause, the decline was certainly one important factor contributing to the stagnation in world oil production over 2005–07. It also unambiguously marks the beginning of a new era for oil pricing dynamics: without the Saudis’ willingness or ability to adjust production to smooth out price changes, any disturbance to supply or demand will have a much larger effect on prices than in earlier periods.

III.B. Demand Even as global supply stagnated, global demand was growing strongly. Particularly noteworthy is oil consumption in China, which has been growing at a 7 percent compound annual rate (calculated logarithmically) since 1990 (figure 9). Chinese consumption in 2007 was 870,000 barrels a day higher than just two years earlier. Figure 9. Oil Consumption, China, 1980–2007 Millions of barrels a day 7 6 5 4 3 2 1 1985

1990

1995

2000

2005

Source: EIA, “World Petroleum Consumption, Most Recent Annual Estimates, 1980–2007” (www.eia.doe.gov/ emeu/international/RecentPetroleumConsumptionBarrelsperDay.xls).

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With global oil production flat, China’s increased consumption meant that consumption in other regions had to decline. U.S. consumption in 2007 was 122,000 barrels a day below its level in 2005; meanwhile Europe’s daily consumption dropped by 346,000 barrels and Japan’s by 318,000 barrels. What persuaded residents of these countries to reduce oil consumption in the face of rising incomes? The answer is a rising price of oil. Consider some ballpark estimates of the size of the price increase required to reduce consumption in the rest of the world by enough to accommodate China’s increased consumption. According to the International Monetary Fund (World Economic Outlook: October 2008, table A1), real gross world product grew by a total of 9.4 percent in 2004 and 2005. As noted above, the income elasticity of petroleum demand in countries like the United States is currently about 0.5, whereas in the newly industrialized countries it may be above unity (Hamilton 2009; Gately and Huntington 2002). World petroleum production, at 85 mbd, was 5 mbd higher in 2005 than in 2003, a 6 percent increase. Thus, it is entirely plausible to attribute the 6 percent increase in world oil consumption between 2003 and 2005 to a shift in the demand curve caused by growth in world income. Real gross world product grew an additional 10.1 percent in 2006 and 2007. Hence, it seems reasonable to suppose that if oil had remained at the 2005 price of $55 a barrel, the quantity demanded would have increased by at least another 5 mbd by the end of 2007. Economic growth slowed significantly in the first half of 2008 but remained positive, and one can conservatively assume that economic growth would have added at least another 0.5 mbd to the quantity demanded in that period, more than enough to absorb the slight increase in global oil production that finally appeared in the first half of 2008. Under these assumptions, the price had to rise between 2005 and the first half of 2008 by an amount sufficient to reduce the quantity that otherwise would have been demanded by 5 mbd, that is, to 85.5 mbd (top panel of figure 10). It is worth commenting on what was new about the contribution of Chinese and world economic growth over this period. Although China’s economy has been growing at a remarkable rate for a quarter century, it has only recently become big enough relative to the global economy to make a material difference. Compare, for example, the 4.9 percent average annual growth in world product achieved during 2004–07 with the 2.9 percent average over the 1990s, a period of robust growth but before China emerged as an important contributor. And judging from the reported gap

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Figure 10. Short-Run Price Determination in the World Oil Market, 2005–08a 2005–2008H1 Price (dollars a barrel) S2005 S2008H1

142 55 D2008H1 (ε = 0.06) D2005 85 90.5 85.5

Quantity (mbd)

2008H1–2008H2 Price (dollars a barrel) S2008H1 S2008H2

142 40 D2008H1 (ε = 0.06) D2008H2 (ε = 0.10) 85.5 86

Quantity (mbd)

Source: Author’s model described in the text. a. Depictions of slopes are exaggerated for clarity. H1 and H2, first and second half of year.

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between China’s total petroleum production and its consumption,5 China was a net exporter of petroleum up through 1992, and its imports were still only 800,000 barrels a day in 1998. By 2007, however, China’s net petroleum imports were estimated to be 3.7 mbd, making it the world’s third-largest importer and a dominant factor in world markets. Global growth in petroleum demand in recent years is thus quite exceptional. And although there have been other episodes when global oil production stagnated over a two-year period, these were inevitably either responses to falling demand during recessions or the physical supply disruptions detailed above. Although figure 10 is drawn with vertical short-run supply curves, the analysis here does not require any particular assumptions about the shortrun supply elasticity. I simply take it as an observed fact that as a result of some combination of shifts of or movements along the short-run supply curve, the quantity supplied in the first half of 2008 was essentially the same as in 2005, and that the price and output for each date represent an intersection of supply and demand. The above exercise simply explores the necessary adjustments if the strong growth of gross world product between the two periods is presumed to have shifted the demand curve to the right by 5.5 mbd. The question is then, What price increase would have been necessary to move the quantity demanded along that second demand curve to a point where it would have been as low as 85.5 mbd? The answer depends, of course, on the slope of that second demand curve. If, for illustration, the short-run price elasticity of demand were ε = 0.06, then the price would have been predicted to rise to $142 a barrel under the above scenario: ε=

Δ ln Q ln 90.5 − ln 85.5 = = 0.06. Δ ln P ln 142 − ln 55

On the other hand, such numerical calculations are extremely sensitive to the assumption one makes about the elasticity. If instead the elasticity were ε = 0.10, the price would only have needed to rise to $97 a barrel to prevent global quantity demanded from increasing. 5. EIA, “World Petroleum Consumption, Most Recent Annual Estimates, 1980–2007” (www.eia.doe.gov/emeu/international/RecentPetroleumConsumptionBarrelsperDay.xls) and “World Production of Crude Oil, NGPL, and Other Liquids, and Refinery Processing Gain, Most Recent Annual Estimates, 1980–2007” (www.eia.doe.gov/emeu/international/Recent TotalOilSupplyBarrelsperDay.xls).

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Figure 11. Crude Oil Stocks of U.S. Refiners, 2007, 2008, and Historical Averagea Millions of barrels 2007

350 300

Average 1990–2007

2008

250 200 Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Source: EIA, “U.S. Stocks of Crude Oil and Petroleum Products” (tonto.eia.doe.gov/dnav/pet/xls/pet_ stoc_wstk_dcu_nus_w.xls). a. Excludes the Strategic Petroleum Reserve.

Which is closer to the correct short-run elasticity, 0.06 or 0.10? Recalling tables 1 and 2, one could easily defend either value, or numbers significantly smaller or bigger. Moreover, as noted by Hughes, Knittel, and Sperling (2008), the relevant elasticity for 2007–08 could have been much smaller than those that governed other episodes. One key variable to help answer this question is the value of inventories. If the price increase between 2005 and the first half of 2008 was greater than needed to equate supply with demand, inventories should have been piling up, whereas if the price increase was too small, inventories would have been drawn down. Reliable data on worldwide stores of oil are lacking, but reasonably good measures are available on the inventories of crude oil held by U.S. refiners. Figure 11 plots the average seasonal pattern of these inventories, along with the actual values in 2007 and 2008. In the first half of 2007, inventories were a bit above trend. But in late 2007 and the first half of 2008, when the price increase was most dramatic, inventories were significantly below normal, suggesting that an assumed elasticity of 0.10 was too high and that the increase in price through the end of 2007 was not sufficient to bring quantity demanded down to equal quantity supplied. Just as academics debate the correct value for the price elasticity of crude oil demand, so do market participants. Many observers have wondered what could have been the nature of the news that sent the price of oil from $92 a barrel in December 2007 to its all-time high of $145 a barrel in July 2008. Clearly it is impossible to attribute much of this move to a major surprise, such as that economic growth in the first half of 2008

JAMES D. HAMILTON

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was faster than expected or that oil production gains were more modest than anticipated. The main uncertainty, I would argue, was over the value of ε. The big news of the first half of 2008 was the surprising observation that even $100 oil was not going to be sufficient to prevent global quantity demanded from increasing above 85.5 mbd, and that no more than 85.5 mbd was going to be available. This explanation of the price shock also requires that market participants could have had little inkling in the first half of 2008 of the massive economic deterioration just ahead. In this they certainly would have had good company. For example, here is the analysis offered publicly by European Central Bank president Jean-Claude Trichet on July 3, 2008:6 On the basis of our regular economic and monetary analyses, we decided at today’s meeting to increase the key ECB interest rates by 25 basis points. . . . [Inflation is] expected to remain well above the level consistent with price stability for a more protracted period than previously thought. . . . While the latest data confirm the expected weakening of real GDP growth in mid-2008 after exceptionally strong growth in the first quarter, the economic fundamentals of the euro area are sound.

And although a growth slowdown in the United States was certainly acknowledged at that point, many were unpersuaded that it would become serious enough to qualify as a true recession. Edward Leamer (2008, p. 29), for example, wrote in August that U.S. economic indicators would “have to get much worse to pass the recession threshold.” One may be able to rationalize the dramatic oil price spike of 2007–08 as a potentially appropriate response to fundamentals. But what about the even more dramatic subsequent collapse? Certainly Trichet, Leamer, and everyone else changed their minds about those assessments of real economic activity as the disastrous economic news of the fall of 2008 came in. But economic collapse alone is not a sufficient explanation for the magnitude of the oil price decline, if the analysis in the top panel of figure 10 is correct. Even a 10 percent drop in global economic activity would undo only the effects of the rightward shift of the demand curve since 2005. Bad as the news in the second half of 2008 was, it did not come close to that magnitude, yet the price of oil by the end of December was down to $40 a barrel, well below the 2005 price of $55. Instead, one would need to attribute a significant part of the price collapse to yet another shift in the elasticity. Whereas a short-run price elasticity of 0.06 might be needed to make sense of the developments of the first half of 2008, a 6. European Central Bank, “Introductory Statement with Q&A,” (www.ecb.int/press/ pressconf/2008/html/is080703.en.html, emphasis in original).

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higher intermediate-run elasticity, as petroleum users made delayed adjustments to the earlier price increases, has to be postulated as another factor contributing to the price decline in the second half of the year (bottom panel of figure 10). It is hardly controversial to suggest that the long-run demand responses to price increases are more significant than the short-run responses. The more-fuel-efficient vehicles sold in the spring and summer of 2008 are going to mean lower consumption, at least from those vehicles, for many years to come. The Energy Information Administration (EIA) reported that U.S. petroleum and petroleum products supplied in 2008Q3 were 9.6 percent lower (logarithmically) than in 2007Q3, a far larger drop in percentage terms than the presumed 6.3 percent rightward shift from the 2005 to the first-half 2008 world demand curve assumed in the top panel of figure 10, and again far in excess of anything attributable to the drop in income alone.

III.C. The Role of Speculation One can thus tell a story of the oil price shock and subsequent collapse that is driven solely by fundamentals. But the speed and magnitude of the price collapse lead one to give serious consideration to the alternative hypothesis that this episode represents a speculative price bubble that popped. One proponent of this view has been Michael Masters, manager of a private financial fund, who has been invited a number of times to testify before the U.S. Senate. Masters (2008) blames the oil price spike of 2007–08 on the actions of investors who bought oil not as a commodity to use but as a financial asset; he claims that by March 2008, commodity index trading funds held a quarter trillion dollars worth of futures contracts. A typical strategy is to take a long position in a near-term futures contract, sell it a few weeks before expiry, and use the proceeds to take a long position in a subsequent near-term futures contract. When commodity prices are rising, the sell price should be higher than the buy, and the investor can profit without ever physically taking delivery. As more investment funds sought to take positions in commodity futures contracts for this purpose, so that the number of buys of next contracts always exceeded the number of sells of expiring ones, the effect, Masters argues, was to drive up the futures price, and with it the spot price. This “financialization” of commodities, according to Masters, introduced a speculative bubble in the price of oil. The key intellectual challenge for such an explanation is to reconcile the proposed speculative price path with what was happening to the physical quantities of petroleum demanded and supplied. To be concrete about the nature of this challenge, consider a representative refiner who pur-

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chases a quantity Zt of crude oil at price Pt per barrel, of which Xt is used up in current production of gasoline and the remainder goes to increase inventories It: I t +1 = I t + Z t − X t .

(1)

This is simply an accounting identity: if the quantity of oil consumed by users (in this case, Xt) is smaller than the quantity physically produced (Zt), inventories must accumulate. If one hypothesizes that, by whatever process, financial speculation results in some particular price Pt, that price necessarily has implications for those who use the product (Xt) and those who produce it (Zt). It seems impossible to discuss a theory of price Pt that makes no reference to the physical quantities produced, consumed, or held in inventory. To explore this issue more fully, consider the following simple model. Suppose that the refiner produces a quantity of gasoline yt, which it sells at price Gt (where both Pt and Gt are measured in real terms), according to the production function yt = F(Xt, It). The second term reflects the idea that refiners cannot operate efficiently with zero inventories. A positive value for the derivative FI(Xt, It) introduces a “convenience yield” from inventories— in other words, a motive for the firm to hold a positive level of inventory even if it anticipates falling crude oil prices (Pt+1 < Pt). The refiner faces a real interest rate of rt and a cost of physically holding inventories C(It+1). The refiner’s objective is thus to choose {Zt, Xt, It+1}Nt=0 so as to maximize N

1

t=0

∏ (1 + r )

∑

t

τ =1

[G F ( X , I ) − C ( I ) − P Z ], t

t

t +1

t

t

t

τ

taking I0 and {Pt, Gt}Nt=0 as given. Note that I pose this as a perfect-foresight problem, since the complications introduced by uncertainty are not relevant for the points I want to make here, and liquid futures markets exist for Pt and Gt. The first-order conditions for this optimization problem are7 Gt FX ( X t , I t ) = Pt

(2) (3)

Pt + C ′ ( I t +1 ) =

1

(1 + r )

[G

t +1

FI ( X t +1 , I t +1 ) + Pt +11 ].

t

7. Specifically, the values of {Xt , Zt , It+1} Nt=0 are determined as functions of {Gt , Pt}Nt=0 from equation 2 for t = 0, . . . , N; equation 1 for t = 0, . . . , N; equation 3 for t = 0, . . . , N − 1, and the terminal condition IN+1 = 0.

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Equation 2 is the optimality condition associated with the firm purchasing one more barrel of crude oil whose marginal cost is Pt and using the crude immediately to refine and sell more gasoline, whose marginal benefit to the firm is GtFX (Xt, It). Equation 3 is the condition required for optimal inventory management. If the firm buys one more barrel of crude today to store as inventory, the marginal cost is Pt + C′(It+1). If the inventory is then used to reduce next period’s crude purchases, the discounted marginal benefit is (1 + rt)−1[Gt+1 FI(Xt+1, It+1) + Pt+1]. If the firm were to face an increase in price (Pt+1 > Pt), with all other prices remaining constant, it would respond by increasing It+1 until the condition given in equation 3 was restored. This plan would be implemented by increasing current crude purchases Zt and decreasing Zt+1. In the market equilibrium that I will finish spelling out shortly, that would put upward pressure on Pt and downward pressure on Pt+1. But it is interesting to comment here on the limiting case of a constant physical storage cost, C′(It+1) = h, and constant convenience yield, FI(Xt+1, It+1) = c, the latter including as a special case zero convenience yield, FI(Xt+1, It+1) = 0, that is, a situation where inventories are already so high that no sales gain would accrue from building them even higher. In this case equation 3 becomes (4)

Pt + h =

1 [G c + Pt +1 ]. (1 + rt ) t +1

In this limiting case, equation 4 becomes an equilibrium condition that would have to characterize the relationship between Pt and Pt+1 in any equilibrium with nonzero inventories. If, for example, the right-hand side of equation 4 exceeded the left, there would be an infinite increase in the demand for crude Zt and an infinite decrease in Zt+1, to which the equilibrium prices Pt and Pt+1 would have to respond until equation 4 was restored. More generally, if C′(It) and FI(Xt, It) are relatively flat functions of It, then the effect of equation 3 is to force Pt and Pt+1 to move closely together. In crude oil markets, the futures price corresponding to Pt+1 serves an information discovery role, any changes in which translate instantaneously into a corresponding movement in spot prices. For example, figure 12 plots f1d, the price of crude oil for the nearest-term futures contract on day d, and f3d, the price of oil for the futures contract expiring two months after the expiration of the contract associated with f1d. The two series move very closely together. On only 7 percent of the 6,421 business days between April 5,

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Figure 12. Prices of Oil Futures Contracts, 1983–2008 Dollars a barrel 140 120 100 80 Near month

60 40

3 months

20 1985

1990

1995

2000

2005

Source: EIA, “NYMEX Futures Prices” (tonto.eia.doe.gov/dnav/pet/pet_pri_fut_s1_d.htm).

1983, and November 12, 2008, were the changes Δf1d and Δf3d of opposite signs. A regression of Δ ln f3d on Δ ln f1d has an R2 of 0.86. Thus, this part of Masters’ claim—that if speculation affected the futures price, the spot price would be forced to move with it—is very much consistent with both theory and evidence. The model can be closed by specifying that crude oil is exogenously supplied, (5)

Zt = Zt ,

and that gasoline demand has a price elasticity of β: (6)

ln F ( X t , I t ) = γ − β ln Gt .

The system of equations 1–3, 5, and 6 then determines {Zt, Xt, It+1, Pt, Gt}Nt=0 – as functions of {Z t}Nt=0. Notice that if the marginal storage cost C′(It+1) is negligible, then equations 1–3 and 5 are homogeneous of degree 0 in {Pt, Gt}Nt=0. Without equation 6—that is, if there were no response of gasoline demand to the price of gasoline—the price of crude oil would be indeterminate. Suppose the market was initially in a situation where all five equations were satisfied, and consider the limiting case where the demand for gasoline is perfectly price inelastic (β = 0). Suppose now that for some reason speculators bid up the futures price of crude (Pt+1 increases). By inventory arbitrage (equation 3),

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Pt would have to rise with it. In this sense, one might claim to have a theory of how financial speculation in Pt+1 could be the determining factor in the price of oil. On the other hand, when the price elasticity β > 0, the above analysis no longer applies. In response to the hypothesized increase in Pt+1 and Pt, the price of gasoline Gt would rise (from equation 2), the quantity of gasoline demanded would decline, and the amount of crude Xt that must be used to meet this demand would be lower. An increase in Pt+1 and Pt induced by speculation would thus cause crude inventories It+1 to accumulate relative to the firm’s desired path. If the price elasticity is small but not zero, this feedback will be subtle, and it might conceivably take some time before mispricing arising from the futures markets would be recognized and corrected. It is interesting to note, however, that the same condition needed to rationalize a speculationbased interpretation of the oil shock of 2007–08—a very low price elasticity of oil demand—is exactly the same condition that allows the event to be attributed to the fundamentals alone. The other possible way in which advocates of the price bubble interpretation might attempt to reconcile their story with the physical side of the petroleum market is by hypothesizing a mechanism whereby the – quantity of oil supplied Z t is itself influenced by the futures price. Given the pressures for growth in petroleum demand from countries like China to continue, if it remains difficult to increase global production, the price pressures of 2008 are only the beginning of the story. Recalling the Hotelling (1931) principle, it would in this situation pay the owners of the resource to forgo current production, in order to be able to sell the oil at the higher future price. One might then hypothesize that oil-producing countries were misled by the speculative purchases of oil futures contracts – into reducing current production Z t in response, by this mechanism reconciling the postulated speculation with the physical dynamics of oil supply and demand (equation 1).8 If so, such miscalculation by oil producers could not have been based on comparing the longer-term futures price with the spot price in 2008. Figure 13 plots the term structure of prices implied by futures contracts on the New York Mercantile Exchange (NYMEX) at the price peak in July 2008. Prices of the very near term contracts sloped modestly upward (for example, the December 2008 contract sold for a higher price than

8. For further discussion see Jovanovic (2007).

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Figure 13. Term Structure of Oil Futures Contracts, July 2008a Dollars a barrel 146 145 144 143 142 141 Aug 2009

Aug 2010

Aug 2011

Source: Norma’s Historical Data. a. Closing prices on July 11, 2008, of NYMEX light sweet crude contracts for settlement in the indicated month.

that for August 2008), but that slope turned distinctly downward after the February 2009 contract, meaning that any producers who used the futures markets to sell their oil forward could expect to sell future production at a lower price than current production. This downward slope from early 2009 onward is inconsistent with a natural Hotelling interpretation of why producers might keep oil in the ground. One might argue instead that producers distrusted the futures markets or felt they could not use them as a significant hedge given the volumes involved. In any case, the high spot price in 2008 meant that a country that had held off production from 2001 to 2008 would have been richly rewarded, and that experience might have persuaded some producers of the benefits of not producing at capacity in 2008 either. Of interest is this Reuters report from April 2008: Saudi Arabia’s King Abdullah said he had ordered some new oil discoveries left untapped to preserve oil wealth in the world’s top exporter for future generations, the official Saudi Press Agency (SPA) reported. “I keep no secret from you that when there were some new finds, I told them, ‘no, leave it in the ground, with grace from god, our children need it’,” King Abdullah said in remarks made late on Saturday, SPA said.9

9. Reuters News Service. “Saudi King Says Keeping Some Oil Finds for Future,” April 13, 2008 (uk.reuters.com/article/oilRpt/idUKL139687720080413).

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With hindsight, it is hard to deny that the price of oil rose too high in July 2008 and that this miscalculation was influenced in part by the flow of investment dollars into commodity futures contracts. It is worth emphasizing, however, that the two key ingredients needed to make such a story coherent—a low price elasticity of demand, and the failure of physical production to increase—are the same key elements of an explanation of the same phenomenon based only on fundamentals. I therefore conclude that these two factors, rather than speculation per se, should be construed as the primary cause of the oil shock of 2007–08. Certainly the casual conclusion that one might have drawn from glancing at figure 1 and hearing some of the accounts of speculation10—that it was all just a mistake, and the price should have stayed at $50 a barrel throughout 2005–08—would be profoundly in error.

IV. Consequences of Past Oil Shocks In essentially any theoretical model of the economic effects of a change in oil prices, a key parameter is the dollar value of energy purchases as a share of total costs, such as the consumption spending share plotted in figure 3. To see why this is a key parameter, consider a firm producing output Yt with inputs of capital Kt, labor Nt, and energy Et. Suppose that the firm is operating at a point where the marginal product of energy is equal to its relative price: (7)

∂F ( K t , N t , Et ) = Pt . ∂Et

Multiplying both sides of equation 7 by Et /F(Kt , Nt, Et) establishes that the elasticity of output with respect to energy is given by the value share, ∂ ln F ( K t , N t , Et ) = αt ∂ ln Et for αt = Pt Et /F(Kt , Nt , Et). Alternatively, consider a consumer facing a π percent increase in the relative price of energy. One short-run option for the

10. Such a conclusion is implied, for example, by the Obama campaign’s website in June 2008, which included a number of quotes from analysts such as Shell President John Hofmeister that the proper range of crude oil is “somewhere between $35 and $65 a barrel.” See the weblog Econbrowser, “How Big a Contribution Could Oil Speculation Be Making?” (www.econbrowser.com/archives/2008/06/how_big_a_contr.html) for details.

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consumer (and indeed, given the empirical evidence reviewed above, not a bad approximation to what actually happens) is to continue to purchase the same quantity of energy as before. If income cannot be increased, this would require that the consumer either reduce saving or cut spending on other items. If αt denotes the consumer’s energy expenditure share, the requisite percentage cut in spending on other items is given by αtπ. A large literature has investigated the economic consequences of previous oil price shocks. Recent refinements include investigations of the following: nonlinearity in the relationship, with oil price increases possibly having a bigger effect than oil price decreases (for example, Hamilton 2003); differences in the causes, with price increases brought about by surging global demand possibly having less of a disruptive effect than those caused by losses in supply (for example, Kilian 2009); and a changing relationship over time, such that the economy is more resilient to an oil price shock today than in the past (for example, Blanchard and Galí 2008). Although these issues are unquestionably quite important, it is useful to look first at some simple linear representations of the basic correlations in the historical data, with a minor automatic adjustment for one source of a possible changing impact over time, namely, changes in αt. This is the approach taken by Edelstein and Kilian (2007), who estimate monthly bivariate autoregressions of the form 6

6

s =1

s =1

6

6

s =1

s =1

x t = k1 + ∑ φ11 x t − s + ∑ φ12 yt − s + ε1t yt = k2 + ∑ φ21 x t − s + ∑ φ22 yt − s + ε 2 t , where yt is a macroeconomic variable of interest and xt is the change in relative energy prices weighted by the expenditure share, x t = α t ( ln Pt − ln Pt −1 ) , αt is the series plotted in figure 3, and Pt the ratio of the personal consumption expenditure (PCE) deflator for energy goods and services to the overall PCE deflator. Thus, for example, a unit shock to xt would result from a monthly 20 percent increase in relative energy prices (ln Pt − ln Pt−1 = 0.20) at a time when energy consumes 5 percent of household budgets (αt = 5.0). A unit shock to xt means that households would suffer a 1 percent loss in their ability to purchase nonenergy items if they attempted to hold real energy consumption fixed following a shock of size xt = 1.

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I reestimated a number of the Edelstein-Kilian regressions for the sample period they used (with the dependent variable running from July 1970 through July 2006). I begin by reporting the results for yt = 100(ln Yt − ln Yt−1), where Yt is real PCE. The top two panels in figure 14 reproduce their orthogonalized impulse-response functions (with energy prices xt ordered first) for the cumulative consequences for the cumulative price change (Xt = Σtj=1 xj , in the left panel) and for real PCE (100 ln Yt, in the right panel) of a unit shock to xt−s. The left panel shows that there is relatively little serial correlation in the energy price change series xt. Almost all of the price consequences appear within the first two months: if xt increases by one unit at time t, one would typically expect another 0.5-unit move up at t + 1, with very minor adjustments thereafter, resulting in an eventual 1.7 percent cumulative loss in purchasing power. Two aspects of the right panel differ from what one would have expected given the simple expenditure impact sketched above. The first is the magnitude of the real PCE response: following a decline that eventually would have reduced consumers’ ability to purchase nonenergy items by 1.7 percent, consumers on average eventually cut their spending by 2.2 percent. Why should consumption spending fall by even more than the predicted upper bound? The second surprising aspect concerns timing: although the change in price immediately reduces purchasing power, the biggest declines in total spending do not appear until six months or more after the initial shock. One way that Edelstein and Kilian seek to explain these anomalies is by breaking down the responses into the various components of consumption. The three middle panels of figure 14 reproduce their findings for the services, nondurables, and durables components of real PCE. The first two responses are in line with the simple expenditure share effects, but the response of expenditure on durable goods is five times as large. The bottom left panel of figure 14 focuses on the motor vehicles component of durables consumption. In contrast to the gradual response seen in the broader consumption categories, here the response is immediate and huge: for example, a 20 percent increase in energy prices, given an energy expenditure share of 5 percent, would result in a 10 percent decrease in spending on motor vehicles within two months. That a direct link might exist between such spending and energy prices is quite plausible, although not simply through the budget constraint effect. For this category of spending a number of other factors are much more important, such as consumers postponing purchases of new vehicles until better information about future

Figure 14. Responses to an Increase in Energy Prices That Reduces Disposable Income by 1 Percenta Response of cumulative price changeb Percent

Response of PCEc Percent

4

4

2

2

0

0

–2

–2

–4

–4 0

0

5 10 15 Months after price increase

5 10 15 Months after price increase

Response of servicesd Percent

Response of nondurables Response of durables Percent Percent

0 –2 –4 –6 –8 –10 –12 –14

0 –2 –4 –6 –8 –10 –12 –14 0

5 10 15 Months after price increase

0 –2 –4 –6 –8 –10 –12 –14 0

5 10 15 Months after price increase

0

5 10 15 Months after price increase

Response of motor vehicles and partse Percent

Response of consumer sentimentf Percent

0 –5 –10 –15 –20

0 –5 –10 –15 –20 0

5 10 15 Months after price increase

0

5 10 15 Months after price increase

Source: Author’s calculations. a. Estimates from bivariate vector autoregressions based on data for July 1970 to July 2006. Dashed lines indicate 95 percent confidence intervals. b. Response of Xt = ⌺ tj=1 xj to a one-unit shock to xt–s plotted as a function of s. c. Response of 100 × the logarithm of real personal consumption expenditure at time t to a one-unit shock to xt–s plotted as a function of s. Reproduces (with renormalization) figure 8a in Edelstein and Kilian (2007). d. Panels in this row reproduce (with renormalization) figure 8b-d in Edelstein and Kilian (2007). e. Panels in this row reproduce (with renormalization) figures 8e and 11a in Edelstein and Kilian (2007). See also footnote 10 in the text. f. Unlike in the other panels, in which the second variable in the vector autoregression, yt = 100(ln Yt – ln Yt–1), represents a rate of change (with the impulse response graphs subsequently translated back into implications for the levels 100 ln Yt), here the variable yt is the index of consumer sentiment itself (from the University of Michigan/Reuters), and the graph shows the consequences for yt+s following a unit shock to xt.

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gas prices is available and shifting purchases to more-fuel-efficient (and perhaps less expensive) vehicles. If one takes as given that there are large and immediate effects of oil price changes on purchases of certain items such as motor vehicles, both the delayed response and the multiplier effect on other categories of spending can be better understood. The shift in spending means a reduction in income for those employed in manufacturing and selling cars. Given the significant technological frictions in relocating the now-underutilized labor and capital to other sectors, the result is a decline in aggregate income and a loss in purchasing power over and above that caused by the initial price increase itself (Hamilton 1988). The bottom right panel of figure 14 presents a second effect identified by Edelstein and Kilian that is also huge and immediate, namely, a drop in consumer sentiment. For whatever reason, consumers found the oil shocks of earlier decades to be very troubling events, with a 20 percent increase in relative energy prices (assuming again a base case value share of αt = 5) producing on average a 15-point drop in the index of consumer sentiment. One can argue whether a response of this magnitude is rational given the size of the shock. However, the budget consequences of spiking gasoline prices are something consumers experience immediately, and such a spike represents an aggregate event that forces everybody to make changes at the same time. Certainly for those whose jobs are related to the automobile industry (and for those who perceive that what happens in that industry will have eventual implications for their own job security), it is quite rational to view these events as carrying pessimistic implications beyond the immediate loss in spending power. In any case, the changes in sentiment observed in these data could easily have made a significant contribution to the subsequent path of both consumption and investment spending. The bottom left panel of figure 14 showed the narrowest effect of the energy price shock, namely, the change in spending on motor vehicles and parts. How big a contribution would this alone have made to the subsequent economic downturns, ignoring any possible multiplier effects? The first column of the top panel of table 3 reports the actual average growth rate of real GDP over the five quarters following each of the four historical oil shocks discussed above. GDP fell over each of these four periods, and all four are included in the list of U.S. recessions. The second column reports results of a very simple calculation: what would average GDP growth have been if there had been zero change in the motor vehicles and parts component of GDP over these five quarters, with all other components of GDP

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Table 3. Growth of Real GDP in Oil Shock Episodes under Alternative Scenarios Percent a yeara Without oil shockc Actual

Without automobilesb

BlanchardGalí (2008)

Hamilton (2003)

Pre-2000 recessions 1974Q1–1975Q1 1979Q2–1980Q2 1981Q2–1982Q2 1990Q3–1991Q3

−2.5 −0.4 −1.5 −0.1

−2.0 +0.4 −1.3 +0.2

−0.1 +0.4 −2.0 +0.5

+2.3 +2.5 +2.0 +3.6

2007–08 recession 2007Q4–2008Q3 2007Q4–2008Q4

+0.7 −0.7

+1.2 −0.0

+1.4 −0.2

+4.2 +3.2

Period

Sources: Blanchard and Galí (2008); Hamilton (2003); Bureau of Economic Analysis data; author’s calculations. a. All data are annual averages. b. Growth in real GDP excluding the contribution from motor vehicles and parts. c. See the text for the details of the computation.

staying the same as reported?11 Although motor vehicles and parts contributed modestly to the change in GDP (0.8 percentage point or less in any episode), it was enough to move the average from negative to positive territory in the cases of the 1979–80 and 1990–91 episodes. This offers some basis for thinking that without the significant downturn in the auto sector each of these two episodes might have been regarded as periods of sluggish growth rather than clear recessions. By contrast, in the more severe 1973–75 and 1981–82 recessions, clearly something more significant than just automobiles was bringing down the economy. I next examine the implications of two earlier studies of the overall effects of oil prices. The first is that by Olivier Blanchard and Jordi Galí (2008), who conclude that oil shocks made a relatively modest contribution to the downturns of the 1970s and are even less important today. Their analysis is based on a vector autoregression (VAR) that has three nominal shocks in addition to oil prices (as captured by the consumer price index, the GDP deflator, and wages) and two output indicators (GDP and total hours worked). All variables are quarterly percentage changes, with the oil price measured as the average price of West Texas Intermediate (WTI) crude oil over the quarter, and a quadratic time trend is included. The 11. This is calculated by subtracting from the growth rate of real GDP the contribution of motor vehicles and parts as reported by the Bureau of Economic Analysis (National Income and Product Accounts, table 1.5.2). Note that this contribution is a negative number in each episode, so that subtracting it raises the GDP growth rate.

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authors estimate two separate versions of the VAR, the first using data only from 1960Q1 to 1983Q1 and the second from 1984Q1 to 2007Q3. I used the VAR coefficients as estimated from the separate subsamples to perform the following calculation. First, I constructed from the estimated coefficients of the VAR a dynamic forecast for each of the six variables for each episode based on information available (that is, the observed values of the six variables) as of the quarter preceding the episode. I then compared this forecast with the ex post realized values of these variables to generate an implied set of forecast errors for each variable one to five quarters ahead. Finally, I decomposed these observed errors into contemporaneously orthogonal components, based on the variance-covariance matrix used by Blanchard and Galí, to arrive at an answer to the following question: what would be the prediction error for each of the variables up to five quarters ahead if one could condition on the ex post realizations of the innovations in oil prices but did not know anything else?12 On the basis of these numbers, I calculated what average GDP growth over 1974Q1–1975Q1 would have been had there been no oil price shock but the other five shocks—to the consumer price index, the GDP deflator, wages, GDP, and hours worked—had been identical to the realized historical residuals. The answer to that “what if” question is reported in the third column of table 3. The Blanchard-Galí estimates imply that had there been no oil shock, the severe downturn of 1974–75 would have been only a very mild recession. Interestingly, although their estimated post1984 effects of oil prices are much smaller than those for their earlier sample, and although the authors did not single out the aftermath of the 1990–91 Gulf War as a separate oil shock, their estimates also imply that had the price of oil not spiked following Iraq’s invasion of Kuwait, the United States might have avoided the 1990–91 recession. 12. The estimated VAR coefficients imply a set of moving-average matrices ψˆ s (as in equation 10.1.19 in Hamilton 1994), and the Cholesky factor of the residual variance ˆ = PˆPˆ′. The s-step-ahead forecast error can then be written yt+s − matrix can be obtained as ⍀ yˆt +s⎟ t−1 = εˆ t+s + ψˆ 1εˆ t+s−1 + . . . + ψˆ sεˆ t for εˆ t, the implied one-step-ahead forecast errors. Define the orthogonalized residuals by vˆ t = Pˆ−1εˆ t, and let pˆ 1 denote the first column of Pˆ. Then the s contribution of {vˆ1t, vˆ1,t+1, . . . , vˆ1,t+s} to this forecast error is calculated as Σ k=0 ψˆ k pˆ 1 vˆ 1,t+s−k, and what yt+s would have been in the absence of the oil shocks is calculated as yt+s − s Σk=0 ψˆ k pˆ 1vˆ1,t+s−k. Note that although the VAR shocks to oil prices and the consumer price index (CPI) are correlated in the data (εˆ 1t is correlated with εˆ 2t), the shocks vˆ1t and vˆ2t are orthogonal in the sample by construction. Thus, when asking what would have happened if vˆ1t had been zero but vˆ2t had been as observed historically, one is implicitly subtracting out that movement in the CPI that is correlated statistically with the oil price and leaving in other, uncorrelated factors.

JAMES D. HAMILTON

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Surprisingly, the Blanchard-Galí estimates imply that the 1981–82 downturn would actually have been more severe in the absence of disturbances to oil prices. The explanation is that their measure for the price of oil, the price of WTI, actually fell between July 1980 and March 1981. Other indicators suggest a very different story: the EIA’s series for the refiner acquisition cost (the series plotted in the middle panel of the third row in figure 5) shows a 27 percent (logarithmic) increase over this same period, the Bureau of Labor Statistics’ producer price index (PPI) for crude petroleum (the series used in Hamilton 1983 and 2003) shows a 42 percent increase, the Bureau of Economic Analysis’ implicit price deflator for consumption expenditure on energy goods and services (the series used by Edelstein and Kilian 2007) shows a 12 percent increase, and the Bureau of Labor Statistics’ (BLS) seasonally adjusted consumer price index for gasoline shows a 14 percent increase. It therefore seems likely that despite the results implied by Blanchard and Galí’s estimation, energy prices were a factor reducing GDP growth in this episode as in the others. For another comparison I turned to the nonlinear specification investigated in Hamilton (2003), where the key result (equation 3.8) was obtained from a regression of quarterly real GDP growth on a constant, four of its own lags, and four lags of the “net oil price increase,” defined as the percentage change in the crude oil PPI relative to its previous three-year high if oil prices made a new three-year high in quarter t, and zero if oil prices ended the quarter lower than at any point over the previous three years. The coefficients for that relationship, estimated over 1949Q2–2001Q3, were as follows: (8)

yt = 0(0..98 + 0(0..22 yt −1 + 0(0..10 yt − 2 − 0(0..08 yt − 3 − 0(0..15 yt − 4 13) 07 ) 07 ) 07 ) 07 ) ot#−1 − 0(.0021 ot#− 2 − 0(.0018 ot#− 3 − 0(.0042 ot#− 4 . − 0(.0024 .014 ) .014 ) .014 ) .014 )

To get a sense of the magnitudes implied by these coefficients,13 I calculated for each quarter in the episode the difference between the onequarter-ahead forecast implied by equation 8 and what that one-quarterahead forecast would have been if the oil price measure o#t−1, . . . , o#t−4 had instead been equal to zero, and I took this difference as a measure of the 13. One could in principle find the answer to an s-period-ahead forecasting equation as in the preceding footnote, although this would require a specification of the dynamic path followed by the net oil price increase variable. No such specification was proposed in Hamilton (2003), and it seems unlikely that spelling one out would change the results significantly from the simpler calculation reported here.

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contribution of the oil shock to that quarter’s real GDP growth. From the fourth column of table 3, it appears that this specification would attribute almost all of the deviation from trend in each of the four recessions to the oil shock alone. To summarize, a range of estimates exist of the contribution of oil shocks to past U.S. recessions. But even the most modest estimates support the claim that the oil shocks made a significant contribution in at least some of these episodes. My conclusion is that had the oil shocks not occurred, GDP would have grown rather than fallen in at least some of these episodes.

V. Consequences of the Oil Shock of 2007–08 This section explores the contribution that oil prices may have made to the most recent recession. I begin by examining what happened to motor vehicle sales in response to the price changes depicted in figure 6. Figure 15 reports sales in the United States of domestically manufactured light vehicles, both cars (top panel) and light trucks (bottom panel). The latter include sport utility vehicles (SUVs), which through 2007 were outselling cars in the U.S. market. Beginning in 2008, sales of SUVs began to plunge, and in May, June, and July they were down more than 25 percent from the same months a year earlier. SUV sales rebounded somewhat when gas prices began to fall in August, only to suffer a second hit in September through December. To what extent was the decline in SUV sales in the first half of 2008 caused by rising gasoline prices as opposed to falling income? One measure relevant for addressing this question is the contrast between sales of light trucks and those of cars. A general drop in income would affect both categories, whereas the effects of rising gasoline prices would hit light trucks much harder than cars. In the event, domestic car sales were down on average by only 7 percent in May, June, and July 2008 compared with the same months in 2007. Even more dramatic are the comparisons for imports. Imported cars were up 10 percent over these same three months (top panel of figure 16); sales of imported light trucks (bottom panel), by contrast, were down 22 percent. Thus, the dominant story in the first half of 2008 was one of American consumers switching from SUVs to smaller cars and more-fuel-efficient imports. Although gasoline prices were likely a key factor behind plunging sales for U.S. automakers in the first half of 2008, falling income appears to have been the biggest factor driving sales back down in the fourth quarter of 2008. Then, in contrast to the first half of 2008, the sales decline was

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Figure 15. Sales of Domestically Produced Cars and Light Trucks, by Month, United States, 2004–08a Cars Thousands

500 400 300 200 100 Jan

Feb

Mar

Apr

May

Jun

2005

2004

Jul

Aug

2006

Sep

Oct

Nov

Dec

2008

2007

Light trucks Thousands 900 800 700 600 500 400 300 200 100 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Source: WardsAuto.com, “Key Automotive Data” (wardsauto.com/keydata). a. All vehicles produced within North America and sold in the United States.

across the board, affecting cars if anything more than SUVs, and imports along with domestically produced vehicles. The result was a significant shock to the auto industry in the first half of 2008, quite comparable in magnitude to what was observed in the wake of the oil shock of 1990. The contribution of motor vehicles and parts to real GDP (measured in 2000 dollars at an annual rate) was $30 billion smaller

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Figure 16. Sales of Imported Cars and Light Trucks, by Month, United States, 2004–08a Cars Thousands

250 200 150 100 50 Jan

Feb

Mar

Apr

May

Jun

2005

2004

Jul

Aug

2006

Sep

Oct

Nov

Dec

2008

2007

Light trucks Thousands 150

100

50

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Source: WardsAuto.com, “Key Automotive Data” (wardsauto.com/keydata). a. All vehicles produced outside of North America and sold in the United States.

in 1991Q1 than it had been in 1990Q3, similar to the $35 billion decline in this sector between 2007Q4 and 2008Q2.14 Granted, that $35 billion in 2007–08 represents a smaller share of GDP than did the lost auto production in 1990–91, but it is still a sizable number, and without that contribution it would be hard to defend the claim that a recession began in 2007Q4. 14. Bureau of Economic Analysis, National Income and Product Accounts, table 1.5.6.

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The last two rows of table 3 include details on this breakdown, looking ahead either four or five quarters beginning with 2007Q4. Average real GDP growth in the four quarters 2007Q4–2008Q3 was actually +0.75 percent at an annual rate. Had there been no decline in automobile output, that number would have been nearly half a percentage point higher. It would be very hard to characterize 2007Q4–2008Q3 as a full year of recession had average growth indeed been +1.2 percent. The Business Cycle Dating Committee of the National Bureau of Economic Research (NBER) reports that it was looking not just at GDP (which even with the decline in automobiles showed clearly positive growth), but also at gross domestic income (GDI), which differs from GDP by only a statistical discrepancy (see Nalewaik 2007). Real GDI growth averaged −0.4 percent over this period,15 offering more justification for the NBER’s recession call. But without the decline in the auto sector, this number, too, would have been positive, albeit very small. The 2007–08 shock was also comparable to that of 1990–91 in terms of the effect on employment in the auto industry. Seasonally adjusted manufacturing employment in motor vehicles and parts fell by 94,000 between July 1990 and March 1991, compared with 132,000 between July 2007 and August 2008.16 Again the latter is relative to a larger economy, but again it is not an inconsequential number. A year-overyear drop in total employment is viewed by some as a defining characteristic of a U.S. recession. This threshold was crossed in May 2008, when 87,000 fewer workers were employed than in May 2007.17 Again without the contribution of automobiles, it would not be at all clear that the economy should have been characterized as being in recession during 2007Q4–2008Q2. Of course, the first half of 2008 saw not just a big decline in auto purchases but also a slowdown in overall consumer spending and a big drop in consumer sentiment, again very much consistent with what was observed after earlier oil shocks. Like SUV sales, consumer sentiment spiked back up dramatically in an initial response to falling gasoline prices at the end of the summer, but, like SUV sales, it then plunged back down as broader economic malaise developed in the fall of 2008. 15. Following Nalewaik (2007), I calculated real GDI by dividing nominal GDI (Bureau of Economic Analysis, National Income and Product Accounts, table 1.10) by the implicit GDP deflator. 16. Bureau of Labor Statistics, “Employment, Hours, and Earnings from the Current Employment Statistics Survey (National)” (series CES3133600101). 17. This is based on the BLS seasonally unadjusted establishment survey data.

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For some more formal statistical evidence and quantification, I turn to several of the studies described in the previous section. I first examine in table 4 how well the models proposed in previous studies perform against data that have become available since those papers were written. To evaluate the Edelstein-Kilian bivariate VARs, I used the parameter values for the relationships as estimated over July 1970 through July 2006 to form forecasts over the postsample interval August 2006 to September 2008. I then compared the postsample one-month-ahead mean squared errors (MSEs) with those that would have been obtained by a univariate autoregression (excluding energy prices) estimated over the same original sample (July 1970 through July 2006). As reported in the last column of table 4, for each of the six Edelstein-Kilian relationships used here, energy prices made a useful contribution to the postsample forecasts. This allows some confidence in using those estimates to measure the contribution that energy prices may have made to the economy’s response to the oil shock of 2007–08. I also used the Edelstein-Kilian relationships as estimated over July 1970 to July 2006 to form a 1- to 12-month-ahead forecast of how these variables might have behaved from September 2007 through September 2008 had there been no oil shock. The top panel of figure 17 presents the results for real PCE. In the absence of any new shocks, the EdelsteinKilian bivariate VAR would have predicted continued growth in PCE at the rate it had grown over the previous half year. In the event, consumption grew much more slowly than predicted through May and then started to decline. The figure also shows the portion of the forecast error at any date attributable to the cumulative surprises in energy prices between September 2007 and the indicated date, calculated as described above. Energy prices can account for about half of the gap between predicted and actual consumption spending over this period. The second and third panels repeat the exercise for the big drops in spending on motor vehicles and in consumer sentiment. Most of the declines in these two series through the beginning of 2008 and about half the decline through the summer of 2008 would be attributed to energy prices, according to the Edelstein-Kilian regressions. I also examined the postsample performance of the Blanchard-Galí VAR as estimated over their second subsample, 1984Q1–2007Q3. In this case I compared their six-variable VAR with a five-variable VAR that leaves out oil prices. Their model with oil prices in fact does somewhat worse at predicting GDP growth rates for data that became available after their study than would a similar VAR without oil prices (third row from the

1984Q1–2007Q3 1949Q2–2001Q3 1949Q2–2001Q3

Blanchard and Galí (2008) Hamilton (2003)

Source: Author’s calculations.

July 1970–July 2006

Sample period

Edelstein and Kilian (2007)

Study

2007Q4–2008Q4 2001Q4–2008Q4 2007Q1–2008Q4

Aug. 2006–Sept. 2008

Evaluation period Real PCE PCE services PCE nondurables PCE durables PCE automobiles Consumer sentiment Real GDP Real GDP Real GDP

Dependent variable

Table 4. Improvements in Postsample Mean Squared Errors Achieved by Alternative Models

AR(6) AR(6) AR(6) AR(6) AR(6) AR(6) VAR(5) AR(4) AR(4)

Comparison model

33 8 23 30 26 9 −11 0 45

Improvement (percent)

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Figure 17. Contribution of Energy Prices and Other Factors to Consumption Spending and Consumer Sentiment, United States, 2007–08 Real PCE

Log units × 100

Predicted given August 2007 data plus subsequent energy pricesa

1,596 1,595

Predicted given August 2007 datab

1,594 1,593

Contribution of energy price shocks

1,592 Actual

1,591 Sep

Dec

Mar

2006

Jun Sep 2007

Dec

Mar

Jun 2008

Sep

Jun 2008

Sep

Real PCE on motor vehicles and parts

Log units × 100 1,305 1,300 1,295 1,290 1,285 Sep Dec 2006

Mar

Jun Sep 2007

Dec

Mar

Univ. of Michigan/Reuters index of consumer sentiment Index, December 1964 = 100 95 90 85 80 75 70 65 60 Sep

Dec 2006

Mar

Jun Sep 2007

Dec

Mar

Jun 2008

Sep

Source: Author’s calculations. a. Forecast made on the basis of information available as of August 2007 plus innovations in the energy price measure over September 2007 through September 2008. b. Forecast made on the basis of information available as of August 2007.

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bottom of table 4). I nevertheless examined how much of the downturn of 2007–08 their coefficients would attribute to oil prices, looking at the errors made in forecasting GDP growth over 2007Q4–2008Q3 or −Q4 on the basis of information available as of 2007Q3, and at the contribution of oil price surprises to these forecast errors. The result of this calculation (third column of table 3) suggests that real GDP growth would have been 0.7 percentage point higher on average in the absence of the oil shock. Thus, the Blanchard-Galí calculations also support the conclusion that the period 2007Q4–2008Q3 would not reasonably be considered the beginning of a recession had there been no contribution from the oil shock. Finally, I looked at the postsample performance of the GDP forecasting regression (equation 8 above) estimated in Hamilton (2003). As seen in the next-to-last row of table 4, this relationship has about the same MSE over the period 2001Q4–2008Q4 as does a univariate autoregression fit to the 1949Q2–2001Q3 data. In part this lack of improvement is due to the fact that the oil-based relationship predicts slower GDP growth than was observed for 2005 and 2006, when the price of oil rose but the economy seemed little affected. It is interesting to note that the historical relationship (equation 8) significantly outperforms a univariate specification when evaluated on the same postsample intervals used to evaluate the Edelstein-Kilian and Blanchard-Galí relationships in table 4. In that case equation 8 achieves a 45 percent improvement in terms of the postsample MSE over the period 2007Q1–2008Q4 compared with a univariate autoregression. Indeed, the relationship could account for the entire downturn of 2007–08 (see the last column of table 3). If one could have known in advance what would happen to oil prices during 2007–08, and if one had used the historically estimated relationship to form a one- to five-quarter-ahead forecast of real GDP, looking ahead to 2007Q4–2008Q4 from 2007Q3, one could have predicted real GDP for both 2008Q3 and 2008Q4 quite accurately (figure 18). That last claim seems hard to believe, since Blanchard and Galí are doubtless correct that the effects of changes in oil prices have lessened somewhat as the economy has become less manufacturing based and more flexible, and given that the housing downturn surely made a critical contribution to the recession that began in 2007. Nevertheless, a few points about the separate contributions of housing and the oil shock deserve mentioning. First, housing had been exerting a significant drag on the economy before the oil shock, despite which economic growth continued. Residential fixed investment subtracted an average of 0.94 percentage point from

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Figure 18. Dynamic Forecasts of Real U.S. GDP from Information Available in 2007Q3 with and without Oil Price Data Available in 2007Q4–2008Q4 Log units × 100 941

Predicted from autoregressiona

940 939 938

Predicted given oil pricesb

937 936

Actual

935 934 Aug

Nov 2007

Feb

May

Aug

Nov

2008

Source: Author’s calculations. a. Dynamic forecast one to five quarters ahead based on coefficients from a univariate AR(4) regression estimated on data from 1949Q2 to 2001Q3 and applied to GDP data through 2007Q3. b. Dynamic conditional forecast one to five quarters ahead based on coefficients reported in equation 8 (which was estimated on data from 1949Q2 to 2001Q3) applied to GDP data through 2007Q3 and conditioning on the ex post realizations of the net oil price increase measure o#t+s for t + s = 2007Q4– 2008Q3.

annual real GDP growth over 2006Q4–2007Q3, when the economy was not in a recession, but only 0.89 percentage point over 2007Q4–2008Q3, which includes the beginning of the recession. At a minimum it is clear that something other than housing must have deteriorated to turn slow growth into a recession. That something, in my mind, includes the collapse in auto purchases, the slowdown in overall consumption spending, and deteriorating consumer sentiment, to which the oil shock indisputably contributed. Second, there is an interaction effect between the oil shock and the problems in housing. Joe Cortright (2008) notes that in the Chicago, Los Angeles, Pittsburgh, Portland (Oregon), and Tampa metropolitan statistical areas, home prices in 2007 were likely to rise slightly in the zip codes closest to the central urban areas but to fall significantly in zip codes with longer average commuting distances. Foreclosure rates also rose with distance from the center. And certainly to the extent that the oil shock made a direct contribution to lower income and higher unemployment, that would also depress housing demand. For example, the estimates in Hamilton

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(2008) imply that a 1-percentage-point reduction in real GDP growth translates into a 2.6 percent reduction in the demand for new homes. Eventually, the declines in income and home prices raised mortgage delinquency rates beyond a threshold at which the overall solvency of the financial system itself came into question, and the modest recession of 2007Q4–2008Q3 turned into a ferocious downturn in 2008Q4. Whether those events would have been avoided had the economy not gone into recession, or instead merely postponed, is a matter of conjecture. Regardless of how one answers that question, the evidence to me is persuasive that had there been no oil shock, economists today would be describing the economy in 2007Q4–2008Q3 as growing slowly but not in a recession. A final question worth examining is why the oil price increases before 2007Q4 failed to have a bigger effect on the economy. Why did consumers respond so little when the price of oil went from $41 a barrel in July 2004 to $65 in August 2005 (a 59 percent increase), yet so strongly to the 86 percent increase from $72 a barrel in August 2007 to $134 in June 2008?18 Equations posed in terms of percentage changes, such as equation 8, would predict that the 2004–05 price increases should also have had significant effects on output. Yet the dollar impact on household budgets of the $62-abarrel price increase in 2007–08 is considerably more than twice that of the $24-a-barrel increase in 2004–05. To explore this possibility more concretely, I looked at the consequences of modifying equation 8 to take into account the changes in the energy budget share over time, replacing o#t with the product ot#αt−1 for αt, the energy share plotted in figure 3.19 This results in a slight improvement in fit for the original sample period (t = 1949Q2–2001Q3), raising the log likelihood from −281.78 for the original specification to −281.47 for the new. The share-weighted regression performs significantly better postsample, producing a 10.8 percent improvement in the MSE over the period 2001Q4–2008Q4 relative to an autoregression with no role for oil prices. For the specific period 2005Q1–2006Q4, the modified specification as estimated over 1949Q2 to 2001Q3 would have predicted an average annual real GDP growth rate of 1.9 percent (calculated logarithmically), a bit below the sluggish 2.5 percent actually observed. Oil prices thus appear to have exerted a moderate drag on real GDP in 2005–06 and made a more significant negative contribution in 2007–08. 18. Oil prices quoted here are monthly averages of daily West Texas Intermediate prices. 19. The monthly series was converted to quarterly data by using only the third month of each quarter. Values for αt for quarters before 1959Q1 were simply set equal to the January 1959 value (7.354).

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The principal reason that Americans ignored the earlier price increases would seem to be that they could afford to do so. By 2007Q4 they no longer could, and the sharp spike in oil prices led to an observed economic response similar to that seen in earlier episodes.

VI. Policy Implications I have raised the possibility that miscalculation of the long-run price elasticity of oil demand by market participants was one factor in the oil shock of 2007–08, and that speculative investing in oil futures contracts may have contributed to that miscalculation. Were any policies available to mitigate the problems that this produced for the economy? One option would have been for the federal government to sell some oil directly out of the Strategic Petroleum Reserve (SPR) in the spring of 2008, perhaps timing the sales to coincide with expiry dates on NYMEX crude oil contracts. If there was speculative momentum buying, such a step might have succeeded in reversing it. At worst, the government would have made a profit on its SPR investment by buying low and selling high. A more conventional policy tool would have been monetary policy. A number of observers (for example, Frankel 2008) have suggested that the very rapid decline in short-term interest rates in 2008Q1 fanned the flames of commodity speculation, with negative real interest rates encouraging investments in physical commodities. In January 2009 Federal Reserve chairman Ben Bernanke offered the following retrospective on that debate: The [Federal Open Market] Committee’s aggressive monetary easing was not without risks. During the early phase of rate reductions, some observers expressed concern that these policy actions would stoke inflation. These concerns intensified as inflation reached high levels in mid-2008, mostly reflecting a surge in the prices of oil and other commodities. The Committee takes its responsibility to ensure price stability extremely seriously, and throughout this period it remained closely attuned to developments in inflation and inflation expectations. However, the Committee also maintained the view that the rapid rise in commodity prices in 2008 primarily reflected sharply increased demand for raw materials in emerging market economies, in combination with constraints on the supply of these materials, rather than general inflationary pressures. Committee members expected that, at some point, global economic growth would moderate, resulting in slower increases in the demand for commodities and a leveling out in their prices—as reflected, for example, in the pattern of futures market prices. As you know, commodity prices peaked during the summer and, rather than leveling out, have actually fallen dramatically with the weakening in global economic activity. As a consequence, overall inflation has already declined significantly and appears likely to moderate further. (Bernanke 2009)

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Bernanke seems here to be taking the position that since the Federal Reserve had been correct about the long run—that ultimately there would be a significant downturn in both the economy and commodity prices, creating strong disinflationary pressure—the short-run consequences (booming commodity prices in the first half of 2008) were less relevant. On the other hand, if indeed the spike in oil prices was one causal factor contributing to the downturn itself, the Federal Reserve can hardly afford to ignore those short-run implications. The evidence examined here suggests that the Federal Reserve needs to give careful consideration to the possible consequences of its actions for relative commodity prices. But although the question of the possible contributions of speculators and the Federal Reserve is an interesting one, it should not distract from the broader truth: some degree of significant oil price appreciation during 2007–08 was an inevitable consequence of booming demand and stagnant production. It is worth emphasizing that this is fundamentally a long-run problem, which has been resolved rather spectacularly for the time being by a collapse in the world economy. However, one may hope that this collapse will prove to be a short-run cure for the problem of excess energy demand. If growth in the newly industrialized countries resumes at its former pace, it will not be too many more years before we find ourselves back in the kind of calculus that was the driving factor behind the problem in the first place. Policymakers would be wise to focus on real options for addressing those long-run challenges, rather than blame what happened in 2008 entirely on a market aberration.

ACKNOWLEDGMENTS I am grateful to Alan Blinder, Lutz Kilian, participants at the Brookings Papers conference, and an anonymous referee for helpful comments on an earlier draft of this paper, and to Davide Debortoli for supplying the Blanchard-Galí data and code.

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References Al-Sowayegh, Abdulaziz H. 1984. Arab Petropolitics. London: Croom Helm. Barsky, Robert B., and Lutz Kilian. 2002. “Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative.” NBER Macroeconomics Annual 2001, vol. 16, pp. 137–83. Bernanke, Ben S. 2009. “The Crisis and the Policy Response.” Stamp Lecture, London School of Economics, January 13 (www.federalreserve.gov/newsevents/ speech/bernanke20090113a.htm). Blanchard, Olivier J., and Jordi Galí. 2008. “The Macroeconomic Effects of Oil Price Shocks: Why Are the 2000s So Different from the 1970s?” In International Dimensions of Monetary Policy, edited by Jordi Galí and Mark J. Gertler. University of Chicago Press. Blinder, Alan S., and Jeremy B. Rudd. 2008. “The Supply-Shock Explanation of the Great Stagflation Revisited.” Working Paper 14563. Cambridge, Mass.: National Bureau of Economic Research. Brons, Martijn, Peter Nijkamp, Eric Pels, and Piet Rietveld. 2008. “A MetaAnalysis of the Price Elasticity of Gasoline Demand: A SUR Approach.” Energy Economics 30, no. 5: 2105–22. Cooper, John C. B. 2003. “Price Elasticity of Demand for Crude Oil: Estimates for 23 Countries.” OPEC Review 27, no. 1: 1–8. Cortright, Joe. 2008. “Driven to the Brink: How the Gas Price Spike Popped the Housing Bubble and Devalued the Suburbs.” Discussion paper. Chicago: CEOs for Cities. www.ceosforcities.org/files/Driven%20to%20the%20Brink %20FINAL.pdf. Dahl, Carol A. 1993. “A Survey of Oil Demand Elasticities for Developing Countries.” OPEC Review 17, no. 4(Winter): 399–419. Dahl, Carol, and Thomas Sterner. 1991. “Analysing Gasoline Demand Elasticities: A Survey.” Energy Economics 13, no. 3: 203–10. Edelstein, Paul, and Lutz Kilian. 2007. “Retail Energy Prices and Consumer Expenditures.” Working paper. University of Michigan. Energy Information Administration. 2006. “A Primer on Gasoline Prices.” Washington (www.eia.doe.gov/pub/oil_gas/petroleum/analysis_publications/primer_ on_gasoline_prices/html/petbro.html). Espey, Molly. 1998. “Gasoline Demand Revisited: An International Meta-Analysis of Elasticities.” Energy Economics 20, no. 3: 273–95. Frankel, Jeffrey A. 2008. “The Effect of Monetary Policy on Real Commodity Prices.” In Asset Prices and Monetary Policy, edited by John Y. Campbell. University of Chicago Press. Gately, Dermot, and Hillard G. Huntington. 2002. “The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand.” Energy Journal 23, no. 1: 19–55. Graham, Daniel J., and Stephen Glaister. 2004. “Road Traffic Demand Elasticity Estimates: A Review.” Transport Reviews 24, no. 3: 261–74.

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Hamilton, James D. 1983. “Oil and the Macroeconomy since World War II.” Journal of Political Economy 91, no. 2: 228–48. ———. 1988. “A Neoclassical Model of Unemployment and the Business Cycle.” Journal of Political Economy 96, no. 3: 593–617. ———. 1994. Time Series Analysis. Princeton University Press. ———. 2003. “What Is an Oil Shock?” Journal of Econometrics 113, no. 2: 363–98. ———. 2008. “Daily Monetary Policy Shocks and New Home Sales.” Journal of Monetary Economics 55, no. 7: 1171–90. ———. 2009. “Understanding Crude Oil Prices.” Energy Journal 30, no. 2: 179–206. Hotelling, Harold. 1931. “The Economics of Exhaustible Resources.” Journal of Political Economy 39, no. 2: 137–75. Hughes, Jonathan E., Christopher R. Knittel, and Daniel Sperling. 2008. “Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand.” Energy Journal 29, no. 1: 113–34. Jovanovic, Boyan. 2007. “Bubbles in Prices of Exhaustible Resources.” Working Paper 13320. Cambridge, Mass.: National Bureau of Economic Research. Kilian, Lutz. 2008. “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?” Review of Economics and Statistics 90, no. 2: 216–40. ———. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review 99, no. 3: 1053–69. Leamer, Edward E. 2008. “What’s a Recession, Anyway?” Working Paper 14221. Cambridge, Mass.: National Bureau of Economic Research. Masters, Michael W. 2008. “Testimony of Michael W. Masters, Managing Member / Portfolio Manager, Masters Capital Management, LLC.” Testimony before the U.S. Senate Committee on Homeland Security and Governmental Affairs (hsgac.senate.gov/public/_files/052008Masters.pdf). Nalewaik, Jeremy J. 2007. “Estimating Probabilities of Recession in Real Time Using GDP and GDI.” Finance and Economics Discussion Series Working Paper 2007–07. Board of Governors of the Federal Reserve System, Division of Research and Statistics and Monetary Affairs.

Comments and Discussion COMMENT BY

ALAN S. BLINDER James Hamilton’s fine paper poses a question that seems especially appropriate, having been presented at this conference just a few days before Passover: Why was this oil price shock different from all other oil price shocks (if indeed it was)? For readers without a working knowledge of the Haggadah, the famous Four Questions posed at each seder are what we would now call (although the ancient rabbis did not) “bullet points” under one umbrella question: “Why is this night different from all other nights?” Analogously, I would phrase Hamilton’s umbrella question as, “Why was the 2002–08 oil shock so different from OPEC I and OPEC II?” Notice that I make the start date 2002, not 2007 as Hamilton does, for that is when the long escalation of the oil price began. I will discuss five subquestions under this umbrella question: —Was it because this shock was demand-driven rather than supplydriven? —Was it because this shock was gradual rather than sudden? —Was it because the U.S. economy is less energy-intensive now than it was then? —Was it because this shock was not accompanied by a large food price shock? or (most conjecturally) —Was it because the U.S. labor market has more wage flexibility now than it did then? As I proceed through this list, it will be apparent that I agree with Hamilton much more than I disagree with him. 262

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DEMAND- VERSUS SUPPLY-DRIVEN OIL SHOCKS. Many observers have emphasized the following apparently stark difference between the oil shocks of the 1970s and the oil shock of this decade:1 OPEC I and II were initiated by supply interruptions, in each case related to war in the Middle East, whereas the oil shock of 2002–08 was driven mainly by rapid growth of world demand, especially from China. This sounds like a fundamental difference. After all, demand shocks and supply shocks induce the opposite covariance between price and quantity. But Hamilton’s useful narrative prompts a reconsideration of this point. As he tells the story, OPEC I and II were supply disruptions greatly magnified by hoarding (which is a demand surge), whereas the 2002–08 run-up resulted from surging demand colliding with stagnant supply. Phrased that way, the two do not sound all that different. Besides, a demand-induced increase in world oil prices is a supply shock to an oil-importing country. So, if this oil shock really was different from the others, we need to look elsewhere for the reasons. Before doing so, one other point about the supply-versus-demand distinction should be made, largely for future reference. Many previous scholars have noted that the apparent macroeconomic impacts of oil shocks are far larger than the predicted neoclassical supply-side effects, which are based on factor substitution along a factor price frontier. The clear implication is that these oil shocks must also have had powerful Keynesian demand-side effects, for example through the “oil tax” draining consumer purchasing power. As Kilian (2008) points out, when strong world GDP growth pushes up the price of oil, oil-importing countries like the United States should also experience rapid export growth, which should offset part of the demandreducing effects of the oil shock. The table below, which comes straight from the National Income and Product Accounts (table 1.1.2), shows that this did indeed happen to the United States in the present decade:

2004 2005 2006 2007 2008

Contribution of net exports to real GDP growth (percentage points) −0.7 −0.2 0 +0.6 +1.4.

GRADUAL VERSUS SUDDEN. I believe economists started using the term supply shock back in late 1973 because of the extreme suddenness of the oil price rise. It is well known, and repeated by Hamilton, that price elasticities 1. I will refer to OPEC I and II as the “oil shocks of the 1970s” for convenience even though OPEC II continued into 1980.

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on both the demand and the supply sides of the oil market are much larger in the long run than in the short run, presumably because substitution (whether in consumption or production) is far more difficult in the short run. It follows that a sudden price shock, such as in 1973–74, is more disruptive than a gradual one, such as in 2002–08. In a Brookings Paper written while the 2002–08 shock was still in progress, William Nordhaus (2007, p. 227) calculated the size of the “oil tax” in that shock and three previous ones on an annualized flow basis:

1973Q3–1975Q4 1978Q4–1981Q1 1990Q3–1990Q4 2002Q4–2006Q2

“Oil tax” as a percent of GDP (annualized) 1.9 2.1 3.0 0.7

Looked at in this way, the most recent oil shock was only about one-third as severe as OPEC I and OPEC II because it developed so gradually. (The 1990 oil shock was the sharpest, but it lasted only one quarter.) That is, indeed, a big difference. No other long, gradual oil price shock like this can be found in the data. DECLINING ENERGY INTENSITY. It is well known that the U.S. economy (like others) uses far less energy per dollar of GDP now than it did before OPEC I. Figure 1, taken from my recent paper with Jeremy Rudd (2008), is one concrete depiction of that fact. The sharp decline in U.S. energy intensity (measured as thousands of BTUs consumed per dollar of real GDP) since the early 1970s is evident, as is the slowdown in that downtrend that began when oil became cheap again in 1985–86. Energy intensity by 2007 was roughly half what it had been in 1973. On this basis alone, the 2002–08 oil shock should have packed only half the punch of the 1970s oil shocks. This may be the only issue on which I disagree with Hamilton, who seems skeptical that “delayed consequences of increased energy conservation following the 1970s oil shocks” played a major role in reducing the demand for oil. The figure, it seems to me, highlights that this was an important difference between now and then. THE FORGOTTEN FOOD PRICE SHOCKS. I have noticed over the years that many economists have dismayingly selective memories when it comes to things that interfere with their favored story lines. In fact, there was another sort of supply shock in the 1970s. Large and durable food price shocks accompanied both OPEC I and OPEC II, but have since been mostly forgotten. Figure 2, which again comes from Blinder and Rudd (2008), shows the rate of change of the food component of the consumer

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Figure 1. Energy Intensity of the Economy, 1950–2007 Percent a year 14 12 10 8 6 4 2 0 1969

1971

1973

1975

1977

1979

1981

1983

1985

Source: Blinder and Rudd (2008). a. Annualized quarterly log differences of the food and beverages component of the personal consumption expenditure deflator.

price index (CPI) from 1968 through 1985. It is clear, as I emphasized in Blinder (1979, 1982), that major food price shocks—featuring sustained double-digit rates of increase—buffeted the economy in 1972–74 and again in 1978–80. The price run-ups were smaller than those for oil, but the weight of food in the CPI is much larger. Figure 2. Consumer Food Price Inflation, 1968–85a Thousands of BTUs per dollar of real GDP

18 16 14 12 10

1955

1960

1965

1970

1975

1980

1985

1990

1995

Source: Energy Information Administration, Annual Energy Review 2007, Table 1.5.

2000

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Much popular discussion at the time, and much econometric work since, failed to disentangle the inflationary effects of the food shocks from those of the oil shocks. In retrospect, I wonder whether economists attributed too much of the economy’s travails in 1973–74 and 1978–80 to the oil shocks, and not enough to the food shocks.2 This is a very important difference between the 1970s and the recent episode, because the recent run-up in oil prices was not accompanied by major food price inflation. In 2007 and 2008, the food component of the CPI rose 5 and 6 percent, respectively, on a December-to-December basis. MORE WAGE FLEXIBILITY TODAY? I come now to my final, and most speculative, possible reason why the most recent oil shock may have been different from all previous ones. Is there greater (real) wage flexibility today than in the 1970s? With flexible real wages, the economy will react to an oil price hike in a more neoclassical and less Keynesian fashion. And, as noted earlier, the neoclassical supply-side effects appear to be smaller than the Keynesian demand-side effects. So if wages have become more flexible, this should have muted the impact of more recent oil shocks on both output and inflation. Both Nordhaus (2007) and Olivier Blanchard and Jordi Galí (2007) offer shreds of evidence to suggest that this is so. DATING THE RECESSION. I conclude with one last, interesting question raised by Hamilton, even though it is not part of the traditional seder inquiries. Hamilton asks whether the four quarters 2007Q4–2008Q3 would have been considered recession quarters—as the NBER Business Cycle Dating Committee does—in the absence of the depressing effects of the oil shock. The overwhelming weight of the evidence that he adduces points to the answer being no, and I agree wholeheartedly. In fact, I would like to push the question one step further. Should the three quarters 2007Q4–2008Q2 be considered recession quarters at all? Real GDP growth rates for these three quarters were (pending further revisions) −0.2, +0.9, and +2.8 percent, respectively. The average is +1.2 percent, which is certainly slow growth but does not look like a recession. By contrast, the quarterly growth rates for the subsequent three quarters (2008Q3–2009Q1) were −0.5, −6.3, and −5.7 percent, respectively, averaging −4.2 percent. Now, that’s a recession. Furthermore, as is well known, virtually every macroeconomic time series took a sharp turn for the worse immediately following the failure of Lehman Brothers on September 15, 2. According to Blinder and Rudd’s (2008, table 4) estimates, the food shock had a larger effect on inflation than the oil shock did in 1973–74, and the two shocks had roughly equal impacts on inflation in 1978–80.

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2008. With full knowledge that several members of the NBER dating committee are participants at this conference, I will close by suggesting that the recession began on that fateful day. REFERENCES FOR THE BLINDER COMMENT

Blanchard, Olivier J., and Jordi Galí. 2007. “The Macroeconomic Effects of Oil Shocks: Why Are the 2000s So Different from the 1970s?” Working Paper 13368. Cambridge, Mass.: National Bureau of Economic Research (September). Blinder, Alan S. 1979. Economic Policy and the Great Stagflation. New York: Academic Press. ———. 1982. “The Anatomy of Double-Digit Inflation in the 1970s.” In Inflation: Causes and Effects, edited by Robert E. Hall. University of Chicago Press. Blinder, Alan S., and Jeremy B. Rudd. 2008. “The Supply-Shock Explanation of the Great Stagflation Revisited.” Working Paper 14563. Cambridge, Mass.: National Bureau of Economic Research (December). Kilian, Lutz. 2008. “The Economic Effects of Energy Price Shocks.” Journal of Economic Literature 46, no. 4: 871–909. Nordhaus, William D. 2007. “Who’s Afraid of a Big Bad Oil Shock?” BPEA, no. 2: 219–38.

COMMENT BY

LUTZ KILIAN James Hamilton has provided an insightful analysis of the latest oil price shock. He makes the case for viewing this episode not merely as a market aberration, but as a systemic and long-term problem that is likely to resurface once the global economy recovers from the current recession. After reviewing time-series plots of the relevant price and quantity data and conventional estimates of the price elasticity of oil demand, he concludes that constraints on the production of crude oil after 2005 and growing demand for crude oil driven by the recent boom in the world economy are the primary explanation of the 2007–08 oil price shock. Having outlined an explanation based purely on economic fundamentals, Hamilton also gives serious consideration to the view that speculation in oil markets may have worsened the oil price spike of 2007–08. One possible view is that speculators in oil-importing countries, anticipating future oil shortages, caused the spot price to increase; however, the fact that oil inventories did not increase substantially in recent years cautions against that interpretation. Another view is that oil-producing countries were misled by rising oil futures prices into reducing current production. Although the analysis in the paper shows that data on the oil futures spread do not support that view, it is conceivable that oil producers nevertheless withheld

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oil supplies in anticipation of even higher oil prices. That conjecture is hard to prove or disprove. Either way, the recent surge in the price of oil was ultimately driven by excess demand for crude oil. The paper highlights the roles of consumer sentiment, of the automobile sector, and of the housing sector in the transmission of the 2007–08 oil price shock. Evidence is presented that this shock was a major factor in causing the current recession and that its impact was magnified by the rising energy share in expenditure. The paper also includes a discussion of two policy tools that might have been used to slow the surge in the price of oil: one is the release of strategic oil reserves to calm speculators; the other is a slower easing of interest rates in 2008. The paper concludes that it would be wise for policymakers to address the long-run policy challenges of booming oil demand and stagnant global oil production, as the recent economic collapse is likely to prove only a short-run cure for the problem of excess demand. I agree with many of the points in this paper, and in only a few instances would I have favored a more subtle interpretation. At the risk of downplaying the many areas of agreement, in this comment I will focus on two main themes that strike me as especially worthy of discussion. One of these is that whereas earlier oil price shocks were primarily caused by exogenous physical disruptions of supply, the price run-up of 2007–08 was caused by strong demand confronting stagnating world production. Although I agree with this analysis of 2007–08 and with the proposition that this latest episode has been different from earlier ones, a growing body of evidence argues against the notion that the earlier oil price shocks were driven primarily by unexpected disruptions of the global supply of crude oil. The paper acknowledges that demand pressures arising from increased global real activity made some contribution to oil price increases during several earlier episodes, but this alternative explanation is never fully investigated. For example, although the paper briefly mentions (and dismisses as implausible) the effect of inflation (and of the devaluation of the dollar) on OPEC supply decisions in 1973, it completely abstracts from shifts in the demand for oil associated with fluctuations in global real activity, except for the analysis of 2007–08. As recent experience has demonstrated, however, such shifts have the potential to cause large fluctuations in the real price of oil. Hence, even if, for the sake of argument, all of the observed oil supply cutbacks in late 1973 or in 1979–80 were exogenous, it would not be self-evident that these supply disruptions, rather than fluctuations in the global business cycle, were the driving force behind the 1973–74 and 1979 increases in the real price of oil.

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Figure 1. Global Real Activity and the Real Price of Oil, 1973–2008 Percent 100

Real oil price (deviation from mean)a

75 50

Global real activity (deviation from trend)b

25 0 −25 −50 −75 −100 1975

1980

1985

1990

1995

2000

2005

Source: Authorís calculations. a. Price is deflated by the U.S. consumer price index. b. Update of the measure described in Kilian (2009).

Moreover, there is good reason to be skeptical of the assertion that oil supply shocks were the primary explanation of all oil price shocks before 2007–08. Not only does Hamilton’s figure 5 show considerable variation in the time-series patterns across oil price shock episodes, arguing against a common explanation, but no mention is made of the crucial point that commonly used measures of exogenous oil supply disruptions explain at most about 20 percent of the observed increase in the real price of oil in 1973–74. Alternative measures that I have proposed (Kilian 2008) imply even lower estimates of the predictive power of exogenous oil supply shocks. This raises the question of what explains the remaining 80 percent of the observed oil price increase. By construction, the answer to this question must have to do with shifts in the demand for oil. Arguably the most important driving force behind the demand for oil is global real activity. Figure 1 plots the real price of crude oil and a measure of global real economic activity for 1973 through the end of 2008. As expected, not all movements in the real price of oil were associated with swings in global real activity, but the three major oil price shock episodes of 1973–74, 1979–80, and 2002–08 all coincided with major surges in global real activity. The attentive reader will notice that the increase in real activity in 1973 predated the increase in the real

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Table 1. Growth in Inflation-Adjusted Prices of Selected Commoditiesa Percent Commodity Crude oil Industrial raw materials Metals

November 1971– February 1974

August 1977– February 1980

June 2001– June 2008

125.3 92.6 95.9

70.7 24.2 27.6

331.5 67.0 235.1

Source: Author’s calculations using data from the Commodity Research Bureau and Kilian (2009). a. Cumulative changes over the indicated period relative to the U.S. consumer price index.

price of oil; in fact, it started in late 1971. The reason for this asynchronicity, as discussed in Robert Barsky and Kilian (2002) and Kilian (2008), is that the price of crude oil before late 1973 was not determined by market forces and remained below its market-clearing level. Had the price of oil been free to move, it would have risen much earlier, in line with other industrial commodity prices. The second major upswing in oil prices coincided with a somewhat smaller surge in global real activity starting in 1978. Finally, it is also evident that the latest oil price shock started in 2003 rather than 2007 and once again coincided with a very large swing in global real activity. How much of an increase in the real price of industrial commodities such global demand swings may cause depends on how elastically the commodities in question can be supplied. It is instructive to contrast the increases in selected aggregate commodity price indices during the three episodes of interest. Table 1 shows that between late 1971 and early 1974, both industrial raw materials and metals prices increased by about 95 percent in real terms, despite a secular downward trend in these prices. Since contemporary sources indicate no important supply shocks in these markets at the time, and since most of the increases predate the oil price increase in late 1973, it is reasonably certain that all of these increases were driven by shifts in global demand (also see National Commission on Supplies and Shortages 1976). The observed increase in the real price of oil is only moderately higher, suggesting that stronger global demand is the explanation of the extra 80 percent increase in the real price of oil. Thus, 1973–74 appears much more similar to the current episode than this paper would have us believe. Likewise, for 1979–80, table 1 suggests that demand pressures seem capable of explaining perhaps a 30 percent increase in the real price of oil. A leading candidate to explain the remainder is rising concern in 1979 about future oil supply shortfalls, since once again exogenous oil

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supply shocks fail to explain the timing and magnitude of these oil price increases. For the period between about 2002 and mid-2008, there is evidence that sustained demand pressures were associated with even larger real commodity price increases than in the 1970s. The reason that the real price of crude oil rose even faster than other industrial commodity prices in this episode—and here I fully agree with the points made in the paper—is that the supply of crude oil, having risen substantially between 2002 and 2005 in response to higher prices, stagnated after 2005. Casual inspection of the data is a good starting point, but more formal regression analysis is required to identify unanticipated movements in global demand and oil supply and to account for their delayed effects on the real price of oil. Kilian (2009) shows that one can incorporate both global oil supply and global real economic activity into a regression framework that allows one to quantify the ability of unexpected physical shortfalls of oil production (“oil supply shocks”) and of demand shocks driven by the global business cycle (“aggregate demand shocks”) to explain the real price of oil. That model also includes a third shock, which may be viewed alternatively as an oil market–specific demand shock, reflecting, for example, shifts in uncertainty about future oil supply shortfalls, or as a measure of the difference between market expectations and econometric expectations of future oil supplies and global real activity. For the purpose of this discussion, I will focus mainly on the first two shocks to maintain consistency with Hamilton’s analysis. Figure 2 illustrates that the increase in the real price of oil from 2002 until mid-2008 was driven by a series of positive aggregate demand shocks associated with shifts in global economic activity. Oil supply shocks played no role. This analysis is very much consistent with this paper’s interpretation of this episode, but it highlights again that this oil price shock really started in 2003 rather than 2007. Figure 2 also shows that the drop in the real price of oil after mid-2008 reflected only in part an unexpected reduction in global real activity. Other factors, presumably associated with the worsening financial crisis, also played some role, as shown in the bottom right panel. Figure 2 also shows that the 1979–80 oil price shock actually reflected a composite of oil supply shocks (in 1980 rather than 1979), global aggregate demand shocks affecting all industrial commodity markets (starting in 1978), and other shocks, especially in 1979 (see Kilian 2009). This evidence is at odds with the view that all earlier oil shock episodes were driven primarily by oil supply disruptions. Likewise it has been shown that there is no evidence that the 1990–91 oil price shock was driven primarily

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Figure 2. Historical Decomposition of Fluctuations in the Real Price of Oil, 1978–84 and 2002–08a Cumulative effect of oil supply shock 1978–84 2002–08 Percent deviation from mean Percent deviation from mean 50

50

0

0

−50

−50 1979 1980 1981 1982 1983 1984

Peak of real global economic activity

2003 2004 2005 2006 2007 2008

Cumulative effect of aggregate demand shock 1978–84 2002–08 Percent deviation from mean Percent deviation from mean 50

50

0

0

−50

−50 1979 1980 1981 1982 1983 1984

2003 2004 2005 2006 2007 2008

Variation not explained by oil supply or aggregate demand shock 1978–84 2002–08 Percent deviation from mean Percent deviation from mean 50

50

0

0

−50

−50 1979 1980 1981 1982 1983 1984

2003 2004 2005 2006 2007 2008

Source: Author’s estimates. a. Estimates from the structural VAR model of Kilian (2009) based on data for February 1973–December 2008.

by oil supply shocks, nor did the Venezuelan crisis of late 2002 and the Iraq War of 2003, which jointly triggered an oil supply disruption not unlike those of the 1970s, have much of an effect on the real price of oil (see Kilian 2008, 2009). Thus, none of the major oil price shocks since the 1970s appear primarily supply-driven. What has changed relative to earlier episodes is the composition of demand and supply shocks, with repeated

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positive global aggregate demand shocks alone explaining most of the run-up in oil prices since 2003. One may question how market participants could have been surprised again and again over the course of several years by strong global real economic activity. Hicks and Kilian (2009) provide evidence from data on professional GDP forecast revisions that this was indeed the case and that the most persistent forecast errors were associated with unexpectedly rapid growth in Asia. Moreover, estimated responses to such forecast errors show a pattern similar to the estimated response to aggregate demand shocks. The other main theme of the paper is that the effect of the latest oil price shock on the U.S. economy has been quite similar to that of earlier ones. This argument is based on recursively identified vector autoregressions in which the oil price is ordered prior to the macroeconomic aggregate of interest. The global oil market model of Kilian (2009) takes the analysis a step further and expresses the VAR oil price innovation as a linear combination of oil demand and oil supply shocks, each of which is predetermined with respect to U.S. macroeconomic aggregates. This highlights two implicit assumptions that Hamilton makes in assessing the effects of oil price shocks. One assumption is that oil price innovations are homogeneous over time. This assumption would be innocuous if all oil price shocks were driven by exogenous oil supply disruptions, but, as has already been shown, oil price innovations reflect both oil demand shocks and oil supply shocks, the composition of which differs from one episode to the next, violating that assumption. The other assumption is that an oil price innovation is not associated with contemporaneous movement in any other macroeconomic variable. This presumption is violated if the oil price innovation is driven by global aggregate demand shocks. In that case, not only will the oil price innovation be correlated with innovations to the price of other industrial commodities, but the demand shock will also have a direct effect on the U.S. economy, for example, through the trade and external finance channel (see Kilian, Rebucci, and Spatafora 2009). This does not mean that one cannot estimate the responses associated with an oil price innovation. Indeed, I have done so in my own work. One does, however, have to be clear that these responses do not represent the causal effect of an innovation to the price of oil, because the ceteris paribus condition is violated. Moreover, one has to keep in mind that these estimates represent the response to a shock of average composition over the sample period. They may be misleading when the composition of the oil price shock in question is atypical by historical standards, as is the case for the 2003–08 episode. Figure 3 illustrates this point. Since the latest oil

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Figure 3. Responses of U.S. Real GDP to Oil Market Shocksa Aggregate demand shock Percent

0 −5

2

4

6 Quarters after shock

8

10

8

10

Real oil price shock Percent

0 −5

2

4

6 Quarters after shock

Source: Author’s estimates. a. The top panel is based on the methodology of Kilian (2009), and the bottom panel on a VAR in the real price of oil and real GDP growth for the same sample period of 1975Q1–2008Q4. Responses are to a 1-standard-deviation shock and are expressed as cumulative quarterly annualized growth rates. Dashed lines are 1-standard-error bands.

price surge was driven primarily by global aggregate demand shocks (as opposed to a more typical mixture of demand and supply shocks), the upper panel focuses on the response of U.S. real GDP to a positive aggregate demand shock. The lower panel shows the response of U.S. real GDP to a real oil price innovation estimated on the same sample. That response reflects the average composition of demand and supply shocks over the entire sample period. Although broadly similar, the exact timing, the magnitude, and at times even the sign of the response estimates differ. A positive global aggregate demand shock has positive, if statistically insignificant, effects on real GDP within the first year, reflecting the sluggish response of industrial commodity and oil prices and the economic stimulus from abroad. Only starting in the second year does the response turn negative, as the stimulus fades and higher oil and industrial commodity prices stifle

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Figure 4. Historical Decomposition of U.S. Real GDP Growth, 1978–84 and 2002–08a Cumulative effect of oil supply shocks 1978–84 2002–08 Real GDP growth Real GDP growth (percent deviation from mean) (percent deviation from mean) 2

2

0

0

−2

−2

−4

−4 1980 1981 1982 1983 1984

Peak of real global economic activity 2003 2004 2005 2006 2007 2008

Cumulative effect of aggregate demand shocks 1978–84 2002–08 Real GDP growth Real GDP growth (percent deviation from mean) (percent deviation from mean) 2

2

0

0

−2

−2

−4

−4 1980 1981 1982 1983 1984

2003 2004 2005 2006 2007 2008

Source: Author’s estimates. a. Estimates based on the methodology of Kilian (2009) using data for February 1973–December 2008. Data are cumulative quarterly annualized growth rates.

economic growth. A shock of average composition implies a decline in real GDP starting in the second quarter, in contrast. The response is significant in the second and third years. An immediate implication is that the effects associated with the 2007–08 oil price increase could not possibly be the same as those associated with earlier oil price shocks, even if it were the case that the earlier shocks were driven by oil supply disruptions. Just how different the implied effects on real GDP are is documented below. In discussing the impact of oil demand and oil supply shocks over extended periods, it is essential to consider the cumulative effect of all of these shocks over time rather than the response to a one-time shock. Figure 4 shows the contribution of aggregate demand and oil supply shocks to the observed variation in U.S. real GDP growth, relative to average growth,

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for selected periods. All of the results are based on the methodology of Kilian (2009). Compared with previous estimates from similar models but shorter time spans, figure 4 reveals a somewhat larger impact of oil supply disruptions on U.S. growth in the early 1980s, coupled with a persistent reduction in growth associated with aggregate demand shocks. In contrast, the primary explanation of below-average U.S. real GDP growth after 2004 is the unexpected increase in global real economic activity that started in 2002. Consistent with the impulse response estimate in figure 3, the initial effect of positive aggregate demand shocks on U.S. real GDP growth was largely positive (see figure 4). Only in 2004 do industrial commodity price and oil price increases start taking their toll. The top panel of figure 4 also suggests that these effects were offset—for some time—by the growthenhancing effect of positive oil supply shocks. Given the unprecedented drop in global real activity of close to 95 percent from the peak in June 2008, shown in figure 1, the fact that the estimated effect is increasing sharply at the end of the sample does not come as a complete surprise. How do these estimates compare with conventional estimates based on VAR models for real GDP growth and the real price of oil? Figure 5 shows that the fully structural VAR model predicts somewhat larger economic contractions in the early 1980s and in 2008 than the VAR model involving real oil price innovations. Interestingly, the overall effect of the demand and supply shocks on U.S. real GDP growth in 2005 through 2007 proved small by the standards of the early 1980s. The negative effect of earlier aggregate demand shocks was initially offset in part by the positive effects of other shocks, including positive oil supply shocks in 2004–06. Thus, only in late 2007 and in 2008 did the full effect of the continued unexpected global expansion make itself felt. This result corroborates the interpretation of the 2007–08 data in the paper. It is also consistent with the observation that the higher oil and industrial commodity prices triggered by repeated positive aggregate demand shocks, as in earlier episodes, caused a reduction in consumer spending, mainly in the residential housing sector and in the automobile sector. What is interesting about the results in figure 4 is that they suggest a somewhat smaller role for the financial crisis in late 2008 than one might have suspected. In that sense I agree with Hamilton that developments in global oil and other commodity markets appear to have played an important role in the latest U.S. recession. In closing, although this paper presents an impressive body of evidence that sheds light on the mechanics of how oil price shocks are propagated, and although I agree with many of its substantive conclusions, my concern is that the narrow causal interpretation of oil price shocks in this paper is

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Figure 5. Explanatory Power of Oil Demand and Supply Shocks Combined and of Real Oil Price Shocks, 1979–85 and 2002–08a Cumulative Effect of Oil Demand and Oil Supply Shocks 1979–85 2002–08 Real GDP growth Real GDP growth (percent deviation from mean) (percent deviation from mean) 4

4

0

0

−4

−4

−8

−8 1980 1981 1982 1983 1984

Peak of real global economic activity

De-meaned actual growth Cumulative effect 2003 2004 2005 2006 2007 2008

Cumulative Effect of Real Oil Price Shocks 1979–85 2002–08 Real GDP growth Real GDP growth (percent deviation from mean) (percent deviation from mean) 4

4

0

0

−4

−4

−8

−8 1980 1981 1982 1983 1984

2003 2004 2005 2006 2007 2008

Source: Author’s estimates. a. Estimates based on the methodology of Kilian (2009) using data for February 1973–December 2008. Data are cumulative quarterly annualized growth rates.

misleading. This is not merely an issue of how to interpret the resulting responses, but one that affects the magnitude of the estimates. For example, although the direction of the estimated effects is broadly similar, the evidence in figure 5 above suggests that the aggregate demand shocks driving the 2007–08 oil price increase may have had greater effects on U.S. real GDP than suggested by models that ignore changes in the composition of oil price shocks. I have also provided evidence that, contrary to the assertion in this paper, demand shocks have always played an important role in oil markets. What is different about the latest episode is that the oil price increase was driven almost exclusively by one specific type of demand shock, reflecting continued unexpected increases in global real activity during 2002–08, primarily

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associated with unexpected growth in emerging Asia (see Hicks and Kilian 2009). From a policy point of view the central question is how much of that unexpected growth reflected an exogenous economic transformation in emerging Asia. An alternative explanation is that the Federal Reserve sustained growth in the United States longer than appropriate, easing monetary policy too early and too much, thus enabling the export-based Chinese economy and the world economy more generally to thrive, and fueling the commodity and oil price boom that contributed to the current collapse of the real economy. I agree with the author that this possibility deserves careful study. A third explanation is that the sustained prosperity in the United States between 2002 and mid-2008 was not directly linked to monetary policy, but to the failure of the Federal Reserve and other regulators to rein in financial and housing markets. It seems unlikely that one could unravel the relative contribution of each explanation without the help of a fully specified multicountry open-economy model. REFERENCES FOR THE KILIAN COMMENT

Barsky, Robert B., and Lutz Kilian. 2002. “Do We Really Know That Oil Caused the Great Stagflation?” NBER Macroeconomics Annual, vol. 16, pp. 137–83. Hicks, Bruce, and Lutz Kilian. 2009. “Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?” Discussion Paper DP7265. London: Centre for Economic Policy Research. Kilian, Lutz. 2008. “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?” Review of Economics and Statistics 90, no. 2: 216–40. _________. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review 99, no. 3: 1053–69. Kilian, Lutz, Alessandro Rebucci, and Nikola Spatafora. 2009. “Oil Shocks and External Balances.” Journal of International Economics 77, no. 2: 181–94. National Commission on Supplies and Shortages. 1976. The Commodity Shortages of 1973–1974: Case Studies. Washington: U.S. Government Printing Office.

GENERAL DISCUSSION Robert Gordon pointed out what he saw as four omissions in the paper. The first was the lack of a systematic macroeconomic theory of commodity supply shocks, such as that developed by Edmund Phelps and himself in the mid-1970s. A low price elasticity of demand for oil means that the energy share in GDP must rise, with the nonenergy share falling by the same amount. A complete theory would

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take into account both the direct effect of higher oil prices in the production function and the indirect effect of crowding out private nonoil real spending. The second was a lack of systematic treatment of inflation. Over the past three decades the response of aggregate inflation to higher oil prices has become more muted. This may have occurred through a smaller response of wages, possibly because of the declining power of labor unions and the decreasing prevalence of cost-of-living escalators in wage contracts. The third was that the paper omitted the housing bubble as a cause of the 2007–08 downturn, instead fully attributing the collapse to the oil price shock. Finally, four additional, interacting shocks occurred during the recession of the mid-1970s and were not considered in the paper: food price shocks, the depreciation of the dollar, the end of the Nixon price controls, and a general slowdown in productivity growth. Robert Hall commended the author on yet again convincingly showing that every recent U.S. recession has been caused by an oil price spike. He recommended, however, that the data be continued beyond the moment of the spike itself. In particular, durable spending, dominated by falling automobile sales, declined steeply until October 2008 and has since then been essentially constant. If financial stress were the most important cause of the current recession, he posited, spending would not have stabilized at that point. Hall also discussed the paper’s demonstration of an asymmetry in the economic response to oil price changes. In 1986 oil prices dropped sharply but did not cause an economic boom; Hall conjectured that if prices had instead risen by the same amount, there would have been a deep recession in 1987. In the current recession, fuel prices at the end of 2008 were significantly lower than at the beginning of the year, yet the economy did not improve and automobile sales remained low. George Perry disputed the claim that oil prices were the prime cause of all recent U.S. recessions. First of all, no important rise in oil prices preceded the 2001–02 recession, and the price spike in 1991 lasted only a few months. A more interesting difference was that the Federal Reserve’s response to the oil price shocks differed across time, and the responses, in turn, were informed by how oil prices were expected to impact core inflation. In the mid- and late 1970s’ oil price shocks, wages in important industries were closely indexed to the consumer price index, leading to significant risk of a price-wage-price spiral in core inflation. The Federal Reserve responded aggressively. Indeed, it deliberately caused the 1980 and 1982 recessions in response to worsening inflation. By contrast, its response to the most recent oil price shock was very mild because inflation has been low in recent years and wages have not moved in response to price shocks.

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Justin Wolfers, citing the discussion of the Romer and Romer paper in this volume, noted that four events may not be enough to prove that oil price shocks are the sole cause of recessions; after all, the chance of four consecutive coin tosses being heads is 1 in 16, whereas statistical significance conventionally requires a probability of 1 in 20 or better. He suggested that if professional recognition could be achieved by being correct four times out of four, many economists would choose that route rather than, say, the much more arduous one of developing a macroeconomic model. Frederic Mishkin seconded George Perry’s comments about the Federal Reserve’s differing reactions to increases in oil prices. The better grounding of inflation expectations in the recent period has allowed the Federal Reserve to keep interest rates lower than would have been possible before, given the same rise in oil prices. Better-grounded inflation expectations also give the Federal Reserve much more flexibility in dealing with negative demand shocks. David Romer conjectured that if, before reading the paper or hearing the presentation, all the conference participants had been asked to state their view on the primary cause of the current recession, oil prices would likely not have garnered many votes. Yet the paper had completely convinced the two discussants, at least, of its remarkable assertion that oil was the primary cause. Romer disagreed, arguing that the paper significantly overstated the role of oil in several ways. First, the effect of oil prices is known to be nonlinear, making it possible for an equation to overfit and overestimate the effect. Second, the regression in the paper that produced the strongest result did not correct for the fact that the energy intensity of the economy has decreased over time. Romer also cited the reasons already given as to why oil is less of a factor in macroeconomic stability today than in the past: wages are more flexible, inflation behaves differently, and the Federal Reserve knows better how to deal with oil price changes. Caroline Hoxby discussed what she saw as an important implication of the paper, namely, that automakers deserve more of the blame for the current recession than has been attributed to them. Their failure to diversify into smaller, more fuel-efficient automobiles suggested that one way in which each oil shock has contributed to causing a recession was through decreased demand for U.S.-made cars. In fact, a large movement in American buying preferences from big cars to smaller ones has followed every oil shock. If U.S. manufacturers had invested in small cars in addition to SUVs, less income would have been lost to other countries such as Japan. Richard Cooper commented on the paper’s use of world growth rates as though they were actually known. He contended that the actual growth rate

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of gross world product is not known, and the rate most commonly used in analysis, that published by the International Monetary Fund, weights countries by purchasing power parity, which overweights countries like China and underweights countries like Japan; the result is a 1-percentage-point overstatement in the overall rate. An accurate measure of demand growth, he proposed, would use market exchange rates. Cooper also wondered about the role of speculation interacting with the fundamental determinants of demand in causing the sharp increase in oil prices. Much as demand for a medical school education might spuriously appear to have risen because the average applicant applies to more schools, so total demand for oil might appear to have risen because of the increase in speculative demand. During the second OPEC shock, Iranian oil production declined steeply, from 4 million barrels a day to 500,000 barrels, significantly hurting BP, which obtained much of its supply from that country. BP’s customers, such as New Zealand, scrambled to order as much oil as they could from anyone who would take orders. This kind of behavior caused the apparent demand for oil to exceed actual final demand, resulting in rising inventories in 1979. In 2008, hedging purchases (for example, by airlines) plus speculative purchases (for example, by hedge funds) in the forward market drove up spot prices—which are determined by near-term forward prices—even when the buyers did not intend to take delivery on all of their purchases. Matthew Shapiro questioned the vertical supply curves used in the paper. In markets for natural resources such as oil, the supply curve in any one period should be approximately horizontal. That is, the flow supply is highly elastic because of intertemporal arbitrage. He agreed that vertical curves might be appropriate in the case of a short-run supply restriction, such as an OPEC embargo. The short-run supply curve can also be steep if there are adjustment costs. Indeed, evidence from a comparison of the asset value of oil in the ground with the spot price of oil during 2008 suggests that short-run supply considerations affected both the increase in oil in the first half of the year and its collapse after the onset of the financial crisis. The slope of the short-run supply curve cannot, however, have a major role in explaining a steady increase in prices over a seven-year period. Philippe Aghion considered the implications of the different types of oil shocks on technological innovation. He noted that in the 1970s the shock had been on the supply side, which had led to research and development in order to deal with a supply shortfall. The current shock was on the demand side but also involved the issue of global warming. He acknowledged that a much longer time series would be necessary to study these effects.

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Benjamin Friedman observed that the paper’s results embedded two interesting propositions. The first was that recessions, and output reactions generally, are invariant to monetary policy. The conventional wisdom holds that a backward shift in the aggregate supply curve presents the Federal Reserve with the unpleasant choice of how much of the shift to absorb in inflation and how much to let a backward shift in aggregate demand cover. The paper insinuated that this notion is wrong, that the actions of the central bank are nothing more than a sideshow. The second proposition was that output appears invariant to the conduct of monetary policy because it was constant across all episodes discussed. Friedman found this proposition quite interesting because Republicans were in charge during some episodes and Democrats in others, inflation was high at the beginning of some episodes and low at the beginning of others, and inflation expectations were well anchored in some but not anchored at all during others. If monetary policy was truly invariant across the entire sample, he noted, that would be an interesting finding in and of itself. Lutz Kilian responded to a number of issues raised in the discussion. He clarified that his view was that unexpected oil price increases are not the one factor that explains recessions but rather are a symptom of deeper shocks to the underlying oil supply and oil demand that affect the economy both directly and indirectly through the prices of oil and other imported commodities. Hence, attempts to distinguish between alternative monocausal explanations of recessions such as “oil” or “money” are inherently misleading. He also observed that, historically, residential housing expenditure has been particularly sensitive to oil price shocks, consistent with Hamilton’s analysis. Regarding the role of asymmetries, he noted that contrary to the conventional wisdom, significant asymmetry is not found in the overall economic response to oil price shocks but may appear in the response of nonresidential investment as a consequence of identification problems and of composition effects. Addressing the comments about the Federal Reserve’s reaction to oil price shocks, he noted that recent research shows that there is no theoretical basis for a mechanical response of monetary policy to oil price shocks, nor is there compelling empirical evidence that the Federal Reserve’s response to oil price shocks has had substantial effects on macroeconomic outcomes in the past. John Campbell remarked that physical activity has to occur in the spot market for oil in order to enforce arbitrage in the futures market, but such activity may never occur in equilibrium because enforcement is not needed. He compared trade in the oil market to that in financial markets, where there is both trade in the assets themselves and trade in futures and other

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derivatives of those assets; in a well-functioning economy, transactions across these market segments do not have to occur to establish the law of one price. Specifically, he pointed out that an exogenous increase in the future oil price allows spot prices to rise without inducing a strong supply response, by contrast with the situation in which high current prices and lower expected future prices generate a large supply reaction. He wondered why oil market participants had formed expectations of persistently high oil prices in the future; a possible explanation was some combination of rational speculation, irrational speculation, and speculation that confused other participants into forming beliefs about future prices that turned out to be unreasonable.

RAFAEL DI TELLA Harvard Business School

ROBERT MACCULLOCH Imperial College London

Why Doesn’t Capitalism Flow to Poor Countries? ABSTRACT We show that capitalism is far from common around the world. Outside a small group of rich countries, heavy regulation of business, leftist rhetoric, and interventionist beliefs flourish. We relate these phenomena to the presence of corruption, with causality running in both directions. The paper presents evidence that, within a country, those who perceive widespread corruption also tend to demand more regulation. As regulation is held constant within a country, this finding is hard to explain if one assumes that causality runs only from regulation to corruption. We also find that over time, increases in corruption in a country precede increases in left-wing voting. To explain our findings, we present a model where corrupt capitalists are disliked, and voting for left-wing policies is a form of punishment available to voters even in weak judicial systems. Evidence on emotions supports this explanation: the frequency with which people report experiencing anger is positively correlated with perceived corruption, but this relationship is significantly weaker when business is heavily regulated.

E

conomists often argue that capitalism outperforms socialism on numerous dimensions. These arguments are so compelling that one might be led to believe that free markets, perhaps with some redistribution, are the norm around the world. In reality, this is not the case. Outside the United States and a small set of other rich countries, public opinion tends to be unimpressed with the performance of capitalism. Resistance to free markets has been observed in former communist countries, in underdeveloped countries in Africa, and in some modern democracies in Europe. In Latin America the phenomenon is especially striking. After a decade of economic reform in the 1990s, a backlash against markets has been observed in most 285

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of the countries in that region. Such skepticism toward capitalism in poor countries is all the more remarkable because, presumably, voters in these countries have the most to gain from the more rapid growth that capitalism might achieve. This paper makes two main points. First, we document that capitalism is indeed relatively more popular in rich than in poor countries. Second, we argue, both empirically and theoretically, that one motivation for such antipathy toward markets originates in the presence of corruption. Economists have connected regulation to corruption before, but with an emphasis on causality going the opposite way: from intrusive regulation to more corruption. We argue instead that in a reasonable theoretical model, causality will run in both directions. Why should corruption invite more regulation? Our interpretation is that widespread or salient corruption causes voters to become upset with capitalists generally and to demand more regulation, higher taxes, or, more broadly, an economic system that is less favorable to business. To put it another way, corruption reduces the public’s voluntary acceptance—the legitimacy—of a country’s commercial institutions and their desire for a system in which capitalists might flourish. Voters who perceive corruption then vote for more regulation as a way of punishing the capitalists, whom they see as undeserving. Moreover, they do so even if the increased regulation generates still more corruption, slower growth, and other economic “bads”; they are willing to incur material costs to obtain outcomes that they see as more fair. Borrowing from the political science literature on the legitimacy of political institutions, we argue that it is worthwhile for economists to study the legitimacy of a country’s commercial institutions, defined as the extent to which there is social consent on the “purpose” of business. We formalize these ideas in a model in which voters expect business to refrain from making money through corrupt means. Given that certain characteristics, or “types,” might be positively correlated across businesspeople within a country—for example, the degree to which they are or appear to be honest—corruption on the part of one may impose a negative externality on all, by inviting higher taxes and a less friendly regulatory environment for business. Targeted legal actions against capitalists who are perceived to be corrupt, such as those taken against the trusts during the administration of U.S. President Theodore Roosevelt a century ago, may address the externality by reducing the demand for widespread inefficient regulation. We present three types of evidence to support our claims. First, we find that within a country (and hence for a given level of regulation), those who

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perceive corruption to be high also tend to demand more regulation. Second, we find that over time, increases in corruption in a country precede increases in left-wing voting. And third, using data on reported emotions from the Gallup World Poll, we find that individual experiences of anger and the perception of business corruption are positively correlated, but that this correlation is weaker in countries where regulation that is detrimental to business is widespread. Of course, the correlations we report may have different explanations from those we propose, but data limitations prevent us from constructing tight tests against these alternatives. Our strategy therefore is to offer some correlations that are suggestive of our proposed mechanism, and to present a model in which corruption plays a central role in eroding trust in the business community. Although our empirical approach is thus limited in its aims, it is sufficient to cast considerable doubt on a narrow version of the prevailing model in political economy, in which the only channel of causality is that going from regulation to corruption. Beyond the empirical limitations, it is worth emphasizing that our paper deals with only a few of the many elements of capitalism, which range from policies on private versus state ownership of business to the extent of regulation and the level of taxation. Indeed, the theoretical mechanisms we propose and the data that are available to us refer to only some of these different aspects, and so we proxy “capitalism” with “policies that improve the economic and social standing of business.” The structure of the paper is as follows. Section I presents the evidence showing that attitudes and policies favorable to capitalism are not common around the world, and section II presents a brief taxonomy of possible explanations, including our main hypothesis, which is that corruption leads to the popular rejection of capitalism. Section III presents the main evidence from tests of that hypothesis, section IV discusses that evidence, and section V presents a model that offers an interpretation of the evidence. Section VI concludes.

I. Capitalism Does Not Flow to Poor Countries This section presents and discusses evidence suggesting that policies and attitudes that can loosely be called pro-capitalist are not observed as frequently in poor countries as economists might expect.1 We examine three types of evidence: party names and platforms that indicate the ideological leanings of those in power; surveys of popular opinion on the desirability 1. On economists’ views of markets, see, for example, Blendon and others (1997).

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of government ownership of business; and measures of the regulatory hurdles faced by those seeking to start a new company.

I.A. Political Rhetoric Is Tilted to the Left in Poor Countries We start by comparing the rhetoric and platforms of political parties across rich and poor countries. One source of data is Thorsten Beck and others (2001), who use a two-step approach covering a maximum of 177 countries over 1975–95. First, they record the party identification of each country’s political leaders, including the chief executive and the party currently in power in the legislature (or the largest party in a governing coalition). Second, they classify these parties according to their preferences regarding greater or less state control of the economy—the standard leftright scale. They infer these preferences from the party’s name and from information on their platforms, taken from a set of standard sources. For example, party names containing words such as “Conservative” or “Christian Democratic” are classified as right-wing, and those containing words such as “Socialist” or “Social Democratic” as left-wing. The “center” category is reserved for parties that are explicitly called “centrist” or that the sources reveal as advocating the strengthening of private enterprise but also supporting a redistributive role for government. The top panel of table 1 uses this classification system and data from a representative year to illustrate the relative prevalence of left- and rightwing governments. We classify countries into three income categories according to real purchasing power per capita, and by ideology according to the orientation of the largest party in government. The data suggest that electorally successful right-wing parties are more common in the top than in the bottom income group and that their frequency relative to left-wing governments is lowest among the poorest group. In other words, governments in poor countries are on average less supportive of capitalism than those in rich countries, as captured by a measure based on party names and platforms. In a working version of this paper (Di Tella and MacCulloch 2002), we showed that this result is not affected when data for a longer sample period, or other periods, or other definitions of government ideology are used. Left-wing governments were more common in the early part of the longer sample than in the later part; however, in both periods right-wing governments were relatively more common in rich countries. This conclusion also holds after controlling for the influence of other variables (for example, the level of political rights as measured by Freedom House, an indicator for whether countries were experiencing civil war, and an indica-

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Table 1. Selected Measures of Attitudes and Policies toward Capitalism, by Country Income, 1992–99 Country income tercilea Measure

Top

Middle

Bottom

b

Ideological leaning of government, 1992 (percent of countries) Right 60.0 45.7 Center 12.6 14.3 Left 27.4 40.0 No. of countries 40 35

15.3 3.9 80.8 26

Preference for greater private or state ownership of business, 1995 (percent of respondents)c Private 46.5 41.4 Neutral 30.6 23.6 State 22.9 35.0 No. of countries 20 22

37.0 21.9 41.1 8

Difficulty of registering a business, 1999d No. of procedures 7.9 Standard deviation 4.2 No. of countries 29

12.2 4.3 27

11.4 3.6 27

Sources: World Bank, World Development Indicators 1995; World Values Survey 1995; Djankov and others (2002). a. Countries are classified according to real purchasing power per capita. b. As determined by the authors using the ideology of the largest party in government, according to the classification scheme of Beck and others (2001). c. Respondents in the 1995 wave of the World Values Survey were asked, “How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between. Sentences: Private ownership of business should be increased [left side]; Government ownership of business should be increased [right side].” A response of 1, 2, 3, or 4 is classified as a preference for private ownership; a response of 5 or 6 as neutral; and a response of 7, 8, 9, or 10 as a preference for state ownership. The panel is based on 70,986 individuals. d. Measured by the number of different procedures that a business start-up has to comply with in order to obtain legal status.

tor for the level of income inequality). Omitting data from countries that were in the Soviet bloc before 1990 likewise does not affect the results. It is worth noting that countries with more unequal distributions of income tend to elect right-wing parties. This point, which has been made informally in contrasting the United States and Europe, is the starting point of Thomas Piketty’s (1995) analysis and, to our knowledge, has not been documented before for a wider range of countries.

I.B. Beliefs about Private versus Government Ownership of Business Cross-country survey data on people’s opinions about various elements of capitalism are available from the World Values Survey (WVS). Coordinated by Ronald Inglehart, the 1995 wave of this survey asked adults

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in over 50 countries several questions of interest.2 One that is directly relevant to this paper concerns the desirability of increasing the private ownership of business: Now I’d like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between. Sentences: Private ownership of business and industry should be increased.

Government ownership of business and industry should be increased.

We categorize those giving answers from 1 to 4 as favoring private ownership, those giving answers from 7 to 10 as favoring state ownership, and those in between as having centrist views.3 The middle panel of table 1 presents the results. It shows that 46.5 percent of respondents in countries in the top third of the world income distribution favor increasing private ownership of business and industry, whereas only 22.9 percent favor increasing government ownership. The proportion favoring private ownership decreases monotonically, and that favoring government ownership increases monotonically, as one reads across the columns. In other words, support for capitalism is weaker in poorer countries, as captured by the prevalence of attitudes favoring increasing government ownership of business and industry.

I.C. Regulation of Entry as a Proxy for Prevalence of Capitalism An alternative approach is to move beyond rhetoric and beliefs and observe whether the policies actually implemented in poor countries are interventionist. We focus on the hurdles in place to start a new business as a proxy for the prevalence of capitalism. Simeon Djankov and others (2002) collected data in various countries on the amount of time, number of screening procedures, and total number of procedures required to register a business.4 These are defined, respectively, as the number of business days 2. Although national random sampling and quota sampling were used, the populations of China, India, and Nigeria, as well as rural areas and the illiterate population in countries generally, were undersampled. 3. Ideally, the data would refer to levels of government intervention rather than simply ownership, but these data are nonetheless useful, particularly in conjunction with data showing that poor countries on average already have more government ownership of business than do rich countries. 4. “Screening procedures” is a subset of the total number of procedures; Djankov and colleagues also collected data on the cost to a firm of obtaining legal status, which we do not include in our analysis.

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“it takes to obtain legal status to operate a firm”; “the number of different steps that a start-up has to comply with in order to obtain a registration certificate that are not associated with safety and health issues, the environment, taxes, or labor”; and “the number of different procedures that a start-up has to comply with in order to obtain a legal status, i.e., to start operating as a legal entity” (Djankov and others 2002, p. 16). The bottom panel of table 1 shows that the total number of procedures required for a start-up company to obtain legal status is monotonically increasing across country income terciles from richest to poorest. Other measures (not reported in the table) display a similar pattern. GDP per capita is negatively associated with the number of days (the correlation is −0.47), number of steps (−0.50), and number of procedures (−0.48) required to start a business; all three correlations are significant at the 1 percent level. In other words, the legal environment in poorer countries tends to be less favorable to capitalism, as captured by the amount of regulation in place that makes it harder for entrepreneurs to start a business.

I.D. Are There Counterexamples in Latin America? Some well-known cases in Latin America appear to be counterexamples to the pattern just described. The “Chicago School” reforms in Chile in the 1970s and 1980s and the administration of President Carlos Menem of Argentina in the 1990s are two cases in point of pro-market governments in developing countries. Closer inspection, however, suggests that these episodes, too, conform to the general pattern. The “Chicago boys” were able to implement their policies only after the military government of General Augusto Pinochet took power. Indeed, a standard informal justification often invoked for military coups in Latin America in the 1970s was that they were the only way that “reasonable” (conservative, nonpopulist) ideas could be implemented, given their weak electoral appeal. In Argentina the center-left Radical and Peronist parties have alternated in government (except when the military was in power) for almost a century. The Peronists are often labeled right-wing because of the role of fascism in shaping the ideology of the party’s founder, Juan Perón. Yet over the last century the labor share of output has been highest under Peronist administrations, and the Peronist march speaks of “fighting capital.” Similarly, it is claimed that the Menem administration in the 1990s turned right-wing, which is a plausible interpretation of Menem’s policies but does not deny the fact that he was elected on a populist platform that included a massive general wage hike (the salariazo). Indeed, “neoliberal” reforms in Latin America have seldom been part of candidates’ electoral

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platforms, and when they were, as in the case of Mario Vargas Llosa’s presidential campaign in Peru in 1990, they failed. The pattern of promarket reforms by unlikely candidates in Latin America is surprisingly widespread: all of the cases described by Susan Stokes (2001) in which actual policies did not match the candidate’s electoral promises involved the implementation of “efficiency-oriented policies of market competition” instead of the promised “security-oriented policies of state intervention.”5

II. Four Possible Explanations The question posed in our title has a number of possible answers. In this section we briefly mention three that appear plausible before offering a fourth that, in our view, better accounts for the observed pattern.

II.A. The Capture Hypothesis According to the capture hypothesis, people want capitalism but their wishes are blocked by entrenched interest groups who deliver bribes (and perhaps issue threats) to politicians in exchange for regulations that favor them. This is close to the consensus explanation among economists today. The related “tollbooth” theory explains regulations as being designed by self-interested politicians and bureaucrats to help them extract bribes.6 Note, however, that if corruption enables unpopular regulation, there is no reason why more corruption would lead voters to desire more regulation, as our findings in this paper suggest.

II.B. The Learning Hypothesis The learning hypothesis holds that people reject capitalism because they fail to understand its benefits. Of course, if people are assumed to make such gross mistakes, it is hard to see how markets that rely on rationality could be good for welfare. A more appealing version is that people are in 5. Stokes (2001, p. 2). A well-known case of a conservative politician veering left once in power is that of U.S. President Richard Nixon, who initiated diplomatic relations with communist China; however, this case is again consistent with the general pattern, as Nixon was the president of a rich country. 6. Standard references include Tullock (1967), Stigler (1971), and Peltzman (1976). On the tollbooth theory, see McChesney (1987), De Soto (1989), and Shleifer and Vishny (1993). More recently, Parente and Prescott (1999) cast these ideas in terms of technology adoption by monopolists, Rajan and Zingales (2003) do so with attention to public confusion over the economic cycle, and Acemoglu and Robinson (2000) with an emphasis on political power.

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the process of learning the correct model, because it is possible in principle that capitalism is not the superior system and that in many circumstances a more heavily regulated economy will actually maximize welfare. Evidence of the superiority of capitalism, in this version, has been accumulating and is known to economists but has not yet reached the voters. Here the seminal paper is Piketty (1995), who proposes a model in which economic agents seek to understand, in the presence of shocks, the connection between work effort and income before deciding on the level of personal taxation. These agents cannot observe other people’s choices regarding effort, nor can they infer them from occupational choices, and so they experiment until they settle on the likely value of the parameter (incomplete learning).7

II.C. Socialism Is Good The third hypothesis argues that people reject capitalism because socialism is in fact better for them. Although the observed failure of some forms of socialism reduces the appeal of this hypothesis (at least in the extreme version), the experience with capitalism of some former communist countries after the collapse of the Berlin Wall has not been impressive either. In fact, the evidence of prolonged economic disorganization after 1989 in some Eastern European countries suggests a related hypothesis: people know how well capitalism works once the state has been developed to the point where it can provide adequate institutional support, but they also know that this might take a long time. In that case, if their discount rate is sufficiently high, people may in fact be better off under socialism. The evidence collected by Olivier Blanchard and Michael Kremer (1997) is consistent with this view. A less extreme view is that a certain amount of regulation or taxation is necessary to help markets function efficiently, for example by addressing externalities, but that this can be accomplished without necessarily abandoning capitalism altogether. (This is sometimes called the “public interest theory” of regulation.) However, to serve as an explanation, this theory has to contend with survey evidence that a majority of voters in rich countries like the United States do not want for themselves the higher levels of government ownership and taxation observed in many poorer countries. 7. For a related discussion in the context of trade policy, see Sachs and Warner (1995) and Buera, Monge-Naranjo, and Primiceri (2008). For evidence on the connection between shocks (crime, oil, or macroeconomic) and pro-market beliefs, see Di Tella, Donna, and MacCulloch (2007, 2008), Di Tella, MacCulloch, and Dubra (forthcoming), and Giuliano and Spilimbergo (2008). On the relationship between the size of a country and the beliefs prevalent within that country, see Alesina and Glaeser (2004).

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No doubt other possible hypotheses might be offered to the question posed in our title. But even within the subset discussed, clear evidence in any one direction is lacking (it would be hard to provide definitive tests), and so there is no clear consensus. Instead we propose another hypothesis, explore its logic, and provide some suggestive evidence in its favor. We call it the “unpleasant capitalists” hypothesis.

II.D. Unpleasant Capitalists According to the “unpleasant capitalists” hypothesis, people reject capitalism because it favors a set of individuals whom they do not like. Although they understand that capitalism would make them better off economically, they would rather introduce regulations and taxes that punish a group of people whom they consider “bad,” and they are unhappy when they observe capitalism conferring benefits on these people. Note that this hypothesis requires that people have other objectives in addition to maximizing their own material payoff, unlike what standard economic models assume. One possible origin of this hostility toward capitalists is a history of corruption in the country: it is easy to dislike the elite of a poor country if they are perceived to have profited from government contracts awarded through corruption and favoritism. In contrast, in a rich country it might be easier to credit the economic elite with genuine wealth creation in the form of new products, greater efficiency, and the like. A related idea is that in some countries capitalists are associated with a hostile foreign power, for example a former colonial master—indeed, we have found some evidence consistent with this idea (results not reported). Such a history could lead to a similar degree of hostility toward “undeserving” capitalists even without the perception of corruption.8 The general idea behind the unpleasant capitalists hypothesis is related to Max Weber’s notion of social legitimacy, but as applied to commercial institutions instead of the state. Weber (1978) described nonmaterial considerations, such as fairness, as giving legitimacy to certain relationships, leading individuals to accept them voluntarily, sometimes even against their own material interest.9 Research in economics on the “ultimatum game” makes a related point. People appear willing in some circumstances to “burn money” (that is, to reject insulting offers), implying that the material payoff is not their sole objective. And, importantly, in some 8. Four related papers are Aghion and others (2009), Alesina and Angeletos (2005), Landier, Thesmar, and Thoenig (2008), and Panizza and Yañez (2005). 9. See also the work in political psychology on system justification by Jost and Banaji (1994).

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variations of this game the “standing” of the proposer of the offer influences the outcome.10 As in the capture hypothesis above, there is a connection between corruption and government intervention, but only under the unpleasant capitalists hypothesis would one expect to observe a stronger public desire to regulate when corruption is greater. Another similarity with the capture hypothesis is that the subgroup of the population that votes can be considered an interest group affecting regulation (although here they are not just maximizing their income).11

III. Corruption Reduces the Appeal of Capitalism: Some Suggestive Evidence Our hypothesis is that lack of capitalism in poor countries is connected to, and is at least in part due to, the existence of widespread corruption in such countries.12 In a simple cross section of countries, Beck and others’ (2001) measure of left-wing government is significantly positively correlated with corruption.13 Of course, such a simple cross-country result could be 10. For example, when Hoffman and others (1994) assigned roles to subjects according to their performance on a general knowledge quiz, proposers became more aggressive in their offers. In research reported by Ruffle (1998), recipients competed on a task affecting the size of the pie in a dictator game. Allocators rewarded skillful recipients more generously, even at the cost of accepting a lower material payoff for themselves. This research also shows that offers to skillful recipients are motivated by a taste for fairness and not by strategic considerations. In Ball and others (2001), the status of participants in a certain market was determined in two different ways: in one, status was assigned according to subjects’ scores on a trivia quiz, whereas in the other, status was randomly assigned. (The assignments were observed by all participants.) Prices (and market surplus) favored the high-status person under both conditions. Rose-Ackerman (2002) discusses the impact of grand corruption on the “social contract.” On consent to taxation, see Levi (1988). 11. This differs from existing normative models of regulation in that it does not need to assume that the objective is to maximize consumption, or that the full population is being counted. Note that a challenge to these models is to explain why people bother voting at all. (For a start, see the model of altruistic voters of Rotemberg forthcoming.) Several normative models of regulation have made the point that the optimal amount of intervention can change in the presence of corruption (see, for example, Ades and Di Tella 1997; Banerjee 1997; and Glaeser and Shleifer 2003 as well as work by sociologists and political scientists on state capacity, such as Evans, Rueschemeyer, and Skocpol 1985 and Woo-Cummings 1999). 12. That corruption is indeed extensive in poor countries is documented by, for example, Mauro (1995) and Knack and Keefer (1995). 13. This finding is robust to the inclusion of other covariates including GDP per capita, income inequality, and dummies for the dominant religion, a recent history of war, and a history of communist rule. The variable measuring right-wing beliefs is positively correlated with income inequality, consistent with the empirical problems of the basic economic model (Meltzer and Richard 1981). For work on the varieties of capitalism, see, for example, the contributions in Hall and Soskice (2001).

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explained by government intervention causing corruption. In this section we present evidence suggesting that this cannot be the whole story. Although the evidence is not conclusive and is often open to alternative interpretations, it nonetheless presents a pattern that is highly unlikely to emerge if the capture hypothesis were the only channel connecting corruption and regulation. To explore whether corruption also creates a demand for government intervention, we use three types of data. First, we use aggregate (countrylevel) data on corruption and the ideology of government to show that surges in a country’s corruption index typically precede the election of left-wing governments, but that ideology lagged is uncorrelated with corruption. Given the quality of the data, this is, of course, only suggestive evidence for the hypothesis that corruption causes regulation. Second, we use survey data to study the correlation between ideological beliefs and the perception of corruption across people within a country at a point in time. We look at both ideological self-placement on a left-right scale and beliefs about the desirability of increasing private (relative to government) ownership of business and industry. The finding of a correlation would be consistent with either of two alternative hypotheses: that a sensibility that makes one prone to observe corruption and a desire for more regulation are fixed traits of left-wing individuals; and that observing corruption causes people to become more left-wing. However, such evidence is difficult to reconcile with a world where only the capture theory is important in explaining the prevalence of left-wing policies. Third, we study the correlation between self-reported experiences of anger (from the 2006 Gallup World Poll) and the perception of corruption within countries. Of course, anger could lead people to vote for less regulation instead of more. Thus, we estimate the correlation in high- and lowregulation countries separately. A lower correlation in a high-regulation sample would be consistent with the hypothesis that the observation of corruption angers people, but that the presence of regulation that interferes with business dampens this reaction. Under the assumption that voters prefer not to experience anger, this evidence suggests the possibility that corruption causes regulation. Although economists have recently begun considering the use of measures of well-being as summary measures of utility, data on individual emotions (which may or may not aggregate into a consistent measure of well-being) may also have research value. Anger is an obvious candidate for researchers interested in political economy. Psychologists have gathered extremely useful evidence for our purposes showing that anger appears to be associated

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with two conditions: the belief that others (as opposed to the situation or oneself) were responsible for some undesirable outcome; and that redress is still possible (and the self can still influence the situation).14 Jennifer Lerner and Larissa Tiedens (2006) discuss evidence showing that anger makes people indiscriminately punitive (and optimistic about their own chances of success at punishing the guilty). Interestingly, anger does not seem to be just a personality trait of left-wing individuals: Deborah Small and Lerner (2008) find that individuals induced to feel anger choose to provide less public assistance to welfare recipients than those induced to feel other emotions.15

III.A. Corruption and Left-Wing Government over Time within Countries Table 2 reports correlations between Beck and others’ (2001) measure of government ideology and the aggregate (country-level) corruption index data from the International Country Risk Guide, taken from Stephen Knack and Philip Keefer (1995). The corruption variable is available for the period 1982–94 and measures analysts’ opinions of the extent of corruption in a country. The estimates are derived from panel regressions using the Arellano and Bond (1991) two-step generalized method of moments (GMM) estimator for dynamic panel datasets that controls for unobserved effects. Our measure of a government’s ideological stance uses the number of legislative seats held by parties of a given ideology: we assign each country’s government a value of −1, 0, or 1 according to whether the largest government party is on the right, center, or left, respectively, using as weights the proportion of seats that the party holds in the legislature. Similar results are obtained when other available definitions are used. We measure time in four-year periods, since four years is the most common duration of electoral terms in our sample. Each observation thus approximates one election cycle in one country; similar results are obtained when the unit of time is one year (and when ordinary least squares is the estimation method). The results of regressing the ideology measure on the first lag of the corruption measure (first column of table 2) show that increases in corrup-

14. See Smith and Ellsworth (1985), Lazarus (1991), and the review by Lerner and Tiedens (2006). A focus on anger is preferable in this context because other negative emotions follow alternative appraisals: sadness (rather than anger) follows negative events that are blamed on situational forces, whereas shame follows such events that are seen as one’s own personal responsibility. Rotemberg (2005) connects anger to macroeconomic phenomena. 15. See also Bodenhausen, Sheppard, and Kramer (1994) on stereotyping and Goldberg, Lerner, and Tetlock (1999) on punishment.

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Table 2. GMM Regressions Relating Left-Wing Government Ideology to Corruptiona Dependent variable Independent variable

Left-Wing Government Ideologyb

Corruptionc

Left-Wing Government Ideology lagged one period

0.74** (0.22)

−0.06 (0.10)

Corruption lagged one period

0.10* (0.05)

0.31** (0.16)

GDP per capita lagged one periodd Wald χ2 (3) z value of Arellano-Bond test for zero autocorrelation in first-differenced errorse

−0.16 (0.18) 38.4 −1.5 Probability > z = 0.14

−0.34 (0.31) 4.3 1.1 Probability > z = 0.29

Sources: Beck and others (2001); Political Risk Services, International Country Risk Guide; World Bank, World Development Indicators 1995; authors’ regressions. a. The table reports results of Arellano-Bond two-step GMM dynamic panel data estimations, controlling for unobserved effects. Data are 137 panel observations from 72 countries and 3 four-year periods over 1982–94. Standard errors are in parentheses. Asterisks denote statistical significance at the *10 percent and **5 percent level. b. Left-Wing Government Ideology is defined as the orientation of the largest party in government, which is classified as either right-wing, centrist, or left-wing and assigned the value −1, 0, or 1, respectively; this value is then weighted by the proportion of seats that the party holds in the national legislature. c. As measured by the International Country Risk Guide country corruption index. The index ranges from 0 to 6 (higher numbers indicate greater corruption in our rescaling) and is based on the opinions of country experts as to the extent to which “high government officials are likely to demand special payments” and “illegal payments are generally expected throughout lower levels of government” in the form of “bribes connected with import and export licenses, exchange controls, tax assessments, police protection, or loans.” d. Adjusted for purchasing power parity in constant 1992 dollars and multiplied by 10,000 for ease of reporting. e. The test reports whether the null hypothesis of zero autocorrelation can be rejected. In both columns the null is not rejected at the 10 percent level of significance.

tion tend to precede increases in the political representation of left-wing parties. The size of the estimated coefficient on the corruption variable (0.10) implies that a 1-standard-deviation increase in corruption (1.5 on a 0–6 scale) corresponds to a change of 25.9 percent of a standard deviation in the government’s ideology [= (1.5 × 0.10)/0.58, where 0.58 is the standard deviation of government ideology]. For comparison, the second column reports the symmetrical exercise, regressing corruption on the first lag of the ideology measure; the results indicate that increases in left-wing representation in government do not tend to precede increases in corruption—the estimated coefficient does not achieve statistical significance.

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III.B. Perceptions of Corruption and Ideology across Individuals within Countries The data we use to investigate individual perceptions come from the 1995 wave of the WVS, which includes three questions that are relevant to our investigation. The first two broadly capture a desire for regulation. The first of these concerns ideological self-placement: “In political matters, people talk of ‘the left’ and ‘the right’. How would you place your views on this scale, generally speaking?” The interviewer then shows the respondent a 1–10 scale, with “Left” written below the number 1 and “Right” below 10. We construct a dummy variable called Left-Winger, which takes the value 1 if the answer is either 1, 2, 3, 4, or 5, and zero otherwise; similar results are obtained when we use information on each of the 10 categories. The second question is that discussed in section I concerning the desired form of ownership of business. The dummy variable Public Ownership captures the respondent’s desire for an increase in public ownership of business, taking the value 1 if the answer is 6, 7, 8, 9, or 10 and zero otherwise; again, similar results are obtained when we exploit all 10 categories. The third question of interest asks about the respondent’s perception of corruption in government: “How widespread do you think bribe taking and corruption is in this country?” The four possible responses are “almost no public officials are engaged in it”; “a few public officials are engaged in it”; “most public officials are engaged in it”; and “almost all public officials are engaged in it.” Because only 4 percent of respondents gave the first answer, we merged the first two categories; thus, we have three variables for perception of corruption—Few Corrupt, Most Corrupt, and All Corrupt—each taking the value 1 according to the respondent’s answer. None of our substantive conclusions depends on our collapsing of the first two categories. Table 3 reports results of our analysis of the responses of more than 50,000 people in 46 countries who answered the questions of interest. We estimated probit regressions of the following form: (1)

Yij = a ( Most Corrupt ij ) + b ( All Corrupt ij ) + c ( Personal Income ij ) + d ( Countryj ) + ε ij ,

where Yij is, alternatively, the Left-Winger or the Public Ownership variable for individual i living in country j, Countryj is a country dummy, and εij is a standard error term that is independent and identically distributed (i.i.d). The first column of table 3 shows a positive and significant correlation within countries between the perception of corruption and Left-Winger. This result survives the exclusion of income as a control as well as the

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Table 3. Probit Regressions of Ideological Orientation on Perceptions of Corruptiona Dependent variable Whole sample

U.S. sample only

Left-Wingerb

Public ownershipc

Left-Winger

Public ownership

Most Corruptd

0.03*** (0.006)

0.006 (0.006)

0.09*** (0.032)

0.005 (0.02)

All Corrupte

0.06*** (0.007)

0.02*** (0.006)

0.11*** (0.04)

0.02 (0.03)

44,962 (45 countries) 0.03

53,182 (46 countries) 0.08

1,182

1,273

0.01

0.02

Independent variable

No. of observations Pseudo-R2

Sources: World Values Survey 1995; authors’ regressions. a. The table reports the marginal effect of moving from one level of perception of corruption to the next higher one on the probability that the respondent will hold left-wing views or favor public ownership of business. Data are survey responses from the 1995 wave of the World Values Survey. All regressions include country dummies and control for household income using dummy variables for each third of the sample income distribution. Standard errors are in parentheses. Asterisks indicate statistical significance at the ***1 percent level. b. Dummy variable equal to 1 if the answer to the following question is 1, 2, 3, 4, or 5, and zero otherwise: “In political matters, people talk of ‘the left’ and ‘the right’. How would you place your views on this scale, generally speaking?” (The interviewer then shows a scale with the numbers 1 to 10, with the word “Left” below 1 and “Right” below 10.) c. Dummy variable equal to 1 if the answer to the following question is 6, 7, 8, 9, or 10, and zero otherwise: “How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between.” (The interviewer shows a scale of numbers with “Private ownership of business and industry should be increased” on the left and “Government ownership of business and industry should be increased” on the right.) d. Dummy variable equal to 1 if the respondent chose the third answer to the following question, and zero otherwise: “How widespread do you think bribe taking and corruption is in this country? 1. Almost no public officials are engaged in it. 2. A few public officials are engaged in it. 3. Most public officials are engaged in it. 4. Almost all public officials are engaged in it.” e. Dummy variable equal to 1 if the respondent chose the fourth answer to the above question, and zero otherwise.

inclusion of a wider set of personal characteristics such as sex, age, age squared, marital status, occupation, employment status, education, and other measures of income (although the sample size drops somewhat). The personal controls enter with the signs that one might expect: for example, people with higher incomes and men tend to lean ideologically toward the right. The key coefficients on the dummies capturing the perception of corruption are monotonic, large, and precisely estimated. To obtain a simple measure of the size of the effect, we report the coefficients in terms of marginal probabilities. A causal interpretation suggests that moving from a situation where people perceive little or no corruption to a situation where all

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officials are perceived to be corrupt raises the probability of self-placement on the left of the political spectrum by 6.1 percentage points. (But see below for an alternative interpretation.) The second column of table 3 reports analogous results for our second dependent variable, Public Ownership. The correlation of this variable with Most Corrupt is also positive and significant, suggesting that people who perceive more corruption tend to want to see an increase in government ownership of business and industry. Similar results are obtained with other measures of economic attitudes available from the WVS; the perception of corruption is also positively correlated with the perception that the poor are unlucky (rather than lazy) and the belief that government should reduce income differences (results not reported).16 The third and fourth columns of table 3 repeat the exercise restricting the sample to the United States, with similar results (although less precisely estimated). As noted above, two interpretations of this correlation are possible. One of these is causal: people who observe an increase in corruption change their beliefs toward the left. The second is not causal, but instead holds that the first regression reported in table 3 simply identifies a fixed trait of leftwingers, namely, that they tend to see corruption everywhere. In either case, however, a surge in a country’s level of corruption would lead to an increase in support for left-wing parties. In the first case the reason is obvious. To understand the second, consider a model of voting behavior involving competition between a right-wing and a left-wing candidate (who display their ideologies as fixed traits) for the vote of an uninformed public. When an exogenous upward shock to corruption takes place (is reported in the media, for example), the public notes that, at least on this issue, the left-wing candidate, who has been vociferating against corruption, has been correct all along. This makes it more likely that the public will think highly of the left-wing candidate from then on.17 16. The laziness question is, “Why, in your opinion, are there people in this country who live in need? Here are two opinions: Which comes closest to your view? 1. They are poor because of laziness and lack of will-power, [or] 2. They are poor because they are unlucky or society treats them unfairly.” 17. Interestingly, the perception of corruption exhibits a nonsystematic pattern with certain noneconomic beliefs: for example, it is positively correlated with the view that homosexuality is never justifiable, which presumably is a trait of the politically conservative. These results are discussed in detail in Di Tella and MacCulloch (2002). A difficult question is why certain beliefs often appear in bundles: for example, conservatives tend to believe both that effort pays and that abortion is wrong. For an attempt to explain part of this phenomenon through the use of metaphor, see Lakoff and Johnson (1980). For a review, see Feldman (2003).

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Note that the perception of corruption could refer to either of two different types of corruption: government corruption (that is, extortion), which is typically initiated by a bureaucrat or politician with authority over a firm that would otherwise be honest; and business corruption (that is, capture), which is typically initiated by a firm approaching a bureaucrat or politician to seek a favorable change in the law. Corruption of the capture variety is likely to be the more damaging of the two to the legitimacy of business.18 Finally, the “unpleasant capitalists” hypothesis would also predict that the strength of the correlation between observing corruption and demanding more regulation will depend on the level of regulation already in place. There are two possible reasons. First, voters might realize that regulation causes corruption (and other “bads”) so that their advocacy of more regulation as a punishment for capitalists is limited by the material costs of this strategy. Second, when regulation is high, acts of corruption may be considered more justifiable: voters may judge that firms had little choice but to bribe their way out of the morass of regulations. (Our model in section V makes this more precise.) Moreover, in high-regulation environments any corruption that might be observed is likely to be interpreted as extortion rather than capture. A simple suggestive test is to repeat the regressions in table 3 but to split the sample into high-, middle-, and low-regulation countries using Djankov and others’ (2002) measure of the number of procedures that a start-up has to comply with in order to obtain legal status. We define a low-regulation country as one where this number is less than 9, and a high-regulation country as one where it is greater than 12. Table 4 summarizes the main coefficients of interest when we reestimate the basic Left-Winger regression in the first column of table 3 separately for the low-regulation and high-regulation samples. In both samples a higher perception of corruption increases the probability of voting left, but the effect is smaller in the high-regulation countries: the coefficient on the All Corrupt variable is more positive for the low-regulation countries

18. In practice, the distinction between capture and extortion is blurred, because a firm being extorted may in turn convince the bureaucrat to deliver other favors, which may harm competitors. Often a firm that submits to extortion is not legally responsible for bribery. One question in the WVS does not talk about business explicitly but instead mentions “big interests” (and yields stronger results). It asks, “Generally speaking, would you say that this country is run by a few big interests looking out for themselves, or that it is run for the benefit of all the people? 1. Run by a few big interests. 2. Run for all the people.”

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Table 4. Probit Regressions of Ideological Orientation on Perceptions of Corruption, by Country Level of Regulationa Dependent variable: Left-Winger Independent variable

b

Low-regulation sample

High-regulation sample

Most Corrupt

0.06 (0.01)

0.02 (0.008)

All Corrupt

0.09 (0.02)

0.04 (0.009)

8,450 (9 countries) 0.02

22,609 (22 countries) 0.04

No. of observations Pseudo-R2

Sources: World Values Survey 1995; Djankov and others (2002); authors’ regressions. a. The dependent variable is the dummy variable for left-wing orientation described in table 3, note b. The table reports the marginal probability of moving from one level of perception of corruption to the next higher level on the probability that the respondent will hold left-wing views. Data are survey responses from the 1995 wave of the World Values Survey. Regressions include country dummies and control for household income as described in table 3. Standard errors are in parentheses. All results are statistically significant at the 1 percent level. b. See table 3, notes d and e, for definitions.

(0.09) than for the high-regulation countries (0.04), and the difference is significant at the 1 percent level. Similar results are obtained when Public Ownership is the left-hand-side variable (results not reported). The perception of corruption in the low-regulation countries increases the probability that a respondent will support more government ownership, whereas the correlation between perception of corruption and Public Ownership in the high-regulation countries is insignificant: the difference in the size of the effect across the two samples is also significant at the 1 percent level.

III.C. Anger at Corruption and the Demand for Regulating Capitalists Our final empirical exercise uses survey data on emotions from the 2006 Gallup World Poll to examine whether people who perceive corruption in business are more likely to experience anger. The results reveal a positive correlation, which, importantly, is weaker where business is heavily regulated. The Gallup data have separate measures for an individual’s perception of business corruption (which we interpret as capture) and of government corruption (which we interpret as extortion). Our Anger dummy variable is assigned a value of 1 when an individual reports having

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felt this emotion “a lot” during the day before the interview. We estimate the following probit regression: (2) Angerij = a ( Government Corruption ij ) + b ( Business Corruption )ij + c ( Regulation j ) + d ( Government Corruption ij

× Regulation j ) + e ( Business Corruption ij × Regulation j )

+ f ( individual controlsij ) + ε ij ,

where Government Corruption takes the value 1 for a positive answer to the question, “Is corruption widespread throughout the government in this country?” and Business Corruption takes the value 1 for a positive answer to the question, “Is corruption widespread within businesses located in this country?” We use two proxies for Regulation, called Number of Procedures and Time to Register, defined as in section I.C. These are objectively defined, measured at the country level, and correlated with other measures of government regulation or intervention in the economy (see Djankov and others 2002). We also test whether the correlation between corruption and anger differs according to the extent of regulation in place. The full sample consists of 68,587 observations across 80 countries worldwide. Number of Procedures is scaled down by a factor of 10, and Time to Register by a factor of 100, for ease in reporting the results. To interpret the results in the first column of table 5, consider a country where 11 regulatory procedures (the sample average) are necessary to start a business. The observation of business corruption is associated with a 4-percentage-point increase in the probability that an individual experienced anger the previous day (from the coefficient on Business Corruption) less the 3.3-percentage-point (= 0.03 × 11) effect due to the negative and significant interaction term between Business Corruption and Number of Procedures. Consequently, the net effect of business corruption in the presence of these regulatory procedures is to increase anger by an (insignificant) 0.7 percentage point. The effect of observing government corruption is different, at least to the extent that it has an insignificant interaction with the number of procedures. Note that the standard deviation of the number of procedures is 4.5, with a range from 2 to 21, and the average share of respondents reporting anger across the countries in our sample is 19.3 percent. To interpret the results in the second column, consider a country where 48 business days (again the sample average) are required to set up a business. The observation of business corruption is again correlated with a

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Table 5. Probit Regressions of Respondent-Reported Anger on Measures of Corruption and Regulationa Dependent variable: Anger Independent variable

5-1

5-2

0.05*** (0.01)

0.03*** (0.01)

Business Corruptionc

0.04*** (0.01)

0.04*** (0.01)

Number of Proceduresd

−0.01 (0.008)

Government Corruption × Number of Procedures

−0.02 (0.01)

Business Corruption × Number of Procedures

−0.03*** (0.01)

Government Corruption

b

Time to Registere

−0.04*** (0.01)

Government Corruption × Time to Register

−0.02 (0.01)

Business Corruption × Time to Register

−0.06*** (0.01)

No. of observations Pseudo-R2

68,587 (80 countries) 0.04

68,587 (80 countries) 0.04

Sources: Gallup World Poll 2006; Djankov and others (2002); authors’ regressions. a. The dependent variable is a dummy variable equal to 1 if the respondent answered yes to the following question, and zero otherwise: “Did you experience the following feeling during a lot of the day yesterday? How about anger?” The table reports the coefficients of the explanatory variables in terms of marginal probabilities. Both regressions include a control variable measuring the respondent’s “satisfaction with standard of living.” Data are 68,587 observations from 80 countries surveyed in 2006. Standard errors clustered at the country level are in parentheses. Asterisks indicate statistical significance at the ***1 percent level. b. Dummy variable equal to 1 if the answer to the following question is positive, and zero otherwise: “Is corruption widespread throughout the government in this country?” c. Dummy variable equal to 1 if the answer to the following question is positive, and zero otherwise: “Is corruption widespread within businesses located in this country?” d. Number of different procedures (divided by 10 for ease of reporting) that a start-up has to comply with in order to obtain legal status in the country. e. Number of business days it takes to obtain legal status to operate a firm, divided by 100 for ease of reporting.

4-percentage-point higher chance that an individual experiences anger, less the 2.9-percentage-point (= 0.06 × 48) effect due to the negative and significant interaction term between Business Corruption and Time to Register. In this case the net effect of business corruption is to increase anger by an (again insignificant) 1.1 percentage points. Government corruption, on the other hand, has an insignificant interaction with Time to

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Register. Note that the standard deviation of the latter variable is 31, and the range is from 2 to 152.19

IV. Discussion Our interpretation of these results is that corruption, especially in the form of capture, reduces the legitimacy of business and commercial institutions. When people observe corruption, they may believe that the rich are less “deserving” and become less accepting of their privileges. This makes populism, or, more precisely, voting in favor of inefficient regulation or taxes, more likely, much as when players in an ultimatum game reject positive offers. Alternatively, voters may experience anger when they see businesspeople earning their positions through bribes or other illegitimate means, and they are placated when business is regulated. Perhaps the main weakness of the unpleasant capitalists approach is that it requires auxiliary hypotheses to explain the precise type of intervention observed. Capitalists can be punished through a variety of means, and we lack strong arguments to explain why voters would choose more regulation when less inefficient forms of punishment, such as redistributive taxation (without affecting the production process), are available. While leaving a full investigation for future research, we provide here some tentative answers. The first is to note that taxation without regulation leaves businesspeople with a high position in the social hierarchy, whereas stronger regulation and control of business send a more direct message that business’s status is diminished. (If this is the true explanation, one would also expect to see it reflected in other aspects of society, such as the extent of conspicuous consumption by businesspeople.) A second possible explanation is that the pre-tax income distribution in a free-market economy with corruption might be perceived as too unfair, requiring economy-wide tax rates so high as to discourage effort (or encourage evasion). Such deadweight losses from taxation might be avoided if the actions that businesspeople can take are limited by regulation at the production stage in certain sectors. A third explanation is that taxation might be less observable to the public than regulation. It is worth noting that more regulation is likely to 19. We also investigated the effects of corruption and regulation on other emotions: regressing love on corruption, regulation, and their interaction yields no significant coefficients on any of these variables, and regressing joy on these same variables yields no significant coefficients on business corruption or the interaction terms. This accords with the view of psychologists that these emotions, although significantly negatively correlated with anger, capture emotions related to different kinds of events.

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prevent more competition and to be associated with higher rents to incumbent firms. One problem here is that it may be difficult for the public to perceive this kind of effect. And, of course, introducing competition, for example by allowing foreign entry, might also benefit incumbents by providing them an opportunity to sell their companies to the new entrants. Thus, a proposal for less regulation is ostensibly even more favorable to business. Finally, some types of mental processes (for example, “categorical thinking” as in Mullainathan and Shleifer 2006) could lead to the grouping of policies into bundles (for example, high taxes, high regulation, and a high level of state ownership; see also footnote 17). An even more difficult problem is that voters would be better off if they were offered the possibility of punishing the unpleasant capitalists individually, rather than punishing all capitalists regardless of blame through higher general regulation. A good legal system would contribute to the emergence and success of a political party that would credibly promise to punish deviant or corrupt capitalists and at the same time promise to push for less regulation.20 It is worth pointing out that U.S. presidents of the trust busting era were not seen as particularly antimarket and in fact included Republican presidents William McKinley, Theodore Roosevelt, and William Howard Taft. McKinley appointed the U.S. Industrial Commission on Trusts, which investigated such well-known business figures as Andrew Carnegie and John D. Rockefeller, and his successors Roosevelt and Taft actually dissolved several trusts.21 More recently, the case of Korea may also illustrate this mechanism. After the 1961 military coup, Korea’s new leader, General Park Chung Hee, decreed

20. Note that businesspeople in general would also benefit from strengthening the legal system because it would eliminate the negative (ideological) externality mentioned in the introduction: without a strong legal system, corrupt capitalists hurt honest capitalists by inviting intrusive regulation for all. The possibility of a corruption trap also exists, whereby beliefs about corrupt capitalists fuel intrusive regulation, prompting more business corruption. See Glaeser and Shleifer (2003) on alternative strategies of law enforcement, with an application to the rise of regulation during the Progressive Era. 21. Eliot Spitzer, when he was New York State attorney general, defended his highprofile cases against “big business” in similar terms: “Does anybody out there really believe that the market is better off with those problems before we revealed them? . . . Just as would anybody want to go back to the world before Teddy Roosevelt, where we broke up the cartels? I think not. And so even though those who pretend to speak for the free market kick vigorously against us when we reveal these problems, . . . the reality is that the market survives only because we reveal these problems, make them eminently clear, and try to confront them in a very real way” (Spitzer 2005, at about minute 32; emphasis added). Spitzer was later elected governor of New York with 69 percent of the vote.

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the Illicit Wealth Accumulation Act. He then arrested some of the country’s more prominent businessmen, including Lee Byung Chull (the head of Samsung), seized their assets, and paraded them through the streets of Seoul carrying placards with legends such as “I am a corrupt swine.” Later on, business groups received favorable treatment, and Park was able to implement policies that were not antimarket and were extremely popular (see Oberdorfer 1997). The unpleasant capitalists hypothesis can also be linked to a literature in anthropology in which mythmaking plays an important role in the construction of society. Couched in these terms, the hypothesis emphasizes that economic organization in developing countries lacks cultural heroes: an American prompted to name a prominent businessperson might think of people who invented great products or built a great company (like Henry Ford or Bill Gates), but a respondent in a developing country is likely to respond with the names of businesspeople who made their wealth in contracts with the state. The perception of Bill Gates as a cultural hero may favor the development of a capitalist system with low taxes, and the lack of such heroes in poor countries could be connected to their rejection of capitalism. In this vein it is also possible to derive a rejection of capitalism from the observation of corruption for efficiency (rather than fairness) considerations. For example, in a simple signal extraction problem involving managerial talent, the observation of corruption reveals to the public that the firm’s manager has decided to spend time and effort lobbying politicians rather than working and innovating, reducing the likelihood that the manager is productive.22 Given the current economic dislocation in the United States, it is of interest to note that during macroeconomic crises there is often the perception of corruption among large companies (particularly banks). This can be exacerbated when firm owners are perceived to be looting their companies even as they are being bailed out by the government (see, for example, Akerlof and Romer 1993). Our paper suggests that the design of macroeconomic bailouts can have lasting influences on the economic system by affecting the perception of how deserving the bailed-out bank owners and other capitalists are.23

22. For a model with these characteristics, see Di Tella and MacCulloch (2006). 23. The debate over regulation and limits to compensation in the wake of the recent bailouts suggests that this paragraph touches on only a few of several relevant considerations. One complication from a normative perspective is that a weak government may also make a crisis (and the necessity of a bailout) more likely.

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V. A Model Linking Corruption and Ideology through Fairness Considerations We now turn to a simple model intended to formalize the idea that “unpleasant capitalists” weaken public support for capitalism. The setup is a two-period model in which workers (who are also voters) in the second period update their evaluation of the altruism of firms, after observing the level of corruption in the first period. From this evaluation, workers decide on the level of taxes to set in the second period. Given the new level of taxes, firms and bureaucrats again jointly decide how much corruption to engage in (which hurts the workers). The preferences we assume imply that workers do not normally confiscate the wealth of the rich, because they would regard that as unfair (see, for example, Akerlof and Yellen 1990 and Rabin 1993). Specifically, individuals are assumed to have “reciprocal preferences” (see Levine 1998 and Rotemberg 2005).

V.A. Preferences Assigning the subscripts b, f, and v to variables corresponding to bureaucrats, firms, and workers, respectively, and denoting by U their material payoffs (apart from any altruistic feelings), we can define their preferences as (3)

Wb = Ub + λ bUv

(4)

Wf = Uf + λf Uv

(5)

Wv = Uv + λ vf λˆ f Uf + λ vb λˆ b Ub ,

( )

( )

where λs is a parameter denoting the unconditional level of altruism of the firms or the bureaucrats toward the workers. (All firms are assumed to be equally altruistic, but their level of altruism is unknown to the workers.) ˆ and is assumed to be an increasing The workers’ level of altruism is λ vs ˆ function of λs, the workers’ best estimate of the firms’ (or the bureaucrats’) level of altruism.24 Without loss of generality we assume that there are no altruistic feelings between firms and bureaucrats. This formulation assumes that workers would want to respond as they themselves have been treated. As stressed by David Levine (1998) and Julio Rotemberg (2005), this function has to adopt some positive values in order to explain voluntary contributions in public goods experiments, and 24. An alternative interpretation of λ is as a measure of the perceived merits of the capitalists (or the bureaucrats).

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some negative values in order to explain rejections of positive offers in ultimatum games. For purposes of this application, it is sufficient to ˆ . For simplicity, let the firms’ assume that λ is an increasing function of λ s altruism parameter take one of two values, λf ∈ {λ1, λ2}. The ex ante probˆ is k and is common ability that the value of the altruism parameter λ f f knowledge. The bureaucrats’ level of altruism, λb ≤ λ1, is assumed to take ˆ = λ, just a single value known to the workers. In this special case, λ b b although in a more general version of the model, λb can also be allowed to take either of two values.

V.B. Government Each worker is endowed each period with an amount, R, of resources that is put into the custody of a bureaucrat (one can think of this as, for example, a flow value of a public good used in national defense). The firm pays a lump-sum tax t to each worker.25

V.C. Technology and Contracts The numbers of firms, bureaucrats, and workers are assumed to be equal, so that the economy is organized as a collection of trios, each consisting of one firm, one bureaucrat, and one worker. The operations of the firm produce output p.

V.D. Corruption (of the Capture Variety) When corruption is present, the firm produces no output, and the players receive the payoffs described in equations 3 through 5, which we now denote Wscorrupt (corruption is observed only within a trio). In this case the worker’s material payoff is 0, and the firm and bureaucrat each obtain R − m, where m is a common moral cost that is privately observed (by the 2 bureaucrat and the firm but not by the worker). Its distribution is common knowledge and is denoted by F(m). When corruption is absent, the firm . In this case the does produce output and the players receive W honest s worker’s material payoff is αv p, and the firm and the bureaucrat receive

25. A standard assumption is that bureaucrats derive some level of enjoyment from the size of the public sector. This effect is already present in the model, arising indirectly since higher taxes increase the payoff to workers, whom bureaucrats care about. Thus, our results can also be derived assuming that bureaucrats care directly about the size of the public sector by letting Ub =g(t), where g is an increasing function of t.

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Figure 1. Timing in the Unpleasant Capitalists Model Period 0 Workers receive endowment R, which is placed in the custody of the bureaucrat.

Bureaucrat-firm-worker trios are formed Workers set the initial level of taxes t0.

Moral cost is revealed to the firm and the bureaucrat.

Firm produces output or engages in corruption.

Workers set the new level of taxes t1.

Moral cost is revealed to the firm and the bureaucrat.

Firm produces output or engages in corruption.

Period 1 Workers observe whether corruption has occurred and receives R, which is placed in the custody of the bureaucrat.

Source: Authors’ model described in the text.

the shares, αf p and αb p, respectively. It is reasonable to assume that the bureaucrat’s material payoff is smaller than the firm’s.

V.E. Timing At the beginning of the first period, the worker receives her endowment, which is placed in the custody of the bureaucrat, and sets the initial level of taxes t0 (figure 1). The bureaucrat-firm-worker trios are then formed. Within each trio, two of the players (the firm and the bureaucrat) learn the value of the common moral cost. Firms then either produce output or engage in corruption with the bureaucrat. At the start of the second period, the worker observes whether there has been corruption (given t0). The ˆ (without information about the realization of worker then estimates λ s the moral cost) and votes on a new level of taxes t1. In the second period there is again a corruption decision (the consequence of the new t) because the worker again receives the endowment, which is placed in the custody of the bureaucrat. A moral cost is again revealed to the firm and bureaucrat, determining whether either production or corruption occurs.26

26. It has to be assumed that the probability that the worker is the median voter is sufficiently small that firms can ignore signaling.

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V.F. Results For a given level of taxes, one can define a threshold moral cost for each altruism parameter such that a firm with a lower moral cost is corrupt. Thus, a firm for which α f p − t + λ fUv ( R + α v p + t ) ≥ R 2 − m

(6a )

produces, where Uf (0) = 0 and is assumed linear for simplicity. Otherwise it is corrupt. Call the level of m for which the equation above holds with equality mf. A similar logic determines mb, the moral cost that makes the bureaucrat indifferent between participating in the corrupt transaction and not. That is, α b p + λ bU v ( R + α v p + t ) = R 2 − mb .

(6 b)

Note that for corruption to occur, both the bureaucrat and the firm need to be willing to deal with each other. Since the honest material payoff to the firm is higher than that to the bureaucrat, the binding moral cost is always the firm’s, mf. The initial level of taxes, t = t0, is set by the workers so as to maximize expected utility, using ex ante probabilities k1 and k2: (7)

{

max t EWv =

2

∑k f =1

}

honest + ∑ k f F ( m f ) Wvcorrupt . ⎣⎡1 − F ( m f )⎤⎦ Wv 1 2

f

f=

After observing the state r, where r ∈ {corruption, honesty}, the worker is able to update her best estimate of the firm’s altruism parameter: λˆ f = λ1 z ( λ1 r ) + λ 2 z ( λ 2 r ) ,

(8)

where z(.) are conditional probabilities. Since the binding moral cost is always the firm’s, updating occurs only with respect to the firm’s level of altruism: (9)

(

z λf

) = k F ( m ) +( k F) ( m ) . kf F mf

corruption

1

1

2

2

The worker’s problem after observing the state r is to set the new level of taxes, t = t1, so as to maximize expected utility: (10)

{

max t EWv r =

∑ z ( λ ) ⎡⎣1 − F ( m )⎤⎦ W 2

f r

f =1

+ ∑ z (λ f 2

f =1

honest v

f

r

) F ( m )W f

corrupt v

}

.

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The first-order condition is given by ∂U v ⎤ − λ vf λˆ f ⎥ ⎣ ∂t ⎦

( )

⎡ ∑ z ( λ ) ⎡⎣1 − F ( m )⎤⎦ ⎢ 2

(11)

f r

f =1

− ∑ z (λ f 2

f =1

r

) ⎛⎜⎝ 1 − λ

f

f

∂U v ⎞ f ( m f ) (Wvhonest − Wvcorrupt ) = 0. ∂t ⎟⎠

Equation 11 suggests that the worker balances her income from taxes against her desire to be fair to the firm and against the incentive costs of high taxes (captured through an increase in corruption and in the size of the unofficial economy). The following proposition can be established: Proposition: 1. Observing corruption increases the desired tax when fairness considerations dominate the decision (because corruption lowers the chance that the firm is altruistic toward the worker). 2. When firms are relatively productive, there is less corruption, ceteris paribus. 3. When taxes are high, corruption does not change the worker’s estimate of the firm’s level of altruism. Proof: 1. Note that z(λ 2⎟ corruption) < k2. Then t1⎟ corruption > t1⎟ honesty, where t1⎟ r = ∂U v − λ vf argmax EWv⎟ r, since the first-order condition reduces to ∂t λˆ f = 0 when fairness dominates considerations of the size of the ˆ ) decreases, which shadow economy. If corruption is observed, λ (λ

( )

vf

f

implies that taxes must rise, assuming ∂ 2Uv /∂t2 < 0. 2. Define a productive firm as one that has a large p (relative to R). Calculate the probability of corruption as ∑ kf F ( m f ) and then note 2

that ∂mf /∂p < 0. 3. The reason is that m2 → m1 as taxes rise.

f =1

The intuition behind our key result—that the observation of corruption leads to higher taxes—is as follows. Firms dislike taxes. An act of corruption means that both the firm and the bureaucrat have been, to some degree, unfair toward the worker. But why should the worker react by punishing the firm and not the bureaucrat? First, recall that the worker gets some of the tax receipts. Second, and more important, for a similar level of altruism, the bureaucrat is always more prone than the firm to be corrupt,

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because it is assumed that the bureaucrat is being paid less than the firm. Thus, the act of corruption reveals only the firm’s level of altruism. This intuition also carries over to the case where bureaucrats can have either of two levels of altruism. It predicts that a person who sees corruption among public officials as widespread will express a dislike of capitalists relative to other groups (such as ethnic or religious minorities). In fact, the correlation between these two questions in the WVS is significant at the 1 percent level and has the predicted sign.27 An alternative explanation exploits the natural distinction between extortion and capture. By assumption, bureaucrats misbehave more than firms in the case of extortion, whereas the opposite is true under capture. Then, if capture cases tend to involve betterknown actors in business and politics than do extortion cases, they will tend to be covered more often in the media and to be more salient in the eyes of the public at elections. The model emphasizes the notion of commercial legitimacy, whereby the privileges (high income, status, laws protecting their activities, and the like) of businesspeople are accepted by the voters. This idea, which parallels the political science notion of legitimacy of the state, is summarized in the model by the degree of mutual respect (or reciprocal altruism) of the different actors.28 In particular, the main variable of interest—the level of taxation—is determined by a combination of self-interest, a sense of fairness toward others, and an incentive constraint arising from the difficulty of producing output in a highly taxed economy.29 This is related (but not identical) to a class of efficiency problems generated by high taxes that prevent the poor from fully taxing the rich. More precisely, in this model the main cost of taxes from the point of view of the voters is that firms hide more of their income (by joining the unofficial economy). Formally, the

27. This important aspect of the model where income differences between bureaucrats and capitalists drive the changes in beliefs against the richer actor can be taken as a metaphor for the differences in power between the two, whereas in the case of extortion the more powerful party is the bureaucrat. 28. This formalizes the idea that “corporations have an obligation to refrain from illegal payoffs as part of the quid pro quo implied by the laws that permit corporations to exist and to operate” (Rose-Ackerman 2002, p. 1889). 29. As in work on why the poor do not expropriate the wealth of the rich (for example, Piketty 1995; Putterman 1996; Roemer 1998; Benabou 2000; Benabou and Ok 2001; and Alesina and Angeletos 2005). Note that even if efficiency considerations were absent, a sufficiently strong desire for fair outcomes would bring about an interior solution. This is desirable given that the correlation between income inequality and taxation across rich countries is weak. We are ultimately more interested in the correlates of the equilibrium level of taxes than in what this level is.

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costs of this outcome are similar to the standard efficiency costs of high taxes.30 One advantage of the present setup is that voters update less when taxes are high, which could capture the idea that corruption is perceived as more “justifiable” when taxes are high. A difficulty for fairness models is that outcomes are judged according to how close they are to a target or “fair” outcome, but there is no natural way to define that outcome. We follow Levine (1998) and Rotemberg (2005, 2008) in assuming that an agent’s feelings toward others are affected by what they believe others feel toward them. Thus, more value is placed on money in the hands of an individual who is thought to be more altruistic. There may be an ideological externality in the sense that the individually rational acts of corrupt firms lead to the belief that all capitalists are undeserving and harmful to the rest of society. A natural extension is to allow different kinds of firms (good and bad) to exist in the economy simultaneously. It then becomes important to specify the extent to which altruism is correlated across firms. In small or stable societies, firms might be perceived to be part of a homogeneous group (as in the present model), and this leads to more updating against all firms (a stronger ideological externality). This provides some justification for the preoccupation of some firms with getting others to adopt forms of corporate social responsibility. Finally, in a repeated-game extension of the model, if a political party offering low taxes credibly promises to control corruption in the future, its appeal may still be less than that enjoyed by the party offering high taxes. The reason is that after observing corruption in the past, reciprocal preferences imply that voters will seek to punish firms by imposing higher taxes. And since corruption will be controlled in the future, there will be no incentive costs of higher taxes in terms of driving firms into the shadow economy, reinforcing the first effect. The regression equations in section III are designed to test the prediction in part 1 of our formal proposition. The desired level of taxes is proxied by the left-right placement of either the government (in section III.A) or the individual (in section III.B). The “anger” regressions in section III.C test for the transmission mechanism suggested by the term interacting the worker’s level of altruism toward firms (which depends on the observed 30. See Johnson and others (2000) and Svensson (2003). Extending the setup to include firm investment shows that corruption can be more damaging than taxes (as long as moral costs are discovered after investments are made), consistent with the arguments in Shleifer and Vishny (1993) and Wei (1997). An emphasis on tax evasion as a response to tax increases (for example, instead of labor supply responses) is consistent with the empirical evidence in Auerbach and Slemrod (1997).

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level of corruption) and the firm’s payoff (which depends on the level of taxes or regulation) in the workers’ utility function.

VI. Conclusion U.S.-style, pro-capitalist political ideas face electoral difficulties in poor countries. The first part of this paper showed, using data on business entry regulation, on the ideological orientation of political parties, and on people’s beliefs about the benefits of private versus government ownership of business, that intrusive regulation and left-wing rhetoric and beliefs are more common in poor countries than in rich ones. The second part of the paper suggested an explanation for these phenomena based on the idea that corruption plays a role in shaping ideologies. We then presented a model in which corruption generates the perception that capitalists are “undeserving” (for example, of their wealth and of the freedom to run their businesses without supervision). When the legal system is slow to punish them, the demand for more regulation, higher taxes, and government intervention to make the environment less business-friendly increases, even if this has material costs. Thus, corruption, even when limited to a small group of businesspersons, might interfere with the spread of capitalism. In some circumstances, however, the government can preserve capitalism by punishing only those capitalists whom the voters perceive as corrupt—as Teddy Roosevelt did almost a century ago. We have presented suggestive evidence consistent with this “unpleasant capitalists” hypothesis. First, we showed that increases in aggregate (country-level) corruption tend to precede electoral gains by left-wing parties in national elections. Second, we showed that in a given country at a given time, people who perceive corruption to be widespread also tend to place themselves toward the left of the ideological spectrum and to demand more government ownership of business and industry. We also found cross-country data on reported emotions, from the Gallup World Poll, to be consistent with the mechanisms involved in our explanation: anger is associated with perceptions of widespread business corruption, but the presence of regulation that makes life harder for business weakens this correlation. We interpret our findings to mean that voters get angry when they see businesspeople engaging in corrupt behavior, and that they are then more likely to elect left-wing governments that will more stringently regulate business, thus reducing their anger. More broadly, the paper shows that corruption has an ideological side to it, eroding the legitimacy of business and hampering the electoral performance of pro-capitalist parties.

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ACKNOWLEDGMENTS We thank our Brookings Panel discussants, George Akerlof and Peter Klenow, as well as Gregory Mankiw, Andrei Shleifer, and Julio Rotemberg, for very helpful suggestions. We also thank Rawi Abdelal, Nittai Bergman, Pedro Dal Bo, Steven Davis, Victor De Gennaro, Juan Dubra, Catherine Duggan, Oded Galor, Amihai Glazer, Ed Iacobucci, Christopher Kingston, Rafael La Porta, Howard Rosenthal, Antoinette Schoar, Enrico Spolaore, Jorge Streb, Nicolas Szekasy, and seminar participants at the University of California, Berkeley, the University of Chicago (applied economics), Brown University, the June 2003 World Bank Conference on Institutions, Enforcement and Corruption (Capri, Italy), the University of Colorado, Columbia University, the 2003 Latin American and Caribbean Economic Association (LACEA) Conference (Puebla, Mexico), the University of Toronto, the Canadian Institute for Advanced Research (Ottawa), the National Bureau of Economic Research Behavioral Macro and Entrepreneurship Conferences, Princeton University, and the October 2003 Wallis Conference on Political Economy (Rochester).

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Comments and Discussion COMMENT BY

GEORGE A. AKERLOF This paper by Rafael Di Tella and Robert MacCulloch is an important one. It establishes and codifies some important facts. Those facts correspond to an important view of the cause and cures of underdevelopment. The paper also gives a new perspective on the detailed nature of that cure. Here are some of the facts that the authors establish. The first is that poor countries tend to have governments that at least rhetorically, but probably also in reality, too, lean left rather than right. Poor countries not only are more likely to have such left-wing governments, but also have higher levels of corruption. They also tend to regulate business more, at least as indicated by measures of the number of procedures and the length of time needed to establish a business. Indeed, these measures suggest that poor countries tend to overregulate business. There appears to be more red tape. Furthermore, the authors establish the conditional possibility that, given income, countries tend to be more left-wing when more corrupt. An increase in corruption (a corruption “shock”) also increases the probability of electing a left-wing government. These facts are consistent with an interesting model that the authors propose. In this model the public reacts to corruption shocks by voting for the left and for more regulation, because they perceive that it is the capitalists who have acted badly. In the spring of 2009, this idea seems remarkably timely. The arguments of this paper take us back nearly two and a half centuries to the very beginning of modern economics. One of the important takeaways from the Wealth of Nations was Adam Smith’s dislike of monopolies, and especially of government-created monopolies, where the producers, with the connivance of the bureaucrats, used their powers to take 322

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advantage of the poor consumers. Monopoly-creating and monopolyprotecting governments were a shield and an abettor of corruption. Every economist understands this image. It seems, then, a paradox that poorcountry voters, rather than eschewing government bureaucracy and its ill consequences, lean left rather than right in response to a corruption shock. Shouldn’t the response to an increase in corruption be to decrease bureaucracy rather than increase it? The authors enumerate at the beginning four different reasons why poor people are electing these left-leaning governments. There may be a political equilibrium of interest groups. Or people may fail to understand the benefits of capitalism. Or they may prefer socialism for its own sake. Or, finally, capitalists may be considered bad people who need to be punished, and punished more, the more corruption there is. The last of these explanations is, of course, especially novel, and the authors present a signalling model with reciprocal altruism that corresponds to it. In that model, corruption is more indicative of bad intentions (Ivan Boesky might call it greed) on the part of capitalists than on the part of bureaucrats. To punish the capitalists, the voters lean left, calling for more bureaucracy. That, of course, is the focus of the authors’ special model. This paper brings forth some new ideas in development economics. One of those ideas concerns how to treat corruption. The authors’ picture of government in this paper is one in which political platforms and parties lie along a one-dimensional spectrum from left to right. George Lakoff (2004) would agree with that characterization. He has found that just knowing that someone is a conservative or a liberal is predictive of a wide range of views that one might think would be mutually independent. Lakoff says that conservatives have one view of the family: there should be a strict father, who makes rules that should be obeyed, whereas liberals have a much more permissive view of the family, in which parents and children negotiate standards regarding behavior. Lakoff claims that such a division will predict views on issues as seemingly disparate and as seemingly independent as whether or not the United States should have invaded Iraq and how much aid should be given to the poor. (The strict father says that Saddam Hussein violated the United Nations resolution and should therefore be punished, and that welfare mothers have violated the rule that families should be independent and should likewise take the consequences.) Corresponding to such a unidimensional view of politics, the potential responses available to the public, as depicted in the paper, are either to elect a more left-wing government, with more regulation and more bureaucrats, including, presumably, more “policemen on the street,” or, alterna-

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tively, to elect a right-wing government, with less regulation and fewer bureaucrats and, presumably, fewer “policemen on the street.” But one implicit and important conclusion of the paper is that governments might be characterized in more than one dimension. Indeed, a two-dimensional approach to politics underlies the authors’ repeated and approving mentions of Theodore Roosevelt. In this approach a response to corruption and to overregulation might instead be to elect a government that is more left wing in the sense of increasing the surveillance and prosecution of corruption, but also more right wing in the sense of decreasing the government regulation of private activities that provides much of the opportunity for corruption. Indeed, one of the important messages of this paper is that such two-dimensional reform is needed in many poor countries. Such reform then gives a twofold approach to capitalism. In terms of Teddy Roosevelt, “Speak softly and carry a big stick” would mean adopting a pro-business policy that imposes relatively little regulation on firms, but aggressively prosecuting those who misuse the freedoms granted under such a policy. Such two-dimensional policies would be both favorable to business and hostile to corruption in ways that cannot be pictured in a one-dimensional left-wing, right-wing trade-off. Thus, one of the important political-economy conclusions of the paper is that politicians in poor countries should emulate Teddy Roosevelt: they should be pro-business on the one hand, but also anticorruption on the other. There is scope for political platforms that are contrary to the current one-dimensional spectrum from left to right. I have suggested to one of the co-authors that he should run for president of Argentina on such a platform. The authors’ fundamental and basic observation that poor countries have biases toward left-wing governments, even where right-wing governments are capable of achieving more rapid economic growth, also poses a conundrum that requires answers. The authors allude to some potential cultural and historical reasons for this phenomenon. Some of these go back to the allegiances and allies of the colonial independence parties. Everyone probably has a favorite personal example. Mine is India. The history of Indian independence reveals the historical origins of left-wing ideas in that country. Biographies of leading Indian nationalists such as Gandhi and Nehru document their close ties to Britain, but, especially, they reveal the extent to which these leaders were influenced by people in Britain who sympathized with the colonized peoples, and who viewed Indians without racial or ethnic stigma. We would today classify as on the left, rather than on the right, those who included colonized peoples as well as their fellow Englishmen, Frenchmen, and so forth in the thought, “All men are created

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equal.” (And in historical fact, many on the right did not believe in equality at all.) I quote the U.S. Declaration of Independence advisedly, because the American revolutionaries—like the Indian nationalists 175 years later— were deeply grateful to prominent sympathizers in England. But there is a deeper reason for left-wing bias in poor countries, beyond the interaction and sympathy with left-wing parties in the “home” country at the time of independence. That deeper reason goes back to the nature of colonial rule itself. One of the great mysteries of modern history (and of economics) is how Europeans came to dominate a good share of the globe. In country after country, sometimes more than a century apart, handfuls of European adventurers took over the local governments and dispossessed the locals. These colonizers came from Britain, France, Spain, Portugal, the Netherlands, and even to some limited extent from Germany, Italy, and Scandinavia. Historians have described these takeovers, such as Hernando Cortés’ conquest of Mexico and the British takeover of Bengal, in minute detail. But the details curiously make the reasons for the takeovers, if anything, more mysterious. What stands out in all the histories is the extent to which the Europeans considered themselves a separate tribe, and the extent to which they denigrated the colonials. Edward Said (1994) described the extent to which the colonized peoples were cast in the role of “Orientals.” Frantz Fanon (1968) described the psychological helplessness engendered in the colonized themselves by this denigration of their culture and the advancement of the culture of the colonizing country on their soil. Left-wing parties’ ideology of equality thus has appeal, beyond its economic prescriptions, to those non-Europeans who, in the postcolonial period, are reacting to the stigmatization that was one of the instruments of their subjugation. The susceptibility of third world governments to manipulation even by minuscule outside forces was, of course, the major fact of the colonial conquest. But, remarkably, this same phenomenon continues into the postcolonial era and is still a reason for these countries to vote on the left. Tim Weiner’s Legacy of Ashes (2007) shows that the Central Intelligence Agency has played a major, if clandestine, role in the politics of many poor countries. Often very small interventions, as in Mohammed Mossadegh’s Iran in the 1950s, have resulted in dramatic political upheavals. The CIA’s ability to influence, and sometimes even topple, local third world governments indicates that the fragility of local governments and institutions discovered by Cortés and Francisco Pizarro in the 16th century, by the Nabobs of Bengal in the 18th and early 19th centuries, and by many others in the history of conquest persists to the present day. Genuine fear in poor third

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world countries of such outside influence gives another reason why third world elites lean to the left, and why they fear American influence. One can add, more than parenthetically, that this fragility of power in the poorest countries is most likely the most essential reason for their poverty, as well as for their government failures, including their inability to curb corruption. In conclusion, two big ideas stand out in this paper. The first is that the poorest countries have a bias toward left-wing regimes. These regimes have served their countries badly. The second big idea is that right-wing regimes, which bolster capitalism, business, and growth, should also take firm stances against corruption. That will be not only useful for growth, but a good political strategy as well, as the voters will come to expect that corruption will be punished. REFERENCES FOR THE AKERLOF COMMENT

Fanon, Frantz. 1968. Black Skin, White Masks. Grove Weidenfeld. Lakoff, George. 2004. Don’t Think of an Elephant! Know Your Values and Frame the Debate: The Essential Guide for Progressives. Chelsea Green. Said, Edward W. 1994. Orientalism. Vintage Books. Weiner, Tim. 2007. Legacy of Ashes: The History of the CIA. Doubleday.

COMMENT BY

PETER J. KLENOW Rafael Di Tella and Robert MacCulloch maintain in this paper that capitalism is unpopular in poor countries because voters perceive capitalists as corrupt. Voters see capitalists bribing politicians and therefore favor policies to limit crony capitalism. Thus, Di Tella and MacCulloch propose a causal pathway from voter-perceived corruption to voter approval of anticapitalist policies. I consider this hypothesis highly plausible and fairly novel to the economic literature. Yet the hypothesis is paradoxical if many anticapitalist policies (for example, the license raj in India) facilitate rather than discourage corruption. One can easily imagine this kind of reverse causality, flowing from regulation to corruption. And to the extent that corruption causes regulation, it may be because incumbent firms lobby politicians to grant them monopoly rights. A related hypothesis, advocated forcefully by Stephen Parente and Edward Prescott (2000), is that anticapitalist policies are chosen precisely so as to create or protect the rents of politically connected firms and workers. Di Tella and MacCulloch first document that developing countries tend to be led by left-leaning parties and to regulate business entry more than

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other countries do, and that survey respondents in these countries profess greater support for government ownership of industries. These observations are positively correlated with perceived corruption not just across countryyears, but also across time within countries (bursts of corruption boost leftleaning parties a few years later) and across individuals within countries (those who perceive more corruption disproportionately favor left-leaning parties and a bigger role for government in running industries). The time-series and cross-individual evidence supports the case that perceived corruption leads to anticapitalist policies, rather than the reverse. But why were there bursts of corruption? And did they occur disproportionately under right-leaning governments in the sample? Perhaps corruption turns voters against incumbent politicians, not capitalism per se. And why do some individuals perceive more corruption than others? Perhaps the personality type that is prone to be outraged by capitalism on ideological or redistributive grounds is also more attuned to instances of corruption by capitalists. Jaime Napier and John Jost (2008) provide related evidence that conservatives report greater subjective well-being than liberals because the former are less troubled by economic inequality. More important, if voters want to limit crony capitalism, why erect barriers to entry? Don’t such barriers favor the corrupt capitalists at the expense of consumers? According to a recent World Bank survey (2008a), entry barriers do in fact limit entry. Of course, such limits could be in the public interest. But Simeon Djankov and others (2002) present a plethora of evidence that these government-imposed barriers reflect “regulatory capture” rather than enlightened corrections of market failures. If the Di Tella and MacCulloch hypothesis is correct, then voters should be availing themselves of more effective ways of curbing corrupt capitalism. First and foremost would be high-profile prosecutions (fines, asset seizures, imprisonment) of exposed corruption. The elite investigating unit in South Africa known as the “Scorpions” comes to mind. Another possibility would be antitrust policies—the opposite of entry restrictions—to drive down incumbent rents. State ownership of industry, likewise, could limit capitalist corruption. Progressive tax rates on business and household income might be even more effective at limiting the benefits of capitalist corruption. Of course, all of these potential palliatives are themselves vulnerable to abuse. Still, it remains far from clear why restricting business start-ups would be at all effective, much less the method of choice, for punishing corrupt capitalists. If capitalist corruption breeds hostility toward capitalism, do poorer countries pursue these alternatives to entry regulation as well? If not, why not?

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A separate question is whether the populace in poor countries is convinced that capitalism maximizes the economic pie (as measured by GDP per capita). Most economists probably believe that it does; World Bank (2008b) is an example of this view. Dani Rodrik is a notable voice of dissent, often citing the disappointment that has followed capitalist reforms in Latin America and the purported success of government industrial policies in East Asia. (See Rodriguez and Rodrik 2001 for a skeptical view of the benefits of openness, for example.) William Easterly and others (1993) and Easterly (2005) argue that there is only a weak relationship between country growth rates and changes in any observed government policies, much less adoption of capitalist policies. Some studies, to be sure, do find large productivity benefits from capitalist reforms. Two examples are Rafael La Porta and Florencio Lo´pez-de-Silanes (1999), on Mexico’s early 1990s privatization wave, and Chang-Tai Hsieh and Klenow (2009), on China’s move away from inefficient state-owned enterprises. If economists do not see the evidence as clear-cut, there is plenty of room for public skepticism about whether capitalism maximizes average incomes. Francisco Buera, Alexander Monge-Naranjo, and Giorgio Primiceri (2008) present a model in which policymakers gradually learn whether “market-oriented policies” or “state intervention” maximize growth in income per capita. These authors use the index constructed by Jeffrey Sachs and Andrew Warner (1995) as a measure of market orientation, and their model allows countries to learn from their own experience and the experience of other countries. They find slow adoption of liberal (that is, pro-market) policies in a large set of countries from 1950 to 2001, because market orientation is associated with only mildly higher average growth rates. They claim that reversals of reforms are easily imaginable given the thin case for market orientation in many countries. Even if people are convinced that capitalism maximizes average income, it may not maximize their own income (or they may not believe it will). This is exactly what Parente and Prescott have in mind when they say that rent seeking results in barriers to competition and entry. But the same point could apply just as well to, say, labor income versus capital income: workers could imagine their share of the pie shrinking even as the overall pie expands in the wake of liberal reforms. Similarly, the subset of the population in a given region, of a given ethnicity, or of a given skill class could suffer from pro-capitalist reforms. Pinelopi Goldberg and Nina Pavcnik (2007) survey the literature and find that globalization (for example, reducing trade barriers) tends to increase

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economic inequality within developing countries. Of course, if the pie expands enough after pro-capitalist reforms, then even those gaining less than proportionately may nonetheless gain; here an example is the rural population in China in recent decades. To recap, Di Tella and MacCulloch propose that poor countries are hostile to capitalism because they associate it with ill-gotten gains to corrupt capitalists. They provide some suggestive pieces of supporting survey evidence, even if the evidence is far from airtight. I think this hypothesis should be taken seriously and subject to much further investigation, thanks to their contribution. REFERENCES FOR THE KLENOW COMMENT

Buera, Francisco J., Alexander Monge-Naranjo, and Giorgio E. Primiceri. 2008. “Learning the Wealth of Nations.” Working Paper 14595. Cambridge, Mass.: National Bureau of Economic Research (December). Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2002. “The Regulation of Entry.” Quarterly Journal of Economics 117, no. 1: 1–37. Easterly, William. 2005. “National Policies and Economic Growth: A Reappraisal.” Chapter 15 in Handbook of Economic Growth, vol. 1A, edited by Philippe Aghion and Steven J. Durlauf. Amsterdam: North Holland. Easterly, William, Michael Kremer, Lant Pritchett, and Lawrence Summers. 1993. “Good Policy or Good Luck? Country Growth Performance and Temporary Shocks.” Journal of Monetary Economics 32, no. 3: 459–83. Goldberg, Pinelopi K., and Nina Pavcnik. 2007. “Distributional Effects of Globalization in Developing Countries.” Journal of Economic Literature 45, no. 1: 39–82. Hsieh, Chang-Tai, and Peter J. Klenow. 2009 (forthcoming). “Misallocation and Manufacturing TFP in China and India.” Quarterly Journal of Economics 124, no. 4. La Porta, Rafael, and Florencio López-de-Silanes. 1999. “The Benefits of Privatization: Evidence from México.” Quarterly Journal of Economics 114, no. 4: 1193–1242. Napier, Jaime L., and John T. Jost. 2008. “Why Are Conservatives Happier Than Liberals?” Psychological Science 19, no. 6: 565–72. Parente, Stephen L., and Edward C. Prescott. 2000. Barriers to Riches. MIT Press. Rodriguez, Francisco, and Dani Rodrik. 2001. “Trade Policy and Economic Growth: A Skeptic’s Guide to the Cross-National Evidence.” NBER Macroeconomics Annual 2000, vol. 15, pp. 261–325. Sachs, Jeffrey D., and Andrew Warner. 1995. “Economic Reform and the Process of Global Integration.” BPEA, no. 1: 1–118. World Bank. 2008a. “Entrepreneurship Survey 2008.” Washington. go.world bank.org/T8G73Z9ZM0. _________. 2008b. The Growth Report: Strategies for Sustained Growth and Inclusive Development. Washington.

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GENERAL DISCUSSION Justin Wolfers suggested some alternative titles for the paper that might make one less comfortable about using the evidence presented, such as “Why Is the United States So Right-Wing?” or “Why Doesn’t the Welfare State Flow to Rich Countries?” or “Why Doesn’t Managed Capitalism Flow to Rich Countries?” Wolfers questioned the authors’ notion that regulation, an inefficient mechanism, serves as punishment for corruption, and he wondered whether the authors were using “capitalism” as a synonym for “the absence of regulation,” when in fact these are very different concepts. For example, the floor of the New York Stock Exchange is arguably the most regulated place in the world, but also the most capitalist. Finally, he suggested that the authors consider running “placebo” regressions using other indicators of emotion, such as perceptions of depression and love, which tend to be highly correlated with each other, in addition to the use of anger, to test whether the anger regression captured a real effect. Luigi Zingales also found the use of the word “capitalism” somewhat problematic and offered a distinction between pro-market policies (designed to increase competition and efficiency) and pro-business policies (aimed at capturing rents for the incumbents). Outside of the United States the experience most people have of “capitalism” is not of the pro-market variety but rather the pro-business one. It is not surprising, then, that they associate capitalism with corruption, and that they respond to corruption with a demand for less free markets. If they could experience the benefits of noncorrupt pro-market policies, Zingales felt, even populists would support markets more strongly. Carol Graham echoed Wolfers’s and the discussants’ comments about the use of anger as a measure, and particularly about the idea that angry people are more likely to perceive corruption. In both the United States and Latin America, people who lean to the right politically tend to be happier and therefore less likely to perceive corruption. She also wondered whether increasing regulation really made angry people calmer. Noting that some Latin American countries, such as Chile and Peru, have seen a rise in popularity of parties that are left-leaning but also pro-market, Graham questioned the true role of regulation against corruption, suggesting instead that a collapse of populist policy could have increased support for marketfriendly policies. Addressing the question of why more demand for regulation is found in countries with higher corruption, Philippe Aghion proposed an answer based not on right versus left but on the concept of social capital. Morecorrupt countries may tend to have less social capital, because in those

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countries even lower-level civil servants demand bribes, thus automatically reducing social capital because poorer people and businesses cannot afford to pay for necessary government services. Aghion also stressed that the causality between corruption and demand for regulation runs both ways: when there is more corruption, people demand more regulation, but the increase in regulation erodes the incentive to invest in social capital, and more corruption results. Trust, he argued, is the key factor in determining the success of deregulation. In a low-trust environment, deregulation will lower trust further unless investments in social capital are made as well. He posited that many transition economies have failed to undertake these “left-wing” investments. Robert Hall interpreted the paper as assuming that capitalism is the natural choice for any country, because capitalist countries have the highest incomes. In a study he had conducted with Charles Jones, however, high incomes were found to be a result of competent and honest government, and in particular of governments that suppress corruption. Capitalism actually scored negatively in the study. The most striking result, Hall stressed, was that government involvement in production is not by itself negatively related to income. Granted, these findings do not fully explain why, for example, the Scandinavian left suppresses corruption and delivers high incomes, while the Syrian left is highly corrupt and achieves abysmal income levels. But, Hall argued, rather than try to get countries to elect parties that follow the Adam Smith doctrine, the emphasis should be placed on getting left-leaning governments to perform well and suppress corruption. Betsey Stevenson followed up on the idea that regulation is not necessarily the same thing as punishment for capitalists and is not necessarily antimarket. Indeed, some regulation is necessary for well-functioning markets. A question worth asking, she suggested, is to what extent regulation substitutes for trust and to what extent it crowds out trust. Citing the recent example of the call for a 90 percent tax rate on executive bonuses in the wake of the AIG episode, she conceded that the public, when angry, may punish capitalists by taking things away from them. However, she noted that regulations introduced under these circumstances may not be part of the punishment and may be supported as a way to make the market function better. Jeffrey Miron wondered whether democracy is the right form of government for poor countries, given that they seem to vote themselves into vicious circles of corruption and regulation. He noted that it is difficult to name any poor democracies that have escaped that trap, but relatively easy to come up with examples of autocratically governed countries that have grown fairly successfully.

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Alan Blinder wondered why, if the problem is anger over corruption, the solution is not more-progressive taxation rather than more regulation. Citing the paper’s claims that people who perceive more corruption tend to favor regulation and government ownership of business, and that sharp increases in corruption tend to precede increases in left-wing voting, he asked the authors, with tongue slightly in cheek, whether they were predicting that the United States would see increases in regulation and nationalization and a larger Democratic majority in Congress over the next few years.

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