Brookings Papers on Economic Activity: Fall 2009

David H. Romer and Justin Wolfers, Editors Brookings Papers O N E C O N O M I C FALL 2009 AC T I V I T Y WILLIAMS ...

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David H. Romer and Justin Wolfers, Editors

Brookings Papers O N

E C O N O M I C

FALL

2009

AC T I V I T Y

WILLIAMS on the Zero Lower Bound on Interest Rates AGARWAL, DRISCOLL, GABAIX, and LAIBSON on Financial Regulation and Older Consumers REIS on Unconventional Monetary Policy HALL on the Government Purchases Multiplier REINHART and REINHART on Monetary and Fiscal Policy in the Great Depression


E C O N O M I C

FALL 2009

DAVID H. ROMER JUSTIN WOLFERS Editors

BROOKINGS INSTITUTION PRESS Washington, D.C.

AC T I V I T Y

Copyright © 2010 by THE BROOKINGS INSTITUTION 1775 Massachusetts Avenue, N.W., Washington, D.C. 20036 ISSN 0007-2303 ISBN-13: 978-0-8157-0407-2

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E C O N O M I C

AC T I V I T Y

FALL 2009

Editors’ Summary

vii

JOHN C. WILLIAMS

Heeding Daedalus: Optimal Inflation and the Zero Lower Bound

1

Comment by Michael Woodford 38 General Discussion 45 SUMIT AGARWAL, JOHN C. DRISCOLL, XAVIER GABAIX, and DAVID LAIBSON

The Age of Reason: Financial Decisions over the Life Cycle and Implications for Regulation

51

Comments by Giovanni Dell’Ariccia and Karen M. Pence 102 General Discussion 111 RICARDO REIS

Interpreting the Unconventional U.S. Monetary Policy of 2007–09

119

Comments by Timothy Besley and Donald L. Kohn 166 General Discussion 176 ROBERT E. HALL

By How Much Does GDP Rise If the Government Buys More Output?

183

Comments by Alan J. Auerbach and Christopher L. House 232 General Discussion 244 CARMEN M. REINHART AND VINCENT R. REINHART

When the North Last Headed South: Revisiting the 1930s Comment by Chang-Tai Hsieh 273 General Discussion 276

251

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

For information about previous issues of the journal, participants in this conference, and agendas for upcoming conferences, visit www.brookings.edu/economics/bpea/bpea.aspx. To purchase subscriptions or single copies, visit www.brookings.edu/press, or contact the Brookings Institution Press at 866-698-0010 or P.O. Box 465, Hanover, PA 17331-0465. Brookings periodicals are available online through both the Online Computer Library Center (contact OCLC subscription department at 800-848-5878) and Project Muse (http://muse.jhu.edu). Archived issues of the Brookings Papers on Economic Activity are available through JSTOR (www.jstor.org). ACCESSING THE JOURNAL

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

Sumit Agarwal Federal Reserve Bank of Chicago William C. Brainard Yale University John C. Driscoll Board of Governors of the Federal Reserve System Xavier Gabaix New York University Robert E. Hall Stanford University David Laibson Harvard University William D. Nordhaus Yale University George L. Perry Brookings Institution Carmen M. Reinhart University of Maryland Vincent R. Reinhart American Enterprise Institute Ricardo Reis Columbia University David H. Romer University of California, Berkeley John C. Williams Federal Reserve Bank of San Francisco Justin Wolfers University of Pennsylvania Michael Woodford Columbia University Jean-Marie Callan Brookings Institution Michael Treadway Brookings Institution Lindsey B. Wilson Brookings Institution

GUESTS WHOSE WRITINGS OR COMMENTS APPEAR IN THIS ISSUE

Alan J. Auerbach University of California, Berkeley Timothy Besley London School of Economics Alan S. Blinder Princeton University Christopher D. Carroll Johns Hopkins University Richard N. Cooper Harvard University Giovanni Dell’Ariccia International Monetary Fund J. Bradford DeLong University of California, Berkeley Karen Dynan Brookings Institution Janice C. Eberly Northwestern University Benjamin A. Friedman Harvard University Linda Goldberg Federal Reserve Bank of New York Gita Gopinath Harvard University Robert J. Gordon Northwestern University Christopher L. House University of Michigan Chang-Tai Hsieh University of Chicago Donald L. Kohn Board of Governors of the Federal Reserve System Deborah J. Lucas Northwestern University Karen M. Pence Board of Governors of the Federal Reserve System Christopher A. Sims Princeton University

Editors’ Summary THE BROOKINGS PANEL ON ECONOMIC ACTIVITY held its eightyeighth conference in Washington, D.C., on September 10 and 11, 2009. All of the papers were related in some way to the remarkable macroeconomic developments of the past two years: the papers considered the zero lower bound on nominal interest rates, consumer financial regulation, unconventional monetary policy, the macroeconomic consequences of fiscal stimulus, and monetary and fiscal policy in the Great Depression. This issue of the Brookings Papers on Economic Activity presents the papers from the conference, comments by the formal discussants, and summaries of the discussions of the papers by conference participants. IN THE FIRST PAPER, John Williams investigates the implications of the fact that monetary policy cannot push nominal interest rates below zero. An earlier literature studied this issue in light of the Bank of Japan’s experience with near-zero rates beginning in the mid-1990s and the Federal Reserve’s experience with very low rates in 2003 and 2004. That literature concluded that although the zero lower bound was likely to be a binding constraint relatively frequently, its average economic cost was likely to be small. Williams reexamines this conclusion in light of the recent crisis, during which most major central banks pushed interest rates close to zero. Williams’s first finding is that the zero lower bound is imposing very large costs in the current episode. He reports that although the downturn would have been almost as severe in the absence of the zero bound, the recovery would have been much faster. He estimates that an unconstrained Federal Reserve would have cut its federal funds rate target by about an additional 400 basis points, and that those cuts would have raised output over the next four years by a cumulative $1.8 trillion. Morevii

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Brookings Papers on Economic Activity, Fall 2009

over, this increased output would have come with little or no cost in terms of the Federal Reserve’s inflation objective. Looking forward, Williams considers the possibility that the recent sharp recession might signal a return to the greater macroeconomic volatility experienced in the 1960s and 1970s. Such a change would greatly increase the probability that the zero bound would become a binding constraint with a low target rate of inflation, fundamentally altering the case for a low target. For instance, Williams finds that a 1 percent annual inflation target would very likely be associated with frequent and costly encounters with the zero bound. A 2 percent target would also likely involve large costs if policymakers follow a conventional interest rate rule; however, these costs could be mitigated substantially if policymakers followed alternative monetary policy rules or used countercyclical fiscal policy more aggressively. Only when the inflation target is set as high as 4 percent can policymakers be confident that the zero lower bound will not prevent them from forcefully countering recessions. This is an important and provocative finding in light of the current debate around the optimal inflation target for the United States. IN THE SECOND PAPER, Sumit Agarwal, John Driscoll, Xavier Gabaix, and David Laibson mount a compelling case that many individuals, particularly older ones, often make poor financial choices. The authors investigate patterns of errors in personal financial decisionmaking across several large-scale databases. A particular strength of these datasets is that they allow the authors to demonstrate quite convincingly that errors in financial decisionmaking are both widespread and quite costly. The financial mistakes they consider include suboptimal use of offers of low interest rates on transfers of credit card balances, misestimation of housing values, and tolerance of excessive interest rates and fee payments. Across a wide range of financial decisions, the authors find that the tendency to make these errors initially declines with age, then flattens during the middle years, and finally rises increasingly steeply during old age. The “age of reason”—the trough of this U-shaped pattern—occurs when people are in their early fifties. This nonlinear pattern likely reflects the offsetting influences of financial experience (which rises with age) and cognitive ability (which generally declines with age). Financial errors by older persons are a particular source of concern because the stakes are often large—personal net worth is typically highest in old age—and the horizon to recover from errors is often limited. The authors then turn to an assessment of the regulatory implications of these findings, structured around a taxonomy of alternative regulatory

EDITORS’ SUMMARY

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regimes. Although their focus is on the particular challenges faced by older adults, much of the discussion is relevant for all vulnerable populations. In rough order from least to most interventionist, the regimes are laissezfaire, disclosure requirements, “nudges” (through choices of defaults, for example), expanded use of advance directives (instructions set out today against a future loss of competency), requirements for financial “driver’s licenses” (where individuals must establish competency to be allowed to make certain financial decisions), enhanced requirements for fiduciaries, protection of assets through sequestration in safe investments, ex post prohibition of financial products found to be deleterious, and mandatory preapproval of financial products by regulators. This taxonomy provides a useful framework to guide regulatory change, although the authors conclude that a clear roadmap for appropriate regulation awaits further empirical evidence on how consumers actually make financial decisions. Given the importance of the issues, much more research on household finance is clearly needed. STANDARD TEXTBOOK ACCOUNTS OF monetary policy describe the role of the central bank primarily in terms of its control of short-term interest rates (in the United States, the federal funds rate). Yet over the past two years the Federal Reserve and other central banks have deployed a whole host of alternative policy instruments, broadly described as “unconventional monetary policy.” In the third paper, Ricardo Reis examines the Federal Reserve’s use of these instruments. Reis’s focus is on the most novel of the new instruments: direct interventions in credit markets. To analyze these, Reis builds a theoretical model describing the interactions among four types of financial actors in addition to the central bank: investors, who have funds to lend but no specialized financial knowledge or skills; traders, who have a specialized ability to evaluate financial instruments; lenders, who have a specialized ability to evaluate investment projects; and entrepreneurs, who seek to borrow to undertake investment projects. This approach gives a central role to imperfect and incomplete information in explaining financial market transactions. These market imperfections not only result in departures from first-best outcomes, but in some circumstances can even lead to a collapse of normal financial flows. Reis then examines the Federal Reserve’s recent interventions through the lens of his model. He finds that their impacts often depend on the details of the interventions, on market conditions, and on the skills or authority the central bank has that other market participants do not. He also

x


finds that in many circumstances loans to traders are a particularly effective form of intervention. The paper then turns to the Federal Reserve’s other instruments. Reis explains why standard concerns about the possibility of insolvency that apply to firms or governments do not apply to the central bank when it increases its liabilities by expanding the quantity of reserves. He also argues that the expansion of reserves will lead to inflation only if the Federal Reserve becomes unduly concerned about the state of its balance sheet, or if deterioration of the balance sheet results in the Federal Reserve losing its independence. Turning to the question of appropriate interest rate policy in the vicinity of the zero lower bound, Reis concludes that the Federal Reserve has followed the precrisis recommendations of many economists—committing to a spell of low interest rates and raising expected inflation—only to a very small extent. ANOTHER DRAMATIC CHANGE IN the policy regime that has resulted from the crisis concerns the role of fiscal policy. Before the crisis, there was broad consensus that monetary policy should be the prime tool of stabilization policy. But with conventional interest rate policy constrained by the zero lower bound, many countries have turned to fiscal policy to stimulate their economies. In the fourth paper, Robert Hall examines one such fiscal tool: increases in government purchases. The title of his paper poses his central question: By how much does GDP rise if the government buys more output? Hall begins with an empirical exploration, focusing on aggregate U.S. data since 1930. His first conclusion is that government purchases have varied so little in the period since the Korean War that any test based on data from that period is almost certain to be uninformative. It is only the sharp shocks to government spending resulting from the Korean War and, especially, World War II that allow the correlation between increases in government purchases and increases in GDP to be estimated with any precision. Over his full sample, Hall finds that each dollar of increased government purchases is associated with an increase in real GDP of about 50 cents. Unfortunately for econometricians, government spending during those wars was not increased in isolation: both also featured major tax increases and (especially World War II) the use of command-and-control measures. Thus, no firm conclusions can be drawn from these episodes, although one can make a case that they provide likely lower bounds on the effects of increases in government purchases. Hall then turns to theory. Again, his first result is negative: he establishes that a baseline real business cycle model cannot deliver any substan-

EDITORS’ SUMMARY

xi

tial positive effect of increases in government purchases on output. He finds that two ingredients are critical to generating such an effect. The first is some force causing firms’ markups of price over marginal cost to fall when output expands, leading to the possibility of a procyclical real wage. The second is a force causing modest increases in the real wage to bring forth substantial increases in the quantity of labor. Hall argues that such a response cannot plausibly occur if households are on their labor supply curves, but can arise if there are labor market and search frictions. He also argues that a third ingredient, complementarity between hours and consumption, is needed for consumption to rise. Adding these features to a calibrated model leads him to conclude that output rises slightly less than one for one with government purchases under normal economic circumstances. Importantly, he also finds that the impact is likely to be considerably larger when the nominal interest rate is at the zero lower bound. IN THE FINAL PAPER, Carmen Reinhart and Vincent Reinhart reexamine the macroeconomic policy lessons from the last previous episode of a large, synchronized global downturn: the Great Depression. That economic slump, like the current one, led policymakers to implement unconventional monetary policy, which for many countries took the form of abandoning the gold standard. The favored interpretation among economic historians has been that this step, by changing expectations and permitting monetary expansion and currency depreciation, was critical to recovery. The key piece of evidence for this interpretation is the finding among a small group of industrialized countries of a quantitatively large and highly statistically significant correlation between how quickly countries left the gold standard and the severity of their depressions: countries that left sooner tended to fare better. Reinhart and Reinhart challenge this finding by showing that it is not robust. In particular, expanding the sample to include developing countries and a few others reduces the estimated effect and renders it insignificantly different from zero. The relationship is even weaker when alternative measures of output behavior are considered. This provocative finding raises the question as to why the strong relationship between exit and recovery disappears in the broader sample. The authors argue that one would not expect leaving gold to be enough to blunt a severe downturn, and they point out that many countries—including some that left gold early—did not experience substantial depreciations of their currencies against the U.S. dollar. Turning to fiscal policy, Reinhart and Reinhart present two main findings. First, fiscal policy during the Depression was procyclical or acyclical

xii


in many countries. Second, even in those where fiscal policy was countercyclical, the stimulus was erratic, so that any potential benefits might have been negated by uncertainty on the part of private agents about future policy. This uneven history makes the Great Depression a poor laboratory for testing the potential value of strong, steady fiscal stimulus in the face of a major worldwide downturn.

JOHN C. WILLIAMS Federal Reserve Bank of San Francisco

Heeding Daedalus: Optimal Inflation and the Zero Lower Bound ABSTRACT This paper reexamines the implications for monetary policy of the zero lower bound on nominal interest rates in light of recent experience. The ZLB contributed little to the sharp output declines in many economies in 2008, but it is a significant factor slowing recovery. Model simulations imply that an additional 4 percentage points of rate cuts would have limited the rise in the U.S. unemployment rate and would bring unemployment and inflation more quickly to steady-state values, but the ZLB precludes these actions, at a cost of $1.8 trillion in forgone U.S. output over four years. If recent events presage a shift to a significantly more adverse macroeconomic climate, then 2 percent steady-state inflation may provide an inadequate buffer against the ZLB, assuming a standard Taylor rule. Stronger countercyclical fiscal policy or alternative monetary policy strategies could mitigate the ZLB’s effects, but even with such policies an inflation target of 1 percent or lower could entail significant costs. Icarus, my son, I charge you to keep at a moderate height, for if you fly too low the damp will clog your wings, and if too high the heat will melt them. —Bulfinch’s Mythology, Chapter XX

J

apan’s sustained deflation and near-zero short-term interest rates beginning in the 1990s prompted an outpouring of research on the implications of the zero lower bound (ZLB) on nominal interest rates for monetary policy and the macroeconomy. In the presence of nominal rigidities, the ZLB will at times constrain the central bank’s ability to reduce nominal, and thus real, interest rates in response to negative shocks to the economy. This inability to reduce real rates as low as desired impairs the ability of monetary policy to stabilize output and inflation. The quantitative importance of the ZLB depends on how often and how tightly the constraint binds, a key determinant of which is the steady-state inflation rate targeted by the central bank. 1

2


If that rate is sufficiently high, the ZLB will rarely impinge on monetary policy and the macroeconomy. If sufficiently low, the ZLB may have more deleterious effects. All else equal, then, the presence of the ZLB argues for a higher steady-state inflation rate. Of course, not all else is equal. Since Martin Bailey (1956), economists have identified and studied other sources of distortions related to inflation besides the ZLB. Several of these—including transactions costs, real distortions associated with nonzero rates of inflation, and nonneutralities in the tax system—argue for targeting steady-state inflation rates of zero or below. Others—including asymmetries in wage setting, imperfections in labor markets, distortions related to imperfect competition, and measurement bias—argue for positive steady-state inflation (see, for example, Akerlof, Dickens, and Perry 1996). Balancing these opposing influences, central banks around the globe have sought to heed the mythical Greek inventor Daedalus’s advice to his son by choosing an inflation goal neither too low nor too high. In practice, many central banks have articulated annual inflation goals centered on 2 to 3 percent (Kuttner 2004). Simulations of macroeconomic models where monetary policy follows a version of the Taylor (1993) rule indicate that with an inflation target of 2 percent, the ZLB will act as a binding constraint on monetary policy relatively frequently (Reifschneider and Williams 2000; Billi and Kahn 2008). But these simulations also predict relatively modest effects of the ZLB on macroeconomic volatility with a 2 percent target, because the magnitude of the constraint will be relatively small and its duration relatively brief. Only with inflation targets of 1 percent or lower does the ZLB engender significantly higher variability of output and inflation in these simulations. In summary, these studies find a 2 percent inflation target to be an adequate buffer against adverse effects arising from the ZLB. The economic tumult of the past two years, with short-term interest rates near zero in most major industrial economies, has challenged this conclusion. As figure 1 shows, the global financial crisis and ensuing recession have driven many major central banks to cut their short-term policy rates effectively to zero; other central banks constrained by the ZLB include the Swedish Riksbank and the Swiss National Bank. Despite these aggressive monetary policy actions, and despite considerable stimulus from fiscal policy, these economies are suffering their worst downturns in many decades (figure 2). In addition, fears of deflation have intensified as falling commodity prices and growing economic slack put downward pressure on prices generally. As figure 3 shows, overall consumer price index (CPI) inflation

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JOHN C. WILLIAMS

Figure 1. Central Bank Policy Rates in Major Industrial Countries, 2003–09 and Forecast 2009–10a Percent per year Sept. 15, 2008 Lehman Brothers fails

6 Bank of England target rate

5 4

Bank of Canada target rate

3

European Central Bank EONIA

2 1

U.S. target federal funds rate Bank of Japan official lending rate 2003

2004

2005

2006

2007

2008

2009

2010

Sources: Board of Governors of the Federal Reserve System, table H.15, “Selected Interest Rates,” various issues; Bloomberg data; foreign central bank data. a. Faded lines are projections based on interest rate futures prices as of September 18, 2009. EONIA, euro overnight index average.

Figure 2. Real GDP in Major Industrial Countries, 2007–09 and Forecast 2009–11a Index, 2007Q4 = 100 104 102

Euro area

100 98

Canada United States United Kingdom

96 94

Japan

92 2007

2008

2009

2010

2011

Sources: Bureau of Economic Analysis data; Consensus Economics, “Consensus Forecasts,” September 2009; European Central Bank data; Haver Analytics; authorís calculations. a. Faded lines are projections based on the September 2009 “Consensus Forecasts.”

4


Figure 3. Inflation in Major Industrial Countries, 2003–09 and Forecast 2010–11a Four-quarter percent change in the CPI 5 4

Canada

Euro area

United States

3 2 United Kingdom

1 0

Japan

–1 –2 2003

2004

2005

2006

2007

2008

2009

2010

2011

Sources: Bureau of Economic Analysis data; Consensus Economics, “Consensus Forecasts,” September 2009; European Central Bank data; Haver Analytics; author’s calculations. a. Faded lines are projections based on the September 2009 “Consensus Forecasts.”

has fallen sharply in all major industrial economies. Much of this decline is due to falling commodity prices, especially energy prices, but core measures of CPI inflation have come down in these economies over the past year as well. Given these conditions, a strong case can be made for the desirability of additional monetary stimulus in the United States and in many other countries. But with rates already effectively at zero, this is not an option, at least in terms of conventional monetary actions. Several central banks have therefore taken unconventional measures, such as changes in the composition and size of the asset side of their balance sheets. But the short- and long-term effects of these unconventional policies remain highly uncertain, and in any case such policies are at best imperfect substitutes for standard interest rate cuts. This paper examines the effects of the ZLB on the current recession and reevaluates the expected future effects associated with the ZLB and the optimal inflation rate in light of new information and research.1 There are

1. I do not examine the issues related to multiple equilibria studied by Benhabib, SchmittGrohé, and Uribe (2001). Instead, like Evans, Guse, and Honkapohja (2008), I assume that discretionary fiscal policy will intervene to ensure that a unique steady state exists toward which the economy tends to revert.

JOHN C. WILLIAMS

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two main findings. First, the ZLB did not materially contribute to the sharp declines in output in the United States and many other economies through the end of 2008, but it is a significant factor slowing their recovery. Model simulations for the United States imply that an additional 4 percentage points of interest rate cuts would have kept the unemployment rate from rising as much as it has and would bring the unemployment and inflation rates more quickly to their steady-state values, but the ZLB precludes these actions. This inability to lower interest rates comes at a cost of about $1.8 trillion of forgone output over four years. Second, if recent events are a harbinger of a significantly more adverse macroeconomic climate than experienced over the past two decades, then a 2 percent steady-state inflation rate may provide an inadequate buffer against the ZLB having noticeable deleterious effects on the macroeconomy, assuming the central bank follows the standard Taylor rule. In such an adverse environment, stronger systematic countercyclical fiscal policy, or alternative monetary policy strategies, or both may be needed to mitigate the harmful effects of the ZLB with a 2 percent inflation target. Even with such policies, an inflation target of 1 percent or lower could entail significant costs in terms of macroeconomic volatility. The paper is organized as follows. Section I examines the effects of the ZLB on the U.S. economy during the current episode. Section II reexamines the assumptions and results of past calculations of the macroeconomic effects of the ZLB under the Taylor rule. Section III evaluates alternative monetary and fiscal policies designed to mitigate the effects of the ZLB. Section IV concludes.

I. Lessons from the Current Recession The ongoing global recession provides compelling proof that the ZLB can be a significant constraint on monetary policy, with potentially enormous macroeconomic repercussions. This section investigates two questions regarding the role of the ZLB in the current episode. First, how should one interpret the widespread phenomenon of central banks lowering their policy interest rates to near zero? Second, what are the consequences of the ZLB in terms of the depth of the recession and the speed of recovery? The fact that central banks have found themselves constrained by the ZLB should not be surprising; in fact, one of the three main “lessons” offered by David Reifschneider and Williams (2000) was that central banks pursuing an inflation goal of around 2 percent would encounter the ZLB

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relatively frequently.2 For example, in a briefing paper prepared for the Federal Open Market Committee (2002) Reifschneider and Williams find that with a 2 percent inflation target, roughly in line with the practices of many major central banks, a calibrated version of the Taylor rule (1993) hits the ZLB about 10 percent of the time in simulations of the Federal Reserve Board’s FRB/US macroeconometric model. Given that inflation has been centered around 2 percent in the United States since the mid-1990s, it was fully predictable that the ZLB would at some point become an issue— either as a threat, as in 2004, or as a reality, as it is today. Indeed, the fact that many central banks have already run up against the ZLB is evidence that they have learned a second lesson from recent research, namely, that policymakers should not shy away from the ZLB, but should instead “embrace” it. A common theme in that research is that when the economy weakens significantly or deflation risks arise, the central bank should act quickly and aggressively to get interest rates down, to maximize the monetary stimulus in the system when the economy is weakening. “Keeping your powder dry” is precisely the worst thing to do. Figure 4 shows nominal and ex post real rates on short-term Treasury securities going back to the 1920s. Despite a low rate of inflation and three recessions, nominal interest rates did not once approach the ZLB in that decade. That the ZLB appears to be a greater problem today than in the 1950s and early 1960s, when inflation was also low, may reflect “better” monetary policy in the more recent period. Indeed, a comparison of estimated Taylor-type rules covering that period and the more recent past indicates that short-term interest rates were far less sensitive to movements in output and inflation during the earlier period (Romer and Romer 2002). Of course, the U.S. economy and financial system were very different 50 years ago, so other factors may also explain the differences in interest rate behavior. To answer the second question, I conduct counterfactual simulations of the Federal Reserve’s FRB/US model in which the Federal Reserve is 2. Note that the lower bound on nominal rates does not necessarily equal zero. On one hand, lowering the rate below some small positive value may generate costly disruptions in money markets and other short-term financing markets. In this case central banks may choose never to lower rates all the way to zero, making the effective lower bound a small positive number. On the other hand, a central bank can in principle lower interest rates below zero by charging interest on reserves. However, there are still limits to how low interest rates can go, because banks and other agents can choose to hold currency instead, which yields zero interest less a holding cost κ, equal to the cost of safely storing and transporting cash. So, instead of a zero bound, there is a −κ lower bound on shortterm rates.

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Figure 4. Interest Rates on Treasury Bills, 1922–2009a Percent per year (quarterly averages) 16 12 Nominal

8 4 0 –4 –8 Realb

–12 –16 1925

1935

1945

1955

1965

1975

1985

1995

2005

Sources: Board of Governors of the Federal Reserve system, tabel H.15, “Selected Interest Rates,” various issues; Bureau of Labor Statistics data; author’s calculations. a. From January 1922 to December 1930, rate on short-term Treasury securities; after December 1930, rate implied by the auction high on three-month Treasury bills. Shaded bands indicate recessions. b. Nominal rate minus the change in the headline CPI over the preceding 12 months.

not constrained by the ZLB.3 These simulations are best thought of as scenarios where the economy enters the current episode with a higher steady-state inflation rate, and therefore the Federal Reserve has a larger interest rate buffer to work with. I consider experiments in which the Federal Reserve is able to lower the federal funds rate by up to 600 basis points more than it has. For comparison, Glenn Rudebusch (2009) finds, based on an estimated monetary policy rule and Federal Open Market Committee (FOMC) forecasts, that in the absence of the ZLB the funds rate would be predicted to fall to about −5 percent. Again, these experiments are not real policy options available to the Federal Reserve. But they allow me to quantify the effects of the ZLB on the recent trajectory of the U.S. economy. In evaluating the role played by the ZLB, it is important to get the timing of events right. Private forecasters did not anticipate until very late in 2008 that the ZLB would be a binding constraint on monetary policy. Figure 5 3. See Brayton and others (1997) for a description of the FRB/US model. In the counterfactual simulations in this paper, I use the version of FRB/US with vector autoregressive expectations. In the stochastic simulations used to evaluate alternative policy rules discussed in sections II and III, I use the version of FRB/US with rational expectations.

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Figure 5. Federal Funds Rate, Actual 2003–08 and Forecast 2008–11a Percent per year (quarterly averages)

5 4 3

Sept. 2008 Sept. 2009

2 Nov. 2008 1

Jan. 2009 2003

2004

2005

2006

2007

2008

2009

2010

Sources: Blue Chip Financial Forecasts, various issues; Board of Governors of the Federal Reserve System, table H.15, “Selected Interest Rates,” various issues. a. Faded lines are Blue Chip forecasts as of the indicated date.

uses the consensus forecast reported in Blue Chip Financial Forecasts to show the expected path of the federal funds rate at various points in 2008 and 2009. At the beginning of September 2008—right before the failure of the investment bank Lehman Brothers and the ensuing panic—forecasters did not expect the funds rate to fall below 2 percent. It was not until early December 2008, when the full ramifications of the panic became clear, that forecasters came to anticipate a sustained period of rates below 1 percent, and the ZLB clearly came into play. In fact, the FOMC cut the target funds rate from 1 percent to a range of zero to 1⁄4 percentage point on December 16, 2008. A similar pattern is seen in forecasts of policy rates in other major industrial economies, whose central banks made their final rate cuts in December 2008 or in 2009. The preceding argument is based on evidence from point forecasts, which typically correspond to modal forecasts. But in theory, economic decisions depend on the full distribution of the relevant forecasts, not just the mode. The possibility that the ZLB could bind in the future may have introduced significant downward asymmetry in forecast distributions of output and inflation in late 2008. Such an increase in the tail risk of a severe recession could have caused households and businesses to curtail spending more than they would have if the ZLB had not been looming on the horizon. Although the evidence is not definitive, forecasts in late 2008 do not appear

JOHN C. WILLIAMS

9

to provide much support for such a channel. Prices for binary options on the federal funds target rate indicate that even as late as early November 2008, market participants placed only about a 25 percent probability on a target rate of 50 basis points or lower in January 2009.4 In addition, the distribution of forecasts for real GDP growth in 2009 from the Survey of Professional Forecasters (SPF) in the fourth quarter of 2008 does not display obvious signs of asymmetric downside risks. In summary, the available evidence suggests that through late 2008, that is, until the ramifications of the financial panic following the failure of Lehman Brothers were recognized, forecasters did not view the ZLB as a binding constraint on policy. Therefore, it is unlikely that it had a significant impact on the major industrial economies before that time, outside Japan. Importantly, this is the period in which these economies were contracting most rapidly. According to monthly figures constructed by Macroeconomic Advisers, the period of sharply declining real U.S. GDP ended in January 2009, with declines of 2 percent in December 2008 and 0.7 percent in January 2009. Real GDP was roughly flat from January through July 2009. Since early 2009, however, the ZLB has clearly been a constraint on monetary policy in the United States and abroad. Interestingly, forecasters and market participants expect that the ZLB will pose a relatively shortlived problem outside Japan. The dashed extensions of the lines in figure 1 show market expectations of overnight interest rates derived from interest rate futures contracts as of September 2009. At that time market participants expected major central banks except the Bank of Japan to start raising rates by early 2010. As shown in figure 5, the Blue Chip forecasters have likewise consistently predicted that the Federal Reserve would start raising rates after about a year of near-zero rates. Even those forecasters in the bottom tail of the distribution of the Blue Chip panel expected the ZLB to constrain policy for only about a year and a half. Based on these expectations that central banks will raise rates relatively soon, one might be tempted to conclude that the effects of the ZLB have been relatively modest. Arguing against that conclusion is the fact that four quarters is the mean duration that the ZLB constrained policy in the model simulations with a 2 percent inflation target reported in Reifschneider and Williams (2000), and that even such relatively brief episodes can inflict costs on the

4. See Carlson, Craig, and Melick (2005) for a discussion of the methodology of computing probabilities from option prices.

10


macroeconomy. Moreover, these forecasts of the path of interest rates may prove inaccurate. I construct my counterfactual simulations starting from a baseline forecast set equal to the August 2009 SPF forecast (Federal Reserve Bank of Philadelphia 2009). The baseline forecast and the counterfactual simulations for short-term interest rates, the unemployment rate, and inflation (as measured by the core price index for personal consumption expenditure, PCE) are shown in figure 6.5 The SPF foresees the unemployment rate remaining above 7 percent through 2012 and core PCE inflation remaining below the median value of the FOMC’s long-run inflation forecasts of 2 percent through 2011. Interestingly, this forecast has the core inflation rate rising over 2010–11, despite continued high unemployment. Such a forecast is consistent with a Phillips curve model of inflation in which inflation expectations are well anchored around 2 percent (Williams 2009). Note that these forecasts incorporate the effects of the fiscal stimulus and unconventional monetary policy actions taken in the United States and abroad. I consider three alternative paths for the nominal federal funds rate and examine the resulting simulated values of the unemployment rate and the core PCE inflation rate. Given the evidence presented above that the ZLB was not a binding constraint until the very end of 2008, I assume in these counterfactual scenarios that additional nominal rate cuts of 200, 400, and 600 basis points occur in 2009Q1. I assume that the entire additional cut occurs in that quarter and that rates are held below the baseline values through 2010Q4, after which the short-term nominal rate returns to its baseline (SPF forecast) value. I assume no modifications of the discretionary fiscal policy actions and unconventional monetary policy actions that are assumed in the baseline forecast. I further assume that the monetary transmission mechanism works as predicted by the FRB/US model; that is, that the disruptions in the financial sector do not change the marginal effect of the additional rate cuts.6 Admittedly, these are strong assumptions, but I do not see better alternatives.

5. Because the SPF does not provide a forecast for the federal funds rate, I use its forecast for the three-month Treasury bill rate as a proxy. In addition, the SPF does not report quarterly figures for 2011 and 2012. I therefore interpolate quarterly figures based on annual figures for those years and the multiyear forecasts for PCE inflation. 6. It can be argued that monetary policy may be more or less effective than usual in the current environment, but there is little empirical evidence to guide any modifications of the model.

11

JOHN C. WILLIAMS

Figure 6. Baseline Federal Funds Rate Forecast and Counterfactual Simulations, 2008–12 Short-term interest rates Percent per year 3 2 1 0 –1 –2 –3 –4 –5 –6

Nominal rate, baseline

With 2 percentage points lower nominal funds rate

Real rate, baseline

With 4 percentage points lower nominal funds rate 2008

2009

2010

2011

2012

Unemployment rate Percent 10 Baseline forecast

9 8 With 2 percentage points lower nominal funds rate

7 6


5 2008

2009

2010

2011

2012

Core PCE inflation Percent With 2 percentage points lower nominal funds rate

2.5


2.0 1.5 1.0

Baseline forecast

0.5 2008

2009

2010

2011

Sources: Survey of Professional Forecasters, August 2009; author’s calculations.

2012

12


I evaluate the simulated outcomes using a standard ad hoc central bank loss function of the form (1)

L=

∑ {( π

2012 q 4

t = 2009 q 1

}

− 2 ) + λ ( ut − u*) , 2

t

2

where π is the core PCE inflation rate, u is the unemployment rate, and u* is the natural rate of unemployment. The inflation goal is assumed to be 2 percent. The SPF forecast runs only through late 2012, so I cannot extend the calculation of the loss beyond that point, nor can I use the optimal control techniques developed by Lars Svensson and Robert Tetlow (2005). Table 1 summarizes the outcomes for the baseline forecast and the alternative policy simulations. The first four columns report the central bank loss for different weights λ on unemployment stabilization and different values for the natural rate of unemployment assumed in the loss function.7 These values for the natural rate cover the range of recent estimates. For example, the median estimate in the most recent SPF survey was 5 percent, and the highest reported estimate was 6 percent. Justin Weidner and Williams (2009) provide evidence suggesting that the output gap is smaller than standard estimates. After taking into account the historical relationship between the output gap and the unemployment gap, they calculate an average estimate of the output gap that suggests that the natural rate of unemployment may currently be as high as 7 percent. The final two columns of table 1 report the simulated values of the unemployment and inflation rates at the end of the forecast period (2012Q4). Cutting the federal funds rate by an additional 200 basis points (second row of table 1) speeds the pace of economic recovery relative to the baseline forecast, bringing the unemployment rate to near 61⁄2 percent by the end of 2012. Meanwhile the reduction in slack and the lower exchange value of the dollar cause core price inflation to rise more quickly back to 2 percent. In fact, core inflation slightly overshoots 2 percent by the end of 2012. This policy reduces the central bank loss function by a considerable amount for all combinations of parameters reported in the table. In the baseline forecast, inflation is below target for nearly the entire forecast period, and the unemployment rate is consistently above the natural rate, so the added 7. Note that I assume the same baseline forecast independent of the value of the natural rate of unemployment used in computing the central bank loss. That is, I treat the natural rate as an unobservable variable that underlies the baseline forecast. In particular, I do not consider alternative baseline forecasts predicated on alternative views of the natural rate.

248.0 193.5 151.0 120.2

4.4 2.5 1.4 1.3

5%

103.8 77.5 63.0

142.0

6%

λ = 1, natural rate of unemployment u* =

46.1 36.0 37.8

68.0

7%

6.6 5.9 5.2

7.3

Unemployment rate u

2.2 2.3 2.5

2.0

Annual inflation π

Outcome in 2012Q4 (percent)

Source: Author’s calculations using data from the August 2009 Survey of Professional Forecasters (SPF; Federal Reserve Bank of Philadelphia 2009). a. Table reports simulations of the FRB/US model and assume an annual inflation target of 2 percent. 2 2 b. L = Σ 2012q4 t=2009q1{(πt − 2) + λ(ut − u*) }. c. Forecasts for short-term interest rates, the unemployment rate, and inflation (annual rate of change in the personal consumption expenditures price index) from the SPF survey; see figure 6.

Baseline forecastc Additional reduction in federal funds rate of 2 percentage points 4 percentage points 6 percentage points

Simulation

Weight on unemployment stabilization λ=0

Central bank loss Lb

Table 1. Forecast Effects of Alternative Monetary Policy Paths, 2009–12a

14


200 basis points of rate cuts moves both objective variables closer to target. Only in the final few quarters of the simulation do trade-offs materialize. Policy would need to be tightened at some point to bring inflation back down to 2 percent. Of course, in all cases the appropriate path for policy in 2012 and beyond depends on the natural rate of unemployment and the path of the economy in later years. The second simulation, which assumes 400 basis points of easing relative to baseline, is more effective at bringing the unemployment rate down and inflation closer to the assumed 2 percent target over most of the forecast period. This policy yields a much lower central bank loss for all parameter combinations reported in the table. The results are striking. Even when the sole objective is the stabilization of inflation (λ = 0), an additional 400 basis points of easing is called for. When the central bank also cares about stabilizing unemployment around its natural rate (λ = 1.0), 400 basis points of easing reduces the central bank loss even with a 7 percent natural rate of unemployment. The third simulation of 600 basis points of easing relative to baseline yields mixed results. It results in a smaller loss over the simulation period as long as the natural rate of unemployment is below 7 percent. But it accomplishes this at the cost of an inflation rate that is 1⁄2 percentage point above the assumed target at the end of 2012. Given these results, such a sharp reduction in rates would be beneficial only if the natural rate of unemployment is not much higher than 5 percent and if it were followed by a much sharper increase in interest rates in 2011 and 2012 than assumed in the simulation. Based on these results, a compelling case can be made that at least an additional 400 basis points of rate reduction in early 2009 would have been beneficial in terms of stabilizing inflation around a 2 percent target, and unemployment around its natural rate. The cost of the ZLB in this episode can be measured as the difference in real output between the baseline forecast and the alternative simulation of an additional 400 basis points of rate cuts. In that simulation, real GDP averages about 3 percent above the baseline forecast over 2009–12 (the unemployment rate averages about 1 percentage point below baseline over this period). An additional 4 percentage points of monetary stimulus thus yields a total increase in output over these four years of about $1.8 trillion. This translates into an increase in output per capita totaling about $5,700 over these four years. The implied increase in consumption is about 2 percent, on average, which translates into a total increase in consumption per capita of about $2,700 over the same period. (These calculations ignore any additional effects on output

JOHN C. WILLIAMS

15

outside the forecast window.) By any measure, these are sizable losses from the ZLB and much larger than standard estimates of the cost of business cycle fluctuations.8 A final caveat is in order. A notable feature of these alternative scenarios is that they entail sizable negative real interest rates for two years. In the second alternative scenario of a 400-basis-point reduction in interest rates, the real funds rate averages below −5 percent during 2009 and 2010. As shown in figure 4, there have been few peacetime episodes of large, sustained negative real interest rates. Although clearly helpful from the perspective of stimulating the economy, such a lengthy period of very negative real rates could have harmful unintended consequences, such as fueling another speculative boom-and-bust cycle (see, for example, Taylor 2007).

II. Reexamining the Lessons from Research These simulations illustrate the large costs associated with the ZLB in the current situation. However, if this recession represents a unique, extraordinary incident, it has no implications for the choice of inflation goal or design of a policy rule regarding the ZLB. Indeed, a third “lesson” from Reifschneider and Williams (2000) is that only in rare instances will the ZLB be very destructive to the macroeconomy, requiring fiscal or other policies to avoid a complete economic collapse. The recent episode—characterized by reckless risk taking on a global scale, poor risk management, lax regulatory oversight, and a massive asset bubble—may be such a 100-year flood. Alternatively, this episode may have exposed some cracks in the analysis of the ZLB’s effects on the ability of central banks to achieve their macroeconomic stabilization goals. In this section I review some key assumptions from the literature and conduct “stress tests” of past research, applying lessons from the past few years. The magnitude of the welfare loss owing to the ZLB depends critically on four factors: the model of the economy, the steady-state nominal interest rate buffer (equal to the sum of the steady-state inflation rate, π*, and the steady-state, or “equilibrium,” real interest rate, r*), the nature of the disturbances to the economy, and the monetary and fiscal policy regime. Recent events have challenged a number of assumptions regarding the structure of the macroeconometric models used in past research on the ZLB. Eventually, 8. The current episode, as projected by the SPF forecast, is an outlier in both depth and duration compared with earlier post–World War II recessions. But as argued in this paper, the ZLB has played a key role in this outcome, a situation that has not occurred since the Great Depression.

16


new models will emerge from the experience of the past few years, but for now I am limited to the models that exist.9 Because the effects of the ZLB depend on the extent of nominal and real frictions (Coenen 2003) and the full set of shocks buffeting the economy, quantitative research into the effects of the ZLB is best done with richer models that incorporate such frictions. For this reason, in this paper I use the Federal Reserve Board’s FRB/US model for my analysis, rather than a small-scale stylized model. One critical aspect of model specification is the assumption that inflation expectations remain well anchored when the ZLB is binding. As discussed in Reifschneider and Williams (2000) and in George Evans, Eran Guse, and Seppo Honkapohja (2008), if inflation expectations are not anchored, the ZLB could give rise to a calamitous deflationary spiral, with rising rates of deflation sending real interest rates soaring and the economy into a tailspin. In the event, inflation expectations have been remarkably well behaved in all major industrial economies. The dashed extensions of the lines in figure 3 show consensus forecasts of overall inflation in several countries. Despite the severity of the downturn, forecasters expect inflation rates to bounce back over this year and next. Long-run inflation expectations in these countries, shown in figure 7, have also been very stable over the past several years, despite the large swings in commodity prices and the severe global recession. Thus far, at least, inflation expectations appear well anchored. But there remains a risk that they could become unmoored, in which case the ZLB poses a larger threat. A second key assumption is the steady-state real interest rate, which, along with the steady-state inflation rate, provides the buffer for monetary policy actions to stabilize the economy. A worrying development over the past decade is the decline in real interest rates. In the United States, the long-run average of the real interest rate—defined as the nominal federal funds rate less the PCE inflation rate—is about 21⁄2 percent, the figure used by Reifschneider and Williams (2000). But the Kalman filter estimate of the equilibrium real interest rate, using the Laubach and Williams (2003) model, has fallen to about 1 percent, as shown in figure 8. Other time series–based estimates show similar or even larger declines. For example, the trend real interest rate computed using a Hodrick-Prescott filter (with a smoothing parameter of 1600) was around zero in the second quarter of 2009. As shown in figure 8, the decline in the Kalman filter estimate of the equilibrium real interest rate coincided with the recent severe downturn 9. Beyond the need for better models of financial frictions, the global nature of the crisis has important implications for the effects of the ZLB and the ability of monetary policy to stabilize the economy (Bodenstein, Erceg, and Guerrieri 2009).

17

JOHN C. WILLIAMS

Figure 7. Long-Run Inflation Expectations in Major Industrial Countries, 2000–April 2009 Percent per year United States

United Kingdom

2.5 2.0

Canada

Euro area

1.5 1.0 Japan

0.5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Source: Consensus Economics, “Consensus Forecasts,” 2000–April 2009, April and October issues.

Figure 8. Estimates of Equilibrium Real Short-Term Interest Rates, 1975–2009 Percent per year One-sided Laubach-Williams estimate 3

2

1

Average real federal funds rate, 1965–2009 Two-sided Laubach-Williams estimate

1980

1985

1990

1995

TIPS 5-year-forward/ 5-year-ahead real ratea

2000

2005

Sources: Board of Governors of the Federal Reserve System, table H.15, “Selected Interest Rates,” various issues; Bureau of Economic Analysis data; author’s calculations. a. TIPS, Treasury inflation-protected securities.

18


and may prove to be an overreaction to it. This hypothesis receives some support from prices of inflation-indexed Treasury securities. Evidently, investors expect real interest rates to remain low over the next five years but to be closer to historically normal levels thereafter. Nonetheless, the massive loss in wealth and high levels of household debt both in the United States and abroad could cause a sizable increase in private saving (Glick and Lansing 2009). All else equal, such an increase in saving would imply that the steady-state real interest rate will remain low for some time. Based on this evidence, a reasonable point estimate of the steady-state real federal funds rate is about 21⁄2 percent, but the risk is real that it could be as low as 1 percent. Of course, the steady-state real rate could be higher than 21⁄2 percent, possibly owing to large fiscal deficits in the United States and abroad (Laubach 2009). In that case the effects of the ZLB would be correspondingly muted. The third key assumption concerns the nature of future disturbances to the economy. Because the ZLB affects events in the lower tail of the distribution of interest rates, the distribution of shocks is a critical factor determining its effects. Reifschneider and Williams (2000; FOMC 2002) based their analysis on the covariance of estimated disturbances from the mid-1960s through the mid-1990s. Other research is based on disturbances from the period of the Great Moderation from the early 1980s on (Coenen, Orphanides, and Wieland 2004; Adam and Billi 2006; Williams 2006). Recent events hint that what were once thought to be negative “tail” events may occur frequently, and that the period of the Great Moderation may suggest an overly optimistic view of the future macroeconomic landscape. Given the limited number of observations since the start of the financial crisis, one cannot yet ascertain whether these events represent a sustained break from the past behavior of disturbances. Given the great deal of uncertainty—much of it difficult or even impossible to quantify—regarding the future economic environment, I take a minimax approach to evaluating policies. Specifically, I look for policies that perform well in very adverse or “worst-case” scenarios as well as in the baseline scenario. I take the baseline scenario to be a steady-state real interest rate of 21⁄2 percent, and I consider disturbances drawn from a joint normal distribution based on model disturbances from 1968 to 2002. I consider alternative adverse scenarios characterized by a steady-state real interest rate of 1 percent and disturbances drawn from more adverse distributions. Of course, these two sources of uncertainty represent only a slice of the spectrum of uncertainty relevant for the ZLB. By taking worst cases from these two sources, my aim is to provide insurance against a wide

19

JOHN C. WILLIAMS

variety of other forms of uncertainty, including model misspecification and unanchored inflation expectations. I follow the simulation methodology of Reifschneider and Williams (2000), with two relatively minor modifications. First, the simulation results reported here are based on a more recent vintage of the FRB/US model from 2004. Second, following Athanasios Orphanides and others (2000) and Reifschneider and Williams (FOMC 2002), I assume that the output gap included in the monetary policy rule is subject to exogenous, serially correlated mismeasurement. The estimates of the simulated moments are based on two sets of stochastic simulations, encompassing 25,000 years of simulated data.10 The use of such extremely long simulations provides reasonably accurate estimates of model-implied moments, effectively eliminates the effects of initial conditions, and ensures that rare events will occur in the simulations. Finally, I assume that automatic stabilizers and other endogenous responses of fiscal variables behave as usual, but that discretionary fiscal policy is not used except in extreme downturns. In what follows, unless otherwise indicated, monetary policy is assumed to follow a Taylor-type policy rule of the form (2)

it = max {0, r *t + π t + 0.5 ( π t − π*) + ϕyt } ,

where it is the nominal interest rate, r t* is the steady-state real interest rate, –π is the four-quarter percent change in the PCE price index, π* is the inflat tion target, and yt is the output gap.11 Following Orphanides and Williams (2002), I refer to the specification with ϕ = 0.5 as the “classic” Taylor (1993) rule; I refer to other specifications as “Taylor-type” rules. The simulated outcomes are evaluated using a slightly different central bank loss function from that used earlier, of the form (3)

{

}

L = E ( π − π*) + y 2 + 0.25 ⴱ ( i − i*) , 2

2

where π is the overall PCE inflation rate, i * = π* + r * is the unconditional mean of the nominal short-term interest rate, and E denotes the unconditional 10. In the analyses using disturbances following the t distribution, I conduct twice as many simulations as in the analyses using normally distributed disturbances. 11. I have included an upward bias in the notional inflation target in the policy rule that is needed for the inflation rate to equal the true target level. As discussed in Reifschneider and Williams (2002) and Coenen, Orphanides, and Wieland (2004), the asymmetric nature of the ZLB implies that the inflation rate will on average be lower than the inflation target in the rule. This upward bias is larger, the more the ZLB constrains policy. I correct for this downward bias by adjusting the inflation target in the policy rule.

20


expectation. Note that I consider only the costs of inflation variability and not the costs of steady-state inflation, on the grounds that current understanding of the costs of steady-state inflation is very limited.12 Thus, I stop short of finding optimal inflation targets. I return to the issue of the costs of steady-state inflation briefly in the concluding section. The top panel of table 2 reports the simulated outcomes under the classic Taylor rule, assuming the shocks are drawn using a normal distribution from the covariance matrix computed from the full sample of disturbances (1968–2002). In terms of the model simulations, the key statistic is the nominal interest rate buffer, which equals the sum of the steady-state inflation rate and the steady-state real interest rate. The first two columns list steady-state inflation rates corresponding to alternative values of r * of 21⁄2 percent (the baseline scenario) and 1 percent, respectively. The third and fourth columns report, for each of these combinations of steady-state inflation and interest rates, the share of the time that the nominal federal funds rate is below 0.1 and 1 percent, respectively. The fifth column reports for each combination the share of the time that the output gap is below −4 percent, representing the trough of a major recession of the type that has occurred in 1958, 1975, 1982–83, and 2009. (For comparison, over 1955Q1–2009Q2, the Congressional Budget Office’s estimate of the output gap was below −4 percent about 6 percent of the time.) The sixth through eighth columns report the corresponding standard deviations of the output gap, the PCE inflation rate, and the nominal federal funds rate. The final column reports the central bank loss. In the baseline scenario, if policy follows the classic Taylor rule, then the ZLB has only minor effects on the magnitude of macroeconomic fluctuations if the inflation target is 11⁄2 percent or higher. Under these assumptions, a 11⁄2 percent inflation target implies that the funds rate will fall below 1 percent 10 percent of the time, and will be below 10 basis points 6 percent of the time. The standard deviation of the unconstrained funds rate is only about 21⁄2 percent. So, with a 4-percentage-point buffer, most episodes where the ZLB is binding are relatively mild, and the effects are minor. These results are consistent with those of many studies finding that with a steady-state nominal interest rate of 4 percent or higher, the ZLB has very modest macroeconomic effects under the Taylor rule.

12. Alternatively, this approach can be justified by assuming that firms increase prices at the steady-state inflation rate without incurring adjustment costs (in an adjustment cost model) or reoptimizing (in a Calvo model).

21

JOHN C. WILLIAMS

Table 2. Simulated Outcomes under a Classic Taylor Rule for Different Shock Distributions Standard deviations of the output gap y, inflation π, and the federal funds rate i

Annual inflation target π* (percent) corresponding to the indicated steady-state real interest rate r*a

Percent of the time the federal funds rate i will be below the indicated value

r* = 2.5%

r* = 1.0%

i = 0.1%

−0.5 0.5 1.5 2.5 3.5 5.5 7.5

Shocks drawn from 1968–2002 covariance, normal distribution 1 23 31 12 3.1 1.5 2.4 2 13 20 8 2.8 1.5 2.4 3 6 10 6 2.6 1.5 2.5 4 4 8 6 2.6 1.5 2.6 5 2 3 6 2.5 1.5 2.6 7 0 0 5 2.5 1.5 2.6 9 0 0 5 2.5 1.5 2.6

i = 1.0%

Percent of the time the output gap will be below −4%

y

π

i

Central bank loss Lb 13.3 11.5 10.6 10.5 10.1 9.9 9.9

Shocks drawn from 1968–2002 covariance, t distribution with 5 degrees of freedom −0.5 1 24 33 13 3.1 1.5 2.4 13.2 0.5 2 13 20 8 2.8 1.5 2.5 11.5 1.5 3 8 13 7 2.7 1.5 2.5 10.8 2.5 4 4 7 6 2.6 1.5 2.7 10.6 3.5 5 3 5 6 2.6 1.5 2.7 10.6 5.5 7 0 0 5 2.5 1.5 2.6 9.9 7.5 9 0 0 5 2.5 1.5 2.6 9.9 −0.5 0.5 1.5 2.5 3.5 5.5 7.5

Shocks drawn from 1968–83 covariance, normal distribution 1 29 38 18 3.7 1.7 2 16 23 12 3.3 1.6 3 9 14 11 3.2 1.6 4 4 7 9 3.0 1.6 5 3 6 9 2.9 1.6 7 2 3 8 2.9 1.6 9 0 0 8 2.9 1.6

2.6 2.8 2.8 2.8 2.9 2.9 2.9

18.4 15.5 14.5 13.6 13.4 13.0 13.0

Source: Author’s calculations. – − π*) + 0.5y }. a. The monetary policy rule is assumed to be it = max{0, r *t + –π t + 0.5(π t t b. L = E{(π − π*)2 + y2 + 0.25* (i − i*)2}, where i* = π* + r*.

If instead the steady-state real interest rate is only 1 percent, then under the classic Taylor rule a 3 percent inflation objective is still sufficiently high to avoid most costs from the ZLB. But with a 2 percent inflation goal, the ZLB binds 13 percent of the time and causes a more noticeable rise in output gap variability: a rise of 0.3 percentage point relative to an inflation goal of 5 percent or higher. The incidence of deep recessions rises as well but remains below 10 percent. Based on this evidence, a lower steady-state real interest rate argues for a higher inflation goal to

22


reduce the costs associated with the ZLB. But it alone does not overturn the basic result of past research that a 2 percent inflation goal is associated with relatively modest costs from the ZLB. This conclusion is reinforced when one considers alternative policy rules that mitigate the problems associated with the ZLB, as discussed below. As noted above, the assumption of normally distributed disturbances may understate the likelihood of tail events of the type recently experienced. To gauge the sensitivity of the results to this assumption, I conduct simulations where the disturbances have the same covariance as before (that is, based on the full 1968–2002 sample) but are assumed to follow the t distribution with 5 degrees of freedom. This distribution is characterized by excess (relative to the normal distribution) kurtosis of 6; that is, it displays significantly fatter tails than the normal distribution. For example, the probability of a 3-standard-deviation (or greater) event is over four times greater with this t(5) distribution than with the normal distribution.13 Allowing for a fatter-tailed distribution of disturbances does not materially affect the results regarding the effects of the ZLB (middle panel of table 2). The ZLB is encountered slightly more often, and the standard deviation of the output gap is in some cases higher, but these effects are nearly lost in rounding. Note that the shocks being considered differ from those in the other simulations; thus, comparison with the simulations using normally distributed disturbances is not exact because of the finite samples of the simulations. Similar results (not reported) were obtained when the disturbances were assumed to follow a Laplace distribution, which has excess kurtosis of 3. More exotic distributions with even greater kurtosis may exist that would have greater effects on these results, but a more critical issue appears to be the covariance of the shocks, rather than the precise shape of the distribution. The effects of the ZLB are far more pronounced when the shocks are drawn from the pre–Great Moderation period. In the simulations reported in the bottom panel of table 2, the disturbances are drawn from a normal distribution where the covariance of disturbances is estimated from the 1968–83 sample. As a result, the ZLB is encountered more frequently and with greater costs in terms of stabilization of the output gap. With a steadystate real interest rate of 21⁄2 percent, a 2 percent inflation target is just on the edge of the region where the ZLB has nontrivial costs in terms of macro13. The choice of 5 degrees of freedom is somewhat arbitrary but near the lower bound of allowable values for the purpose at hand. In particular, the degrees of freedom of the distribution must exceed 4 for finite second and fourth moments to exist.

JOHN C. WILLIAMS

23

economic variability. Inflation goals of 11⁄2 percent or lower entail moderate increases in output gap variability. The combination of a 1 percent steady-state real interest rate and greater volatility of disturbances poses the greatest threat to macroeconomic stabilization in a low-inflation environment. In this case inflation goals of 2 to 3 percent are associated with some increase in output gap variability, and a 1 percent goal entails a significant increase. Yet even in these extreme cases, the effects on inflation variability are quite modest, reflecting the effects of the assumption of well-anchored expectations. How big are these losses? One metric is the fraction of the time the output gap is below −4 percent. In the adverse environment of shocks drawn from the 1968–83 shock covariance and a steady-state real interest rate of 1 percent, this figure rises from 9 percent to 18 percent when the inflation target is reduced from 4 percent to 1 percent. The standard deviation of the output gap rises by 0.7 percentage point. For comparison, the standard deviation of the output gap during the Great Moderation period was 2 percentage points, according to Congressional Budget Office estimates. The comparable figure for 1965–80 was 2.7 percentage points. Thus, moving from a 4 percent inflation target to a 1 percent target yields an increase in output gap variability in these model simulations comparable to switching from the Great Moderation period to the 1965–80 period. Moving from a 4 percent inflation target to a 2 percent target entails an increase in output gap variability comparable to switching from the Great Moderation period to the period from 1955 to 1965, when the standard deviation of the output gap was 2.3 percentage points, or 0.3 percentage point above that during the Great Moderation period.

III. Alternative Monetary and Fiscal Policies The results reported above indicate that in a particularly adverse macroeconomic environment of large shocks and a low steady-state real interest rate, the ZLB may cause a significant deterioration in macroeconomic performance when monetary policy follows the classic Taylor rule with a very low inflation target. As discussed in Reifschneider and Williams (2000; FOMC 2002) and Gauti Eggertsson and Michael Woodford (2003), alternative monetary policy strategies can improve upon the performance of the classic Taylor rule in a low-inflation environment. Several such modifications are examined here. In addition, I consider the use of countercyclical fiscal policy to mitigate the effects of the ZLB. Throughout the following discussion, I assume the worst-case adverse macroeconomic environment of a 1 percent steady-state real interest rate and disturbances

24


drawn from the covariance matrix computed from the shocks of the pre–Great Moderation period.

III.A. Modifying the Taylor Rule One way to achieve greater stabilization of the output gap even at low steady-state inflation rates and in an adverse environment is to have the policy rule respond more aggressively to movements in the output gap. Table 3 reports simulation results for alternative values of the coefficient Table 3. Simulated Outcomes for Alternative Monetary Policy Responses to the Output Gapa

Annual inflation target π* (percent)b

Percent of the time the federal funds rate i will be below the indicated value i = 0.1%

i = 1.0%


Standard deviations of the output gap y, inflation π, and the federal funds rate i y

π

i

Loss Lc

1 2 3 4 5 7 9

Coefficient on the output gap in the monetary policy rule ϕ = 0.5 29 38 18 3.7 1.7 2.6 16 23 12 3.3 1.6 2.8 9 14 11 3.2 1.6 2.8 4 7 9 3.0 1.6 2.8 3 6 9 2.9 1.6 2.9 2 3 8 2.9 1.6 2.9 0 0 8 2.9 1.6 2.9

18.4 15.5 14.5 13.6 13.4 13.0 13.0

1 2 3 4 5 7 9


27.3 15.2 13.1 12.4 12.0 12.0 12.0

1 2 3 4 5 7 9


31.2 17.1 13.4 12.8 12.5 13.0 13.2

Source: Author’s calculations. a. All simulations use shock covariance data for 1968–83 and assume a steady-state real interest rate r* equal to 1. – − π*) + ϕy }. b. The monetary policy rule is assumed to be it = max{0, r*t + –π t + 0.5(π t t c. L = E{(π − π*)2 + y2 + 0.25 ⴱ (i − i*)2}, where i* = π* + r *.

JOHN C. WILLIAMS

25

on the output gap, ϕ, in the monetary policy rule in equation 2. A larger response to a widening output gap reduces output gap variability and allows the central bank to reach output and inflation goals, at some cost of interest rate variability, even at inflation goals as low as 2 percent. For example, assume the goal is to have outcomes like those under the classic Taylor rule (ϕ = 0.5) unconstrained by the ZLB, but with an inflation target of 2 percent. The Taylor-type rule with the stronger response to the output gap of ϕ = 1.5 yields outcomes for output gap and inflation rate variability close to those of the unconstrained classic Taylor rule, at the cost of somewhat greater interest rate variability. Outcomes similar to that of the unconstrained classic Taylor rule can be achieved with an inflation goal of 3 percent by setting ϕ = 1.0. Interestingly, too strong a response to the output gap can be counterproductive at very low steady-state interest rates. This outcome likely reflects the asymmetry of the policy response resulting from the ZLB. When the output gap is positive, policy tightens sharply. But when the output gap is negative, the policy response may be truncated by the ZLB. This strongly asymmetric response causes output gap variability to rise at very low inflation rate targets in the adverse macroeconomic environment. A stronger response to inflation in the Taylor-type rule has little impact on the effects of the ZLB (not shown).14 None of these modified Taylor rules performs well with an inflation target of 1 percent in the adverse macroeconomic environment. In all three cases the standard deviation of the output gap rises sharply, and the fraction of the time that the output gap is below −4 percent is extremely high, between 17 and 20 percent. These figures decline dramatically when the inflation target is raised to 2 percent. Other modifications to the Taylor-type rule can also be effective at offsetting the effects of the ZLB in low-inflation environments. The top two panels of table 4 report the results from a modified Taylor-type rule proposed by Reifschneider and Williams (2000). According to this policy rule, realized deviations of the interest rate from that prescribed by the rule owing to the ZLB are later offset by negative deviations of equal magnitude. Note that this does not necessarily imply that the central bank is promising to raise inflation above its target in the future, but only that it makes up for “lost monetary stimulus” by holding the interest rate low for a period after the ZLB no longer binds. 14. There are other reasons, however, for a stronger response to inflation, such as the better anchoring of inflation expectations in an economy with imperfect knowledge, as discussed in Orphanides and Williams (2002, 2007).

26


Table 4. Simulated Outcomes for Alternative Monetary Policy Rulesa

Annual inflation target π* (percent)

Percent of the time the federal funds rate i will be below the indicated value i = 0.1%

i = 1.0%


Standard deviations of the output gap y, inflation π, and the federal funds rate i π

i

Loss Lb

1 2 3 4 5 7 9

Classic Taylor rule with lagged adjustment (ϕ = 0.5)c 18 26 12 3.7 1.6 12 19 10 3.2 1.6 7 13 9 3.0 1.6 4 8 8 3.0 1.6 3 5 8 2.9 1.6 0 1 8 2.9 1.6 0 0 8 2.9 1.6

2.8 2.8 2.8 3.0 2.9 2.9 2.9

19.4 14.7 13.6 13.7 13.0 13.0 13.0

1 2 3 4 5 7 9

Taylor-type rule with lagged adjustment (ϕ = 1.0)c 32 40 12 3.6 1.6 21 28 7 2.9 1.6 16 22 6 2.5 1.6 5 15 6 2.5 1.6 2 9 6 2.5 1.6 0 3 5 2.5 1.6 0 1 5 2.5 1.6

3.2 3.4 3.4 3.5 3.5 3.6 3.6

27.6 13.6 11.7 11.7 11.8 12.0 12.0

2.4 2.6 2.6 2.6 2.7 2.7 2.7

14.5 11.9 11.0 10.2 10.1 10.2 10.2

1 2 3 4 5 7 9

24 15 11 5 2 0 0

y

Optimized inertial policy ruled 33 10 3.4 22 8 2.9 16 7 2.7 8 6 2.5 4 6 2.5 1 6 2.5 0 6 2.5

1.4 1.4 1.4 1.4 1.4 1.4 1.4

Source: Author’s calculations. a. All simulations use shock covariance data for 1968–83 and assume a steady-state real interest rate r* equal to 1. Alternative monetary policies are described in the text. b. L = E{(π − π*)2 + y2 + 0.25* (i − i*)2}, where i* = π* + r*. c. The policy rule is as described in Reifschneider and Williams (2000), in which realized deviations of the interest rate from that prescribed by the rule owing to the ZLB are later offset by opposite deviations of equal magnitude. – − π*) + 0.12y . d. The policy rule is i ut = 0.96i ut−1 + 0.04* (r* + πˆ t ) + 0.04(π t t

This modified rule nearly eliminates the effects of the ZLB for inflation targets as low as 3 percent, and it significantly reduces them for lower inflation targets. If the inflation goal is 2 percent, the modified rule with a greater response to the output gap of ϕ = 1.0 yields the same outcomes as the unconstrained Taylor rule in this adverse environment. In rational expectations models like FRB/US, policies with inertial responses to movements in inflation and output gaps perform much better

27

JOHN C. WILLIAMS

than static Taylor-type rules and closely approximate the outcomes under fully optimal policies (Woodford 2003; Levin and Williams 2003). The key benefit of inertial rules is that they generate expectations of the future path of policy that reinforce the direct effects of the policy actions on the economy. Here I examine the performance of an inertial policy rule taking the form (4)

itu = 0.96itu−1 + 0.04 ( r * + π t ) + 0.04 ( π t − π*) + 0.12yt ,

where i tu is the prescription for the federal funds rate unconstrained by the ZLB. The coefficient on the lagged funds rate, at near unity, imparts a great deal of inertia to policy (also frequently referred to as “interest rate smoothing”). The actual setting of the funds rate must satisfy the ZLB: (5)

it = max {0, itu } .

As shown in Reifschneider and Williams (2000), policy rules like this perform very well in the presence of the ZLB because they promise to keep interest rates low in the future and to allow inflation to rise above its long-run target following bouts of excessively low inflation. In forwardlooking models like FRB/US, this expectation of high future rates of inflation boosts the current rate of inflation. This inertial policy rule delivers better macroeconomic performance with a 2 percent inflation target than does the classic Taylor rule unconstrained by the ZLB. The bottom panel of table 4 reports the simulated outcomes from the inertial version of the Taylor-type rule where the parameters of the rule were chosen to yield minimum weighted variances of inflation, the output gap, and the nominal interest rate. Nonetheless, in this worst-case environment there are limits to what this simple rule can accomplish, and performance suffers noticeably as the inflation goal is lowered much below 2 percent. I obtain very similar results for a policy rule that targets the price level growing at a deterministic trend rather than the inflation rate. (Eggertsson and Woodford 2003 find that such a rule performs well in the presence of the ZLB.) Price-level targeting rules are closely related to the inertial rules described above but imply a stronger mechanism to raise inflation above the long-run target rate following an episode of below-target inflation. Based on this evidence, there is little gain from switching from an optimized inertial policy to an explicit price-level targeting regime, even with very low steady-state inflation rates.

28


A potential problem with these alternative policy approaches is that the public may be confused by monetary policy intentions in the vicinity of the ZLB. For example, the asymmetric policy rule I have described represents a significant deviation from the standard reaction function, which could have unintended and undesirable consequences (Taylor 2007). More generally, all of these alternative policies rely heavily on expectations of future policy actions to influence economic outcomes. As shown by Reifschneider and John Roberts (2006) and by Williams (2006), if agents do not have rational expectations, episodes of a binding ZLB may distort expectations, reducing the benefits of policies that work very well under rational expectations. In particular, inertial and price-level targeting policies cause inflation to rise above the long-run target following an episode where the ZLB constrains policy. Such a period of high inflation could conceivably undermine the public’s confidence in the central bank’s commitment to price stability and lead to an untethering of inflation expectations. Indeed, central banks are averse to declaring any desire to see a sustained rise in inflation above the target level (Kohn 2009; Walsh 2009). One method to minimize public confusion is for the central bank to clearly communicate its expectations, including the anticipated policy path, as discussed by Woodford (2005) and by Rudebusch and Williams (2008).15 Another approach is to back up that communication with interventions in foreign exchange markets, as proposed by Bennett McCallum (2000), Svensson (2001), and Günter Coenen and Volker Wieland (2003), or by targeting the short to the middle end of the yield curve of Treasury securities, a strategy analyzed by Bruce McGough, Rudebusch, and Williams (2005). An additional potential problem with highly inertial and price-level targeting policies is that, historically, the price level and interest rates tend to be relatively high as the economy enters a recession, because inflation tends to be high near the end of an expansion.16 In these circumstances, such policies imply delayed policy responses early in a downturn. The current episode illustrates this dilemma. As shown in figure 3, inflation had been consistently running above 2 percent in several countries well into 2008. Although model simulations do not bear out these concerns, perhaps there

15. Although a few central banks publish interest rate paths, and the Bank of Canada recently made clear statements about its intended path, most central banks remain unwilling to provide such clear communication of their future policy intentions. 16. This observation is related to the strong correlation between the slope of the yield curve and recessions (Rudebusch and Williams 2009). Past recessions are preceded by periods of monetary tightening in response to periods of high inflation.

JOHN C. WILLIAMS

29

is something missing from the dynamics in the models or the assumed monetary policies.

III.B. Countercyclical Fiscal Policy The active use of countercyclical fiscal policy has been excluded from consideration in most quantitative research on the ZLB, including the simulations reported above. The experience of the past decade suggests that this assumption is too stringent and may overstate the future effects of the ZLB by ignoring the ways in which fiscal policy can substitute for monetary policy. The past decade has seen the active use of discretionary countercyclical fiscal policy in many countries. For example, Japan aggressively used fiscal policy to stimulate the economy during the 1990s and in the current recession. The International Monetary Fund (2009) expects discretionary fiscal stimulus to average 1 percent of GDP in the G-20 economies over 2008–10, above and beyond automatic stabilizers and measures to support the financial sector. Economic theory is clear that in the presence of nominal rigidities, government spending can be useful at reducing the macroeconomic costs associated with the ZLB (see, for example, Eggertsson 2009; Christiano, Eichenbaum, and Rebelo 2009; and Erceg and Lindé 2009). Consider the case where, following a negative shock to the economy, the short-term interest rate declines but cannot fall enough to offset the shock. As a result, the real interest rate rises, consumption falls, and inflation falls. These consequences reduce household welfare. A temporary increase in government purchases will increase output and raise wages and thereby marginal cost, which in turn boosts both current and expected inflation. Given a fixed short-term nominal interest rate constrained by the ZLB, the rise in expected inflation lowers the real interest rate, causing consumption to rise. As a result, the increase in government spending reduces the fluctuations in inflation and the output gap and raises welfare.17 In principle, any number of policies aimed at strengthening automatic stabilizers or countercyclical fiscal policy more generally could help mitigate the problems caused by the ZLB. Reifschneider and Roberts (2006), using simulations of the FRB/US model, provide an example of the effects 17. In contrast to the case of government spending, the effects of changes in income taxes when the ZLB is binding can be counterintuitive. In models without credit and liquidity constraints, lowering income taxes can be counterproductive because it lowers marginal costs and thus inflation (Eggertsson and Woodford 2006). In such a model, raising taxes during a downturn can improve welfare. In models with liquidity-constrained consumers, a tax cut can also raise demand.

30


of fiscal policy stimulus when the ZLB is constraining policy. Here I consider one simple experiment based on a systematic fiscal policy rule for federal government purchases excluding employee compensation and investment purchases. The estimated fiscal reaction function for this category of purchases (which make up about half of total purchases) in the FRB/US model is given by (6)

gt = 0.55gt −1 + 0.07 gt − 2 + 0.19 gt − 3 − 0.0004 yt + 0.0027 yt −1 + γ ( it −1 − itu−1 ) + ε t ,

where g is the logarithm of federal purchases in this category, yt is the output gap, and i u is the federal funds rate that would occur absent the ZLB. In the baseline model, γ = 0. I consider the effects of a sustained increase in federal purchases when the ZLB constrains monetary policy by setting γ = 0.02. This value implies that a 1-percentage-point interest rate gap owing to the ZLB causes total federal purchases to rise by 1 percent in the next period. Lags in fiscal policy implementation are approximated by the lag structure of this equation. This modified fiscal reaction function cuts the macroeconomic effects of the ZLB in half for low steady-state interest rates of 3 and 4 percent. The bottom panel of table 5 shows the outcomes from this experiment for the Taylor-type rule with ϕ = 1. The top panel shows the outcome of the same rule without the fiscal response. In the worst-case scenario, an inflation target of 3 percent is sufficient to avoid effects from the ZLB. An inflation target of 2 percent suffers a small increase in output variability. This specification for the fiscal reaction function is in no way meant to be optimal or even desirable, but rather is intended only to illustrate the effects of countercyclical fiscal policy aimed at mitigating the effects of the ZLB on the economy. Further research is needed in this area to devise better countercyclical fiscal policy rules.

III.C. Unconventional Monetary Policy Actions The preceding discussion and analysis abstracted from unconventional monetary actions, implicitly assuming that these are not used or are ineffective. However, the events of the past year provide ample evidence that central banks possess and are willing to use tools other than the overnight interest rate. James Clouse and others (2003) and Ben Bernanke and Vincent Reinhart (2004) describe alternative policy tools available to the Federal Reserve. In the current crisis, a number of such alternatives have been put

31

JOHN C. WILLIAMS

Table 5. Simulated Outcomes of Alternative Fiscal Policiesa Percent of the time the federal funds rate i will be below the indicated value


Standard deviations of the output gap y, inflation π, and the federal funds rate i

Annual inflation target π* (percent)b

i = 0.1%

2 3 4 5 6 8 10

Government does not increase spending when ZLB is reached 34 41 17 4.6 2.1 2.6 16 22 8 3.1 1.7 3.3 11 15 6 2.7 1.6 3.4 8 12 6 2.6 1.6 3.4 6 10 6 2.5 1.6 3.5 2 3 5 2.5 1.6 3.6 0 1 5 2.5 1.6 3.6

2 3 4 5 6 8 10

i = 1.0%

y

π

Government increases spending when ZLB is reached 31 39 12 3.9 2.0 16 23 7 2.8 1.6 12 17 6 2.6 1.6 8 12 6 2.5 1.6 6 10 6 2.5 1.6 2 3 5 2.5 1.6 0 1 5 2.5 1.6

i

2.8 3.2 3.3 3.4 3.5 3.6 3.6

Loss Lc 27.3 15.2 13.1 12.4 12.0 12.0 12.0 21.2 13.3 12.2 11.8 11.9 12.0 12.0

Source: Author’s calculations. a. All simulations use shock covariance data for 1968–83 and assume a steady-state real interest rate r* equal to 1. – − π*) + y }. b. The monetary policy rule is assumed to be it = max{0, r*t + –π t + 0.5(π t t c. L = E{(π − π*)2 + y2 + 0.25 * (i − i*)2}, where i* = π* + r*.

to use. Several central banks, including the Bank of England, the European Central Bank, the Federal Reserve, and the Bank of Japan, have instituted programs to buy or guarantee assets such as commercial paper and mortgagebacked securities. Finally, the Bank of Japan, the Bank of England, and the Federal Reserve have expanded their holdings of longer-term securities through the creation of reserves. Many of these programs are aimed at improving the functioning of impaired or distressed markets. Similarly, the Federal Reserve’s purchases of the debt of government-sponsored enterprises such as Fannie Mae and Freddie Mac, and of mortgage-backed securities, were aimed at particular sectors—housing and finance—that appeared to be functioning poorly. Future recessions may not be accompanied by severe financial market disruptions, in which case these tools would not be as useful at offsetting the shock. An open question is whether policies that expand the central bank’s balance sheet, such as quantitative easing or purchases of longer-term

32


government securities, are effective at stimulating the economy. Bernanke, Reinhart, and Brian Sack (2004) provide evidence that shocks to the supply of government securities do affect their prices and yields. Announcements by the Bank of England and the Federal Reserve regarding plans to buy longer-term government securities were followed by large movements in yields, providing additional support that such policy actions can be effective (see Meier 2009 for a summary of the U.K. experience). Nonetheless, a great deal of uncertainty surrounds the magnitude and duration of these effects. In addition, some observers fear adverse consequences from such actions if taken on a large scale, including the risk of large losses and the concern that inflation expectations may become unmoored. Further careful study is needed before these policy options can be counted on as effective substitutes for more traditional monetary policy actions.

IV. Conclusion The zero lower bound has significantly constrained the ability of many central banks to stimulate the economy in the current recession. Counterfactual simulations suggest that the ZLB will impose significant output costs on the U.S. economy. Although these simulations focus on the effects of lower U.S. interest rates on the U.S. economy, comparable simulations for other economies where the ZLB has constrained monetary policy—such as Japan and Europe—would no doubt also show that the ZLB has entailed significant costs during the recent episode. A useful extension of the simulations reported in this paper would be to calculate the costs of the ZLB in a model of the global economy. If the recent episode represents a unique, extraordinary incident, it has no particular implications for future monetary policy with respect to the ZLB. In particular, a 2 percent inflation target should provide an adequate buffer for monetary policy in the future. If, however, the era of the Great Moderation is over but the steady-state real interest rate remains very low, the ZLB may regularly interfere with the ability of central banks to achieve macroeconomic stabilization goals. The analysis in this paper argues that an inflation target of 2 percent may be insufficient to keep the ZLB from imposing sizable costs in terms of macroeconomic stabilization in a much more adverse macroeconomic climate if monetary policy follows the standard Taylor rule. Given these results, it is important to study and develop monetary and fiscal policies that effectively counter the effects of the ZLB, should the future macroeconomic environment prove adverse. Arguably, the applica-

JOHN C. WILLIAMS

33

tion of some of these approaches over the past two years has helped combat the massive shocks that have buffeted the global economy. Improving these policies and developing new ones into systematic, predictable responses to economic conditions will help make them more effective in the future. In addition, an important lesson from the recent crisis, not addressed in this paper, is the critical need for effective regulation and supervision of financial markets to avoid the shocks to the global economy that ignited the crisis and led to recession. Finally, this paper has examined only the costs associated with the ZLB, abstracting from the many other sources of distortions related to steady-state inflation. Unfortunately, relatively little research has weighed the costs of the ZLB against these other influences in a coherent, empirically supported framework (see Billi and Kahn 2008 for a review).18 More research on these issues is needed.

ACKNOWLEDGMENTS This paper benefited greatly from comments by Richard Dennis, Benjamin Friedman, Michael Woodford, and by the editors and other participants at the Brookings Papers conference. I thank Justin Weidner for excellent research assistance. The opinions expressed are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco, the Board of Governors of the Federal Reserve System, or anyone else in the Federal Reserve System.

18. Much of the literature focuses on the welfare costs related to holding zero-interestbearing assets; both Feldstein (1997) and Attanasio, Guiso, and Jappelli (2002) convincingly show that these are trivial. These costs are even lower now that the Federal Reserve and many other central banks pay interest on reserves.

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Comment and Discussion COMMENT BY

MICHAEL WOODFORD This paper by John Williams reassesses the significance for the choice of an inflation target of a consideration stressed by Lawrence Summers (1991), namely, that too low a target will interfere with the success of monetary stabilization policy, because policy will too frequently be constrained by the zero lower bound on nominal interest rates. This argument began to be taken more seriously by both central bankers and monetary economists after Japan reached the zero bound in the late 1990s, and I think the consensus view regarding the desirable level for an inflation target shifted at least slightly higher after that experience. Many countries had lowered their inflation rates (often in a series of steps) over the course of the 1980s and 1990s, but it was not clear whether this process of disinflation should be regarded as having been completed, or at what point it would be appropriate to stop. Before the Japanese experience showed that the zero bound could indeed be a binding constraint, an important current of opinion argued for the desirability of zero inflation (full “price stability”) or even mild deflation (to reduce the opportunity cost of holding money to zero, as called for by Friedman 1969). After observing the Bank of Japan’s inability to pull the Japanese economy out of its continuing deflationary slump over a period of years, many economists came to accept that inflation targets on the order of 2 percent a year—a level already commonplace among the industrial nations with explicit targets—were quite possibly low enough. Williams asks, however, whether recent events—in which the Federal Reserve and a great many other central banks found themselves at the effective lower bound to which they were willing to reduce their policy rates, amid a global recession—might not justify a further upward revision in inflation targets. The question Williams poses is not whether the zero 38

COMMENT and DISCUSSION

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lower bound is a reason to forgo the benefits of deflation at the rate called for by Friedman, or even the benefits of full price stability, but whether even a 2 percent inflation target does not make the zero lower bound too great a constraint on effective stabilization policy. After all, central banks like the Federal Reserve have found themselves constrained by the zero bound during the current crisis, despite explicit or implicit inflation targets on the order of 2 percent. The general tenor of the paper’s conclusions is that it might indeed be prudent to aim for a moderately higher rate of inflation, perhaps as high as 4 percent a year, which, in the context of U.S. policy, would mean aiming for a rate clearly higher than that pursued for the past two decades. This conclusion contrasts with that reached by Williams himself in his analyses of the issue only a few years ago (for example, Reifschneider and Williams 2000; Williams 2006), which represent, in my view, some of the best work available on this topic. What accounts for the difference? Williams proposes two reasons in particular for greater caution now about the suitability of a low inflation target. One is that past studies may have assumed too low a frequency of large shocks. This is a crucial issue, since the zero bound is a constraint on interest rate policy only to the extent that one would sometimes like to be able to cut real interest rates substantially, and how often that situation arises depends on the size of certain kinds of real disturbances to the economy. Williams proposes that studies that parameterize shock processes based on data from the “Great Moderation” period of the mid- to late 1980s and the 1990s may underestimate the frequency of large shocks, on the grounds that this period may have been atypically calm; as a robustness check, he instead conducts stochastic simulation exercises using a shock distribution estimated on the basis of data from the period 1968–83 only, so as to exclude the arguably atypical years that followed. The other reason for caution is that past studies may have assumed too high a value for the average equilibrium real rate of interest. This is also a crucial issue, since the average level of nominal interest rates associated with a given inflation target—and hence the number of percentage points by which interest rates can be cut, if necessary, before hitting the zero lower bound—is greater, the higher the average equilibrium real rate of interest. Williams cites estimates suggesting that the equilibrium real rate of interest in the United States has fallen in recent years, from a range of 2.5 to 3 percent a year in the 1980s to only about 1 percent at present. If this represents a permanent structural change, he suggests, it may be appropriate to simulate the consequences of alternative policy rules assuming an

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average equilibrium real rate as low as 1 percent. Both of these proposed changes in the numerical assumptions used in his stochastic simulations increase the degree to which the zero bound is predicted to interfere with economic stabilization, under a Taylor rule with an implicit inflation target of 2 percent a year. Although I am sympathetic with the view that it is important to undertake sensitivity analysis of quantitative conclusions to alternative assumptions, especially with regard to the values of parameters about which one cannot claim that the historical record provides conclusive evidence, it is difficult to be sure how much weight to place on the results obtained under Williams’s “worst-case” scenarios (those that use shocks from the period 1968–83 and assume an equilibrium real rate of only 1 percent). One could also argue, with some plausibility, that 1968–83 was a period of atypically high macroeconomic instability. (Some of that instability may have been due to policy mistakes, rather than to genuinely exogenous disturbances to economic fundamentals, but it may show up as larger residuals in the equations of the structural model used in Williams’s exercise, owing to misspecification of some of the model equations. In that case, residuals of this size should not be expected to be a recurrent feature of economic dynamics under a stable policy rule that provides a stronger nominal anchor.) It is even less clear that it makes sense to assume that the equilibrium real rate of interest will continue to be 1 percent a year. If the equilibrium real rate has fallen by an entire percentage point (or more) leading up to and during the current financial crisis (as indicated by the Laubach-Williams estimates shown in Williams’s figure 8), this is surely due to temporary disruption of the financial system, rather than some kind of permanent structural change that happens to coincide precisely in time with the crisis. Hence, it is plausible to assume that the equilibrium real rate should again be 2 percent or higher, once the recent problems in the financial sector have largely been overcome. Other factors that have contributed to a somewhat lower equilibrium real rate of return over the past decade, such as the remarkable accumulation of dollar assets by Asian central banks, may well prove temporary as well, leading to an equilibrium real rate of return more like that observed in past decades. Even under his worst-case assumptions, Williams’s results provide modest support at best for an inflation target higher than 2 percent. The main results that he emphasizes concern the stability of output and inflation under a “simple” Taylor rule with one implicit inflation target or another; Williams argues that the greater stability of real activity under an inflation target of 3 or even 4 percent is sufficient to offset the harm done by the


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higher average rate of inflation under such rules. But as his paper shows, more sophisticated monetary policy rules could achieve better outcomes, even with an average inflation rate of 2 percent (or less). In fact, as stressed by previous papers such as Reifschneider and Williams (2000) and Eggertsson and Woodford (2003), a “simple” Taylor rule is a relatively poor form of policy rule in the case that the zero bound sometimes binds, because it is a commitment to a purely forward-looking policy. This means that once the zero bound ceases to bind, monetary policy is immediately conducted in the same way as it would be if the bound had not constrained policy. Hence, the central bank’s opportunity to commit itself to a systematic approach to policy is not used to create expectations about how policy will be conducted after an episode in which the zero bound is reached that respond in any way to that situation. In fact, the advantage of a higher inflation target in the simulations in this paper derives entirely from the consequences of having people expect a higher inflation target immediately following the exit from a period in which the zero bound has been a binding constraint; the expectation of a higher inflation rate at that time lowers the expected real rate implied by the zero nominal interest rate floor, and this reduces the distortions caused by the existence of that floor. But to achieve this benefit, it is (at least in principle) not necessary to have a higher inflation target all of the time; it suffices to follow a policy that allows higher inflation for a very brief period following any period in which the zero bound causes one to undershoot one’s normal target; the inflation target can still be 2 percent (or even lower) at all other times. The “history-dependent” policy rules proposed in Reifschneider and Williams (2000) and in Eggertsson and Woodford (2003) serve exactly this purpose. As long as the fraction of the time in which the zero bound is a binding constraint is not too large, such rules achieve a substantially higher level of welfare than any purely forward-looking (or constant-inflationtarget) policy, as shown by Eggertsson and Woodford (2003), and the optimal rule involves an average inflation rate that is only slightly higher than would be optimal if the zero bound were never a constraint. Indeed, it is not obvious that any increase in the average inflation rate is necessary in order to deal with the zero lower bound in a reasonably effective way. Eggertsson and Woodford show, in an admittedly simple model, that a good approximation to optimal policy can be achieved—and the distortions resulting from the zero bound under a constant inflation target largely avoided—by a simple price-level targeting rule that implies a zero rate of inflation over the long run. Under this rule the only commitment to inflation is a commitment to make up for any decline in the price level that occurs

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during the period in which the zero bound prevents the target from being hit; this brings about inflation expectations of the size necessary to prevent the zero bound from creating significant distortions (for if there were more substantial distortions, the sharper decline in prices would automatically create correspondingly higher expectations of inflation as soon as inflation can be achieved with a nonnegative interest rate). An important question about this solution to the problem of the zero bound is whether it is likely that such a commitment to subsequent reflation can actually be made credible to the public, so that inflation expectations are affected in the desired way. (Walsh 2009 discusses the skepticism of many central bankers about such proposals.) One might argue that restricting attention to simple Taylor rules with alternative constant inflation targets is sensible, on the grounds that more complex rules would not be understood or believed in. But the kind of commitment that would solve the problem is not too difficult to explain; as just noted, it would simply require commitment to a price-level target or, more realistically, to a target path for the price level (or for some other nominal variable, such as nominal GDP). Moreover, even if one supposes that private sector inflation expectations cannot be shifted by mere announcements about future policy intentions, it would be desirable to explicitly analyze the kind of policy that would best shape those expectations in a way that mitigates the distortions caused by the zero bound. Williams (2006) addresses this question in the context of an explicit model of “learning” dynamics and finds that some rules that would be effective at stabilizing the economy despite the zero bound under an assumption of rational expectations are less desirable under learning dynamics. Nonetheless, he finds that “a robust strategy to cope with both imperfect knowledge and the zero bound is to respond more strongly to inflation” when not at the zero bound “than would be optimal under rational expectations.” Even with learning dynamics, such a rule is found to be “effective at stabilizing inflation and output . . . even with a low inflation target” (Williams 2006, abstract). Another possible response to a perceived inability to make a credible commitment to history-dependent policy is to use fiscal policy to prevent a severe contraction and deflation once the zero bound is reached. Under the assumption that the central bank follows a simple Taylor rule except when the zero bound is reached, New Keynesian models often imply quite large output multipliers for increases in government purchases while monetary policy continues to be constrained by the zero bound (Eggertsson 2009; Christiano, Eichenbaum, and Rebelo 2009). Of course, such a policy response is not under the control of the central bank. Nonetheless, reliance


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on fiscal policy to mitigate the problems that could otherwise be created by the zero bound has two advantages: first, that the deviation from policy as usual is one that can already be observed at the time that one wishes for the policy to be credible (that is, when the zero bound constrains monetary policy), and second, that the effectiveness of the policy does not depend on any change in expectations about how policy will be conducted after the unusual circumstances cease to obtain. (In fact, the analyses just cited imply that the output effect of government purchases is greatest when the public does not expect that the increased government purchases will continue after the zero bound no longer constrains monetary policy.) Relative to a policy of creating higher inflation expectations while at the zero bound by aiming at a higher inflation target all of the time, the alternative of activist fiscal policy during crises has the advantage of not increasing economic distortions at other times. This is a substantial advantage if the economy is not at the zero bound too much of the time. Finally, even supposing that the relevant choice is among alternative inflation targets under a simple Taylor rule, with no assistance from fiscal policy, and even accepting Williams’s worst-case assumptions under which the policy simulations should be conducted, the quantitative results that Williams announces do not make all that strong a case for an inflation target higher than 2 percent. According to the bottom panel of his table 2 (the case of shocks drawn from the 1968–83 distribution), and under the assumption that the steady-state real interest rate r* = 1 percent, increasing the inflation target from 2 percent to 4 percent would lower the standard deviation of log output from 3.3 percent to 3.0 percent, with no measurable effect (to two significant digits) on the standard deviations of inflation or interest rates. But would this degree of improvement in the stability of aggregate output really justify 2 percentage points higher inflation? The most serious reason to fear that an increase in the Federal Reserve’s implicit inflation target to 4 percent could do real harm is the likelihood that such a shift would increase doubts about the extent to which the Federal Reserve is truly committed to any inflation target at all: if, as a result of a recession, the inflation target can be increased from the 1.5 to 2 percent range that many members of the Federal Open Market Committee were thought to prefer in the recent past to a target of 4 percent, what further shifts in the inflation rate might the Federal Reserve find acceptable in response to further unforeseen events? Such considerations are not taken into account in Williams’s simulations, which assume perfect constancy and perfect credibility of whichever inflation target is contemplated. But even supposing that the consequences would be those indicated in table 2, one

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might easily conclude that 2 percentage points higher inflation at all times would increase distortions by more than the improved output stabilization would reduce them. The “loss measure” reported in table 2 assumes that losses result only from squared deviations of inflation, output, and the nominal interest rate from their steady-state values, even though the steady-state values of inflation and of the nominal interest rate are quite different in the equilibria associated with alternative inflation targets. Thus, the gains reported from moving from the 2 percent inflation target to a 4 percent target take no account at all of any reason why a higher average inflation rate (or a higher average nominal interest rate) would be undesirable. It therefore abstracts entirely from the considerations that are at the center of most discussions of the optimal inflation target (as surveyed, for example, in Schmitt-Grohé and Uribe 2009). One might easily assign values to those other considerations that would outweigh the improvement in output stabilization shown in the table. As a simple example, the familiar Calvo model of staggered price adjustment implies the existence of losses deriving from the misalignment of prices that are adjusted at different times that are (in a second-order approximation to expected utility, as discussed in Woodford 2003, chapter 6) proportional to E[π t2 ] rather than to E[(πt − π*)2]. If one assumes that the losses due to inflation variability are of this sort, then the term E[(πt − π*)2] in the loss measure (equation 3) should be replaced by E [ π 2t ] = π*2 + E ⎡⎣( π t − π*) ⎤⎦ . 2

When this substitution is made (but otherwise assuming the relative weights on the three stabilization objectives given in equation 3), the loss measure associated with the 2 percent inflation target (in the bottom panel of table 2) increases from 15.5 to 19.5, while the loss associated with the 4 percent inflation target increases from 13.6 to 29.6. Hence, the increase in the inflation target would result in substantially greater losses rather than the modest improvement that the table suggests. Indeed, under this correction the 1 percent inflation target (the lowest considered in the table) would be the one with the lowest losses. If one were also to take account of the “shoe-leather costs” resulting from unnecessary economizing on cash balances, which should be an increasing function of i *, this conclusion would only be strengthened. Thus, the case that is presented for the desirability of an inflation target higher than 2 percent is quite weak. My own summary of Williams’s results


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would be that even under assumptions that are chosen to be as unfavorable as possible to a low inflation target—larger shocks, a very low assumed value for r*, restriction of attention to simple Taylor rules, and no help from fiscal policy—stochastic simulations that take into account the zero lower bound on nominal interest rates provide little support for the desirability of an inflation target as high as 2 percent, let alone an even higher target. REFERENCES FOR THE WOODFORD COMMENT

Christiano, Lawrence, Martin Eichenbaum, and Sergio Rebelo. 2009. “When Is the Government Spending Multiplier Large?” Working Paper no. 15394. Cambridge, Mass.: National Bureau of Economic Research (October). Eggertsson, Gauti B. 2009. “What Fiscal Policy Is Effective at Zero Interest Rates?” Staff Report no. 402. Federal Reserve Bank of New York (November). Eggertsson, Gauti B., and Michael Woodford. 2003. “The Zero Bound on Interest Rates and Optimal Monetary Policy.” BPEA, no. 1: 139–211. Friedman, Milton. 1969. “The Optimum Quantity of Money.” In The Optimum Quantity of Money and Other Essays. Chicago: Aldine. Reifschneider, David, and John C. Williams. 2000. “Three Lessons for Monetary Policy in a Low-Inflation Era.” Journal of Money, Credit, and Banking 32 no. 4, part 2: 936–66. Schmitt-Grohé, Stephanie, and Martín Uribe. 2009. “The Optimal Rate of Inflation.” Columbia University (November). Summers, Lawrence. 1991. “How Should Long-Term Monetary Policy Be Determined?” Journal of Money, Credit, and Banking 23, no. 3, part 2: 625–31. Walsh, Carl E. 2009. “Using Monetary Policy to Stabilize Economic Activity,” presented at the Federal Reserve Bank of Kansas City Symposium on Financial Stability and Macroeconomic Policy, Jackson Hole, Wyo. Williams, John C. 2006. “Monetary Policy in a Low Inflation Economy with Learning.” Working Paper no. 2006-30. Federal Reserve Bank of San Francisco (September). Woodford, Michael. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press.

GENERAL DISCUSSION Robert Hall suggested that the literature on the zero lower bound overlooks the possibility of a negative federal funds rate. A negative funds rate can be achieved by charging banks for holding reserves rather than paying interest on them. Then banks would pay each other to take their unwanted reserves, generating the pressure to lend that achieves the desired stimulus. Hall argued that economists should encourage Congress to permit the Federal Reserve to charge for reserves.

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Christopher Sims observed that the Swedish Riksbank already has a negative-interest-rate policy, which it implements by charging interest on reserves. However, there are limits to how far one can push interest rates into negative territory by this means. Cash pays zero interest, which means that if the interest rate is negative enough, banks will substitute cash in the vault for reserve balances and so avoid the negative interest rate. Benjamin Friedman agreed with Hall that it is just an administrative matter whether the Federal Reserve charges interest on banks’ accounting balances of reserves. Moreover, under the right legislation, it would not matter whether the reserves were held in the form of balances at the Federal Reserve or vault cash. Both could be charged. Sims responded that although the Federal Reserve could indeed charge banks interest on vault cash, it cannot charge interest on cash in general. And so long as there exists an asset (cash) that pays nonnegative nominal interest, the effective marginal cost of borrowing cannot be pushed much below zero. Christopher House noted that the reason that the money supply is not discussed as a policy instrument in modern monetary models with price rigidity is that the money demand side of the model can usually be inverted to express policy in terms of a nominal interest rate. This works as long as the nominal interest rate is above the lower bound. An alternative way of conducting monetary policy in such an event would be to focus less on the federal funds rate, and more on other types of policy instruments. In fact, House pointed out, the Federal Reserve has been doing some of this already through quantitative easing. Vincent Reinhart underscored House’s point by noting that one’s degree of concern about the zero lower bound depends on one’s assessment of how effective policy is at the lower bound. He also observed that the treatment of the inflation goal in Williams’s paper is symmetric, but central bankers often sound very asymmetric in discussing their inflation goals: shortfalls of inflation below the target seem to represent evidence of increased credibility, whereas any excess poses a danger of losing credibility. He wondered what the right inflation goal is if one believes that central bankers will deal with departures from it asymmetrically. David Laibson noted that the paper did not address the effects of downward nominal wage rigidity on the calculation of the optimal inflation rate. He wondered to what extent the issue is still as relevant as it was when George Akerlof, William Dickens, and George Perry first brought it to the Panel’s attention in their 1996 Brookings Paper, or whether it is now considered to be a second-order issue in light of subsequent research. If the issue is still important, how does it affect the paper’s analysis? George Perry responded


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that their research indicated that the issue remained relevant as of not very long ago. They still found an abrupt discontinuity below zero, which made sense given how labor markets are thought to work. But the Akerlof, Dickens, and Perry results had been subject to an important amendment: they are relevant to an economy near full employment, not one in deep recession. Christopher Sims thought the Federal Reserve ought to talk about at least being willing to tolerate higher inflation than it has previously targeted. He did not see a price-level target, which Michael Woodford had espoused in his comment, as a good idea: although it might be attractive in the current situation because of the zero lower bound, it is not so attractive in a situation following high inflation, perhaps accompanied by a recession, because then the price-level target would indicate a need to deflate. What is needed in that case is to convince the public that the Federal Reserve is willing to tolerate higher future inflation, and that is not the same as a price-level target. David Romer highlighted for the Panel what he saw as a striking number in the paper: $1.8 trillion, which is roughly the cumulated output cost over the next several years of the zero lower bound, or the difference between output as currently projected and what output would be with an additional 400 basis points of stimulus. That is a huge number and raises major policy issues. Romer observed that the academic literature that predated the crisis gave a clear answer about what policy is supposed to do in this situation: it should promise temporarily high inflation. He was therefore puzzled, not by the fact that central bankers have not done that, but by the fact that they never seemed to seriously consider it. He felt that the view due to Kenneth Rogoff, that the world needs conservative central bankers, has perhaps been taken a little too far. Today’s central bankers seem allergic to inflation at any level and unwilling even to talk about it under any circumstances. Alan Blinder reminded the Panel that when one is talking about whether inflation should be 2 or 3 or 4 percent, as opposed to whether it should be zero or 12 percent, another issue becomes relevant, namely, bias in the inflation measure. The science of inflation measurement is not at the state where it can confidently distinguish between zero and 2 percent, or between 2 and 4 percent inflation. Justin Wolfers felt that the paper’s treatment of the natural rate of unemployment might understate the situation. Disinflation can easily start to cause long-term unemployment problems, which can in turn cause the natural rate to creep upward, resulting in large, persistent output shortfalls. If there is even a reasonable probability of such an effect, Wolfers asserted, the magnitudes involved could well swamp just about everything else and boost the figure of $1.8 trillion.

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Timothy Besley argued that it is premature to try to assess the welfare implications of the zero lower bound, because so far we have little experience of periods when interest rates are hitting the bound on a regular basis, or of how central banks can and will respond. He recalled from his tenure on the Bank of England Monetary Policy Committee that as Britain approached the zero lower bound, it was clear that there would be a lot of pressure for some sort of action when the lower bound was reached, but very unclear what would be done. Monetary policymakers still have a lot to learn about effective policy at the lower bound, which can only come from experience. Besley also noted that the coordination of fiscal and monetary policy becomes more important when one begins to encounter the zero bound with some regularity, and such coordination could lead to fundamental changes in the nature of the central bank’s independence. Richard Cooper observed that the available quantitative information concerning the lower bound comes overwhelmingly, or even exclusively, from the United States, yet the paper tries to draw lessons that go well beyond the United States. More empirical data from other countries are needed. He supposed that wage rigidity in the United States is less than what it was two decades ago, and he was unsure whether the same would be true of other countries. To the extent that wage rigidity is important, one has to look not only at these macroeconomic phenomena, but also at the allocative implications of an inflation target, because if nominal wages are rigid, one can get much more movement in real wages with a higher inflation target. Christopher Carroll noted that only two or three years ago, before the dramatic events of recent months, some academic papers had come to the conclusion that hitting the zero lower bound was essentially a zeroprobability event and could be ignored. Now the zero-probability event has happened, and the response seems to be “Oh, when you recalibrate with the new data, it’s not a zero-probability event any more, just rare.” Carroll wondered whether this reaction is an unhealthy sign that the whole literature has gone seriously astray and has overestimated the state of knowledge on some fundamental questions, by requiring that all answers to those questions be shoehorned into the model through their implications for a couple of model parameters such as the target inflation rate or the cost of quadratic inflation deviations. He proposed a return to some more foundational questions that have not been satisfactorily settled. For example, what are the costs of inflation, and are they captured in the current models? He thought Stanley Fischer may have come close to the truth with his idea that the biggest cost of higher inflation is that it creates more uncertainty, but then inflation becomes simply an intermediary indicator of uncertainty, in which


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case the real issue becomes the effect of monetary policy on uncertainty, not on inflation. Benjamin Friedman commented that the current behavior of central banks seems to provide dramatic evidence that they are not solely concerned about rising inflation. With the zero lower bound currently binding, one would want to have a more negative real interest rate, and yet it is precisely at this moment that the Federal Reserve seems most eager to demonstrate that it will seek at all costs to withdraw the excess liquidity in a way that will not lead to inflation. Friedman also wondered whether the paper could make the point more persuasively that hitting the zero lower bound did not make the current recession any deeper. The claim, as he understood it, was that the zero lower bound could not have contributed to the precipitous decline in GDP in the first quarter of 2009, because of the usual lags with which monetary policy affects the economy. But in light of the role that expectations play, more justification is needed to say that there was no difference between, on the one hand, the Federal Reserve cutting the funds rate to zero and telling the public that it was through, and on the other, the Federal Reserve making very clear that it was willing to do whatever was required to prop up the economy. Ricardo Reis commented that the value of an inflation target is as a means of conveying the central bank’s commitment to a given level of inflation, so that agents come to expect that level of inflation in their pricing and output decisions. Just five years ago, when the optimal inflation target was being discussed, there was agreement that it should be between around zero and 2 percent. No one was seriously proposing 4 percent. Now, however, the discussion is whether it should be 2 or 4 or maybe even 5 percent. It seemed to Reis that when the economy gets into serious difficulty, the discussion turns to exit strategies, and higher targets suddenly become acceptable. But he worried that once out of the crisis, those higher rates would again become laughable and the discussion would return to lower rates. There thus seemed to be a disconnect in the current discussion: as long as the target itself is being reassessed every few years, it undermines the commitment that justifies the desire for a target in the first place. Gita Gopinath reminded the panel of another policy option available to policymakers confronting the zero lower bound, namely, depreciating the currency. This was raised by Ben Bernanke during the Japanese deflation but seldom comes up in other contexts. There are some reasons why this is so, but perhaps depreciation should be discussed as one of the ways of dealing with the lower bound.

SUMIT AGARWAL

XAVIER GABAIX

Federal Reserve Bank of Chicago

New York University

JOHN C. DRISCOLL

DAVID LAIBSON

Board of Governors of the Federal Reserve System

Harvard University

The Age of Reason: Financial Decisions over the Life Cycle and Implications for Regulation ABSTRACT Many consumers make poor financial choices, and older adults are particularly vulnerable to such errors. About half of the population between ages 80 and 89 have a medical diagnosis of substantial cognitive impairment. We study life-cycle patterns in financial mistakes using a proprietary database with information on 10 types of credit transactions. Financial mistakes include suboptimal use of credit card balance transfer offers and excess interest rate and fee payments. In a cross section of prime borrowers, middle-aged adults made fewer financial mistakes than either younger or older adults. We conclude that financial mistakes follow a U-shaped pattern, with the cost-minimizing performance occurring around age 53. We analyze nine regulatory strategies that may help individuals avoid financial mistakes. We discuss laissez-faire, disclosure, nudges, financial “driver’s licenses,” advance directives, fiduciaries, asset safe harbors, and ex post and ex ante regulatory oversight. Finally, we pose seven questions for future research on cognitive limitations and associated policy responses.

M

ost households in the United States have accumulated a substantial pool of wealth by the time they retire. Among households with a head aged 65–74, median net worth—including net home equity but excluding public and private defined-benefit claims—was $239,400 in 2007, according to the 2007 Survey of Consumer Finances (SCF).1 Moreover, household 1. For this group of households, mean net worth, again excluding defined-benefit accounts, was $1,015,700 in 2007. However, the mean is dominated by the right tail of the distribution, and saving for these households is motivated by many considerations other than retirement (particularly the desire to make bequests and the need for capital in ongoing privately held businesses).

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wealth is likely to grow much more quickly than income in the next three decades, as more and more households experience a full career’s worth of accumulation in defined-contribution pension accounts such as 401(k)s.2 In addition to their accumulated assets, households with a head aged 65–74 often have a complex set of balance sheet liabilities: the SCF reports that in 2007, 47.9 percent had debt secured by a residential property, 26.1 percent had installment loans, and 37 percent carried credit card balances from month to month. Nearly two-thirds (65.5 percent) of households in this age range had at least one form of debt. In this paper we seek to raise a red flag about the increasingly large and complex balance sheets of older adults. Substantial retirement savings are critical for the well-being of this group in light of the increasing average length of retirement, the rising expectation of independent living through most of retirement, and more modest defined-benefit flows for most retirees. However, many older adults are not in a good position to manage their finances, or even to delegate that management safely to others. We document this concern in four ways. First, we review the literature on age-based patterns in cognitive function. Performance on novel cognitive tasks—what psychologists call fluid intelligence—declines dramatically over one’s adult life. In the cross section, the fluid intelligence of the average individual falls by about 1 percentile unit each year from age 20 to age 80 (Salthouse forthcoming and authors’ calculations). Many mechanisms contribute to explaining this pattern, including (confounding) cohort effects, normal aging effects, and dementia. The prevalence of dementia explodes after age 60, doubling with every five years of age to more than 30 percent after age 85 (Ferri and others 2005). Moreover, many older adults without a strict diagnosis of dementia still experience substantial cognitive impairment. For example, the prevalence of the diagnosis “cognitive impairment without dementia” is nearly 30 percent between ages 80 and 89.3 Drawing these facts together, about half the population aged 80–89 have a diagnosis of either dementia or cognitive impairment without dementia. Second, we supplement these existing findings with new longitudinal evidence from the University of Michigan’s Health and Retirement Study (HRS). Our new evidence eliminates the confound of cohort effects by 2. Poterba, Venti, and Wise (2008). Today fewer than half of the private sector workforce have a defined-contribution plan at their current employer. However, current legislative proposals are likely to expand coverage. 3. Plassman and others (2008). They define cognitive impairment without dementia as a Dementia Severity Rating Scale score between 6 and 11.

AGARWAL, DRISCOLL, GABAIX, and LAIBSON

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estimating age effects that control for person fixed effects. When we do this, we find even stronger age-based patterns. Our results imply that age-based selection effects are very strong—the most impaired subjects tend to drop out of these surveys—explaining why cross-sectional patterns of cognitive decline understate the true age-based decline. Third, using a new dataset, we document a link between age and the quality of financial decisionmaking in debt markets. In a cross section of prime borrowers, middle-aged adults borrow at lower interest rates and pay less in fees than do either younger or older adults. Averaging results across 10 different types of credit transactions, we find that fee and interest payments are minimized around age 53. These measured effects are not explained by differences in observed risk characteristics. Combining multiple datasets, neither do we find evidence that selection effects or cohort effects explain our results. The leading explanation for the patterns that we observe is that experience and acquired knowledge rise with age, but fluid intelligence declines with it. Fourth, we review the contributions of other authors who have studied age effects on financial decisionmaking. A small but rapidly growing body of convergent research highlights the cognitive vulnerabilities of older adults. Questions remain about the identification of age, cohort, and time effects. Moreover, even if we were certain that older adults make many suboptimal financial decisions, it is not clear what society should do about it. We next present a discussion of mutually compatible policy options for addressing the identified problems. Although some of our field evidence suggests that younger adults are also at risk, our regulatory analysis emphasizes older adults, for four reasons. First, older adults have much more at stake, since they control far more financial resources, both absolutely and as a fraction of their total net worth, than people in their 20s. Second, older adults cannot as easily bounce back from their financial mistakes, since their cognitive and physical disabilities frequently make it difficult to return to work. Third, younger adults may make frequent financial mistakes, but they rarely have severe cognitive incapacity. The behavior of a foolish 20-year-old credit card user bears little comparison to that of an 80-yearold suffering from dementia. For example, one regularly hears stories about friends’ aging relatives who lend or give a substantial fraction of their wealth to con artists (see, for example, Choi-Allum 2009). Fourth, in the United States at least, retirees effectively have fewer regulatory protections than do most workers. This is an unintended consequence of the nation’s system of defined-contribution retirement savings. Definedcontribution pension accounts are stringently regulated by the Employee

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Retirement Income Security Act (ERISA).4 However, almost all retirees eventually roll their accumulated balances out of ERISA-regulated accounts into Individual Retirement Accounts (IRAs), which are regulated with a much lighter touch. For example, the broker-dealer securities firms that manage most IRAs have no fiduciary duty toward their customers. Thus, the system currently provides the least regulation for precisely the age group with the greatest vulnerability. We identify and discuss nine policy options, listed here in approximate order from least to most paternalistic: —Laissez-faire —Increased disclosure —“Libertarian paternalism” (the greater use of advice, defaults, and other nudges) —Financial “driver’s licenses” —Advance directives (instructions set out today against a future loss of competence) —Expanded fiduciary requirements —Protection of assets through sequestration in known safe investments (“safe harbors”) —Default regulatory approval of financial products, with ex post sanctions for violation of standards —Mandatory ex ante regulatory review of financial products. We discuss the pros and cons of each of these different regulatory models, without arguing for the adoption of any one of them. We believe that natural experiments are needed to determine their efficacy, and we recognize that the stronger interventions have the potential to generate large social costs as well as benefits. Finally, we identify seven critical research questions that need to be answered before policymakers can identify an optimal regulatory design. These questions highlight how little is currently known about the financial choices of older adults. The paper is organized as follows. Section I discusses medical and psychological evidence on changes in cognitive function over the life cycle. Section II discusses evidence from the HRS on age-based changes in 4. ERISA requires plans to provide participants with information about the plan, including important information about plan features and funding; establishes fiduciary responsibilities for those who manage and control plan assets; requires plans to establish a grievance and appeals process for participants; and gives participants the right to sue for benefits and over breaches of fiduciary duty. (See the U.S. Department of Labor’s webpage on ERISA at www.dol.gov/dol/topic/health-plans/erisa.htm.)


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cognitive function, controlling for person fixed effects. Section III discusses the evidence on errors made by older adults in debt markets. Section IV discusses the broader literature on economic decisionmaking over the life cycle. Section V analyzes the nine policy approaches listed above. Section VI concludes by posing our seven questions for future research.

I. Psychological and Medical Evidence on Cognitive Decline among Older Adults Fluid intelligence (performance on novel tasks) can be measured along many different dimensions, including working memory, reasoning, spatial visualization, and cognitive processing speed. Figure 1, from a forthcoming paper by Timothy Salthouse, illustrates each of these kinds of tasks. Fluid intelligence shows a robust age pattern in cross-sectional datasets of adults (Cattell 1987; Salthouse 2005; Salthouse forthcoming). Adults in their early 20s score on average about 0.7 standard deviation above the adult mean, and adults in their early 80s about 1.0 standard deviation below Figure 1. Examples of Memory and Analytical Tests Memory Study the following words and then write as many as you can remember Goat Door Fish Desk Rope Lake Boot Frog Soup Mule

Reasoning Select the best completion of the missing cell in the matrix

Spatial Visualization Select the object on the right that corresponds to the pattern on the left

Perceptual Speed Classify the pairs as same (S) or different (D) as quickly as possible

Source: Salthouse (forthcoming). Reprinted with permission.

56


Figure 2. Performance on Memory and Analytical Tasks by Age Percentile

Z-score

1.0

Pattern comparison (N = 6,547)

84

Matrix reasoning (N = 2,440) 0.5

69 Word recall (N = 2,230) 50

0.0 Spatial relations (N = 1,618) –0.5

31

–1.0

16

–1.5

7 20

30

40

50 60 Chronological age

70

80

90

Source: Salthouse (forthcoming). Reprinted with permission.

the mean. This implies a decline of about 1 percentile of the total adult population per year after age 20, assuming the distribution is Gaussian. Indeed, this decline is remarkably smooth from age 20 to age 90 (figure 2). The measured pattern results from some combination of true age effects, cohort effects (Flynn 1984), and selection effects. We return to the issue of identification of age effects in the next section, where we report new evidence from the HRS. Neurological pathologies represent one important pathway for age effects on performance in older adults. For instance, dementia is primarily attributable to Alzheimer’s disease (in about 60 percent of cases) and vascular disease (in about 25 percent). The prevalence of dementia doubles with every five additional years of life (Ferri and others 2005; Fratiglioni, De Ronchi, and Agüero-Torres 1999).5 For example, the top panel of table 1 reports that the prevalence of dementia in North America rises from 3.3 percent at ages 70–74 to 6.5 percent at ages 75–79, 12.8 percent at ages 80–84, and 30.1 percent for adults at least 85 years of age (Ferri and others 2005). 5. There is also a growing literature that identifies age-related changes in the nature as opposed to the quality of cognition (see Park and Schwarz 2000; Denburg, Tranel, and Bechara 2005). Mather and Carstensen (2005) and Carstensen (2006) identify age-related variation in cognitive preferences. Subjects with short time horizons and older subjects attend to negative information relatively less than do subjects with long time horizons or younger subjects.

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Table 1. Prevalence of Cognitive Impairment by Age in North America Percent Age Dementia 60–64 65–69 70–74 75–79 80–84 85 and over

Prevalence

95 percent confidence interval

0.8 1.7 3.3 6.5 12.8 30.1

0.6–1.0 1.5–1.9 2.7–3.9 5.5–7.5 11.8–13.8 27.9–32.3

Cognitive impairment without dementia 71–79 16.0 80–89 29.2 90 and over 38.8 All ages 22.0

11.5–20.5 24.3–34.1 25.6–52.0 18.5–22.5

Sources: Ferri and others (2005); erratum to Plassman and others (2008).

Many older adults suffer from a less severe form of cognitive impairment, which is diagnosed as cognitive impairment without dementia. The prevalence of this diagnosis rises from 16.0 percent at ages 71–79 to 29.2 percent at ages 80–89 (bottom panel of table 1). All told, about half of adults in their 80s suffer from either dementia or cognitive impairment without dementia. Age-driven declines in fluid intelligence, however, are partly offset by age-related increases in crystallized intelligence (sometimes called experience or knowledge).6 Most day-to-day tasks, such as buying the right amount of milk at the grocery store, rely on both fluid and crystallized intelligence. For most tasks we hypothesize that net performance is humpshaped with respect to age. Formally, this would result from the following conditions: general task performance is determined by the sum of fluid and crystallized capital, crystallized capital accumulates with diminishing returns (for m < n, the nth learning experience generates less crystallized capital than the mth), and fluid capital falls linearly (or perhaps concavely) over the life cycle (figure 3). Consequently, middle-aged adults may be at a decisionmaking sweet spot: they have substantial practical experience and have not yet suffered significant declines in fluid intelligence. Supporting this 6. Experience may be acquired directly, or it may be acquired indirectly through communication with peers. As people build up their social networks over their lifetime, their external sources of experience become better and better developed. However, these networks tend to fray as individuals retire and leave well-developed work-based relationships. Peer mortality also contributes to a late-life decline of social networks. These channels suggest that the experiential knowledge derived from social networks follows a concave life-cycle pattern.

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Figure 3. Relationship of Net Cognitive Performance to Crystallized and Fluid Capital Task performance

Crystallized capital

Performance

Fluid capital

Age

hypothesis is the fact that results of experience-based cognitive tests—for example, those testing vocabulary and other types of acquired knowledge— follow a hump-shaped pattern (Salthouse 2005).

II. Evidence from the HRS The medical and psychological evidence on age-based patterns in cognitive function reviewed in the previous section is confounded by cohort effects and selection effects. In any cross section of subjects, the older subjects not only are older but also were born in different cohorts from the others. Moreover, these patterns are affected by different selection mechanisms. For example, older adults have relatively more health problems (both physical and cognitive) that cause them to selectively drop out of surveys.

II.A. Evidence from the Main HRS Sample In light of these problems, it is useful to analyze data that follow individuals longitudinally. The HRS is an excellent source for such analysis for cognitive variables (Ofstedal, Fisher, and Herzog 2005; McArdle, Smith, and Willis forthcoming). Since its beginning in 1992, the HRS has surveyed a nationally representative sample of more than 22,000 Americans over the age of 50. These longitudinal surveys are conducted every two years. For reasons of data comparability, we use all of the waves from 1993 to 2006.7 7. See Ofstedal and others (2005) for a complete description of the cognitive scales in the HRS.


59

Our analysis proceeds in two parallel ways. First, we undertake a “naive” analysis (mirroring the methods described in the previous section) that simply plots mean performance by age (in integer years), ignoring the potential role of cohort and selection effects. Second, we conduct a “controlled” analysis that traces out the performance trajectory using only intra-individual differences. To do this, we calculate the slope at age a as the average slope for all subjects who are observed in adjacent survey waves straddling age a.8 In other words, the slope at integer age a is calculated as 1 N Ω( a )

x i ,w +1 − x i ,w , − Ai ,w i ,w +1

∑( ) A

i ∈Ω a

where xi,w is the task performance of subject i in HRS wave w, Ai,w is the decimal age of subject i in wave w, Ω(a) is the set of subjects who appear in adjacent waves at ages straddling age a, and N is the cardinality of Ω. Note that this average slope implicitly controls for person fixed effects, since the slope is calculated by averaging individual slopes. We then trace out the life-cycle trajectory using these average slopes, starting from the value of the naive analysis at an initial age. To reduce sampling noise, the initial age is chosen as the first age for which we have at least 1,000 observations in our combined sample (including all of the HRS waves from 1992 to 2006). The top left panel of figure 4 plots naive and controlled performance in the immediate word recall task. The interviewer reads a list of 10 simple nouns, and the respondent is immediately asked to recall as many of them as possible, in any order. At age 51 the average performance is 6.2 words out of 10. By age 90 the average (controlled) performance is 3.0 words out of 10. The top right panel of figure 4 plots naive and controlled performance in the delayed word recall task. This is the same as the previous task except that a second task taking about five minutes intervenes between recitation and recall. At age 51 the average performance is 5.4 words out of 10. By age 90 the average (controlled) performance is 2.1 words out of 10. The middle left panel of figure 4 plots naive and controlled performance in the serial sevens task, in which the respondent is asked to count 8. In our notation, responses at dates t and t′ are adjacent if⎟ t′ − t⎟ < 5 years and the respondent does not answer the same question at another date t ″ between t and t′. For example, if a specific question was not included in the 2004 wave but was included in the 2002 and 2006 waves (and the respondent answered the question in both those waves), then the adjacent responses would be in 2002 and 2006. Two decimal ages A < A′ straddle integer age a iff A < a < A′.

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Figure 4. Performance by Age on Six Tasks in the Health and Retirement Studya Immediate word recall Score

Score

6

5

5

4

Naive

4

3

Controlled b 50

60

70 Age

80

Delayed word recall

90

50

60

70 Age

80

90

Telephone Interview of Cognitive Status

Serial sevens Score

Score

9.0

3.0

8.5 2.5

8.0 50

60

70 Age

80

60

90

70

80

90

Age

Chance of disease question Percent giving correct answer

Split lottery question Percent giving correct answer

50 70

40

60

30

50

20 50

60

70 Age

80

90

Sources: HRS data and authorsí calc ulations. a. See the text for descriptions of the tasks. b. Controlling for cohort and selection effects.

50

60

70 Age

80

90


61

backward by 7 from 100. The respondent is scored on a 1-to-5 scale, with 1 point awarded for each successful subtraction. At age 51 the average score is 3.2 out of 5. By age 90 the average (controlled) score is 2.2 out of 5. The middle right panel of figure 4 plots naive and controlled performance in the Telephone Interview of Cognitive Status (TICS) task. The researcher asks the respondent the following 10 questions and assigns 1 point for each correct answer: What is the current year? What is the current month? What is the current day of the month? What is the current day of the week? What do you usually use to cut paper? What do you call the kind of prickly plant that grows in the desert? Who is the current president? Who is the current vice president? Count backward from 20 to 10 (twice). At age 63 the average score is 9.2 out of 10. By age 90 the average (controlled) score is 7.5. Finally, we present two measures of practical numeracy. The bottom left panel of figure 4 plots naive and controlled performance in response to the following question: If the chance of getting a disease is 10 percent, how many people out of 1,000 would be expected to get the disease? At age 53, 79 percent of subjects answer correctly. By age 90, 50 percent answer correctly. The bottom right panel of figure 4 plots naive and controlled performance in response to the following question: If five people all have the winning numbers in the lottery and the prize is $2 million, how much will each of them get? We believe that this question is imprecisely posed, since the logical answer could be either $2 million or $400,000. However, the results are still interesting, since the fraction answering $400,000 (the official correct answer) drops precipitously with age. At age 53, 52 percent answer $400,000. By age 90, only 10 percent do.

II.B. Evidence from ADAMS Data The Aging, Demographics, and Memory Study (ADAMS) of the National Institute on Aging conducts in-person clinical assessments of cognitive function and dementia for a subsample of HRS respondents (Langa and others 2005). The ADAMS uses a much smaller sample than the HRS. Only 856 respondents were surveyed in the initial 2002 ADAMS wave. We use both the 2002 wave and a follow-up wave in 2006. Figure 5 plots naive and controlled Clinical Dementia Ratings (CDRs) for the ADAMS sample (using just the 2000 wave), using sampling weights to correct for overrepresentation of certain groups. The CDR score is on a 0-to-4 scale, where 0 is healthy. A score of 1⁄2 represents very mild dementia; 1 represents mild dementia; 2 represents moderate dementia; 3 represents

62


Figure 5. Clinical Dementia Ratings by Age from the Aging, Demographics, and Memory Study (ADAMS) CDR (4 = maximum) Severe 3.0 2.5 Moderate 2.0

Controlled

1.5 Mild 1.0 Naive

Very mild 0.5

75

80

85

90

Age Source: ADAMS data and authors’ calculations.

severe dementia; 4 is the highest dementia rating on the scale.9 Scoring is done by a panel of clinicians who base their judgments on the entire battery of tests in the ADAMS. Hence, the CDR has a very high signal-to-noise ratio, since the CDR score is derived from hundreds of survey questions posed to a respondent and his or her caregivers. At age 75 the average score is 0.4, which is near the threshold for “very mild” dementia. At age 99 the average (control) score is 3.2, implying that the average respondent has severe dementia. Note that figure 5 shows a large gap between the naive average and the controlled average CDR scores. The naive averages are highly misleading, since they are affected by selection bias.

II.C. Summary of Evidence from the HRS The HRS data paint a clear picture of declining cognitive function with age. They also suggest that selection effects may be more important than cohort effects. Cohort effects are predicted to cause the naive profiles to fall more steeply than the control profiles, since older cohorts have fewer educational advantages. Selection effects, in contrast, should cause the 9. See “Clinical Dementia Rating (CDR) Scale,” Washington University, St. Louis (alzheimer.wustl.edu/cdr/PDFs/CDR_OverviewTranscript-Revised.pdf) for a description.


63

naive profiles to fall less steeply than the control profiles, since the individuals with the poorest cognitive function tend to exit the sample. Selection bias seems to be more important in the HRS data, since our controlled profiles are steeper than our naive profiles.

III. Financial Services and Age: Evidence on the Inverse U-Shape of Performance In this section we document a U-shaped curve with age in the prices people pay in 10 different types of credit transactions: credit card balance transfer offers, home equity loans, home equity lines of credit, automobile loans, mortgages, personal credit cards, small business credit cards, credit card late payment fees, credit card overlimit fees, and credit card cash advance fees. We discuss three forms of prices paid: interest rates (measured as annual percentage rates, or APRs), fee payments, and balance transfer costs. In each case we conduct a regression analysis that identifies age effects and controls for observable factors that might explain the patterns by age. Thus, unless otherwise noted, in each context we estimate a regression of the following type: (1)

F = α + β × spline(age) + γ × controls + ε ,

where F is a measure of the price paid by the borrower (for example, the APR), “controls” is a vector of control variables intended to capture alternative explanations in each context (for example, measures of credit risk), and “spline(age)” is a piecewise linear function that takes the consumer’s age as its argument, with knot points at ages 30, 40, 50, 60, and 70. We then plot the fitted values for the spline on age, computing the intercept using the sample means for the controls. The regressions are either pooled panel or cross-sectional regressions, depending on the context.10 Below we discuss the nature of the products and the prices paid, present graphs of fitted values on the spline coefficients, and discuss possible explanations for our findings. We describe the data in the appendix and provide summary statistics for the datasets and the regression results in the online appendix.11 10. As variants, we tried having knot points at every five years, and replacing the spline with a set of dummy variables for age. The results were qualitatively and quantitatively similar, also showing a U-shaped pattern by age. 11. Online appendices for all papers in this issue may be found on the Brookings Papers webpage (www.brookings.edu/economics/bpea) under “Conferences and Papers.”

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III.A. Three Financial Choices We first examine three of the above financial transactions: credit card balance transfer offers, home equity loans, and home equity lines of credit. The U-shaped pattern by age for suboptimal balance transfer behavior is a relatively clean example and thereby merits special emphasis. In the other two cases we are able to tease out the mechanism leading to higher interest payments for younger and older borrowers, namely, mistakes made in estimating the value of one’s home. EUREKA MOMENTS: OPTIMIZING THE USE OF CREDIT CARD BALANCE TRANSFERS.

Credit card holders frequently receive offers to transfer account balances from an existing card to a new card charging a substantially lower APR for an initial period from six months to a year or more (a “teaser” rate). The catch is that all payments on the new card are applied first to the transferred balance, and to new purchases (which are subject to a higher APR) only after the transferred balance has been paid off. The optimal strategy for the cardholder during the teaser-rate period, then, is to make all new purchases on the old credit card and none on the new card until all the transferred balances have been paid off. We categorize cardholders in our dataset by the speed with which they converge on this optimal strategy. Some (about one-third) identify this optimal strategy immediately, before making any purchases with the new card. Others (slightly more than one-third) never identify the optimal strategy during the teaser rate period. The remaining third discover it after one or more billing cycles as they observe their surprisingly high interest charges. These borrowers make purchases for one or more months and then have a “eureka” moment, after which they implement the optimal strategy.12 The top panel of figure 6 plots the frequency of eureka moments for each of five age groups. The plot of those who never experience a eureka moment shows a pronounced U-shape by age; in contrast, the plot of those who implement the optimal strategy immediately has a pronounced inverted U-shape. Plots for eureka moments in the interior of the time space (that is, those that occur strictly after the first month) are flat.13 The “No eureka” line implies that the groups with the greatest frequency of maximal confusion 12. We thank Robert Barro of Harvard University for drawing our attention to this type of potentially tricky financial product. We also note that changes in regulation proposed in May 2008 by the Federal Reserve, the National Credit Union Administration, and the Office of Thrift Supervision would forbid banks from applying payments solely to transferred balances. 13. Although the average share of borrowers in each of the intermediate categories is small—on the order of 5 percent—summing over all the months yields a fraction of borrowers equal to one-third of the total.

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Figure 6. “Eureka” Moments in Credit Card Balance Transfers by Age of Borrower Fraction of borrowers experiencing eureka momenta Percent

50

No eureka

40 30 In month 1 20 10

In month 2a

18–24

25–34

35–44 Age

45–64

Over 65

Propensity ever to experience eureka moment Percent 85

75

65

55

45 30

40

50 Age

60

70

Source: Authors’ calculations. a. Fraction experiencing eureka moment in each of months 3 through 6 is below 10 percent for all ages.

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are younger adults and older adults. The group most likely to adopt the optimal strategy immediately is adults aged 35–44. The bottom panel of figure 6 plots the fitted values of the age splines for the propensity ever to experience a eureka moment. Note that unlike in the other figures, higher values here indicate a smaller propensity to make mistakes. Consistent with the evidence so far, a performance peak occurs in middle age.14 In section III.C we discuss possible explanations for this and the other age-related differences we observe.15 HOME EQUITY LOANS AND LINES OF CREDIT. Figure 7 plots the fitted values on the splines for age for interest rates paid on home equity loans and lines of credit. The lines have a pronounced U-shape: the relatively young and the relatively old face APRs that can be 50 basis points or more higher than what the middle-aged pay. For these two examples, we believe we understand the mechanism leading to the differences by age: misestimation of the value of one’s home. The amount of collateral offered by the borrower, as measured by the loanto-value (LTV) ratio, is an important determinant of loan APRs. Higher LTV ratios imply higher APRs, since the fraction of collateral is lower. At the financial institution that provided our data, borrowers first estimate their home’s value and then apply for a loan or credit line falling into one of three categories depending on the implied LTV estimate: 80 percent or less, between 80 and 90 percent, and 90 percent or greater. The financial institution then independently verifies the home’s value using an industry-standard methodology and constructs its own LTV measure, which can therefore differ from the borrower’s.16 The pricing of the loan or credit line depends on the LTV category that the borrower falls into, and not on where precisely the LTV falls within that category.17 If the borrower has overestimated the value of the home, 14. We also check for the possibilities that the relatively old and the relatively young might have lower levels of debt or less access to credit than the middle-aged. We find that neither credit card debt nor the number of open credit cards varies in economically or statistically significant ways with age. 15. The online appendix presents a formal hypothesis test for the U-shaped curves; we reject the null hypothesis of a flat age-based pattern in 9 out of the 10 cases. 16. Agarwal (2007) provides evidence that younger households are more likely to overstate, and older households to understate, their home’s value. Bucks and Pence (2006) present evidence that many borrowers do not accurately estimate their home’s value. 17. We have verified this practice in our dataset by regressing the APR on both the bank’s estimated LTV and dummy variables for whether the bank’s LTV falls into one of the three categories. Only the coefficients on the dummy variables were statistically and economically significant. Ben-David (2008) also shows that there are discrete jumps in lending rates at LTV cutoff points.

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Figure 7. Interest Rates Offered on Home Equity Loans and Lines of Credit by Age of Borrower Annual percentage rate (APR)

6.0 Loans 5.5

5.0 Lines of credit 4.5

30

40

50 Age

60

70

Source: Authors’ calculations.

so that the financial institution’s LTV is higher than the borrower’s, the institution will direct the buyer to a different loan with a higher interest rate corresponding to the higher LTV. In such circumstances the loan officer is also given some discretion to depart from the financial institution’s normal pricing schedule to offer a higher interest rate than the officer would have offered to a borrower who had correctly estimated her LTV. If the borrower has underestimated the home’s value, however, the financial institution need not direct the buyer to a loan with a lower interest rate corresponding to the financial institution’s lower LTV estimate; the loan officer may simply choose to offer the higher interest rate associated with the borrower’s estimate.18 Since the APR paid depends on the LTV category and not on the LTV itself, home value misestimation leads to higher interest rate payments only if the financial institution’s estimated LTV falls in a different category than the borrower’s. If, in contrast, the borrower’s estimated LTV was, say, 60 percent, but the financial institution’s estimate was 70 percent, the borrower would still qualify for the highest-quality loan category 18. Even if the financial institution’s estimate of the true home value is inaccurate, that misestimation will not matter for the borrower as long as other institutions use the same methodology.

68


(LTV < 80 percent) and would not suffer an effective interest rate penalty. We define a rate-changing mistake (RCM) to have occurred when the borrower’s LTV falls in a different category from the financial institution’s, for instance when the borrower estimates an LTV of 85 but the financial institution calculates an LTV of 75 percent (or vice versa). We find that, on average, making an RCM increases the APR by 125 basis points for loans and 150 basis points for credit lines (controlling for other variables, but not age). To examine the importance of RCMs, we first study the APRs offered to consumers who do not make an RCM. The top panel of figure 8 plots the fitted values from reestimating the regressions of APRs on borrower characteristics and age splines, but now conditioning on borrowers who do not make an RCM. The plots show only slight differences in APR paid by age. For home equity loans, the difference in APR between a borrower at age 70 and a borrower at age 50 has shrunk from 36 basis points to 8 basis points; for home equity lines of credit, it has shrunk from 28 basis points to 4 basis points. For a borrower at age 20, the APR difference over a borrower at age 50 has shrunk to 3 basis points for both loans and lines of credit. We conclude that among borrowers who do not make an RCM, the APR is essentially flat with age. Therefore, the U-shape of the APR age curve is primarily driven by RCMs. We next investigate who makes RCMs. The bottom panel of figure 8 plots the propensity to make an RCM by age for home equity loans and credit lines. The figure shows U-shapes for both. Borrowers at age 70 have a 16-percentage-point greater chance of making a mistake on a home equity loan than borrowers at age 50 (the difference for lines of credit is 19 percentage points); the comparable numbers for borrowers at age 20 relative to age 50 are 35 and 41 percentage points. The unconditional average probability of making an RCM is 24 percent for loans and 18 percent for credit lines. This age effect is consistent with the cost of an RCM calculated above and the additional probability of making an RCM with age. For example, as noted, a 70-year-old has a 16-percentage-point additional chance of making an RCM on a home equity loan. Multiplying this by the average APR cost of an RCM for home equity loans of about 125 basis points gives an expected incremental APR paid of about 26 points. The analogous difference for lines of credit is 23 basis points. These differences are very close to the estimated differences of about 23 and 28 basis points. We conclude that in this example we have identified the reason for the U-shape of home equity APRs as a function of age (as always, controlling for other characteristics). Younger and older consumers have a greater tendency to misestimate the value of their home, which leads them to make

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Figure 8. Rate-Changing Mistakes in Home Equity Loan and Line of Credit Applications by Age of Borrower APRs for borrowers who do not make an RCMa Percent 5.75 5.50

Loans

5.25 5.00 4.75 Lines of credit 4.50 4.25 30

40

50 Age

60

70

Propensity to make an RCM Percent 90 80 70 60

Loans

50 40 30 20 10

Lines of credit 30

40

50 Age

60

70

Source: Authors’ calculations. a. A rate-changing mistake (RCM) occurs when the borrower’s estimate of the value of the home differs from the lender’s estimate by enough to place the borrower’s implied loan-to-value (LTV) ratio in a different interest rate category from the lender’s implied LTV. The categories are LTV ≤ 80 percent, 80 percent < LTV< 90 percent, and LTV ≥ 90 percent; loans in higher LTV categories may be charged higher interest rates.

70


an RCM, which in turn results in their borrowing at a higher APR than they could have obtained. On the other hand, for consumers who do not make an RCM, the APR is essentially independent of age. Given the large costs associated with an RCM, one might ask why borrowers do not make a greater effort to accurately estimate the value of their home. One possibility is that potential borrowers are not aware that credit terms will differ by LTV category or, if they are aware of this fact, they may not know by how much the terms differ. This feature of loan pricing may thus be a shrouded attribute, in the sense of Gabaix and Laibson (2006).

III.B. Seven Other Financial Choices In this section we present results on the seven other credit prices we studied: interest rates on personal credit cards, auto loans, mortgages, and small business credit cards (figure 9), and three types of credit card fees (late payment, overlimit, and cash advance; figure 10). In all seven cases, plots of fitted values on the coefficients on the age splines are U-shaped by Figure 9. Interest Rates Paid by Type of Loan and Age of Borrower Personal credit card loans Annual percentage rate

Automobile loans Annual percentage rate

18.00

9.25 9.00 8.75 8.50 8.25

17.75 17.50 17.25 30

40

50 Age

60

70

Mortgages Annual percentage rate

30

40

50 Age

60

70

Small business credit card loans Annual percentage rate

15.75 15.50 15.25 15.00 14.75

12.75 12.50 12.25 12.00 11.75 30

40

50 Age


60

70

30

40

50 Age

60

70

71


Figure 10. Incidence of Credit Card Fees by Age of Borrower No. of fees incurred per month 0.32 0.30

Late fee

0.28

Cash advance fee

0.26 0.24 0.22 0.20 0.18

Overlimit fee

30

40

50 Age

60

70


age, although the amplitudes vary. In each case, in the underlying regressions we have tried to control for other variables that might explain differences in the cost of suboptimal financial choices by age. Data and regression results are presented in the online appendix. Visual inspection of the age splines in all cases suggests that fees and interest rates paid are lowest for customers in their late 40s or early 50s. To estimate the minimum more precisely, we reestimate each model, replacing the splines from age 40 to 50 and from age 50 to 60 with a single spline running from age 40 to 60 and the square of that spline. Table 2 reports our estimates of the “age of reason,” the point in the life span at which financial mistakes are minimized, for each type of transaction. The mean age of reason in these estimates is 53.3 years, and the standard deviation calculated by treating each study as a single data point is 4.3 years. The lowest age of peak performance is that for the eureka moment task. Interestingly, that task is arguably the most dependent on analytical capacity and the least dependent on experience, since the kinds of balance transfer offers that we study were new financial products when our data were collected. Hence, it is not surprising that the peak age for succeeding at that task would be earlier than for the other tasks. However, since we lack a rigorous measure of the difficulty of a task, the interpretation of the eureka case remains speculative.

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Table 2. Age of Peak Performance for 10 Financial Tasks Age Task

Mean

Standard deviation

Minimizing APR on: Home equity loans Home equity lines Credit cards Automobile loans Mortgages Small business credit cards

55.9 53.3 50.3 49.6 56.0 61.8

4.2 5.2 6.0 5.0 8.0 7.9

Experiencing eureka moment Avoiding credit card late fees Avoiding credit card overlimit fees Avoiding credit card cash advance fees Average

45.8 51.9 54.0 54.8 53.3

7.9 4.9 5.0 4.9 4.3


It would be useful to measure such effects in other decision domains, such as savings choices, asset allocation choices beyond stocks, and health care choices. We have described a simple procedure for doing this: first identify the general shape of the age effect, as in equation 1, using controls and age splines, and then estimate a linear-quadratic equation to localize the peak of performance. George Korniotis and Alok Kumar (forthcoming-a) confirm our U-shape hypothesis in their study of investment skills. It may also be possible to develop models that predict the age of peak performance. There is a growing consensus that analytically intensive problems, like those in mathematics, are associated with younger peak ages (see Simonton 1988; Galenson 2006; Weinberg and Galenson 2005). Analogously, problems that require more experiential training have older peak ages. For instance, Benjamin Jones (2005) finds that the peak age for natural scientists drifted higher over the 20th century. Relative to 100 years ago, more experience now needs to be accumulated to reach the cutting edge of scientific fields. In this paper we found that what is arguably the most analytically demanding task—deducing the best way to exploit lowinterest balance transfers—is associated with the youngest age of peak performance. It would be useful to study the association between analytically demanding problems and peak age.

III.C. Possible Explanations Each credit market has idiosyncratic factors that may contribute to the hump-shaped age patterns that we have measured. But the recurrence of


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that pattern across all 10 outcomes above suggests that there may also be some common underlying explanation. In this section we discuss several such possible explanations, including cognitive age effects, selection effects, and cohort effects. We do not find evidence for selection or cohort effects that could explain our results, but our data do not allow us to definitively rule them out. AGE-RELATED EFFECTS. One possible explanation for the U-shaped pattern of mistakes is a combination of two age-based effects: diminishing returns to learning, and the age-based decline in analytical function we documented in section I. Relatively young borrowers tend to have low levels of experience but a high degree of analytical function, whereas older borrowers tend to have high levels of experience but relatively lower analytical function. We discussed these mechanisms in section I and explained how these offsetting trends could produce a hump-shaped pattern in financial sophistication. This hypothesis of two offsetting age-based effects also provides a possible explanation of the location of peak performance. We hypothesize that peak performance reflects a trade-off between rising experience and declining analytical function. If so, the sooner people start experimenting with a financial product, the earlier the peak of performance should be. To evaluate this hypothesis for each financial product, we first construct the age distribution of the users of this product in our dataset and calculate the age at the 10th percentile of the distribution, which we call “age10%.” This is a proxy for the age at which people typically start using the product. We then regress the location of peak performance on that variable, with the following results: peak = 33 + 0.71 × age10% . The adjusted R2 for the equation is 0.62, and the standard errors on the intercept and the coefficient are, respectively, 5.7 and 0.19.19 Thus, we reject the null hypothesis of no relationship between the peak variable and age10%. Products whose use begins later in life tend to have a later performance peak. SELECTION EFFECTS. The cross-sectional age effects that we measure are probably also partly attributable to differences in the pool of borrowers by age group—a selection effect. For example, in the total population of U.S. households, retirees borrow less than other adults, as the life-cycle 19. The effect is robust to the choice of percentiles other than the 10th for the start variable. For instance, the correlation between peak age and median age of users for the product is 0.83.

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consumption model predicts. Older adults who are borrowing may therefore be unrepresentative of the population of all older adults. Likewise, older adults who are borrowing might be less financially savvy than 30- or 50-year-old borrowers, since borrowing might be less of a bad signal at these younger ages.20 Below we describe several ways of measuring the role of sample selection in determining our results. Lack of financial sophistication in relatively older (or younger) borrowers should be reflected in those borrowers having less education, income, or net worth than nonborrowers of the same age. To make such comparisons, we calculate for each age group the ratio of the median educational attainment (or income, or net worth) of borrowers in that group to the median for all members of the group. We want to determine whether these ratios differ across age groups. In other words, are borrowers differentially selected across different age groups? Since our proprietary dataset contains information only on borrowers, we cannot use it to make these calculations. However, we can make such comparisons using the SCF. Using data from the 1989, 1998, 2001, and 2004 SCFs, we compute the above ratios for education, income, and net worth; results are presented in the online appendix. We find that within age groups, borrowers almost always have higher income and more education than the population as a whole, and often have higher net worth. Moreover, older borrowers appear to have relatively higher income and more education relative to their peers than middle-aged borrowers do. Hence, these data suggest that the selection effects by age go in the opposite direction from that predicted: older borrowers appear to be a more affluent and better educated pool than middle-aged borrowers. We present additional results in the online appendix showing that borrowing by age does not appear to vary by race, and that older borrowers do not appear to have disproportionately lower incomes, lower FICO (credit) scores, or more debt than older people generally. None of these analyses lend support to the idea that sample selection effects contribute to the U-shaped patterns that we see in the data. If in fact a higher proportion of older borrowers are unsophisticated, then that lack of sophistication is somehow evident only in borrowing rates and fee payments, and not in credit scores, default rates, educational attainment, income, or net worth. Although we concede that this is a logical possibility, we know of no explanation for why such a lack of sophistication would appear so selectively in the data. 20. They could also be riskier, in ways not captured by the risk measures we include—a hypothesis that we consider (and reject) below.


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Older borrowers in our cross section might make relatively less sophisticated financial choices than younger borrowers because they belong to cohorts that have less human capital than younger cohorts (see, for example, Flynn 1984). For example, older cohorts may be less mathematically literate than younger cohorts, or they may use less sophisticated search technologies: for instance, they may be less inclined to use the Internet to compare financial products. Finally, older cohorts may have grown up with different financial products than those now available. Without a true panel dataset with information going back 20 years or more, we cannot measure the role of cohort effects in explaining the observed U-shaped pattern relative to other explanations. However, several facts make us think that cohort effects cannot be the whole explanation. First, education-based cohort effects cannot explain the pattern of declining mistakes that we observe over the first half of adulthood. Second, we observe the U-shaped pattern over a broad range of products, some of which, such as mortgages, have seen substantial changes in their institutional characteristics over time, while others, such as auto loans, have not. Third, if cohort effects were important, one would expect to see differences in prices paid between male and female borrowers, on the grounds that the current cohort of older female borrowers have tended to be less involved in financial decisionmaking than their male contemporaries. In fact, we find no substantive differences between men and women. Finally, for two products—auto loans and credit cards—we have data from 1992, 10 years earlier than the data used for our other studies. Replicating our analysis for these data results in the same U-shaped pattern. In summary, although cohort effects are probably present in our data, we doubt that they play an important role in explaining the U-shaped pattern. Cohort effects are most likely to make some contribution to the decline in performance that we measure after middle age. The improvement in performance up to middle age is harder to explain with cohort stories, although some preference-based cohort effect might be generating this pattern.21 RISK EFFECTS. Some of our results could be driven by unobserved variation in default risk that is not reflected in the risk measures (such as FICO scores) that we use as control variables. For instance, the U-shaped pattern of APRs could be due to a similar pattern of default by age. We test this alternative hypothesis by analyzing default rates on credit cards, auto loans, and home equity loans and credit lines. We estimate a linear regression in COHORT EFFECTS.

21. See Malmendier and Nagel (2007) for examples of how cohort effects affect preferences for equities.

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which the default rate is modeled as a weighted sum of an age spline, log income, and all of the standard risk measures that are in our data. When we plot fitted values by age (see the online appendix for the chart), we find a pronounced inverted U-shape for home equity loans and credit lines, implying that the young and the old have lower default rates than the middle-aged. Credit cards and auto loans show a slightly inverted U-shape, and the curve for small business credit cards is about flat. Hence, these results contradict the hypothesis that our APR results are driven by an unmeasured default risk. Finally, note that age-dependent default risks would not explain the observed patterns in credit card fee payments or in the suboptimal use of credit card balance transfers. OPPORTUNITY COST OF TIME. Some age effects could be generated by age-related variation in the opportunity cost of time (Aguiar and Hurst 2007). However, standard opportunity cost effects would predict that retirees pay lower prices, which is not what we observe in our data. Nevertheless, our findings and those of Aguiar and Hurst are not necessarily contradictory. Shopping for a familiar commodity—for instance, a gallon of milk—is much less analytically demanding than shopping for a complicated and somewhat unfamiliar product that can differ across many dimensions, such as a mortgage. Hence, we are not surprised to see older adults shopping more effectively for food even while losing ground in relatively more complex domains. In addition, shopping at stores and supermarkets may be a more pleasant activity than shopping at banks and other lenders, leading consumers to devote more time to the former. The 2007 SCF provides some support for the idea that shopping intensity for loans decreases with age. That survey asked borrowers whether, when borrowing money or obtaining credit, they shop around a great deal, moderately, or not at all. For borrowers under age 35, 24 percent report shopping around a great deal, 60 percent moderately, and 15 percent almost no shopping. The corresponding figures for those aged 75 and over are 15 percent, 40 percent, and 46 percent. The online appendix presents a full table of results by SCF age class as well as the text of the survey question. DISCRIMINATION AND OTHER SUPPLY FACTORS. The presence of age effects might also be interpreted as evidence for some kind of age discrimination. Banks may explicitly choose to charge older and younger borrowers higher APRs, or they may simply market products that happen to have higher APRs or fees more aggressively to the young or the old. We believe these explanations to be unlikely for two reasons. First, the U-shaped pattern shows up in contexts, such as credit card fee payments and failures to optimally use balance transfer offers, in which discrimination is not rele-

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Table 3. Differences in the Cost of Selected Financial Products by Age Interest rate or probability difference relative to age 50a Product or task Product Home equity loan Home equity line of credit Auto loan Mortgage Personal credit card Small business credit card

Task Eureka moment Avoiding credit card late fees Avoiding credit card overlimit fees Avoiding credit card cash advance fees

At age 25

At age 75

Total cost difference relative to age 50b (dollars a year) At age 25

At age 75

Difference in APR (basis points) 73 40 68 51 20 12 6 15 17 5 26 14

284 296 8 25 2 3

146 265 4 62 1 2

Difference in probability of experiencing (percentage points) 8 11 2 2 1 1 2 1

37 8 4 8

13 8 4 4

Source: Authors’ calculations. a. For the six products, difference between the APR paid by a borrower of the indicated age and that of a 50-year-old borrower. For the four tasks, difference between the probability that a borrower of the indicated age will succeed at the task and the same probability that a 50-year-old will. b. For the six products, the difference in APRs times the average debt level by age (see the online appendix). For the eureka moment, the difference in probability times the APR difference for personal credit cards times the balance transferred. For the credit card fees, the difference in probability times $35 (the typical fee amount) times 12 months. This may understate the true cost difference because multiple fee payments may trigger a rise in the interest rate on cash advance balances.

vant, since the products are the same and all cardholders face the same rules. Second, firms are likely to avoid age discrimination for legal reasons or to avoid the costs of negative publicity.22

III.D. How Large Are the Effects? The effects we find have a wide range of dollar magnitudes, as reported in table 3. We estimate that for home equity lines of credit, 75-year-olds pay about $265 more a year, and 25-year-olds about $296 more, than 50-year-olds. For other quantities, such as credit card fees, the implied age differentials are small: up to $10 a year for each kind of fee. The importance of these effects goes beyond the economic significance of 22. Charles, Hurst, and Stephens (2008) document racial differences in lending rates at auto finance companies, but not at banks.

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each individual case, however: the consistent appearance of a U-shaped pattern of costs in such a wide variety of circumstances points to a phenomenon that might also apply to many other areas.23 An important question is whether this pattern translates into other choice domains, including saving, asset allocation, and health care. Indeed, in domains for which we lack data, the effects might be larger. For instance, our sample probably does not contain older adults with severe dementia, for which the effects might be stronger. In section VI we estimate the fraction of GDP that may be wasted because of poor financial decisionmaking.

IV. Other Work on Economic Decisionmaking over the Life Cycle Our analysis is part of a recent literature that studies the effects of aging and cognitive function on the use of financial instruments (see, for example, Willis 2007; McArdle and others forthcoming), which in turn is part of a broader literature on household finance (Campbell 2006). In their work on financial literacy, Annamaria Lusardi and Olivia Mitchell (2006, 2007) find declines in the mastery of basic financial concepts, such as the ability to calculate percentages or perform simple division, in adults after age 50. In light of our findings, other researchers have offered to look for age patterns of financial mistakes in their own datasets. Lucia Dunn (personal communication, June 2007) has reported to us that the Ohio State Survey on credit cards shows a U-shaped pattern of credit card APR terms by age. Fiona Scott Morton (personal communication, May 2007) has reported that in her dataset of indirect auto loans (loans made by banks and finance companies using the dealer as an intermediary; see Scott Morton, Zettelmeyer, and Silva-Risso 2003), loan markups show a U-shaped age pattern. Luigi Guiso (personal communication, April 2007) finds that when picking stocks, consumers achieve their best Sharpe ratios at about age 43, and this effect appears to be entirely driven by the willingness to hold stocks in the first place. Ernesto Villanueva (personal communication, April 2007) finds that mortgage APRs in Spanish survey data (comparable to the U.S. Survey of Consumer Finances) follow a U-shaped curve by age. A relationship between age and performance has been noted in many nonfinancial contexts. Survey data suggest that labor earnings peak around 23. For example, Korniotis and Kumar (2008a) confirm our U-shape hypothesis in their study of investment skills.


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age 50 (Gourinchas and Parker 2002), or after about 30 years of work experience (Murphy and Welch 1990). Kelly Shue and Erzo Luttmer (2009) find that older and younger voters make disproportionately more errors in voting than do middle-aged voters. John Graham, Campbell Harvey, and Manju Puri (2008) find that older CEOs tend to be more risk averse (see Simonton 1988 for a survey). A recent literature reports systematic differences in rationality between groups of people, particularly with respect to financial decisionmaking. Barry Scholnick, Nadia Massoud, and Anthony Saunders (2008) find that wealthier people make fewer mistakes on their credit cards, and Agarwal and others (2008) reach a similar conclusion concerning more experienced people. Victor Stango and Jonathan Zinman (2009) document that naive consumers substantially underestimate loan interest rates when asked to infer them from principal, maturity, and monthly payments. Korniotis and Kumar (2008) find that investors who perform better on standard intelligence tests obtain better risk-adjusted returns (see also Korniotis and Kumar forthcoming-b). In experimental contexts, Shane Frederick (2005) identifies a measure of IQ: people with higher scores on cognitive ability tasks tend to exhibit fewer and weaker psychological biases. Daniel Benjamin, Sebastian Brown, and Jesse Shapiro (2006) find that subjects with higher intelligence test scores, or less cognitive load, display fewer behavioral biases. Several researchers have looked at the response of consumers to credit card teaser rates. Haiyan Shui and Lawrence Ausubel (2005) show that consumers prefer credit card contracts with low initial rates for a short period to ones with somewhat higher rates for a longer period, even when the latter are ex post more beneficial. Consumers also appear reluctant to switch contracts even when they would benefit from doing so (Agarwal and others 2006). Stefano DellaVigna and Ulrike Malmendier (2004) theorize that financial institutions set the terms of credit card contracts to reflect consumers’ poor forecasting ability over their future consumption. Many of these effects are discussed in the literature on behavioral industrial organization, which documents and studies markets with boundedly rational consumers and rational firms.24 In some of that literature, the effects depend on having both naive and sophisticated consumers in the market. The present paper suggests that those naive consumers will disproportionately be younger and older adults. 24. Examples from this literature include DellaVigna and Malmendier (2004), Gabaix and Laibson (2006), Heidhues and Ko˝szegi (2009), Malmendier and Shanthikumar (2007), Mullainathan and Shleifer (2005), and Spiegler (2006).

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V. Regulatory Responses In this section we discuss nine mutually compatible policy responses (and some hybrids) to the problems we have identified, both specifically with respect to older adults and more generally with respect to their applicability to financial decisionmakers of all ages. We analyze the pros and cons of each approach without arguing for the adoption of any one of them, recognizing that strong regulatory interventions have the potential to generate large social benefits but also large social costs. The nine approaches are discussed approximately in order from least to most paternalistic. This ordering is somewhat arbitrary since some approaches have multiple dimensions, some of which might be more or less paternalistic than others.

V.A. Laissez-Faire Laissez-faire is surely not the first-best policy. As we have noted, about half of decisionmakers between ages 80 and 89 are significantly cognitively impaired. The competitive equilibrium is unlikely to be efficient when agents routinely make significant cognitive errors. A growing body of anecdotal evidence finds that overpriced financial products are being targeted at older adults (for example, Choi-Allum 2009). The competitive equilibrium works as follows: Some older adults will make bad decisions (for example, overpaying for financial services or losing their money in fraudulent schemes), generating economic rents for those who can exploit these decisions. These rents are partially dissipated, however, because the aggressive or manipulative sales tactics needed to capture them are costly, and because fraudulent sellers face the risk of legal punishment. In equilibrium, the zero-economic-profit condition still applies, but the social allocation is inefficient. To put it more intuitively, when sellers must spend a dollar of their own resources (their time, legal defense fees, and so forth) to convince a pool of older adults to give them a dollar in rent, excess profits will be zero, but there is a social deadweight loss of one dollar. In equilibrium, then, wasteful marketing and bad products will survive even if competition eliminates all excess profits. (See Gabaix and Laibson 2006 for a related argument.) Laissez-faire policies are nevertheless serious candidates on our list of optimal policies, because the laissez-faire approach could be second-best optimal. Strong regulatory interventions are problematic for many familiar reasons. Regulations are usually administratively costly. They may harm the interests of households who are financially sophisticated or who have sophisticated and trustworthy advisers. Policymakers may have conflicts


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of interest, and even well-intentioned policymakers make mistakes. For all these reasons, we do not rule out laissez-faire policies. In addition, laissezfaire policies are compatible with voluntary advance directives (discussed below), in which rational household members, recognizing the possibility of their own future cognitive decline, set up protective mechanisms ahead of time, in the form of family oversight, competent and trustworthy financial advisers, and formal trusts. However, such delegation-based solutions are limited by seven factors: the failure to anticipate, when cognitively healthy, the possibility of one’s own future cognitive decline; the mistaken belief that one will recognize one’s own cognitive decline and respond optimally by progressively delegating decisionmaking as it occurs; procrastination; the difficulties that external parties face in determining when key thresholds of cognitive decline have been crossed, so that control can be transferred efficiently; administrative costs, particularly when the trustee is a third party such as an attorney; a lack of financially sophisticated family members, capable of making good financial decisions on the declining adult’s behalf; and a lack of trustworthy family members. The last of these is particularly important, since, counterintuitively, family members are often a poor choice to play an oversight role. Of course, altruism is strong in many families, and many family members do have intimate knowledge of each other’s preferences. However, family members also face a conflict of interest when they are residual claimants on a parent’s estate. Hence, many older adults will lack an unconflicted, lowcost agent to whom they can safely delegate decisionmaking authority.

V.B. Disclosure Full and fair disclosure has been the primary goal of financial regulatory systems since the 19th century, and it is at the heart of many current congressional proposals. Legislation to strengthen disclosure requirements has recently been introduced in many different domains, including mutual fund fees, 401(k) fees, and mortgage origination fees. However, we are skeptical that improved disclosure will be effective in improving financial choices. Even for cognitively healthy populations, there is scant evidence that increases in disclosure improve decisionmaking. In a series of recent studies of middle-aged adults, additional disclosure and consumer education made surprisingly little difference. In one study, employees with low saving rates were randomly assigned to a treatment in which they were paid $50 to read a short survey explaining their 401(k) plan, including a calculation of how much money they would

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personally gain by taking full advantage of the employer match. Relative to a control group, this group did not significantly increase its average 401(k) saving rate (Choi, Laibson, and Madrian 2007). The bankruptcy of the Houston-based Enron Corporation and the huge losses suffered by many of its employees who had invested their 401(k)s largely in Enron stock had no effect, despite intense media coverage, on the willingness of newly hired workers at other firms to invest their 401(k) contributions in employer stock. This was true even for newly hired workers at other firms in Houston (Choi, Laibson, and Madrian 2005). Employer-sponsored financial education seminars have been shown to have little effect on 401(k) enrollment (Madrian and Shea 2001b). A new, easy-to-read summary prospectus proposed by the Securities and Exchange Commission (SEC) seems to have no effect on investor choices (Beshears and others 2009a). Finally, making mutual fund fees overwhelmingly salient does not lead investors to minimize them, even when allocating real money among index funds. In one study, subjects were asked to allocate $10,000 among four S&P 500 index funds. To help with their choice, the subjects were told what an index fund is, given a one-page summary sheet comparing the fees of the four index funds, and given the prospectus of each fund. Only 10 percent of the subjects put all $10,000 in the fund with the lowest costs (Choi and others 2007). The subjects of these studies were all adults in the workforce. It is likely that disclosure would be even less effective on retired older adults experiencing significant declines in cognitive function. We wish to emphasize that we are not opposed to disclosure. There is no evidence that it hurts, and it is certainly possible that it makes a small positive difference. For example, the study using the SEC summary prospectus found that shortening and simplifying mutual fund prospectuses would save paper and printing costs and decision time, even if it had no effect on asset allocation. So when improved disclosure is itself inexpensive or reduces other costs, it is surely a good idea. But one should not expect disclosure to resolve the regulatory concerns raised by the findings in this paper.

V.C. Libertarian Paternalism: Advice, Defaults, and Other Nudges In the last decade a growing body of research has suggested that gentle institutional “nudges” can improve behavior without mandating any particular behavior. Richard Thaler and Cass Sunstein (2003, 2008) refer to such interventions as “libertarian paternalism,” because the social planner is acting paternalistically by nudging behavior in one direction, but simultaneously maintaining a libertarian stance by allowing the actor to reject


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the nudge at minimal cost. Prominent examples of nudges include automatic 401(k) enrollment with an opt-out feature (Madrian and Shea 2001a; Choi and others 2002) and automatic saving rate escalators (Thaler and Benartzi 2004). In practice, such nudges work when the nudge is aligned with the intentions of the person being nudged, for example when employees want to be enrolled in their company’s 401(k) plan but for whatever reason fail to enroll on their own. But nudges are rejected when the nudge is misaligned with those intentions. For example, when workers are automatically enrolled at a saving rate that they deem too high (say, 12 percent of income), almost all opt out (Beshears and others 2009b). Likewise, automatic annuitization of defined-benefit accumulations is often rejected in favor of lump-sum payouts (Mottola and Utkus 2007). It is also important that those being nudged not be subject to forceful opposing influences. In the case of automatic 401(k) enrollment, workers overwhelmingly perceive that they save too little (Choi and others 2002), and no third party stands to gain a significant rent by convincing them not to enroll. In other words, no third party has a strong incentive to nudge in the other direction. Unfortunately, benevolent institutional nudges, whether by the government or by other agents, will probably provide little protection for older adults. These benevolent nudges will often be outweighed by malevolent ones emanating from marketers and unscrupulous relatives (see ChoiAllum 2009). Older adults with low financial literacy or significant cognitive impairment may be no match for highly incentivized parties with malevolent interests and ample opportunities to nudge in the wrong direction. Here, too, we are not opposed to the intervention in principle. Nudges can partly protect older adults and other vulnerable economic agents. However, we suspect that nudges will only be weakly protective in an environment where older adults are soft (and increasingly wealthy) targets for those with conflicts of interest.

V.D. Financial “Driver’s Licenses” Another set of proposals would require that individuals pass a “license” test before being allowed to make nontrivial financial decisions, such as opting out of “safe harbor” investment products (Alesina and Lusardi 2006; see section V.G below). Such proposals would need to overcome several logistical problems. Can a test be devised that reliably separates qualified from unqualified investors, without generating too many false negatives or false positives? Can it be administered at a reasonable social

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cost? If the test were administered over the Internet, what would prevent coaching by parties with conflicts of interest? Who would be required to take the test? How often would retesting be required? Would it be required often enough to catch people as they transition (often very quickly) into a state of significant cognitive impairment? Would such a test be politically feasible if it primarily targeted older adults?

V.E. Strengthening Fiduciary Responsibilities Regulators could also increase the fiduciary duties of individuals who sell financial products. In the extreme, all sales of financial products to individuals could be required to be conducted by an agent with a fiduciary duty toward the buyer. The word “fiduciary” originates from the Latin words fides and fiducia, which mean, respectively, “faith” and “trust.” Under common law, fiduciaries are legally bound to act at all times for the sole benefit and interest of a beneficiary—the principal—and to avoid conflicts of interest and selfdealing. Because of these legal obligations, the principal can trust the fiduciary to do the right thing. In an influential decision, Judge Benjamin Cardozo wrote that a fiduciary “is held to something stricter than the morals of the market place. Not honesty alone, but the punctilio of an honor the most sensitive, is then the standard of behavior.”25 In the United States, many types of advisers—lawyers, guardians, executors, trustees, conservators of estates, corporate directors, corporate officers, and majority shareholders—bear fiduciary duties. Investment advisers also have a fiduciary duty, which is legislated in the Investment Advisers Act of 1940. As we mentioned in the introduction, the Employee Retirement Investment Savings Act (ERISA) imposes a fiduciary duty on employers that sponsor retirement plans, for their decisions that affect plan participants, who in turn have the right to sue over breaches of that duty. However, many types of financial representatives and salespeople have no legal fiduciary responsibilities, and a substantial fraction of financial services are sold by such agents. For example, an annuity salesperson who cold-calls potential clients may legally receive large commissions (which are often shrouded from the client) for selling them financial products with large markups. Neither these agents nor the registered representatives of securities broker-dealer firms are considered investment advisers, and 25. Alhough Cardozo would eventually sit on the Supreme Court, this landmark decision was handed down during his earlier tenure on the New York Court of Appeals. In Meinhard v. Salmon, 164 N.E. 545 (N.Y. 1928), the court held that business partners have a fiduciary duty in the course of activities associated with their partnership.


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consequently they do not have a full fiduciary duty. Brokers’ duties are established in the Securities Exchange Act of 1934, which does not set a fiduciary standard. One implication is that many workers have much greater effective regulatory protection than retirees. Employer 401(k) plans and other definedcontribution plans are regulated by ERISA, which, as noted, imposes a strong fiduciary duty on the employer. IRAs, in contrast, have a much lower level of protection, since the representatives of the securities broker-dealer firms that manage these accounts are not full-fledged fiduciaries. Thus, when a retiree rolls money over from her 401(k) account to an IRA, as happens with the overwhelming majority of 401(k) assets, her savings lose the fiduciary protection she enjoyed as an employee. Of course, nobody forces retirees to make these rollovers, but the financial services industry has a strong incentive to encourage them, since fees are higher in practice outside of 401(k) accounts.26 Mandating fiduciary responsibilities, even weak ones, on the sellers of financial services would give them a stronger incentive to design and market products that meet high standards. But it might also generate new administrative and compliance costs and slow financial innovation. If these inefficiencies were to prove considerable, an alternative would be to mandate fiduciary duties only in certain circumstances, for example when the buyer is above some age threshold. Other intermediate solutions would be to impose on IRA asset managers the same fiduciary duties as 401(k) plan sponsors, or to strongly discourage rollovers from 401(k)s to IRAs.

V.F. Mandatory Advance Directives One way to address directly the impact of cognitive decline on financial decisionmaking would be to require older adults to put in place a financial advance directive before reaching a certain age. Such mandatory advance directives could take many forms. For instance, older adults might be required to create a durable power of attorney, so that somebody would be able to manage their financial affairs in the event of their incapacity.27 Alternatively, older adults might be required to create a streamlined version of a revocable living trust. Such trusts enable individuals to pass management of their assets to a fiduciary in the event of the principal’s incapacity. 26. In principle, investors could purchase low-fee products in their IRAs. In practice, they overwhelmingly fail to do so. 27. Alternatively, this could be a springing power of attorney, which takes effect only in the event of their incapacity.

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Entirely new legal protections might also be created. For example, a fiduciary could be appointed to approve all “significant financial transactions” involving the principal’s funds after the principal reaches a designated age. A significant financial transaction might be defined as any transaction representing more than a certain share (which could decrease with the principal’s age) of the principal’s net worth. The principal would preset this threshold for fiduciary approval at the time the advance directive is created. To make the process easier, a default schedule could be established, for example, beginning at 50 percent of net worth at age 75 and falling as the principal ages. If a seller then enters into a financial transaction with the principal without formal approval from the principal’s fiduciary, and it is subsequently determined that the transaction exceeded the relevant threshold, the transaction could be nullified. In addition, the directive would stipulate who will judge the principal’s mental capacity and how the principal’s assets would be administered in the event the principal is judged no longer mentally competent. Finally, the details of the directive (including the choice of fiduciary) could be changed at any time if the principal can demonstrate mental competency. As an alternative to these fiduciary-based models, the principal could also be allowed to place her assets in a safe harbor (see next subsection) that eliminates the need for a fiduciary. This option would appeal to families that do not have substantial assets and would therefore not find the appointment of a fiduciary to be cost-effective. Mandating advance directives would pose several problems. First, it might be perceived by some older adults as an unfair restriction targeted against them. Second, the imposition of a fiduciary would create transactions costs. Third, any attempt to define a safe harbor would be politically contentious and would doubtless give rise to a great deal of lobbying. An independent agency would probably be needed to partly insulate the safe harbor regulations from political pressure.

V.G. Protected Assets: A Life-Cycle Safe Harbor A portion of a retiree’s financial assets could be protected in a mandatory safe harbor, with four basic features:28

28. We use the term “safe harbor” with two complementary meanings in mind. First, the standard legal usage is relevant, since the firms managing these safe harbor assets and following the relevant regulatory guidelines would be protected from lawsuits. Second, the investors themselves would be protected from exploitation by those same regulatory guidelines.


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—First, an asset base would be identified. This would probably include all forms of savings that have benefited from federal tax relief, such as qualified defined-contribution plans and all types of IRAs. —Second, when the principal reaches a specified age, a portion of this asset base would be irrevocably placed in a safe harbor account, that is, one that is permitted to hold only certain types of assets. For example, at age 591⁄2,29 every account in the asset base could be required to distribute 50 percent of the balance into such an account. In principle, the safe harbor accounts could be managed by the same company that managed the original accounts, obviating the need for direct government involvement. —Third, the investor would choose from among a limited range of highly regulated, low-cost investment options for the assets within the safe harbor. These options might include a fixed annuity, a variable annuity, a bond mutual fund, and a life-cycle mutual fund (which would contain a diversified portfolio of stocks and bonds). —Finally, the account would have a minimum and a maximum rate of drawdown. The minimum rate could be set according to current rules on required minimum distributions from tax-deferred accounts.30 The maximum rate, which would rise with age, would be set to preserve the assets so that they provide a nontrivial stream of retirement income until the principal’s death. Such a system would provide four benefits. First, a substantial fraction of the retiree’s assets would be protected from high fees, from suboptimal investments, and from fraud. Second, the pooling of the assets in the safe harbor into a small number of investment vehicles would achieve scale economies and might mitigate adverse selection problems in the case of annuitized assets. Third, retirees would be at least partly constrained from spending down their assets prematurely. Fourth, society at large would benefit because household savings would cover a larger fraction of longterm care and other medical expenses.31 At first glance, the proposal just described may seem novel. But it is actually just a generalization of mandatory annuitization schemes that are 29. This transfer to the safe harbor would occur just before early withdrawal penalties are terminated at age 591⁄2. The transfer would incur no penalty, since the assets would remain in the retirement savings system. Individuals who try to avoid the transfer to the safe harbor by prematurely withdrawing assets from their tax-deferred accounts would face a 10 percent penalty. 30. Note that Roth IRAs are not currently covered by lifetime RMDs. 31. Under the proposal made here, fewer assets could be transferred during one’s lifetime to one’s children, and so those assets would be available to cover some of the long-term care costs that would otherwise be borne by Medicaid.

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already commonplace in Europe, Latin America, and Asia (Antolin, Pugh, and Stewart 2008). The proposal has two potential disadvantages. First, it would meaningfully restrict individual choice by requiring households to invest some of their retirement savings in a limited menu of assets and by constraining their consumption path. Second, it would create the potential for political manipulation by empowering a regulator to select and monitor the asset menu. As we argued above, an independent agency might be needed to insulate the regulator.

V.H. Default Regulatory Approval: The Dietary Supplements Model Up to this point we have focused on interventions that primarily target the individual investor. Regulations could instead target the financial products themselves. One such regime would mimic the regulatory model currently used for nutritional supplements: new financial products would be allowed in the market without specific formal approval in advance but would be monitored for adverse effects. The other, discussed in the next subsection, would require that new financial products obtain explicit regulatory approval before being marketed. The Dietary Supplement Health and Education Act of 1994 established a novel regulatory framework for nutritional supplements.32 Unlike food additives and drugs, dietary supplements do not need to be approved by the Food and Drug Administration (FDA) before being brought to market, nor are they subject to formal requirements for ex ante safety and efficacy testing. Instead, the supplement manufacturer does its own due diligence, applying safety and marketing rules that have been established by the FDA. The manufacturer is responsible for ensuring that its supplement adheres to these established regulatory standards for safety and truthful labeling. The manufacturer does not even need to register the supplement with the FDA. The FDA is responsible for taking action against any unsafe supplements that it identifies in the marketplace. Under this system, dietary supplements thus have default regulatory approval. Financial products could be regulated in much the same way. Safety and quality standards would be established by the relevant regulator, typically the SEC, and financial services firms would then evaluate their own products to determine whether they comply with the standards. Such a sys32. Dietary supplements, also known as nutritional supplements, include vitamins, minerals, botanical and herbal remedies, fiber, fatty acids, amino acids, and other substances believed to improve health. The current regulatory regime is described on the FDA’s webpage “Dietary Supplements” (www.fda.gov/Food/DietarySupplements/default.htm).


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tem would avoid the need for rigorous and time-consuming regulatory review for each new product and would encourage innovation. Naturally, this system would work well only if the regulator could successfully identify socially optimal ex ante standards. This might be difficult. Such standards would need to be based both on the characteristics of the product being marketed and on those of the buyer. For instance, products that might be appropriate for young investors (such as equity-based leveraged ETFs) might be deemed inappropriate for an 85-year-old retiree.

V.I. Mandatory Explicit Regulatory Approval The final regulatory approach draws on the model currently used by the FDA for drugs (as opposed to dietary supplements). New drugs must undergo extensive, documented testing for safety and efficacy and may not be sold until formally approved by the FDA. This testing takes years and is generally extremely costly to the pharmaceutical firm developing the drug. Once approved, new drugs may be sold only by prescription until the FDA formally approves them, in a separate process, for overthe-counter sales. Financial products could be made subject to such an ex ante review process and tested in small-scale trials much as drugs are today. For example, a new credit product could be offered (at regular cost) to 10,000 clients during a trial period. Their behavior could be studied anonymously, and they could be anonymously surveyed about their experiences. The survey could be designed by the financial regulator or outsourced to a third party monitored by the regulator and could ask such questions as the following: —Do you feel that this product generates value that exceeds the fees you are paying for it? —What are the fees on this product? (“Don’t know” would be an option here.) —Would you recommend this product to any of your friends? —In your view, should this product be approved for sale? Naturally, a product could be approved even if a sizable minority of customers did not understand it or did not like it. However, if a large enough fraction expressed reservations about its value or did not understand its costs, this would be grounds for further study and possibly rejection. The regulator would try to judge the aggregate benefits and harms generated by the product, recognizing the possibility that modest harm to many consumers might be offset by large gains to a small number. Such testing would be socially costly. It would delay the release of new products, increase costs for financial services firms, and discourage

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innovation. But it could also prevent the marketing of socially undesirable products. The net social benefit is not easy to evaluate. Finally, note that testing could occur after a product has been released. For example, the regulator could be given the authority to compel a product’s developer to perform the analysis described above on products that have been found after their introduction to be potentially problematic. Such ex post selection for testing might be superior to ex ante testing of all new products. Financial service firms would have an incentive to design products that do not attract the potentially costly scrutiny of the regulator.

VI. Seven Open Questions for Future Research This paper has undertaken three main tasks. First, we reported evidence that older adults experience substantial declines in cognitive function over time. Then, we reported evidence that economic behavior and economic mistakes show strong age-based patterns in the cross section, even among a population of individuals who are screened to be prime borrowers. Finally, we discussed nine potential policy responses. We emphasized that we are agnostic about what regulatory interventions (if any) should be adopted. We do not think that the jury is in on many different dimensions of the problem. Economic behavior among older adults is still poorly understood. Moreover, even if older adults are making substantial financial mistakes, it is not clear what a well-intentioned policymaker should do about it. Much more research is needed—including field experiments that study different regulatory regimes—before the best solutions can be identified.33 In this concluding section we identify seven open questions that we hope academic researchers and policymakers will consider as they wrestle with these problems in the future. First, how widespread and important are losses due to poor financial decisionmaking? What fraction of aggregate wealth, and of the wealth of older adults, is lost because of poor choices? What are the utility costs? In our analysis of economic behavior, we were able to study only a set of decisions with relatively moderate costs, such as credit card fees. We believe that these costs are just the tip of the iceberg. We studied these particular transactions because the data are of good quality, but the bulk of the mistakes that are probably being made lie elsewhere. Older adults make many decisions with potentially enormous costs. Should I refinance my 33. The law and economics literature also studies those issues. See, for example, BarGill (2009) and Marotta-Wurgler (2007).


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home and draw down some of my equity? Should I buy a complicated (high-fee) annuity? Should I cash out my current annuity (paying a surrender charge) so I can buy a better one? Should I invest a substantial fraction of my wealth in a high-return CD offered by a broker calling from an offshore bank? Should I invest a substantial fraction in a structured finance product that has a high return and that I am assured is completely safe? Should I participate in a real estate investment pool with a minimum investment of $100,000? Measuring and aggregating these costs is an important research program. To get a sense of how they might add up, consider a few quantitative examples. Wealth dissipation in the annuity market is estimated at about 6 percent of the value of each annuity purchased, for a total of $16 billion in 2008.34 Korniotos and Kumar (forthcoming-a) estimate that older investors with accounts at a discount brokerage earn 3 percent less in risk-adjusted equity returns than middle-aged clients of the same broker. Even if the true aggregate figure is just 0.3 percent of total financial assets held by older adults, this would translate into $40 billion of underperformance per year.35 It is unclear whether these amounts are transfers between parties or deadweight losses. As an order of magnitude for the stakes, Kenneth French (2008) estimates total fees paid in active financial management to be about 0.7 percent of the market value of equity per year, which is equivalent to about 0.7 percent of GDP. This may mostly be a deadweight loss, because the active traders (as well as the marketing experts who advertise these funds) could be productively engaged in other activities. The active traders are engaged primarily in trying (usually unsuccessfully) to transfer wealth to their clients from other people. The transfers themselves are not a deadweight loss, but the time spent attempting to achieve those transfers is. Physics Ph.D.s might be more usefully employed in labs rather than at hedge funds. Second, what demographic characteristics predict poor financial decisionmaking? In this paper we have used only data on age, but clearly other demographic measures (years of education, field of education) would be 34. See LIMRA (formerly the Life Insurance and Market Research Association), “Annuity Sales Estimates, 1999–2008” (www.limra.com/PDFs/newscenter/databank/Annuity Estimates99_08.pdf), for the volume of the annuities market. A typical commission on the sale of an annuity ranges from 6 to 12 percent of the face value of the contract. Annuities with small or zero commissions exist but represent a small part of the market. 35. The Federal Reserve estimates that households own financial assets worth about $40 trillion, excluding deposits (Flow of Funds, table B.100). The Survey of Consumer Finances implies that 34 percent of that wealth is owned by households with a head age 65 or older.

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helpful. One would also like to know whether cognitive tests are predictive of poor financial decisionmaking, and if so, what kinds of tests (knowledge-based, logic-based, or others). Is it possible to inexpensively and accurately measure an individual’s current level of cognitive function or to predict future changes? Third, to what extent do people anticipate their own cognitive decline or recognize it when it happens? And if they do, are they able to delegate their financial affairs or protect themselves in other ways (for example, by politely ending the conversation when they are solicited over the phone to attend a free lunch or investment seminar)? We do not know how malleable older adults really are. One often hears anecdotes about aging widows who vaguely realize that they are experiencing some cognitive decline but still fall prey to sophisticated and sociable brokers. Are these stories representative? Or are the vast majority of older adults able to protect themselves because they recognize their own vulnerability? Fourth, does financial education help? Is it cost-effective? Is it relevant in a changing financial environment? As discussed above, the evidence on the impact of financial education is mixed and not particularly encouraging. For example, Agarwal and others (2009) find that mortgage counseling does not help consumers choose lower-risk mortgage products. Fifth, do third parties help? Who should be empowered to serve as an external adviser or decisionmaker? A family member? a friend? an independent trustee?36 Family members sometimes make problematic trustees—as illustrated in cases ranging from King Lear to Brooke Astor—but how prevalent are such problems? What is the evidence on the effectiveness of different types of trustee? How does the market for fiduciaries operate? Sixth, what is the market response to this situation, and does it help or hurt? There are theoretical reasons to worry that the market mechanism might be inefficient in the market for advice. Advice markets suffer by definition from information asymmetries between providers and recipients (see the survey in Dulleck and Kerschbamer 2006). In markets with inattentive consumers and shrouded attributes (Gabaix and Laibson 2006), perverse situations with high fees can persist as bona fide economic equilibria when there are enough naive consumers and the only profitable business model is to offer a product with low base prices and high “surprise” fees. How important empirically are these perverse market equilibria? Are professional fiduciaries trustworthy or not? There is much anecdotal evi36. See Li (2009) for empirical evidence on financial advice by family members.


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dence of problems, for example of outrageously high fees, but a systematic quantification is needed. Finally, what is the appropriate regulatory response? If the market for third-party advice and fiduciary services functioned well, the market equilibrium would have three phases. Early in life, each individual would write a plan for his or her future consumption and investment, contingent on major events (including cognitive decline). Then, cognitive testing and observation would monitor the individual for the onset of significant cognitive decline. Finally, once this prespecified threshold has been crossed, the original contingent plan would be enforced by a fiduciary, or the individual’s assets would be placed in a financial instrument with a statecontingent payout scheme. Indeed, the market already provides financial products with this feature: for example, annuities eliminate complex asset decumulation decisions. More sophisticated institutions will be designed. However, for all of the reasons reviewed in this paper, the unregulated market solution may not work well. Government intervention is probably desirable, but the ideal form of that intervention remains unclear. More empirical analysis and field experiments are needed to identify the regulatory response that best balances the marginal costs against the potential benefits.

APPENDIX

Data Description Eureka Moments We use a proprietary panel dataset with data from several large financial institutions, later acquired by a single financial institution that made balance transfer offers nationally. The offers were not made conditional on closing the old credit card account. The dataset contains information on 14,798 individuals who accepted such balance transfer offers over the period January 2000 through December 2002. The bulk of the data consists of the main billing information listed on each account’s monthly statement, including total payment, spending, credit limit, balance, debt, purchase APRs, cash advance APRs, and fees paid. We also observe the amounts of all balance transfers, the start and end dates of the balance transfer teaser rate offer, and the initial teaser APR on the balance transfer. At a quarterly frequency, we observe each customer’s credit bureau rating (FICO) score and a proprietary (internal) credit “behavior” score. We have

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credit bureau data about the number of other credit cards held by each accountholder, total credit card balances, and mortgage balances. We also have data on the age, sex, and income of the accountholder, collected when the account was opened. In this sample, borrowers did not pay a fee for balance transfers.

Home Equity Loans and Lines of Credit We use a proprietary panel dataset constructed from records from a national financial institution that has issued home equity loans and home equity lines of credit. This lender has not specialized in subprime loans or other market segments. Between March and December 2002, the lender offered a menu of standardized contracts for home equity credit.37 Consumers chose either a loan or a credit line, either a first or a second lien, and an incremental loan amount, which, given an estimate of the property’s value, resulted in an LTV ratio of less than 80 percent, between 80 and 90 percent, or between 90 and 100 percent. Thus, in effect, the lender offered 12 different contract choices.38 We ran separate regressions for home equity loans and lines of credit, conditioning in each case on not having a first mortgage and on the LTV ratio categories described above; hence, we control for contract type. All loans have the same five-year maturity. For 75,000 such contracts, we observe the contract terms, demographic information about the borrower (age, years at current job, and home tenure), financial information (income and debt-to-income ratio), and risk characteristics (FICO score and LTV).39 We also observe the borrower’s estimate of the property’s value and the loan amount requested.

37. Other interest rates in the economy varied considerably during this time period. One might therefore ask whether the age results we report are an artifact of borrowers of different ages happening to disproportionately borrow earlier or later in the sample. We observe no pattern in the distribution by month of borrowing by age over the sample. In alternative versions of the regressions including dummy variables for the month of loan origination, we obtain nearly identical results. 38. We focus on APRs across contracts for four reasons. First, the contracts in the dataset do not differ in points (origination fees as a percent of the loan) charged or in other charges to the borrower. Second, we verified that even conditioning on contract choice, some borrowers pay higher APRs than others. Third, we controlled for borrower risk characteristics. Fourth, we show in the paper that the residual variation in APRs is explained by the propensity to make an identifiable mistake in the loan acquisition process. 39. We do not have internal behavior scores (a supplementary credit risk score) for these borrowers. Such scores are performance based and are thus not available at loan origination.


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Auto Loans We use a proprietary dataset of auto loans originated at several large financial institutions that were later acquired by another institution. The dataset comprises observations on 6,996 loans originated for the purchase of new and used automobiles. Observed loan characteristics include the automobile’s value and age, the loan amount and LTV, the monthly payment, the contract rate, and the date of origination. Observed borrower characteristics include credit score, monthly disposable income, and age.

Mortgages We use a proprietary dataset from a large financial institution that originates first mortgages in Argentina. Using data from another country provides suggestive evidence about the international applicability of our findings. The dataset covers 4,867 owner-occupied, fixed-rate, first mortgage loans originated between June 1998 and March 2000 and observed through March 2004. We observe the original loan amount, the LTV and appraised home value at origination, and the APR. We also observe borrower financial characteristics (including income, second income, years on the job, and wealth measures such as second home ownership, car ownership, and car value), risk characteristics (the borrower’s Veraz score—a credit score similar to the U.S. FICO score—and mortgage payments as a percentage of after-tax income), and demographic characteristics (age, sex, and marital status).

Small Business Credit Cards We use a proprietary dataset of small business credit card accounts originated at several large institutions that issued such cards nationally. The institutions were later acquired by a single institution. The panel dataset covers 11,254 accounts originated between May 2000 and May 2002. Most of the businesses are very small, are owned by a single family, and have no public financial records. The dataset has all the information collected at the time of account origination, including the business owner’s self-reported personal income, the number of years the business has been in operation, and the age of the business owner. We also observe the quarterly credit bureau score of the business owner.

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Credit Card Fees We use a proprietary panel dataset from several large financial institutions that offered credit cards nationally; these institutions were later acquired by a larger financial institution. The dataset contains a representative random sample of about 128,000 credit card accounts followed monthly over a 36-month period from January 2002 through December 2004.40 The bulk of the data consists of the main billing information listed on each account’s monthly statement, including total payments, spending, credit limit, balance, debt, APRs on purchases and cash advances, and fees paid. At a quarterly frequency, we observe each customer’s credit bureau (FICO) score and a proprietary (internal) credit “behavior” score. For each cardholder we have credit bureau data on the number of other credit cards held, total credit card balances, and mortgage balances. We also have data on the age, sex, and income of the cardholder, collected when the account was opened. Further details, including summary statistics, are available in the online appendix.

ACKNOWLEDGMENTS Xavier Gabaix and David Laibson acknowledge support from the National Science Foundation (DMS-0527518). Laibson acknowledges financial support from the National Institute on Aging (R01AG-021650, R01-AG-1665, P30-AG-012810). The views expressed in this paper are those of the authors and do not represent the policies or positions of the Board of Governors of the Federal Reserve System or the Federal Reserve Bank of Chicago. For their helpful comments we thank the editors and Marco Basetto, John Beshears, Stephane Bonhomme, James Choi, David Cutler, Giovanni Dell’Ariccia, Ray Fair, Luigi Guiso, Gur Huberman, Erik Hurst, Brigitte Madrian, Ulrike Malmendier, Karen Pence, Mitchell Petersen, Richard Rosen, Timothy Salthouse, Fiona Scott-Morton, Jesse Shapiro, William Sharpe, Paolo Sodini, Nicholas Souleles, Richard Suzman, Jonathan Zinman, and participants at various conferences and seminars. We thank Jacqueline Barrett and Kyle Chauvin for excellent research assistance.

40. The credit cards do not have annual fees and do not differ in terms of rewards points or other benefits.


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Comments and Discussion COMMENT BY

GIOVANNI DELL’ARICCIA 1 Those whom the gods love die young. Yet we grow old always learning new and interesting things. This paper by Sumit Agarwal, John Driscoll, Xavier Gabaix, and David Laibson does some justice to both these statements. And like all good economic papers, it identifies a trade-off. You can be young and foolish: at the peak of your cognitive abilities, but also very ignorant. Or you can be old and a bit slow: having accumulated a lot of experience, but with depleted analytical capital. In the middle is an “age of reason” at which you have learned enough and are still able to exploit your experience and to process new information. The paper argues that these dynamics are reflected in a life-cycle pattern of financial mistakes and performance. Its findings are relevant from a policy perspective, especially given the continuing aging of the population in most advanced economies. But I suspect they will be intriguing (and a bit scary) even for those who are not particularly interested in economic policymaking or behavioral finance. I will structure my comments as follows. First, I will attempt to provide alternative explanations for the main empirical patterns identified in the paper. The authors present relatively convincing evidence linking the lifecycle pattern of high interest rates paid and other financial mistakes to progressive cognitive decline. However, it is worth exploring whether a framework that does not rely on bounded rationality can produce a similar pattern. In particular, I will focus on whether more standard, industrial organization–based stories can deliver similar results. Second, I will delve into how such a framework can explain the paper’s two most convincing 1. The views expressed in this paper are those of the author and do not necessarily represent those of the International Monetary Fund.

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COMMENTS and DISCUSSION

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pieces of empirical evidence: the “eureka moments” on credit card balance transfers and the “rate-changing mistakes” on home equity loan applications. Finally, I will briefly discuss the paper’s policy implications. The paper hypothesizes that net cognitive performance is hump-shaped with respect to age. This results from assuming that performance is determined by the sum of analytical capital, which declines linearly with age, and experiential capital, which rises with age but exhibits diminishing returns. The question is whether other plausible stories or models can produce a similar U-shaped life-cycle pattern in the pricing of financial products. I will propose two such stories, both of which borrow elements from the literature on relationship lending. The first examines how age can affect the expected value of a bank-borrower relationship. The second looks at how a bank’s ability to extract rents from a borrower may vary with the borrower’s age. These models are not meant to be realistic or particularly sophisticated. They are just simple examples of how some of the empirical evidence in the paper could be explained with models of bank competition that do not rely on bounded rationality. Banks value lending relationships. One reason may be that having a relationship with a customer makes it easier to sell additional products to that customer. Or, in a more complex model, it may be that relationships allow banks to gather valuable private information about the borrower. This can explain the widespread practice of offering introductory teaser rates on credit cards and other loans,2 and that of offering attractive terms to borrowers willing or likely to buy multiple products. For example, according to the president of one major bank, in Europe before the current crisis the margin on prime mortgages was so low that banks were losing money on them. However, selling those mortgages brought in new customers who would then buy wealth management services, trading services, and the like. Relationships, however, are not forever. They can be broken by exogenous shocks, such as changes in the preferences or needs of the customer, or when the customer moves or dies. The longer the expected life of a relationship, the greater its value. The value of a relationship will also depend on the degree to which it allows the bank to extract rents from the customer. The higher the switching costs and informational barriers that prevent competing lenders from successfully poaching each other’s customers, the more a relationship is worth. 2. See Petersen and Rajan (1995) for evidence of how bank market power affects introductory rates.

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Figure 1. A Model of the Bank-Customer Relationship over the Life Cycle Percent

Probability of an exogenous interruption of the relationship

Mortality rate, θ

Probability of other exogenous interruption of the relationship, µ Age

Age

Source: Authorís model described in the text.

Consider now the following barebones model. Two banks compete on the interest rate to extend loans to customers of equal creditworthiness but different ages. Banks obtain profits from these loans but also benefit from establishing a relationship with the customer. Relationships, however, can be broken with probability θ by the death of the customer, or with probability µ by some other exogenous shock such as the customer moving to a different state. θ is obviously increasing with age. But it is reasonable to assume that µ is decreasing with age, since, for example, older people are less likely to change jobs (left-hand panel of figure 1). Then, normalizing the bank’s cost of funds to 1, the profit from extending a loan at a gross interest rate r is ϕr − 1 + δ (1 − θ ) (1 − μ ) V , where ϕ is the probability that the borrower will repay the loan, δ is the discount factor, and V is the value of the relationship. With Bertrand competition driving profits to zero, in equilibrium we have rˆ =

1 − δ (1 − θ ) (1 − μ ) V , ϕ

which, for a broad range of distribution functions for θ and µ, delivers a U-shaped life-cycle pattern for interest rates consistent with the evidence


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in the paper (right-hand panel of figure 1). However, in this case the age-dependent pattern reflects changes in the probability of an exogenous interruption of the relationship rather than cognitive decline. If, in addition, one assumes that middle-aged clients are those most likely to be interested in multiple banking products (so that V follows a hump-shaped pattern with respect to age), this pattern is reinforced further. A model of bank competition under asymmetric information can deliver a similar pattern. Consider another simple framework where banks learn about their clients’ creditworthiness over the course of a relationship.3 Further, assume that a portion λ of these borrowers are captives of their bank in that they find it difficult to signal their credit quality to competing lenders. It follows that inside lenders will charge the monopoly interest rate, R, to the captive borrowers but will be forced to offer the competitive rate, r, to those borrowers that can signal their quality. The average rate observed on the market will then be a function of the proportion of captive borrowers, r* = λR + (1 − λ)r. It is also reasonable to assume that the proportion of captive borrowers is a function of age: young borrowers with limited track records and credit histories, and older borrowers with few current transactions, may find it more difficult than middle-aged borrowers to signal their quality. Then, consistent with the evidence in the paper, the average interest rate charged to borrowers will follow a U-shaped pattern with respect to age. These simple models show that a U-shaped pattern of interest rates with respect to age can be generated with models of bank competition, without resorting to unconventional assumptions. I now turn to the two most convincing pieces of empirical evidence in the paper: the eureka moments on credit card balance transfers and the rate-changing mistakes on home equity loan applications. For these, my task is more difficult. Let me start with the eureka moments. A set of borrowers are offered the opportunity to transfer their current credit card balances to a new card charging a very low (teaser) interest rate on the transferred balance for a limited period. The catch is that new purchases on the new card face a high APR, and payments on the new card are credited first to the transferred (low APR) balance. In that case the optimal strategy is to take advantage of the low-interest-rate offer by transferring existing balances but to continue to use the old card for new purchases. And the evidence in the paper shows, after controlling for individual factors, that the per3. This is a common assumption in models of competition under asymmetric information. See, for example, Sharpe (1990) and Dell’Ariccia and Marquez (2004).

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centage of borrowers that actually follow this strategy has a hump-shaped pattern with respect to age. Put differently, a higher proportion of middleaged than of younger and older borrowers discover the optimal strategy and put it into practice. The simple models described above cannot immediately explain this pattern of financial mistakes. Yet they are not necessarily irrelevant to the issue. Consider the following argument. The paper implicitly assumes that the interest rate charged on new purchases with the new card is higher than the rate paid on the old credit card. If this is true, the proposed strategy is indeed optimal. If not, things are more complicated, and it may be optimal for borrowers to use the new card for at least some purchases. But the authors’ dataset lacks information on the interest rates borrowers were paying on their old cards. Consistent with the models above, it is not unreasonable to assume that middle-aged people on average pay lower rates on their existing credit cards and have greater access to low-interest-rate credit cards or other low-cost sources of funds generally. Then the eureka moment result could be explained in terms of a heterogeneity of optimal strategies rather than of cognitive abilities. In fact, without information about the rates paid on the old cards, the two explanations are observationally equivalent. Now let me turn to the evidence on rate-changing mistakes. The authors find that the average interest rate paid by borrowers on home equity loans follows the by-now-familiar U-shaped pattern with respect to age. However, the pattern disappears for borrowers that provide a correct estimate of the value of their home. Put differently, conditional on borrowers applying for the “right” loan (one with the loan-to-value ratio appropriate for their home), the interest rate they are charged is roughly constant with respect to age. What drives the age pattern is the larger percentage of younger and older applicant borrowers who grossly underestimate the value of their home. This is the strongest piece of evidence in support of the authors’ hypothesis, and it is especially difficult to explain with an industrial organizationbased model. Of course, one could argue that some borrowers make more mistakes not because they are cognitively impaired or less experienced, but because they have fewer opportunities to learn. For instance, borrowers who interact more often with the banking system might receive multiple loan offers and consequently have a more accurate estimate of the value of their home. This group might consist disproportionately of middle-aged borrowers. Alternatively, it might be that loan officers are more willing to suggest the optimal strategy to their more valuable customers, who are again


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disproportionately those of middle age. But these assumptions are far from standard and represent a much more convoluted explanation of the evidence than the cognitive decline model. Finally, I want to turn briefly to the policy implications of the paper. Obviously these depend on the mechanism one believes is at play. I believe the authors present convincing evidence that cognitive decline is at least partly responsible for the documented life-cycle pattern in financial performance. That said, the authors are rightly cautious when it comes to policy recommendations. In addition to the alternative possible explanations for the evidence (such as those discussed above), there is the question of how economically significant the identified age effects are. The paper reports relatively small economic damages associated with aging, which would not by themselves justify ad hoc regulatory intervention. However, as the authors argue, these costs may be the tip of the iceberg. Financial vulnerability in old age is a much broader issue, including exposure to outright fraud that can put one’s entire life savings at risk. Perhaps more important, several of the more paternalistic proposals in the paper can infringe on individual freedom. This raises two questions. The first is one of principle: Should society constrain an individual’s right to control her own wealth just because she belongs to a group that is statistically more likely to make financial mistakes? What if similar results were found for groups identified by sex, or ethnicity, or education? The second question is a practical one: Several of the policies discussed in the paper would likely be difficult to implement, or politically infeasible, or both. After all, as Karen Pence documents in her discussion of this paper, the United States imposes very few restrictions on driving by senior citizens, even though the externalities are far more evident. That said, the evidence in the paper could be safely used to provide guidance for resource allocation in the context of consumer protection programs. For example, education and efforts against predatory lending could be targeted to more vulnerable areas and groups. REFERENCES FOR THE DELL’ARICCIA COMMENT

Dell’Ariccia, Giovanni, and Robert Marquez. 2004. “Information and Bank Credit Allocation.” Journal of Financial Economics 72, no. 1: 185–214. Petersen, Mitchell A., and Raghuram G. Rajan. 1995. “The Effect of Credit Market Competition on Lending Relationships.” Quarterly Journal of Economics 110, no. 2: 407–43. Sharpe, Steven A. 1990. “Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationships.” Journal of Finance 45, no. 4: 1069–87.

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COMMENT BY

KAREN M. PENCE Older and younger individuals are more likely to pay higher prices for financial products than individuals in their middle years. Sumit Agarwal, John Driscoll, Xavier Gabaix, and David Laibson document this fact in several consumer finance markets, including markets for mortgages, credit cards, and auto loans. The age-related pattern holds after controlling for other features of the credit contracts and for borrowers’ other demographic, financial, and risk characteristics, suggesting that older and younger individuals could have paid less than they did. I follow the authors in labeling this phenomenon a “mistake,” although determining this with certainty would require knowledge of the full range of each borrower’s options, which is lacking. The authors assemble evidence from the medical, psychological, and economic literatures that suggests that older individuals make mistakes because of declining cognitive ability, whereas younger individuals appear to make mistakes because of inexperience. Although both groups are more prone than middle-aged individuals to make mistakes, the paper focuses primarily on the financial decisionmaking of older individuals. The authors explore a range of possible regulatory responses, including doing nothing, requiring a financial “driver’s license” to invest in nonstandard products, strengthening the fiduciary duties of financial salespeople, and requiring explicit regulatory approval of financial products. The authors decline to take a stand on which interventions, if any, are desirable. The empirical work in this paper is clear and convincing, and the authors place their findings in a broad and rich context. However, many of the possible regulatory responses may prove politically unpalatable: the political system seems reluctant to impose restrictions on the behavior of older individuals, even when such restrictions may be warranted. I describe below two situations in which both younger and older individuals are more likely to make mistakes, yet regulations are considerably more comprehensive for younger than for older individuals. DRIVING. Teenage drivers and older drivers are both more likely than other drivers to be involved in car crashes (Liu, Utter, and Chen 2007). As drivers age, the advantage of their greater experience is eventually outweighed by physical factors such as degradation of reflexes, vision, and hearing, and decreases in strength, mobility, and ability to process information (Islam and Mannering 2006). In addition, because of their relative frailty, older drivers are more likely to be injured or killed in a crash: drivers 65 or older have almost three times the odds of drivers 24 or younger of being seriously injured in an auto accident (Liu and others 2007).

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Figure 1. Automobile Fatalities by Driver Age, 1999–2003 Driver fatalities Per billion miles driven

100 80 60 40 20

16

18

22

27

35

45 55 Driver age

65

72

77

82

87

72

77

82

87

Nonmotorist fatalitiesa Per billion miles driven 7 6 5 4 3 2 1 16

18

22

27

35

45 55 Driver age

65

Source: Author’s calculations from data in Tefft (2008). a. Persons killed in auto accidents who were not riding in an automobile.

The top panel of figure 1 shows driver deaths by age, scaled by billions of miles driven by members of the age group. The age-related pattern is dramatic: on average, 34 drivers aged 16 and 17, 5 drivers in their forties, and 96 drivers age 85 or older were killed per billion miles driven over the 1999–2003 period.

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The bottom panel of the figure shows nonmotorist (such as pedestrian) deaths by driver age, again scaled by billions of miles driven. This figure isolates the age-related pattern due to driver error rather than the relative frailty of older drivers. The pattern is still apparent but is less dramatic: on average 7 nonmotorists were killed by drivers aged 16 and 17, about 2 were killed by drivers in their forties, and 5 were killed by drivers aged 85 or older. Figure 1 suggests that the argument for age-based regulation of driving is compelling. And indeed, states are highly involved in regulating teenage driving. Almost all have adopted a form of “graduated licensing,” which includes some combination of a learner’s period during which parents may have to certify a certain amount of supervised driving, and an intermediate period during which night driving and teenage passengers may be prohibited or limited (Insurance Institute for Highway Safety 2009a). States regulate older drivers, however, with a substantially lighter touch. Twenty-four states place no restrictions on older drivers, and in Tennessee, licenses issued to drivers 65 years or older never expire (Insurance Institute for Highway Safety 2009b). Four states and the District of Columbia forbid licensing administrators to treat people differently solely on the basis of advanced age, and only New Hampshire and Illinois require road tests for older drivers. CREDIT CARDS. As the paper documents, both younger and older individuals appear to pay higher credit card interest rates and fees than those in between. Policymakers and consumer groups have also raised concerns about the use of credit cards by students and the elderly, but only the concerns about the former have been translated into law.1 Legislation enacted in 2009 imposes broad changes on the credit card industry. The Credit Card Accountability, Responsibility, and Disclosure Act has no provisions specific to the elderly, but it does place significant restrictions on the access of young borrowers to credit cards. An applicant under the age of 21 may open a credit card account only with a co-signer age 21 or older, unless the applicant submits evidence of independent means to repay. Issuers are not allowed to send unsolicited prescreened credit offers to individuals under the age of 21, and they may not increase the credit lines of accounts with a co-signer without the co-signer’s permission. POLICY IMPLICATIONS. The above two examples suggest that policymakers and the public are comfortable protecting young individuals from their 1. See General Accounting Office (2001) for an overview of the issues surrounding students and credit cards, and García (2007) for an example of concern about older households’ credit card debt.


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inexperience, but are less comfortable protecting older individuals from their declining mental or physical abilities. Thus, popular support may be low for many of the regulatory responses outlined in this paper. Perhaps support would be higher if the costs of the financial mistakes that older people make were more apparent. This paper cannot make that case, because the costs of the mistakes the authors identify are not particularly large. For example, they show that older borrowers pay perhaps $5 to $20 more in credit card rates and fees than middle-aged borrowers. In the most costly example of the 10 that the authors document, older borrowers may pay around $250 more on their home equity lines annually than middle-aged borrowers. In contrast, mistakes that older households make in managing and investing their retirement savings may have significant quality-of-life consequences. As the authors note, more research is needed about the consequences of these and other mistakes for policymakers to formulate appropriate regulatory responses. However, research alone may not be enough. For example, the costs of driving by the elderly are large and well measured, but the policy response does not seem commensurate. REFERENCES FOR THE PENCE COMMENT

García, José A. 2007. “Borrowing to Make Ends Meet: The Rapid Growth of Credit Card Debt in America.” New York: De–mos. www.demos.org/pubs/ stillborrowing.pdf. General Accounting Office. 2001. “Consumer Finance: College Students and Credit Cards.” Washington. Insurance Institute for Highway Safety. 2009a. “U.S. Licensing Systems for Young Drivers.” Arlington, VA. www.iihs.org/laws/pdf/us_licensing_systems.pdf. ———. 2009b. “Licensing Renewal Provisions for Older Drivers.” www.iihs.org/ laws/OlderDrivers.aspx. Islam, Samantha, and Fred Mannering. 2006. “Driver Aging and Its Effect on Male and Female Single-Vehicle Accident Injuries: Some Additional Evidence.” Journal of Safety Research 37, no. 3: 267–76. Liu, Cejun, Dennis Utter, and Chou-Lin Chen. 2007. “Characteristics of Crash Injuries among Young, Middle-Aged, and Older Drivers.” National Highway Traffic Safety Administration Technical Report DOT HS 810 857. Washington. Tefft, Brian C. 2008. “Risks Older Drivers Pose to Themselves and to Other Road Users.” Journal of Safety Research 39, no. 6: 577–82.

GENERAL DISCUSSION Linda Goldberg said she accepted the idea that experience rises with age while cognitive abilities decline, and she found the evidence for a U-shape of costs persuasive. She was not yet

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convinced, however, that the former explains the latter. Issues also remain regarding the self-selection of borrowers: are older borrowers different from the rest of the older population, given that the general expectation, from a life-cycle perspective, is for borrowers to be younger? She also observed that with product bundling, customers purchasing the most sophisticated or broadest set of products might be offered lower interest rates. Also, other issues relating to supply of credit need to be controlled for before deciding whether regulation or some other policy is appropriate. Robert Hall conceded that there are quite noticeable age effects in financial performance, but he noted that the correlation is very low: one can easily think of extreme outliers, like Warren Buffett. The paper’s results might be more powerful if expressed in terms of a relationship to some aptitude score rather than to age. With respect to mortgages, the presumption is that the amount a person pays depends on the cost of serving that person as well as on the person’s skill in choosing financial products. For example, the U-shaped relationship between mortgage expense and age might reflect higher costs of originating mortgages for the young and the old. Hall also noted that mandatory mortgage counseling appears to have been quite successful at improving consumers’ choice of a mortgage. Apart from that, however, Hall shared the general frustration with the lack of good policy options, particularly regarding labeling and disclosure. The only kind of labeling that might provide a useful model, he thought, is nutrition labeling, for which some excellent research was done to determine the absolute minimum amount of information necessary and how to present it. The so-called Schumer box now required of credit card issuers should look like the nutrition label on the side of a box of cereal, but instead it is lengthy and difficult to read. Hall concluded that there is still a long way to go on disclosure, but he doubted that even better disclosure would do much to resolve the problem. Alan Blinder observed that the authors were very agnostic about remedies, almost to the point of suggesting that nothing will work, and he urged a little less agnosticism. He suggested a distinction between policies that rely on engaging the brain through improving financial literacy or greater disclosure, and policies that are more paternalistic, such as good default options, nudges, and fiduciary standards, which he thought the paper too easily dismissed. Blinder disputed the contention that stockbrokers are obligated only to avoid misrepresentation. There is a suitability standard for brokers, which prohibits them from pressuring


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anyone, let alone the elderly, into inappropriate investments. He agreed, however, that there is a lot of room to raise fiduciary standards and prevent abuse. Christopher House agreed with Goldberg that differences in performance across age per se do not seem to be the key, and he suggested that it would be better to think in terms of the level of cognitive ability. He further observed that although cognitive ability does decline with age, there are often other people present who have the interests of an aging household in mind. For example, just as parents encourage their children to drive safely, save their money, and choose a good subject for their major, so the grown children of older people often give them advice about finances and other important decisions. The presence of these familial supports complicates the situation and argues for caution in devising a regulatory structure that might interfere with these supports. Benjamin Friedman also urged a clearer distinction between nudges and disclosure, especially if the authors generally approve of the former and are skeptical of the latter. The discussion of nudges in the paper reminded Friedman that in the recent book Nudge by Cass Sunstein and Richard Thaler, many of the proposed nudges amounted to no more than disclosure. Friedman was also skeptical of the idea that small-scale trials would be very informative. A mantra of the financial industry is that securities are not bought; they are sold. In a small-scale trial, however, firms lack the incentive to develop the well-financed marketing effort to sell securities or other financial products to people who otherwise would not buy them. Thus, if a small-scale trial suggests that a given intervention leads only a few people to buy an inappropriate product, that sheds little light on what will happen when the industry is unleashed on a large population of prospective buyers and thus has an incentive to sell aggressively. Christopher Sims argued that not enough is known about the process by which younger people start taking over decisionmaking for their elderly relatives, and especially what happens to people who lack educated, financially well off, or even living children to take care of them. The issue goes well beyond finance. When a person gets Alzheimer’s, for example, eventually not only financial decisions become difficult, but also what to eat in the morning, what to wear, and how to maintain one’s home. Adding to the problem is that Alzheimer’s patients often lose all perspective on their abilities. A further problem is that some charities seem to increase the frequency of their requests for contributions addressed to older people, because they know that elderly people, especially those with Alzheimer’s,

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often forget how much they have given in the recent past. For some relatively wealthy households, the financial impact of this practice may be as important as the credit card issues discussed in the paper. Christopher Carroll framed his comment by quoting the philosopher Isaiah Berlin, who said that one of the most important individual rights is the right to make your own mistakes. Any rigorous investigation into optimal financial regulation has to take into account the fact that people often learn valuable lessons from their mistakes. Indeed, this could be one reason why the experience profile is so sharply upward sloping. Berlin’s point is less persuasive, however, with respect to decisions that one cannot easily revisit, such as lifetime saving decisions. This suggests that the need for regulation of financial products should depend in part on whether the transaction is likely to be a one-time event, or one that is likely to be repeated with the benefit of learning. Justin Wolfers thought the paper should do more to highlight not just the types of mistakes being made and their relationship to the life cycle, but also the consequences of those mistakes. He observed further that the economics profession at the moment is thinking a lot about methodology, and in particular about what contributes to making results “robust.” The present paper gathers a wealth of evidence across many different decisions and different datasets, which adds robustness in a different sense than adding another control variable. It is a wonderful case study in that respect. The paper also succeeds in identifying examples of behavior that are unambiguously suboptimal. For example, when using a credit card with a teaser rate to which one has transferred a balance, it is absolutely clear that one should not use that credit card for purchases. There is no possible story where such a transaction represents a rational decision. Ricardo Reis suggested two explanations for why there are more legal restrictions on the young than on the old. First, the old vote and the young do not. Second, when a teenager has a horizon of 60 or 70 years of driving ahead but is prohibited from driving for the first 3, the cost does not seem as high as when a 65-year-old with 15 years of expected life ahead is prevented from driving. This may be what leads older people to resist restrictions on their actions more than the young. Reis also stressed the distinction between fiduciary duties and regulatory authority. The authors drew a parallel between financial products and medicines, but Reis believed the right model for financial products was much closer to prescription drugs than to over-the-counter drugs. Prescription drugs must be approved for sale by the FDA, but in addition they have to be recommended by a doctor for each patient individually and prescribed in the right doses. The


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doctor’s approval is as important as the FDA’s, if not more important. Similarly, any governmental authority approving a financial product will always have to rely ultimately on the broker or seller bearing some fiduciary responsibilities. Reis added that there is a separate question about fiduciary responsibility in the health field, namely, whether doctors are making poor financial decisions for their patients and for taxpayers. Doctors are trained for 15 years and indoctrinated into thinking about their patients first; they take an oath to do so and can be not only disbarred but jailed for a wrong medical decision. Yet there is no comparable sanction for their decisions that go against the financial interest of a patient, not to mention that of the taxpayer. Alan Auerbach disagreed with Reis’s comparison, arguing that there is a big difference between making a decision in the interest of a patient and making a decision in the interest of the taxpayer. Although this setup is one reason for the current problems in the health care system, it can be fully rational and in the interest of the patient for the doctor to make a decision against the taxpayer’s interest. Justin Wolfers added, with respect to the FDA analogy, that off-label drug use is enormous. Medicines may be approved for one use, yet most of the sales of that drug may end up being used for something else. Much the same thing could well happen under stronger financial regulation. Chang-Tai Hsieh wondered whether the phenomenon that the paper identified is big or small. He suggested two possible dimensions for gauging its importance. First, is the choice of a financial product among the most important decisions a person makes, or merely one of many important decisions? People might, for example, be making serious mistakes in other areas, such as schooling for their children, or what kind of home to buy. The potential welfare consequences from these mistakes could be even larger than those from poor financial decisions. Second, is age really the most important source of variation in cognitive ability? Suppose one could administer a test that accurately measures that variation, and in particular how much of the variance can be explained by age. The latter result, Hsieh predicted, is likely to be small; other considerations, such as differences in income, might well be more important. Timothy Besley suggested looking not just at mean differences between age groups, but also at the variance. One reason why there is less resistance to intervening with the young could be that competence at financial decisionmaking is fairly tightly distributed within some age groups: most young people are more or less equally bad at making such decisions, but the distribution seems to fan out as people get older. This would have dif-

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ferent redistributive implications at different points in the age distribution. Besley also observed that what matters is the margin and not the average: the paper’s results support doing something about both the old and the young, but they do not yet imply that the marginal return to intervening with those groups is highest. Besley argued further that one should not give up on the role of tort law in resolving some of these issues. To be sure, not all regulatory issues can be resolved in court ex post, but fines and other standard tort mechanisms do have their place. Robert Hall responded by suggesting that in the United States the tort system effectively does not apply to financial products at all. Most financial products sold in this country require that the buyer sign a mandatory arbitration agreement. This protects the providers of financial products from liability but amounts, in Hall’s view, to the forfeiture of tort rights by the purchaser. Christopher Carroll countered that although at present there is no effective role for the court system in financial matters, that does not mean that it is not a useful thing to contemplate. In fact, he noted, one large credit card issuer recently claimed to have moved away from the arbitration requirement. Karen Dynan seconded Hsieh’s comment on the importance of measuring the costs of people’s financial mistakes. Regulation is not costless; the question for policy is whether the losses associated with it are worth bearing. Dynan said she was sympathetic to the view that cognitive abilities decline with age, but she offered an alternative possible explanation for the paper’s results: older people might put less time and effort into financial decisions because of shifting priorities. Many people toward the end of life say they wish they had spent less time working. They might say the same thing about trying to figure out exactly the right interest rate on a credit card. It would be interesting to see whether older people do try as hard as younger ones when making financial decisions. William Nordhaus commented that the paper reminded him of certain issues in the energy area, where there are also well-documented inefficiencies, the reasons for which are poorly understood. He saw three similarities across the two sets of issues. First, the inefficiencies are stubborn and do not disappear over time. For well over three decades now, the United States has had various policies in place to improve energy efficiency, yet first-price bias in the purchasing of automobiles and the overdiscounting of fuel use appear almost as prevalent today as when they were first investigated in the 1970s. No one should underestimate how difficult it will be to change this kind of behavior. Second, careful labeling of energy-consuming goods like appliances appears to have made some difference, but it is


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necessary not just to do the calculations, but also to undertake a fair number of behavioral studies. Third, one should never underestimate Congress’s ability to devise very poor, heavily interventionist policies. When designing new financial regulations, policymakers would do well to remember that the SUV was invented basically to get around energy regulations and fuel economy standards. The ability and the incentives of financial engineers to devise the financial equivalent of SUVs should not be taken lightly. George Perry wondered whether, instead of allowing certain products to be sold but forbidding certain groups of people to buy them, the solution might be to place restrictions on what is available for anyone to buy. This would raise obvious issues of freedom of choice, but on the other hand no one disagrees that the Bernie Madoffs of the world should be put in jail and should not be allowed to peddle fraudulent goods. Perry noted that banks continue to charge outrageous fees on debit card overdrafts, and on card accounts generally, and although the amounts are small compared with the loss of one’s home, they are nonetheless considerable. Perry could think of no other area where regulation permits what is universally agreed to be an unfair price, yet companies remain free to set these unconscionable fees.

RICARDO REIS Columbia University

Interpreting the Unconventional U.S. Monetary Policy of 2007–09 ABSTRACT This paper reviews the unconventional U.S. monetary policy responses to the financial and real crises of 2007–09, dividing these responses into three groups: interest rate policy, quantitative policy, and credit policy. To interpret interest rate policy, it compares the Federal Reserve’s actions with the literature on optimal policy in a liquidity trap. This comparison suggests that policy has been in the direction indicated by theory, but it has not gone far enough. To interpret quantitative policy, the paper reviews the determination of inflation under different policy regimes. The main danger for inflation from current actions is that the Federal Reserve may lose its policy independence; a beneficial side effect of the crisis is that the Friedman rule can be implemented by paying interest on reserves. To interpret credit policy, the paper presents a new model of capital market imperfections with different financial institutions and roles for securitization, leveraging, and mark-to-market accounting. The model suggests that providing credit to traders in securities markets is a more effective response than extending credit to the originators of loans.

T

he last two years have been an exciting time to be a student of monetary policy and central banking. Variability in the data is what allows us to learn about the world, and variability has not been in short supply in the United States, with wide swings in asset prices, threats to financial stability, concerns about regulation, sharply rising unemployment, and a global recession. But these have been difficult times to be a central banker. The limited tools at the disposal of the Federal Reserve have been far from sufficient to put out so many fires, and many of the challenges have caught central bankers unprepared for what not so long ago seemed highly improbable. This paper reviews the Federal Reserve’s actions in 2007–09 and interprets them in the light of economic theory. “Interpret” is the operative word here, since any attempt to describe and evaluate all that has happened would 119

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Figure 1. Interest Rates Targeted by the Federal Reserve, August 1989–August 2009 Percent a year 9 8 7

Discount rate

6 5 4

Federal funds rate

3 2 1

Interest on reserves

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Sources: Federal Open Market Committee press releases; Federal Reserve statistical release H.15, ìSelected Interest Rates, ” various issues; and author’s calculations.

be doomed to fail. On the one hand, so much has already happened that it would take a book, or perhaps many books, to describe and account for it all. On the other hand, the crisis and its repercussions are far from over, so that any assessment runs the risk of quickly becoming obsolete. I will therefore avoid, as far as I can, making pronouncements on what policies seem right or wrong, even with the benefit of hindsight, and I will not give a comprehensive account of all the events and policies. My more modest ambition is to provide an early summary of monetary policy’s reaction to the crisis thus far, to interpret this reaction using economic theory, and to identify some of the questions that it raises. I start in section I with brief accounts of the crisis and of the Federal Reserve’s responses. These fall into three categories. The first is interest rate policy and concerns the targets that the Federal Reserve sets for the interest rates that it controls. Figure 1 illustrates the recent changes by plotting two key interest rates targeted by the Federal Reserve over the last 20 years. These rates are as low today as they have been in this entire period, and the Federal Open Market Committee (FOMC) has stated its intent to keep them close to zero for the foreseeable future.1 1. Operating procedures for the discount window changed in January 2003, and therefore a consistent discount rate series for the whole period does not exist. For the federal funds rate in 2009, I plot the upper end of the range targeted by the Federal Reserve. The figure also shows the interest rate on reserves that was introduced in October 2008, discussed further below.

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Figure 2. Adjusted Reserves and Monetary Base, 1929–2009 Percent of GDP

20 Monetary base 15 10 Adjusted reservesa

5

1930

1940

1950

1960

1970

1980

1990

2000

Source: Federal Reserve Bank of St. Louis, Federal Reserve Economic Data (FRED). a. Reserves are adjusted for the effects of changes in statutory reserve requirements on the quantity of base money held by depositories.

Figure 2 illustrates the second set of policies, which I label quantitative policy. These involve changes in the size of the balance sheet of the Federal Reserve and in the composition of its liabilities. The figure plots an adjusted measure of reserves held by banks in the Federal Reserve System and the monetary base (currency plus reserves), both as ratios to GDP, since 1929. In September 2009 adjusted reserves were equal to 6.8 percent of GDP, a value exceeded in the history of the Federal Reserve System only once, between June and December 1940. The monetary base is as large relative to GDP as it has ever been in the last 50 years. The third set of policies, which I label credit policy, consists of managing the asset side of the Federal Reserve’s balance sheet. To gauge the radical change in the composition of these assets since the crisis began, figure 3 plots the ratios of U.S. Treasury bills and of all Treasury securities held by the Federal Reserve to its total assets.2 From a status quo where the Federal Reserve held almost exclusively Treasury securities, in the last two years it has switched toward holding many other types of assets and, more recently, toward securities with longer maturities. I start my assessment in section II with this last group of policies, because they are the least understood in theory. Using a new model of 2. U.S. Treasury bills are three-month securities; total Treasury securities include bonds and notes, which have longer maturities. The figure includes only securities held outright, not those held as part of repurchase agreements.

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Figure 3. U.S. Treasury Securities Held Outright by the Federal Reserve, June 1996–August 2009 Percent of total assets 90 80 70

All Treasury securities

60 50 40 30 20

Bills only

10 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Sources: Board of Governors of the Federal Reserve System, “Credit and Liquidity Programs and the Balance Sheet,” statistical release H.4.1, “Factors Affecting Reserve Balances,” various issues.

capital markets, I investigate the effects of the Federal Reserve’s different investments on the availability of credit.3 In the model, four groups of actors—entrepreneurs, lenders, traders, and investors—all have funds that must be reallocated through the financial system toward investment and production, but frictions among these groups may lead to credit shortages at different points in the system. Different credit programs implemented by the central bank will have different effects depending on whether they tighten or loosen these credit constraints, and depending on the equilibrium interactions between different markets. Drawing on the model, section III goes on to suggest that whereas the Federal Reserve’s credit policies to date have been directed at a wide range of markets and institutions, focusing the central bank’s efforts on senior secured loans to traders in securities markets would be the most effective way to fight the crisis. Next, in section IV, I move to quantitative policy and ask the following question: do the recent increases in reserves and in the central bank’s balance sheet undermine the ability of the current policy regime to control inflation? I show that according to a standard model of price-level determination, the regime is threatened only if the Federal Reserve becomes excessively concerned with the state of its balance sheet, or if it gives in to pressure from the fiscal authorities, effectively surrendering its independence. 3. The model is a simple version of the more complete analysis in Reis (2009).

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Finally, in section V, I turn to interest rate policy. I briefly survey the literature on optimal monetary policy in a liquidity trap, which recommends committing to higher than normal inflation in the future and keeping the policy interest rate at zero even after the negative real shocks have passed. Although the Federal Reserve’s actions fit these prescriptions qualitatively, they seem too modest relative to what theory calls for. Section VI concludes.

I. What Has the Federal Reserve Been Up To? There already exist some thorough descriptions of the events of the U.S. financial crisis of 2007–09.4 After a brief and selective summary, this section catalogs the policies followed by the Federal Reserve in response to these events.

I.A. The Financial Crisis and the Real Crisis In August 2007 an increase in delinquencies in subprime mortgages led to a sharp fall in the prices of triple-A-rated mortgage-backed securities and raised suspicions about the value of the underlying assets. Because many banks held these securities, either directly or through special investment vehicles, doubts were cast over the state of banks’ balance sheets generally. Through 2007 the fear became widespread that many banks might fail, and interbank lending rates spiked to levels well above those in the federal funds market. This increase in risk spreads diffused over many markets, and in a few, notably the markets for commercial paper, private asset-backed securities, and collateralized debt obligations, the decline in trading volume was extreme, apparently due to lack of demand. In the real economy, the U.S. business cycle peaked in December 2007, according to the National Bureau of Economic Research. Unemployment began rising steadily from 4.9 percent in December 2007 to just over 10 percent in October 2009, and output decelerated sharply in 2008Q1. Net acquisition of financial assets by households fell from $1.02 trillion in 2007 to $562 billion in 2008 and to just $281 billion and $19 billion in the first and second quarters of 2009, respectively, according to the Federal Reserve’s Flow of Funds Accounts. As of the start of 2008, however, there was still no sharp fall in total bank lending. In March 2008 the investment bank Bear Stearns found itself on the verge of bankruptcy, unable to roll over its short-term financing. The government, in a joint effort by the Federal Reserve and the Treasury, stepped 4. See Brunnermeier (2009), Gorton (2009), and Greenlaw and others (2008).

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in and arranged for the sale of Bear Stearns to JP Morgan Chase, providing government guarantees on some of Bear Stearns’ assets. Risk spreads remained high, and the asset-backed securities market was effectively closed for the rest of the year, but some calm then returned to markets until the dark week of September 15 to 21 arrived. The extent of the crash during these seven days probably finds its rival only in the stock market crash of October 1929. It was marked by three distinct events. The first, on September 15, was the bankruptcy of Lehman Brothers, the largest company ever to fail in U.S. history. This investment bank was a counterparty in many financial transactions across several markets, and its failure triggered defaults on contracts all over the world. The second event was the bailout of American International Group (AIG), one of the largest insurance companies in the world, on the evening of September 16. The bailout not only signaled that financial losses went well beyond investment banks, but also increased the uncertainty about how the government would respond to subsequent large bankruptcies. The third event, on September 20, was the announcement of the first version of the Troubled Asset Relief Program, or TARP (also known as the “Paulson plan” after Treasury Secretary Henry Paulson), which, although potentially far-reaching, was both short on detail and vague in its provisions. In the six months that followed, the stock market plunged: having already fallen 24.7 percent from its peak a year earlier, the S&P 500 index fell another 31.6 percent from September 2008 to March 2009. Most measures of volatility, risk, and liquidity spreads increased to unprecedented levels, and measures of real activity around the world declined. Which of the three events was the main culprit for the financial crisis that followed is a question that will surely motivate much discussion and research in the years to come.5 Through all these events, the Treasury cooperated with the Federal Reserve while also pursuing its own policies in response to the crisis. Today, these include a plan to invest up to $250 billion in banks to shore up their capital, assistance to homeowners unable to pay their mortgages, and up to $100 billion of TARP money in public-private investments to buy 5. The situation at the time looked so dire that the head of the International Monetary Fund, Dominique Strauss-Kahn, stated apocalyptically on October 11 that “Intensifying solvency concerns about a number of the largest U.S.-based and European financial institutions have pushed the global financial system to the brink of systemic meltdown” (“Statement by the IMF Managing Director, Dominique Strauss-Kahn, to the International Monetary and Financial Committee on the Global Economy and Financial Markets, Washington, October 11”).

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underperforming securities from financial institutions. Since March 2009 some stability has returned to financial markets, with risk spreads shrinking and the stock market partly recovering. Forecasts of unemployment and output, however, have yet to show clear signs of improvement. Finally, inflation as measured using the year-on-year change in the consumer price index has fallen from 4.1 percent in December 2007 to −1.3 percent in September 2009. Inflation forecasts for the coming year, as indicated by the median answer in the Survey of Professional Forecasters, have fallen from 3.6 percent in the last quarter of 2007 to 0.7 percent in the third quarter of 2009, and the forecast for average inflation over the next 10 years has risen slightly, from 2.4 percent to 2.5 percent.

I.B. The Federal Reserve’s Actions during the Crisis The Federal Reserve typically chooses from a very narrow set of actions in its conduct of monetary policy. It intervenes in the federal funds market, where many banks make overnight loans, by engaging in open-market operations with a handful of banks that are primary dealers. These operations involve collateralized purchases and sales of Treasury securities, crediting or debiting the banks’ holdings of reserves at the central bank. The Federal Reserve announces a desired target for the equilibrium interest rate in the federal funds market and ensures that the market clears close to this rate every day. Over the course of the last two years, however, the Federal Reserve’s activities have expanded dramatically. Table 1 provides snapshots of these recent actions at three points in time: in January 2007, before the start of the crisis (and representative of the decade before); at the end of December 2008, in the midst of the crisis; and in August 2009. The Federal Reserve’s policies fit into three broad categories.6 The first is interest rate policy. Starting from a target for the federal funds rate of 5.25 percent for the first half of 2007, the Federal Reserve gradually reduced that target to effectively zero by December 2008.7 In its policy announcements, the Federal Reserve has made clear that it expects to keep

6. For alternative descriptions of the policy responses to the crisis, see Cecchetti (2009) for the United States and Blanchard (2009) for an international perspective, as well as the many speeches by governors of the Federal Reserve available on its “News & Events” page (www.federalreserve.gov/newsevents/default.htm). An up-to-date exposition is the Federal Reserve’s statement of its “Credit and Liquidity Programs and the Balance Sheet” (www.federalreserve.gov/monetarypolicy/bst.htm). 7. More precisely, in December 2008 the Federal Reserve started announcing upper and lower limits for this rate, which at that time were 0.25 percent and zero.

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Table 1. Balance Sheet of the Federal Reserve, Selected Dates, 2007–09a Billions of dollars Assets Securities held outright U.S. Treasury bills U.S. Treasury notes and bonds Agency debt Repurchase agreements Direct loans Gold Foreign reserves Other assets Total Memorandum: federal funds target rate Securities held outright U.S. Treasury bills U.S. Treasury notes and bonds Agency debt Repurchase agreements Direct loans Gold Foreign reserves Other assets New asset categories Term Auction Facility (TAF) Commercial Paper Funding Facility (CPFF) Maiden Lane Total Memorandum: federal funds target rate

Liabilities and capital January 3, 2007 Federal Reserve notes 277.0 Commercial bank reserves 501.9 U.S. Treasury deposits 0 Reverse repurchase agreements 39.8 Other liabilities 1.3 11.0 Total liabilities 20.5 16.7 Capital 878.5 Total

781.3 20.0 6.2 29.7 10.6 847.9 30.6 878.5

5.25% December 31, 2008 Federal Reserve notes 18.4 Commercial bank reserves 457.5 U.S. Treasury deposits 19.7 Reverse repurchase agreements 80.0 Others 193.9 11.0 Total liabilities 579.8 40.3 Capital

853.2 860.0 365.4 88.4 56.8 2,223.8 42.2

450.2 334.1 73.9 2,265.9

Total

2,265.9

0.0–0.25%

this rate at zero for an extended period.8 Starting in October 2008, the Federal Reserve has also been paying interest on both required and excess reserves held by commercial banks; since December 2008 the interest rate on these reserves (shown in figure 1) has been the same as the upper end of the target range for the federal funds rate. This implies that banks no 8. The December 2008 press release of the FOMC stated that “the Committee anticipates that weak economic conditions are likely to warrant exceptionally low levels of the federal funds rate for some time.” The commitment to low interest rates has been reaffirmed at every meeting since then, with slightly different wording since March 2009.

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Table 1. Balance Sheet of the Federal Reserve, Selected Dates, 2007–09 (Continued) Billions of dollars Assets Securities held outright U.S. Treasury bills U.S. Treasury notes and bonds Agency debt Repurchase agreements Direct loans Gold Foreign reserves and other assets New asset categories Term Auction Facility (TAF) Commercial Paper Funding Facility (CPFF) Maiden Lane Mortgage-backed securities Central bank liquidity swaps Total Memorandum: federal funds target rate

Liabilities and capital August 19, 2009 Federal Reserve notes 18.4 Commercial bank reserves 717.7 U.S. Treasury deposits 111.8 Reverse repurchase agreements 0 Others 106.3 11.0 Total liabilities 76.7 Capital 221.1 53.7 61.7 609.5 69.1 2,063.8

Total

871.5 818.8 240.2 68.4 14.4 2,013.3 50.5

2,063.8

0.0–0.25%

Sources: Board of Governors of the Federal Reserve System, “Credit and Liquidity Programs and the Balance Sheet,” statistical release H.4.1; “Factors Affecting Reserve Balances,” various issues; and Federal Reserve Bank of New York, “Treasury and Federal Reserve Foreign Exchange Operations,” various issues. a. Items may not sum to totals because of rounding.

longer pay an effective tax on reserves held at the central bank beyond the legal requirements. It also means that the Federal Reserve in the future has at its disposal a new policy instrument, the spread between the federal funds rate and the rate on reserves.9 Finally, the Federal Reserve has purchased other securities with the stated intent of affecting their prices and yields, but there is little evidence of success.10 9. The Federal Reserve also controls the interest rate that it charges banks that borrow from it directly at the discount window. Although banks rarely use the discount window during normal times, this facility can be important during crises. 10. For instance, in April 2009 Vice Chairman Donald Kohn stated that “the Federal Reserve has begun making substantial purchases of longer-term securities in order to support market functioning and reduce interest rates in the mortgage and private credit markets” (“Policies to Bring Us Out of the Financial Crisis and Recession,” speech delivered at the College of Wooster, Wooster, Ohio, April 3, 2009). Chairman Ben Bernanke stated that “The principal goal of these programs is to lower the cost and improve the availability of credit for households and businesses” (“The Federal Reserve’s Balance Sheet,” speech delivered at the Federal Reserve Bank of Richmond 2009 Credit Markets Symposium, Charlotte, N.C., April 3, 2009).

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The second category, which I label quantitative policy, concerns the size of the Federal Reserve’s balance sheet and the composition of its liabilities. Historically, the bulk of these liabilities has consisted of currency in circulation plus bank reserves (most of which the banks are required by law to hold at the level mandated by the Federal Reserve) and deposits of the Treasury and foreign central banks. With the onset of the crisis, the first change in quantitative policy was that the Federal Reserve’s balance sheet more than doubled. Reserves accounted for much of this increase and are now mostly voluntary, since the penalty for holding reserves instead of lending in the federal funds market effectively disappeared once the interest rates on both became the same. The other significant change was that the U.S. Treasury became the single largest creditor of the Federal Reserve. As a means of providing the Federal Reserve with Treasury securities to finance its lending programs, the Treasury has greatly expanded its account, and in August 2009 it held more than one-tenth of the Federal Reserve’s total liabilities. The third category is credit policy. This consists of managing the composition of the asset side of the Federal Reserve’s balance sheet. At the start of the crisis, the central bank’s assets were similar in composition to what they had been since its founding: mostly U.S. Treasury securities, with over one-third in Treasury bills and the remainder made up of Treasury bonds and notes together with modest amounts of foreign reserves. Rounding out the balance sheet were other assets (such as gold), but almost no direct loans. By the height of the crisis in December 2008, however, this picture had changed dramatically, following the announcement of several new asset purchase programs.11 The Federal Reserve’s December 31, 2008, balance sheet reveals several important changes in its assets from two years earlier. Starting from the top of the assets column, the first is a significant shift in the average maturity of Treasury securities held from short to long. The second is a dramatic increase in direct loans, with the Federal Reserve for the first time lending directly to entities other than banks. These included loans to primary dealers through the 28-day TSLF and the overnight PDCF and, through the TALF, to investors posting as collateral triple-A-rated asset-backed securities on student loans, auto loans, credit card loans, and Small Business 11. These included the Term Auction Facility (TAF), the Term Securities Lending Facility (TSLF), the Primary Dealer Credit Facility (PDCF), the Commercial Paper Funding Facility (CPFF), the Term Asset-Backed Securities Loan Facility (TALF), the Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), and the Money Market Investor Funding Facility (MMIFF).

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Administration loans.12 The third is an almost 30-fold increase in foreign reserves, reflecting a swap agreement with foreign central banks to temporarily provide them with dollars against foreign currency. The next three changes take the form of entirely new asset categories. First, through the TAF, the Federal Reserve started lending to banks for terms of 28 and 84 days against collateral at terms determined at auction. These auctions provide a means to lend to banks that preserves the recipients’ anonymity, to prevent these loans from being seen by the market as a signal of trouble at the debtor bank. In December 2008 these credits to banks accounted for almost one quarter of the Federal Reserve’s assets. Second, through the CPFF, the Federal Reserve bought 90-day commercial paper, thereby financing many companies directly without going through the banks. Finally, the Federal Reserve created three limited-liability companies, Maiden Lane LLC and Maiden Lane LLC II and III, to acquire and manage the assets associated with the bailouts of AIG and Bear Stearns. By August 2009 some of these programs had been reduced significantly in scope, in particular the holdings of commercial paper and foreign reserves. Others, however, continue to grow. In particular, in March 2009 the Federal Reserve announced it would purchase up to $300 billion in long-term Treasury bonds and $1.45 trillion in agency debt and mortgage-backed securities; it expects to reach these goals by the end of the first quarter of 2010. These changes were announced at the FOMC meeting of March 2009 but had been under discussion for a few months before that. A large share of these purchases is already reflected in the August balance sheet.

II. A Credit Frictions Model of Capital Markets The crisis of 2007–09 has witnessed credit disruptions involving multiple agents in many markets, it has seen large swings in asset-backed securities, and it has propagated from financial markets to the real economy. Unfortunately, no off-the-shelf economic model contains all of these ingredients. Before I can interpret the Federal Reserve’s policies, I must therefore take a detour to introduce a new model that captures them. Financial markets perform many roles, including the management of risk and the transformation of maturities. In the model I abstract from these 12. The Federal Reserve also made funds available to lend to the money market, through the MMIFF for money market funds, and through the AMLF programs for banks to finance purchases from money market funds. The first program was never used; the funds under the AMLF are included in the “direct loans” item on the balance sheet, but the balance is currently zero.

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better-understood roles to focus on another role of financial markets: the reallocation of funds toward productive uses. I take as given a starting distribution of funds across agents, and I study how trade in financial markets shifts these funds to where they are needed, subject to limits due to asymmetries of information. The model merges insights from the theory of bank contracts based on limited pledgeability (Holmström and Tirole 2009) with the theory of leverage based on collateral constraints (Kiyotaki and Moore 1997; Matsuyama 2007). It is a simpler version of a model fully developed in Reis (2009). The appendix lays out the model in more detail.

II.A. Setting up the Model: Agents The model has three periods, no aggregate uncertainty, and a representative consumer-worker. She supplies labor L in all three periods, earning a wage W in each period, and consumes a final good C″ in the last period, which is a Dixit-Stiglitz aggregator of a continuum of varieties. The economy has only one storable asset, in amount H, which I will refer to as capital. It consists of claims issued by the government, which can be redeemed for the consumption good in the final period. The government levies a lump-sum tax on the representative household in the last period to honor these claims.13 The representative household has four different types of financial agents, each endowed with an initial allocation of capital. First, there are many investors behaving competitively, who hold capital M. Agents of the second type are entrepreneurs. There is a continuum of them in the unit interval associated with each variety of the consumption good. In the first period they must hire F units of labor to set up operations.

13. A few notes are in order regarding this capital. First, it is a very crude way to introduce an asset in this economy that is used as a means of payment. However, it allows me to keep the focus on the credit frictions and to avoid having to describe in detail the underlying theory of money or assets. Second, although I assume that, like money, capital pays a zero net return, generalizing the model to include a positive return does not change the results qualitatively. Third, I use the term “capital” and not “money” because these assets can be thought of as broader than just high-powered money. They represent any claims that can be exchanged for consumption goods in the last period, and so they refer to all assets in this economy. Fourth, these assets could be private claims issued by the representative consumer, if the consumer could commit to their repayment, thus dispensing with the need for a government or taxes. However, decentralizing this economy to justify the existence of the representative consumer is a difficult task. Fifth, an alternative would be to assume that H is a physical good that can be stored without depreciating and can be transformed into the final consumption good in the final period. This leads to predictions similar to those in this paper, but messier algebra.

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Further labor is then hired in the second and third periods, to produce monopolistically in the last period a variety of consumption goods in amount Y i″. The production function is (1)

⎧ L ′ L ′′ ⎫ Y i′′= Ai′ min ⎨ i , i ⎬ . ⎩ v 1− v⎭

At the optimal choice of labor in the second and third periods, v will be the fraction of labor employed in the second period. Exogenous productivity, A′i , is independently and identically distributed across the continuum of firms and is revealed in the second period, before the labor decision is made for that period. With probability 1 − ϕ it equals a, and with probability ϕ it is zero. Therefore, if I ∈ [0, 1] projects are funded in the first period, only N = (1 − ϕ)I yield positive output in the last period. This production structure captures the maturing process of investments, with expenses incurred in every period in order to obtain a payoff in the last period, together with the risk that setup costs may not be recouped if the technology turns out to be worthless. The entrepreneurial capital available is K, which is smaller than WF, so that entrepreneurs must seek outside financing. Agents of the third type are lenders. Their distinguishing feature is that only they have the ability to monitor the behavior of entrepreneurs. If investors were to finance entrepreneurs directly, they could not prevent them from running away with all of the funds. Lenders, in contrast, can prevent the entrepreneurs from absconding with more than a share δ of sales revenue. Entrepreneurs can therefore pledge 1 − δ of this revenue to lenders and zero to all other agents.14 I assume that the pledgeable revenue is enough to ensure positive pledgeable profits to lenders. A lender will provide the capital needed to start the project, WF − K, as well as a line of credit in the second period to pay wages WL′. To fund these investments, lenders have capital D in the first period and may receive a new infusion D′ in the second period. If they require further financing, they can issue and sell securities, guaranteed by the loans they make, totaling S for price Q in the first period, and S′ for price Q′ in the

14. This limited pledgeability constraint has a long tradition in the modeling of capital market imperfections: see Matsuyama (2007) and Holmström and Tirole (2009) for recent reviews. Note that one can reinterpret the F setup costs as the cost to lenders to set up the monitoring technology to which only they have access, allowing them to capture 1 − δ of the revenue.

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second period.15 These securities pay one unit of capital in the last period, if the project is in operation. In the data, lenders include all providers of financing to the nonfinancial sectors, including commercial banks, primary issuers of commercial debt, some brokers, and others. Traders are the fourth and final group of agents. Although they cannot monitor loans, together with lenders they have the unique ability to understand and trade the lenders’ securities. In particular, in the first period, lenders could try to sell as many securities as they wanted whether they had proper backing or not. Traders are the only agents who can verify that a recently issued security has proper backing. Traders also observe the realization of productivity in the second period, whereas investors do not. They therefore perform the role of intermediating between lenders and investors so that the latter have access to the securities. In the United States, traders include investment banks, hedge funds, special investment vehicles set up by commercial banks, and many others. Traders have capital E in the first period, and an additional E′ is available to them in the second period. They can also obtain funds from investors, but I assume that another friction prevents investors from effectively owning the traders and acquiring access to their information technology. I again use a pledgeability constraint, assuming that investors can seize at most a share 1 − µ of the assets of a trader, so that this is the trader’s maximum liability.16 Therefore, in the first period, the trader’s total assets are E/µ, where µ gives the inverse of the leverage multiplier. In the second period, because traders enter with assets equal to the securities S, and these are marked to market, their entering equity is E + [(1 − ϕ)Q′ − Q]S/Q, reflecting the capital gain (or loss) made on these investments. Because the trader can get new loans against this marked-to-market equity position, the trader can invest a further [(1 − µ)/µ][(1 − ϕ)Q′/Q − 1]S in the second period. This ability to use capital gains to boost leverage is also emphasized by Arvind Krishnamurthy (forthcoming) and by Andrei Shleifer and Robert Vishny (2009).17

15. Note that S is the total revenue from selling the security in the first period, so that S/Q is the number of securities sold paying this amount of capital in the third period. The same applies to S′. 16. I assume that even if traders abscond with the securities, they can show up to redeem them in the last period. 17. Lenders cannot obtain direct financing from investors, since in equilibrium their assets will consist solely of the outstanding loans. Only lenders can monitor these loans, so seizing the lenders’ assets would produce zero revenue.

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II.B. Setting Up the Model: Financial Markets Having presented the agents, I now describe the markets in which they interact in each period. In the first period, entrepreneurs need financing to set up their firms. Because of the need for monitoring, only lenders are willing to provide them with capital. Lenders behave competitively in funding each project, but once a lender is matched with an entrepreneur, they stay together until the last period. If lenders do not have enough capital, they can issue securities, which only traders will choose to buy since only they can ensure that the securities have proper backing. Investors deposit funds with traders. I assume that K + D + E < WF, so that all funds of all agents, including the investors, are required to set up all the projects. In the second period, entrepreneurs require more capital and obtain it from their line of credit with their lender. The lender may issue more securities, and traders can again choose to buy them. In this period, however, investors can also buy the preexisting securities, because lenders and traders have signaled, by trading them in the first period, that these securities are properly backed. However, investors cannot distinguish the securities backed by assets for which A′i = a from those for which A′i = 0. Therefore, as long as Q′ > 1 − ϕ, they will refrain from buying securities directly in this market. Lenders and traders, on the other hand, can distinguish between the two types of securities, so if investors stay out, the price of the A′i = 0 securities is zero, and Q′ refers to the price of the A′i = a securities. Finally, in the third period, entrepreneurs obtain the revenue from sales, pay the last-period workers, and pay back the lenders. The lenders, in turn, use part of the proceeds to repay the holders of securities backed by the loans, and traders return the funds belonging to investors. In the end, all agents return their capital to the representative household. All of these financial market participants are risk-neutral and aim to maximize their last-period payoff. Figure 4 summarizes the timing and the flows of funds just described. I assume that there is enough liquidity to sustain the social optimum, where all projects get funded and marginal costs depend only on wages and productivity, which is equivalent to assuming that total capital H exceeds the setup and up-front labor costs at the efficient level. The problem I focus on here is the allocation of this liquidity, in the presence of the frictions captured by the parameters δ, ϕ, and µ.

II.C. Closing the Model To close the model, I need a few more ingredients, which are spelled out in more detail in the appendix. The first is the demand for each variety of

Entrepreneurs can abscond with share δ of loans.

Traders

Investors

Can buy securities from lenders but cannot tell good from bad

Extend capital to traders in first two periods

Traders can abscond with share μ of investors’ capital.

Mark securities to market

Trade securities in first two periods

Obtain leverage from investors

Only a share ϕ of projects are productive.

Hire labor in second period

Realize revenue in third period

Monitor loans Sell securities to traders and investors

Incur fixed cost in first period

Source: Author’s model described in the text.

Frictions

Agents

Lenders Lend to entrepreneurs

Entrepreneurs

Borrow from lenders

Figure 4. Characterization of Markets in the Credit Frictions Model

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the goods, which is isoelastic: Y i″ = C″Pi″m/(1−m), where C″ is total final consumption, and Pi″ is the price of the good. The lender and the entrepreneur jointly decide the optimal scale of production for the productive firms in the second and third periods so as to maximize joint returns: (2)

max { Pi ′′Yi ′′− WLi′′− WLi′ Q ′} ,

Pi′′,Yi′′, Li′ , Li′′

subject to the production function in equation 1 and demand for the good. The optimality condition is v ⎞⎛W⎞ ⎛ Pi ′′= m ⎜ 1 − v + , ⎝ Q ′ ⎟⎠ ⎜⎝ a ⎟⎠

(3)

together with L′ = v(L′ + L″). I assume that m ∈ [1, 2], so that markups are between 0 and 100 percent, and that (1 − δ)m > 1, so that the pledgeable profits to lenders are positive. In a symmetric equilibrium, the production of all firms is the same and equal to Y. Total consumption is then C = NmY, which is increasing in the number of goods produced because variety is valued. Moreover, all prices are the same in equilibrium, which, since consumption goods and capital have the same price, implies that N1−mPi″ = 1, so the static cost-of-living price index is constant. Finally, the labor supply function is C″ = W, which follows from assuming log preferences over consumption and linear disutility of labor supply. Combining all of these equations provides the solution for the following endogenous variables: total employment L′ + L′′ in the second and third periods, wages W, and the pledgeable amount of operating profits π: (4)

L ′ + L ′′ =

1 m (1 − v + v Q ′ ) (1 − ϕ ) I

(5)

a [(1 − ϕ ) I ] W = m (1 − v + v Q ′ )

(6)

π i (Q ′, I ) ≡ (1 − δ ) Pi ′′ Yi ′′− WLi′′−WLi′ Q ′

m −1

=

m

2

[(1 − δ ) m − 1] a (1 − v + v Q ′ )[(1 − ϕ ) I ]

2−m

.

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II.D. Equilibrium Conditions in the Financial Markets Two restrictions on prices must hold so that there are no arbitrage opportunities that would allow for infinite profits. First, since a security bought in the first period for price Q will, with probability 1 − ϕ, be worth Q′ in the second period, but zero otherwise, and since lenders can sell it in the first period and buy it back in the second period, it must be that Q ≤ (1 − ϕ)Q′. Otherwise, lenders would make infinite expected profits.18 Second, and similarly, because lenders can hold cash between the second and the third period with a guaranteed return of 1, it must be that Q′ ≤ 1. I now characterize the equilibrium securities price and investment in the first period. In the securities market in the first period, if Q < (1 − ϕ)Q′, traders strictly prefer to buy securities rather than hold cash, and so their total demand is E/µ. If Q = (1 − ϕ)Q′, they are indifferent between cash and securities, and so they will be willing to buy any amount of securities below E/µ. Turning to the supply of securities, if Q < (1 − ϕ)Q′, it equals total investment minus the capital of the entrepreneurs and the lenders: WFI − K − D. If Q = (1 − ϕ)Q′, the lender is indifferent between issuing this amount of securities and any higher amount. Equating demand and supply for Q < (1 − ϕ)Q′ and substituting for equilibrium wages from equation 5 gives the first-period securities market equilibrium condition (SM):

(7)

⎤⎛ v⎞ E⎞ ⎡ m ⎛ . Im = ⎜K + D + ⎟ ⎢ ⎥ ⎜1 − v + m −1 ⎝ μ ⎠ ⎢⎣ a (1 − ϕ ) F ⎥⎦ ⎝ Q ′ ⎟⎠

In (I, Q) space this defines a vertical line for Q between zero and (1 − ϕ)Q′. The expected profits of lenders in the first period are Q(1 − ϕ)Iπ(Q′, I) − WFI + K. There is free entry into this sector, so lenders will enter as long as there are available projects, and profits are strictly positive. If Q is above a certain level Q*, then I = 1, and lenders earn positive rents in exchange for their monitoring services.19 If Q ≤ Q*, then lenders’ profits are driven to

18. The fact that capital gains on a portfolio of securities are always nonnegative is a consequence of the lack of aggregate uncertainty. It is straightforward to extend the model to include uncertainty; since all agents are risk-neutral, this would change little in the analysis after replacing expected for actual values. WF − K 19. Q* is defined as Q* = . (1 − ϕ ) π (Q ′,1)

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zero, so Q(1 − ϕ)Iπ(Q′, I) − WFI + K = 0. Solving this equation for I and replacing for pledgeable profits from equation 6 gives (8)

a (1 − ϕ )

m −1

Q [(1 − δ ) m − 1] ⎫ ⎧ v ⎞ ⎛ . I m ⎨F − ⎬ = Km ⎜ 1 − v + ⎝ Q ′ ⎟⎠ mI ⎩ ⎭

This is the zero-profits equilibrium condition (ZP), when Q ≤ Q* and investment is below 1. It defines investment implicitly as an increasing function of Q. Intuitively, as the price of securities increases, projects become cheaper to finance, so the amount of entrepreneurial capital needed per project falls and more projects are funded. Turning to the securities market in the second period, if 1 − ϕ < Q′ < 1, the demand comes solely from traders and equals (9)

S′ =

E ′ ⎛ 1 − μ ⎞ ⎡ (1 − ϕ ) Q ′ ⎤⎛ E ⎞ − 1⎥ ⎜ ⎟ . +⎜ ⎟ ⎢ μ ⎝ μ ⎠⎣ Q ⎦⎝ μ ⎠

Here the first term is the demand from the new capital, and the second is the extra demand from leveraging capital gains. If Q′ = 1, the trader is indifferent between zero and the amount in equation 9. As Q′ falls, the expected capital gain for traders is smaller, and so they have fewer funds with which to demand securities. If Q′ falls all the way to 1 − ϕ, then investors start buying securities directly, satisfying the supply at that price. The supply of securities comes from lenders who need capital to cover their outstanding credit lines; thus, it equals (1 − ϕ)IWL′ − D′ if Q′ < 1. Replacing for the equilibrium labor and wage from equations 4 and 5 gives the supply function for securities in the second period: (10)

S′ =

va (1 − ϕ )

m −1

I m −1

m 2 (1 − v + v Q ′ )

2

− D ′.

This is increasing in Q′, since a higher price of securities implies a lower marginal cost of production and therefore an increase in the scale of each firm. This requires more funds to finance operations, and hence higher credit lines and more securities issued. When Q′ = 1, the lenders become indifferent between supplying this and any higher amount. Equations 7 through 10 provide four conditions to determine the four endogenous variables: the equilibrium price of securities in the first and second periods (Q and Q′), the amount of investment in the first period (I), and the scale of operations and funding in the second period (S′). Together

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these define the equilibrium in this economy.20 There are three possible equilibria, which I describe next.

II.E. The Three Equilibrium Cases The first case is the efficient economy, where, in spite of the financial frictions, all projects are still funded (I = 1), and financing does not add to the marginal cost of firms: Q′ = 1. One can show that this will be the case if δ, µ, and ϕ are each below some threshold. Intuitively, if δ is not too high, then the lenders are able to appropriate enough of the entrepreneurs’ revenue so that their profits are high enough and they will wish to finance all the projects. If µ is low enough, the friction impeding the flow of funds from investors to traders is not too severe, and so their funds can satiate the market for securities. Finally, if ϕ is low enough, the expected profits of lenders in the first period are high, inducing full investment, and investors put a high lower bound on the price of securities in the second period. The second case is the other extreme, that of a catastrophic economy, where the price of securities in the second period has fallen to 1 − ϕ. Investors start buying securities directly, but because they cannot distinguish profitable from unprofitable assets, for each dollar they spend on a worthwhile security, ϕ/(1 − ϕ) dollars buy a worthless security, squandering their funds and destroying resources. This low price of securities implies that the marginal cost of production (1 − v + v/Q′) is high, so that each firm will operate at a small, inefficient scale. And as Q falls even lower, below (1 − ϕ)2, the cost of financing to set up projects in the first period becomes very high, and few firms are set up in the first place. In between these two extremes is the constrained economy, depicted in figure 5. As the left-hand panel of figure 5 shows, the equilibrium price of securities and the level of investment in the first period are determined, taking as given the price of securities in the second period. The vertical line is the SM condition in equation 7, and the upward-sloping curve is the ZP condition in equation 8. The right-hand panel shows the equilibrium price in the second period and the scale of the projects, taking as given the price and investment from the previous period. The zigzag line depicts the demand function in equation 9, and the curve is the supply function in equation 10. In this economy there is an extensive-margin inefficiency, as I < 1 in equilibrium. Traders do not have enough assets, because of either 20. With these four variables determined, equilibrium wages and hours worked are determined by equations 4 and 5. Equilibrium output and consumption follow from using the production function and the market clearing condition in the goods market.

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Figure 5. Equilibrium in a Constrained Economy First period

Second period

Q Q⬘(1 – ϕ)

Q⬘ SM

ZP

1

Demand

Supply

Q* 1–ϕ

1

I

S


low capital or tight leverage constraints imposed by investors, so the price of securities Q is below Q*, making the up-front cost of investing too high relative to future revenue. There is also an intensive-margin inefficiency, since Q′ < 1, and so the marginal costs of production exceed W/a. Operating firms will hire too little labor and produce too little output, because there is too little second-period capital in the hands of traders to satisfy the lenders’ residual need for funds.21 Intuitively, for the economy to operate efficiently, investors’ capital must reach entrepreneurs, either directly from lenders or through the securities market from traders and investors. In the efficient economy, this happens because entrepreneurs have all the capital they need to set up and operate projects. In the constrained economy, leverage constraints on traders are too tight, so that there are insufficient funds in the securities markets in both periods, and the pledgeability constraint and technological risk prevent lenders’ capital from being enough. In the catastrophic economy, investors enter the securities market directly, but do so with great waste since they are unable to pick securities. There is severe mispricing and misallocation of capital, as worthless and worthwhile investments face the same marginal cost of capital in an inefficient pooling equilibrium.22 21. One can see that the efficient equilibrium in this graph would require that the SM line lie to the right of I = 1 so that, in the second period, demand and supply would coincide over a line segment in the region at the top where they are horizontal. The catastrophic equilibrium occurs when the supply curve intersects the demand curve in its lower horizontal segment. 22. One feature of this model, as well as of most models of credit frictions, is that there is too little borrowing. Some have argued that the current crisis is due rather to too much borrowing, but to my knowledge this has not yet been formalized.

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To understand better the role of each of the three frictions in the model, consider what happens in equilibrium as each is shut down. First, if all projects are productive (ϕ = 0), then there is no “lemons” problem in the securities market. This implies that the knowledge traders use in picking securities is no longer valuable, and investors can buy securities directly from lenders. Since there is no limit to the amount of securities that lenders can issue, and since investors have all the necessary capital to fund all projects and run them efficiently, the only equilibrium is the efficient one. Second, assume that traders can no longer abscond with capital without being detected (µ = 0). In this case investors will be willing to invest all their funds with traders, who in turn will buy all the securities issued by lenders. Again, the unique equilibrium is the efficient case. Finally, if the banks have a perfect monitoring technology, they can reap all of the revenue from projects (δ = 0). Lenders will then be very willing to lend, a condition reflected in figure 5 by Q* being quite low, making it more likely that the efficient equilibrium obtains. It is still possible, however, that the friction in the leveraging of traders is so strong that they cannot obtain from investors even the small amount of funds required to fund all projects, and so the constrained equilibrium persists if the SM line is to the left of I = 1.

III. Interpreting the Federal Reserve’s Actions: Credit Policy In terms of the model just described, the financial events and crisis described in section I.A can be interpreted as a combination of two effects. First, the downgrading of many securities, following downward revisions of the value of the assets backing them, can be interpreted as an increase in ϕ in the model. Second, the withdrawal of funds from the financial sector and the fears about the solvency of many financial institutions can be interpreted as an increase in µ. Both of these changes can be interpreted as technological changes, or instead as changes in beliefs about the quality of assets. The economy in 2007–09 can then be seen as moving to a constrained equilibrium like that depicted in figure 5, or perhaps even as on the way to the catastrophic equilibrium. A policymaker would like to intervene to correct this serious misallocation of funds. Credit policy in this economy consists of transferring the capital trapped in investors’ hands to other agents or, alternatively, issuing more claims on final output (and correspondingly taxing more consumption in the final period). What the central bank can achieve with these actions depends on what is assumed about its knowledge and skills.

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One extreme is the case where the central bank has no special powers beyond those available to private investors. In terms of the model, this translates into the central bank having neither the ability to monitor loans, nor the know-how to pick securities, nor the power to seize more than a share of the traders’ assets. In this case any injection of credit by the central bank in the market is equivalent to an increase in the capital of investors M. This does not affect any of the equilibrium conditions in the model, since the problem to be solved is not a lack of funds but their misallocation. Worse, if the central bank misguidedly tries to pick securities, invest in traders, or make loans directly to entrepreneurs, the model predicts that its suboptimal behavior will lead to possibly heavy losses, as money is absconded and investments turn sour. At the other extreme, consider the case where the central bank can become a lender, able to monitor the behavior of borrowers and ensure that the funds it lends are put to good use. Then, by lending the needed funds to entrepreneurs, the policymaker could reach the social optimum, with no intervention by financial firms. This seems unrealistic and indeed results in absurd predictions: if the central bank could lend as effectively as anyone else, why have a financial system at all? Three intermediate cases are both more interesting and more realistic.

III.A. The Central Bank as a Senior Secure Investor In the first intermediate case, I assume that the central bank has the ability to make loans to financial institutions that are sure to be fully repaid. In the model this maps into the policymaker both being able to distinguish good projects from bad and having some monitoring technology that ensures that lenders repay the central bank out of the revenue from projects before they or the securities holders get paid. In reality this might be achieved by imposing the condition that central bank loans are senior to those of other creditors, or by the central bank using its regulatory power. In the model a transfer of funds X from the central bank to lenders in the first period raises their initial capital from D to D + X, while leaving their profits unchanged as X is returned in the final period.23 Figure 6 depicts the effect this has on the equilibrium. The SM line in the first period shifts to the right, leading to an increase in investment and a rise in the price of securities. The extensive margin moves closer to the efficient level. These changes, in turn, lead to an increase in the supply of securities in the second 23. This assumes that the central bank is not trying to profit from the loan, so that the net interest rate it charges is zero.

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Figure 6. Effect of Injecting Credit through Loans to Lenders and Traders First period

Second period

Q Q⬘(1 – ϕ)

Q SM

ZP

1

Demand

Supply

Q* 1–ϕ

1 Initial equilibrium

S

I

Lender-case equilibrium

Trader-case equilibrium


period, since I is higher, so that the amount needed for the credit lines rises, as well as to a decline in demand, since the increase in Q lowers expected capital gains for traders. Therefore, the price of securities in the second period unambiguously falls, raising marginal costs and leading to a worsening of the intensive margin. Second-round effects then follow as the lower Q′ lowers the expected profits of lenders, shifting the zeroprofit condition to the left and lowering investment, and so on. As a result of the central bank’s actions, more firms are in operation, but each at a smaller, inefficient scale. For comparison, consider what happens if the first-period loans X are made to traders instead, as also portrayed in figure 6. Their total assets in the first period increase to E/µ + X, which has exactly the same effect on the first-period equilibrium as the transfer of funds to lenders in the previous scenario. However, in the second-period market, the increase in the assets of traders implies that they will have higher capital gains. Because traders mark their equity to market, they now have an extra source of funds with which to demand securities in the second period, so that the demand curve will be to the right of that in the previous case (in the figure this is drawn as unchanged from the initial case). Therefore, the price of secondperiod securities falls less than it did in that case. This intervention does not give rise to the same intensive-margin inefficiency that the loan to lenders did. Alternatively, consider the case where the central bank lends to traders or lenders in the second period rather than the first. Examination of the two

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equilibrium conditions, equations 9 and 10, shows that E′/µ and D′ enter symmetrically; it follows that loans to traders and loans to lenders would have an equivalent effect, raising Q′ and improving intensive-margin efficiency. At the same time, they would lower investment in the first period (see equation 7) and so worsen the extensive margin.24 Note that the crucial difference between the first and the second periods in the model is whether the securities are coming due next period or not. The indifference between lending funds to traders and lending them to lenders applies only to the securities that are about to mature; for all other securities, loans to traders are more effective because they affect the traders’ equity and leverage in future periods. The theory therefore suggests that providing funds to traders of new securities is more effective than providing them to lenders. The intuition is that, by accruing capital gains, traders can use increases in their equity to raise their leverage and draw more of the plentiful funds in the hands of investors to where they are needed in the securities markets. For the Federal Reserve, however, it is more natural to extend loans to commercial banks, as this involves little departure from its usual procedures. The creation of the popular 90-day loans under the TAF, which banks can use instead of the overnight loans available in the federal funds market, is an example of directing funds to lenders. Programs such as the TSLF, the PDCF, and the TALF are closer to the injection of funds into traders that the model recommends.

III.B. The Central Bank as a Buyer of Securities Next, consider the stricter case where the central bank has the knowhow to evaluate securities in the second period, distinguishing those that are associated with profitable firms from those that are worthless. In this case the central bank can use its funds X to buy securities directly, shifting the demand curve in the right-hand panel of figure 5 to the right. In the model this is precisely equivalent to lending funds to traders or lenders in the second period, as was just discussed. It is less effective than lending to traders in the first period because it does not draw investors’ funds into the market. The Federal Reserve followed this path during the latter part of 2008 through the CPFF. This agrees with the model’s prescriptions, since it has the same effect on the equilibrium as loans to traders, but the latter in 24. Leaving the constrained equilibrium and reaching the efficient one would require large loans in either or both periods. If that is not possible, then a well-calibrated increase in the funds available to traders in both periods could simultaneously improve both extensiveand intensive-margin efficiency.

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reality are likely easier to manage and less risky. Moreover, in practice, once the central bank starts picking which securities to buy, it opens itself to political and lobbying pressures that may prove dangerous.

III.C. The Central Bank as an Equity Investor Through its public-private partnerships and its capital stakes in banks, the Treasury has become an equity holder in many financial firms. The Federal Reserve has not done so explicitly, although its uncomfortable actions in support of the rescue of Bear Stearns and AIG make it close to being a de facto investor.25 In terms of the model, this case differs from the previous one because the purchases of securities by the traders increase not by X but rather by X/µ. That is, with the central bank now taking an equity stake, the new funds can be leveraged up, drawing more capital from investors into the securities market. In terms of the model, this is unambiguously better than providing loans, but only if the central bank can prevent its new partners from absconding with a share µ of the assets.26 Moreover, in real life it requires that the government behave like a profit-maximizing shareholder in the firms. Both conditions may not be met, and both surely come with some risk.

IV. Interpreting the Federal Reserve’s Actions: Quantitative Policy The large increase in outstanding reserves and in the size of the Federal Reserve’s balance sheet can cause worries. If “inflation is always and everywhere a monetary phenomenon,” as in Milton Friedman’s famous dictum, then the creation of so much money in the past two years might indicate that inflation is to come. However, there are good reasons, both empirical and theoretical, to be skeptical of the tight link between money and inflation that a strict mone25. The Federal Reserve’s discomfort with these actions is clear in Chairman Bernanke’s speech of April 3, 2009, cited above: “[The purchases covered by Maiden LLC] are very different than the other liquidity programs discussed previously and were put in place to avoid major disruptions in financial markets. From a credit perspective, these support facilities carry more risk than traditional central bank liquidity support, but we nevertheless expect to be fully repaid. . . . These operations have been extremely uncomfortable for the Federal Reserve to undertake and were carried out only because no reasonable alternative was available.” 26. In reality, agents receiving the funds need not literally abscond with them. They may instead pick dishonest partners, exert too little effort, or divert company investments toward private gains.

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tarist stance would suggest. The attempts at money targeting in the United States and the United Kingdom in the early 1980s were a failure, and even though Japan in the 1990s increased reserves on a scale similar to that in the United States recently, deflation persisted. Conventional models of inflation predict that reserves are irrelevant for the setting of interest rates or the control of inflation.27 This section discusses these theoretical arguments and examines to what extent the crisis may require their modification.

IV.A. A Simple Model of Price-Level Determination Consider the following model of price-level (Pt ) determination with no uncertainty: (11)

(1 + i ) P t

t

Pt +1 = Ct +1 βCt

(12)

M t Pt = L ( it − itm , Ct )

(13)

PG + it −1 Bt −1 = PT + Vt + Bt − Bt −1 t t t t

(14)

Bt = BtP + BtF

(15) Vt + itm−1 M t −1 + BtF − BtF−1 + K t − K t −1 = M t − M t −1 + it −1 BtF−1 + qt −1 K t −1 (16)

ln (1 + it ) = χΔ ln ( Pt ) + x t .

Equation 11 is the Euler equation for consumption, which equates the real interest rate (the gross nominal rate 1 + it divided by gross inflation Pt+1/Pt ) to the discounted change in the marginal utility of consumption, which with log utility equals consumption growth. Equation 12 is the demand for real reserves (Mt /Pt). It depends negatively on the opportunity cost of holding reserves instead of bonds, which is the difference in the interest rates paid on the two assets (it − itm). When this difference is zero and the other determinants of the demand for reserves are held fixed, the private sector is indifferent toward holding any amount of reserves above some satiation level.28 27. See Woodford (2008), among many others. 28. One assumption implicit in these two equations is that real money balances do not affect the marginal utility of consumption. Although deviations from this strict separability assumption can have strong theoretical implications for monetary policy (Reis 2007), empirically the deviations seem small (see section 3.4 in Woodford 2003).

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The next two equations refer to the behavior of the Treasury. Equation 13 is the government budget constraint. On the left-hand side are government spending (Gt) and interest payments on outstanding bonds (Bt ). On the right-hand side are revenue from taxes (Tt), transfers from the Federal Reserve (Vt), and issuances of new debt. Equation 14 is the market clearing condition for government debt, which may be held either by the Federal Reserve (BtF ) or by private agents (BtP ). The final two equations apply to the central bank. It makes transfers to the Treasury, pays interest on reserves, and buys either government securities or private assets (Kt). These uses of funds are financed by issuing new reserves and by the interest collected on the government bonds and on the portfolio of private securities with return qt. The last equation is the policy rule for the interest rate, with χ > 1 and policy choices xt.29 To focus on the price level, I take consumption as exogenous, and to focus on monetary policy, I treat government spending as also exogenous. The Federal Reserve’s policy is captured by its interest rate policy (its choices of interest rates {xt , it, i tm}), its quantitative policy (its choices regarding the amount of reserves and transfers to the Treasury {Mt , Vt}), and its credit policy (its choices regarding what assets {BtF, Kt} to hold). The Treasury’s policy is captured by its choices regarding taxation and debt issuance {Tt, Bt}.30 The goal is to determine the price level Pt as a function of these nine policy variables, subject to the six equations above and a set of initial and terminal conditions.31 A policy regime can be defined as a choice of which of these policy variables will be exogenously chosen and which must be accommodated endogenously.

IV.B. The Precrisis Policy Regime For most of the last 20 years, the press releases and commentary following meetings of the FOMC have focused on the current choice of innovations to the short-term interest rate xt, and its likely future path.

29. Adding a real activity variable to bring this rule close to a Taylor rule would change nothing in the analysis. 30. In the world outside the model, this sharp distinction between fiscal and monetary policy has become blurred by the recent cooperation between the Federal Reserve and the Treasury in addressing the crisis. F , Bt−1, Kt−1, and the terminal conditions come from 31. The initial conditions are Mt−1, Bt−1 consumer optimization with no outside assets and nonnegative holdings of money and bonds: P /Pt+j = 0 and limj→∞βju′(Ct+j)Mt+j /Pt+j = 0. limj→∞βju′(Ct+j)Bt+j

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Combining equations 11 and 16 and solving forward, the unique bounded solution for the price level is (17)

Δ ln ( Pt ) =

∞ ln (β ) + ∑ χ − j −1 ⎡⎣ Δ ln (Ct +1+ j ) − x t +1+ j ⎤⎦. 1 − χ j=0

Regardless of any other policy choice, interest rate policy alone determines inflation. As long as the other policy choices respect the constraints imposed by the equilibrium in equations 11 through 16, understanding and forecasting inflation involves focusing solely on the target rates announced by the FOMC. However the other variables are determined, it is the federal funds rate that determines inflation, according to the model. Turning to the other variables, the policy rule in equation 16 determines endogenously the observed short-term interest rate it . The other exogenous interest rate is itm, the interest rate on reserves, which before October 2008 was zero. The money demand equation (equation 12) then implied that total reserves Mt were determined endogenously. Therefore, there was no independent quantitative policy, as the size of the Federal Reserve’s balance sheet had to accommodate the fluctuations in the demand for reserves. As for credit policy, before 2007 the Federal Reserve chose to hold almost no private securities (Kt ≈ 0) and to hold government bonds roughly in line with the amount of reserves in circulation (BtF ≈ Mt ). The Federal Reserve’s budget constraint, equation 15, reduces to Vt ≈ it −1 M t −1

(18)

in steady state. With these policy choices, the Federal Reserve obtained net income from seigniorage every period, rebating almost all of it to the Treasury to keep its accounting capital roughly constant. Finally, turning to fiscal policy, combining the result in equation 18 with the Treasury’s budget constraint in equation 13, the market clearing condition for bonds in equation 14, and the transversality conditions gives (19)

BtP = Pt ( Gt − Tt ) + (1 + it −1 ) BtP−1 − ΔM t , ⎡ Pt + j ( Tt + j − Gt + j ) + ΔM t + j ⎤ P ⎥ = Bt −1 . Π kj = 0(1 + it −1+ k ) j=0 ⎣ ⎦ ∞

(20)

∑⎢

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The fiscal authorities can choose a path for deficits subject to the intertemporal solvency constraint in equation 20, and the total outstanding U.S. debt evolves endogenously to satisfy equation 19. Monetary policy has been independent of fiscal policy in that the Federal Reserve chooses xt taking only its mandate into account, regardless of the fiscal choices of the Treasury. Fiscal policy is dependent on monetary policy insofar as changes in reserves will affect the flow of seigniorage to the Treasury, but since the term ΔMt+j has in the history of the Federal Reserve been tiny relative to the government’s operating balance Pt+j (Tt+j − Gt+j), this dependence has been close to irrelevant. Until recently, both the independence of the central bank to set interest rates and control inflation and the accommodation of reserves to interest rate policy were seen as hallmarks of good monetary policy.32 Some have even argued that this policy regime partly explains the decline in macroeconomic volatility in the two decades before the crisis.33

IV.C. Is the Pre-2007 Status Quo Sustainable? The crisis has brought significant changes in monetary policy. However, these by themselves do not imply that the determination of the price level must be different from what was just described. According to the model, monetary policy can still independently choose the path for interest rates {xt}, and this alone still suffices to determine current and future inflation. The changes in policy only have to affect variables in the system other than inflation. First, because it can now pay interest on reserves, the central bank can choose exogenously either it − itm or the quantity of reserves Mt. Unlike before, when the interest rate on reserves was fixed at zero, the central bank may now wish to set a target for the amount of reserves in the market, as long as it adjusts i tm accordingly. Moreover, if it continues the current policy of setting itm = it, the central bank can also target any level of reserves above the satiation level (Mt /Pt)*. This policy has at least two virtues. First, it allows the central bank to inject as much liquidity as necessary to sustain the efficient equilibrium described in the previous section. Second, it eliminates the implicit tax on reserves that existed before 2008 and that Friedman (1960, 1969) and Marvin Goodfriend (2002), among many others, had criticized well before the crisis for being inefficient. 32. See Woodford (2003) and Mishkin (2007). 33. See, for instance, Chairman Bernanke’s speech on “The Great Moderation,” delivered at the Eastern Economic Association, Washington, February 20, 2004.

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Turning to credit policy, the Federal Reserve can gradually sell its holdings of private securities, receiving in return government bonds until these are again approximately equal to reserves. The only substantial change is that now, with the removal of the implicit tax on reserves, transfers to the Treasury become zero. Since they were small to start with, this should have no visible effect on government finances and fiscal policy. The balance sheet of the Federal Reserve can stay larger than before, with reserves beyond the satiation level at whatever amount is supplied. The announced intentions of the Federal Reserve are roughly consistent with the scenario just described. The Federal Reserve has been firm in its commitment to set interest rates so as to control inflation and to maintain its independence.34 Moreover, there is no indication that the decision to pay interest on reserves will be reversed. The Federal Reserve has also indicated that it would like to lower its holdings of private securities to as close to zero as possible as soon as it can.35 One source of uncertainty is what the Federal Reserve will do about quantitative policy in the aftermath of the crisis. The Federal Reserve has indicated that once it becomes possible, it will lower reserves and reduce the size of its balance sheet.36 The theory in this and the previous sections suggests that this is unnecessary, as there is nothing wrong with keeping reserves at high levels. Importantly, this much higher level of reserves is

34. From the joint statement of the Federal Reserve and the Treasury on March 23, 2009: “The Federal Open Market Committee (FOMC) determines monetary conditions in the United States, subject to its congressional mandate to foster maximum sustainable employment and stable prices. The Federal Reserve’s independence with regard to monetary policy is critical for ensuring that monetary policy decisions are made with regard only to the long-term economic welfare of the nation.” From the same statement: “Actions that the Federal Reserve takes, during this period of unusual and exigent circumstances, in the pursuit of financial stability, such as loans or securities purchases that influence the size of its balance sheet, must not constrain the exercise of monetary policy as needed to foster maximum sustainable employment and price stability.” 35. As Vice Chairman Kohn put it in a speech in May 2009, “An important issue with our nontraditional policies is the transition back to a more normal stance and operations of monetary policy as financial conditions improve and economic activity picks up enough to increase resource utilization. These actions will be critical to ensuring price stability as the real economy returns to normal” (“Interactions between Monetary and Fiscal Policy in the Current Situation,” speech delivered at Princeton University, May 23, 2009). 36. As Chairman Bernanke stated in his April 3 speech, cited above, “We have a number of tools we can use to reduce bank reserves or increase short-term interest rates when that becomes necessary. . . . Many of our lending programs extend credit primarily on a short-term basis and thus could be wound down relatively quickly. . . . The Federal Reserve can conduct reverse repurchase agreements against its long-term securities holdings to drain bank reserves or, if necessary, it could choose to sell some of its securities.”

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not inflationary. Once the Federal Reserve started paying interest on reserves, eliminating the implicit tax on reserves, the old money multiplier that linked reserves to the price level broke down.

IV.D. The Capital and Fiscal Risks to the Status Quo The main risk to the scenario just described comes from the Federal Reserve’s flow of funds in equation 13. Now that interest is being paid on m reserves, and now that reserves have more than doubled, the term i t−1 Mt−1 m can become significant as soon as it−1 increases from zero in tandem with the federal funds rate. Moreover, with the Federal Reserve holding a significant amount of private securities, the return on these securities may prove negative, lowering revenue by the amount qt−1Kt−1.37 How can the Federal Reserve make up for this budget shortfall? There are two separate issues, one real and one illusory. To start with the latter, if the Federal Reserve suffers significant losses on its portfolio, its accounting capital may become negative. If the Federal Reserve were an ordinary company, this would mean that it was bankrupt, as its liabilities would exceed its assets. However, the Federal Reserve is not an ordinary company, because its liabilities are special. Negative capital is a problem for an ordinary company because it lacks the assets to pay its creditors if they all demand to be paid at once. But the Federal Reserve’s two main creditors are currency holders and banks holding reserves. Neither can show up at the central bank and demand to be paid with assets. Currency issued by the Federal Reserve is legal tender, and the holding of reserves can be required by law. This means that there cannot be a run of creditors on the Federal Reserve. Thus, the accounting capital of the Federal Reserve is a vacuous concept. If there is a concern, it is because, as Tiago Berriel and Saroj Bhattarai (2009) document, most central banks, including the Federal Reserve, seem to worry about their capital. As those authors show, if the central bank worries about trying to maintain a target level of capital in its balance sheet, this will move the path of interest rates away from what would be desirable. 37. The Federal Reserve has repeatedly stated that it believes the risk of losses is minimal (see, for example, Chairman Bernanke’s Stamp Lecture at the London School of Economics, “The Crisis and the Policy Response,” January 13, 2009), because in most of its programs it is taking triple-A-rated securities as collateral and imposing significant haircuts. There is reason to be a little skeptical, however. First, if the investments were riskless, one would expect that private investors would not be so reluctant to make them. Second, there is a certain irony in appealing to the high ratings of the collateral when the financial crisis has been marked by suspicions about the value of collateral and the reliability of ratings agencies.

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The real issue is whether there is a need for outside funds. The Federal Reserve, like any other agent, has a budget constraint. Rearranging equation 13, (21)

itm−1 M t −1 − qt −1 K t −1 = it −1 BtF−1 − ΔBtF − ΔK t + ΔM M t − Vt .

The issue is that the left-hand side may become large, requiring additional funds on the right-hand side to maintain equality. The five terms on the righthand side give the five possible sources of these funds. The first of these is the interest collected on the government bonds the central bank holds. Because m it−1 ≥ it−1 , any budget shortfall that arises from paying interest on reserves is at most equal to the interest rate times the difference between reserves outstanding and government securities held. The Federal Reserve’s balance sheet on August 19, 2009, reported in table 1, shows that at that date, even if the annual interest rate were as high as 5 percent, this would amount to just over $10 billion a year.38 If the Federal Reserve exchanges a few of its private assets for government securities, as it already plans to do by the end of 2009, it can reach the normal state where BtF > Mt and the interest on reserves is more than covered by the interest received on government securities. The danger therefore comes almost entirely from the possibility of large losses on the central bank’s holdings of private assets. The second and third terms on the right-hand side of equation 21 show that the Federal Reserve can sell its assets—either the government securities or the private assets—to cover these losses. This cannot go on forever, as the Federal Reserve will eventually run out of assets. But considering the over $2 trillion in assets that the Federal Reserve holds, this would require quite catastrophic losses for a sustained period.39 Another option is to print money or raise reserves, raising Mt. If the economy is already satiated with reserves, this extra printing of money will have no effect on the macroeconomy, as banks will be happy to accept these extra reserves as payment. There is no private or social cost to creating excess and possibly idle reserves.40 38. This is calculated by multiplying 0.05 by the sum of bank reserves plus Treasury deposits minus securities held outright. This maps onto the worst-case scenario, where the Treasury closes its deposit account with the Federal Reserve, demanding that its $240.2 billion in bonds be given back. Excluding this possibility, then already BtF > Mt. 39. Stella (2009, table 9) tries to quantify this risk and arrives at a worst-case scenario of losses of $78 billion on the existing assets. 40. Note that this option relies on the existence of a finite satiation level in the demand for reserves, beyond which people are indifferent about the real money balances they hold. Otherwise, printing money would compromise the Federal Reserve’s target for inflation.

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Only the final option is more troublesome. To pay for its budget shortfall, the Federal Reserve might choose to rely on a steady stream of financing from the Treasury (Vt < 0). The financial independence of the Federal Reserve from Congress has been a guarantee of its overall independence.41 Should transfers from the taxpayer to the Federal Reserve, requiring the approval of Congress, become a regular occurrence, political pressures on the setting of interest rates would become inevitable. There is a real danger that this would lead to permanent increases in inflation in exchange for only short-lived boosts to output, as the U.S. economy falls into the time-inconsistency trap described by Finn Kydland and Edward Prescott (1977). In the extreme, this loss of independence could even trigger a change in the policy regime. In particular, consider the scenario where Congress limits the fiscal plans of the executive branch by imposing a target for government debt as a ratio to GDP (or consumption): Bt /Ct. The Treasury could accommodate this target by cutting deficits. But it might instead choose a value for nominal deficits exogenously, consistent with an equilibrium.42 The equilibrium price level would be (22)

Pt =

Bt Ct

∑

∞ j =1

β ( Tt + j − Gt + j ) Ct + j j

and inflation would be determined solely by the government’s fiscal choices. The Federal Reserve would then be forced to accommodate these fiscal policies by effectively handing over control of nominal interest rates, with xt determined endogenously to satisfy (23)

x t = Δ ln ( Pt +1 ) − χΔ ln ( Pt ) + Δ ln (Ct +1 ) − ln β.

This fiscalist determination of inflation requires the Treasury to be dominant over the Federal Reserve in setting policy—a situation that the literature has described as the fiscal authorities being active and the central bank passive.43 41. Indeed, conventional measures of central bank independence typically consider budgetary independence from the legislative bodies a prerequisite (see the recent survey in Cukierman 2008). 42. This mechanism is described in Sims (1994) and Woodford (1995) and is discussed and criticized in Canzoneri, Cumby, and Diba (2001) and Bassetto (2008). 43. For further exploration of the implications of this fiscal theory of the price level within the context of the current crisis, see Sims (2009) and Cochrane (2009).

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V. Interpreting the Federal Reserve’s Actions: Interest Rate Policy A key feature of the crisis of 2007–09 is that short-term interest rates have been almost zero. This is only the second time that this has happened in the last century in the United States, the other being the period of the Great Depression in the 1930s. Many economists refer to this situation as a “liquidity trap,” since zero is the lowest possible target for the federal funds rate, and transitory increases in the money supply lead investors, now indifferent between money and bonds, to simply substitute one for the other. Conventional monetary policy appears powerless. An extensive literature argues that this appearance is incorrect. Motivated by the experience of Japan in the 1990s, researchers over the past decade have characterized the challenges in a liquidity trap and offered some policy advice to confront them.44 They argue that in a liquidity trap, not only is interest rate policy not ineffective, but indeed choosing the right path for interest rates becomes particularly important. To understand this point, recall the Fisher equation equating the real interest rate, rt , to the nominal interest rate, it , minus expected inflation, Et(Δ Pt+1): (24)

rt = it − E t [ Δ ln ( Pt +1 )].

Recall further that the (linearized) Euler equation with log utility for optimal consumption states that expected consumption growth between date t and date t + s is equal to the sum of short-term real interest rates across the two periods: E t [ ln (Ct + s ) − ln (Ct )] = E t ⎛ ∑ rt + j ⎞ . ⎝ j=0 ⎠ s −1

(25)

Intuitively, the higher the long-term real interest rate, which is equal to the expected path of short-term real interest rates, the greater the incentive to save, postponing consumption today for consumption in the future. 44. This work, in turn, builds on earlier analyses of monetary policy during the Great Depression. Romer (1992), in particular, makes a compelling case for the powerful role of monetary policy in ending the Depression.

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The challenge for interest rate policy is that the financial crisis and its spillover to the real economy have led to a fall in the real interest rate needed for the economy to respond efficiently. If inflation expectations remain stable and low, equation 24 may imply that the nominal interest rate would have to become negative to generate the needed real interest rate. But because the nominal interest rate has a zero lower bound, this cannot happen, and consequently real interest rates remain too high.45 Equation 25 then implies that these excessively high real interest rates drive down current consumption, worsening the recession. The “Brookings answer” to this problem was given in two papers published in this journal. First, Paul Krugman (1998) emphasized that monetary policy is particularly potent in this situation if it can steer inflation expectations. The way out of the trap is to raise inflation expectations by whatever means possible, so that the short-term real interest rate can fall, encouraging consumption. Then, Gauti Eggertsson and Michael Woodford (2003) identified a practical way for the central bank to affect inflation expectations, by committing to keep nominal interest rates low into the future, even after the shocks leading to the crisis have subsided. This would lower expected future short-term real interest rates, producing the fall in long-term real interest rates needed to drive real activity up. There are several other ways to raise inflation expectations, bring down real interest rates, and stimulate the economy. Devaluing the currency is one, and another is to purchase government debt with a permanent increase in the money supply that is allowed to persist after the crisis has passed. A more institutional approach that would prevent the problem from appearing in the first place would be for the central bank to announce a price-level target, since this would require that current deflation be offset by higher future inflation to get back on target. A final alternative would be for the central bank to commit to lower long-term nominal interest rates, as this would be equivalent to committing to a lower path of short-term rates.46 It 45. The nominal interest rate on any safe security cannot be negative, because selling this security short and keeping the proceeds as cash until the security matures would result in positive profits and create an arbitrage opportunity. This is only approximately correct since the expected return on money is not exactly zero but slightly negative, as deposit accounts pay fees, and cash held in one’s pocket may be stolen. Nevertheless, it is likely very close to zero. Goodfriend (2000) and Buiter and Panigirtzoglou (2003) have revived an old proposal by Silvio Gesell for the government to tax money, effectively removing the lower bound on interest rates and therefore eliminating the possibility of liquidity traps. 46. On exchange rate policy see Svensson (2003), on debt purchases see Auerbach and Obstfeld (2005), on price-level targeting see Eggertsson and Woodford (2003), and on lowering long-term interest rates see Bernanke (2002).

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is important to note that these are not alternatives to increasing inflation expectations by committing to low nominal interest rates into the future. Rather, they are different ways to implement the same policy, namely, a decrease in the real interest rate, through its relationship with other macroeconomic variables. How do the Federal Reserve’s actions compare with these theoretical suggestions? Although the Federal Reserve has not announced a commitment to allow higher inflation than average in the near future, in the way that a price-level target would suggest, it has announced its commitment to do what it can to prevent deflation. The FOMC announcements following every meeting so far in 2009 have stated the intention to keep the target for the federal funds rate at zero for an extended period. These are signs that the advice of Krugman, Eggertsson, and Woodford is being followed, but only halfway, as the Federal Reserve has also signaled that it will not tolerate either temporary or permanent abovenormal inflation.47 Meanwhile the Federal Reserve has made no commitment to any of the other alternatives. First, announcing a devaluation of the dollar is not an option, since this is the domain of the Treasury, not the Federal Reserve. Second, there has been little purchasing of government debt: the dollar value of Treasury-issued securities plus agency debt held by the Federal Reserve in August 2009, at $847.9 billion, was not dramatically greater than the $778.9 billion it held in January 2007. Although the Federal Reserve has announced that it will expand its purchases of government bonds substantially in the coming months, it has also indicated that this might be temporary, as it returns to a balance sheet similar in size to that in the past once the crisis subsides. Third, the change in the maturity composition of these securities toward longer-term bonds is consistent with an effort to lower long-term interest rates, but there is little evidence that this portfolio shift can have any effect beyond what the announcement of lower future short-term interest rates will achieve. A crucial part of the Federal Reserve’s policy is its future actions, after the crisis subsides, and these remain to be seen. In particular, the FOMC has not clearly stated that it will keep interest rates at zero even after the financial shock disappears, an important component of optimal policy according to the theory just discussed.

47. This was clearly stated by Vice Chairman Kohn on October 9, 2009.

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VI. Conclusion This paper has provided a critical analysis of the Federal Reserve’s policy actions of the past two years. It has catalogued monetary policy into three types according to whether it affects interest rates, the size of the Federal Reserve’s balance sheet, or the allocation of its credit across different assets. With regard to interest rate policy, the Federal Reserve has followed the advice derived from theory by committing to fight deflation and to keep nominal interest rates at zero for the foreseeable future. It has deviated from the theoretical recommendations by not committing to higher-thanaverage inflation in the future, and especially by not providing a clear signal that it will keep nominal interest rates low for some time even after the crisis is over. With regard to quantitative policy, at least theoretically there is no reason why the path of short-term nominal interest rates should cease to determine inflation, or why the conventional separation between monetary and fiscal policy should have to be revisited. Both of these features have been lauded as hallmarks of the success of monetary policy in the past two decades. However, the combination of an expansion in the Federal Reserve’s balance sheet, the introduction of interest payments on reserves, and the holding by the Federal Reserve of assets with risky returns does pose a danger. The Federal Reserve might face significant budget shortfalls, and overreacting to these may lead to the central bank surrendering its independence from fiscal policy, potentially compromising both of the hallmarks above. Finally, regarding credit policy, the paper has introduced a new model of how the financial market allocates funds to investment and of the credit frictions in that process. I have considered the merits of different interventions as indicated by the model, conditioned on alternative beliefs about the knowledge and power of the Federal Reserve. The model suggests that using senior loans to inject funds into firms that trade asset-backed securities is an effective way to intervene in the financial markets. Theoretically, this seems superior to lending funds to the originators of loans, and superior to buying securities directly, and perhaps superior even to taking equity stakes in financial firms. The Federal Reserve’s actions over the past two years have included almost all of these alternatives. Perhaps this was wise, since so little is known in this area. What is more likely is that looking back in a few years and using either the model in this paper or others that will follow, some of the Federal Reserve’s credit policies will be seen as ineffective or even harmful.

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Although the paper has touched on many different topics, models, and policies, I have not addressed every facet of the crisis or of the role of monetary policy during a crisis. For example, I have considered neither aggregate risk and changes in risk spreads nor the potential for bank runs.48 Nor have I discussed the role of foreign investors and the external deficit, or compared the Federal Reserve’s actions with those of other central banks around the world. Finally, I have not emphasized the political economy trade-offs that the different policies involve, which may become important in the near future. This interpretation of the Federal Reserve’s actions has thus enjoyed the privileges of being selective in the choice of topics and of having some hindsight in addressing them. Neither was available to the Federal Reserve and other central banks in the past two years. Moreover, as is almost always the case when an academic writes about policy, the tone and spirit of this interpretation are based implicitly on the premise that theory runs ahead of practice. The events of the past two years have been humbling on that score, providing a lesson to academics like me that we must be less confident about this premise than usual.

APPENDIX

Details of the Model This appendix complements the setup and solution of the model described in the text.

The Problem of the Representative Consumer-Worker The consumer-worker in the model faces the following optimization problem: max ln (C ′′ ) − ( LS + LS ′ + LS ′′ )

( A.1) ( A.2) ( A.3)

C ′′ , LS , LS ′ , LS ′′

s.t. :

∫

N 0

Pi ′′ Ci′′di + H ′′ = W ( LS + LS ′ + LS ′′ ) + Payoff C ′′ =

( ∫ C ′′ di ) . m

N

0

1m

i

48. On risk spreads, see Cúrdia and Woodford (2009), and on bank runs, see Allen, Babus, and Carletti (2009).

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Expression A.1 gives the consumer-worker’s preferences. Utility is logarithmic in total consumption and linear in labor supplied; these functional forms make the algebra easier. Equation A.2 is the budget constraint. On the left-hand side are the uses of funds in the third period, namely, to purchase the consumption good from the firms and to pay taxes H″. On the right-hand side are the sources of the funds: wages received from labor and income received (“payoff”) from the four financial participants in the last period. Because utility is linear in labor supply in all three periods, there is a single wage. Since capital is transferred across periods at zero net return, this is the single intertemporal budget constraint. Finally, equation A.3 is the Dixit-Stiglitz aggregator mapping the consumption of different varieties onto the final composite goods, with elasticity of substitution m/(m − 1). The optimality conditions are ( A.4)

1=

(∫

N 0

P ′′1 (1− m) di i

)

1− m

( A.5)

Ci′′= C ′′Pi ′′ m (1− m)

( A.6)

C ′′ = W .

The Problem of Agents in the Financial Market Investors start in period 1 with capital M. Their budget constraints for each period are ( A.7)

Inv + H I = M

( A.8)

Inv ′ + H I′ + Sec ′ = H I

( A.9)

H I′′ = H I′ + Inv + Inv ′ + (1 − ϕ ) S ec ′ Q ′ .

In words, in the first period they invest Inv in traders and keep Ht in capital. In the second period they invest an additional Inv′, buy securities in amount Sec′, and keep the remainder H 1′ in capital. In the third period they receive back their previous investments from traders at zero net return and receive the payoff of the 1 − ϕ securities they bought in the previous period that were productive, ending with total capital H"1.

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Entrepreneurs start with capital K. The aggregate budget constraint (summed over all entrepreneurs) in each period is ( A.10)

WFI + H E = K + Loan

( A.11)

WL ′N + H E′ = Loan ′ + H E

( A.12)

WL ′′N + H E′′ = δP ′′Y ′′N + H E′ .

In the first period entrepreneurs use their own capital and the loans from lenders to pay their fixed costs, with HE left over. In the second period they have this capital plus new loans, which they spend on the operating costs of their firms, leaving H E′ for the next period. Finally, in the last period, they receive the share δ of revenue and end with total capital H"E. The budget constraints of the lending sector in the aggregate in each period are ( A.13)

Loan + H L = D + S

( A.14)

Loan ′ + H L′ = D ′ + S ′ + Sec ′ + H L

( A.15)

(1 − ϕ ) S

Q + S ′ Q ′ + (1 − ϕ ) Sec ′ Q ′ + H L′′ =

(1 − δ ) NP ′′Y ′′ + H ′ . L

In the first period lenders start with capital D and obtain extra capital S by selling securities. They use this to make loans and retain a nonnegative amount of capital HL. In the next period they receive new capital, sell new securities to traders and investors, and can use this and the capital saved from the last period to increase their lending, through the credit lines extended to the entrepreneurs, while potentially holding some capital for the following period. Finally, in the last period, they receive a share 1 − δ of the firms’ revenue and must pay back the holders of securities backed by the loans to the surviving firms. Finally, the aggregate budget constraints of the traders in each period are ( A.16)

S + H T = E + Inv

( A.17)

S ′ + H T′ = E ′ + Inv ′

( A.18)

Inv + Inv ′ + H T′′ = (1 − ϕ ) S Q + S ′ Q ′ .

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In the first period traders buy securities S and hold capital HT, using their starting funds E plus Inv received from investors. The same applies in the second period. In the third period the investments are repaid at zero net cost, and the securities earn a nonzero return. The pledgeability constraints on investment are ( A.19)

Inv ≤ (1 − μ ) S

( A.20)

Inv ′ ≤ (1 − μ ) {S ′ + [(1 − ϕ ) Q ′ − Q ] S Q} .

The second term in the pledgeability constraint in the second period is the capital gain on the securities bought in the previous period. The possible absconding of traders with the assets is not included in these constraints, because this never happens in equilibrium. The capital holdings for all agents are nonnegative: HI, HI′, H″I , HE, HE′, HE″, HL, HL′, HL″, HT, HT′, HT″ are all greater than or equal to zero.

Optimality Conditions for Financial Agents Each of the risk-neutral financial agents wants to maximize its final capital. I focus here on the case where, in equilibrium, there is some inefficiency, so Q < 1 − ϕ, and Q′ < 1. The other cases are similar. Investors want to maximize HI″. As long as Q′ < 1 − ϕ, they will buy no securities, Sec′ = 0, since doing so leads to a negative return. Moreover, they are indifferent between holding capital and placing it with traders, and I assume that they invest as much as they can, subject to the pledgeability constraint. Entrepreneurs earn strictly positive profits. Therefore, the return from applying their capital in the firm exceeds that from keeping it idle, and HE = H′E = 0. The optimal number of projects started and the optimal amount of labor hired are determined in section II.C. Lenders are willing to sell securities at a positive return to traders, and therefore they must not be holding capital at zero return, so HL = H′L = 0. The optimal choice of Loan and Loan′ was determined in section II.C, and the optimal issues of S and S′ were stated in section II.D and derive from the budget constraints. Traders earn a positive net return on the securities. Since capital earns a zero return, they choose HT = H ′T = 0. Since they pay zero return to investors, they will want to draw funds from them to the extent possible. The pledgeability constraints therefore hold with equality. Combining the

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RICARDO REIS

pledgeability and budget constraints gives the demand for securities in the text, S = E/µ and S′ = E′/µ + [(1 − µ)/µ][(1 − ϕ)Q′/Q − 1]E/µ.

Market Clearing Conditions and Walras’s Law I start by summing the budget constraints for the four financial agents, to obtain the market clearing conditions for capital within the financial market. This gives ( A.21)

H I = M + K + D + E − WFI

( A.22)

H I′ = H I + D ′ + E ′ − WL ′N

( A.23)

H I′′+ H E′′ + H L′′ + H T′′ = NP ′′Y ′′ − NWL ′′ + H I′.

The first two conditions determine the capital left over with investors at the end of the first two periods. They show that as long as M is large enough, HI > 0 and H′I > 0, an assumption that I maintain throughout the analysis. This in turn translates into an assumption for total initial capital, since the market clearing condition for capital between the representative household and financial institutions in the first period is H = M + K + D + D ′ + E + E ′.

( A.24)

The payoff from financial firms to households in the last period is ( A.25)

Payoff = H I′′ + H E′′ + H B′′ + H L′′

( A.26)

= P ′′Y ′′N − WL ′′N + H I′

( A.27)

= P ′′Y ′′N − WL ′′N − WL ′N − WFI + H ,

where the second equation comes from the market clearing condition for capital in the third period, and the third from using the market clearing conditions in the other periods. Noting that market clearing in the goods market implies that ∫ N0 Pi″ Ci″ di = P″Y″N, and therefore this last expression can be rewritten as ( A.28)

∫

N 0

Pi ′′ Ci′′di + H = WFI + WL ′N + WL ′′N + Payoff .

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Finally, since the labor market clearing conditions are FI = LS, L′N = LS′, and L"N = LS", this expression becomes the budget constraint of the representative consumer. This verifies Walras’s law and confirms that all funds have been accounted for.

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References Allen, Franklin, Ana Babus, and Elena Carletti. 2009. “Financial Crises: Theory and Evidence.” Annual Review of Financial Economics 1 (forthcoming). Auerbach, Alan J., and Maurice Obstfeld. 2005. “The Case for Open-Market Purchases in a Liquidity Trap.” American Economic Review 95, no. 1: 110–37. Bassetto, Marco. 2008. “Fiscal Theory of the Price Level.” In The New Palgrave Dictionary of Economics, edited by Lawrence E. Blume and Steven N. Durlauf. London: MacMillan. Bernanke, Ben S. 2002. “Deflation: Making Sure ‘It’ Doesn’t Happen Here.” Remarks at a meeting of the National Economists Club, Washington, November 21. Berriel, Tiago C., and Saroj Bhattarai. 2009. “Monetary Policy and Central Bank Balance Sheet Concerns.” B.E. Journal of Macroeconomics (Contributions) 9, no. 1. Blanchard, Olivier. 2009. “The Crisis: Basic Mechanisms, and Appropriate Policies.” CESifo Forum 10, no. 1. Brunnermeier, Markus K. 2009. “Deciphering the Liquidity and Credit Crunch 2007–2008.” Journal of Economic Perspectives 23, no. 1: 77–100. Buiter, Willem H., and Nikolaos Panigirtzoglou. 2003. “Overcoming the Zero Bound on Nominal Interest Rates with Negative Interest on Currency: Gesell’s Solution.” Economic Journal 113, no. 490: 723–46. Canzoneri, Matthew B., Robert E. Cumby, and Behzad T. Diba. 2001. “Is the Price Level Determined by the Needs of Fiscal Solvency?” American Economic Review 91, no. 5: 1221–38. Cecchetti, Stephen G. 2009. “Crisis and Responses: The Federal Reserve in the Early Stages of the Financial Crisis.” Journal of Economic Perspectives 23, no. 1: 51–75. Cochrane, John H. 2009. “Understanding Fiscal and Monetary Policy in 2008–2009.” University of Chicago. Cukierman, Alex. 2008. “Central Bank Independence and Monetary Policymaking Institutions: Past, Present and Future.” European Journal of Political Economy 24, no. 4: 722–36. Cúrdia, Vasco, and Michael Woodford. 2009. “Conventional and Unconventional Monetary Policy.” CEPR Discussion Paper 7514. London: Centre for Economic Policy Research. Eggertsson, Gauti B., and Michael Woodford. 2003. “The Zero Bound on Interest Rates and Optimal Monetary Policy.” BPEA, no. 1: 139–211. Friedman, Milton. 1960. A Program for Monetary Stability. Fordham University Press. ———. 1969. The Optimum Quantity of Money: and Other Essays. Chicago: Aldine. Goodfriend, Marvin. 2000. “Overcoming the Zero Bound on Interest Rate Policy.” Journal of Money, Credit, and Banking 32, no. 4: 1007–35.

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———. 2002. “Interest on Reserves and Monetary Policy.” Economic Policy Review 8, no. 1: 77–84. Gorton, Gary. 2009. “Information, Liquidity, and the (Ongoing) Panic of 2007.” American Economic Review 99, no. 2: 567–72. 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. Holmström, Bengt, and Jean Tirole. 2009. “Wicksell Lectures: Inside and Outside Liquidity.” idei.fr/doc/by/tirole/wicksell_lectures.pdf. Kiyotaki, Nobuhiro, and John Moore. 1997. “Credit Cycles.” Journal of Political Economy 105, no. 2: 211–48. Krishnamurthy, Arvind. Forthcoming. “Amplification Mechanisms in Liquidity Crises.” American Economic Journal: Macroeconomics. Krugman, Paul R. 1998. “It’s Baaack: Japan’s Slump and the Return of the Liquidity Trap.” BPEA, no. 2: 137–187. Kydland, Finn E., and Edward C. Prescott. 1977. “Rules Rather Than Discretion: The Inconsistency of Optimal Plans.” Journal of Political Economy 85, no. 3: 473–91. Matsuyama, Kiminori. 2007. “Aggregate Implications of Credit Market Imperfections.” Working Paper no. 13209. Cambridge, Mass.: National Bureau of Economic Research. Mishkin, Frederic S. 2007. Monetary Policy Strategy. MIT Press. Reis, Ricardo. 2007. “The Analytics of Monetary Non-Neutrality in the Sidrauski Model.” Economics Letters 94, no. 1: 129–35. ———. 2009. “Where Should Liquidity Be Injected during a Financial Crisis?” Columbia University. Romer, Christina D. 1992. “What Ended the Great Depression?” Journal of Economic History 52, no. 4: 757–84. Shleifer, Andrei, and Robert W. Vishny. 2009. “Unstable Banking.” Working Paper no. 14943. Cambridge, Mass.: National Bureau of Economic Research. Sims, Christopher A. 1994. “A Simple Model for Study of the Determination of the Price Level and the Interaction of Monetary and Fiscal Policy.” Economic Theory 4, no. 3: 381–99. ———. 2009. “Price Level Determination in General Equilibrium.” Presented at the annual meeting of the Society for Economic Dynamics, Istanbul, July 2–4. Stella, Peter. 2009. “The Federal Reserve System Balance Sheet: What Happened and Why It Matters.” IMF Working Papers WP/09/120. Washington: International Monetary Fund.

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Svensson, Lars E. O. 2003. “Escaping from a Liquidity Trap and Deflation: The Foolproof Way and Others.” Journal of Economic Perspectives 17, no. 4: 145–66. Woodford, Michael. 1995. “Price-Level Determinacy without Control of a Monetary Aggregate.” Carnegie-Rochester Conference Series on Public Policy 43: 1–46. ———. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press. ———. 2008. “How Important Is Money in the Conduct of Monetary Policy?” Journal of Money, Credit and Banking 40, no. 8: 1561–98.


TIMOTHY BESLEY The past two years have seen central banks respond with unprecedented aggressiveness to the consequences of the global downturn. Following the collapse of Lehman Brothers in September 2008, interest rates were eventually cut to their effective lower bound by a number of central banks, including the Federal Reserve. Many central banks have now engaged in a variety of unconventional policy measures to increase the degree of monetary stimulus and to improve the operation of financial markets, particularly credit markets. These have involved a variety of actions that often fall collectively under the heading of “quantitative easing,” although as Ricardo Reis notes in his paper, there are conceptually distinct components to the Federal Reserve’s strategy. There has been much in this episode to challenge practitioners and students of monetary policy. In his paper Reis sets out to provide a framework for thinking through the theoretical motivation for the Federal Reserve’s actions. While focusing on the U.S. experience, he touches on issues of more general importance for academics and policymakers. And to some degree the issues that he discusses have been on the mind of many central bankers the world over. I come at this through the lens of my own experience as a member of the Bank of England’s Monetary Policy Committee between 2006 and 2009. The context of recent events is important for understanding the bigger picture. The period known as the Great Moderation had encouraged a view that the science of monetary policy and its practical implementation had reached maturity. Reflecting this, central banks were more or less universally charged with ensuring price stability and had enjoyed a good measure of success over the recent past. Whether as cause or as effect, most major central banks had been granted some kind of operational independence to 166


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achieve this mandate. This had been accompanied by a remarkably stable real economy, although the direction of causation between this stability and good policy was always subject to debate. This period of stability was important since it fueled a belief that episodes of macroeconomic instability like those experienced in the 1970s, and a fortiori the 1930s, had been largely consigned to the history books. A standard macroeconomic tool kit supported the inflation control and stabilization strategies of central banks, with a central role for dynamic stochastic general-equilibrium (DSGE) models. Unlike a previous generation of practical macroeconomic models, these incorporated microeconomic foundations grounded in stylized depictions of optimizing behavior and forward-looking behavior derived from policymakers’ likely responses to economic shocks. They therefore offered the promise of harmony between economic theory and policy practice. The chief problems underpinning the current crisis were, of course, the build-up of leverage and the inflation of asset prices. This is the classic historical pattern observed before the onset of most financial crises in the past (see Reinhart and Rogoff 2009). The available macroeconomic models, particularly those developed by central banks, were ill equipped to spot the dangers associated with this leverage and to understand the implications for economic instability. Central banks have also traditionally been given a central role in guaranteeing financial stability. But just what this meant in practice was less clear than their responsibility for controlling inflation and for economic stabilization more generally. And there was nothing of comparable sophistication to the DSGE models to help them fulfill this role. Most of the debate about redesigning policy has therefore rightly focused on how to refashion the policy architecture to prevent a repeat of the severe difficulties in financial markets that precipitated the current crisis. Although there have been plenty of useful suggestions, we are still some way from fully understanding what types of policies are needed and what kind of institutional framework will be necessary to support them. It seems unlikely, however, that this wider macroprudential role can be achieved solely through monetary policy, narrowly defined. It is now commonplace to observe that the standard model used to study the conduct of monetary policy had scant role for financial frictions. It has been well understood for a while that the operation of the financial system should affect how the principal tool of monetary policy, the overnight interest rate, affects real activity. The workhorse DSGE models, however, largely marginalized these frictions. Its microfoundations basically had interest rate

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policy acting on the economy through two Euler equations: one for firms’ investment decisions, and the other to represent households’ intertemporal optimization. The first hints that something was amiss in the global economy were firmly apparent by 2006. Sitting as I did on the Monetary Policy Committee, I distinctly recall how, in 2006–07, our steering of overnight rates was having relatively little impact on the economy or in reining in credit creation. Little concrete guidance for policy was coming from either the output gap (however measured) or inflation. The economic expansion was not remarkably rapid, and neither was inflation signaling an overheating economy. Instead, most of the danger signs were in asset prices and leverage growth. In the end, interest rates were probably kept too low for too long. But manipulation of overnight rates was never going to be an adequate tool for combating the problems of excess credit creation during an expansion such as this one. Of course, there were many (mainly theoretical) models linking financial systems and real activity. Particularly important were those that described the propagation, even the amplification, of shocks via the collateral channel. But these were mainly models where the behavior of financial intermediaries played little role. The key feature of the current crisis, and of many crises before it, is that the shock was rooted inside the financial sector itself. To anyone familiar with financial history (see, for example, Kindleberger 1989), there is perhaps nothing surprising about this. Recent events thus provide a big challenge to economic modeling and policy analysis. Similar-looking events have occurred in the past, but they were rare; hence, deciding what is specific to the present case and what emerges as a general phenomenon requires careful judgment. It is a pipe dream to think that one can build a coherent macroeconomic model that does justice to the richness of modern financial sectors. Models are generally useful in economics because they are intelligible—they are judicious simplifications that can describe general effects. But the complexity of the events leading up to a major global crisis like this one can almost certainly not be captured in a stylized model. In this innovative and interesting paper, Ricardo Reis does three main useful things. First, he offers a bird’s-eye view of the Federal Reserve’s reaction in the face of a liquidity trap. Second, by dissecting the Federal Reserve’s actions into three components—credit policy, quantitative policy, and interest rate policy—he provides a useful analytical framework for thinking these actions through. Finally, as part of this exercise, Reis sketches a novel model of financial frictions and develops some aggregate


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implications. His paper is a nice mixture of restatement of aspects of the conventional wisdom and new insights. It is a useful contribution to the debate in this important area. In their response to the current crisis, many central banks have engaged in some kind of unconventional monetary policy in one or more of Reis’s three categories. Table 1 below gives a sense of the kinds of measures adopted before August 2009 by a number of leading central banks. A common feature of many of the unconventional policies that Reis discusses is a blurring of the distinction between monetary and fiscal policy. This can be explicit, as in the case of monetary finance of a fiscal transfer, or more implicit, as when a central bank increases the risk on its balance sheet. In time, therefore, the lessons from these unconventional policies may lead us to modify our understanding of what it means for a central bank to be independent. Interest rate policy can be motivated in more or less conventional terms, but when the overnight rate reaches its zero lower bound, unconventional policy can be thought of as a second-best way of implementing a negative shadow interest rate, in part by influencing inflation expectations. This has been the approach taken, for example, by the Bank of England, which has mainly been purchasing government securities at medium maturities and boosting the supply of narrow money. Other central banks, such as Sweden’s Riksbank and the Bank of Canada, have accompanied such actions with communications about the likely time path of interest rates, emphasizing a desire to keep the interest rate low. These strategies are not linked to the fallout of the financial crisis per se, but rather to the negative demand shock that accompanied it. Increasing the money supply through purchasing government securities is what Reis calls quantitative policy. Here he restates the new conventional wisdom that “reserves are irrelevant for the setting of interest rates or the control of inflation.” And he restates the logic of the model and points out that there is no reason why the expansion of reserves by the Federal Reserve (or by other central banks) need be inflationary. I cannot help but feel, however, that the success of this view was partly responsible for policymakers’ lack of attention to the consequences of the growth of money and credit in the run-up to the financial crisis. The subsequent collapse of money and credit growth is a useful diagnostic of the true underlying stance of monetary policy in the downturn. In his study of credit policy, in particular, Reis focuses on an immediate problem in the aftermath of the crisis: the fact that the supply of credit to the real economy is now impaired. And I agree with him that, in the near

Sources: Central bank websites.

Purchases of public sector securities Commitment to low interest rates Exceptional long-term fixed-rate operations Purchases of corporate bonds Purchases of equities Purchases of commercial paper Purchases or funding of asset-backed securities

Measure

x x

x x

Federal Reserve

x

x

European Central Bank

x x x

x

Bank of Japan

Table 1. Unconventional Measures Adopted by Major Central Banks Since Mid-2008

x

x

x

Bank of England

x

x

Bank of Canada

x

x

Swiss National Bank

x

x x

Swedish Riksbank


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term, economies that have experienced this financial shock will face the problems that motivate his model, resulting in an inefficiently low level of financial intermediation. In undertaking unconventional measures, the Federal Reserve and other central banks have certainly been mindful of their potential to mitigate this outcome. Indeed, this has been the cornerstone of the Federal Reserve’s policy response. Such a credit policy creates a stimulus through a direct effect on asset prices and by influencing the equilibrium interest rates and quantities of credit available to businesses and households. But there are important judgments to be made about where the greatest marginal effect can be found. Also, public purchases of private sector assets create nontrivial governance issues and public risk management concerns, making it almost certain that central banks will wish to exit from this regime at the earliest opportunity. Although I admire what Reis is doing in this paper, at least one important feature is missing from the core model of financial markets that he uses to study credit policy in the current context. This is the impossibility of the model to generate excess credit creation. Although this omission is probably not relevant in the aggregate at the moment, it is possible for central banks to distort the allocation of credit, causing excess credit creation in some areas. Thus, it is important to consider the sectoral credit impact as well as the aggregate effects. Better understanding of excess credit creation is important, since, as I have already mentioned, it is a feature of almost all significant financial crises and certainly of the current one. To fulfill their financial stability mandate, central banks will need to think more carefully about how to design credible structures with appropriate policy instruments to prevent such excess credit growth from recurring. Asking monetary policymakers to “lean against the wind” in such situations is almost certainly inadequate. Although Reis’s model is useful, it does not offer progress on this issue, by design. This remains an open challenge for modeling. It is probably too early to discern the pros and cons of different unconventional policies. Moreover, in the end their effects will be overdetermined, making it unlikely that any amount of evidence will ever show their impact precisely. The Federal Reserve and other central banks have acted decisively to combat the downturn. A good deal of policy experimentation is already in train, which will doubtless inform the next generation of policy models. Reis’s paper is a fine example of how quickly leading monetary economists have also been responding to the challenges thrown up by this astonishing episode.

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REFERENCES FOR THE BESLEY COMMENT

Kindleberger, Charles P. 1989. Manias, Panics, and Crashes: A History of Financial Crises. New York: Basic Books. Reinhart, Carmen M., and Kenneth S. Rogoff. 2009. This Time Is Different: Eight Centuries of Financial Folly. Princeton University Press.

COMMENT BY

DONALD L. KOHN I appreciate the opportunity to comment on Ricardo Reis’s paper. In this paper, Reis classifies critical aspects of monetary policy over the past two years and uses models and his own analysis to interpret and evaluate these policies. I very much enjoyed reading the paper and thinking through the issues he raises and will discuss a few of them in my remarks.1 An important contribution of the paper is a new, stylized model of capital market frictions, which is used to study how credit policies affect capital allocation by providing funds to different kinds of financial intermediaries, including nonbanks and institutional investors. I am not going to comment much on the details of the model, but I do want to draw attention to the conclusion from the model that favors the provision of central bank credit to socalled traders, which are characterized as financial intermediaries that leverage their own capital as well as client funds to invest in securitized loans. The Federal Reserve has indeed recognized the importance of securitization, and working together with the Treasury, it created the Term AssetBacked Securities Loan Facility, or TALF, precisely to support the market for securitized assets. In addition, by making credit available to primary dealers, it supported trading and liquidity in a variety of securities markets. However, this stylized model does not capture the heterogeneity in lending activity that we see in the economy. In the model, all loans are equally eligible for securitization once they have been originated by the lenders. Providing funds to traders thus benefits all lenders and entrepreneurs similarly. In reality, not every borrower would benefit equally if the Federal Reserve were to backstop only the securitized loan market without providing liquidity to commercial banks and other institutions. Banks and other intermediaries are at least as important in ensuring a healthy flow of credit

1. The views presented here are my own and not necessarily those of other members of the Board of Governors of the Federal Reserve System or the Federal Open Market Committee. Elmar Mertens and Roberto Perli of the Board’s staff contributed to these remarks.


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to creditworthy borrowers, and it would be very disruptive if a scramble by such intermediaries to meet funding shortfalls in a panic led to fire sales of assets or a freeze in lending. Thus, lending to banks should remain a central part of the Federal Reserve’s toolbox. I agree with Reis that, at least prior to his effort, no off-the-shelf model was available for analyzing much of what has happened over the past two years, and further research in this direction is essential. Still, we at the Federal Reserve were certainly not without guidance from well-established principles when we formulated policies to address the financial crisis. In designing our liquidity facilities we were guided by the time-tested precepts derived from the work of Walter Bagehot.2 Those precepts hold that central banks can and should ameliorate financial crises by providing ample credit to a wide set of borrowers, as long as the borrowers are solvent, the loans are provided against good collateral, and a penalty rate is charged. Such lending addresses discontinuities in investor behavior in a crisis in which uncertainty sets off flights to liquidity and safety that feed on themselves and then circle back on the economy in adverse feedback loops—a dynamic not fully captured by Reis’s model. The liquidity measures we took during the financial crisis, although unprecedented in their details, were generally consistent with Bagehot’s principles and aimed at short-circuiting these feedback loops. The Federal Reserve lends only against collateral that meets specific quality requirements, and it applies haircuts where appropriate. Beyond the collateral, in many cases we also have recourse to the borrowing institution for repayment. In the case of the TALF, we are backstopped by the Treasury. In addition, the terms and conditions of most of our facilities are designed to be unattractive under normal market conditions, thus preserving borrowers’ incentives to obtain funds in the market when markets are operating normally. Apart from a very small number of exceptions involving systemically important institutions, such features have limited the extent to which the Federal Reserve has taken on credit risk, and the overall credit risk involved in our lending during the crisis has been small. In Reis’s view, if the collateral had really been good, private institutions would have lent against it. However, as has been recognized since Bagehot, private lenders, acting to protect themselves, typically severely curtail lending during a financial crisis, irrespective of the quality of the

2. My colleague Brian Madigan (2009) evaluated the Federal Reserve’s recent policies from this perspective.

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available collateral.3 The central bank—because it is not liquidity constrained and has the infrastructure in place to make loans against a variety of collateral—is well positioned to make those loans in the interest of financial stability, and can make them without taking on significant credit risk, as long as its lending is secured by sound collateral. A key function of the central bank is to lend in such circumstances to contain the crisis and mitigate its effects on the economy. Reis’s model does not directly address central banks’ long-term asset purchases, but in one place the paper seems to question their effectiveness. Our framework for this aspect of our credit policies relied on preferred habitats of investors and imperfect arbitrage. There was ample evidence that private agents had especially strong preferences for safe and liquid short-term assets in the crisis; in those circumstances, sizable purchases of longer-term assets by the central bank can have an appreciable effect on the cost of capital to households and businesses. The marked adjustments in interest rates in the wake of the announcements of such actions, both in the United States and elsewhere, suggest that market participants also saw them in this light. Reis raises the possibility that our credit policies, together with the payment of interest on reserves, could leave the Federal Reserve dependent on the fiscal authorities for funding our expenses, with adverse implications for our ability to conduct a sound monetary policy. This outcome seems extremely remote. As I have already noted, the Federal Reserve’s exposure to credit losses is quite limited. Certainly, the Federal Reserve’s interest expense will increase when short-term rates move up from their current very low level because of the payment of interest on reserve balances. However, the Federal Reserve will continue to earn substantial net income over the next few years under all but the most remote contingencies, for at least two reasons. First, currency, on which we pay no interest, will remain a substantial portion of our liabilities. And second, we will have sizable earnings on our assets. Short-term interest rates would have to rise very high very quickly for interest on reserves to outweigh the interest we are earning on our longer-term asset portfolio. With the global economy weak and inflation low, a large and rapid rise seems quite improbable. Moreover, even in the unlikely event that a sharp rise in interest rates forced us to

3. In their Brookings Paper of last year, Stephen Morris and Hyun Song Shin (2008) pointed out that the drying up of securitized lending was an important aspect of the constriction on liquidity and the forced deleveraging of this crisis.


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suspend remittances to the Treasury temporarily, we would still maintain our ability to implement monetary policy to foster our statutory objectives of maximum employment and stable prices. As Reis points out, paying interest on reserve balances also has important benefits and will play a key role in our exit from unusually accommodative policies when the time comes. Raising the interest paid on those balances should provide substantial leverage over other short-term market interest rates because banks generally should not be willing to lend reserves in the federal funds market at rates below what they could earn simply by holding reserve balances.4 Against that background, Reis questions why the Federal Reserve is highlighting the availability of reserve-draining tools, since the level of reserves should not impede the usual transmission mechanism of tighter policy working through interest rates. However, neutralizing or draining reserves could be helpful in tightening the link between the interest rate on excess reserves and other short-term interest rates. And the presence of a large volume of reserves on bank balance sheets—even when remunerated—could have undesired effects on the portfolio decisions of banks. So we continue to develop tools that enable the Federal Open Market Committee (FOMC) to drain or neutralize large volumes of reserves were the FOMC to decide that doing so would support its objectives.5 Finally, Reis notes that the theoretical literature on monetary policy at the zero lower bound commonly prescribes targeting higher-than-normal inflation rates even beyond the point of economic recovery, so that real interest rates decline by more and thus provide greater stimulus for the economy. The arguments in favor of such a policy hinge on a clear understanding on the part of the public that the central bank will tolerate increased inflation only temporarily—say, for a few years once the economy has recovered— before returning to the original inflation target in the long term. Notably, although many central banks have put their policy rates near zero, none have adopted this prescription. In the theoretical environment considered by the 4. I would also note that there are large participants in the federal funds market—the government-sponsored housing enterprises—that are not eligible to receive interest from the Federal Reserve and thus may be willing to make reserves available in the federal funds market at rates lower than the interest rate paid on reserves. 5. For example, the Federal Reserve could drain liquidity by engaging in reverse repurchase agreements with a range of counterparties, or it could offer banks the option of term deposits, which would then not be available for lending in the federal funds market. The Federal Reserve could also sell a portion of its holdings of securities. Any combination of these tools, in addition to the payment of interest on reserves, may prove very valuable when the time comes to tighten the stance of monetary policy—although, as the FOMC has said, that time is not likely to come for an extended period.

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paper, long-run inflation expectations are perfectly anchored. In reality, however, the anchoring of inflation expectations has been a hard-won achievement of monetary policy over the past few decades, and we should not take this stability for granted. Models are by their nature only a stylized representation of reality, and a policy of achieving “temporarily” higher inflation over the medium term would run the risk of altering inflation expectations beyond the horizon that is desirable. Were that to happen, the costs of bringing expectations back to their current anchored state might be quite high. But while the Federal Reserve has not attempted to raise medium-term inflation expectations as prescribed by the theories discussed in the paper, it has taken numerous steps to lower real interest rates for private borrowers and keep inflation expectations from slipping to undesirably low levels in order to prevent unwanted disinflation. These steps include the credit policies I discussed earlier, the provision of forward guidance that the level of short-term interest rates is expected to remain “exceptionally low for an extended period” conditional on the outlook for the economy and inflation, and the publication of the longer-run inflation objectives of FOMC members. REFERENCES FOR THE KOHN COMMENT

Madigan, Brian F. 2009. “Bagehot’s Dictum in Practice: Formulating and Implementing Policies to Combat the Financial Crisis.” Speech delivered at the Financial Stability and Macroeconomic Policy symposium sponsored by the Federal Reserve Bank of Kansas City, Jackson Hole, Wyo., August 20–22. Morris, Stephen, and Hyun Song Shin. 2008. “Financial Regulation in a System Context.” BPEA, no. 2: 229–74.

GENERAL DISCUSSION Deborah Lucas observed that there has not been a sharp distinction between fiscal policy and monetary policy in the recent crisis, and that a lot of what has been described as monetary policy was actually fiscal policy. Although she understood the need for immediate action, she found it worrisome that many economists seem to have so easily accepted the Federal Reserve’s decision to take actions outside the normal course of oversight by Congress. Lucas went on to cite a piece of anecdotal evidence about the possible consequences. The advance business of the Federal Home Loan banks, a large source of capital for smaller banks, has dropped by about a third since the crisis began. The banks’ explanation is that the Federal Reserve is outcompeting them in the business of making risky loans: their customers are no longer interested in borrowing from them because they are getting such sweet deals from the central bank.


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In Lucas’s view, the issue came down to one question: Why do we value the Federal Reserve’s independence? The answer, presumably, is that the Federal Reserve needs to be able to make the difficult and unpopular decisions necessary to keep inflation down. Lately, however, the Federal Reserve has been making the popular decisions. That should not become a habit. Robert Gordon raised what he viewed as a fundamental issue about the ability of a central bank to manage expectations. When the Japanese had their dismal decade, and the Bank of Japan finally woke up and began to relax monetary policy, inflation expectations did not spontaneously revive. Gordon thought that Paul Krugman’s 1998 Brookings Paper on the liquidity trap in Japan, although it drew nice parallels with the late 1930s that are still relevant today, was off base in implying that a central bank can control inflation expectations by exhortation. The alternative model, which holds that inflation expectations are based on inertia and experience, seems closer to the reality. Gordon argued further that one hears it too often asserted today that large federal deficits are bound to lead to inflation, when in fact the risk of deflation is much more serious; if current forecasts are correct, the unemployment gap will soon have never been so high for so long. Either the modern version of the Phillips curve is going to be contradicted over the next four years, or a significant deflation is likely in the absence of a collapse of the dollar or an explosion of oil prices. Gita Gopinath observed that a frequently heard argument in the run-up to the crisis was that one reason why interest rates were so low was the global savings glut. She wondered whether the Federal Reserve still held that view and whether it will continue to play an important role in the postcrisis recovery period. She also wondered how much the outcome relies on China retaining its appetite for U.S. assets and on China’s high saving rate. Christopher Sims agreed with Donald Kohn that the likelihood of negative seigniorage and balance sheet problems at the Federal Reserve is quite remote, but just how remote, he argued, depends on whether the Federal Reserve has thought through what it would do in that situation, and on how completely people are convinced that the Federal Reserve is prepared for it. The problem, as Sims saw it, is not the possibility of a small or even a fairly large capital loss on the private assets. It has to do with what would happen if there were a sudden shift in sentiment, in which, for example, the dollar’s role as a reserve asset suddenly started to deteriorate, the exchange rate started dropping, and inflationary pressure began to build. The need for extraordinarily high interest rates might arise, especially if the public

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does not understand exactly how the Federal Reserve intends to address the problem. Sims also agreed with Kohn on the importance of inflation expectations being well anchored and on the difficulty of restoring the anchor once lost. But if an anchor can stabilize the boat in a storm, it can also drag it under the water. Japan, after all, has very well anchored inflation expectations. The problem is that one would like to anchor expectations from above and below, by convincing people that the Federal Reserve is concerned not only about possible future inflation, but also about inflation falling below roughly 2 percent. Sims disputed the idea, implied in the paper, that there is no social cost to the Federal Reserve creating interest-bearing reserves. That is true in terms of paper and printing costs, but interest-bearing reserves are interest-bearing government debt. The only reason a central bank balance sheet matters is that its independence depends on its ability to raise revenue through seigniorage. If the central bank’s capital is seriously insufficient, there are limits to how strongly anti-inflationary it can be without recourse to explicit fiscal backing. Sims also underlined the point made by others that the lines between monetary and fiscal policy are becoming blurred. Interest-bearing reserves do not count against the federal debt limit right now. But if interest rates were to rise substantially while reserves remained at their current level, Congress might well become interested in this distinction. The Federal Reserve and academic economists should be thinking about what happens in these extreme scenarios: both about what would be good fiscal policy, and about what the Federal Reserve should ask for if it should fall under this kind of fiscal pressure. David Romer thought the paper’s model provided a helpful framework for thinking about multiple layers of frictions in a very complicated environment. But although the model is as simple as one could make it, it is still hard to grasp intuitively. He asked where the special ability of the Federal Reserve to improve outcomes was coming from and suggested two possibilities: either it is a special technological skill that makes the Federal Reserve good at keeping people from absconding, or it is an ability to essentially break private contracts. Private individuals cannot make their debt more senior than a prior lender’s, but the Federal Reserve can, and the knowledge of this capability presumably feeds back into the equilibrium. Romer also wondered whether there is something unique about the independence of the Federal Reserve that requires that it retain the ability to set its own budget. Many government agencies are and should be independent, including the


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judiciary, the district attorneys, and the Federal Election Commission. And many of those agencies could legitimately derive a stream of revenue from pursuing their government business. Yet Congress in all cases sets their budget. Why is the Federal Reserve different? Christopher Carroll found it striking that both Donald Kohn and Timothy Besley—the one a current, the other a former policymaker—had expressed great concern about the lack of useful macroeconomic models for the current circumstances. A century and a half after Bagehot, this might still be the most important question for macroeconomists to address today. Why, then, does there seem to be an intellectual block on the topic? One reason might be that macroeconomists are reluctant to abandon the equilibrium rational expectations approach, but in a model where everyone understands everything about how the entire model works, there is not the feedback from past events into expectations about the future needed to capture what is going on. Carroll argued that developing such a well-articulated framework ought to be macroeconomists’ top priority. Benjamin Friedman agreed with Sims on the need for symmetry in central banks’ expression of objectives for inflation, whether it be embodied in formal inflation targets or in terms of stated objectives within a dualmandate system. The classic example of the lack of such symmetry today is the European Central Bank, which cannot even bring itself to say out loud that deflation is bad and instead expresses its objective as “inflation of no more than 2 percent.” Friedman agreed with the paper’s assertion that there is nothing particularly wrong with a central bank having negative capital. What, then, is the real risk to Federal Reserve independence? The prospect of the Federal Reserve needing outright funding from Congress is remote, he argued, but the problem is that certain policies were pursued precisely because Congress would not have provided that funding. Friedman conjectured that in the event the Federal Reserve does realize serious losses on its recently acquired assets, it will become clear that it has been pursuing fiscal policy without congressional approval, and the political reaction to that is likely not to be subtle. It might not be limited to taking away the Federal Reserve’s authority to set its own budget, as David Romer suggested, or placing other limits on its powers while leaving its independence with respect to monetary policy intact. The fallout could be a diminution of the Federal Reserve’s independence not only with respect to its own budget but also on more fundamental matters. Vincent Reinhart noted that in the models that monetary policymakers use, policy works its effects through current and expected short-term interest

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rates. He saw the fundamental question raised by this paper as whether unconventional policies should be viewed as commitment devices to lock in that current and expected future path of the short-term rate, or rather as a revealed preference, that is, as demonstrating that policymakers actually believe that monetary policy works through different channels. On that score, the various facilities that the Federal Reserve has created and the actions it has taken amount to a very long list of potential channels of monetary policy. They probably mark, in Reinhart’s view, the beginning of a substantial expansion of the way macroeconomics is done, either to explain what policymakers thought they were doing in recent months or, having identified the channels through which policy actually works, coming up with different models. Michael Woodford sympathized with the Federal Reserve’s concern that any tolerance of temporarily higher inflation on its part might be misunderstood and pose a threat to its hard-won credibility. Indeed, he argued, monetary policymakers need to think about not only what will happen if people understand what the central bank says, but as a practical matter, what alternative interpretations people might give to its words and actions, and what will happen as a consequence. Theoretical analyses that simply assume that people understand the same things the policymaker understands fail to address this. Policymakers need to think more about how to explain policy so that it can be understood and therefore effective, rather than simply give up on the public’s ability to understand a nuanced policy. Woodford himself had previously proposed one approach to better public understanding, one that described monetary policy in terms of catch-up to some price-level path or a path of nominal GDP. Woodford felt the Federal Reserve has been too reluctant to think about having an articulated strategy as the basis for the credibility of its inflation commitment. The Federal Reserve appears to perceive no payoff from such a strategy, and instead simply lets the public observe the inflation rates delivered in the past. The current situation, however, illustrates a weakness of that approach: when one asks if it is ever possible to have temporarily higher inflation expectations without undermining longer-run inflation credibility, the answer has to be no, because that undermines the basis of the Federal Reserve’s approach to controlling long-run inflation expectations. An explicit strategy, such as price-level path targeting, that justified the temporary inflation, in the context of a strategy that would nonetheless imply no change in long-run inflation, would allow a way around this constraint. John Williams observed that one way to improve the management of inflation expectations might be to publish interest rate forecasts, as is done in


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New Zealand, Norway, and Sweden. He also proposed that one could move partway out the yield curve—using purchases not of long-term Treasuries, but of those in the short to middle range of maturities—as a way of reinforcing the view that the central bank intends to keep interest rates low for a while. Such policies could be useful for a central bank seeking to maintain a low inflation target in a zero-lower-bound world. Williams also worried that the next recession might not look at all like the present one. It might be one where the only tools available are the short-term interest rate and maybe some other quantitative easing policies. The toolbox would be smaller because in the absence of a financial crisis, the alternative tools being used so freely today would not be appropriate. Richard Cooper wondered whether anything beyond the discomfort of its accountants prevents the Federal Reserve from having negative capital forever. The capital of a central bank, after all, seems to be a completely arbitrary number. This is more evident with other central banks than with the Federal Reserve, because a much larger share of their assets is in the form of foreign exchange reserves, so that the relationship between the central bank’s assets and its liabilities is changing all the time as exchange rates change. Cooper reminded the Panel that a policy of devaluing the currency does not generalize to all countries. Moreover, a deliberate depreciation of the dollar is not a feasible policy for the United States, because other countries will not allow it. We now have a global capital market, with large pools of savings outside the United States and the United Kingdom. Long-term interest rates today are determined in that market, and not by the monetary policy of any particular country or countries, including the United States. David Laibson observed that although the traditional bank run, where people line up outside the bank to withdraw deposits, is no longer a threat, there is now something that could be called a “modern” bank run, in which institutional sources of short-term credit are suddenly withdrawn because of worries that everyone else is withholding credit. This kind of bank run is obviously what afflicted Bear Stearns and Lehman Brothers, and the concern was that it would spread through the entire financial sector that was not in some sense federally insured. Laibson understood Federal Reserve policy as partly motivated by the need to avoid this kind of disorderly liquidation, and many of its actions follow from that motive. He wondered why the discussion was not more about the Federal Reserve’s efforts to avoid these bank runs as an organizing principle for a lot of the transactions that ended up changing policy in general, and the Federal Reserve’s balance sheet specifically. Linda Goldberg responded by pointing out that much of the debate on changes in the financial architecture is specifically targeted at this issue and

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on expanding the resolution authority of the Federal Deposit Insurance Corporation, which currently is limited to banks and does not extend, for example, even to bank holding companies. Xavier Gabaix observed that monetary policy during the crisis has exhibited creativity and originality, but the same cannot be said about fiscal policy, which has been desperately old-fashioned and uncreative. He would like to see some institutional innovation in fiscal policy, perhaps the creation of a Federal Reserve equivalent of fiscal policy. For example, some ability on the part of the central bank to change things like payroll taxes during a downturn would be useful. Gabaix argued that it is good to have fair-minded technocrats making these decisions rather than short-term-focused politicians. Carmen Reinhart recommended expanding the paper’s scope to a more international setting, given that the crisis is global. A global framework would be particularly useful for the treatment of inflation expectations, because in only two postwar banking crises did the crisis country’s currency actually appreciate: Japan in the 1990s and the United States today. More generally, when one looks at all the new facilities that have been created, it is clear that the buyers were coming from somewhere else, and the setting of interest rates thus has a distinctly global element.

ROBERT E. HALL Stanford University

By How Much Does GDP Rise If the Government Buys More Output? ABSTRACT During World War II and the Korean War, real GDP grew by about half the increase in government purchases. With allowance for other factors holding back GDP growth during those wars, the multiplier linking government purchases to GDP may be in the range of 0.7 to 1.0, a range generally supported by research based on vector autoregressions that control for other determinants, but higher values are not ruled out. New Keynesian macroeconomic models yield multipliers in that range as well. Neoclassical models produce much lower multipliers, because they predict that consumption falls when government purchases rise. Models that deliver higher multipliers feature a decline in the markup ratio of price over cost when output rises, and an elastic response of employment to increased demand. These characteristics are complementary to another Keynesian feature, the linkage of consumption to current income. The GDP multiplier is higher—perhaps around 1.7—when the nominal interest rate is at its lower bound of zero.

M

ajor contractions in economic activity bring policies of temporary expansion in government purchases of goods and services. The severe contraction that hit the U.S. and world economies in 2008 was no exception. The need for fiscal expansion was particularly acute because monetary policy had driven nominal short-term safe interest rates down to zero without heading off the contraction. Fiscal policy, including increases in federal purchases and in state and local purchases financed by federal grants, was an important part of the government’s response to a severe recession. A major issue for fiscal policy is how much total output increases when the government temporarily buys more goods and services. The ratio of the output increase to the purchases increase is the government purchases multiplier. I emphasize that my concern in this paper is with government purchases, not all of government spending, which includes transfers and interest payments as well as purchases. I assume in all cases that the products 183

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the government purchases enter preferences in a separable fashion: they do not affect households’ marginal rate of substitution between consumption and work or between consumption this year and in any future year. Military spending is the obvious example. If instead the government provided consumers with goods and services they would have purchased anyway, the resulting multiplier would be lower. In the extreme case, where the government purchases consumption goods and provides them to consumers, the multiplier would be zero in the standard life-cycle model. I exclude effects that operate through externalities. One such effect arises from the fact that the government, as the nation’s agent for collective action, may have uses for output that exceed the private value of the output. For example, law enforcement is underprovided by private action and may be underprovided by current government action. If the increase in government purchases includes more spending on law enforcement, its value may exceed its direct contribution to GDP. I leave out that increased value, which could be attributed either to the purchases or to the increase in GDP that occurs because more enforcement makes other production more efficient. Another example is road building, where the benefits accrue mainly in the future, because roads are part of the public capital stock. I omit benefits related to externalities not because I think they are unimportant, but because I want to focus on a limited, strictly macroeconomic question. Thus, as a general matter, I do not offer a welfare analysis of government purchases, but rather one important piece of a welfare analysis, having to do with the aggregate effects, mainly in the labor market, of the government’s increase in product demand. I assume that no special distortionary taxes apply during the brief period of the countercyclical purchases; the government balances its budget in the long run with whatever taxes it normally uses. I also do not comment on the other major branch of fiscal stimulus, tax reductions. An analysis of fiscal stimulus in the form of higher transfers or lower taxes would make use of the conclusion about the effects of higher purchases on overall economic activity, because it is a fair presumption that the effects of higher consumer purchases are similar to the effects of higher government purchases. But I do consider the effects of the subsequent financing of increased government purchases, both explicitly in the models I study and implicitly in my empirical work, which assumes that the public knows that the government must eventually service the debt it has issued to pay for its higher purchases. Here my focus on temporary increases in purchases is critical: permanent increases have a different effect because households will respond by cutting consumption in anticipation of permanent increases in taxes, a

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wealth effect. I demonstrate the irrelevance of any wealth effect for temporary programs of higher government purchases. The paper describes a closed economy. In effect, it is about the world economy, although I use U.S. data to find parameter values. In the context of the events of 2008 and 2009, a global focus is appropriate, because every major economy has suffered substantial declines in employment and output, and many have responded with increases in government purchases. I start with a discussion of the direct evidence from simple regressions about both the output multiplier and the analogous consumption multiplier for government purchases. Given the reasonable assumption that movements in military purchases are exogenous and the fact that they account for much of the variation in government purchases, the natural approach is to study the econometric relationship between output and consumption, on the one hand, and military spending, on the other. The resulting multipliers are about 0.5 for output and slightly negative for consumption. Although the standard errors of these estimates are agreeably small, the estimates are under suspicion for understating the multipliers, because the bulk of the evidence comes from the command economy of World War II and may not be relevant to today’s market economy. Omitting World War II from the sample yields similar multipliers with rather larger standard errors, based largely on the Korean War buildup, but these too are questionable because that buildup was accompanied by large increases in tax rates. Changes in military purchases from the Vietnam War period, the Reagan years, or the two wars in Iraq are not large enough to deliver usable estimates of the multipliers. I conclude that the evidence from U.S. historical experience on the magnitude of the multipliers only makes the case that the multiplier is above 0.5. I next report evidence from vector autoregressions (VARs), which find fairly consistently that the output multiplier is in the range from 0.5 to 1.0 and that the consumption multiplier is somewhat positive. To varying extents, these estimates include adjustments for factors such as taxes that may correct downward biases in the simple regressions. The paper then turns to models, specifically those derived from the blending of neoclassical and Keynesian theory that has flourished in the past decade under the name New Keynesian economics. Following many earlier authors, I demonstrate that the purely neoclassical general-equilibrium model without unemployment yields the pretty much unshakable conclusion that increases in government purchases come largely out of investment and consumption and do not raise output substantially. The output multiplier is well under 1, and the consumption multiplier is quite negative. The

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reason is that increased output in this type of model can come only from increased employment. Without a reservoir of unemployed workers to draw down, any increase in labor input must drive the wage down, resulting in less labor supply. The neoclassical model thus predicts small increases in output and fairly large declines in consumption. A key idea of modern macroeconomics that results in more reasonable multipliers is that the margin of price over cost falls during expansions; that is, the markup ratio declines as output rises. Often this property is expressed as stickiness of the price level: prices stay constant during a boom that raises input costs. Other rationalizations based on oligopoly theory or other principles deliver the result directly. The declining markup permits the wage to rise, or at least not fall as much as it would with constant markup during an expansion. Hence, it permits the household to supply much more labor when the government increases its claim on output. A second key idea of modern macroeconomics needed to rationalize a reasonably positive output multiplier is elastic labor supply. Research based on household data is adamant that the elasticity of labor supply is below 1 even after adjustment for the income effect. Such an elasticity precludes a substantially positive output multiplier with any reasonable response of the markup to changes in output. It takes both a declining markup and elastic labor supply to generate a substantial output multiplier. My approach to rationalizing a high wage elasticity of labor supply starts from the observation that most of the cyclical movement in work effort takes the form of variations in unemployment. I raise the elasticity of labor supply to incorporate the response of unemployment to changes in labor demand, following a search-and-matching approach to the labor market. A standard dynamic general-equilibrium model with a sufficiently responsive markup and realistically elastic effective labor supply (including the response of unemployment) yields an output multiplier as high as just below 1, in accord with the direct evidence. One might think that the traditional Keynesian hypothesis of rigid wages would be a close cousin of elastic labor supply, but this thought turns out to be quite wrong. An unresponsive wage constrains the immediate effect of an increase in government purchases to zero, because employment and thus output are determined entirely by the equality of the marginal product of labor and the wage. This predetermination of output remains in an economy where the markup ratio declines with higher output. The standard model with responsive markup and elastic labor supply still generates a negative consumption multiplier. I show that adding com-

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plementarity between hours worked and consumption—a topic of extensive recent research—can tame the negative multiplier. The logic is that employed people consume significantly more market goods and services than do the unemployed, who have more time to create nonmarket equivalents. My preferred specification for matching the observed positive multiplier for output and the slightly negative multiplier for consumption has a substantial negative response of the markup of price over cost to changes in output, a fairly elastic response of employment to changes in labor demand, and a degree of complementarity of consumption and work estimated from micro data. Modern models generally embody the life-cycle model of consumption, where households use credit markets to smooth consumption. It is widely believed that replacing this feature of models with a traditional consumption function linking consumer spending to current income will boost the output and consumption multipliers. The issue then becomes by how much an increase in government purchases crowds out investment. Traditional Keynesian models assume rigid real wages, in which case output is determined on the demand side of the labor market by firms equating the marginal product of labor to the fixed real wage. With output unresponsive, crowding out is complete and the output multiplier is zero. Adding partial borrowing constraints to an otherwise standard New Keynesian model does boost the consumption multiplier. Multipliers are not structural constants. They describe the responses of endogenous variables to changes in the driving force of government purchases. Multipliers depend on monetary policy. In normal times, monetary policy leans against the expansionary effect of increased government spending, reducing the multipliers. But when monetary policy lowers nominal interest rates to their minimum value of zero, the offsetting effect disappears, and so an economy at the lower bound has higher multipliers. In an economy with an output multiplier for government purchases of just under 1 in normal times, the multiplier rises to 1.7 when monetary policy becomes passive with a zero nominal interest rate. I conclude that the efficacy of stimulus from higher government purchases depends on two features of the economy: a markup of price over cost that declines as output expands, and a substantially wage-elastic labor supply or the equivalent. Both features are related to traditional Keynesian views about price and wage stickiness: the negative response of the markup can be viewed as price stickiness, and elastic labor supply as wage stickiness. Both features appear to describe the U.S. economy, although research on this topic is still far from definitive.

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I review the evidence on the movement of the markup ratio as output expands. The hypothesis of a negative response implies that the share of profit in total income should fall during expansions. In fact, that share rises. The most promising rationalization involves a substantial amount of wage smoothing. Then the observed increase in profit during booms is the combination of two phenomena: an increase associated with wage contracts that give management the bulk of the benefit of higher revenue, partly offset by a decline in profit per unit of output. But this is pure guesswork— I lack any handle on measuring wage smoothing. There is no meaningful factual support for the key hypothesis that the markup ratio declines with output. I show that the expansion of government purchases so far enacted to deal with the severe current recession is too small to add meaningfully to our knowledge on this subject—or to offset much of the loss in output. A debate about whether the government purchases multiplier is 1.0 or 1.5 is completely off the point in this respect.

I. Regression Estimates of Output and Consumption Multipliers I begin by estimating the government purchases multipliers for output and consumption in simple (ordinary least squares) regressions and in VARs.

I.A. Estimates from Simple Regressions on Military Purchases The most direct way to measure the government purchases multipliers is to exploit large and arguably exogenous fluctuations in military spending. I start with a review of that evidence for the United States over the past 80 years, using the following specification: (1)

z t − z t −1 g − gt −1 = mz t + εt . yt −1 yt −1

Here z is either y for the output multiplier my or c for the consumption multiplier mc. The equation also contains a constant (not shown). Note that using the same denominator on the left and the right preserves the normal definition of the multiplier as the dollar change in output or consumption per dollar of increased government purchases. In this approach I am treating the change in nonmilitary government purchases as one of the sources of the noise εt. Because these purchases grow smoothly, their difference has little variability. The alternative of using

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military spending as an instrument for total purchases gives essentially identical results. I assume that the change in military spending g is uncorrelated with the non-g component of the right-hand-side variable εt. This identifying assumption has two aspects. First, military spending does not respond to forces determining GDP or consumption, such as monetary or financial forces, but only to geopolitical events. I have long believed that this aspect of the identifying assumption is among the more plausible in macroeconomics. Second, no other determinants of output or consumption growth change when government purchases change. The basis for this aspect of the identifying assumption is much weaker. In particular, when military spending rises substantially, two other policy responses may occur: command-type interventions in the economy, including rationing, and increases in taxes. Both of these presumably decrease consumption demand and thus reduce output growth. The result is a failure of the identifying assumption in the direction of a negative correlation between the disturbance εt and military spending, and thus a downward bias in the estimate of the multiplier mz. I conclude that the value of the multiplier is probably better interpreted as a lower bound than as an unbiased estimate. Because the movements in GDP and consumption induced by changes in government purchases have essentially the same dynamics as the changes in purchases, it is not necessary (in fact, it is inefficient) to find the innovation in g and then track the response to the innovation, as would occur in a VAR. The advantage of a VAR is that it can account for other influences, notably taxes, and isolate the causal effect of government purchases. The simple regression considered here confounds the effects of wartime increases in purchases with the effects of accompanying tax increases. Temporary increases in purchases for stimulus purposes are not accompanied by comparable tax increases. I discuss evidence from VARs in the next subsection. To form the differences in the data, I use the various versions of National Income and Product Accounts table 1.1.6, Real Gross Domestic Product, Chained Dollars. Each version of the table uses a different base year for the deflator. For the overlap years, I take the average of the two measures of the two changes; these are usually identical to two digits. I use this approach because the deflator for military spending drifts relative to the GDP deflator, and I wish to retain the usual interpretation of the multiplier as the effect of one current dollar of purchases on GDP or consumption, also measured in current dollars.

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Table 1. Ordinary Least Squares Estimates of Government Purchases Multipliers for Military Spendinga Period 1930–2008 1948–2008 1960–2008 1939–48 1949–55 1939–44 1945–49

GDP multiplier

Consumption multiplier

0.55 (0.08) 0.47 (0.28) 0.13 (0.65) 0.53 (0.07) 0.48 (0.56) 0.36 (0.10) 0.39 (0.08)

−0.05 (0.03) −0.12 (0.10) −0.09 (0.29) −0.05 (0.02) −0.18 (0.05) −0.11 (0.03) −0.04 (0.05)

Source: Author’s calculations. a. Numbers in parentheses are standard errors.

Table 1 shows the results of the regressions for output and consumption. The top row shows that, over the entire sample 1930 through 2008, the output multiplier is just over half, with a standard error of 0.08, and the consumption multiplier is close to zero, although slightly negative, with a standard error of 0.03. The higher precision of the consumption multiplier estimate arises because the change in consumption has a much lower volatility than does the change in real GDP. As I noted earlier, estimates of the multiplier that include the huge changes in military spending during World War II are biased downward because important parts of the economy were run on command principles during the war. Direct controls on consumption through rationing arguably held back consumption growth that would have occurred under free-market conditions. Other factors, including the draft and the wartime surge in patriotism, result in an upward bias. Although I am inclined to believe that the net bias is downward, there is no solid evidence one way or the other. The other rows in table 1 show the evidence from various subperiods. The second row starts the sample in 1948, after the rise and fall of wartime military purchases. The multiplier estimates are similar to those for the whole period but with much larger standard errors. The confidence interval for the output multiplier runs from about zero to about 1. The confidence interval for the consumption multiplier remains fairly tightly concentrated near zero.

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The third row of the table starts the sample in 1960, several years after the Korean War. It shows that military spending did not move enough during the Vietnam War, the Reagan buildup, or the two wars in Iraq to allow precise estimation. The estimates are fully consistent with those in the first two rows but are almost completely uninformative about the output multiplier. They do, however, rule out larger positive or negative values of the consumption multiplier. The fourth row reinforces the message of the earlier rows by showing that the results for just the period enclosing the World War II expansion and contraction of military spending are virtually identical to those for the whole period. Essentially all the identifying power comes from the large movements during World War II. The fifth row looks at the years enclosing the Korean War. The estimates are similar to those found for the periods including World War II but have much larger standard errors, especially for the output multiplier. The last two rows of table 1 break World War II into its expansion phase, ending in 1944, and a phase containing the military contraction and the resumption of normal economic conditions, from 1945 to 1949. One of the strengths of the parsimonious specification I use is its ability to deliver useful results with a small number of observations. The results are interesting because many economists—most recently, Lawrence Christiano, Martin Eichenbaum, and Sergio Rebelo (2009)—believe that the multipliers are higher when the economy is slack. The U.S. economy was extremely slack in 1939, the first year of the expansion phase in the table. The results here give no support to the view of higher multipliers in a slack economy. The downward multipliers found for the period from 1945 to 1949 are virtually identical to those for the expansion from slack starting in 1939. Both are measured with good precision. Robert Barro and Charles Redlick (2009) consider similar evidence in a regression framework that includes tax rates and other determinants of GDP along with government purchases. They use data starting in 1917 and so take advantage of World War I, another period when the military component of purchases rose dramatically. Their estimates of the output multiplier range from 0.59 to 0.77; the estimate for all data starting in 1917 is 0.64, with a standard error of 0.10. Their estimates of the consumption multiplier are close to zero. They do not report results without the tax variables, but it appears that their inclusion somewhat increases the estimates. Thus, tax increases with negative effects tend to coincide with increases in government purchases.

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The most important lesson from the data on military purchases is that all the real information comes from big wars. The standard errors in table 1 reflect this fact, rising sharply when the big wars are omitted. Another way to see the point is to observe that the regression coefficient is mz =

∑ Δz Δg ∑ ( Δg ) t

t

t

2

τ

τ

Δzt

∑ Δg ( Δg )

2

t

(2)

=

t

t

∑ ( Δg ) τ

=

∑W

t

t

2

τ

Δzt . Δgt

Here Δzt is the change in real GDP or consumption as a fraction of initial real GDP less its mean, and Δgt is the change in military purchases as a fraction of GDP less its mean. Thus, the overall estimate of the multiplier is a weighted average of year-to-year observed multipliers, where the weights Wt depend on the square of the growth in military purchases. Figure 1 shows these weights calculated from the data on military purchases and real GDP. The only visibly positive weights are for the two wars. Of the two, World War II is vastly more informative. There is little hope of learning much about the multipliers from any data after the mid-1950s. Note that the weights are the same for the output and the consumption multipliers. I conclude that the regression evidence from big wars demonstrates that the government purchases multiplier is probably at least 0.5, based on the hypothesis that the net effect of biases is downward. World War II does not yield a higher estimate of the multiplier than does the Korean War, despite the fact that the buildup starting in 1940 was from a much more slack economy than the one starting in 1950. Possible explanations for the failure to find the expected relationship between initial slack and the multiplier include more aggressive command interventions in the earlier mobilization and the fact that World War II involved enormous expansions in motor vehicles, ships, and aircraft, all highly specialized industries subject to bottlenecks.

I.B. Estimates from Vector Autoregressions VARs are a more powerful approach to measuring multipliers, in principle. The simple regressions in the previous section take all the movements

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ROBERT E. HALL

Figure 1. Annual Weights Implicit in OLS Estimates of Output and Consumption Multipliersa Weight 0.35 0.30 0.25 0.20 0.15 0.10 0.05 1935

1945

1955

1965

1975

1985

1995

2005

Source: Authorís calculations. a. Each weight derives from the square of military spending in that year.

in real GDP and consumption not attributable to changes in government purchases as noise, captured by the residual. Even if these movements arise from driving forces that are uncorrelated with military purchases, so that the estimated multipliers are unbiased, the estimates have a high sampling error. A VAR can soak up much of the noise by associating it with other causal factors, thus generating more precise estimates than a simple regression. Further, a VAR can take account of effects that are correlated with changes in government purchases that result in biases in the simple regressions. Probably the main effect of this type is that from the tax rate, although this correlation can be captured in a simple regression as in Barro and Redlick (2009). By far the biggest increase in government purchases over the sample included in the VARs reported below occurred during the Korean War, when tax rates also increased substantially. Olivier Blanchard and Roberto Perotti (2002), Jordi Galí, David LópezSalido, and Javier Vallés (2007), Perotti (2008), Andrew Mountford and Harald Uhlig (2008), and Valerie Ramey (2009) estimate VARs subject to a variety of identification schemes, all of which basically rely on the exogeneity of movements of government purchases. Blanchard and Perotti consider two versions of their VAR, one with a deterministic trend and the other with a stochastic trend. Ramey estimates elasticities rather than multipliers; I convert these to multipliers by dividing by the ratios of

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Table 2. Literature Estimates of Government Purchases Multipliers from Vector Autoregressionsa Estimate Source

Type of multiplier

Blanchard and Perotti (2002, table 4)

Output

Galí, López-Salido, and Vallés (2007, table 1)

Output Consumption

Perotti (2008, figure 3)

Output Consumption

Mountford and Uhlig (2008, table 4)

Output

Ramey (2008, figure 10a)

Outputb

Ramey (2008, figure 10b)

Consumptionc

On impact

After 4 quarters

After 8 quarters

0.90 (0.30) 0.41 (0.16) 0.07 (0.10) 0.70 (0.20) 0.10 (0.05) 0.65 (0.39) 0.30 (0.10) 0.02

0.55 (0.80) 0.31 (0.34) 0.11 (0.19) 1.00 (0.50) 0.30 (0.20) 0.27 (0.78) 0.50 (0.25) −0.17

0.65 (1.20) 0.68 (0.45) 0.49 (0.28) 1.20 (0.50) 0.40 (0.25) −0.74 (1.95) 0.90 (0.35) −0.09

Source: Literature cited. a. Numbers in parentheses are standard errors. b. Ramey (2008) states results for both output and consumption as elasticities, which here have been converted to multipliers. c. Separate elasticities were estimated for durables, nondurables, and services, so standard errors for total consumption are unavailable.

government purchases to GDP and to consumption of nondurables and services. Ramey’s innovation is to identify shocks to government purchases from events presaging rises in military spending, which she weights by the present value of the predicted increase in military purchases. Table 2 shows the estimated multipliers for real GDP and in some cases consumption for the above five studies at three points in time after an innovation in government purchases: on impact, after four quarters, and after eight quarters. None of the estimated output multipliers is as high as 1 at impact. The impact multipliers range from 0.3 to 0.9. The variation arises from differences in identification strategies. Perotti, and Galí and his coauthors, find consumption multipliers as high as 0.49, whereas Ramey’s estimates are only slightly positive or negative. The difference again arises from her identification strategy rather than the other authors’ use of the innovation in all government purchases. The standard errors in table 2 indicate the wide range of uncertainty in the responses, especially at longer lags. Note that all of these studies use the same data, so that their estimated coefficients are highly correlated with each other. The standard

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errors are indicative of the overall uncertainty from VARs; they would not be smaller for an average across the various estimates. One important difference between these earlier VAR estimates and the question pursued in this paper is that government purchases rose very persistently in response to innovations over the period from 1948 to the present. The Korean War was the exception to the general rule that military spending is transitory: it remained high after the end of that war because of the intensification of the Cold War. By contrast, the increase in government purchases to offset a recession is intended to be transitory.

I.C. Conclusions on the OLS and VAR Estimates Empirical work using simple regressions or more elaborate VARs finds output multipliers in the range from 0.5 to 1.0, with a few exceptions, and consumption multipliers in the range from somewhat negative to 0.5. All of this work is limited in its ability to measure multipliers for the period from 1948 onward by the lack of variation in government purchases, especially in its most exogenous component, military purchases. Figure 1 showed that essentially all the information comes from World War II and the Korean War. Both the simple regressions and the VARs infer the multipliers entirely or mainly from the rise in military spending starting in 1940 (for the simple regressions only) and again in 1950, and the VARs are probably only partly successful in adjusting for taxes and other confounding forces. Thus, one cannot say that the evidence rules out multipliers above 1.0. In the rest of the paper, I will speak as if the evidence clearly supports an output multiplier a bit below 1 and a consumption multiplier a bit negative. To avoid painful repetition, I will not comment each time on the weakness of the evidence on this point.

II. Multipliers Derived from Structural Macroeconomic Models Today, most research-oriented macroeconomic models combine, in varying proportions, ideas from dynamic optimization. In the majority of these models, households choose consumption to balance present against future satisfaction, according to the life-cycle-permanent-income principle, although some households may face binding borrowing constraints. In almost all models, firms choose inputs so as to maximize firm value, subject to the wage for labor and the rental price for capital. In many models, firms are price-setting monopolists facing fairly but not fully elastic demand. A popular assumption is that a firm keeps price constant for an extended

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period of random length, after which the price pops to its value-maximizing level. Few modern macroeconomic models embody any monetary sector. Rather, consistent with modern central bank practice, the economy has a Taylor rule relating the interest rate to the rate of inflation. Finally, models view households as having preferences that govern labor supply, but they may permit a varying gap between labor demand and labor supply, on the view that the wage is sticky in the shorter run but clears the labor market in the longer run. I omit consideration of macroeconomic models used in proprietary forecasting. I do not have access to information about the underlying economic principles of those models. In particular, I do not comment on the analysis by Christina Romer and Jared Bernstein, which uses an average of multipliers from “a leading private forecasting firm” and the Federal Reserve’s FRB/US model (Romer and Bernstein 2009, p. 12). I do find that their fairly high estimate of the output multiplier is in line with the findings of a model applied to the conditions of 2009 with the federal funds rate at its lower bound of zero. The class of models favored by academic macroeconomists and many central banks has a neoclassical growth model at its core. With prices adjusted frequently to firm value–maximizing levels and wages adjusted frequently to market-clearing levels, the economy grows reasonably smoothly along a full-employment path, with some volatility associated with changing rates of productivity growth, changing levels of market power, changing preferences, and other driving forces. A topic of intense debate is how much of the observed volatility of output and employment would occur without price and wage stickiness. Two recent developments in general-equilibrium macroeconomics are worth noting. First is the development of coherent theories of unemployment, which are replacing oversimplified ideas that unemployment is just the gap between labor supply and labor demand. Second is the recognition that the models are missing truly important features of financial markets, especially the widening of spreads that occurs in a financial crisis and recession between the interest rates that private sector borrowers pay and the safe government rate. My discussion of models and their implications for the output multiplier for government purchases adheres to the general philosophy of the class of models sketched above. I begin with the neoclassical growth model core. A single equation from that model—the first-order condition for balancing consumption of goods and services against work effort—has played a huge role in the literature on government purchases multipliers over the

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past 30 years. When that equation is given its full role, as in a simple neoclassical model, the consumption multiplier for government purchases is quite negative. Much of the history of commentary on government purchases multipliers looks for alterations in the model that boost the consumption multiplier toward or even above zero, in accord with the empirical studies that do not generally find very negative values. The consumption-work trade-off is irrelevant in a sticky-wage model, because workers can be off the labor supply function implied by the firstorder condition. But an otherwise neoclassical model with a sticky wage cannot have much of an output multiplier, and it cannot have a nonnegative consumption multiplier, as I will show.

II.A. The Neoclassical Starting Point Suppose people have preferences described by the within-period utility function (3)

c1 − 1 σ h1 + 1 ψ −γ . 1−1 σ 1+1 ψ

Here σ describes the curvature of utility with respect to consumption of goods and services, c; it is the intertemporal elasticity of substitution and the reciprocal of the coefficient of relative risk aversion. The parameter ψ describes the curvature of utility with respect to the volume of work, h, and is the Frisch elasticity of labor supply. Finally, the parameter γ controls the overall disamenity of work. With the price of goods and services normalized at 1 and a real wage of w, the first-order condition for the optimal mix of consumption and work is (4)

wc −1 σ = γh1 ψ .

Under what conditions will an increase in government purchases (or any other source of higher employment and output) actually raise work effort h? If work effort does rise, the real wage must fall, given that the capital stock is a state variable whose level cannot change immediately. For h to rise, the left-hand side of the equation must rise, despite the fall in the real wage. The only way for the product to fall is for c−1/σ to rise by a higher proportion than the wage falls. This rise requires, in turn, that consumption fall. Much of the history of formal macroeconomics of the past three decades rests on this issue. In this model any driving force that raises product demand and thus employment and output must depress consumption,

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contrary to the evidence and common sense. The real business cycle model broke the conundrum by invoking a stimulus that raised wages: it took bursts of productivity growth to be the driving force of employment fluctuations, rather than the changes in product demand that had generally been the primary driving force in earlier models. But the real business cycle model implies that an increase in government purchases achieves an increase in hours of work and output by depressing consumption through wealth and intertemporal substitution effects. The model is fundamentally inconsistent with increasing or constant consumption when government purchases rise. Parameter values that alleviate but do not avoid the property of consumption declines with higher government purchases are low values of intertemporal substitution, σ, and high values of the elasticity of labor supply, ψ. Advocates of the real business cycle model have adopted the second enthusiastically but have been less keen on low σ, because σ = 1 (log of consumption) is needed to match the absence of a trend in hours of work as real wages have risen. Another helpful feature of preferences is to introduce complementarity of consumption and hours, but again this cannot deliver an increase in consumption along with an increase in hours of work. I discuss complementarity in section II.D. To see how the basic marginal-rate-of-substitution condition limits the multiplier, consider the simplest static general-equilibrium model. The technology is Cobb-Douglas: y = hα .

(5)

Capital is fixed and normalized at 1. The real wage is the marginal product of labor: (6)

w = αh −(1− α ) .

Output is divided between consumption and government purchases g: (7)

y = c + g.

Combining the first-order condition from equation 4 and the two previous equations, I get a single equation to describe general equilibrium: (8)

( y − g)

−1 σ

=

γ 1+α1 ψ −1 y . α

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It is convenient to normalize the model, without loss of generality, so that output is 1 at a designated level of government purchases g. This implies γ = α (1 − g )

(9)

−1 σ

.

Then the output multiplier is (10)

my =

dy α = . dg α + σ (1 − g ) (1 − α + 1 ψ )

Because α ≤ 1 and ψ > 0, the conclusion follows, under the assumptions adopted so far, that the output multiplier cannot exceed 1. Further, the output multiplier is an increasing function of the labor supply elasticity ψ, an increasing function of the labor elasticity of production α, and a decreasing function of the consumption curvature parameter σ. Conditions under which the output multiplier is close to 1 are the following: highly elastic labor supply (large ψ) and low diminishing returns to labor (α close to 1); high curvature of utility in consumption (σ close to zero); or government purchases close to all of output (g close to 1). Because all output is either consumed or purchased by the government, the consumption multiplier is simply the output multiplier less 1. Thus, under the assumptions I have made so far, the consumption multiplier is never positive. Note that the expansion in output that occurs in this economy with an increase in government purchases g results in a lower wage: employers would not be willing to increase employment and lower the marginal product of labor if the cost of labor did not decline. The parameter ψ controls the response of labor supply to the lower wage. A higher value of ψ results in a larger decline in hours from the decrease in the wage, in the substitution sense (again, ψ is exactly the Frisch wage elasticity of labor supply). The reason that a higher value of ψ results in a larger increase in hours when g increases is the income effect, which also depends on ψ. The consumption curvature parameter σ also enters the income effect. For parameters that bring the multiplier close to 1, the income effect is swamping the substitution effect. Notice as well that the labor elasticity α enters the output multiplier because it controls the wage depression accompanying the increase in output. With α close to 1, diminishing returns are weak and the substitution effect is correspondingly smaller, so there is less offset to the income effect.

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The elasticity of the production function with respect to the labor input, α, is widely believed to be around 0.7. The critical (and controversial) parameter in the model is ψ. Empirical work with household data suggests that ψ lies in the range from 0.2 to 1.0 (see the papers cited in the appendix to Hall 2009). With σ at the fairly standard value of 0.5 and g at 0.2, the output multiplier is about 0.4, at the low end of the range of empirical findings, and the consumption multiplier is −0.6, out of line with all of the empirical evidence. I will now consider a set of modifications of the model that improve its match to the evidence. These incorporate, in turn, variations in the markup of price over cost, unemployment, complementarity of consumption and hours of work, and a negative response of investment to changes in government purchases. The last modification requires moving to a dynamic model.

II.B. Endogenous Markup of Price over Cost The neoclassical model assumes competition in output and labor markets. The New Keynesian branch of macroeconomics drops that assumption in favor of market power in product markets and makes the extent of market power depend on the state of the economy. Forces, such as higher government purchases, that expand output also make the economy more competitive, with a lower markup of price over cost. New Keynesian and many other macroeconomic models take the product price as sticky. In a monetary economy, this hypothesis can take the form of a sticky nominal price level combined with variations in factor prices. My approach is to continue to normalize the price of output at 1, so that the implication of price stickiness is that factor prices are inside the competitive factor-price frontier. Firms have market power. That power is high in slumps and low in booms; hence, markups are countercyclical. The relationship between price stickiness and countercyclical markups has been noted by many authors, notably Julio Rotemberg and Michael Woodford (1992). Sticky-price models generally derive the variable markup from the Calvo pricing model and Spence-Dixit-Stiglitz preferences, but I will take it for now as a primitive feature of the economy. I build this feature into the earlier model with a constant-elasticity relationship between the markup and output: µ(y) = y−ω. I continue to normalize the reference level of output, the point where I take the derivative for the multiplier, at 1. Now the wage equals the marginal revenue product of labor, (11)

w=

1 αh −(1− α ) . y− ω

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The output multiplier becomes (12)

my =

dy α = . dg α + σ (1 − g )[1 − (1 + ω ) α + 1 ψ ]

The more responsive the markup to changes in output (the higher ω), the higher the output multiplier. Further, the output multiplier can now exceed 1, and thus the consumption multiplier can be positive. The condition for an output multiplier above 1 is (13)

ω>

1− α +1 ψ . α

If ψ = 0.5, the markup elasticity ω needed to deliver an output multiplier of 1 is 3.3, far above the plausible range. With ω = 0.5, the output multiplier is 0.5 and the consumption multiplier is −0.5.

II.C. Unemployment and the Employment Function Even today, many general-equilibrium models struggle to explain the volatility of employment without explicit consideration of unemployment. But good progress has occurred in this area. Monika Merz (1995) and David Andolfatto (1996) introduced unemployment as described by Dale Mortensen and Christopher Pissarides (1994) into otherwise neoclassical models. Blanchard and Galí (2007) did the same for the New Keynesian model. With a Nash wage bargain, the wage is sufficiently flexible that fluctuations in driving forces of reasonable volatility cause almost no movement in unemployment, as Robert Shimer (2005) showed in an influential paper. Blanchard and Galí introduced sticky, non-Nash wages to generate realistic unemployment volatility. Hall (2009) developed a more general framework based on a broad family of bargaining solutions and with standard preferences to replace the linear preferences in Mortensen-Pissarides. That framework describes an employment function n(w, λ) that gives the fraction of the labor force employed (1 minus the unemployment rate). Here w is the wage in the sense of the marginal product of labor; the actual compensation paid to workers may differ because of two-part pricing and wage smoothing. λ is the marginal utility of consumption. Its inclusion as an argument arises because of the diminishing marginal rate of substitution between consumption and work. A second function, h(w, λ), is the Frisch supply function for hours of work per employed worker (not to be

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confused with hours per person, the variable considered in models that disregard unemployment). I assume that an efficient relationship between worker and employer results in the setting of hours on the basis of the marginal product of labor, and I show that this assumption results in a reasonable account of the movements of hours per employed worker. For the purposes of studying a transitory alteration in the economy such as countercyclical government purchases, λ can be taken to be roughly constant, so the functions become n(w) and h(w). Further, the size of the labor force does not change significantly in response to the forces causing the business cycle, so I can standardize it at 1 and write the total volume of work effort as n(w)h(w). This object replaces the labor supply function in a general-equilibrium model. I take the Frisch elasticity of hours per employed worker—the elasticity of h(w)—to be 0.7, based on research surveyed in the appendix to Hall (2009). This elasticity is a cousin of the compensated elasticity of labor supply and must be nonnegative according to the standard theory of household behavior. This elasticity is far below the level needed to explain the observed volatility of total hours of work per person. The employment function n(w) is not the result of household choice. Rather, as in the Mortensen-Pissarides model, it is determined by the interaction of jobseekers and employers in the labor market. If the marginal product of labor rises and compensation paid to workers does not rise as much (compensation is sticky), then employers put more resources into recruiting workers, the labor market tightens, and unemployment falls. Thus, with sticky compensation, n(w) is an increasing function of the marginal product of labor, w. The stickier compensation, the higher the elasticity. I find that the elasticity is 1.2 (Hall 2009, table 1, p. 300). Compensation is quite sticky: under a Nash bargain, the elasticity would be only barely positive. The elasticity of work effort n(w)h(w) is, accordingly, 1.9. The conclusion of this analysis is that the use of a standard labor supply specification with a fairly high elasticity, namely, 1.9, properly captures both the lower elasticity of the choice of hours by employed workers and the elasticity resulting from sticky compensation in a search-and-matching setup following Mortensen and Pissarides. For almost 30 years, a chorus of criticism (including, I confess, my voice) fell upon Finn Kydland and Edward Prescott (1982) and the proponents of general-equilibrium models with elastic labor supply. Now it turns out that their specification fits neatly into the Mortensen-Pissarides framework, with Nash bargaining replaced by some other type of bargaining that results in a sticky level of compensation.

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With the Frisch wage elasticity ψ raised to 1.9, the output multiplier becomes 0.8 and the consumption multiplier −0.2, an important step toward realism.

II.D. Consumption-Work Complementarity Although the empirical finding of a somewhat negative consumption multiplier is hardly new (see Hall 1986), the model considered here so far yields consumption multipliers that are rather more negative than those estimated in empirical studies. One further ingredient, consumption-work complementarity, helps to close the gap. Florin Bilbiie (2009) shows that complementarity cannot turn the consumption multiplier positive in models that lack a negative response of the markup to increases in output, but it can bring the multiplier close to zero. Christiano, Eichenbaum, and Rebelo (2009) discuss the role of complementarity in connection with variable markups and cite a number of earlier treatments of this subject for preferences that assume a particular pattern of complementarity. In the Frisch framework, as laid out in Hall (2009), complementarity means that goods and services consumption rises when the wage rises, with marginal utility held constant. Equivalently, it means that the marginal utility of consumption rises when an individual moves from nonwork to work or when the individual works more hours. I have not found any studies of the cross effect in a Frisch system or in other representation of preferences. But the dependence of consumption on work levels, with wealth or marginal utility held constant, has been the subject of an extensive recent literature. Mark Aguiar and Erik Hurst (2005) provide a well-known study of the subject. The “retirement consumption puzzle”— the drop in consumption of goods and services upon cessation of work— is resolved nicely by complementarity. A retired person relies more on home production and less on purchases in the market, given the availability of time previously devoted to work. The same point applies to changes in consumption during a spell of unemployment, with the possibly important difference that retirement is more likely to be a planned, expected event than is unemployment. Some of the decline in consumption observed among the unemployed may be the result of imperfect insurance markets and lack of liquid savings. Hall and Paul Milgrom (2008) set out a family of preferences with complementarity: (14)

c1 − 1 σ h1 + 1 ψ − χc1−1 σ h1+1 ψ − γ . 1−1 σ 1+1 ψ

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Positive values of the parameter χ introduce an increase in the marginal utility of consumption c that depends on hours of work h (provided, as I assume, σ < 1). I use the following parameter values: σ = 0.4, ψ = 1.54, χ = 0.334, and γ = 1.1. The Frisch elasticities for these parameter values are —own-price elasticity of consumption: −0.5 —wage elasticity of hours of work: 1.9 —elasticity of consumption with respect to the wage: 0.4. See the appendix to Hall (2009) for a discussion of the household-level evidence on the own-price elasticity of consumption and the cross elasticity. In the latter case, the evidence relates to the decline in consumption that occurs at retirement or upon unemployment. Hall and Milgrom show how to calculate the cross elasticity to match the consumption decline. With the negative of the elasticity of the markup, ω, at 0.5, the output multiplier is 0.97 and the consumption multiplier is −0.03, figures easily consistent with the empirical evidence.

III. Dynamic Modeling The output multiplier is relatively high in the static model because of the income effect. In a dynamic version of the model, the analogue of the income effect is the wealth effect: when people feel poorer because of current and future government purchases, they work harder. When the program of purchases is transitory, as I assume throughout this paper, the wealth effect can be much smaller than the corresponding static income effect. Put differently, the wealth effect would be comparable to the static income effect if the increase in purchases were permanent, but if the increase is transitory, people will smooth their work effort and consumption. They accomplish the smoothing by investing less. The economy thus pays for temporary government purchases in part by cutting investment rather than by increasing output, so the output multiplier is smaller. To incorporate the investment effect, one needs a dynamic model that characterizes the investment process. I will use James Tobin’s now-standard approach, based on the distinction between installed capital and newly produced investment goods. The price of installed capital is q in units of investment goods, which I take to be the same as consumption goods, in a one-sector model. The flow of investment equates the marginal benefit of investment, the price q, to the marginal installation and acquisition cost, which I take to be linear in the flow of investment as a fraction of the earlier capital stock:

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(15)

qt = κ

kt − kt − 1 + 1. kt − 1

The parameter κ measures the capital adjustment cost: if κ = 0, q is always 1 and there are no adjustment costs. If κ is large, most fluctuations in the demand for capital are absorbed by the price of installed capital, q, rather than causing changes in the amount of installed capital. In that case the decline in investment when government purchases increase will be small, and the earlier analysis of a static economy will yield a fairly accurate estimate of the output and consumption multipliers. Capital rents for the price (16)

bt = qt −1 ( rt + δ ) − Δqt .

The interest rate rt is the net marginal product of capital; δ is depreciation. Capital demand in period t equals capital supply as determined in the previous period: (17)

(1 − α )

yt = kt − 1 . μbt

At the beginning of a period, the stock of installed capital is kt−1; people choose hours of work ht. At the end of the period, output yt becomes available and is allocated to government purchases gt, consumption ct , and investment, including adjustment cost, resulting in the new capital stock, kt. The law of motion for capital is κ ( kt − kt − 1 ) kt + = (1 − δ ) kt −1 + yt − ct − gt . 2 kt − 1 2

(18)

I continue to consider only a real model and to embody sticky prices in the form that matters for my purposes, the countercyclical markup that a sticky product price implies. Worker-consumers order their paths of hours and goods consumption according to the utility function in equation 14. The first-order condition for the optimal mix of consumption and work is (19)

wc −1 σ [1 − χ (1 − 1 σ ) h1+1 ψ ] = h1 ψ [ − χ (1 + 1 ψ ) c1−1 σ − γ ].

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The economy’s discounter is (20)

mt,t +1 = β

ct−+11σ 1 − χ (1 − 1 σ ) ht1++11 ψ . ct−1 σ 1 − χ (1 − 1 σ ) ht1+1 ψ

The Euler equation for consumption is (21)

(1 + r ) m t +1

t,t + 1

= 1.

Following a government purchases shock, purchases decline from an initial level g + g– with a rate of persistence of φ: (22)

gt = g + gφ t .

Capital at the end of period T is required to be at the economy’s stationary level: kT = k*. For reasonably large T, the result is very close to the infinite-horizon solution. I use the value k0 = k* for the initial capital stock before the government purchases shock. I use the solution to the nonstochastic perfect-foresight model as a (close) approximation to the impulse response of a stochastic model to an innovation in government purchases in an AR(1) equation with persistence φ. I take T = 80 quarters, but the model has the turnpike property that makes T essentially irrelevant to the results as long as it is more than a decade. I take the parameter κ that controls capital adjustment cost to be 8 at a quarterly rate, corresponding to 2 at an annual rate, a representative value from the literature on this subject. Table 3 gives parameter values for the base case and for a number of variants, to illustrate the roles of the various features added to the original neoclassical model. I picked the value of the markup response parameter, ω = 0.7, to yield a reasonable value of the output multiplier. All the other parameters are drawn as described earlier from my review of earlier research. For the cases described in table 3, table 4 shows some of the properties of the dynamic model in terms of impulse response functions, comparable to those shown earlier for the structural VAR results. The first pair of columns, labeled “On impact,” reports the multipliers, defined as the immediate effects of one dollar of increased government purchases on output or consumption, in dollars of real GDP. In the base case the multipliers are 0.98 for output and −0.03 for consumption. After four quarters the output effect becomes smaller, 0.68, and the consumption effect remains essentially the

Source: Author’s calculations.

Base Constant markup No adjustment cost No complementarity Less elastic labor supply

Case

0.4 0.4 0.4 0.5 0.4

Consumption curvature σ 1.54 1.54 1.54 1.9 0.5

Labor supply elasticity ψ

Table 3. Parameter Values for the Neoclassical Model and Variants

0.334 0.334 0.334 0.0 0.334

Complementarity of work and consumption χ 1.103 1.103 1.103 1.102 0.617

Labor weight γ

8 8 0 8 8

Capital adjustment cost κ

0.7 0.0 0.7 0.7 0.7

Markup response ω


Base Constant markup No adjustment cost No complementarity Less elastic labor supply

Case

0.98 0.60 0.98 0.92 0.40

Output multiplier

Output multiplier 0.68 0.41 0.69 0.65 0.24

−0.03 −0.16 −0.03 −0.15 −0.25

−0.02 −0.12 −0.02 −0.10 −0.21


After 4 quarters


On impact

Table 4. Government Purchases Multipliers Derived from Impulse Response Functions

0.48 0.28 0.48 0.46 0.13

Output multiplier

−0.01 −0.10 −0.01 −0.07 −0.18


After 8 quarters

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same, at −0.02; after eight quarters they shrink even further. Recall that the increase in government purchases declines at a 30 percent annual rate, so that much of the change in the response is the direct result of the decline in the stimulus from the extra purchases. Eliminating the New Keynesian property of a markup ratio that declines with output and replacing it with a constant markup of zero (that is, dropping ω from 0.7 to 0; second row of table 4) alters the responses dramatically. The impact multipliers become 0.60 for output and −0.16 for consumption, both of which are small relative to the earlier evidence. Again, these become even smaller as the impulse dies out over four and eight quarters. On the other hand, removing adjustment costs for capital formation (third row of table 4) has essentially no effect. The reason is simple. If the output multiplier is about 1 and the consumption multiplier is zero, the effect of government purchases on investment must be about zero (here the closed-economy assumption is important). To put it differently, one effect of the government purchases is to drive up the real interest rate and inhibit investment. The second effect is the accelerator: investment increases because businesses add capacity to serve the demand for more output. In the base case the two effects offset each other. Because nothing happens to investment when government purchases increase, adjustment costs are irrelevant to their effect on other variables. The fourth row of table 4 shows that dropping the complementarity of work and consumption has a small downward effect on the output response and a larger downward effect on the consumption response, pushing it into unrealistic territory. Thus, complementarity—a feature of household production and preferences well supported by recent research—helps to make the model’s properties fit the data. The bottom row of the table shows the overwhelming importance of elastic labor supply (including the large part of the elasticity arising from unemployment) in bringing the model into agreement with the data. With less elastic labor supply, all the other features of the model, including the price stickiness that accounts for the variable markup, leave its output response at about a third of the realistic value and its consumption response deeply negative. Although I favor modeling the elastic response with a labor supply function, the New Keynesian literature (not to mention its Keynesian predecessors) speaks of the same response as wage stickiness. Some of this distinction is only one of vocabulary, but I will show later that a sticky wage does not result in as realistic a model as does elastic labor supply.

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IV. Other Issues In this section I address three issues: whether estimates of the government purchases multiplier are affected by such factors as the frequency of price adjustment and the response of the central bank; how the estimates change when nominal interest rates are near their zero lower bound; and whether they change noticeably when the model includes a wealth effect.

IV.A. Is an Analysis without Consideration of the Price Level Appropriate? In most modern macroeconomic models, including all of those to be discussed in section VII, the central bank intervenes in the economy to stabilize the price level or the rate of inflation. Consequently, the bank’s policy rule is part of the model, and the government purchases multipliers depend on this rule. The more draconian the response to inflation, the lower the multipliers. The analysis in this paper does not ignore this point but puts it in the background: the central bank’s policy rule is one of the determinants of the elasticity ω of the markup of price over cost. To explore the relationship between the standard New Keynesian model and the reduced-form approach taken in this paper, based on the negative response of the markup ratio to output, I created a version of the New Keynesian model embodying all the same features and parameters as the benchmark model just discussed, altered to include the Calvo (1983) sticky-price specification with a parameter θ, the quarterly probability that a price remains fixed, and an elasticity of demand ⑀ = 5 facing each producer whose price is sticky. The model also includes a standard Taylor rule governing the path of the price level in relation to the interest rate. The online appendix to this paper gives a full description and code for the model.1 In the New Keynesian model, the stickiness of prices is the fundamental source of variation in the markup of price over cost: such variations occur when firms are hit by demand surprises that raise marginal cost during the time when the price is fixed. Marginal cost rises because firms move up their short-run marginal cost functions, and because the wage rises. Many New Keynesian models invoke sticky wages as well as sticky prices, but I continue to rely on a high wage elasticity to explain larger movements in employment in the face of small changes in wages. 1. Online appendices for all papers in this issue may be found on the Brookings Papers webpage (www.brookings.edu/economics/bpea) under “Conferences and Papers.”

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Table 5. Price Persistence, Multipliers, and Markup Elasticities in a New Keynesian Model Price persistencea θ 0.60 0.70 0.80 0.89 0.90 0.95

Output multiplier


Elasticity of the markup ratio ω

0.60 0.62 0.68 0.95 1.02 1.60

−0.21 −0.20 −0.18 −0.07 −0.04 0.20

0.06 0.13 0.29 0.75 0.84 1.24

Source: Author’s calculations. a. Probability that the price remains fixed in a given quarter.

Table 5 reports the multipliers corresponding to varying degrees of price stickiness, as controlled by the parameter θ. A value of θ between 0.8 and 0.9 delivers an output multiplier in the range just below 1 and a consumption multiplier that is only barely negative. The implied frequency of price change is between 20 percent and 10 percent per quarter. Christiano, Eichenbaum, and Rebelo (2009) take θ to be 0.85. I conclude that the reduced-form approach taken in this paper, based on a negative elasticity of the markup ratio with respect to output, provides a reasonable basis for inferring the effects of changes in government purchases on output and consumption. From the perspective of the issues studied in this paper, it is not necessary to take separate stands on the various ingredients of a nominal model, including the frequency of price adjustment and the response of the central bank. What matters is the reduction in the markup when output expands. The model here is compatible with any explanation for that negative relationship, including explanations that do not depend on sticky prices, such as that of Rotemberg and Garth Saloner (1986).

IV.B. The Importance of the State of the Economy The output and consumption multipliers are derivatives of two endogenous variables with respect to an exogenous shock. They are not fundamental structural parameters invariant to the state of the economy. Quite the contrary, the multipliers are themselves endogenous. The state of the economy in 2009 provides a perfect example. With extreme slack in the economy and the federal funds rate at essentially zero, there are good reasons to believe that the government purchases multipliers are higher than in normal times.

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Christiano, Eichenbaum, and Rebelo (2009) find that the government purchases multiplier in a New Keynesian model becomes large when the economy hits the zero nominal interest rate bound. In a model with an output multiplier of 0.9 in normal times, the multiplier rises to 3.9 when the nominal bank interest rate hits the zero bound and the central bank loses the ability to stimulate the economy by cutting that interest rate. In the simple New Keynesian model of the previous section, the central bank follows a Taylor rule that increases the nominal interest rate by 1.5 percentage points for each percentage point of inflation. At the zero bound, the coefficient becomes zero. The output multiplier rises from 0.95 to 1.72 and the consumption multiplier from −0.07 to 0.26.

IV.C. The Wealth Effect Much of the modern literature on multipliers takes the key difference between neoclassical real business cycle (RBC) models and traditional models to be the former’s inclusion of a wealth effect on consumption. Galí, López-Salido, and Vallés (2007, p. 228, footnotes omitted) provide a clear statement of the standard view of the difference between the two models: The standard RBC and the textbook IS-LM models provide a stark example of such differential qualitative predictions. The standard RBC model generally predicts a decline in consumption in response to a rise in government purchases of goods and services (henceforth, government spending, for short). In contrast, the IS-LM model predicts that consumption should rise, hence amplifying the effects of the expansion in government spending on output. Of course, the reason for the differential impact across those two models lies in how consumers are assumed to behave in each case. The RBC model features infinitely-lived Ricardian households, whose consumption decisions at any point in time are based on an intertemporal budget constraint. Ceteris paribus, an increase in government spending lowers the present value of after-tax income, thus generating a negative wealth effect that induces a cut in consumption. By way of contrast, in the IS-LM model consumers behave in a non-Ricardian fashion, with their consumption being a function of their current disposable income and not of their lifetime resources. Accordingly, the implied effect of an increase in government spending will depend critically on how the latter is financed, with the multiplier increasing with the extent of deficit financing.

A related issue is that some critics of the use of temporary increases in government purchases have argued that their effect is blunted by the public’s expectation of higher future taxes. The model says the opposite: the expectation of higher future taxes lowers wealth, stimulates work effort, and discourages consumption. The output multiplier is higher and the consumption multiplier more negative in a model with the wealth effect than without it. Other critics believe that the public is unaware of the future burden of higher government purchases and are skeptical of

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stimulus estimates that include the wealth effect. To evaluate this issue, I examined the response of the model with elastic labor supply and an elasticity of the markup with respect to output, ω, of 0.6 to an immediate increase in purchases followed by a decline at a rate of 30 percent per year. This model embodies the wealth effect. I compared the multipliers in that model with those in an otherwise identical model in which the increase in immediate purchases was paid back, so to speak, by a decrease in purchases at the end of the solution period with the same present value. Recall that the immediate increase is g, the persistence rate is φ, and the economy’s discount factor is β. The repayment in the last period is T

(23)

g ⎛ 1⎞ ⎜⎝ β ⎟⎠ 1 − βφ .

This alteration in the model lowers the output multiplier by 0.022 and makes the consumption multiplier 0.001 point more negative. These changes are in the expected direction but are trivial in magnitude. I conclude that it hardly matters whether the public anticipates the future taxes needed to finance a temporary increase in government purchases. Ricardian neutrality is irrelevant in this respect. This calculation also demonstrates the unimportance of the wealth effect for temporary increases in government purchases. The standard view, quoted above, applies to permanent increases but not to the type of temporary increase that occurs in a countercyclical stimulus.

V. Sticky Wages The results so far rely on what I have elsewhere called “equilibrium wage stickiness” (Hall 2005). The wage and the volume of work together represent an equilibrium in the bargain between worker and employer, but because the wage responds weakly to changes in labor demand, employers find it desirable to recruit more aggressively when demand is strong; their efforts tighten the labor market and reduce unemployment. An earlier view of wage stickiness rejects the equilibrium concept and supposes that the wage can be sticky in the sense of preventing a worker-employer pair from achieving bilateral efficiency. Hall (2009) argues that this disequilibrium sticky-wage view is unnecessary to an understanding of employment fluctuations—equilibrium stickiness is enough. Here, on the

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contrary, I explore briefly the implications of an extreme form of disequilibrium sticky wages, namely, a fixed real wage. For a discussion of the details of a different and less extreme form in New Keynesian models based on Calvo wage setting, see Jesús Fernández-Villaverde and Juan Rubio-Ramírez (2009). This version of the model differs from the earlier version in that the consumption–work effort condition of equation 19 no longer holds, and the wage w is now fixed at its stationary value for the baseline level of government purchases. The effect is to make labor supply infinitely elastic at the fixed wage, rather than fairly elastic around a wage determined by wealth. The fixed-wage model implies that the output and consumption multipliers are exactly zero. Absent the markup response, this proposition follows directly from the observation that firms hire up to the point that the marginal revenue product of capital equals the wage. The response of the markup does not alter this proposition. Putting the markup response into the profitmaximization condition for the firm’s choice of labor input and restating in terms of labor input h1 and capital k0 yields what I call the extended labor demand function: 1

(24)

1 ⎤ 1− α(1+ ω ) ⎡ h1 = ⎢αk0(1− α )(1+ ω ) ⎥ . w⎦ ⎣

With k0 at its historical, preshock level, the only potentially endogenous variable here is the wage. If it is fixed, labor input in the first postshock period is also fixed, and so output and consumption are fixed. By contrast, in the baseline model of this paper, where the wage is endogenous, a change in the wage can alter employment and output. Now comes the surprise: the labor demand function extended to include the markup response, in the above equation, slopes upward! In the base case α = 0.7 and ω = 0.7, so 1 − α(1 + ω)= −0.19, and the exponent on the wage in the extended labor demand function is more than 5. The baseline model gets its brisk response of employment and output from a small wage increase that stimulates both demand and supply. In the fixed-wage case, a strong response does emerge once time goes by and the capital stock expands, thus increasing labor demand. Figure 2 compares the impulse response functions for the fixed-wage and the baseline models. The fixed-wage response builds slowly for an extended period. Output remains high even 15 years after the shock to government purchases, many, many years after purchases have returned to normal.

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Figure 2. Impulse Response Functions for the Baseline and Fixed-Wage Models Response (dollars per initial dollar of government purchases) Fixed wage

1.6 1.4 1.2 1.0 0.8 0.6 0.4

Baseline (elastic labor supply)

0.2 12

24

36 Quarters

48

60

72


VI. Departures from the Life-Cycle Model of Consumption One of Keynes’s contributions to macroeconomic theory was the consumption function, where current consumption depends mainly on current income. As the life-cycle model became the standard framework for thinking about consumption behavior, researchers developed hybrid models in which some households have full access to capital markets, and therefore smooth consumption according to the life-cycle principle, while others— those who would borrow if they could—are constrained to consume current income. Despite a quarter century of research within this framework, substantial disagreement prevails about the fraction of consumption governed by the life-cycle model. Note that the issue is the fraction of consumption, not the fraction of consumers. Given that more prosperous households are surely less likely to be constrained, the fraction of constrained consumption is less than the fraction of constrained consumers. To the extent that the factual premise of this paper holds—that the output response to government purchases is robust and close to dollar for dollar, whereas the consumption response is essentially zero—the idea that consumption responds mainly to current income is completely unsupported. The reason is that the ratio of the consumption response to the output response is the perfect instrumental variables estimator of the marginal

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propensity to consume if a simple consumption function links output (income) and consumption. If one takes the evidence in table 1 seriously, the marginal propensity to consume is slightly negative and estimated with precision, provided at least the Korean War is included in the sample. Obviously, a negative marginal propensity to consume is profoundly inconsistent with the idea of a consumption function, so the appropriate conclusion is that important forces other than current income, such as the forces implicit in the life-cycle model, determine consumption. Despite the problems with inference based on the behavior of consumption during wars, I think the hypothesis that current income has a large effect on consumption faces an uphill battle with the data. The standard view of the government purchases multiplier—as expressed, for example, in the quote from Galí and coauthors in the previous section— is that a Keynesian consumption function delivers fairly high multipliers. If the consumption function reflects borrowing constraints on the unemployed, some alteration of the labor supply part of the earlier model is needed: the notion of a constraint takes labor income as exogenous, not partly the choice of the worker. The development of a full model with heterogeneous households, some facing more limited choices than discussed earlier, is beyond the scope of this paper. Instead, I will pair the consumption function with another assumption of many Keynesian models, that of wage rigidity, as discussed in the previous section. Employers choose total hours of work, h, so as to equate the marginal revenue product of labor to the prescribed wage. I drop both the consumption Euler equation (equation 21) and the first-order condition for labor supply (equation 19) and replace them with a Solow-style consumption function, (25)

ct = (1 − s ) yt ,

–. For consistency and the earlier assumption that the wage is a constant, w with the other results in this paper, I choose the saving rate s to be its stationary value in the neoclassical model, just under 0.2. Note that this is the saving rate out of gross output and includes depreciation, which is why it exceeds normal ideas about net saving, which treat it as saving out of income net of depreciation. The relevant equations from the earlier model are the equation for –, employment conditional on the wage w (equation 24), evaluated at w = w and the law of motion of the capital stock, κ ( kt − kt − 1 ) kt + = (1 − δ ) kt −1 + yt − ct − gt . 2 kt − 1 2

(26)

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ROBERT E. HALL

Figure 3. Impulse Response Functions for the Fixed-Wage Model with Consumption Proportional to Output Response (dollars per initial dollar of government purchases)

0.8

Output response

0.6 0.4 0.2

Consumption response

12

24

36 Quarters

48

60

72


The model behaves as a Solow growth model, converging to stationary values of output, capital, and consumption, which I take to equal their values in the baseline model. Figure 3 shows the impulse response functions for the consumption function model. Because the model embodies a fixed wage, the immediate response of both output and consumption is zero. The responses build over time but are not as strong as in the case of a fixed wage as shown in figure 2. Not surprisingly, the simple consumption function delivers a distinctly positive consumption multiplier, not far below the output multiplier. The intertemporal substitution response that depresses consumption in the model with life-cycle consumption is absent. The relationship between this model and the simple expenditure model of the purchases multiplier is easy to explain. The simple expenditure model takes investment as exogenous. Letting i denote investment and neglecting time subscripts, (27)

y=

i+g , s

the standard expenditure solution with multiplier my = 1/s. In contrast, the consumption function model makes investment endogenous, declining when

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output rises. Government purchases crowd out investment in this model. Because consumption has to rise by more than 80 percent of the increase in output, crowding out is severe in the presence of a consumption function. A number of investigations of the role of partial borrowing constraints, discussed in the next section, suggest that they can increase the output multiplier under conditions different from the model studied here, which is extreme. This model takes wages as fixed for 20 years, and it assumes that all consumption is tied to current income, contrary to the conclusions of the literature on borrowing constraints.

VII. Multipliers Inferred from New Keynesian Structural Models The term “New Keynesian” refers to the class of models combining a full treatment of the production side of the economy, life-cycle consumption behavior, sticky wages, and markup ratios that respond negatively to output increases because of sticky prices. Another name often used for the class is dynamic stochastic general-equilibrium or DSGE models. These models are widely used in recent macroeconomic research, especially at central banks. Although the characterization of the effects of monetary policy has been the main use of New Keynesian models, four studies have examined responses to government purchases. Galí, López-Salido, and Vallés (2007) consider a fairly standard New Keynesian model, with one nonstandard element: a fraction of consumers λ simply consume all their labor income rather than follow the life-cycle principle. Although these authors also consider a competitive labor market with a flexible wage, I will discuss only their results for a sticky wage, for the reasons discussed earlier in this paper: a sticky wage appears to be essential to generate meaningfully positive government purchases multipliers. The results of Galí and coauthors confirm this proposition. In their baseline model, they take the quarterly persistence of the effect of the government purchases shock to be 0.9, about the same as the annual persistence of 0.7 that I used earlier. At their preferred value of the fraction of consumption subject to rule-of-thumb behavior, λ = 0.5, the output multiplier on impact is 1.9 and the consumption multiplier is 1.0 (Galí and others 2007, figure 3, p. 250). With life-cycle consumption behavior, λ = 0, the output multiplier is 0.75 and the consumption multiplier is slightly negative. Intermediate values of λ come close to matching the consumption multipliers found in the VARs reviewed earlier in this paper.

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López-Salido and Pau Rabanal (2006) find similar results in a model based on a leading New Keynesian model, that of Christiano, Eichenbaum, and Evans (2005). With some consumption governed only by current income and the remainder by the life-cycle principle, the impact output multiplier is just above 2 and the consumption multiplier just above 1 (Christiano and others 2005, figure 1, p. 19). With the standard New Keynesian specification where all consumption follows the life-cycle principle, the output multiplier is slightly above 1.0 and the consumption multiplier is slightly negative. Günter Coenen and Roland Straub (2005) study the New Keynesian model of Frank Smets and Raf Wouters (2003), an outgrowth of the Christiano, Eichenbaum, and Evans model. They consider both the original model and one altered so that about a quarter of consumption tracks current income rather than following the life-cycle principle. For the original model, the consumption multiplier is −0.14 on impact, and the output multiplier is 0.68 (1 plus the consumption multiplier of −0.14 plus the investment multiplier of −0.18) (Smets and Wouters 2003, figure 1, p. 457). When about a quarter of consumption is constrained, the consumption multiplier is −0.05 on impact, and the output multiplier is 0.77 (1 plus the consumption multiplier of −0.05 plus the investment multiplier of −0.18). John Cogan and coauthors (2009) also use the Smets-Wouters New Keynesian model to measure the output multiplier. Their model assumes that all consumption follows the life-cycle principle. For the transitory burst of government purchases in the February 2009 stimulus bill, they find an output multiplier of about 0.5 (Cogan and others 2009, figure 2, p. 12). These four papers make similar assumptions about the single most important feature of a model with respect to multipliers, namely, the response of the markup ratio to increases in output. The first two illustrate the importance of the controversial issue of the fraction of consumption governed by the life-cycle principle. Absent a substantial departure from the life-cycle principle, the models agree that the output multiplier is between 0.5 and 1.0 and that the consumption multiplier is around zero, values consistent with the OLS and VAR evidence.

VIII. Negative Response of the Markup Ratio to Output Rotemberg and Woodford (1999) provide a complete discussion as of a decade ago of the many empirical and theoretical issues relating to variations in the markup ratio.

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VIII.A. Earlier Research on Cyclical Changes in the Markup Research on variations in the markup of price over marginal cost falls into two categories: models where alterations in competition are a driving force of the business cycle or are part of such a driving force, and models where markups fall passively when output expands, because product prices are sticky but some elements of cost are not. For purposes of understanding the effects of fiscal policy, the issue is the markup, not price stickiness itself. Thus, both strands of research are relevant to the issue of the output multiplier for government purchases. One easy way to tell the two strands apart is to see whether sticky prices are derived, as in the first set of models, or assumed, as in the second. From the perspective of the fiscal issue, it does not seem to matter which way the model gets to the property of a countercyclical markup. Rotemberg and Woodford (1999, pp. 1112–29) survey this literature thoroughly.

VIII.B. Theoretical Models with Countercyclical Markup Rotemberg and Saloner (1986) launched the modern literature on the relationship between competition and economic activity. The starting point is a model of oligopoly in which a collusively determined high price is an equilibrium because rivals will revert to competition to punish a deviator who tries to capture a large volume of sales by beating its rivals’ price for one period. The potential deviator compares the immediate profit in one period with the present value of its share of the collusive profit. Deviation is more likely when demand is temporarily strong, so that the immediate profit exceeds that present value. Some episodes in real-world oligopolies seem to fit the model. Rotemberg and Woodford (1992) carried the idea of a declining markup in a boom over to a general-equilibrium setting. Since the publication of their well-known paper, it has been understood that a countercyclical markup is an important ingredient in models that take demand fluctuations as a driving force. Miles Kimball (1995) provides an extensive discussion of the role of markup variation in a sticky-price New Keynesian setting. Mark Bils (1989) developed a model of countercyclical markups based on customer loyalty. In an expanding economy where customers are seeking suppliers of products they have not previously consumed, sellers compete aggressively and customers enjoy low prices. Markups are low. In a slump, customers buy from their established suppliers and do not look

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for suppliers of new goods. Sellers respond by setting higher prices to reflect the less elastic demand of their customer base. Chris Edmond and Laura Veldkamp (2009) consider the effect of changes in the distribution of income over the business cycle. They conclude that booms are periods when income shifts toward lower-income consumers with more elastic demand, so that the optimal markups of sellers fall. To the extent that increases in government purchases compress the distribution of income in the same way as other driving forces, this mechanism would support the assumption in this paper about the negative relationship between output and markups.

VIII.C. Empirical Research on the Cyclical Movements of the Markup Ratio If the markup ratio falls in booms and rises in recessions, the share of income captured by labor should rise in booms and fall in recessions, given that the markup adds to the income of business owners. In other words, labor’s share should be procyclical. To formalize this idea, note that marginal cost is w , ∂Y ∂L

(28)

where w is the wage, Y is output, and L is labor input. This relationship comes from the envelope theorem property that a cost-minimizing firm is indifferent among increases in any of its inputs. Then the markup ratio µ is μ=

(29)

p w ∂Y ∂L

=

pY L ∂Y wL Y ∂L

=

α , s

where α is the elasticity of output with respect to labor input and s is the share of labor compensation wL in total revenue pY. If the elasticity α is constant—the Cobb-Douglas case—the intuition about the relationship

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Figure 4. Markup Ratio and Employment, 1948–2009 Index

Percent

1.04 1.02

Cobb-Douglas index of the markup ratioa (left scale) 105

1.00 0.98 0.96

100

0.94 0.92

95

0.90 0.88 0.86

Employment rateb (right scale)

90

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Source: Author’s calculations. a. Reciprocal of the Bureau of Labor Statistics index of the labor share of income. b. One minus the unemployment rate.

between labor’s share and the markup is confirmed: a countercyclical markup requires a procyclical labor share. To check this proposition against U.S. data, I construct two series from Bureau of Labor Statistics (BLS) data. One is the reciprocal of the BLS index of the labor share (BLS series PRS84006173), which I call the Cobb-Douglas index of the markup ratio. The other is the employment rate, which is 100 minus the standard unemployment rate (BLS series LNS14000000). According to the simplest version of the countercyclical markup hypothesis, the markup index should move in the opposite direction from the employment rate: as employment grows in a boom, the markup index should decline. Figure 4 shows the two series. Although their relationship is far from systematic, it is clear that they tend to move in the same direction: booms are times when the markup index rises along with employment, and recessions are times when the markup index falls with employment. To put it differently, business owners’ share of income does not fall in booms, on account of lower markups; rather, it rises. The two most recent expansions are the leading examples of declining labor and rising business shares; the markup index reached an all-time high at the most recent cyclical peak at the end of 2007.

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Figure 4 is only a first cut at testing the countercyclical markup hypothesis. Research has focused on two factors omitted from the figure. One is the measurement of the labor share of compensation. In the numerator of the share, wL, the appropriate measure of the wage is the marginal cost to the firm of adding another hour of work. If the incremental hour is more expensive than the average hour, the use of the average wage in the numerator will understate the true value of labor’s share. If the understatement were the same in booms and slumps, it would not affect the conclusion to be drawn from the figure. But if the incidence of higher marginal wages is greater in booms than in slumps, the properly calculated share will be less countercyclical than the one based on the average wage, and the Cobb-Douglas index will be less procyclical or possibly even countercyclical, as the hypothesis requires. Bils (1987) pursued this approach. The second factor omitted from the figure is variation in the elasticity of the production function, α. If the elasticity of substitution between labor and capital is less than 1, the elasticity falls if the labor-capital ratio rises: low substitution means that production saturates in one input if that input rises relative to another. The markup ratio is the elasticity divided by the labor share. If the elasticity falls more than in proportion to the labor share as the economy expands, the true markup ratio could fall even though the Cobb-Douglas index of the markup ratio rises. Christopher Nekarda and Ramey (2009) pursue this approach. They conclude that the variation in the labor elasticity of the production function with an elasticity of substitution of 0.5 is insufficient to deliver a countercyclical markup ratio. Bils (1987) estimated the cyclical movements in the markup ratio by estimating the changes in the marginal cost of labor and applying the envelope theorem to infer changes in the marginal cost of output. He found that a larger fraction of workers are subject to the 50 percent overtime premium requirement of the Fair Labor Standards Act in booms than in recessions. Given that employers could have avoided the increase in the marginal cost of labor by using more of other factors, but did not, he inferred a corresponding increase in the marginal cost of output. Then he found that prices are not as cyclical as marginal cost, leading to the inference that the markup of price over marginal cost must shrink in booms and widen in recessions. Nekarda and Ramey (2009) revisit Bils’s findings in much the same framework, but with new, broader data and sufficient alterations to reverse the finding in favor of procyclical markup ratios. They discuss evidence that the effective overtime premium is not the statutory 50 percent that

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Bils used, but rather may be 25 percent. They also question the definition of the business cycle that Bils employed. Extension from manufacturing to the entire economy appears to be the most important factor distinguishing their work from Bils’s. The framework in Bils’s and in Nekarda and Ramey’s work is robust in a number of important ways. First, it makes no assumptions about the supply of capital services. The results apply with any type or magnitude of capital adjustment costs and variable utilization of installed capital (see Rotemberg and Woodford 1999, p. 1079). Second, they apply for any type of pricing, including customer pricing where the choice of the price depends on complicated intertemporal factors. The price is taken as data. Customer pricing should be visible in the data as higher profits and lower labor shares in slack markets, when firms are exploiting their installed base. Firms should forgo profit in strong markets, when it pays to set prices low to sign up new customers who will remain loyal when conditions weaken. One important factor bearing on the measurement of cyclical fluctuations in markup ratios has escaped empirical consideration so far, to my knowledge. Employers may smooth wage payments to their workers rather than pay a wage equal to current marginal revenue product, as assumed in the research on the cyclical behavior of the labor share. Jonathan Thomas and Tim Worrall (1988) present a representative model where employers insure workers against some of the idiosyncratic risk of working for a particular firm. In their model the wage payment remains constant as long as it remains within the bargaining set of the worker and the firm. For employment relationships with substantial match-specific capital, the wage can remain constant despite large changes in demand for the firm’s products. The result is a substantial bias in favor of a countercyclical labor share and thus a procyclical markup ratio. Although this issue is well understood, no good solution has appeared so far. Pissarides (2009) surveys the literature on wage flexibility and finds a strong consensus that the wages of newly hired workers are more sensitive to the business cycle than are the wages of continuing workers. This finding supports the hypothesis of wage smoothing. I conclude that the cyclical behavior of the labor share does not provide direct support for the hypothesis of a countercyclical markup ratio. The simple Cobb-Douglas markup ratio derived from the labor share is distinctly procyclical. Attempts to adjust it through improved measurement of the marginal wage and through consideration of fluctuations in the labor

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elasticity of the production function do not seem to deliver big enough adjustments to overcome this procyclical character. In the absence of effective adjustments for wage smoothing, however, I believe the hypothesis of a countercyclical markup ratio is still an open issue.

VIII.D. Indirect Evidence on the Cyclical Behavior of the Markup Ratio Bils and James Kahn (2000) use inventory movements to shed light on the cyclical movements of marginal cost. Earlier research, based on a fixed target ratio of inventories to sales, had concluded that procyclical inventory investment showed that marginal cost falls in booms, because otherwise firms would schedule the investment during times when production was cheap, in times of low output. Bils and Kahn demonstrate that the movements of marginal cost cannot be big enough to induce such rescheduling of production. They go on to show that countercyclical markups do alter inventory holding cost enough over the cycle to explain the movements of inventories, if the target inventory-sales ratio is itself sensitive to the holding cost, given an extreme assumption about the cost of labor. The assumption is that all of the procyclical movement of measured productivity is actually variation in work effort. Under this assumption, labor becomes cheap in booms of the type that last occurred in the early 1980s, in the recovery following the recession of 1981–82. That assumption is not only extreme but unverifiable. In any case it fails to account for the events of the following three business cycles, when productivity rose during recessions. It strains credulity that people were working harder than usual in the troughs of 1991, 2001, and today. Research on the response of prices to cost increases has some bearing on the behavior of the markup ratio. To the extent that prices remain fixed when costs rise, the markup ratio falls. As I noted earlier, models incorporating the popular Calvo price-stickiness mechanism have this property. Bils and Yongsung Chang (2000) studied highly disaggregated prices. They found stronger responses of prices to changes in materials and fuel costs than to changes in wages, productivity, and output (taken as a measure of the position of the firm on its marginal cost schedule). The weaker response to wages is consistent with wage smoothing, which introduces an error of measurement. The quick response to certain categories of cost is inconsistent with the Calvo model. Bils and Chang favor theories of price stickiness based on modern limit pricing models, where firms deter entry of rivals by depressing the profits available to entrants.

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Table 6. Effects of the February 2009 Stimulus Measure and of an Alternative, Front-Loaded Measure Item Actual stimulus purchases, fiscal year (billions of dollars) Actual stimulus purchases, calendar yeara (billions of dollars) GDP (billions of dollars) Actual stimulus purchases, calendar year (percent of GDP) Effect on GDP (percent) Hypothetical front-loaded stimulus purchases, calendar year (percent of GDP) Effect on GDP (percent)

2009

2010

2011

Sum

34.8

110.7

76.3

221.8

62.5

102.1

57.2

221.8

13,700 0.46

14,043 0.73

14,604 0.39

1.57

1.10 0.71

1.28 0.50

0.70 0.35

3.08 1.56

1.35

0.94

0.62

2.90

Sources: Congressional Budget Office; author’s calculations. a. A small amount of purchases, projected by the Congressional Budget Office to occur in fiscal 2012, is omitted from the figure for calendar 2011.

IX. Application to the Government Purchases Stimulus of 2009 The fiscal stimulus measure passed in February 2009 included increases in federal purchases of goods and services. The top row of table 6 gives the Congressional Budget Office’s estimates of likely purchases under the measure by fiscal year (October through September). The second row restates the figures by calendar year, assuming equal spending within the fiscal year by quarter. The third row gives rough estimates of GDP for the three years 2009, 2010, and 2011, and the fourth row states the stimulus purchases as percents of GDP. The fifth row shows the results of inserting the fourth row into the model with the preferred parameter values. These are the base case values in table 3, but with the markup-response parameter ω set at 1.29 to match the response in the New Keynesian model at the constant nominal interest rate of zero that prevailed when the policy was adopted in February 2009. I substitute the fourth row into the model in place of the exponentially declining pattern used in the earlier runs of the model. This row shows the powerful anticipation effects in the model, based on the assumption that, as of the beginning of 2009, decisionmakers believed that purchases of the magnitude shown in the table would materialize in the three years. The purchases stimulus raises GDP in 2009 by 1.10 percent, with further effects of 1.28 percent in 2010 and 0.70 percent in 2011. The model disputes the common view that the long ramp-up in purchases will delay the effects of the stimulus until long after they would

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be most beneficial. Rather, announcing future purchases delivers immediate stimulus. Back-loading is a desirable feature of a stimulus program. All this is according to a simple model that overlooks many potentially important features of the economy. The calculations also rest critically on the projection that the stimulus purchases will ramp down in 2011 and end in 2012, a proposition that is under dispute. The bottom two rows of table 6 show the effects of an alternative, frontloaded time pattern of stimulus purchases. I assume, as in the earlier runs of the model, that a burst of new purchases dies off at 30 percent per year rather than rising in the second year. Unlike in the earlier runs, here the purchases go to zero in the fourth year, to make the policy more comparable to the three-year horizon of the February 2009 stimulus measure. I standardize the front-loaded policy to have the same total amount of purchases over the three years. The effect in 2009 is somewhat larger in the front-loaded case than in the actual back-loaded policy, but the three-year sum of the effects on GDP of the front-loaded policy is smaller. The model suggests that the much-criticized slow ramp-up of the stimulus was actually beneficial. Table 6 makes it clear that the purchases component of the stimulus package passed in February 2009 could not possibly have closed much of the shortfall of GDP from normal levels. The shortfall is around $1.2 trillion for 2009. No conceivable multiplier could permit $62.5 billion of added purchases to close much of a gap of that magnitude.

X. Concluding Remarks I am persuaded that GDP rises by roughly the amount of an increase in government purchases, and possibly rather more when monetary policy is passive because of the zero bound. I am aware that neoclassical models have no hope of explaining such a high multiplier, even if extended to include unemployment along the lines discussed in this paper. I am impressed by the success of New Keynesian models in matching the observed multiplier, because these models were developed for rather different purposes and estimated using data containing essentially no variation in government purchases. Notwithstanding this success, I am concerned about the weak factual support for the key mechanism underlying the New Keynesian explanation of the multiplier, namely, the decline in the markup ratio that accompanies an increase in output. The behavior of profit margins suggests on its face that the markup ratio rises with output. The only plausible way for falling

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markups to fit the data is through a lot of wage smoothing. I think there is room for new ideas outside the New Keynesian framework to explain the high value of the multiplier along with other mysteries about aggregate economic behavior.

ACKNOWLEDGMENTS I am grateful to the editors and to my Brookings Panel discussants, and to Robert Barro, Susanto Basu, Jordi Galí, Jonathan Parker, Fabrizio Perri, Valerie Ramey, and Ricardo Reis for guidance and comments. A file containing the calculations is available on my website.

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References Aguiar, Mark, and Erik Hurst. 2005. “Consumption versus Expenditure.” Journal of Political Economy 113, no. 5 (October): 919–48. Andolfatto, David. 1996. “Business Cycles and Labor-Market Search.” American Economic Review 86, no. 1: 112–32. Barro, Robert J., and Charles J. Redlick. 2009. “Macroeconomic Effects from Government Purchases and Taxes.” Working Paper no. 15369. Cambridge, Mass.: National Bureau of Economic Research (September). Bilbiie, Florin O. 2009. “Nonseparable Preferences, Fiscal Policy Puzzles and Inferior Goods.” Journal of Money, Credit, and Banking 41, no. 2–3: 443–50. Bils, Mark. 1987. “The Cyclical Behavior of Marginal Cost and Price.” American Economic Review 77, no. 5 (December): 838–55. ———. 1989. “Pricing in a Customer Market.” Quarterly Journal of Economics 104, no. 4 (November): 699–718. Bils, Mark, and Yongsung Chang. 2000. “Understanding How Price Responds to Costs and Production.” Carnegie-Rochester Conference Series on Public Policy 52: 33–77. Bils, Mark, and James A. Kahn. 2000. “What Inventory Behavior Tells Us about Business Cycles.” American Economic Review 90, no. 3 (June): 458–81. Blanchard, Olivier, and Jordi Galí. 2007. “Real Wage Rigidities and the New Keynesian Model.” Journal of Money, Credit, and Banking 39, no. 1: 35–65. 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. Calvo, Guillermo A. 1983. “Staggered Prices in a Utility-Maximizing Framework.” Journal of Monetary Economics 12, no. 3 (September): 383–98. Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans. 2005. “Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy.” Journal of Political Economy 113, no. 1: 1–45. Christiano, Lawrence, Martin Eichenbaum, and Sergio Rebelo. 2009. “When Is the Government Spending Multiplier Large?” Working Paper no. 15394. Cambridge, Mass.: National Bureau of Economic Research (October). Coenen, Günter, and Roland Straub. 2005. “Does Government Spending Crowd in Private Consumption? Theory and Empirical Evidence for the Euro Area.” International Finance 8, no. 3: 435–70. Cogan, John F., Tobias Cwik, John B. Taylor, and Volker Wieland. 2009. “New Keynesian versus Old Keynesian Government Spending Multipliers.” Working Paper no. 14782. Cambridge, Mass.: National Bureau of Economic Research (March). Edmond, Chris, and Laura Veldkamp. 2009. “Income Dispersion and Countercyclical Markups.” Journal of Monetary Economics 56, no. 6: 791–804. Fernández-Villaverde, Jesús, and Juan F. Rubio-Ramírez. 2009. “A Baseline DSGE Model.” University of Pennsylvania (October).

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Galí, Jordi, J. David López-Salido, and Javier Vallés. 2007. “Understanding the Effects of Government Spending on Consumption.” Journal of the European Economic Association 5, no. 1: 227–70 (March). Hall, Robert E. 1986. “The Role of Consumption in Economic Fluctuations.” In The American Business Cycle: Continuity and Change, edited by Robert J. Gordon. University of Chicago Press for the National Bureau of Economic Research. ———. 2005. “Employment Fluctuations with Equilibrium Wage Stickiness.” American Economic Review 95, no. 1 (March): 50–65. ———. 2009. “Reconciling Cyclical Movements in the Marginal Value of Time and the Marginal Product of Labor.” Journal of Political Economy 117, no. 2 (April): 281–323. Hall, Robert E., and Paul R. Milgrom. 2008. “The Limited Influence of Unemployment on the Wage Bargain.” American Economic Review 98, no. 4 (September): 1653–74. Kimball, Miles S. 1995. “The Quantitative Analytics of the Basic Neomonetarist Model.” Journal of Money, Credit, and Banking 27, no. 4, part 2 (November): 1241–77. Kydland, Finn E., and Edward C. Prescott. 1982. “Time to Build and Aggregate Fluctuations.” Econometrica 50, no. 6 (November): 1345–70. López-Salido, J. David, and Pau Rabanal. 2006. “Government Spending and Consumption-Hours Preferences.” Working Paper no. 02/2006. Barcelona: la Caixa (November). Merz, Monika. 1995. “Search in the Labor Market and the Real Business Cycle.” Journal of Monetary Economics 36, no. 2: 269–300. Mortensen, Dale T., and Christopher A. Pissarides. 1994. “Job Creation and Job Destruction in the Theory of Unemployment.” Review of Economic Studies 61, no. 3: 397–415. Mountford, Andrew, and Harald Uhlig. 2008. “What Are the Effects of Fiscal Policy Shocks?” Working Paper no. 14551. Cambridge, Mass.: National Bureau of Economic Research (December). Nekarda, Christopher J., and Valerie A. Ramey. 2009. “The Cyclical Behavior of the Price-Cost Markup.” University of California, San Diego (July). Perotti, Roberto. 2008. “In Search of the Transmission Mechanism of Fiscal Policy.” In NBER Macroeconomics Annual 2007, edited by Daron Acemoglu, Kenneth Rogoff, and Michael Woodford. MIT Press. Pissarides, Christopher A. 2009. “The Unemployment Volatility Puzzle: Is Wage Stickiness the Answer?” Econometrica 77, no. 5: 1339–69. Ramey, Valerie A. 2009. “Identifying Government Spending Shocks: It’s All in the Timing.” Working Paper no. 15464. Cambridge, Mass.: National Bureau of Economic Research (October). Romer, Christina, and Jared Bernstein. 2009. “The Job Impact of the American Recovery and Reinvestment Plan” (January). Washington: Obama-Biden Transition Project. otrans.3cdn.net/45593e8ecbd339d074_l3m6bt1te.pdf.

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Rotemberg, Julio J., and Garth Saloner. 1986. “A Supergame-Theoretic Model of Price Wars during Booms.” American Economic Review 76, no. 3 (June): 390–407. Rotemberg, Julio J., and Michael Woodford. 1992. “Oligopolistic Pricing and the Effects of Aggregate Demand on Economic Activity.” Journal of Political Economy 100, no. 6 (December): 1153–1207. ———. 1999. “The Cyclical Behavior of Prices and Costs.” Chapter 16 in Handbook of Macroeconomics, vol. 1B, edited by John B. Taylor and Michael Woodford. Amsterdam: Elsevier. Shimer, Robert. 2005. “The Cyclical Behavior of Equilibrium Unemployment and Vacancies.” American Economic Review 95, no. 1: 24–49. Smets, Frank, and Raf Wouters. 2003. “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area.” Journal of the European Economic Association 1, no. 5 (September): 1123–75. Thomas, Jonathan, and Tim Worrall. 1988. “Self-Enforcing Wage Contracts.” Review of Economic Studies 55, no. 4 (October): 541–53.


ALAN J. AUERBACH Robert Hall has produced a very useful paper that provides an overview of the literature and much additional analysis regarding the impact of government purchases on GDP. As the paper’s title makes clear, the focus here is exclusively on the effects of government purchases, which now constitute just over half of all government spending exclusive of interest payments. The remainder of the government budget consists of transfer payments, which are typically lumped together with taxes when their effects on output are considered, because, like taxes, they have no direct impact on GDP; transfer payments affect GDP only through their impact on private consumption, private investment, and net exports. Although national income accounting is a logical exercise based on reasonable, time-tested conventions, one should keep in mind that the distinctions in the accounts are sometimes more sharply drawn than the underlying reality. For example, if the government appropriates money for a “shovel-ready” project, this is considered a government purchase, even if the project is a classic Keynesian one in which the shovels are used to dig and fill in ditches. With very minor modification, this hypothetical program could just as easily have been classified as an expansion of transfer payments, with no direct impact on GDP. The change in classification would not affect the further macroeconomic consequences of the policy, but it would have a big effect on the policy’s measured multiplier. Thus, care is needed in drawing conclusions about the relative effectiveness of purchases and transfer payments based on their relative multipliers, a caveat less relevant for the current paper than for the broader related literature. What is relevant even for an analysis exclusively of purchases is that their effects on consumption, investment, and net exports should depend on the nature of the purchase. For example, a productive government investment, 232


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unlike a disguised transfer payment, might raise the marginal product of private capital and therefore encourage private investment. This is a point worth keeping in mind when making conjectures about the effects of quickly adopted antirecession policies, and one of the many reasons why multipliers estimated using historical episodes might not apply in the present circumstances. Hall’s paper relies on the two main tools of the literature to draw conclusions about the effects of government purchases on output: simple time-series econometrics with relatively few restrictions imposed, and general-equilibrium simulation models based on structural equations, calibrated using parameters based on either auxiliary estimates or educated guesses guided by theory. Each tool has its advantages and disadvantages. Time-series methods reveal patterns actually present in the data, whereas a model’s predictions are only as accurate as the model itself is realistic. But time-series methods may be only of limited use in predicting the effects of policies when either the policies or the economic environment departs from historical experience; a structural model can easily be used for such an exercise. The two approaches therefore are naturally complementary, and Hall utilizes them in this fashion. He asks whether a calibrated model can generate predictions that are consistent with the empirical evidence, as a way of assessing the validity of both the model and the time-series analysis. The basic conclusion of this exercise is yes. In particular, Hall argues that the most plausible time-series estimates, which find that government purchases increase GDP overall but crowd out private consumption, are consistent with what is implied by models based on optimal household and firm behavior. In what is perhaps the paper’s most valuable contribution, he further shows which elements of the model are critical to this result and which are not. In particular, the two critical elements to generating a large enough multiplier are very elastic labor supply and countercyclical producer markups. Somewhat less critical, but helpful in limiting the negative consumption response, are complementarity of work and consumption and limited-horizon consumption responses to changes in incomes, both of which have received some empirical support in the recent literature. And of little importance at all are wealth effects, since it does not take a very long horizon to get close to Ricardian equivalence when a temporary spending policy is being analyzed. As to the empirical support for the two key components, responsive labor supply and countercyclical markups, Hall argues that a very elastic employment response is consistent with equilibrium in a model of job search and wage bargaining even if the hours elas-

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ticity is small, and he suggests, less convincingly, that the jury is still out on the presence of countercyclical markups, even though they do not seem to be present in the data. In short, Hall tells a story that hangs together reasonably well, but not all the pieces fit quite right. All in all, I find Hall’s analysis to be relatively convincing as to the plausibility of the empirical results he reviews early in the paper. Like him, I find the most convincing results on the effects of government purchases to be those based on the methodology of Valerie Ramey and Matthew Shapiro (1998), recently updated by Ramey (2009), which use large military spending buildups to identify exogenous government spending shocks. As Hall himself illustrates nicely in his figure 1, however, these results are based on some very unique and now quite dated natural experiments— mostly World War II and the buildup to it—so it is very hard to know what they reveal about what is of greatest concern right now, namely, the effects of nonmilitary government purchases on economic activity when the economy is in deep recession, short-term government interest rates are effectively zero, and the government’s ability to meet its fiscal commitments is quite unclear. Here one relies on the structural models, and there is little empirical evidence against which to test the models’ predictions. The fact that they are consistent with the empirical results does not imply that they will do a very good job in 2009, so one is still left having to evaluate the models by judging the plausibility of their assumptions. To me, it is quite plausible—as argued by Lawrence Christiano, Martin Eichenbaum, and Sergio Rebelo (2009) and by Gauti Eggertsson (2008)— that the fiscal multiplier will be larger now, with a slack economy and zero nominal interest rates. In Hall’s own analysis using a simple dynamic New Keynesian model, a larger fiscal multiplier results because the Taylor rule that otherwise would raise nominal interest rates in response is inoperative: because interest rates are already constrained to be higher than the monetary authority would like them to be, raising the desired interest rate has no effect on monetary policy. But the multipliers in these cited papers rise by more than in Hall’s analysis, and so one wonders whether there is more to the story. As to other issues, I can think of reasons why nonmilitary spending could have stronger positive effects than military spending on private domestic output, but also reasons why the effects could be weaker. And consistent with what is known from the literature on fiscal consolidations (for example, Perotti 1999), I worry that the benefits of today’s expansionary fiscal policies may be undercut by concerns about the


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government’s commitment to keeping the policies temporary and about its willingness to confront the nation’s long-term fiscal challenges. Indeed, the current recession has seen an unprecedented increase in the perceived probability of default on U.S. Treasury obligations implied by credit default swap prices (Auerbach and Gale 2009). Although this increase has subsided since early 2009 and may have had more to do with the recession and gyrations in credit markets, these prices are still elevated relative to where they were in the past. I also wonder about some of the more specific conclusions Hall reaches when evaluating the 2009 stimulus package. In particular, he performs jujitsu on critics of the package’s slow implementation of government purchases: using his simple structural model, he estimates (table 6) that a front-loaded stimulus of the same total size would have had smaller effects on GDP than the actual package. In Hall’s words, “Back-loading is a desirable feature of a stimulus program.” I am not sure exactly what generates this result, but I assume that it has something to do with an announced change in government spending having effects on private behavior similar to an immediate one, but without the crowding-out effect. It would have been helpful if the discussion were more explicit on this point. In particular, one would expect this result to be sensitive not only to the credibility of policy announcements, but also to the extent to which household behavior is forward looking. (Hall does devote some space to the question of how consumption constraints affect multipliers. Much of this discussion is helpful, although the analysis done using the fixed-real-wage model is less so because of that model’s strange properties.) In summary, Hall has produced an interesting and thought-provoking paper, compelling further thought about the channels through which government purchases might affect output, both in normal times and in the very abnormal present time. In the process he has exposed one of the profession’s dirty little secrets: that economists really have very little idea what the multiplier is for government purchases adopted as part of a stimulus package, during a deep recession with a binding zero bound on interest rates and a serious fiscal calamity just around the corner. The aggregate empirical evidence relates to episodes quite different in nature from this one, and the available structural models consist of many reasonable components with untested assumptions filling the interstices. In a context where the only way to generate empirical evidence is through big wars and deep recessions, one is hesitant to wish for more data, but evidence, as well as careful modeling, is needed to move the state of knowledge forward.

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REFERENCES FOR THE AUERBACH COMMENT

Auerbach, Alan J., and William G. Gale. 2009. “The Economic Crisis and the Fiscal Crisis, 2009 and Beyond.” Tax Notes 125, no. 1 (October 5): 101–30. Christiano, Lawrence, Martin Eichenbaum, and Sergio Rebelo. 2009. “When Is the Government Spending Multiplier Large?” Working Paper no. 15394. Cambridge, Mass.: National Bureau of Economic Research (October). Eggertsson, Gauti B. 2008. “Can a Tax Cut Deepen the Recession?” Federal Reserve Bank of New York (December). Perotti, Roberto. 1999. “Fiscal Policy in Good Times and Bad.” Quarterly Journal of Economics 114, no. 4 (November): 1399–1436. Ramey, Valerie A. 2009. “Identifying Government Spending Shocks: It’s All in the Timing.” Working Paper no. 15464. Cambridge, Mass.: National Bureau of Economic Research. Ramey, Valerie A., and Matthew D. Shapiro. 1998. “Costly Capital Reallocation and the Effects of Government Spending.” Carnegie-Rochester Conference on Public Policy 48 (June): 145–94.

COMMENT BY

CHRISTOPHER L. HOUSE Early in 2009, the American Recovery and Reinvestment Act (ARRA) was passed largely on the grounds that it would provide necessary stimulus to the economy, which was, and still is, suffering from one of the worst recessions in the postwar period. The act’s provisions are projected to cost roughly $787 billion over the next decade. This sum is divided into three broad groups: tax cuts (largely consisting of a $400 payroll tax credit for low- and middle-income families and an extension of the alternative minimum tax exemption) make up roughly $288 billion; transfers to state and local governments are roughly $144 billion; and increased federal spending accounts for roughly $355 billion. The bulk of the funds will be spent by the end of 2013. Against this backdrop, many researchers have begun to reexamine whether stimulative fiscal policies like the ARRA are effective. Robert Hall’s paper addresses two questions relevant to this research: First, empirically, how much does economic activity increase when the government purchases more goods and services? Second, what do existing macroeconomic models say about the likely effects of government spending on the economy, and do the models’ insights match the empirical evidence? Hall casts his analysis in terms of the magnitude of the government spending multiplier: the change in real GDP caused by a temporary increase in real government purchases of one dollar. Hall’s paper reviews both empirical evidence and theory to try to get at these questions. The empirical evidence consists primarily of estimates of


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the change in GDP (and consumption) associated with a change in military spending. This strategy, which other researchers (notably Ramey and Shapiro 1998 and Ramey 2009) have also used, is based on the plausible assumption that changes in military spending are driven by geopolitical events unrelated to economic conditions. Although the evidence is far from conclusive, the magnitude of the spending multiplier for output appears to be between 0.5 and 1.0. The estimated consumption multiplier is near zero and slightly negative. Hall then studies a macroeconomic model to see whether it can provide additional insights into the magnitude of the multiplier. The model has a basic neoclassical substructure but allows for non-neoclassical features such as a high labor supply elasticity and a countercyclical markup. Hall concludes that neoclassical models necessarily produce small output multipliers and negative consumption multipliers. Larger multipliers are possible only if the markup is sufficiently countercyclical and if labor supply is sufficiently elastic. EMPIRICAL ANALYSIS. Because Hall assumes that military purchases are exogenous to other determinants of economic activity, he uses OLS estimates to gauge the economy’s reaction. For his entire sample from 1930 to 2008, he obtains an output multiplier of roughly 0.55 with a standard error of 0.08. The multiplier for consumption is −0.05 with a standard error of 0.03. Different subsamples produce different estimates and standard errors, but the output multiplier is always less than 1.0 and the consumption multiplier is always negative and close to zero. Although Hall’s approach is a natural one to take and, in my assessment, provides essentially the best information we have, it suffers from a severe lack of data. When one examines the time path of real government spending, the immediate sense is that there have been perhaps five or six large, sharp changes in military spending, and little else. Before the onset of World War II, annual U.S. military spending was roughly $18 billion, or 2 percent of GDP. By 1944 military spending had increased to almost $1.2 trillion, or 65 percent of GDP. A similarly dramatic swing occurred at the end of the war as military spending fell. The Korean War also led to large variations in government spending. Annual military spending rose from $171 billion to $467 billion at the start of the war. These few observations stand out from the remainder of the dataset and exert extraordinary weight on the estimates. The remaining observations consist of smaller absolute changes, which are smaller still as a percent of GDP. We are effectively left to base our estimates on perhaps five or six data points. Table 1 and figure 1 in Hall’s paper reflect the importance of these data points in his estimates.

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An important consequence of the lack of data is that one cannot control for other factors that would likely influence the multiplier. One would expect that production would expand more in response to an increase in government purchases if the monetary authority accommodated the expansion in spending than if it did not. Similarly, if taxes were to rise with military spending (as they did at the beginning of the Korean War), the expansion in economic activity would likely be smaller. Finally, one would anticipate that increases in government purchases might be more stimulative during a business cycle trough than at a peak, since more idle resources would be available for production. Although controlling for these factors is surely important, it is not possible with such a limited data sample. THEORETICAL ANALYSIS. To augment the empirical analysis, Hall examines the predictions of a model that allows for government spending shocks. He argues that unless the model has sharply countercyclical markups and highly elastic labor supply, the implied multiplier is low. He considers variations of the model that allow for hand-to-mouth behavior on the part of consumers, as well as nonadditively separable utility, and concludes that the basic result holds even under these modifications. Here I draw attention to one feature that Hall does not emphasize: the important role that investment demand can play in influencing the multiplier. From a purely neoclassical perspective, there are good reasons to anticipate multipliers less than 1.0. Faced with an increase in government spending, the representative household in a neoclassical model has only three options: work more, consume less, or invest less. Typically the household chooses a combination of these options, and as a result, the multiplier is less than 1.0. How much less depends on the relative elasticities of each of these margins of adjustment. I argue below that under typical conditions, the most appealing margin for the representative household is the investment margin. In the absence of investment adjustment costs, the representative household can allow investment to vary substantially without experiencing sharp reductions in utility. Indeed, in an instructive limiting case, investment demand is completely crowded out and the multiplier is zero even when the markup is highly cyclical and labor is highly elastic. To analyze the role of investment, I introduce an additional variable implied by Hall’s model. Let vt be the shadow value of capital at date t. I assume that preferences are described by the simple additively separable case in equation 3 in Hall’s paper. In this case the shadow value vt can be expressed recursively as (1)

vt = β (1 − α ) ct +σ1 htα+1 kt−+α1 + β (1 − δ ) vt +1 , −

1

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or as the discounted sum, (2)

vt = β (1 − α ) ∑ j = 0 [β (1 − δ )] ct +σj +1htα+ j +1 kt−+αj +1 . ∞

j

−

1

At the optimum, the marginal benefit of an additional unit of capital (v) 1 equals the marginal cost of acquiring it. This requires that ct− –σ = vt.1 I make two short-run approximations. Specifically, I assume that the backward-looking variable kt and the forward-looking variable vt are approximately constant in the short run. The accuracy of these approximations requires that the fiscal stimulus be sufficiently temporary and that the capital goods be sufficiently long-lived (that is, the capital goods should have a sufficiently low depreciation rate).2 I discuss these approximations further below. Treating the capital stock as fixed in the short run is permissible because the stock of long-lived capital goods is much larger than the flow of investment. As a result, even with dramatic variations in investment, the capital stock changes only slightly in the short run. With the capital stock fixed, the remaining endogenous variables can be expressed in terms of the equilibrium change in the shadow value of capital v. Let x˜t denote the percent deviation of a variable x from its steady-state value; that is, x˜t ≡ (xt − –x )/ –x . With some algebra, one can show that the percent change in output is (3)

y t =

α × vt . 1 ωα ⎞ ⎛ + (1 − α ) ⎜ 1 − ⎝ 1 − α ⎟⎠ ψ

The expression in the denominator is the difference between the slope of the labor supply curve and the slope of the effective labor demand curve (the labor demand curve taking the change in the markup into account). As long as effective labor demand slopes downward, this term is positive. In this case output increases only if the shadow value v increases. Hall focuses on conditions under which the coefficient multiplying v is very large. For a given change in v, the higher this coefficient is, the larger the multiplier will be. Specifically, the multiplier will be high if the markup is 1. Mechanically, v is the Lagrange multiplier on the capital accumulation constraint; it is not Brainard-Tobin’s q. Instead, q is the ratio of v and the marginal utility of consumption. 2. The discussion here draws on the analysis in Barsky, House, and Kimball (2007) and House and Shapiro (2008). See those papers for a more detailed discussion of the approximations. See also House (2009).

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very countercyclical (high ω), or if labor supply is very elastic (high ψ), or if labor demand is very elastic (α near 1). Note also that for output to increase, v must increase. Since v is equal to the marginal utility of consumption in equilibrium, c must fall, implying a negative consumption multiplier. How much will the shadow value v fall? In most models the shadow value moves only slightly. The short-run approximation mentioned above treats v as constant, so that v˜t = 0. To understand this approximation, look again at equation 2. The fiscal stimulus will influence v by causing changes 1 in ct− –σ , h tα, and k t−α. Because the fiscal stimulus is temporary, these changes are temporary, and most of the future terms in equation 2 remain close to their steady-state values. As a result, the difference between vt and its steady-state level is attributed entirely to the changes in the first few terms in the expression. Provided that the household is sufficiently patient and the depreciation rate sufficiently low, the value of capital is anchored by the future, long-run terms in the expression. Put differently, for sufficiently 1 long-lived capital goods, transitory changes in ct− –σ , h tα, and kt−α have negligible influences on vt. Naturally, the payoff from investing in a long-lived capital good is dictated by future, long-run considerations and is approximately independent of short-run events. As a result, assuming that the future is only slightly influenced by a temporary fiscal stimulus, the shadow value v is approximately constant in the short run. Using the short-run approximation vt ≈ –v , equation 3 implies that there is no change in total output, and thus the multiplier is zero. This is true regardless of the parameter values for ψ and ω. Of course, the approximations – vt ≈ –v and kt ≈ k are exactly true only for arbitrarily short-lived fiscal policies or arbitrarily low depreciation rates. For longer-lived policies like the ARRA and for realistic depreciation rates, the approximations are not exact. To judge the accuracy of the approximations away from the lowdepreciation limit, I solve the model out exactly allowing for v and k to move endogenously in response to the policy. I solve the model and compute the multiplier for a variety of model specifications and depreciation rates. The parameter values used for each variation of the model are given in table 1.3 3. I hold the investment-GDP ratio constant when I vary the depreciation rate. In addition to the parameter values in the table, α = 0.65, β = 0.98, φ = 0.70, σ = 0.20, the consumption-GDP ratio is 0.60, and the ratio of government spending to GDP is 0.20. These parameter values are held constant across all simulations. Consumption-labor complementarity modifies the labor supply condition (equation 20 in Hall’s paper). In log deviations, the modified labor supply 1 1 condition used in the simulations here is h˜t = − c˜t + w˜ t + θ(h˜t − c˜t). Hand-to-mouth ψ σ consumers set c˜t = y˜t ; other consumers behave according to the permanent income hypothesis.

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Table 1. Parameter Values

Model

Frisch elasticity (ψ)

Markup cyclicality (ω)

1.00 1.00 1.00 ∞ 1.00

0 0.30 0 0 0

Baseline Cyclical markup Hand-to-mouth consumers Infinite Frisch elasticity Labor-consumption complementarity

Consumption-labor Fraction of complementarity hand-to-mouth (θ) consumers 0 0 0 0 0.70

0 0 0.70 0 0

Figure 1 reports the multipliers for the model specifications given above and for several different depreciation rates. Each line in the figure corresponds to a variation of Hall’s model. For each variation I compute the multiplier for a range of depreciation rates, which are plotted on the horizontal axis. For purposes of comparison, the vertical lines in the figure indicate depreciation rates for vehicles (roughly 17 percent), general equipment (roughly 10 percent), and structures (2 to 3 percent). There are two things to note about the figure. First, when the model includes New Keynesian features, the value of the multiplier is higher. As Hall emphasizes, higher

Figure 1. Simulated Government Purchases Multipliers for Various Models and Depreciation Rates Multiplier 0.6 Infinite Frisch elasticity 0.5

Labor-consumption complementarity

0.4 0.3

Cyclical markup Baseline

0.2 0.1

Hand-to-mouth consumption 2

4

Source: Authorís calc ulations.

6

8 10 12 14 Depreciation (percent a year)

16

18

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labor supply elasticities and greater cyclicality of the markup result in higher output multipliers. Second, as the depreciation rate falls, the multiplier approaches zero in all of the specifications. This is a consequence of the near constancy of the shadow value v for long-lived investments. As the depreciation rate falls, the shadow value becomes more anchored by the long-run terms in equation 2 and less influenced by temporary fiscal stimulus. The reason the multiplier approaches zero as the depreciation rate falls is that the elasticity of investment demand approaches infinity. In this case the representative household does not need to reduce consumption or increase work. Instead, since the shadow value of capital is approximately unchanged, the increase in government spending can be accommodated nearly entirely by a temporary reduction in investment. That the shadow value is nearly constant is equivalent to saying that the investment demand curve is nearly flat. Of course, the fact that government spending crowds out investment is nothing new. This effect is present in the standard IS-LM model. An outward shift in the IS curve results in some crowding out, depending on the elasticity of investment. In neoclassical and New Keynesian models without adjustment costs, the IS curve is extremely elastic, and crowding out is nearly complete. In the data, investment is indeed crowded out by government spending. Taking the estimates in table 1 in Hall’s paper as given, one can calculate the implied investment multiplier. This is simply the first column of the table plus the second column minus 1. For the entire sample the investment multiplier is roughly −0.5. In the limiting case with low depreciation rates, neoclassical and New Keynesian models have the investment multiplier close to −1.0, which implies that the output multiplier is close to zero. To undo the extreme elasticity of investment demand and restore some of the traditional effects of fiscal policy, some sort of investment adjustment friction can be added to the model. Adjustment costs temper the extent to which investment is crowded out. An extreme form of adjustment cost would be one that assumes that investment is predetermined in the short run.4 To illustrate the effects of investment adjustment costs, I recalculate the multipliers under the assumption that investment cannot change, that is, that investment demand is completely inelastic. 4. Basu and Kimball (2005) consider sticky investment in a New Keynesian framework. House and Mocanu (2009) analyze investment planning costs in a model of heterogeneous firms and fixed adjustment costs.

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Figure 2. Simulated Government Purchases Multipliers with Investment Held Fixed Multiplier

Keynesian

2.0 1.5 Hand-to-mouth consumption

Infinite Frisch elasticity

1.0 Baseline Cyclical markup

0.5

2

4

6

Labor-consumption complementarity

8 10 12 14 Depreciation (percent a year)

16

18


Figure 2 shows the implied output multipliers for the model specifications considered previously. These are substantially larger than their counterparts in figure 1. Notice also that the multipliers do not approach zero as they did before. In fact, the depreciation rate does not influence the equilibrium at all. Since investment is assumed to be exogenous, the depreciation rate influences only the value of capital. Given investment, the remainder of the model is static. The figure also includes multipliers for a “Keynesian” specification that combines the mechanisms in the other model specifications. Because investment typically has an extremely high elasticity of demand, the specification of investment supply often plays a crucial role in determining the magnitude of the government spending multiplier. Hall’s base calibration (table 3 in his paper) is chosen so that the output multiplier is near 1.0 and the consumption multiplier is close to zero. In this case investment does not change much in equilibrium, and thus investment adjustment costs play little role in influencing the multiplier. This calibration features high labor supply elasticity, a high degree of consumption-labor complementarity, and a high cyclicality of the markup (high enough to imply an upward-sloping effective labor demand curve). Hall provides a balanced and scholarly approach to a timely and relevant topic. Unfortunately, neither data nor theory speaks very loudly on this

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issue. Ultimately, only a limited amount of data are available on which to base conclusions, and depending on the treatment of investment, the models that are available allow for a wide range of behavior associated with temporary increases in government purchases. Whether the current stimulus measures will have the desired impact is unclear. The most one can hope for is that the consequences will become apparent once new data become available. REFERENCES FOR THE HOUSE COMMENT

Barsky, Robert B., Christopher L. House, and Miles S. Kimball. 2007. “Sticky-Price Models and Durable Goods.” American Economic Review 97, no. 3: 984–98. Basu, Susanto, and Miles Kimball. 2005. “Investment Planning Costs and the Effects of Fiscal and Monetary Policy.” Working paper. Boston College and University of Michigan. House, Christopher L. 2009. “Fixed Costs and Long-Lived Investments.” Working Paper no. 14402. Cambridge, Mass.: National Bureau of Economic Research. House, Christopher L., and Matthew D. Shapiro. 2008. “Temporary Investment Tax Incentives: Theory with Evidence from Bonus Depreciation.” American Economic Review 98, no, 3: 737–68. House, Christopher, and Ana-Maria Mocanu. 2009. “The Timing of Investments in Fixed-Cost Models: Theory, Evidence and Implications.” Working paper. University of Michigan. Ramey, Valerie. 2009. “Identifying Government Spending Shocks: It’s All in the Timing.” Working Paper no. 15464. Cambridge, Mass.: National Bureau of Economic Research. 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.

GENERAL DISCUSSION David Romer described two alternative views of reduced-form estimates of the government purchases multiplier: one is that it is impossible to know anything, because the data come from such few and unusual episodes, and the other is that the direction of the bias is known. Given that most of the variation in the data comes from World War II, and given all the other factors that were clearly biasing the multiplier downward at that time, 0.5 can be taken rather confidently to be the lower bound. Bradford DeLong argued that the New Keynesian model is built on foundations of sand. The only intelligent way to view it is as an attempted exercise in mental consistency, a way to try to organize certain beliefs while leaving aside the reasons for those beliefs. Most of the time the fiscal multiplier is taken to be very low because the labor supply elasticity is low


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and markups are not strongly countercyclical, and almost all of the rest of the time the fiscal multiplier is very low because the Federal Reserve has a strong view about what nominal spending will be and acts to offset whatever fiscal policy initiatives Congress attempts. DeLong argued that there are times—namely, when the federal funds rate is essentially zero, and the effects of standard open-market operations on relative prices are unclear because cash and Treasury securities look like nearly perfect substitutes— when a combination of quantitative easing and banking recapitalization on the demand side of the credit channel, and of government asset purchases and guaranty policies on the supply side, together with fiscal expansion, have a role to play that they normally do not. It is hard, however, to justify any particular numbers that attempt to answer how much each of these supplements to normal monetary policy tools should be contributing. Robert Gordon addressed the problem of having to rely on data from World War II and the Korean War, which were not only long ago but also atypical because supply constraints were in effect. The problem could be alleviated, he argued, by using newly available data on the buildup to World War II. These data show that between the second quarter of 1940 and the fourth quarter of 1941, the ratio of government spending to potential output rose from 12 percent to 25 percent; this period thus offers a controlled natural experiment in the size of the government spending multiplier. The annual growth rate of real GDP during this period was among the fastest in recorded history, at roughly 18 percent. The ratio of actual to potential GDP rose by 16 percentage points; increased government spending accounted for about 9 percentage points out of that increase, leaving quite a bit unexplained. Consumption and investment both rose by approximately the same amount, underlining the point that the model needs to include investment as well as consumption. These numbers suggest that the multiplier for that five-quarter period was about 1.75, of which about 1.0 came from the government, 0.4 from consumption, and 0.4 from investment. Michael Woodford suggested that as he understood the logic of the results derived from the standard New Keynesian model, the size of the government expenditure multiplier depends on whether real interest rates go up, and by how much, in response to the increase in government purchases. In a classical model with market clearing and price equal to marginal cost, real interest rates must rise in response to an increase in government purchases. This results in a crowding out of private spending, and thus a multiplier of less than 1, and monetary policy cannot affect real interest rates. The difference in the New Keynesian model is that monetary policy can affect

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the real interest rate. The size of the multiplier thus depends on what is assumed about monetary policy. If monetary policy is thought to increase real interest rates in response to an output increase, the multiplier will be small, but if monetary policy accommodates a fiscal expansion and keeps real interest rates from rising, the multiplier could be 1 or higher. Woodford also emphasized that the zero lower bound literature implies the importance of distinguishing between periods when the nominal interest rate is at the zero lower bound and periods when it is not. The theoretical models imply much bigger multipliers at the zero bound than in normal circumstances when monetary policy is described by something like a Taylor rule. If, in the regression sample, something more like the Taylor rule typically applied, empirical estimates should very much underestimate the multiplier that would be relevant under present circumstances, when the federal funds rate is at the zero lower bound. Deborah Lucas noted that great emphasis has been placed on the role of the Federal Reserve in changing expectations about the duration and severity of a downturn, but generally the models do not build that in. She wondered why that role is not more central to the analysis of how the effects of government expenditure are treated. John Williams pointed out the importance of the accelerator and of financial constraints on investment. In an environment with lots of slack, a zero interest rate, and financial constraints, the effects of fiscal policy on investment may be different than otherwise. He also said it would be interesting to see more foreign evidence on countercyclical government spending at the zero bound, in particular from Japan. Christopher Carroll proposed that another way to work with the available data would be to look not only at episodes in foreign countries, but also at geographical variation within the United States. Not nearly enough work has been done, for example, on whether one can measure the effects on a metropolitan area of a new highway being approved for construction. This approach could also help resolve the difficult issue of teasing apart the effects of monetary policy and fiscal policy when both are active at the same time. Monetary policy applies uniformly across the entire country, whereas state and local government spending affects mainly the state or locality. Asking what “the” government spending multiplier is, he argued, is like asking what “the” temperature is. Both vary over time and space. The really interesting intellectual questions involve the extent to which the whole set of other important factors causes the multiplier to vary. William Brainard suggested that the paper discuss the implications of the production function having a lower short-run than long-run elasticity


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of substitution between capital and labor. He noted that a putty-clay model has quite different implications than does the Cobb-Douglas function as calibrated to long-run factor shares for cyclical fluctuations in productivity and in the factor shares themselves. With a putty-clay model, a significant fraction of what are frequently labeled productivity “shocks” driving output are simply movements along the short-run production function and cyclical movements in factor shares. Although Brainard was skeptical of the importance of firms’ expectations of the future price level for their output and employment decisions, he believed that expectations about the timing and strength of recovery are important to decisions about whether to lay off workers during a downturn and whether to add labor as demand picks up. Richard Cooper noted three serious omissions from the paper’s model as it applies to the current situation. First, it has no financial sector. When nominal incomes increase, it means fewer foreclosures, so mortgages continue to be paid, and directly and indirectly, through changes in markups, commercial mortgages get paid. What happens to these mortgage payments? Does the financial sector recycle them in new loans, or does it simply absorb them so as to improve capital ratios? Either way, how does the Federal Reserve respond? Second, the model assumes a closed economy. Yet imports are a substantial component of both consumer and nonconsumer expenditure. The United States is big enough that its stimulus policies should generate noticeable feedback effects from the rest of the world. Third, and conversely, because there is no “rest of the world” in the model, there is no effect on the United States from stimulus programs elsewhere. In fact, all the major countries of the world have stimulus packages in place, which should stimulate imports from the United States. A realistic model would therefore have an export component. Cooper further observed that investment as measured in the national accounts is too broad a category to be very useful. It includes what might be called “loosely productive” investment, such as investment in housing. Growth of one type of investment will have very different implications for the productive capacity of the economy than growth in another, and it is worth noting that the most interest-sensitive component of investment is housing investment. Ricardo Reis observed that the New Keynesian model is a very large umbrella that incorporates a lot of things, but at the most fundamental level it is about retaining the neoclassical model while allowing for nominal rigidities and for an effective monetary policy. The foundations of the model are solid, he argued, and as much work has been done on the foundations of nominal rigidities as on the foundations of an aggregate production

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function, for instance. When those foundations are described as being shaky, what is usually meant is that the details or the implementation of the model are shaky. It is the particular model of nominal rigidities that may be shaky, not the presence of some form of nominal rigidities. Vincent Reinhart underlined the point that the fiscal multiplier is likely to be largest when monetary policy is pinned to the zero bound. That raises an issue about the applicability of data from two big war buildups in estimating the fiscal multiplier, especially because at that time the Federal Reserve was constrained not by a lower but by an upper bound on interest rates. The Treasury Support Program put a ceiling on interest rates at various points on the yield curve, and there was automatic accommodation of policy. Reinhart wondered whether one should expect multipliers closer to that experience, given that policy is similarly constrained today. Janice Eberly raised the issue of investment adjustment costs and capital adjustment costs. She thought that putting investment adjustment costs in the model, or making investment a state variable, probably would have dramatic effects. Some work along these lines has been done by Lawrence Christiano, Martin Eichenbaum, and Charles Evans, whose model works well in a monetary setting trying to replicate impulse response functions, but does not perform as well with firm-level data. A gap emerges between matching firm-level moments and matching aggregate moments, which suggests that before putting investment adjustment costs into the aggregate model, the source of the smoothness in the aggregate data needs to be fleshed out. Simply including investment adjustment costs is too ad hoc. Especially in the current situation, parameters from normal times should not be imposed on the investment data. David Laibson suggested a micro foundation for the Keynesian consumption function, namely, hyperbolic discounting. Agents end up putting all their wealth into illiquid assets, and thus their consumption becomes highly responsive to changes in their high-frequency labor income. Laibson also expressed interest in seeing a welfare analysis corresponding to the simulations showing what the implicit shadow value of government expenditure is and how these different experiments translate into welfare consequences. Linda Goldberg seconded Cooper’s observation that the paper’s model is a closed economy model, and she suggested fleshing it out with some international influences. U.S. auto imports experienced a big uptick in July, which suggests that some of the stimulus from the “Cash for Clunkers” program was felt outside of the United States. International considerations


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are also relevant to the financing of government expenditure; if it is financed abroad, an important question is how elastic the available savings are. That elasticity could have a strong impact on whether there is an interest rate response, how large it is, and how much investment might be crowded out. Goldberg also mentioned some recent work by Giancarlo Corsetti and others on the dynamics of this financing in an open-economy model, which finds that a key part of the adjustment mechanism depends on the assumptions made about exchange rate adjustment.

CARMEN M. REINHART University of Maryland

VINCENT R. REINHART American Enterprise Institute

When the North Last Headed South: Revisiting the 1930s ABSTRACT The U.S. recession of 2007–09 is unique in the post–World War II experience in the broad company it kept. Activity contracted around the world, with the advanced economies of the North experiencing declines in spending more typical of the developing economies of the South for the first time since the 1930s. This paper examines the role of policy in fostering recovery in that earlier decade. With nominal short-term interest rates already near zero, monetary policy in most countries took the unconventional step of delinking currencies from the gold standard. However, analysis of a sample that includes developing countries shows that this was not as universally effective as often claimed, perhaps because the exit from gold was uncoordinated in time, scale, and scope and, in many countries, failed to bring about a substantial depreciation against the dollar. Fiscal policy was also active—most countries sharply increased government spending—but was prone to reversals that may have undermined confidence. Countries that more consistently kept spending high tended to recover more quickly.

T

he financial and economic dislocations of the past two years have been sharp and widespread. Yet there is ample precedent for such crises— and for the economic adjustment that follows to be wrenching. Among the advanced economies, those earlier crises occurred either before World War II or in open economies that were out of sync with the global cycle.1 Crashes and severe contractions have been more common in emerging market economies. In the current episode, however, activity collapsed in unison in developed and developing countries around the world. Indeed, 1. Reinhart and Rogoff (2009) provide many comparisons and a full explanation of the data.

251

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the rarity of current circumstances is why we rely on an event three-quarters of a century old, the Great Depression, as the main comparator.2 Given the importance of that precedent in understanding the current contraction, it is useful to cast a sharp focus on the role that policy actions played in shaping recovery in the 1930s. Unconventional monetary policy action has been called (Svensson 2003) a “foolproof way” of preventing deflation, especially in an open economy that can generate additional demand through depreciation of its currency. But when the global pie is shrinking, such action may be less effective. In the 1930s, moving off the gold standard bought fiscal authorities in many countries more space for stimulus because their central banks had room on their balance sheets to purchase more government securities and to generate additional income. It also allowed each country to devalue relative to gold.3 Those actions, however, were mostly uncoordinated in time, scale, and scope. As a consequence, the record of success among countries abandoning the gold standard, both in avoiding a severe contraction and in speeding the recovery, is quite mixed. The 1930s also saw massive increases in government spending in many countries, but fiscal authorities were prone to reverse themselves. As a result, some of the direct benefits of that spending were offset by harmful effects stemming from its volatility.

I. The Appropriate Precedent The Business Cycle Dating Committee of the National Bureau of Economic Research has put the peak of the current U.S. cycle at the end of 2007. There is no equivalent formalism at the world level, but indicators for most other countries started turning down about six months later, consistent with the view that the United States led the way down. Robert Barro and José Ursúa (2008) have demonstrated that occasional large, adverse shocks hit national economies without the reason for those shocks always being clear. The current episode is particularly unusual because so many economies around the world contracted simultaneously. Table 1 provides a historical perspective on the rarity of events like those of recent years, by documenting changes in real exports during past systemic crises from 1890 to today, for samples ranging from 35 to 111 countries. The episodes included in the table are those that saw spikes in the number 2. Eichengreen and O’Rourke (2009) provide useful comparisons to that episode as well. 3. This strategy is discussed in Eichengreen (1992) and Romer (1992).

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CARMEN M. REINHART and VINCENT R. REINHART

Table 1. Declines in Real Exports during Crisis Episodes Countries experiencing real export decline (percent of total)

Any

Greater than 15 percent

Largest decline (percent)

Median change (percent)

Episode

Year

No. of countries

Barings crisis

1890 1891 1907 1908 1920 1921 1929 1930 1931 1932 1967 1973

35 35 73 75 70 73 94 94 95 95 104 111

34.3 57.1 31.5 66.7 48.6 76.7 43.6 88.3 100.0 80.0 48.5 11.7

5.2 8.6 4.1 20.0 31.4 54.8 13.8 48.9 88.4 62.1 23.3 4.5

−18.0 −47.5 −27.4 −33.6 −60.9 −73.7 −36.2 −51.2 −73.5 −53.8 −92.0 −79.4

2.2 −1.0 6.9 −5.4 1.4 −19.8 1.3 −13.9 −33.3 −17.1 0.4 39.1

1975 1981 1982 1991 1992

110 106 108 93 95

41.8 60.4 62.3 57.0 36.8

29.1 31.1 29.2 26.9 14.7

−78.3 −70.7 −77.2 −75.8 −65.6

1.8 −3.6 −4.0 −1.4 3.9

1995 1997 1998 2001 2008 2009a

105 109 107 108 87 42

23.8 40.4 51.4 74.1 86.2 100.0

11.4 13.8 22.4 28.7 52.9 92.9

−79.3 −83.8 −62.4 −39.8 −74.1 −62.2

9.4 2.8 −1.4 −9.6 −16.6 −36.6

70 99

41.1 33.0

18.0 14.6

−82.2 −92.0

4.5 9.2

Panic of 1907 Commodity crash Great Depression

Sterling crisis End of Bretton Woods regime First oil shock Latin American debt crisis Nordic crises Exchange Rate Mechanism crisis Tequila crisis Asia, Russia, LTCM crisesb September 11 “Great Contraction” Averages

1890–1939 1957–2008

Sources: Reinhart and Rogoff (2009, appendix A); League of Nations, Statistical Yearbook, various issues; national sources; Maddison (2004); Mitchell (2003a, 2003b, 2003c). a. Through April. b. LTCM, Long Term Capital Management, the large hedge fund that failed in 1998.

of banking crises worldwide, as reported by Carmen Reinhart and Kenneth Rogoff (2009). As is evident from the table, it is not unprecedented for a majority of countries to experience declines in real exports coincident with systemic financial crises. Many of the median changes listed in the last column are negative, and the largest declines (which the preceding column reports for each country) are quite large indeed. The scale of the most recent

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experience, however, has only one precedent, namely, the early 1930s: more than four-fifths of countries in both periods saw contractions in exports of greater than 15 percent. The scope of the problem also distinguishes the Great Depression and the current, second “Great Contraction”: only in those two episodes did virtually all of the nations of the world witness shrinking trade flows. No other crisis period in the past century matches that experience. The commonality of the experience in these two episodes makes an examination of the setting of policy in the 1930s relevant for consideration today. We consider the actions of the monetary and those of the fiscal authorities in turn.

II. Monetary and Exchange Rate Policy during the Great Depression The painful adjustment in activity around the world during the early 1930s strained the confidence of many public officials in the speed with and extent to which the market system would correct itself. As a consequence, the range of policy response was wide. The major form that monetary policy experimentation took was to expand central bank balance sheets by lowering the gold content of the home currency. As will be discussed below, countries devalued relative to gold at different points over the decade and by different amounts. The mechanism through which this proved expansionary can best be understood by considering a single country’s experience. In the United States, the key decision in the early 1930s that shifted the stance of monetary policy decisively toward ease was not made by the nation’s principal monetary authority, the Federal Reserve. Rather, it was the devaluation of the dollar in terms of gold by newly inaugurated President Franklin Roosevelt,4 followed by a sharp increase in gold inflows as a result of political instability in Europe, that produced a marked relaxation of monetary conditions, through a large increase in high-powered money.5 As shown in figure 1, high-powered money in the United States (essentially, currency in circulation, vault cash, and bank deposits with the Federal Reserve) increased by 60 percent from March 1933 to May 1937 (the trough 4. There were two steps in this process. Executive Order 6102 in April 1933 lowered the gold content of the dollar and prohibited the public from holding gold. The value of the dollar in terms of gold was lowered again with the Gold Reserve Act of 1934. 5. Eichengreen (1992), Romer (1992), and Bernanke (2004) explain the mechanics. Important earlier contributions include Choudhri and Kochin (1980) and Hamilton (1988).

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Figure 1. Monetary Conditions in the United States, 1929–39 Billions of dollars

Billions of dollars Dollar devalued against gold

14

30 12 M1 (left scale) 25 10 20

High-powered money (right scale)

1930

1931

1932

8

1933

1934

1935

1936

1937

1938

1933

1934

1935

1936

1937

1938

Percent a year

5 Three-month Treasury bill rate

4 3 2 1

1930

1931

1932

Sources: Friedman and Schwartz (1963, tables A.1 and B.3); Board of Governors of the Federal Reserve System (1943, pp. 439–42 and 448–451).

and peak, respectively, of the business cycle); the M1 measure of the money supply expanded by about the same amount from 1933 to 1937. Short-term nominal interest rates, proxied in the bottom panel by the three-month Treasury bill rate, were already close to zero. Thus, in the decade from 1932 onward, policy impetus cannot be measured by reference only to the level of the short-term interest rate. Then as now, the size and composition of the monetary authority’s balance sheet had the potential to influence financial markets and the

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economy. An enlarged balance sheet also provided fiscal authorities more space to be aggressive, if they felt so inclined.6 All this meets the definition of “unconventional” monetary policy and quantitative easing (as in Bernanke and Reinhart 2004). In the standard rendering, there were three acts to this episode of quantitative easing. In the first act, in 1932, U.S. policymakers extended their mistake of the prior three years of not addressing a crisis of confidence. After the stock market crash of 1929, the public sought to build up a cushion of safe assets. For households, this meant holding more currency; for banks, the demand for reserves rose. Declines in asset values and increased demand for liquidity strained the financial system, leading to a daisy chain of bank failures, which further heightened demand for safe assets.7 High-powered money did expand, but by too little to offset increases in desired currency and reserve holdings, as detailed by Milton Friedman and Anna Schwartz (1963) and by Philip Cagan (1965).8 In the second act, President Roosevelt’s decision to devalue relative to gold in 1933 triggered an expansion in the monetary authority’s balance sheet and sent a clear signal of the intent to reflate.9 In the final act, policymakers repeated their initial mistake and contracted policy by sterilizing gold inflows in 1936 and increasing reserve requirements in 1937, stalling the expansion of high-powered money.10 This third act highlights the danger of a premature exit from policy accommodation, as Christina Romer has recently pointed out.11 It is the middle act, the move off the gold standard, that has been most widely praised and that offers the best evidence that unconventional policy action can spur recovery. 6. Open market purchases of Treasury securities can lower yields on government debt if assets are imperfect substitutes for each other. Even if they are perfect substitutes, the swap of interest-bearing government debt for non-interest-bearing reserves works to lower debt service. Also, a decline in real interest rates improves measures of debt sustainability. Two issues arise, however. First, the macroeconomic effects will depend on whether the public capitalizes the income stream of central bank profits. Second, paying interest on reserves lessens the reduction in the debt burden associated with open market purchases. 7. James (2009) argues that these two episodes are distinct. The year 1929 marked a major asset revaluation, and 1931 was a year of banking collapse. 8. This failure can be explained as the Federal Reserve being either hamstrung by the gold standard (as argued in Eichengreen 1992) or focused too much on reserve supply rather than reserve demand (as in Meltzer 2003). Either case amounts to a lack of willingness to use the appropriate policy tools, not a lack of ability. Hsieh and Romer (2006) show that a short-lived monetary accommodation in 1932 did not trigger concerns in markets or among policymakers about a destabilizing exit from the gold standard. 9. Romer (1992) stresses the multiplier effects of the former; Eichengreen and Temin (2000) emphasize the change in the zeitgeist as rekindling inflation expectations. 10. Meltzer (2003) and Orphanides (2004) review this experience. 11. Christina D. Romer, “The Lessons of 1937,” The Economist, June 18, 2009.


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But although the U.S. experience can be interpreted that way, the wider international record is more mixed. Devaluing a nation’s currency in terms of gold has three distinct effects.12 First, the home-currency value of the monetary authority’s resources expands. If, as in the U.S. case in 1933, short-term interest rates are near the zero bound, this amounts to unconventional monetary policy. Second, if other countries remain at an unchanged gold parity (or devalue by less than the home country), the exports of the home country become priced more competitively on world markets. Third, devaluation might be interpreted as a regime switch, signaling higher inflation in the future and therefore working to lower real interest rates immediately. Table 2 gives a year-by-year chronology of countries’ exits from the gold standard during the 1930s, along with some information about the course of economic contraction and recovery in each country. The first column reports the year that output peaked—usually 1928 or 1929. The second column reports the peak-to-trough decline in real output. This was, indeed, a wrenching contraction, with the 29 percent decline in the United States among the worst. Small open economies that were reliant on commodity production, such as Chile, Nicaragua, and Uruguay, were hit especially hard. Closed economies, such as Italy and Portugal, in contrast, fared better. The last column in the table provides a metric for recovery: the number of years it took for output to return to the previous peak. This seems an intuitive way to measure a downturn, but it is also quite conservative. Ongoing expansion in potential output implies that a return to prerecession output is not synonymous with an elimination of economic slack. What is striking in this column is how varied was the experience and how long was the typical path to recovery. In the event, abandoning the gold standard was not a foolproof solution for economic recovery. Figure 2 plots for each country in table 2 the peakto-trough decline in real GDP against the number of years it took after 1929 for the country to devalue or leave the gold standard. There is no obvious association between the timing of the devaluation and the severity of the downturn. Early leavers (those in 1929 and 1930) experienced output contractions ranging from 13 to 36 percent. Late exiters (from 1933 onward) suffered output declines from 6 to 32 percent. In his work with different coauthors on the interwar gold standard, Barry Eichengreen has argued that devaluation against gold was an engine 12. Eichengreen and Sachs (1986) work through the effects in a simple model.

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Table 2. Depth and Duration of the Great Depression by Year of Exit from the Gold Standard

Country

Year of business cycle peak

Peak-to-trough decline in real GDP per capitaa (percent)

Years until return to precrisis real GDP

December 1929 and 1930 exits from gold standard 1926 17.3 1929 17.8 1929 19.4 1928 13.3 1929 36.1 1929 24.1

10 7 15 8 17 6

United Kingdom Austria Canada Finland Germany Japan Norway Sweden Chile El Salvador Hungary India Korea Malayab Mexico Portugal

1931 exits from gold standard 1929 1929 1928 1929 1928 1929 1929 1930 1929 1928 1929 1929 1928 1929 1929 1929

6.6 23.4 29.0 6.1 17.8 9.3 1.9 4.8 46.6 11.3 11.4 8.2 12.7 17 31.1 2.4

5 10 12 5 7 4 3 4 16 9 7 31 5 35 16 2

Colombia Costa Rica Greece Nicaragua Peru Romania

1932 exits from gold standard 1929 3.8 1928 15.7 1930 6.4 1929 43.0 1929 25.4 1931 8.0

3 9 4 24 6 7

United States Guatemala Honduras Philippines

1933 exits from gold standard 1929 1930 1931 1929

28.9 23.6 32.0 13.1

10 6 36 8

Italy

1934 exits from gold standard 1929

6.4

6

Belgium

1935 exits from gold standard 1928 10.4

11

Australia New Zealand Argentina Brazil Uruguay Venezuela

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Table 2. Depth and Duration of the Great Depression by Year of Exit from the Gold Standard (Continued)

Year of business cycle peak

Country

Peak-to-trough decline in real GDP per capitaa (percent)

1936 exits from gold standard 1929 1929 1929 1929 1929

France Netherlands Switzerland Netherlands East Indiesc Poland

Years until return to precrisis real GDP

15.9 16.0 9.8 14.3 24.9

10 21 9 9 9

Sources: Reinhart and Rogoff (2009); Eichengreen (1992); League of Nations, Statistical Yearbook, various issues; Officer (2001); Maddison (2004); Mitchell (2003a, 2003b, and 2003c). a. GDP is measured in 1990 international Geary-Khamis dollars. b. Present-day Malaysia and Singapore. c. Present-day Indonesia.

Figure 2. Timing of Exit from the Gold Standard and Severity of Output Decline, 1929–36a Peak-to-trough decline in real GDP per capitab (percent) 45 40 35 30 25 20 15 10 5 0–1

2 3 4 5 6 Years from 1929 to exit from gold standard

7

Sources: Reinhart and Rogoff (2009); Eichengreen (1992); League of Nations, Statistical Yearbook, various issues; Officer (2001); Maddison (2004); Mitchell (2003a, 2003b, 2003c). a. Squares indicate countries in the original sample of 19 countries; circles indicate those in the expanded sample. b. GDP is measured in 1990 international Geary-Khamis dollars.

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of reflation.13 The simple scatterplot in figure 2 suggests that the benefits were not always evident. But the figure implicitly differs from the prior literature in three ways: the choice of the measure of activity, the window of observation, and the country coverage; these differences can be addressed systematically. Table 3 reports regressions that seek to explain various measures of economic recovery in the 1930s for different sets of countries. The first column reproduces what might be called “exhibit A” for those arguing that the change in the exchange rate regime was crucial in fostering economic recovery. That column, following the literature, relies on information collected in real time by the League of Nations. The change in industrial production from 1929 to 1937 for the 19 countries for which data are available from that source is regressed against the number of years after 1929 that the country exited the gold standard. For these 19 countries, leaving the gold standard had a statistically significant effect on industrial production over that common time period. Indeed, the coefficient on the timing variable is quite large. Leaving the gold standard at the start rather than at the end of the period prevented a decline in industrial output of more than one-half. It might be argued that the more pronounced effect on industrial production in part reflects the higher cyclical amplitude of this narrower slice of economic activity. It could also be due to the greater dependence of manufacturing on international trade. The second column therefore repeats the exercise for the same countries but uses the change in real GDP per capita in place of the change in industrial production. The results, although smaller, remain statistically significant and quantitatively important for this broader measure of activity. According to this estimate, delaying the exit from the beginning to the end of this fixed window is associated with about a 20 percent loss in real GDP per capita. The previous literature’s use of the League of Nations sample puts particular weight on the experience of large countries and of countries in Europe. The third column therefore broadens the sample to include 39 countries, including many in Latin America. Although the coefficient on the timing variable remains negative, it is no longer statistically significant. Thus, some of the purported benefits of the 1930s regime switch are apparently sensitive to the country set. In addition, because countries left the gold standard at different times, the literature’s use of a single time period to measure recovery across that 13. See Eichengreen (1992), Eichengreen and Sachs (1985), and Eichengreen and Temin (2000).

0.36 19.75

45.19 (9.13) −7.33 (2.35)

Industrial productionb

0.17 12.41

13.75 (5.74) −2.74 (1.48)

Real GDP per capitab

0.06 15.80

9.29 (4.81) −1.99 (1.35)

Real GDP per capita

0.00 11.07

18.10 (3.37) −0.35 (0.95)

0.31 9.62

7.87 (4.52) 1.02 (0.95) 3.56 (5.49) 14.81 (3.92) 6.28 (5.43)

Dependent variable: Peak-to-trough decline in real GDP per capita (percent)

0.00 8.44

10.18 (2.57) 0.21 (0.72)

0.10 8.35

5.85 (3.92) 0.86 (0.83) −1.47 (4.76) 5.31 (3.40) 5.77 (4.71)

Dependent variable: Years until return to precrisis level (percent)

Sources: Authors’ regressions using data from League of Nations, Statistical Yearbook, various issues; Eichengreen (1992); Officer (2001); Maddison (2004); Mitchell (2003a, 2003b, 2003c). a. Sample consists of 39 countries except where noted otherwise. Numbers in parentheses are standard errors. b. Sample consists of 19 countries.

Adjusted R2 Standard error of the regression

Dummy for British Commonwealth

Dummy for Latin America

Exit from gold standard (years after 1929) Dummy for Axis power

Constant

Independent variable

Dependent variable: Change in industrial production or real GDP per capita, 1929–37 (percent)

Table 3. Regressions Explaining the Depth and Duration of the Great Depression by Date of Exit from the Gold Standarda

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experience might be inappropriate. An alternative is to determine the width of the observation window country by country. We ran regressions using the date of exit from the gold standard to explain, first, the peak-to-trough decline in real GDP per capita (fifth and sixth columns in table 3), and second, the number of years it took for real GDP per capita to return to its precrisis level (final two columns). Because of the varied country set, the table reports both the simple regression using the date of exit and an augmented one that also includes dummy variables for whether the country was an Axis power, in Latin America, or a member of the British Commonwealth. In no case does the date of exit from the gold standard help to explain the depth or duration of the downturn, confirming the message from the earlier scatterplot. All told, the evidence that countries exiting the gold standard early fared better is apparently fragile. Once one expands the sample to a broader set of countries and considers other measures of the business cycle, leaving the gold standard early was not always a reliable route to a shorter or less severe recession. Why did exiting the gold standard generate so little benefit in the larger sample? The answer in part was already evident in table 2. Countries left the gold standard at different times. Moreover, when they did leave, the range of variation in bilateral exchange rates vis-à-vis the U.S. dollar was wide, indicating that policymakers did not follow a common roadmap. The greatest number of countries left in 1931, but those that did so in 1932 adjusted by more. A few that moved to some form of floating arrangement saw their currencies unhelpfully appreciate against the dollar. The important point for the United States is that almost all of these devaluations relative to gold produced an appreciation of the dollar, which added to the force of contraction domestically. Not until 1933 was some of that force pushed back, and even then the dollar still appreciated against the currencies of many economies. For contemporaneous observers, these swings in bilateral exchange rates smacked of “beggar thy neighbor” policy. From that experience was born a mistrust of floating exchange rates and a desire for a more managed system, famously expressed by Ragnar Nurkse (1944), among others. The net effect of these currency changes was to worsen the external drag on the U.S. economy, exactly when the appropriate policy was to reflate. Some sense of the net external drag can be gotten from figure 3, which plots effective exchange rate indices between the United States and five country groups. The base is set to 100 in 1929, and the shaded area represents the range from plus to minus 15 percent of that parity. There are

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Figure 3. Exchange Rates of Selected Country Groups against the U.S. Dollar, 1929–38 Local currency units per dollar, 1929 = 100a Latin Americab 180 160 U.K. and Nordicsc Asiad

140 120 100

1929 parity ± 15%

Canada

80

Other Europee

60 1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

Sources: Reinhart and Rogoff (2009) and sources cited therein; authorsí calc ulations. a. Each index is calculated from the simple unweighted average for the countries in the group. b. Argentina, Brazil, Chile, Colombia, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Peru, Uruguay, and Venezuela. c. Finland, Norway, Sweden, and United Kingdom. d. China, India, Japan, Korea, Netherlands East Indies, Philippines, Sri Lanka, Taiwan, and Thailand. e. Austria, Belgium, France, Germany, Greece, Hungary, Italy, Netherlands, Poland, Portugal, and Romania.

three main messages. First, the range of variation of nominal exchange notes was fairly narrow, except in Latin American countries, suggesting that external relative price adjustment was not the crucial means of rebalancing. Second, Canada’s vaunted embrace of floating exchange rates produced a result suggestive of considerable management in that market outcome.14 Third, most of the lines follow a track above 100 (that is, an appreciation of the U.S. dollar), implying that exchange rates worked to offset some of the domestic U.S. monetary policy stimulus. This brings to mind Robert Mundell’s (1968) insights about the N + 1 currency problem. In a system of N + 1 floating exchange rates, depreciation of the N currencies must come from an appreciation of the N + 1 currency. This creates a need for the economy using that anchor currency to overcompensate with domestic stimulus for that force of external restraint. The advantage of the gold standard was that all N could cheapen their currencies without putting a special burden on any one nation, given that the N + 1 14. Indeed, “fear of floating” in the Calvo and Reinhart (2002) sense seems evident.

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price was the value of gold. In the event, however, the adjustment was not so smooth.

III. Fiscal Policy during the Great Depression Sustained fiscal impetus in the major countries was similarly needed in the 1930s. And it was tried in many countries, but seldom consistently. Indeed, in many cases fiscal policy contracted as the national economy shrank, worsening the downturn. This record of policymaking is summarized in table 4, which reports for a group of 30 countries the year in which economic activity hit its cyclical low, as well as real government spending in that year, indexed so that the 1929 level equals 100.15 We rely on government spending to measure the fiscal impetus, rather than the more commonly used budget balance, for two reasons. First, revenue typically falls off in economic contractions, irrespective of policy intent, thus worsening the fiscal balance without necessarily providing much impetus. Second, we are somewhat more confident about the reliability of spending data over time and across countries than about that of revenue (see Kaminsky, Reinhart, and Végh 2005). The countries in table 4 are listed according to the co-movement of government spending and the economic cycle, from the most procyclical to the most countercyclical. Quite clearly, fiscal policy sometimes imparted considerable restraint rather than stimulus. As the third column shows, real government spending contracted in at least one year in 24 of the 30 countries, sometimes by a large amount. The United States was not among the countries where the trend of real government spending amplified the business cycle: by the trough in 1933, real government spending was almost twice its level of 1929. Figure 4 plots real government spending in the United States and Canada, again indexed to 100 in 1929. There were three distinct episodes of large increases in spending, first at the end of President Herbert Hoover’s administration in

15. Because of data limitations inherent in a large historical sample, the table uses statistics on central government spending only, which is problematic for countries with a federal system that allows discretion at the state or province level, such as the United States and Argentina, among others. Local budgetary pressures may have necessitated spending retrenchment that offset federal impetus. An additional issue is that real government spending is constructed using nominal spending from Mitchell (2003a, 2003b, 2003c), deflated by available price indexes. Again this is done for comparability across countries, but these measures do not always align well with readings from the national income and product accounts, where available.

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Table 4. Real Government Spending in Selected Countries, 1929–36a

Country Chile Peru Venezuela Finland Austria Germany Netherlands East Indies Brazil Mexico Japan Colombia Norway New Zealand Argentina Uruguay Hungary India Poland Australia Belgium Greece United Kingdom Korea France Canada Portugal Sweden Netherlands Italy United States Median Standard deviation

Year of trough in real GDP per capita

Real government spending at trough (1929 = 100)

Largest annual decline in real spending (percent)

Year of largest decline in real spending

1932 1932 1932 1932 1933 1932 1934 1931 1932 1931 1931 1931 1932 1932 1933 1932 1938 1933 1931 1932 1932 1931 1932 1932 1933 1936 1933 1934 1934 1933

53.0 55.7 73.8 79.4 79.9 94.1 94.9 96.4 96.9 99.7 102.0 105.5 106.1 110.2 110.7 111.8 112.6 114.0 115.7 116.2 117.5 118.3 120.2 138.9 149.0 151.9 152.3 154.2 178.5 191.6

34.4 25.7 33.2 28.7 21.8 9.8 4.0 15.4 7.2 8.9 32.7 None 3.7 3.9 13.3 10.2 9.9 None 3.1 0.2 38.8 None None None 11.9 3.9 None 3.6 31.1 2.1

1932 1932 1931 1932 1933 1931 1931 1931 1931 1931 1929 None 1932 1931 1929 1932 1932 None 1929 1932 1931 None None None 1933 1930 None 1934 1929 1933

111.3 31.9

Sources: Mitchell (2003a, 2003b, 2003c); Reinhart and Rogoff (2009) and sources cited therein; and authors’ calculations. a. Central government only.

1932 and then under Roosevelt in 1934 and 1936. Contrary to the popular perception, Hoover did significantly enlarge the role of government.16 In contrast, the governments of the United Kingdom and the Nordic countries 16. Akerlof and Shiller (2009) applaud the aggressiveness of both Hoover and Roosevelt but lament the unevenness of their policies.

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Figure 4. Real Government Spending for Selected Country Groups, 1929–38 1929 = 100a 250

Canada and United States

225 200 175 150

U.K. and Nordicsb

125 100 Latin Americac

75 1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

Sources: Mitchell (2003a, 2003b, 2003c); Reinhart and Rogoff (2009) and sources cited therein; and authors’ calculations. a. Each index is calculated from the simple unweighted average for the countries in the group. Spending is central government spending only. b. Nordics are Finland, Norway, and Sweden. c. Argentina, Brazil, Chile, Colombia, Mexico, Peru, Uruguay, and Venezuela.

provided less impetus, and fiscal policy in the Latin American countries was decidedly procyclical until 1934. The last group was no doubt hampered by a lack of access to funding, as well as institutional problems evident in Argentina and Brazil, among other countries. Although fiscal impetus was forceful in some countries, in almost all it was also erratic. Figure 4 further reveals that each of the three large increases in spending in the United States and Canada was followed by some retrenchment. The impetus from government spending in the United States in 1932, 1934, and 1936 appeared on track to provide considerable lift to the economy, but after each of those years real spending dropped off, imparting an arithmetic drag on expansion. The fact that fiscal expansion has been aggressive in many countries in 2009 works to help contain the contraction in the global economy. That it will continue to do so is far from assured, if history is any guide. Table 5 examines the ebbs and flows of real government spending across countries from 1929 to 1939. The first column reports the most conventional measure of spending volatility, the standard deviation of annual percentage

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Table 5. Volatility of and Reversals in Real Government Spending in Selected Countries, 1929–39

Country Italy Greece Peru United States Brazil Finland Portugal Japan Chile Colombia Venezuela France Germany Canada Sweden Austria Uruguay Argentina Netherlands Mexico Korea Netherlands East Indies India Hungary United Kingdom Norway Poland Australia New Zealand

Standard deviation of annual changes in real government spending (percent)

Amplitude of largest fiscal reversala (percentage points)

Year of largest reversal

No. of reversals with amplitude > 10 percentage points

105.4 61.3 45.0 33.8 31.5 30.6 29.7 28.9 27.6 27.6 24.7 21.5 21.2 16.6 16.5 16.4 16.4 15.2 12.5 12.0 11.9 11.7 10.0 9.6 9.0 8.6 7.8 7.2 6.5

227.7 159.2 64.8 47.5 55.0 33.0 66.6 42.3 51.5 68.0 46.8 15.1 21.5 39.9 31.4 18.5 24.1 21.2 20.7 24.4 16.6 30.3 13.5 20.1 12.8 11.9 12.8 11.8 8.9

1937 1931 1929 1933, 1935b 1933 1929 1937 1937 1929 1929 1929 1936 1929 1933 1934 1931, 1932b 1929 1935 1932 1937 1932 1938 1931 1932 1933 1933 1937 1932 1932

3 3 5 3 5 4 2 4 4 3 4 2 2 2 2 3 2 3 3 1 3 1 2 1 1 1 1 1 0

Sources: Mitchell (2003a, 2003b, and 2003c); Reinhart and Rogoff (2009) and sources cited therein; authors’ calculations. a. A reversal is defined as a year of rising followed by a year of declining government spending; the amplitude of a reversal is calculated as growth in year t minus growth in year t + 1. For instance, if real spending rose by 15 percent in year t and declined by 12 percent in the following year, the amplitude would be 15 − (−12) = 27 percentage points. b. Reversals in the two years were comparable in amplitude.

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changes in spending. Fiscal policy was indeed volatile in this period, with six countries posting a standard deviation of spending of more than 30 percent. A second indicator of the inconsistency of fiscal policy is the frequency with which government spending sharply reverses course. We calculate the “amplitude” of such a reversal as the sum of the percentage changes in spending in two consecutive years in which the first year sees a rise in spending and the second a decline. The second column of table 5 reports the amplitude of the largest such reversal in real spending growth for each country, the third lists the year of that reversal, and the fourth reports the number of times such reversals exceeded 10 percentage points in amplitude. Again and again, fiscal policy lacked follow-through in providing consistent impetus. Every one of the countries in the table experienced a reversal of real spending in at least one year of the decade, and all but one country suffered at least one reversal with an amplitude of more than 10 percentage points. This volatility of fiscal spending could, in principle, have blunted some of the force of the fiscal impetus if it rendered economic planning more Table 6. Regressions Explaining the Depth and Duration of the Great Depression by Volatility of Government Spending, 1929–39a

Independent variable Constant Annual change in real government spending divided by its standard deviationb Dummy for Axis power

Dependent variable: Peak-to-trough decline in real GDP (percent) 14.22 (11.42) 1.49 (7.71)

25.15 (11.26) −9.50 (7.90)

0.00 11.04

3.38 (5.70) 15.54 (4.91) 3.05 (5.21) 0.29 9.83

Dummy for Latin America Dummy for British Commonwealth Adjusted R2 Standard error of the regression

Dependent variable: Years until return to precrisis level 10.51 6.56 −0.83 4.42

9.88 (7.15) −1.58 (5.01)

0.00 6.33

−0.75 (3.62) 3.61 (3.11) 5.19 (3.30) 0.14 6.24

Dependent variable: Growth in real GDP per capita, 1929–37 −2.32 14.05 5.64 9.48

−7.51 (15.83) 11.86 (11.10)

0.01 13.57

−4.72 (8.01) −9.63 (6.90) −4.14 (7.32) 0.09 13.82

Source: Authors’ regressions. a. Sample consists of 30 countries in all regressions. Numbers in parentheses are standard errors. b. Sample covers 1929–39.


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difficult. Table 6 examines the extent to which such a mechanism was at work: using data from a sample of 30 countries from 1929 to 1939, the table reports regressions that attempt to explain the variation in the depth and duration of the business cycle with a measure of the growth of real government spending standardized by its volatility; to be precise, it is the average annual change in real government spending from 1929 to 1939, divided by its standard deviation. These regressions were performed with and without dummy variables for regions and for whether the country was an Axis power; the regressions including the dummy variables also include the logarithms of real GDP per capita and population in 1928, to capture any effects of country size. As is evident from the first pair of regressions, the depth of the cycle appears unrelated to the volatility of government spending. However, the remaining regressions at least produce coefficients that match the intuition. Higher standardized spending hastened the return of output to precrisis levels and added to real GDP growth. However, the standard deviation of spending is in the denominator of that explanatory variable, implying that greater volatility of real government spending tended to delay economic recovery and to reduce the net change in real GDP per capita. Thus, there may have been real costs associated with policy wavering.

IV. Some Lessons Unconventional monetary policy and aggressive fiscal policy were used extensively in the 1930s, in a considerable number of countries. They were not, however, employed consistently. Monetary policy was hampered by beggar-thy-neighbor problems as countries devalued relative to gold at different times and by different amounts. As a consequence, countries derived less benefit from exiting the gold standard than they could have, if indeed they saw any benefit at all. The United States was in the vanguard of aggressive use of fiscal policy at the central government level, but there and in many other countries this fiscal impetus was partly reversed soon after. The net effect was to raise volatility—and therefore uncertainty—and potentially to lessen the stimulus provided. A message from the 1930s is that national authorities must recognize that the openness of the global economy sometimes works to blunt the effectiveness of policy in one country. In the 2000s the N + 1 currency has been the U.S. dollar, whose special reserve-currency status meant that the United States received flight-to-safety flows even as it was the epicenter of

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the financial crisis.17 Like the appreciating U.S. dollar in the first part of the 1930s, this flight to the safe haven by capital holders outside the United States, by bolstering the dollar, augments the forces of restraint at home. Such a force may strengthen the case for concerted fiscal stimulus, but here an unpleasant reality intrudes: financial markets do not view all countries alike. Some have a history of uncertain repayment of their debt. Indeed, as shown by Reinhart, Rogoff, and Miguel Savastano (2003), some countries are “debt intolerant” and tend to default at debt-to-income ratios that elsewhere would be an entry ticket to European Monetary Union under the Maastricht Treaty. Progress in institution building has been significant in many of these emerging market economies. But national authorities take that lingering lack of acceptance very seriously and are unlikely to act in a fashion that threatens a reminder of earlier excesses. This implies that the advanced economies may be the only agents with significant scope for fiscal stimulus during a global crisis.18

ACKNOWLEDGMENTS We have benefited from the comments of our discussants, the editors, other participants at the Brookings Panel conference, and Kenneth Rogoff. We also thank Meagan Berry, Adam Paul, and Gregory Howard for their assistance.

17. We raised this point in Reinhart and Reinhart (2008) when we asked whether the United States was “too big to fail.” Note the parallel with the discussion of the Federal Reserve’s failure in the early 1930s. Policymakers need to recognize that safe-haven flows increase demand, necessitating even greater increases in supply. 18. Another reason the advanced economies may have to shoulder more of the burden is systematic differences in fiscal multipliers across the North and the South, as discussed in Ilzetzki, Mendoza, and Végh (2009).


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References Akerlof, George A., and Robert J. Shiller. 2009. Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism. Princeton University Press. Barro, Robert J., and José F. Ursúa. 2008. “Macroeconomic Crises since 1870.” BPEA, no. 1: 255–350. Bernanke, Ben S. 2004. Essays on the Great Depression. Princeton University Press. Bernanke, Ben S., and Vincent R. Reinhart. 2004. “Conducting Monetary Policy at Very Low Short-Term Interest Rates.” American Economic Review 94, no. 2: 85–90. Board of Governors of the Federal Reserve System. 1943. Banking and Monetary Statistics, 1890–1941. Washington. Cagan, Phillip. 1965. Determinants and Effects of Changes in the Stock of Money, 1875–1960. Columbia University Press. Calvo, Guillermo A., and Carmen M. Reinhart. 2002. “Fear of Floating.” Quarterly Journal of Economics 117, no. 2: 379–408. Choudhri, Ehsan U., and Levis A. Kochin. 1980. “The Exchange Rate and the International Transmission of Business Cycle Disturbances: Some Evidence from the Great Depression.” Journal of Money, Credit and Banking 12, no. 4, part 1: 565–74. Eichengreen, Barry. 1992. Golden Fetters: The Gold Standard and the Great Depression 1919–1939. Oxford University Press. Eichengreen, Barry, and Kevin H. O’Rourke. 2009. “A Tale of Two Depressions.” VoxEU (June). www.voxeu.org/index.php?q=node/3421. Eichengreen, Barry, and Jeffrey Sachs. 1985. “Exchange Rates and Economic Recovery in the 1930s.” Journal of Economic History 45, no. 4: 925–46. ———. 1986. “Competitive Devaluation and the Great Depression: A Theoretical Reassessment.” Economics Letters 22, no. 1: 67–71. Eichengreen, Barry, and Peter Temin. 2000. “The Gold Standard and the Great Depression.” Contemporary European History 9, no. 2: 183–207. Friedman, Milton, and Anna Jacobson Schwartz. 1963. A Monetary History of the United States, 1867–1960. Princeton University Press. Hamilton, James D. 1988. “The Role of the International Gold Standard in Propagating the Great Depression.” Contemporary Policy Issues 6, no. 2: 67–89. Hsieh, Chang-Tai, and Christina D. Romer. 2006. “Was the Federal Reserve Constrained by the Gold Standard During the Great Depression? Evidence from the 1932 Open Market Purchase Program.” Journal of Economic History 66, no. 1: 140–76. Ilzetzki, Ethan, Enrique Mendoza, and Carlos Végh. 2009. “How Big (Small) Are Fiscal Multipliers?” University of Maryland. James, Harold. 2009. The Creation and Destruction of Value. Harvard University Press.

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Kaminsky, Graciela L., Carmen M. Reinhart, and Carlos A. Végh. 2005. “When It Rains, It Pours: Procyclical Capital Flows and Policies.” In NBER Macroeconomics Annual 2004, edited by Mark Gertler and Kenneth Rogoff. MIT Press. Maddison, Angus. 2004. Historical Statistics for the World Economy: 1–2003 AD. Paris: Organisation for Economic Co-operation and Development. www.ggdc. net/maddison/. Meltzer, Allan H. 2003. A History of the Federal Reserve, Volume 1: 1913–1951. University of Chicago Press. Mitchell, Brian R. 2003a. International Historical Statistics: Africa, Asia, and Oceania, 1750–2000. London: Palgrave Macmillan. ———. 2003b. International Historical Statistics, Europe, 1750–2000. London: Palgrave Macmillan. ———. 2003c. International Historical Statistics: The Americas, 1750–2000. London: Palgrave Macmillan. Mundell, Robert A. 1968. International Economics. New York: Macmillan. Nurkse, Ragnar. 1944. International Currency Experience: Lessons of the Interwar Period. Geneva: League of Nations. Officer, Lawrence H. 2001. “Gold Standard.” In EH.Net Encyclopedia, edited by Robert Whaples (October 1). eh.net/encyclopedia/article/officer.gold.standard. Orphanides, Athanasios. 2004. “Monetary Policy in Deflation: The Liquidity Trap in History and Practice.” North American Journal of Economics and Finance 15, no. 1: 101–24. Reinhart, Carmen M., and Vincent R. Reinhart. 2008. “Is the US Too Big to Fail?” VoxEU (November). www.voxeu.org/index.php?q=node/2568. Reinhart, Carmen M., and Kenneth S. Rogoff. 2009. This Time Is Different: Eight Centuries of Financial Folly. Princeton University Press. Reinhart, Carmen M., Kenneth S. Rogoff, and Miguel A. Savastano. 2003. “Debt Intolerance.” BPEA, no. 1: 1–62. Romer, Christina D. 1992. “What Ended the Great Depression?” Journal of Economic History 52, no. 4: 757–84. Svensson, Lars E. O. 2003. “Escaping from a Liquidity Trap and Deflation: The Foolproof Way and Others.” Journal of Economic Perspectives 17, no. 4: 145–66.

Comment and Discussion COMMENT BY

CHANG-TAI HSIEH Why did monetary policymakers fail to stem the Great Depression of the 1930s? A conventional view, largely due to Barry Eichengreen and Jeffrey Sachs (1986) and Ben Bernanke (2004), holds that the gold standard was key. Adherence to the gold standard forced economies experiencing capital outflows to contract and was the key mechanism by which deflation was spread throughout the world. The link to gold also prevented central banks from acting as the lender of last resort when faced with a financial panic, and it placed constraints on fiscal authorities who might otherwise have engaged in expansionary spending or tax policies. A key stylized fact that supports this interpretation is that among the industrialized countries, the depth and length of the depression were correlated with how long a country stayed on the gold standard. The downturn was more muted in countries, such as the United Kingdom, that were among the first to exit the gold standard, and longer in countries, such as France, that were among the last. This paper by Carmen Reinhart and Vincent Reinhart challenges this interpretation of the role of the gold standard by marshalling new facts. Figure 2 of their paper shows that the statistical relationship between the date of exit from the gold standard and the magnitude of the depression disappears when the sample is broadened beyond the industrialized countries. For example, several countries that were mainly primary commodity producers were among the first to leave the gold standard (most of them in 1929, two full years before the United Kingdom’s departure in 1931), yet suffered some of the worst downturns. If one interprets this finding as evidence that adherence to the gold standard did not affect the depth and severity of the Great Depression, it potentially changes the standard interpretation of its causes. The question is whether this reinterpretation is warranted. 273

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In fact, the authors provide two interpretations of their new finding. First, they argue that it shows that departure from the gold standard is less effective “when the global pie is shrinking.” Although this interpretation might be correct, the paper presents no evidence to support it. If the global pie was shrinking, by definition it was shrinking for industrialized and nonindustrialized countries alike. Why, then, might such a decline weaken the effectiveness of exiting the gold standard more for the latter than for the former? For example, is a larger share of manufacturing output in the nonindustrialized countries exported, making these countries more susceptible to downturns in world export markets? More generally, does the response to exiting the gold standard differ depending on whether a country is more or less dependent on world trade? Or is the argument that the nonindustrialized countries specialize largely in commodities, whose price elasticity of demand is less than that of other goods? In that case, what might be driving the results is that the output elasticity to changes in the terms of trade for the nonindustrialized countries is not the same as that for the industrialized countries. The second interpretation offered by the paper harkens back to the argument by Ragnar Nurkse (1944). In brief, the argument is that one country’s departure from the gold standard might not necessarily translate into a decline in the terms of trade if other countries are depreciating against that country’s currency at the same time. (As an aside, it would be useful if the paper couched the discussion in terms of the real exchange rate, that is, net of changes in domestic prices or wages on both sides.) Again, here it would be useful to know how exactly this interpretation fits with the observation that departure from the gold standard was associated with economic recovery in industrialized but not in nonindustrialized countries. The paper emphasizes the fact that exchange rate adjustment in most countries came largely at the expense of the United States. This may well be true for 1931 and 1932, but the United States’ departure from the gold standard in 1933 was quickly followed by a recovery. The paper needs to show that departure from the gold standard was associated with depreciation for the industrialized countries and not for the nonindustrialized countries. A casual reading of figure 3 suggests that the evidence on this point is not clear. Over all, the European countries (other than the United Kingdom and the Nordic countries) did see their currencies depreciate relative to the U.S. dollar. The Canada–U.S. exchange rate, however, was basically unchanged. The pound sterling actually appreciated against the U.S. dollar, yet this was the country where the downturn was the smallest. On the other hand, the Latin American currencies saw the largest depreciation against


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the U.S. dollar, yet these were the countries where the downturn was most severe. Let me propose a third explanation. To interpret the correlation between the timing of exit from the gold standard and subsequent economic outcomes as causal, one needs to be sure that the cross-country variation in the timing of the exit from gold is not driven by forces that might also drive the economic outcomes one is measuring. In his book Golden Fetters (1992), Barry Eichengreen provides a wealth of narrative evidence that the degree of commitment to the gold standard among the industrialized countries was largely driven by ideology and domestic political considerations. Although these political forces might also have an independent effect on economic policy (that is, other than through their effect on exchange rate policy), this is not the same as a story where, for example, countries that exited the gold standard, or exited sooner, were the ones that were hit the hardest by adverse economic shocks. And the argument that variation in the degree of commitment to the gold standard is exogenous to economic forces is less plausible for the nonindustrialized than for the industrialized countries. For example, isn’t the fact that Argentina, Brazil, Australia, New Zealand, Uruguay, and Venezuela (table 2 in the paper) were the first countries to exit the gold standard driven by the severe decline in world prices for their exports? If so, then how does one disentangle the effect of the decline in export prices from the effect of exit from the gold standard? It might well be the case that the exit from the gold standard stimulated output, but this effect is overwhelmed by the economic shocks that prompted the country to exit the gold standard in the first place. In sum, the new facts presented by Reinhart and Reinhart have the potential to overturn what we thought we knew about the causes of the Great Depression. But much more needs to be done to show that their interpretation of their new facts—that the timing of departure from the gold standard did not contribute to recovery from the Great Depression— is the right one. REFERENCES FOR THE HSIEH COMMENT

Bernanke, Ben S. 2004. Essays on the Great Depression. Princeton University Press. Eichengreen, Barry. 1992. Golden Fetters: The Gold Standard and the Great Depression 1919–1939. Oxford University Press. Eichengreen, Barry, and Jeffrey Sachs. 1985. “Exchange Rates and Economic Recovery in the 1930s.” Journal of Economic History 45, no. 4: 925–46. Nurkse, Ragnar. 1944. International Currency Experience: Lessons of the Interwar Period. Geneva: League of Nations.

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GENERAL DISCUSSION Linda Goldberg thought it worth recalling the specific circumstances in which exchange rate changes can make a difference toward recovery from a recession. The classic mechanism is expenditure switching: changes in exchange rates change the relative prices of goods in different countries. But the amount by which such changes help a country in recession depends in part on the degree to which production is vertically integrated. The proportion of U.S. imports that consists of components and raw commodities rather than final goods for consumption is higher today than in the past, and this limits the effects that one can expect through the exchange rate channel. Goldberg also suggested introducing financial globalization variables into the analysis. For example, there is evidence that the more globalized banks are less sensitive to U.S. monetary policy than other banks, because the globalized banks are able to transfer liquidity among their different subsidiaries. This does not make monetary policy completely ineffective, but it does shift the incidence of monetary policy to those countries that are host to the global counterparties in these intrafirm capital transactions. Alan Auerbach pointed out that state and local spending was a much larger share of U.S. government spending in the 1930s than it is today. In the current recession, state and local government responses have tended to be procyclical, and this effect needs to be taken into account. Auerbach also observed that the paper dealt only with government spending, and he suggested looking at the tax policy response to the recession in different countries as well. Finally, he wondered to what extent recent fiscal policy actions in different countries have been expressly designed to avoid international leakages, perhaps in response to the greater openness of economies in general.

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