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FEDERAL RESERVE BANK OF ST. LOUIS

R E V IE WVOLUME 84, NUMBER 6NOVEMBER/DECEMBER 2002

Flation

William Poole and Robert H. Rasche

A Case Study of a Currency Crisis: The Russian Default of 1998

Abbigail J. Chiodo and Michael T. Owyang

Asset Mispricing, Arbitrage, and Volatility

William R. Emmons and Frank A. Schmid

Regime-Dependent Recession Forecasts and the 2001 Recession

Michael J. Dueker

Investment-Specific Technology Growth: Concepts and RecentEstimates

Michael R. Pakko

Director of Research

Robert H. Rasche

Deputy Director of Research

Cletus C. Coughlin

Review Editor

William T. Gavin

Research Economists

Richard G. Anderson

James B. Bullard

Michael J. Dueker

Thomas A. Garrett

R. Alton Gilbert

Hui Guo

Rubén Hernández-Murillo

Kevin L. Kliesen

Christopher J. Neely

Michael T. Owyang

Michael R. Pakko

Jeremy M. Piger

Patricia S. Pollard

Frank A. Schmid

Daniel L. Thornton

Howard J. Wall

David C. Wheelock

Managing Editor

George E. Fortier

Assistant Editor

Lydia H. Johnson

Graphic Designer

Donna M. Stiller

Review is published six times per year by the ResearchDivision of the Federal Reserve Bank of St. Louis and may be accessed at our new Web address:<research.stlouisfed.org/publications/review>.Single-copy subscriptions are also available free of charge. Send requests to: Federal Reserve Bank of St. Louis, Public Affairs Department, P.O. Box 442, St. Louis, MO 63166-0442, or call (314) 444-8808 or 8809.

The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.

© 2002, The Federal Reserve Bank of St. Louis. Articles may be reprinted, reproduced, published, distributed, displayed, and transmitted in their entirety if this copyright notice is included. Please send a copy of any reprinted, published, or displayed materials to George Fortier, Research Division, Federal Reserve Bank of St. Louis, P.O. Box 442, St. Louis, MO 63166-0442; [email protected]. Please note:Abstracts, synopses, and other derivative works may be made only with prior written permission of the Federal Reserve Bank of St. Louis. Please contact theResearch Division at the above address to request permission.

R E V I E W

NOVEMBER/DECEMBER 2002 i

ContentsVolume 84, Number 6

1 Flation

William Poole and Robert H. Rasche

“Flation” is an article adapted from a speech ofthe same title presented before the InternationalMass Retail Association Leadership Forum,Scottsdale, Arizona, January 21, 2002.

7 A Case Study of a Currency Crisis:The Russian Default of 1998

Abbigail J. Chiodo and Michael T. Owyang

This paper uses a currency crisis frameworkto analyze the currency devaluation and debtdefault of post-Soviet Russia in August 1998.The authors show that even though the Russianeconomy recorded positive growth immediatelypreceding the default, the atmosphere wasreflective of an impending crisis. The authorsthen consider the symptoms of a currencycrisis—specifically public and private debtresponsibilities, devaluation expectations, andcontractionary monetary policy—and showthat they were present in Russia at that time.Three generations of currency crisis modelsare reviewed, followed by speculation that theRussian default was a product not only of fiscaldeficits but also of a fragile financial systemand contractionary monetary policy. Theauthors address the possibility that the usualprescription for a currency crisis, that is,increasing interest rates, may have acceleratedthe default and that a case-by-case prescriptionmay afford a better solution than a blanketpolicy of increasing interest rates in the faceof devaluation.

19 Asset Mispricing, Arbitrage, and Volatility

William R. Emmons and Frank A. Schmid

Market efficiency remains a contentious topicamong financial economists. The theoreticalcase for efficient markets rests on the notionof risk-free, cost-free arbitrage. In real markets,however, arbitrage is not risk-free or cost-free.

In addition, the number of informed arbi-trageurs and the supply of financial resourcesthey have to invest in arbitrage strategies islimited. This article builds on an importantrecent model of arbitrage by professionaltraders who need—but lack—wealth of theirown to trade. Professional abitrageurs mustconvince wealthy but uninformed investors toentrust them with investment capital in orderto exploit mispricing and push market pricesback toward intrinsic value. The authors intro-duce an objective function for the arbitrageurthat resembles real-world contracts. Also, theauthors calibrate the objective function to showthat arbitrage generally has a price-stabilizinginfluence and reduces volatility in asset returns.

29 Regime-Dependent Recession Forecastsand the 2001 Recession

Michael J. Dueker

Business recessions are notoriously hard topredict accurately, hence the quip that econ-omists have predicted eight of the last fiverecessions. This article derives a six-month-ahead recession signal that reduces the numberof false signals outside of recession, withoutimpairing the ability to signal the recessionsthat occur. In terms of predicting the 1990-91and 2001 recessions out of sample, the newrecession signal, like other signals, largelymisses the 1990-91 recession with its six-month-ahead forecasts. In contrast, a recessiononset in April or May 2001 was predicted sixmonths ahead of the 2001 recession, which isclose to the actual turning point of March 2001.

37 Investment-Specific Technology Growth:Concepts and Recent Estimates

Michael R. Pakko

The strength of U.S. productivity growth inrecent years has been attributed to techno-logical improvements that are, in some sense,embodied in new types of capital equipment.However, traditional growth theory and growthaccounting techniques—which emphasize therole of disembodied, neutral technologicalprogress—are deficient in explaining thisphenomenon. In this article, Michael R. Pakkooutlines a model of investment-specific techno-


logical change that has become popular fordescribing the notion of capital-embodiedgrowth and summarizes some recent estimatesof the importance of this type of technologicalprogress for assessing U.S. productivity trends.

49 Working Paper Series

57 Review Index 2002

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R E V I E W

NOVEMBER/DECEMBER 2002 1

FlationWilliam Poole and Robert H. Rasche

“Flation”—not inflation, not deflation—islifted from the title of a book by Abba P.Lerner.1 For the past 35 years in the

United States and, indeed, in most of the world,policymakers and the public in general have beenfocused on the issue of inflation—that is, the con-tinual upward drift in prices of the overwhelmingfraction of goods and services produced in theeconomy. Sometimes the drift was more of a gallop.For most of this period, the upward trend was alsocharacteristic of the prices of assets such as land,houses, and equities. Inflation, prevalent thoughit has been in our recent economic experience,has not been the norm for most of U.S. history. Inthe early 1930s, exactly the opposite experienceoccurred: deflation, or a continual downward driftin the prices of goods, services, and assets.

Deflation has a frightening history. Simultane-ously with the deflation of the early 1930s, the U.S.unemployment rate soared to about 25 percent in1933 at the depth of the Great Depression. Althoughdeflation ended in 1933, the damage to the economywas so great that poor economic conditions persisteduntil the United States became involved in WorldWar II in 1941. Moreover, the economic history ofthe 1990s in Japan is characterized by deflation. TheJapanese economy has stagnated, and unemploy-ment there has risen today to levels not seen in over40 years. From these and other episodes aroundthe world, many people associate deflation with“hard times.”

The purpose of this analysis is not to get into adiscussion of whether a little deflation is compatiblewith prosperity, although within limits it may be.The more important point is that, without question,substantial deflation is inconsistent with prosperity.Thus, deflation is every bit as serious an issue asinflation; however, the U.S. economy today doesnot run any significant risk of deflation.

Obviously, not everyone agrees with this judg-ment. Based on a few recent observations of month-to-month price changes, some commentators haveused the “D” word to express their concern aboutthe current state of the U.S. economy. The objectivein this paper is to explore this subject and, we hope,make a contribution to public understanding of theissue.

First, we will elaborate on what we believe isthe appropriate objective for Federal Reserve policy.Second, we will explain the generally accepted defi-nitions of inflation and deflation, and discuss thefundamental sources of these phenomena. Third, wewill review aspects of price behavior in our economyand discuss how data should be interpreted to deter-mine the inflationary or deflationary state of theeconomy. Finally, although the issue concerns thebehavior of the aggregate price level, we will exam-ine some particular sectoral price changes to helpbetter understand the aggregate price level.

THE APPROPRIATE POLICY OBJECTIVEFOR THE FEDERAL RESERVE

Our monetary policy framework is this. Firstand foremost, the central bank must maintain acommitment to price stability. An operational defi-nition of price stability is an environment in whichthe inflation rate, properly measured and averagedover several years, is zero. All of our inflation dataare subject to measurement errors. Experts in suchmeasurements generally agree that current priceindexes, despite statisticians’ best efforts, still leaveinflation measures that have some upward bias.Hence, in terms of the various inflation indexes, wecan say that price stability prevails when broad priceindexes exhibit small positive average values formeasured inflation and that year-to-year fluctuationsaround that average are well contained.

If the price level comes unstuck, yielding infla-tion or deflation, all sorts of other problems willarise. Nevertheless, within the confines of the goalof price stability, the central bank has some flexibil-ity to lean against fluctuations in output and employ-ment. However, the central bank ought not to pursuethe goal of stabilizing economic activity so aggres-sively that it runs any substantial risk of compromis-ing the goal of price stability.

Finally, in leaning against fluctuations in growthand employment, the central bank ought not to

1 Lerner, Abba P. Flation. New York: Quadrangle Books, 1972.

William Poole is the president and Robert H. Rasche is a senior vicepresident and director of research at the Federal Reserve Bank of St.Louis. This article is adapted from a speech of the same title presentedbefore the International Mass Retail Association Leadership Forum,January 21, 2002. The authors appreciate comments provided bycolleagues at the Federal Reserve Bank of St. Louis. The views expresseddo not necessarily reflect official positions of the Federal ReserveSystem.

© 2002, The Federal Reserve Bank of St. Louis.

have goals for levels of the economy’s growth andunemployment rates per se. Within a wide range,no one knows what the economy’s equilibrium rateof growth is or what rate of unemployment willclear the labor market in the long run. The biggestrisks of a major monetary policy mistake occur if acentral bank attempts to target the levels of realvariables.

Achieving the objective of price stability, asdefined above, will yield a highly stable economy.When the market has confidence in Fed policy, short-run changes—that is, over a few months or even afew quarters—in the rate of inflation or deflation willtend to be self-reversing rather than self-reinforcing.

THE DEFINITION AND SOURCES OFINFLATION AND DEFLATION

At the beginning of the great inflation of 1965-80,there was a wide disparity of professional opinionabout the fundamental source of inflation or defla-tion in an economy. One proposition came to beknown as the “monetarist view.” This view held thatsustained inflation or deflation was always a mone-tary phenomenon; that is, that the only source oflong-run positive or negative trends in the generallevel of prices in an economy is the creation of anexcess or insufficient supply of money balancesrelative to the growth of the productive capacity ofthat economy. Milton Friedman of the University ofChicago was the most publicly visible proponentof this proposition. The Federal Reserve Bank of St.Louis, in particular the president of the Bank at thattime, Darryl Francis, and the Research staff werevocal advocates of this proposition in the policyarena during the late 1960s and early 1970s. A read-ing of the Memorandum of Discussion of the FederalOpen Market Committee (FOMC) for this periodmakes clear that there were sharp debates over theseissues. The FOMC is the Fed’s main monetary policy-making body, and the public record of that periodshows that Darryl Francis was a vigorous advocateof the monetarist view.

The proposition that the central bank is thesource of ongoing inflation or deflation was a dis-tinct minority view 35 years ago. In the FOMC, DarrylFrancis was usually the only one expressing thisview. The development of economic theory and theeconomic history of the past three decades haveproduced a major change in both professionalthinking and public attitudes toward the sources ofinflation and deflation. Economists are now largely

in agreement that if the central bank does not achievethe goal of price stability, no one else can. Manycentral banks around the world, starting with theReserve Bank of New Zealand in 1990, have acknowl-edged this responsibility and have adopted explicitnumeric inflation targets.

This view also spread into public thinking aboutinflation in the United States. Paul Volcker, formerchairman of the Board of Governors of the FederalReserve System, is widely credited for the disinflationthat occurred in the United States in the early 1980s.Chairman Greenspan is applauded for the additionalprogress in the 1990s that brought the U.S. inflationrate to the lowest level in almost 40 years.

Today the Federal Reserve accepts its responsi-bility for the trend rate of inflation. However, a centralbank is not responsible for month-to-month wigglesin the inflation statistics. Nor should a central bankattempt to react to short-run variations, since thesources of such noise are beyond its control andlikely to average out over a period of a few monthsor at most a couple of years. One obvious reasonfor not reacting to short-run developments is thatan unknown part of these changes in the reportedinflation rate is purely measurement error, or statisti-cal noise.

Professional opinion has also changed about thesource of deflation in the 1930s. It is now widelyacknowledged that, at a minimum, the intensity ofthe Great Depression was magnified by the failureof the Federal Reserve to provide sufficient liquidityto the economy in the face of widespread bank fail-ures. The Federal Reserve in turn has learned fromthat experience. When the U.S. economy has beenthreatened by liquidity crises in recent years—suchas the stock market crash of 1987, the Asian crisesand Russian default of 1998, and the terrorist attackof September 11, 2001—the Fed has moved rapidlyto inject large amounts of liquidity into the econ-omy. Liquidity crises have been averted, inflationhas remained low and stable, and deflation has notoccurred.

Experience elsewhere has not been as benign.Over the period from 1981 through 1990, theJapanese economy grew at an annual rate of 3.7percent and the inflation rate (measured by thegross domestic product [GDP] price index) averaged1.5 percent per year. The situation in Japan in the1990s has been remarkably different. The Japaneseeconomy has struggled in and out of recession, andreal growth from 1991 to 2000 averaged only 1.1percent. Over the same period, very low inflation

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has turned into deflation. From 1991 to 1996, theJapanese consumption deflator rose at an averageannual rate of only 0.5 percent; for 1996 to 2000,the rate was –0.2 percent. Asset prices fell dramati-cally. The decline of the Nikkei equity price indexfrom a value of close to 40,000 in late 1989 to itsrecent level of less than 10,000 is common knowl-edge. What is not as well known outside Japan isthat land and real estate prices over the past decadehave experienced equally dramatic declines as thoseseen in equity markets. In April 1993 an index ofhousing prices in Japan stood at 42.35 million yen.By April 2001 it had fallen to 36.52 million yen, anannual average rate of decline of 1.7 percent.2 Theindex of residential land prices reached a peak inMarch 1991 of 109.7 and fell to 81.7 by September2001, an annual average rate of decline of 2.4 per-cent. The decline in commercial land prices waseven larger. From a peak of 111.7 in September 1991,the index of these prices fell to 49.1 in September2001, an annual average rate of decline of 5.6 per-cent.3 In terms of the impact on Japan’s output andemployment, the large deflation of asset prices wasprobably more important than the gentle deflationof goods prices.

What is responsible for the incredible differ-ence in the performance of the Japanese economybetween the 1980s and 1990s? Japan’s money stock(using Japan’s own preferred measure, M2+CDs)grew at an average annual rate of 7.9 percent from1981 through 1990, but only at 2.3 percent per yearover the decade from 1991 through 2000. A con-clusion consistent with research on this issue isthat the ongoing stagnation and deflation that theJapanese economy has experienced in the pastdecade is likely related to an insufficient supply ofliquidity by the Bank of Japan. Slow money growthis not the whole story, but is certainly a significantpart of it.

RECENT PRICE BEHAVIOR IN THEU.S. ECONOMY

Public discussion of inflation in the UnitedStates generally is focused on the consumer priceindex (CPI) published monthly by the Bureau ofLabor Statistics. The monthly change in the overallCPI is the so-called “headline” inflation number.The CPI is very visible; it has been widely reportedfor years and is used to construct cost-of-livingadjustments in union wage contracts and SocialSecurity benefits.

Sometimes reference is also made to a “core”inflation rate, usually measured by the CPI excludingprices of food and energy products. The rationalefor excluding food and energy prices is that they canbe quite volatile, and hence longer-term inflationtrends can be obscured when they are included.

Starting in 2000, the FOMC chose to focus on adifferent measure of inflation: changes in the priceindex for personal consumption expenditures in thenational income accounts. This measure of inflation,which for convenience we will call the “consumptionprice index,” is reported monthly by the Bureau ofEconomic Analysis of the Department of Commerce.Although this index receives less public attentionthan the CPI, it is preferred by the FOMC becausethe methodology used in its construction reducesthe measurement bias relative to that in the CPI;also, the coverage of goods and services in this indexis believed to better represent consumption patterns.For example, prices of medical services are includedin the CPI only to the extent that such services arepaid directly by consumers. Prices of all medicalservices are included in the consumption priceindex whether those services are paid for directlyby consumers or are paid for on behalf of consumersby third parties such as insurance companies.

In recent years, inflation as measured by theconsumption price index has been lower than thatmeasured by the CPI.4 Although the following discus-sion will refer primarily to the consumption priceindex, no important issues depend on whether thefocus is on that index or the CPI.

What should we expect to observe in an econ-omy where price stability prevails? If it were possi-ble to measure the average level of prices with littleor no bias in such an economy, then over a periodof time an average measured inflation rate very closeto zero should be observed. From month-to-monthor quarter-to-quarter, positive or negative changesof the inflation index will occur, but over time thesewould average out to about zero.


2 The Housing Loan Progress Association. “Price Survey of the HousingMarket.” <http://jin.jcic.or.jp/stat/stats/>.

3 National Land Agency.

4 In August 2002 the Bureau of Labor Statistics introduced a new measureof consumer prices—the chained consumer price index for all urbanconsumers (C-CPI-U). Monthly data are available from December 1999.The objective of the new index is to reduce the substitution bias thatis present in the CPI-U. Between December 1999 and December 2000(the only period for which final estimates of the C-CPI-U are available),the inflation rate measured by the C-CPI-U differs from that measuredby the consumption price index by only 0.1 percent.

FEDERAL RESERVE BANK OF ST. LOUIS Poole and Rasche

What about prices of individual goods and ser-vices under such conditions? There would likely bea dispersion of changes in the prices of individualgoods and services around zero. In fact, prices ofsome goods and services could be continually falling,while prices of other goods and services could becontinually rising. It is perfectly normal to experiencedivergent trends of individual prices under condi-tions of overall price stability. Thus, trends in theprices of individual goods or services cannot be usedto judge whether an economy is experiencing infla-tion or deflation.

An important influence on inflation data in theUnited States over the past three years has been thebehavior of energy prices on world markets. In 1998,energy prices collapsed as world demand droppeddramatically in response to the crises in Asian econ-omies. Petroleum inventories rose unexpectedlyand major producers, including OPEC nations, cutproduction to stabilize prices and adjust inventories.In 1999 and 2000, energy prices rose sharply aseconomic activity boomed in the United States andother major industrialized economies at a time whenworld inventories of oil were particularly low. Lead-ing up to 2002, as the U.S. economy sank into reces-sion and economic growth slowed in Europe, energydemand growth slowed and energy prices on worldmarkets fell again.

The average inflation rate over the four years1994 through 1997 was 2.7 percent per year asmeasured by the consumption price index. Theaverage inflation rate over the four years from 1998through 2001 was 1.7 percent per year. The coreinflation component of the consumption price indexhas fallen from 2.1 percent in the earlier period to1.6 percent in the latter period. The conclusionfrom these observations is that there has been asmall reduction in trend inflation, whether measuredby the total or the core consumption price index,over the past four years.

No estimates of the biases in the index are solarge as to suggest that the true rate of inflation isnow negative—that is, the U.S. economy is not in adeflationary situation. What, then, is the origin ofthe “deflation threat” that has been featured in someeconomic and newspaper commentaries? Some ofthese discussions appear to concentrate unduly onparticular prices and on short-run data collected inthe immediate aftermath of the September 11 terror-ist attacks. The change in the price index for personalconsumption expenditures for September 2001compared with August 2001 was reported at –0.4

percent. The decline is attributable to falling energyprices and to a statistical artifact of the decisionmade by the Bureau of Economic Analysis in mea-suring insurance claim payments as a result of theSeptember 11 attacks. The December 2001 consump-tion price index showed a decline of 0.2 percentfor the month and led to further press speculationabout deflation. Again, it is necessary to emphasizethat a focus on very short-term movements in theprice indexes can lead to misinterpretation of theunderlying trends of inflation or deflation in aneconomy.

CHANGES IN RELATIVE PRICES

One of the great strengths of the U.S. economyis that prices of individual goods and services fluc-tuate freely. These price changes allow markets tosignal how our productive resources can be allocatedmost efficiently. The disparity among inflation ratesfor particular goods and services over longer periodsof time is significant. From 1980 to 2000, the over-all consumption price index rose 95 percent. Con-sider price behavior in a half-dozen categories withinoverall personal consumption expenditures. Pricesof personal computers and peripheral equipmentstand out: such prices are estimated to have fallenby 99 percent since 1980. Note that despite thisdramatic price decline, people do not talk about thecomputer industry suffering from deflation. This isa growth industry, driven by dramatic innovationsand increases in efficiency.

Prices of durable goods are estimated to haveincreased by 20 percent since 1980, considerablyslower than the general inflation over this period.Prices of nondurables are estimated to have increasedby 65 percent since 1980; nondurable goods priceshave risen more than durable goods prices, but stillconsiderably less than the overall rate of inflation.Prices of food and beverages are estimated to haveincreased 79 percent since 1980, somewhat slowerthan the overall rate of inflation.

Consider some examples at the other extreme.Since 1980, prices of tobacco and smoking productsare estimated to have increased 480 percent andprices of medical services by 197 percent. In thetables that show prices by various sectors, widedifferences in experience such as those mentionedhere can be seen.

Are falling prices, or prices that increase slowlyrelative to the general rate of inflation, indicative of“hard times” for particular industries? Sometimes,but certainly not always. Consider personal comput-



ers and consumer electronics in general (the latteris included in the durable goods component of theconsumption price index). These are goods that havedemonstratively high income and price elasticities.What that means is that the amounts consumersbuy increase a lot as incomes rise and/or prices fall.Over time, as consumer incomes have increasedand prices have fallen, the size of the market forthese high-elasticity products has increased dramat-ically. Color TVs, camcorders, VCRs, DVDs, and per-sonal computers, to name a few such products, areall now common household items in the UnitedStates. Many consumers can remember when theseproducts were either unknown or owned by relativelyfew households.

This is an important point: expansion of themarkets for certain products occurred simultane-ously with a fall in prices. Price deflation for thesegoods was not inconsistent with prosperity in theindustries producing them. Indeed, declining priceswere essential to expanding these markets. Thefall in prices was the result of rapid productivityincreases from innovations in the production ofthese items and/or their components. Firms foundit profitable to cut prices and expand production.Workers in these industries found their improvedproductivity rewarded in higher wages. Consumers,workers, and shareholders all have benefited, eventhough prices have fallen substantially over time.

High-demand elasticities are a critical elementin such success stories. In contrast, consider marketsfor basic agricultural products in the United States.Productivity improvement in U.S. agricultural pro-duction over the years has been tremendous. Pricesof these products have also fallen relative to goodsin general over the long run. However, both incomeand price elasticities for agricultural products arerelatively low. Hence, economic growth and declin-ing prices have not produced large increases inconsumption. As a result, fewer and fewer workershave been required over time to produce more thanenough output to satisfy both domestic and foreigndemand. Farms have gone out of business, the num-ber of people engaged in agricultural productionhas decreased, and in recent years farm incomehas been sustained by large “emergency” farmappropriations out of the federal budget. Becauseof the low price and income elasticities for agricul-tural goods, deflation in this industry means hardtimes for many farmers.

Health care provides a really interesting caseof relative price changes. In part, the rapid rate ofprice increase here represents innovation in the

form of new products and/or improved procedures.Such price changes really reflect significant qualityimprovements. Ideally such quality improvementswould be incorporated into the measurement of astandardized unit of medical services. With someconsumer durables, such as automobiles, statisticianshave been quite successful in measuring qualityimprovement. In other areas, capturing qualitychange into the measurement of a standard unit ofoutput is difficult if not impossible.

As an example, consider laparoscopic surgeryto remove the gall bladder. Not that long ago, gallbladder surgery required a substantial period ofhospitalization, during which patient activity levelswere significantly restricted. Today, with laparoscopicsurgery, the length of the hospital stay is muchshorter and patient discomfort much less. Moreover,the patient can resume reasonably normal activity,including going to work, after a short postoperativeperiod. The patient and/or a third-party payer maypay the surgeon substantially more today to removethe gall bladder than 35 years ago, but does thisincrease mean that the price properly measured isdramatically higher? A well-constructed price indexmight adjust for the reduction in the pecuniary costof confinement—fewer hospital days—from theimproved technology. However, it is unlikely thatany price index would reflect the improved qualityof the procedure represented by the reduced non-pecuniary costs of confinement and the shorterrecovery time now available. Hence the reportedchange in the price index for such a procedurecertainly overstates the true rate of price change.

FLATION AND THE FED

The Fed’s goal is to maintain low and steadyinflation, so that expectations of changes in inflationdo not enter importantly in the decisions businessesand households make. Using several different mea-sures of inflation expectations, it is clear that long-term expected inflation has changed little in recentyears. There is no evidence that changing inflationexpectations figure importantly in economic deci-sions at this time.

Substantial variability in prices of individualgoods is consistent with stability in the overall infla-tion rate. The variability serves to allocate and re-allocate resources across different sectors of theeconomy, according to changes in consumer tastesand differential trends in productivity advancement.Simply put, it is normal that some industries aregrowing while others are contracting.


FEDERAL RESERVE BANK OF ST. LOUIS Poole and Rasche

A common business problem is to determine asuccessful pricing strategy. One aspect of pricingstrategy is directly relevant to this discussion. Whena firm cuts prices to stimulate sales, it may not besuccessful if its customers believe that even deeperprice cuts are around the corner. An expectation offalling prices may, temporarily, reduce rather thanincrease sales. It is for this reason that generalizeddeflation can be so dangerous to the economy. Awidespread expectation of falling prices may leadto declining demand across much of the economyas people wait for lower prices in the future. Declin-ing demand may force layoffs, which further depresshousehold and business confidence. Conversely,inflation expectations can lead to rising demandsand anticipatory buying.

Many analysts seem to view low inflation andhigh employment as competing goals. That is cer-tainly not the only possible scenario. Maintaininglow and stable inflation contributes mightily to over-all economic stability. Consider the situation in theweeks following the terrorist attacks of September 11,2001, when the economic outlook was highlyuncertain. The auto industry was successful in sellinga record number of cars in October 2001 throughprice cuts in the form of zero-interest financing. Ifconsumers had reacted by expecting even deeperprice cuts and had delayed purchases, the situationin early 2002 would have been very different. Over-all, consumers view price cuts in today’s environ-ment as a buying opportunity, not as a forecast offurther price cuts to come.

Clearly, the stability in the overall price environ-ment—stability in longer-run expectations—is whatallows temporary price cuts to work to boost salesand is an important element in stabilizing the generaleconomy. The current U.S. situation does not matchcases in the United States and elsewhere that histori-cally have been associated with ongoing deflation.The Federal Reserve pursued an expansionary mone-tary policy throughout 2001 that has contributedto restoring equilibrium to the U.S. economy. Whatpolicy actions will be appropriate going forward willhave to be determined as evidence arrives on thestrength and durability of the economic expansion.We must be vigilant, but today it is likely that weenjoy flation—no “in” and no “de.”




A Case Study of aCurrency Crisis: TheRussian Default of 1998Abbigail J. Chiodo and Michael T. Owyang

A currency crisis can be defined as a specula-tive attack on a country’s currency that canresult in a forced devaluation and possible

debt default. One example of a currency crisisoccurred in Russia in 1998 and led to the devaluationof the ruble and the default on public and privatedebt.1 Currency crises such as Russia’s are oftenthought to emerge from a variety of economic condi-tions, such as large deficits and low foreign reserves.They sometimes appear to be triggered by similarcrises nearby, although the spillover from these con-tagious crises does not infect all neighboring econ-omies—only those vulnerable to a crisis themselves.

In this paper, we examine the conditions underwhich an economy can become vulnerable to acurrency crisis. We review three models of currencycrises, paying particular attention to the events lead-ing up to a speculative attack, including expectationsof possible fiscal and monetary responses to impend-ing crises. Specifically, we discuss the symptomsexhibited by Russia prior to the devaluation of theruble. In addition, we review the measures that wereundertaken to avoid the crisis and explain why thosesteps may have, in fact, hastened the devaluation.

The following section reviews the three genera-tions of currency crisis models and summarizes theconditions under which a country becomes vulner-able to speculative attack. The third section examinesthe events preceding the Russian default of 1998 inthe context of a currency crisis. The fourth sectionapplies the aforementioned models to the Russiancrisis.

CURRENCY CRISES: WHAT DOESMACROECONOMIC THEORY SUGGEST?

A currency crisis is defined as a speculativeattack on country A’s currency, brought about by

agents attempting to alter their portfolio by buyinganother currency with the currency of country A.2This might occur because investors fear that thegovernment will finance its high prospective deficitthrough seigniorage (printing money) or attempt toreduce its nonindexed debt (debt indexed to neitheranother currency nor inflation) through devaluation.A devaluation occurs when there is market pres-sure to increase the exchange rate (as measured bydomestic currency over foreign currency) becausethe country either cannot or will not bear the costof supporting its currency. In order to maintain alower exchange rate peg, the central bank must buyup its currency with foreign reserves. If the centralbank’s foreign reserves are depleted, the governmentmust allow the exchange rate to float up—a devalu-ation of the currency. This causes domestic goodsand services to become cheaper relative to foreigngoods and services. The devaluation associated witha successful speculative attack can cause a decreasein output, possible inflation, and a disruption inboth domestic and foreign financial markets.3

The standard macroeconomic frameworkapplied by Fleming (1962) and Mundell (1963) tointernational issues is unable to explain currencycrises. In this framework with perfect capital mobil-ity, a fixed exchange rate regime results in capitalflight when the central bank lowers interest ratesand results in capital inflows when the central bankraises interest rates. Consequently, the efforts of themonetary authority to change the interest rate areundone by the private sector. In a flexible exchangerate regime, the central bank does not intervene inthe foreign exchange market and all balance of pay-ment surpluses or deficits must be financed byprivate capital outflows or inflows, respectively.

The need to explain the symptoms and remediesof a currency crisis has spawned a number of modelsdesigned to incorporate fiscal deficits, expectations,and financial markets into models with purchasingpower parity. These models can be grouped intothree generations, each of which is intended toexplain specific aspects that lead to a currency crisis.

1 Kharas, Pinto, and Ulatov (2001) provide a history from a fundamentals-based perspective, focusing on taxes and public debt issues. Weendeavor to incorporate a role for monetary policy.

2 The speculative attack need not be successful to be dubbed a currencycrisis.

3 Burnside, Eichenbaum, and Rebelo (2001) show that the governmenthas at its disposal a number of mechanisms to finance the fiscal costsof the devaluation. Which policy is chosen determines the inflationaryeffect of the currency crisis.

Abbigail J. Chiodo is a senior research associate and Michael T. Owyangis an economist at the Federal Reserve Bank of St. Louis. The authorsthank Steven Holland, Eric Blankmeyer, John Lewis, and RebeccaBeard for comments and suggestions and Victor Gabor at the WorldBank for providing real GDP data.


First-Generation Models

The first-generation models of a currency crisisdeveloped by Krugman (1979) and Flood and Garber(1984) rely on government debt and the perceivedinability of the government to control the budgetas the key causes of the currency crisis. These modelsargue that a speculative attack on the domesticcurrency can result from an increasing currentaccount deficit (indicating an increase in the tradedeficit) or an expected monetization of the fiscaldeficit. The speculative attack can result in a suddendevaluation when the central bank’s store of foreignreserves is depleted and it can no longer defend thedomestic currency. Agents believe that the govern-ment’s need to finance the debt becomes its over-riding concern and eventually leads to a collapseof the fixed exchange rate regime and to speculativeattacks on the domestic currency.

Krugman presents a model in which a fixedexchange rate regime is the inevitable target of aspeculative attack. An important assumption in themodel is that a speculative attack is inevitable. Thegovernment defends the exchange rate peg with itsstore of foreign currency. As agents change the com-position of their portfolios from domestic to foreigncurrency (because rising fiscal deficits increase thelikelihood of devaluation, for example), the centralbank must continue to deplete its reserves to staveoff speculative attacks. The crisis is triggered whenagents expect the government to abandon the peg.Anticipating the devaluation, agents convert theirportfolios from domestic to foreign currency by buy-ing foreign currency from the central bank’s reserves.The central bank’s reserves fall until they reach thecritical point when a peg is no longer sustainableand the exchange rate regime collapses. The keycontribution of the first-generation model is itsidentification of the tension between domestic fiscalpolicy and the fixed exchange rate regime.4

While the first-generation models help explainsome of the fundamentals that cause currency crises,they are lacking in two key aspects. First, the stan-dard first-generation model requires agents to sud-denly increase their estimates of the likelihood ofa devaluation (perhaps through an increase inexpected inflation). Second, they do not explainwhy the currency crises spread to other countries.

Second-Generation Models

The second-generation models suggested byObstfeld (1994), Eichengreen, Rose, and Wyplosz

(1997), and others are particularly useful in explain-ing self-fulfilling contagious currency crises. Onepossible scenario suggested by these models involvesa devaluation in one country affecting the price level(and therefore the demand for money) or the currentaccount by a reduction of exports in a neighboringcountry. In either case, devaluation in a neighboringcountry becomes increasingly likely.

Eichengreen, Rose, and Wyplosz (1997) findthat a correlation exists between the likelihood ofdefault across countries. That is, the probability ofa speculative attack in country A increases whenits trading partner, country B, experiences an attackof its own. They estimate that a speculative attacksomewhere in the world increases the probabilityof a domestic currency crisis by about 8 percent.The spillover from one currency crisis into neighbor-ing countries can be attributed to a number of differ-ent scenarios. First, an economic event, such as awar or an oil price shock, that is common to a geo-graphical area or a group of trading partners canaffect those economies simultaneously; in addition,an individual shock can be transmitted from onecountry to another via trade. Second, a devaluationor default in one country can raise expectations ofthe likelihood of a devaluation in other countries.Expectations can rise either because countries areneighboring trade partners or because they havesimilar macroeconomic policies or conditions (e.g.,high unemployment or high government debt). Sincethe crises are self-fulfilling, these expectations makethe likelihood of devaluation increase as well. Lastly,a devaluation can be transmitted via world financialmarkets to other susceptible countries. Any combi-nation of scenarios can serve as an explanation ofthe apparent international linkages that are responsi-ble for the spread of speculative attacks from onecountry to another.

Third-Generation Models

The literature on contagious currency criseshas helped clarify the spread of devaluations andtheir magnitudes. However, the first two generationsof models have not provided a policy recommenda-tion for the central bank in the face of a crisis. Indeed,Krugman’s first-generation model suggests that acrisis cannot be thwarted—that once a devaluationis expected, it is inevitable. Thus, third-generation


4 Obstfeld (1986) outlines a multiple equilibrium model in which acurrency crisis is brought about when government policy (financinga deficit through seignorage, for example) causes agents to expect acrisis and push the economy to a bad equilibrium.

Chiodo and Owyang R E V I E W

currency crisis models suggested by Krugman (1999)and Aghion, Bacchetta, and Banarjee (2000, 2001)examine the effects of monetary policy in a currencycrisis.

These models argue that fragility in the bankingand financial sector reduces the amount of creditavailable to firms and increases the likelihood of acrisis. They suggest that a currency crisis is broughton by a combination of high debt, low foreignreserves, falling government revenue, increasingexpectations of devaluation, and domestic borrow-ing constraints. Firms’ access to domestic loans isconstrained by assuming they can borrow only aportion of their wealth (somewhat similar to requir-ing the firm to collateralize all domestic loans). Inthese lending-constrained economies, the creditmarket does not clear: interest rates rise, but notenough to compensate investors for the increase inperceived default risk. Increasing the domestic inter-est rate, then, does not raise the supply of domesticlending in the normal fashion. Moral hazard, a firm’sability to take its output and default on its loan, forcesbanks to restrict lending. Therefore, increasingthe interest rate reduces the amount of loans as itincreases firms’ incentive to default.

These third-generation models offer a role formonetary policy (aside from the decision to abandonthe exchange rate peg) through a binding creditconstraint in an imperfect financial market. If firms’leverage in the domestic market is substantiallyreduced, they may be forced to accumulate a largeamount of foreign-denominated debt. When, indomestic markets, the amount of available lendingdepends on the nominal interest rate, the centralbank can deepen a crisis by further reducing firms’ability to invest. The typical prescription for a cur-rency crisis is to raise interest rates and raise thedemand for domestic currency.5 However, in thethird-generation models, an interest rate increasecan greatly affect the amount of lending and furtherrestrict firms’ access to financial capital. In caseswhere lending is highly sensitive to the interest rate,an increase in the nominal interest rate can bedetrimental, altering the productive capacity of theeconomy by stifling investment. The perceived dropin output puts additional pressure on the exchangerate, perhaps through actual or expected tax revenue,exacerbating the crisis. In this situation, an alterna-tive strategy for the central bank is warranted: it isactually beneficial to lower the interest rate to spurinvestment.6

These three generations of models suggest four

factors that can influence the onset and magnitudeof a currency crisis. Domestic public and privatedebt, expectations, and the state of financial marketscan, in combination with a pegged exchange rate,determine whether a country is susceptible to acurrency crisis and also determine the magnitudeand success of a speculative attack. In the nextsection, we provide an example of a recent currencycrisis, keeping these four factors in mind.

THE RUSSIAN DEFAULT: A BRIEF HISTORY

After six years of economic reform in Russia,privatization and macroeconomic stabilization hadexperienced some limited success. Yet in August1998, after recording its first year of positive econ-omic growth since the fall of the Soviet Union, Russiawas forced to default on its sovereign debt, devaluethe ruble, and declare a suspension of payments bycommercial banks to foreign creditors. What causedthe Russian economy to face a financial crisis afterso much had been accomplished? This sectionexamines the sequence of events that took place inRussia from 1996 to 1998 and the aftermath of thecrisis. (For a timeline, see Table 1.)

1996 and 1997

Optimism and Reform. In April 1996, Russianofficials began negotiations to reschedule the pay-ment of foreign debt inherited from the formerSoviet Union. The negotiations to repay its sovereigndebt were a major step toward restoring investorconfidence. On the surface, 1997 seemed poisedto be a turning point toward economic stability.

• The trade surplus was moving toward a balance between exports and imports (seeFigure 1).

• Relations with the West were promising: theWorld Bank was prepared to provide expandedassistance of $2 to $3 billion per year and theInternational Monetary Fund (IMF) continuedto meet with Russian officials and provide aid.

• Inflation had fallen from 131 percent in 1995to 22 percent in 1996 and 11 percent in 1997(see Figure 2).

• Output was recovering slightly.


5 Flood and Jeanne (2000) argue that increasing domestic currencyinterest rates can act only to speed devaluation.

6 The expansionary monetary policy in this case is assumed not to beinflationary since it only alleviates liquidity constraints.

FEDERAL RESERVE BANK OF ST. LOUIS Chiodo and Owyang



A Timeline of Russian Events

April 1996 Negotiations with the Paris and London Clubs for repayment of Soviet debt begin.

1997 Trade surplus moving toward balance.Inflation around 11 percent.Oil selling at $23/barrel.Analysts predict better credit ratings for Russia.Russian banks increase foreign liabilities.Real wages sagging.Only 40 percent of workforce being paid fully and on time.Public-sector deficit high.

September/October 1997 Negotiations with Paris and London Clubs completed.

November 11, 1997 Asian crisis causes a speculative attack on the ruble.CBR defends the ruble, losing $6 billion.

December 1997 Year ends with 0.8 percent growth.Prices of oil and nonferrous metal begin to drop.

February 1998 New tax code submitted to the Duma.IMF funds requested.

March 23, 1998 Yelstin fires entire government and appoints Kiriyenko.Continued requests for IMF funds.

April 1998 Another speculative attack on the ruble.

April 24, 1998 Duma finally confirms Kiriyenko’s appointment.

Early May 1998 Dubinin warns government ministers of impending debt crisis, with reporters in the audience.

Kiriyenko calls the Russian government “quite poor.”

May 19, 1998 CBR increases lending rate from 30 percent to 50 percent and defends the ruble with $1 billion.

Mid May 1998 Lawrence Summers not granted audience with Kiriyenko.Oil prices continue to decrease.Oil and gas oligarchs advocate devaluation of ruble to increase value of their exports.

May 23, 1998 IMF leaves Russia without agreement on austerity plan.

May 27, 1998 CBR increases the lending rate again to 150 percent.

Summer 1998 Russian government formulates and advertises anti-crisis plan.

July 20, 1998 IMF approves an emergency aid package (first disbursement to be $4.8 billion).

August 13, 1998 Russian stock, bond, and currency markets weaken as a result of investor fears of devaluation; prices diminish.

August 17, 1998 Russian government devalues the ruble, defaults on domestic debt, and declares a moratorium on payment to foreign creditors.

August 23-24, 1998 Kiriyenko is fired.

September 2, 1998 The ruble is floated.

December 1998 Year ends with a decrease in real output of 4.9 percent.

NOTE: CBR, Central Bank of Russia.

Table 1

• A narrow exchange rate band was in placekeeping the exchange rate between 5 and 6rubles to the dollar (see Figure 3).

• And oil, one of Russia’s largest exports, wasselling at $23 per barrel—a high price byrecent standards. (Fuels made up more than45 percent of Russia’s main export commodi-ties in 1997.)

In September 1997, Russia was allowed to jointhe Paris Club of creditor nations after reschedulingthe payment of over $60 billion in old Soviet debtto other governments. Another agreement for a23-year debt repayment of $33 billion was signeda month later with the London Club. Analysts pre-dicted that Russia’s credit ratings would improve,allowing the country to borrow less expensively.Limitations on the purchase of government securitiesby nonresident investors were removed, promotingforeign investment in Russia. By late 1997, roughly30 percent of the GKO (a short-term governmentbill) market was accounted for by nonresidents. Theeconomic outlook appeared optimistic as Russiaended 1997 with reported economic growth of 0.8percent.

Revenue, Investment, and Debt. Despite theprospects for optimism, problems remained. Onaverage, real wages were less than half of what theywere in 1991, and only about 40 percent of thework force was being paid in full and on time. Percapita direct foreign investment was low, and regu-

lation of the natural monopolies was still difficultdue to unrest in the Duma, Russia’s lower houseof Parliament. Another weakness in the Russianeconomy was low tax collection, which caused the



Percent

250

200

150

100

50

0Dec1995

Mar1997

Jun1998

Sep1999

Dec2000

SOURCE: IMF.

CPI InflationPercent Change over Previous Year

Figure 2

Russian Merchandise Trade Balance

US$ Millions

Exports

Imports

30,000

25,000

20,000

15,000

10,000

5,0001994:Q1 1996:Q1 1998:Q1 2000:Q1

SOURCE: CBR.

Figure 1

Exchange Rate

Ruble/US$

35

30

25

20

15

5

0

10

Dec1995

Mar1997

Jun1998

Sep1999

Dec2000

SOURCE: IMF (end of period data).

Figure 3

public sector deficit to remain high. The majorityof tax revenues came from taxes that were sharedbetween the regional and federal governments,which fostered competition among the differentlevels of government over the distribution. Accord-ing to Shleifer and Treisman (2000), this kind oftax sharing can result in conflicting incentives forregional governments and lead them to help firmsconceal part of their taxable profit from the federalgovernment in order to reduce the firms’ total taxpayments. In return, the firm would then maketransfers to the accommodating regional govern-ment. This, Shleifer and Treisman suggest, mayexplain why federal revenues dropped more rapidlythan regional revenues.

Also, the Paris Club’s recognition of Russia as acreditor nation was based upon questionable qualifi-cations. One-fourth of the assets considered tobelong to Russia were in the form of debt owed tothe former Soviet Union by countries such as Cuba,Mongolia, and Vietnam. Recognition by the ParisClub was also based on the old, completely arbitraryofficial Soviet exchange rate of approximately 0.6rubles to the dollar (the market exchange rate atthe time was between 5 and 6 rubles to the dollar).The improved credit ratings Russia received fromits Paris Club recognition were not based on animproved balance sheet. Despite this, restrictionswere eased and lifted and Russian banks beganborrowing more from foreign markets, increasingtheir foreign liabilities from 7 percent of their assetsin 1994 to 17 percent in 1997.

Meanwhile, Russia anticipated growing debtpayments in the coming years when early creditsfrom the IMF would come due. Policymakers faceddecisions to decrease domestic borrowing andincrease tax collection because interest paymentswere such a large percentage of the federal budget.In October 1997, the Russian government was count-ing on 2 percent economic growth in 1998 to com-pensate for the debt growth. Unfortunately, eventsbegan to unfold that would further strain Russia’seconomy; instead of growth in 1998, real GDPdeclined 4.9 percent.

The Asian Crisis. A few months earlier, inthe summer of 1997, countries in the Pacific Rimexperienced currency crises similar to the one thateventually affected Russia. In November 1997, afterthe onset of this East Asian crisis, the ruble cameunder speculative attack. The Central Bank of Russia(CBR) defended the currency, losing nearly $6 billion(U.S. dollars) in foreign-exchange reserves. At the

same time, non-resident holders of short-termgovernment bills (GKOs) signed forward contractswith the CBR to exchange rubles for foreign cur-rency, which enabled them to hedge exchange raterisk in the interim period.7 According to Desai(2000), they did this in anticipation of the ruble los-ing value, as Asian currencies had. Also, a substantialamount of the liabilities of large Russian commercialbanks were off-balance-sheet, consisting mostlyof forward contracts signed with foreign investors.Net obligations of Russian banks for such contractswere estimated to be at least $6 billion by the firsthalf of 1998. Then another blow was dealt to theRussian economy: in December 1997, the pricesof oil and nonferrous metal, up to two-thirds ofRussia’s hard-currency earnings, began to drop.

1998

Government, Risk, and Expectations. Withso many uncertainties in the Russian economy,investors turned their attention toward Russiandefault risk. To promote a stable investment environ-ment, in February 1998, the Russian governmentsubmitted a new tax code to the Duma, with fewerand more efficient taxes. The new tax code wasapproved in 1998, yet some crucial parts that wereintended to increase federal revenue were ignored.Russian officials sought IMF funds but agreementscould not be reached. By late March the politicaland economic situation had become more dire, and,on March 23, President Yeltsin abruptly fired hisentire government, including Prime Minister ViktorChernomyrdin. In a move that would challengeinvestor confidence even further, Yeltsin appointed35-year-old Sergei Kiriyenko, a former banking andoil company executive who had been in governmentless than a year, to take his place.

While fears of higher interest rates in the UnitedStates and Germany made many investors cautious,tensions rose in the Russian government. The execu-tive branch, the Duma, and the CBR were in conflict.Prompted by threats from Yeltsin to dissolve Parlia-ment, the Duma confirmed Kiriyenko’s appointmenton April 24 after a month of stalling. In early May,during a routine update, CBR chair Sergei Dubininwarned government ministers of a debt crisis withinthe next three years. Unfortunately, reporters werein the audience. Since the Asian crisis had heightenedinvestors’ sensitivity to currency stability, Dubinin’s


7 The requirement of forward contracts was the CBR’s way of preventingruns on its foreign currency reserves.


restatement of bank policy was misinterpreted tomean that the Bank was considering a devaluationof the ruble. In another public relations misunder-standing, Kiriyenko stated in an interview that taxrevenue was 26 percent below target and claimedthat the government was “quite poor now.” In actual-ity, the government was planning to cut governmentspending and accelerate revenue, but these planswere never communicated clearly to the public.Instead, people began to expect a devaluation ofthe ruble.

Investors’ perceptions of Russia’s economicstability continued to decline when LawrenceSummers, one of America’s top international-financeofficials, was denied a meeting with Kiriyenko whilein Russia. An inexperienced aide determined thatSummers’s title, Deputy Secretary of the Treasury,was unworthy of Kiriyenko’s audience and the twonever met. At the same time, the IMF left Russia,unable to reach an agreement with policymakerson a 1998 austerity plan. Word spread of these inci-dents, and big investors began to sell their govern-ment bond portfolios and Russian securities,concerned that relations between the United Statesand Russia were strained.

Liquidity, Monetary Policy, and Fiscal Policy.By May 18, government bond yields had swelledto 47 percent. With inflation at about 10 percent,Russian banks would normally have taken thegovernment paper at such high rates. Lack of con-fidence in the government’s ability to repay thebonds and restricted liquidity, however, did notpermit this. As depositors and investors becameincreasingly cautious of risk, these commercialbanks and firms had less cash to keep them afloat.The federal government’s initiative to collect moretaxes in cash lowered banks’ and firms’ liquidity.8Also, in 1997, Russia had created a U.S.-style trea-sury system with branches, which saved moneyand decreased corruption, yet also decreased theamount of cash that moved through banks. Thebanks had previously used these funds to buy bonds.Also, household ruble deposits increased by only1.3 billion in 1998, compared with an increase of29.8 billion in 1997.

The CBR responded by increasing the lendingrate to banks from 30 to 50 percent, and in two daysused $1 billion of Russia’s low reserves to defendthe ruble. (Figure 4 shows the lending rate.) However,by May 27, demand for bonds had plummeted somuch that yields were more than 50 percent andthe government failed to sell enough bonds at its

weekly auction to refinance the debt coming due.Meanwhile, oil prices had dropped to $11 per

barrel, less than half their level a year earlier. Oiland gas oligarchs were advocating a devaluation ofthe ruble, which would increase the ruble value oftheir exports. In light of this, the CBR increased thelending rate again, this time to 150 percent. CBRchairman Sergei Dubinin responded by stating“When you hear talk of devaluation, spit in the eyeof whoever is talking about it” (quoted in Shleiferand Treisman, 2000, p. 149).

The government formed and advertised an anti-crisis plan, requested assistance from the West, andbegan bankruptcy processes against three compa-nies with large debts from back taxes. Kiriyenkomet with foreign investors to reassure them. Yeltsinmade nightly appearances on Russian television,calling the nation’s financial elite to a meeting atthe Kremlin where he urged them to invest in Russia.In June the CBR defended the ruble, losing $5 billionin reserves.

Despite all of the government efforts being made,there was widespread knowledge of $2.5 to $3 billion


8 As a result of a 1998 elimination of tax-offsets paper issued by govern-ment agencies to pay for goods and services, the receipts of whichcould be used to decrease their tax duties, banks and companies wereforced to provide more cash to pay their taxes, thus lowering theirliquidity.


Lending Rate

Percent

160

140

120

100

80

40

0

60

20

Jul1996

Feb1997

Nov1997

Mar1998

Jun1998

SOURCE: CBR.

Mar2000

Nov2000

Figure 4

in loans from foreign investors to Russian corpora-tions and banks that were to come due by the endof September. In addition, billions of dollars in rublefutures were to mature in the fall. In July the IMFapproved additional assistance of $11.2 billion, ofwhich $4.8 billion was to be disbursed immediately.Yet between May and August, approximately $4billion had left Russia in capital flight, and in 1998Russia lost around $4 billion in revenue due to sag-ging oil prices. After losing so much liquidity, theIMF assistance did not provide much relief.

The Duma, in an effort to protect natural monop-olies from stricter regulations, eliminated crucialparts of the IMF-endorsed anti-crisis program beforeadjourning for vacation. The government had hopedthat the anti-crisis plan would bring in an additional71 billion rubles in revenue. The parts that the Dumaactually passed would have increased it by only 3billion rubles. In vain, lawmakers requested that theDuma reconvene, lowering investors’ confidenceeven further.

Default and Devaluation. On August 13, 1998,the Russian stock, bond, and currency marketscollapsed as a result of investor fears that thegovernment would devalue the ruble, default ondomestic debt, or both. Annual yields on ruble-denominated bonds were more than 200 percent.The stock market had to be closed for 35 minutesas prices plummeted. When the market closed, it

was down 65 percent with a small number of sharesactually traded. From January to August the stockmarket had lost more than 75 percent of its value,39 percent in the month of May alone. (Figure 5shows the Russian stock market’s boom and bust.)Russian officials were left with little choice. OnAugust 17 the government floated the exchange rate,devalued the ruble, defaulted on its domestic debt,halted payment on ruble-denominated debt (primar-ily GKOs), and declared a 90-day moratorium onpayment by commercial banks to foreign creditors.

The Aftermath

Russia ended 1998 with a decrease in real out-put of 4.9 percent for the year instead of the smallgrowth that was expected. The collapse of the rublecreated an increase in Russia’s exports while importsremained low (see Figure 1). Since then, directinvestments into Russia have been inconsistent atbest. Summarized best by Shleifer and Treisman(2000), “the crisis of August 1998 did not only under-mine Russia’s currency and force the last reformersfrom office…it also seemed to erase any remainingWestern hope that Russia could successfully reformits economy.”

Some optimism, however, still persists. Figure6 shows Russian real GDP growth, which grew 8.3percent in 2000 and roughly 5 percent in 2001—lower but still positive. Imports trended up in thefirst half of 2001, helping to create a trade balance.At the same time, consumer prices grew 20.9 percentand 21.6 percent in 2000 and 2001, respectively,compared with a 92.6 percent increase in 1999.Most of the recovery so far can be attributed to theimport substitution effect after the devaluation; theincrease in world prices for Russia’s oil, gas, andcommodity exports; monetary policies; and fiscalpolicies that have led to the first federal budget sur-plus (in 2000) since the formation of the RussianFederation.

HOW DO THE THEORIES EXPLAINTHE RUSSIAN CRISIS?

As discussed earlier, four major factors influencethe onset and success of a speculative attack. Thesekey ingredients are (i) an exchange rate peg and acentral bank willing or obligated to defend it with areserve of foreign currency, (ii) rising fiscal deficitsthat the government cannot control and thereforeis likely to monetize (print money to cover the deficit),(iii) central bank control of the interest rate in a



The Russian Stock Market

Daily Observations (Sept. 1, 1995=100)

600

500

400

300

200

0

100

Jan1996

Dec1996

Dec1997

Dec1998

Dec1999

SOURCE: <http://red-stars.com/financial>.

Dec2000

Nov2001

Figure 5

fragile credit market, and (iv) expectations of devalu-ation and/or rising inflation. In this section we dis-cuss these aspects in the context of the Russiandevaluation. We argue that an understanding of allthree generations of models is necessary to evaluatethe Russian devaluation. Krugman’s (1979) first-generation model explains the factors that madeRussia susceptible to a crisis. The second-generationmodels show how contagion and other factors canchange expectations to trigger the crisis. The third-generation models show how the central bank canact to prevent or mitigate the crisis.

The Exchange Rate and the Peg

When the ruble came under attack in November1997 and June 1998, policymakers defended theruble instead of letting it float. The real exchangerate did not vary much during 1997. Clearly a pri-mary component of a currency crisis in the modelsdescribed here is the central bank’s willingness todefend an exchange rate peg. Prior to August 1998,the Russian ruble was subject to two speculativeattacks. The CBR made efforts both times to defendthe ruble. The defense was successful in November1997 but fell short in the summer of 1998. Defend-ing the ruble depleted Russia’s foreign reserves.Once depleted, the Russian government had nochoice but to devalue on August 17, 1998.

Revenue, Deficits, and Fiscal Policy

Russia’s high government debt and falling rev-enue contributed significantly to its susceptibilityto a speculative attack. Russia’s federal tax revenueswere low because of both low output and the oppor-tunistic practice of local governments helping firmsconceal profits. The decrease in the price of oil alsolowered output, further reducing Russia’s ability togenerate tax revenue. Consequently, Russia’s revenuewas lower than expected, making the ruble ripe fora speculative attack. In addition, a large amount ofshort-term foreign debt was coming due in 1998,making Russia’s deficit problem even more serious.Krugman’s first-generation model suggests that agovernment finances its deficit by printing money(seigniorage) or depleting its reserves of foreigncurrency. Under the exchange rate peg, however,Russia was unable to finance through seigniorage.Russia’s deficit, low revenue, and mounting interestpayments put pressure on the exchange rate. Print-ing rubles would only have increased this pressurebecause the private sector would still have been able

to trade rubles for foreign currency at the fixed rate.Thus, whether directly through intervention in theforeign currency market or indirectly by printingrubles, Russia’s only alternative under the fixedexchange rate regime was to deplete its stock offoreign reserves.

Monetary Policy, Financial Markets,and Interest Rates

During the summer of 1998, the Russian econ-omy was primed for the onset of a currency crisis.In an attempt to avert the crisis, the CBR intervenedby decreasing the growth of the money supply andtwice increasing the lending rate to banks, raisingit from 30 to 150 percent. Both rate hikes occurredin May 1998, the same month in which the Russianstock market lost 39 percent of its value. The risein interest rates had two effects. First, it exacerbatedRussia’s revenue problems. Its debt grew rapidly asinterest payments mounted. This put pressure onthe exchange rate because investors feared thatRussia would devalue to finance its non-denominateddebt. Second, high government debt prevented firmsfrom obtaining loans for new capital and increasingthe interest rate did not increase the supply of lend-ing capital available to firms. At the same time, for-



Percent

10

8

2

–2

–6

–10

Real GDP GrowthQuarterly Change from Previous Year

6

4

0

–4

–8

1995:Q1 1996:Q2 1997:Q3 1998:Q4 2000:Q1

SOURCE: Russian Statistics Committee and International Bank for Reconstruction and Development staff estimates.

2001:Q2

11

Figure 6

eign reserves held by the CBR were so low that thegovernment could no longer defend the currencyby buying rubles.

Expectations

Three components fueled the expectations ofRussia’s impending devaluation and default. First,the Asian crisis made investors more conscious ofthe possibility of a Russian default. Second, publicrelations errors, such as the publicized statementto government ministers by the CBR and Kiriyenko’srefusal to grant Lawrence Summers an audience,perpetuated agents’ perceptions of a political crisiswithin the Russian government. Third, the revenueshortfall signaled the possible reduction of the publicdebt burden via an increase in the money supply.This monetization of the debt can be associatedwith a depreciation either indirectly through anincrease in expected inflation or directly in orderto reduce the burden of ruble-denominated debt.Each of these three components acted to push theRussian economy from a stable equilibrium to onevulnerable to speculative attack.

CONCLUSION

In this paper we investigate the events that leadup to a currency crisis and debt default and thepolicies intended to avert it. Three types of modelsexist to explain currency crises. Each model explainssome factor that has been hypothesized to cause acrisis. After reviewing the three generations of cur-rency crisis models, we conclude that four keyingredients can trigger a crisis: a fixed exchangerate, fiscal deficits and debt, the conduct of monetarypolicy, and expectations of impending default. Usingthe example of the Russian default of 1998, we showthat the prescription of contractionary monetarypolicy in the face of a currency crisis can, undercertain conditions, accelerate devaluation. Whilewe believe that deficits and the Asian financial crisiscontributed to Russia’s default, the first-generationmodel proposed by Krugman (1979) and Flood andGarber (1984) and the second-generation modelsproposed by Obstfeld (1984) and Eichengreen, Rose,and Wyplosz (1997) do not capture every aspect ofthe crisis. Specifically, these models do not addressthe conduct of monetary policy. It is therefore nec-essary to incorporate both the first-generationmodel’s phenomenon of increasing fiscal deficitsand the third-generation model’s financial sectorfragility. We conclude that the modern currency

crisis is a symptom of an ailing domestic economy.In that light, it is inappropriate to attribute a singleprescription as the prophylactic or cure for a cur-rency crisis.

REFERENCES

Aghion, Philippe; Bacchetta, Philippe and Banerjee, Abhijit.“A Simple Model of Monetary Policy and Currency Crises.”European Economic Review, May 2000, 44(4-6), pp. 728-38.

___________; ___________ and ___________. “CurrencyCrises and Monetary Policy in an Economy with CreditConstraints.” European Economic Review, June 2001, 45(7),pp. 1121-50.

Ahrend, Rudiger. “Foreign Direct Investment Into Russia—Pain Without Gain? A Survey of Foreign Direct Investors.”Russian Economic Trends, June 2000, 9(2), pp. 26-33.

Burnside, Craig; Eichenbaum, Martin, and Rebelo, Sergio.“On The Fiscal Implications of Twin Crises.” WorkingPaper No. 8277, National Bureau of Economic Research,May 2001.

Desai, Padma. “Why Did the Ruble Collapse in August 1998?”American Economic Review: Papers and Proceedings, May2000, 90(2), pp. 48-52.

Economist. “Surplus to Requirements.” 8 July 2000, p. 79.

Eichengreen, Barry; Rose, Andrew and Wyplosz, Charles.“Contagious Currency Crisis.” March 1997.<http://www.haas.berkeley.edu/~arose/>.

Fischer, Stanley. “The Russian Economy at the Start of1998.” U.S.-Russian Investment Symposium, HarvardUniversity, Cambridge, MA, 9 January 1998.

Flemming, Marcus. “Domestic Financial Policies UnderFixed and Under Floating Exchange Rates.” IMF StaffPapers, 9 November 1962.

Flood, Robert P. and Garber, Peter M. “Collapsing ExchangeRate Regimes: Some Linear Examples.” Journal ofInternational Economics, August 1984, 17(1-2), pp 1-13.

___________ and Jeanne, Olivier. “An Interest Rate Defenseof a Fixed Exchange Rate?” Working Paper WP/00/159,International Monetary Fund, October 2000.

Kharas, Homi; Pinto, Brian and Ulatov, Sergei. “An Analysisof Russia’s 1998 Meltdown: Fundamentals and MarketSignals.” Brookings Papers on Economic Activity, 2001,0(1), pp. 1-67.



Krugman, Paul. “A Model of Balance-of-Payment Crises.”Journal of Money, Credit, and Banking, August 1979, 11(3),pp. 311-25.

___________. “Balance Sheets, the Transfer Problem, andFinancial Crises.” International Tax and Public Finance,November 1999, 6(4), pp. 459-72.

Malleret, Thierry; Orlova, Natalia and Romanov, Vladimir.“What Loaded and Triggered the Russian Crisis?” Post-Soviet Affairs, April-June 1999, 15(2), pp. 107-29.

Mudell, R.A. “Capital Mobility and Stabilization Policy UnderFixed and Flexible Exchange Rates.” Canadian Journal ofEconomics, November 1963.

Obstfeld, Maurice. “Rational and Self-Fulfilling Balance-of-Payments Crises.” American Economic Review, March 1986,76(1), pp. 72-81.

___________. “The Logic of Currency Crises.” CahiersEconomiques et Monetaires, Banque de France, 1994, 43,pp. 189-213.

Popov, A. “Lessons of the Currency Crisis in Russia and inOther Countries.” Problems of Economic Transition, May2000, 43(1), pp. 45-73.

Russian Economic Trends. Various months.

Shleifer, Andre and Treisman, Daniel. Without A Map:Political Tactics and Economic Reform in Russia. Cambridge,MA: MIT Press, 2000.

Velasco, Andrés. “Financial Crises in Emerging Markets.”National Bureau of Economic Research Reporter, Fall1999, pp.17-19.




Asset Mispricing,Arbitrage, and VolatilityWilliam R. Emmons and Frank A. Schmid

After nearly four decades, academic econ-omists continue to debate financial-marketefficiency as vigorously as ever.1 The original

theoretical arguments put forward in favor of effi-cient markets were based on the notion of stabiliz-ing speculation in the form of arbitrage (Friedman,1953). Simply put, arbitrage is “the simultaneouspurchase and sale of the same, or essentially similar,security in two different markets for advantageouslydifferent prices” (Sharpe and Alexander, 1990). Intheory, a perfectly hedged trading position of thissort could be executed at no cost (as the short-saleproceeds are used to finance the long position).Vigilant traders on the look-out for just such arbi-trage opportunities would ensure that no one couldconsistently “beat the market”—the hallmark ofefficient markets theory.

The academics’ logical case for efficient marketsboils down to a pair of simple rhetorical questions:Why would utility-maximizing traders leave unex-ploited any profitable opportunities (after adjustingproperly for risk)? And if no risk-adjusted “freelunches” exist, how could market prices be predict-able enough to make money? For several decades,empirical evidence piled up both for and againstmarket efficiency. As of the early 1990s, neitherside could claim total vindication. As the 1990sprogressed, however, the weight of the evidenceseemed to tip toward those who claimed asset priceswere, at least to some extent, predictable (Campbell,Lo, and MacKinlay, 1997, Chaps. 2 and 7).

The academic asset-pricing literature today isdominated by attempts to explain why and to whatextent the price movements of financial assets arepredictable. One potential explanation of stock-return predictability is that markets are efficient(“no free lunch”) but expected returns are time-varying, perhaps being linked to the business cycle.For example, expected returns may be highest wheneconomic risks are perceived to be high, such as ator near the bottom of a business cycle. Conversely,

expected returns may be lowest when economicrisks are perceived to be low, at or near a business-cycle peak. Thus, the simple random-walk modelof stock returns may be false, but a relevant notionof market efficiency survives because high returnsare earned only by taking large amounts of risk. Adifferent type of explanation of return predictabilityrejects market efficiency and focuses on marketimperfections of various sorts, such as incompletestock-market participation by households, significanttransactions costs, changes in investor sentiment,or limited wealth and liquidity resources to conductarbitrage (as in the current article).2

Whatever its economic explanation, mountingevidence of return predictability leads Campbell, Lo,and MacKinlay (1997, p. 24) to suggest that it is timefor financial economists to focus their attentionon the “relative efficiency” of a market instead ofcontinuing the all-or-nothing battle of attrition thatis characteristic of much of the earlier market effi-ciency literature.

As we now understand more clearly, the originalcase for efficient markets probably leaned too heav-ily on the notion of risk-free, cost-free arbitrage toeliminate all profitable trading strategies immedi-ately. In real markets, arbitrage is neither as easynor as effective as economists once had assumed.For one thing, financial markets are not completeand frictionless, so arbitrage in general is risky andcostly. In addition, it is not realistic to assume thatthe number of informed arbitrageurs or the supplyof financial resources they have to invest in arbitragestrategies is limitless.

This article builds on an important and insight-ful recent model of arbitrage by professional traderswho need—but lack—wealth of their own to trade(Shleifer and Vishny, 1997). Professional arbitrageursmust convince wealthy but uninformed investorsto entrust them with investment capital in order toexploit mispricing and push the market back towardthe ideal of efficiency. Unfortunately, arbitrageurscannot prove that they recognize the intrinsic (or“fundamental”) values of the assets they claim aremispriced. Even worse, it is possible the assets will

1 For early statements of the theory of efficient markets and the unpre-dictability of asset-price movements, see Fama (1965), Muth (1960),or Samuelson (1965). For a recent summary of the evidence for returnpredictability and its implications for efficient-markets theory, seeCampbell, Lo, and MacKinlay (1997, Chap. 2).

2 Ironically, Keynes (1936, Chap. 12) clearly foreshadowed the recentinterest in investor sentiment and liquidity for understanding stockmarket behavior, but was forgotten for decades as the efficient-marketshypothesis dominated the academic discussion.

William R. Emmons is an economist and Frank A. Schmid is a senioreconomist at the Federal Reserve Bank of St. Louis. William V. Bockprovided research assistance.


become even more mispriced before revertingeventually to their intrinsic values. Having incurredlosses, the outside investors may demand theirmoney back at this point even though the expectedprofit of staying invested actually has increased.

Thus, market efficiency may depend ultimatelyon the successful resolution of a principal-agentproblem that exists between informed but wealth-constrained arbitrageurs and uninformed wealthyinvestors. The resulting degree of market efficiencymay change over time and differ across markets,and it could depend importantly on factors such asthe outside investors’ use of performance-based(“feedback”) strategies when deciding on the possi-ble termination of ongoing investment mandates.

After developing a simple model of wealth-constrained professional arbitrage that departs inseveral important aspects from the canonical Shleiferand Vishny (1997) model, we calibrate our modelto illustrate its qualitative features. We show that theexistence of professional arbitrageurs mitigates—but cannot eliminate—mispricing in the marketrelative to intrinsic values, regardless of how sensi-tive the outside investors are to arbitrageurs’ pastperformance in deciding whether to remain investedwith them. We also show that arbitrage dampensthe unconditional volatility of asset returns, whichwe measure as the expected value of squared returns.Most importantly, the presence of arbitrageurs limitsboth the degree of increased mispricing and levelof volatility during a financial crisis, which we defineas a period of heightened volatility and acute short-age of liquidity.3 This result points out that profes-sional arbitrageurs tend to stabilize markets evenwhen they are wealth-constrained. Other papersshow that investors who use “positive feedback”trading strategies—such as portfolio insurers—tendto destabilize markets (Grossman and Zhou, 1996).

We analyze a three-date (two-period) model ofan aspiring professional arbitrageur (or “convergencetrader” in the language of Kyle and Xiong, 2001,and Xiong, 2001) who must obtain financing frominvestors less informed than he is about the intrin-sic value of a financial asset—that is, its liquidationvalue at the end of the second period. In addition tothese two types of individuals, there are noise traderswho have wealth to invest but who misperceive theasset’s intrinsic value. It is the noise traders whodrive the asset’s price away from the intrinsic value.

The investors provide the arbitrageur with fundsto invest in an underpriced asset at the outset of themodel. The price is observed again at the end of the

first period, at which time the investors may “rollover” their funds with the arbitrageur or demandtheir money back if they have lost confidence in hisability. The asset will assume its intrinsic value at theend of the second period with certainty, althoughonly the arbitrageur knows in advance what thatvalue is. Consequently, the two-period return on thearbitrageur’s private information would be both posi-tive and risk-free if he could be assured of financing.

Our set-up highlights the fact that a two-periodrisk-free arbitrage nevertheless can be risky over aone-period horizon in the presence of noise tradersand financial constraints on the arbitrageur. Therisk arises because the arbitrageur needs outsideinvestors, and these outside investors might revisetheir beliefs about the arbitrageur’s talent at theinterim date, based on the return the arbitrageurachieved in the first period. If the investors down-wardly revise their beliefs about the arbitrageur’sabilities because the fund lost money due to a deep-ening of the mispricing, they might withdraw theirmoney precisely when the expected return on thearbitrage is at its maximum. One implication is thatthe arbitrageur will invest “strategically”—that is,he will not invest as much initially as he would in aworld without wealth constraints—in order to hedgeagainst the possibility of being unable to exploiteven greater mispricing should it occur one periodahead. Of course, this is not a new finding; for paperswith similar results, see Grossman and Vila (1992),Shleifer and Vishny (1997), or Gromb and Vayanos(2001).4 Our paper’s contributions in this respect


3 Myron Scholes (2000) suggests that the global financial crisis of 1997-98 was characterized by an increase in volatility, especially in equitymarkets, and a flight to liquidity (that is, a preference by many investorsfor assets whose liquidity was expected to be good). The crisis wasaccentuated by the “negotiated bankruptcy” of Long-Term CapitalManagement (LTCM), a hedge fund in which Scholes himself was apartner. According to Scholes, prior to the crisis, LTCM “was in thebusiness of supplying liquidity” and therefore its demise worsenedthe crisis by eliminating the liquidity it had been supplying. A theoreticalanalysis relevant to this episode is Xiong (2001).

4 The first rigorous investigations of the multi-period investment problemwere Merton (1971, 1973) and Breedon (1979). Merton concluded thata trader should keep a constant fraction of his wealth invested in therisky asset at all times. The fraction depends on the asset’s expectedreturn and risk and the investor’s degree of risk aversion. Grossmanand Vila (1992) added leverage and solvency constraints to the dynamictrader’s problem. Their trader optimally commits more wealth to therisky asset the shorter is the investment horizon and the further fromthe leverage constraint (not just today but prospectively in the future)the trader finds himself. Campbell and Viceira (1999) is a recent exami-nation of the problem under the assumption that the investor is awarethat the probability distributions from which asset returns are drawnchange over time.

Emmons and Schmid R E V I E W

are a more realistic objective function for the arbi-trageur and a set-up in which the arbitrageur’s trad-ing significantly affects the asset’s price. Our modelgenerates interior solutions and we provide cali-brated illustrations of the model’s results. WhileShleifer and Vishny (1997) assume that the arbi-trageur maximizes assets under management, weassume that he maximizes his income. The arbi-trageur’s income is determined by an incentivescheme that resembles real-world contracts of hedgefund managers.

THE MODEL

There are three types of agents in the model.Noise traders have wealth but misperceive the intrin-sic value of a financial asset. Professional arbitrageurshave no wealth or borrowing capacity but know theintrinsic value of the financial asset. Investors havewealth but no insight into the financial asset’s intrin-sic value. Unlike noise traders, investors know thatthey cannot recognize the asset’s intrinsic value.All parties are risk-neutral.

The investors may provide the arbitrageur withfunds to invest in an underpriced asset at the outsetof the model (see Figure 1). We refer to this arrange-ment as a hedge fund. Noise traders misperceivethe intrinsic value of at least one financial asset inthe economy, which generates arbitrage opportuni-ties that so-called “long-short” investment strategiesseek to exploit. Asset mispricing implies that thereare relatively overpriced and relatively underpricedassets, which means that a portfolio that is long onthe relatively undervalued asset and short on therelatively overvalued asset trades below intrinsicvalue.

We treat a market-neutral long-short portfolioas a single, complex financial asset. Arbitrage is the

process of acquiring a long-short portfolio and hold-ing it until its price returns to the portfolio’s intrinsicvalue. The long-short portfolio that any arbitrageurmight hold defines a market segment of a largerarbitrage industry. We assume that arbitrageurs arehighly skilled people who pursue proprietary tradingstrategies and therefore enjoy a monopoly in theirsegment. For simplicity only, we make the assump-tion that the operating costs in the arbitrage industryare zero.

The risk-free rate of return, and therefore theopportunity cost of capital, is zero. For simplicity,we assume that risky assets trading at fair value—including the stock market index—also have anexpected return of zero. This implies that there areno priced systematic risk factors in the economy,that is, there is no equity risk premium.

The asset trades at three moments in time, t(t=1,2,3). We capture the influence of the noisetraders’ misperceptions of the intrinsic value of theasset at times t1 and t2 with the parameters S1 andS2, respectively. There is no fundamental risk in themodel because the price of the asset will revert tothe intrinsic value at a known date (t3) with certainty(so S3=0).

The supply of the financial asset is unity. Noisetraders’ demand for the financial asset at time t(t=1,2,3) is expressed as

(1) , 0 ≤ St<V,

where pt is the price of the financial asset and St isthe misperception of the noise traders about theintrinsic value of the financial asset. Because thefinancial asset in question is a long-short portfoliowhose value is underestimated by the noise traders,the noise traders demand less than one unit of the

QN

V Spt

t

t

= −


FEDERAL RESERVE BANK OF ST. LOUIS Emmons and Schmid

Figure 1

Timeline of the Model

Noise traders’misperception, S1,

in place.

Arbitrageur allocates financial resources to asset and cash.

Noise traders’misperception

might deepen to S2.

If asset price reverts to intrinsic value, V,

arbitrageur liquidates.

Noise traders’misperception

corrects (S3= 0).

Hedge fund winds down.

t1 t2 t3

financial asset. Without misperception (S=0), thenoise traders would be willing to absorb the unitsupply of the asset or, in other words, the asset wouldtrade at the intrinsic value ( pt=V ).

The arbitrageur is compensated in two ways inaccord with actual practice—via an up-front “man-agement fee” and an after-the-fact performance-based “incentive fee.”5 At the beginning of eachperiod, he receives a fraction (α ) of the assets undermanagement, and at the end of the period he receivesa fraction (β ) of any positive return on the portfolio.This corresponds to compensation structures inreal-world hedge funds, where managers typicallycollect α=1 percent or α=2 percent of the equitycapital, plus β=20 percent of any positive returnon the fund’s equity. We assume that the arbitrageurinvests his entire fee income in the fund. This isbecause he recognizes the profitability of the fund’sactivities.

The variable Ft denotes the total financialresources available to the arbitrageur at time t(t=1,2,3). The value of F1 is exogenous, while thequantities F2 and F3 are determined in the model.The startup capital, F1, is provided solely by theinvestors, while the arbitrageur acquires the shareα in F1 immediately as part of his compensation.The arbitrageur acquires additional equity at t2 inthe amount of a fraction α of the outsiders’ sharein F2. Furthermore, the arbitrageur acquires equityin the fund through capital gains on his equityposition and through his share β in the capitalgains on the outsiders’ equity. The quantity F3 is thefund’s liquidation value. Note that the arbitrageuris both the general equity partner of the fund andits manager, receiving compensation from outsideinvestors (limited partners) according to the feeschedule described above.

We assume that the fund raises equity capitalonly at the outset—at t1. This assumption preventsthe arbitrageur from diluting initial investors’ equitystakes later on. Remember that the arbitrageur’scompensation depends not only on the return onbut also on the amount of the outsiders’ equitycapital under management. The arbitrageur there-fore might have an incentive to raise fresh capitalat t2, particularly if he expects low returns in thesecond period. This would dilute the fund’s existinginvestors’ equity stakes. Thus, we assume (in keepingwith typical hedge-fund arrangements) that the fundcloses to new and existing investors after raisingthe initial capital. Reinvested capital gains are conse-

quently the sole source of additional equity capitalin the second period.

At time t2, the price of the asset either revertsto V or it does not. If the asset price is V at t2, thearbitrageur liquidates the fund and holds cash untilt3. If the asset price does not equal V at t2, the arbi-trageur invests aggressively—albeit not all of thefund’s cash—in the underpriced asset. This portfoliothen generates a risk-free return because the assetprice rises to V at t3 with certainty.

The arbitrageur’s (that is, the hedge fund’s)demand for the asset at the interim date, t2, is givenby

(2)

where D2 is the amount of the hedge fund’s demandin dollars. The amount F2 – D2 ≥ 0 is held in cash.Because total demand aggregated across noisetraders and the arbitrageur must equal the assetsupply of one unit (QN2+QA2=1), the price of thefinancial asset at t2 is determined by combining (1)and (2):

(3)

The condition D2<S2 implies that the asset stilltrades at a discount to the intrinsic value at t2: p2<V.This assumption recognizes the arbitrageur’s incen-tive not to bid up the price all the way to intrinsicvalue immediately—an implication of the fact thatthe arbitrageur will be compensated during the lastperiod for achieving a positive return on investment.

As shown by Grossman and Vila (1992), thearbitrageur does not want to invest all of F1 in theasset at t1, either. After all, the asset may becomeeven more underpriced at t2, in which event he willwant to increase his investment (“double up”). WithD1 denoting the amount the arbitrageur invests inthe asset at t1, we have

(4) ,

which implies the initial asset price will be QA

Dp1

1

1

=

p V S D D S2 2 2 2 2 , 0 .= − + ≤ <

QADp

D F22

22 2 , 0 ,= ≤ ≤


5 Amin and Kat (2001) report that hedge funds typically charge theirinvestors a fixed annual “management fee” of 1 or 2 percent of assetsunder management plus an “incentive fee” of 15 to 25 percent of thefund’s realized annual return. The incentive fee is waived if a particular“hurdle rate” has not been achieved, which can be a fixed number ora reference rate such as the T-bill rate plus or minus a spread. Mostfunds also apply a “high-water mark” provision, which requires thefund to make up any past losses before the incentive fee is paid. Weassume the hurdle rate is zero, and we exclude any high-water markfor the sake of simplicity.


(5)

The condition D1<S1 implies p1<V, which againcaptures the fact that the arbitrageur will not bidthe price all the way up to the asset’s intrinsic valuebecause of the incentives built into his compensa-tion schedule.

The investors have prior beliefs about the arbi-trageur’s talent in exploiting possible asset mispric-ing, but are not perfectly informed. Investors updatetheir beliefs about the arbitrageur’s talent using asimple Bayesian learning rule, which is based solelyon the arbitrageur’s past performance. When pastreturns are poor, investors don’t know for surewhether the poor returns are due to a random error(noise), a deepening of noise trader misperception(bad luck), or truly inferior investment talent. Pullingsome of their money from the hedge fund after theasset mispricing has deepened—that is, when theexpected return on the long-short portfolio ishighest—is the investor’s rational response to theproblem of inferring the arbitrageur’s (unobservable)talent from data that are ambiguous (that is, observa-tionally equivalent under more than one possibleeconomic structure).

The investor’s rule of updating his beliefs aboutthe arbitrageur’s talent implies that, if the hedgefund loses money during the first period, the fundfaces withdrawals at the interim date, t2. Specifically,we assume that the withdrawals at t2 are a multipleof the hedge fund’s posted gross return (that is,before management fees) at t2, denoted R2, shouldthis return be negative. Remember that, whileinvestors can withdraw capital, they cannot injectadditional funds. Thus, the supply of funds in thesecond period is the following6:

(6)

where γ is a parameter that determines the respon-siveness of the investor to past performance. Forγ=1, poor first-period returns do not shake theconfidence of investors in the arbitrageur’s talent.At the other extreme, responsiveness that becomesunboundedly large implies that even a small first-period loss is multiplied into a huge withdrawal offunds. Note that the outside investors may withdrawonly what is theirs. This means that, even if the out-siders pull all of their money, the arbitrageur’s equitystake remains and the fund can stay in business.

FF R F R R

F R R2

1 2 1 2 2

1 2 2

1 1 1 1 1 0

1 0=

⋅ ⋅ +( ) + ⋅ −( ) ⋅ +( ) > − ≤ <⋅ +( ) >

α α γγ, , if

, if ,

p V S D D S1 1 1 1 1 , .= − + < The arbitrageur knows that—despite a temporarydeepening of the mispricing—the price of the assetwill revert to intrinsic value at t3 for certain, so hewill keep his own money invested, come what may.

Our multiplicative feedback rule provides thearbitrageurs with what may be a more realistic incen-tive structure than the linear feedback rule in Shleiferand Vishny (1997). Our feedback rule does not penal-ize small negative returns quite as severely for ahigh degree of responsiveness, γ, as is the case inShleifer and Vishny. For a responsiveness coefficientof γ=5, for instance, a gross return in the first period,R2, of –1 percent reduces the fund’s equity capitalby approximately 4 percentage points. A 5 percentloss, on the other hand, leaves the fund with approxi-mately 77 percent of its equity capital at the begin-ning of the next period. We provide more resultsfrom the model below.

The gross return of the hedge fund in the firstperiod, R2, is given by

(7)

The fund’s first-period return consists of its returnon the financial asset, normalized by the total fundsavailable for investment.

For simplicity, we assume a specific form ofuncertainty about noise trader sentiment at t2, S2.With probability 1 – q (0<q<1), noise traders recog-nize the true value of the asset, which implies S2=0.In this case, the arbitrageur liquidates at t2 and holdscash until t3. Then the arbitrageur’s assets undermanagement at t3 would amount to

(8)

where

On the other hand, noise trader misperceptiondeepens to S2 with probability q, S2=S>S1(>0). Ifnoise traders continue to misperceive the intrinsicvalue of the asset, the hedge fund’s assets at t3 willamount to the following:

RF D D

Vp

F

FS2

01 1 1

11

1

2

( ).= =

− + ⋅ −

F F F RS S S3

02

01 2

02 2 2(1 ),= = == ≡ ⋅ +

RF D D

pp

F

F

Dp p

pF2

1 1 12

11

1

12 1

1

1

( ).=

− + ⋅ −=

⋅ −



6 Some hedge funds have “lock-up” periods of one to three years, whileothers allow investors to withdraw money with only a few weeks’notice. As a result of the poor quality of investors’ information aboutthe arbitrageur’s talent, the arbitrageur’s past performance often is amajor determinant of the resources he receives to manage, regardlessof the actual arbitrage opportunities available to him.

(9)

where

THE ARBITRAGEUR’S OPTIMIZATIONPROGRAM

The arbitrageur’s total income consists ofmanagement fees and capital gains on reinvestedmanagement fees. The expected value of the man-agement fees, MF, equals the sum of the expectedvalues of the management fees collected at t1(MF1),at t2(MF2), and at t3(MF3). The expected value of thecapital gains is CG. The arbitrageur’s maximizationproblem therefore is

(10)

where the management fees are

(11)

(12) and

(13)

and where

and

The quantity MF2S2=S represents the income the

arbitrageur collects at t2 should the noise traders’

R

F D DV

pF

FS S

S S S S S SS S

S S

S S3

2 2 22

2

2

2

2 2 2

2

2

2

( ).=

= = ==

=

==− + ⋅ −

+ ⋅ − ⋅ ⋅ − ⋅=β α(1 ) (1 ) ,20

12q R FS

MF q F R F

R F

S S S

S

20

20

20

1

20

1

2 2 2

2

(1 ) ( [ ]

(1 ) )

= = =

=

= ⋅ − ⋅ − ⋅ + ⋅

− ⋅ ⋅ − ⋅

α α

β α

1

+ ⋅ ⋅ ⋅ − ⋅=β αq R FS Smax{0, } (1 ) ,2 12

MF q F R F

R F

S S S S S S

S S

2 2 2 1

2 1

2 2 2

2

( [ ]

max{0, } (1 ) )

= = =

=

= ⋅ ⋅ − ⋅ + ⋅

− ⋅ ⋅ − ⋅

α α

β α

1

MF q R

F R MF MF

S S

S S S S S S

3 3

2 2 1 2

2

2 2 2( (1 ) )

= ⋅ ⋅

⋅ − + ⋅ −

=

= = =

β

MF MF MFS S S2 2 2

02 2 ,= += =

MF F1 1,= ⋅α

Max MF MF MF CGD D1 2,

1 { ,+ + +2 3 }

RF D D

pp

F

FS S

S S

2

1 1 12

11

1

2

2

( ).=

=

=− + ⋅ −

= ⋅ + ⋅ + −==V

pD F R DS S

S S

22 1 2 2

2

2(1 ) ,γ

FV

pD F DS S

S SS S

32

2 2 22

2

2( )==

== ⋅ + −misperception deepen in the first period, whileMF2

S2=0 is the fee income if the asset price revertsto intrinsic value. The arbitrageur also captures capi-tal gains on the equity he builds from the reinvestedmanagement fees. The expected value of the capitalgains, CG, equals

(14)

The arbitrageur’s choice variables are D1(≤ F1)and D2(≤ F2

S2=S ), which are the amounts the arbi-trageur invests in the asset at t1 and t2, respectively.Unless the asset reverts to intrinsic value at t2( p2=V ),the t2 price of the asset given in equation (3) is afunction of the t2 choice variable, D2. Similarly, thet1 price of the asset given in equation (5) is a functionof the choice variable, D1.

SOLUTION TO THE MAXIMIZATIONPROBLEM

We solve the maximization problem numeri-cally. We hold constant all of the following: V=1;F1=S1=0.2; S2=0.4; q=1– q=0.5; α=0.02; andβ=0.2. Note that F1=S1=0.2 means that the arbi-trageur has sufficient buying power to eliminate thet1 mispricing entirely if so desired. Also, note that0.4=S2>S1=0.2 means that noise trader mispercep-tion may deepen between t1 to t2—that is, the assetmay become even more mispriced. For the valueschosen for S1, S2, and q, noise trader misperception,S, is as likely to double as it is to vanish. Thus, theexpected value of noise trader misperception in thesecond period, q·S2, equals the noise trader misper-ception observed in the first period, S1.

We vary γ, the responsiveness to past perfor-mance of fund withdrawals, from γ=1 (no responsive-ness by the investors to past investment performance,that is, no withdrawals) to γ=20 (extreme respon-siveness) with a step length of unity. We use a gridsearch method to solve the maximization problem.This involves varying D1 and D2 independently invery small increments within their bounds, 0 ≤ Di ≤Fi ( i=1,2), to find the maximum of the objectivefunction.

The findings of the grid search are displayedin Figures 2 through 5. The first important point tomake concerns the extent to which the presenceof the hedge fund affects asset mispricing. Figure 2shows that the mispricing is less pronounced in eachperiod than it would be without the hedge fund.

CG q R MF R R MF

q R MF

S S S S S S S S

S

= ⋅ ⋅ ⋅ + + ⋅

+ − ⋅ ⋅

= = = =

=

( [ ] )

(1 )

2 1 3 3 2

2 1

2 2 2 2

2

10 .



Remember that, without arbitrage, the first-periodprice, p1, and the expected value of the second-periodprice, E[p2], both would equal 0.8 (shown as a dashedline). On the other hand, without noise traders, theasset would trade at unit value in both periods (notshown). The hedge fund almost halves the differencebetween the expected value of the second-periodprice, E[p2] (shown as solid circles), and the asset’sintrinsic, unit value. In fact, the degree of investorresponsiveness, γ, has little bearing on E[p2], whichapproaches the value of approximately 0.8873(shown as a solid horizontal line) as γ approachesinfinity. By comparison, the degree of responsivenesshas a strong impact on the first-period price, p1(shown as open boxes). This is because the arbitra-geur treads even more cautiously when putting onthis trade in the first period when he knows that theinvestors penalize negative returns with sizeablewithdrawals. In fact, the higher is γ, the more cashthe arbitrageur holds in the first period, and therefore,the lower is p1. As the degree of investor responsive-ness, γ, goes to infinity, the amount the arbitrageurinvests in the first period goes to zero and, conse-quently, the first-period price, p1, converges to 0.8—the value the asset would adopt if there were nohedge fund in the market (shown as a dashed line).Thus we conclude that the hedge fund pushes theprice of the asset (or its respective expected value)toward the intrinsic, unit value in both periods. Thisis our first main finding.

Figure 3 shows the unconditional volatility ofthe asset’s returns for various degrees of investorresponsiveness, γ. The unconditional volatility iscalculated as the expected value of the squaredreturns over the two periods. For low values ofinvestor responsiveness, volatility increases as γincreases. For high values of responsiveness, a fur-ther increase in γ reduces volatility monotonically.As γ goes to infinity, volatility approaches a level(as shown by the solid line) that is lower than thevolatility level at γ=1 (as indicated by the leftmostsymbol), which is the benchmark case of unwaver-ing investor confidence in the hedge fund manager.The reason for this “volatility hump” lies in the exis-tence of two opposite effects. All else equal, thehigher γ is, the bigger is the drop in the asset’s pricefrom t1 to t2 should the noise traders’ misperceptiondeepen. On the other hand, the higher γ is, the loweris the price of the asset at t1 because the arbitrageurputs less money to work. For low values of investorresponsiveness, the volatility-increasing effect domi-nates. For increasingly higher values of γ, this effectbecomes progressively weaker until it vanishes foran infinitely large degree of investor responsiveness.

It is important to note that the hedge fundgreatly reduces asset price volatility, regardless ofthe degree of investor responsiveness. The uncon-ditional volatility without the hedge fund runs at0.5694 (not shown), which is a multiple of the volatil-ity that we observe even at the degree of responsive-



Effect of Investor Responsiveness on Asset Prices

p1

E(p2)

Ass

et P

rice

s p

1, E(p

2)

Responsiveness of Cash Flows ( )γ

0.90

0.88

0.86

0.84

0.82

0.80

0.780 2 4 6 8 10 12 14 16 18 20

Figure 2


0 2 4 6 8 10 12 14 16 18 20

Unc

ond

itio

nal V

ola

tilit

y

0.090

0.085

0.080

0.075

0.070

0.065

0.060

Effect of Investor Responsiveness on Asset Price Volatility

Figure 3

ness that generates the highest level of volatility.Thus, we conclude that the hedge fund unambigu-ously reduces unconditional volatility. This is oursecond main finding.

Another way to look at the impact of arbitrageon volatility is to ask how the market behaves whenasset mispricing deepens. Such an event—if severe—might cause, or occur alongside, a financial crisis.Figure 4 shows, for the case of a deepening noisetrader misperception of the asset’s intrinsic value,the first-period asset return as a function of investorresponsiveness. The absolute value of the percentagedecline of the asset price increases with investorresponsiveness, γ. For an infinitely high value of γ,the arbitrageur holds cash in the first period andthen invests aggressively at t2, although he does notinvest all the cash available. The horizontal line inFigure 4 signifies the first-period return for thisborderline case of an infinite degree of responsive-ness. Note that, without a hedge fund, the first-periodreturn would amount to a negative 25 percent (notshown), which is more than twice as much (in abso-lute value) as what is observed even with a degreeof responsiveness of zero (that is, γ equal to one).Hence, we conclude that the presence of a hedgefund dampens volatility in the event of a deepeningof noise trader misperception, as might occur in afinancial panic. This is our third main finding.

Finally, we are interested in the question of howinvestor responsiveness affects the arbitrageur’s

profit, that is, his incentive to set up a hedge fundand engage in arbitrage. Figure 5 shows the arbi-trageur’s profit as a function of γ. Not surprisingly,the profit of the arbitrageur decreases monotonicallywith increased investor responsiveness to past per-formance. The monotonic decline in the profitabilityof arbitrage with increasing investor responsivenessto past performance is a manifestation of the factthat liquidating a hedge portfolio when the expectedreturn from arbitrage is highest is counterproduc-tive—that is, it runs against “the nature of the trade.”

CONCLUSION

Even financially constrained professionalarbitrageurs may be able to exploit asset mispricingif they can link up with rational but uninformedinvestors. To achieve this goal, the two parties mustovercome—at least to a degree—the problem ofasymmetric information about the arbitrageur’stalent. The result of such an endeavor is a hedgefund that goes long on (comparatively) underpricedassets and short on (comparatively) overpriced assets.As a byproduct, the impacts of noise trader misper-ceptions on asset prices and volatility are reduced.This holds for any degree of responsiveness to pastperformance (“feedback”) of the investors’ confi-dence in the arbitrageur’s talent.

This article builds on the dynamic-investmentliterature that reaches back at least to Merton (1971).




0 2 4 6 8 10 12 14 16 18 20

Firs

t-Pe

rio

d A

sset

Ret

urn

for

S=S 2

–0.025

–0.050

–0.075

–0.100

–0.125

Effect of Investor Responsiveness on Asset Return When MisperceptionDeepens

Figure 4


0 2 4 6 8 10 12 14 16 18 20A

rbit

rage

ur’s

Exp

ecte

d P

rofit

0.0142

0.0140

0.0138

0.0136

0.0134

0.0132

0.0130

Effect of Investor Responsiveness on Arbitrageur’s Profit

Figure 5

Shleifer and Vishny (1997) provided an insightfulmodel of wealth-constrained arbitrageurs that canbe, and has been, extended in several directions.We add several realistic features to the professionalarbitrageur’s problem in the canonical model, includ-ing the ability to build an equity stake in his hedgefund over time, and a potentially more realistic multi-plicative (rather than linear) investor feedback rule.Like Shleifer and Vishny, we assume that the hedgefund can influence the market price. Hedge fundsdo, in fact, sometimes move market prices becausethey operate in specialized market segments thathave limited liquidity. It is also true, however, thathedge funds alone cannot prevent asset-price volatil-ity or occasional mispricing—which might deepenbefore it eventually corrects.

REFERENCES

Amin, Guarav S. and Kat, Harry M. “Hedge Fund Performance1990-2000: Do the ‘Money Machines’ Really Add Value?”Working paper, University of Reading (UK), 15 May 2001.

Breedon, Douglas T. “An Intertemporal Asset Pricing Modelwith Stochastic Consumption and Investment Opportuni-ties.” Journal of Financial Economics, September 1979,7(3), pp. 265-96.

Campbell, John Y.; Lo, Andrew and MacKinlay, A. Craig.The Econometrics of Financial Markets. Princeton, N.J.:Princeton University Press, 1997.

___________ and Viceira, Luis M. “Consumption andPortfolio Decisions When Expected Returns Are TimeVarying.” Quarterly Journal of Economics, May 1999, 114(2),pp. 433-95.

Fama, Eugene F. “The Behavior of Stock Market Prices.”Journal of Business, 1965, 38, pp. 34-105.

Friedman, Milton. “The Case for Flexible Exchange Rates,”in Essays in Positive Economics. Chicago: University ofChicago Press, 1953.

Gromb, Denis and Vayanos, Dimitri. “Equilibrium and Welfarein Markets with Financially Constrained Arbitrageurs.”

Working paper, London Business School and MassachusettsInstitute of Technology, October 2001.

Grossman, Sanford J. and Vila, Jean-Luc. “Optimal DynamicTrading with Leverage Constraints.” Journal of Financialand Quantitative Analysis, June 1992, 27(2), pp. 151-68.

___________ and Zhou, Zhongquan. “Equilibrium Analysisof Portfolio Insurance.” Journal of Finance, September1996, 51(4), pp. 1379-1403.

Keynes, John Maynard. The General Theory of Employment,Interest, and Money. New York: Harcourt, Brace & World,1936.

Kyle, Albert S. and Xiong, Wei. “Contagion as a WealthEffect.” Journal of Finance, August 2001, 56(4), pp. 1401-40.

Merton, Robert C. “Optimal Consumption and PortfolioRules in a Continuous-Time Model.” Journal of EconomicTheory, December 1971, 3(4), pp. 373-413.

___________. “An Intertemporal Capital Asset Pricing Model.”Econometrica, September 1973, 41(5), pp. 323-61.

Muth, J. “Optimal Properties of Exponentially WeightedForecasts.” Journal of the American Statistical Association,1960, 55, pp. 299-306.

Samuelson, Paul. “Proof That Properly Anticipated PricesFluctuate Randomly.” Industrial Management Review,1965, 6, pp. 41-49.

Scholes, Myron S. “Crisis and Risk Management.” AmericanEconomic Review Papers and Proceedings, May 2000, 90(2),pp. 17-21.

Sharpe, William P. and Alexander, Gordon. Investments.4th Edition. Englewood Cliffs, N.J.: Prentice Hall, 1990.

Shleifer, Andrei and Vishny, Robert W. “The Limits ofArbitrage.” Journal of Finance, March 1997, 52(1), pp. 35-55.

Xiong, Wei. “Convergence Trading with Wealth Effects: AnAmplification Mechanism in Financial Markets.” Journalof Financial Economics, November 2001, 62(2), pp. 247-92.




Regime-DependentRecession Forecastsand the 2001 RecessionMichael J. Dueker

Business recessions, as a major source ofnondiversifiable risk, impose high costs onsociety. Since firms cannot obtain “recession

insurance,” they try to foresee recessions and reducetheir exposure ahead of time. Consequently, fore-casting business cycle turning points has remainedan important endeavor. Of course, the difficulty isderiving reliable methods to forecast business cycleturning points. Previous studies found that accuraterecession forecasts remain elusive (Filardo, 1999;Del Negro, 2001; Chin, Geweke, and Miller, 2000).Forecasts of economic output are not a good substi-tute for forecasts of business cycle turning pointsbecause less than 20 percent of all months pertainto recessions. Hence, empirical models of outputgrowth focus largely on explaining variation inoutput growth during economic expansions, sincethis variation accounts for the lion’s share of thesample variance.

Throughout the 1990s, recession forecastingmodels relied exclusively on the 1990-91 recessionfor out-of-sample confirmation (Estrella and Mishkin,1998; Birchenhall et al., 1999; Friedman and Kuttner,1998). Out-of-sample confirmation is particularlyimportant for recession forecasting because reces-sions are infrequent, making it tempting to overfitspecific episodes in sample. In general, recessionforecasting models failed to predict the 1990-91recession out of sample. The occurrence of the 2001recession raises the question: Was the 1990-91 reces-sion uniquely difficult to predict or is recession fore-casting a failed enterprise? If recession forecastingmodels were to repeat in 2001 their dismal perfor-mance from the 1990-91 recession, then doubtsabout such models would mount with justification.In this article, I examine the out-of-sample forecastsfrom recession forecasting models with three levelsof sophistication. All three models concur with theprevious finding that the 1990-91 recession was

hard to predict. The simplest of the three modelslargely misses the 2001 recession, but two regime-switching models come quite close to predictingthe onset date of the recession six months aheadof time. One innovation to recession forecastingintroduced here is to allow the critical probabilitylevel for a recession to be predicted to depend onthe current state of a Markov switching process—hence, regime-dependent recession forecasts.

In this way, the forecasts presented here respondto the criticism that economists equivocate too muchwhen it comes to their recession forecasts. Whenrecession forecasts are expressed as probabilitystatements, it is tempting to claim ex post that theex ante probability of recession from the forecastingmodel was “close enough” to either one or zero to beconsidered a correct forecast. For example, if themodel suggested that a recession would occur witha probability of 35 percent, then after the fact themodel builder could try to justify either outcome:If a recession ensued, the model builder could citethe jump in probability from zero to 35 percent asevidence that a large shift toward recession wasdetected; if no recession ensued, the model buildercould say that the 35 percent probability was far from100 percent and did not indicate recession. To avoidsuch ambiguity, economists are often asked to makespecific calls as to whether the economy will or willnot be in recession six months from now. A yes/norecession signal comes from comparing the fore-casted probability of recession with a critical valuedetermined prior to the out-of-sample forecasting.

The dependent variable I use to separate businessrecessions from expansions is based on the businesscycle turning points defined by the National Bureauof Economic Research (NBER). As in Birchenhall et al.(1999), I use the composite index of leading indica-tors (CLI) from the Conference Board as the explana-tory variable in the probit forecasting models. TheCLI receives much attention as a harbinger of futurebusiness cycle conditions. It has ten components:manufacturing hours, consumer expectations, stockprices, initial unemployment claims, building permits,the money supply, the spread between short- andlong-term interest rates on government securities,vendor performance, manufacturing orders forconsumer goods, and manufacturing orders forcapital goods. Figure 1 shows the upward trend inthe CLI and its three-month percentage changes.The decreased volatility in the three-month changesince the early 1980s is symptomatic of decreasedvolatility in the business cycle.

Michael J. Dueker is a research officer at the Federal Reserve Bank ofSt. Louis. Mrinalini Lhila and John Zhu provided research assistance.


The 1990-91 and 2001 recessions can be usedto evaluate the out-of-sample performance of themodel and the leading indicators. I find that the1990-91 recession is the anomaly in that the reces-sion signal emanating from the leading indicatorslargely misses it. The 2001 recession, in contrast,was largely predictable from the leading indicatorssix months ahead.

The probit model takes the monthly movementsin the leading indicators and translates them intoprecise probability statements regarding the likeli-hood of recession. When dealing with qualitativeevents such as recessions, however, it is often desir-able to define a specific recession yes/no signal fromthe probit probabilities. That way, the forecastingmethod either correctly “calls” the recession or itdoes not. Birchenhall et al. (1999) use this approachto say that a recession is signaled if the logit proba-bility of recession exceeds a critical value. If onechooses the critical value to maximize the in-samplefit, then out-of-sample confirmation of recessionforecasting models becomes particulary important,given Robert Lucas’s dictum: Beware of econometri-cians bearing free parameters. The critical value thatdefines a recession signal is an example of whatLucas calls a free parameter because—although itis not an inherent part of the econometric model—it combines with the model estimates to suggest thatthe model fits the data well. Out-of-sample confirma-tion helps ensure that the free parameters are notsimply overfitting the in-sample data.

THE SIMPLE PROBIT MODEL

I study three probit forecasting methods. One isthe simple probit model and the other two are basedon a Markov switching probit from Dueker (1997).A probit model can be used to predict a qualitativevariable such as a recession indicator, Rt, where

Rt=1 if the economy is in NBER recession in period t=0 otherwise.

One way to think of a probit model is to assume thata normally distributed latent variable, R*, lies behindthe recession indicator:

(1)

where u is a normally distributed error team and Xis the leading indicator explanatory variable laggedk periods—the forecasting horizon. A probabilityof recession is associated with each possible valueof the latent variable, where the latent variable isassumed to be negative during expansions and posi-tive during recessions. In this case, the forecastedprobability of recession is

(2)

where Φ(.) is the cumulative standard normal densityfunction.

The log-likelihood function for a simple probitmodel is

(3)

COEFFICIENT VARIATION VIA MARKOVSWITCHING IN THE PROBIT MODEL

The log-likelihood function in equation (3) high-lights the assumption in the probit model that therecession outcomes conditional on available infor-mation are independently distributed from monthto month. This assumption is questionable unlessthe econometric model allows for considerableserial correlation in the recession probabilities. Asin Dueker (1997), one way to achieve this degree ofserial correlation is to introduce serial correlationin the model’s coefficients by making them subjectto Markov switching.

The simplest interpretation of Markov switchingcoefficients in a probit model is that they capturetime variation in the variance of the error term ufrom equation (1). In the low-variance regime thevariance would still be normalized to one, but in

L R R X

R R X

tt

t t k

t t t k

= ∑ ( )+ −( ) ( )

−

−

lnProb =1

lnProb =01 .

Prob =1 ,R c c Xt t k( ) = − +( )−1 0 1Φ

R c c X ut t k t* ,= − + − +−0 1


Dueker R E V I E W

Index of Leading Indicators

3-Month Change in Index

1959 1965 1971 1977 1983 1989 1995 20010

20

40

60

80

100

120

–10

–5

0

5

10

15

20Index

Figure 1

the high-variance regime the variance would begreater than one:

Variance switching implies that the coefficients arerestricted to change by the same percentage betweenregimes. I do not impose this condition because Ido not want to restrict the signs of the coefficients,for example, to be the same in both regimes. Never-theless, conditional heteroskedasticity helps motivatewhy the coefficients might be subject to regimeswitching, since volatility is one aspect of the econ-omy that does vary across the business cycle. Becauseit is not the only aspect of the economy that variesacross the business cycle, however, we keep themodel more general by not restricting the regimeswitching to variance switching.

In a Markov switching model, the parameterschange values according to an unobserved binarystate variable, St, which follows a Markov process:

(4) St equals 0 or 1Prob (St=0|St–1=0)=p

Prob (St=1|St–1=1)=q.

In this way, the coefficients take on either of twovalues and thereby change the magnitude of theshock needed to induce a recession:

(5) Rt=1 if

=0 if .

The transition probabilities, p and q, indicatethe persistence of the states and determine theunconditional probability of the state St=0 to be(1 – q )/(2 – p – q ). Since the state is unobserved andmust be inferred as a probability, allowance for twostates means that the expected values of the coeffi-cients can lie anywhere between the high and lowvalues corresponding to the two states. In the esti-mation, Bayes’ rule is used to obtain filtered proba-bilities of the states in order to sum over possiblevalues of the unobserved states and evaluate thelikelihood function, as in Hamilton (1990):

(6)

Prob ,

Prob , Prob ,

Prob , Prob ,=

S R X

S R X R X

S s R X R S s X

t t t

t t t t t

t t t t t ts

= =( ) =

=( ) = =( )=( ) = =( )∑

−

− − −

− − −

0 0

0 0 0

0

1

1 2 1

1 2 101

St

u c S c S Xt t t t k≤ ( ) + ( ) −0 1

u c S c S c S Xt t t t t k> 0 1 1( ) + ( ) + ( ) −

Prob =1 | High Variance

/ / ,

R

c c X

t

t k

( )= − +( ) >−1 10 1Φ σ σ σ .

(7)

.

The probability in equation (7) is called the one-period-ahead prior probability because it is not con-ditional on the recession outcome at time t. For aforecast horizon of several months, we need to usethe transition probabilities to derive a k-period-aheadprobability of the state variable:

(8)

where G is the transition matrix of the Markov statevariable.

For forecasting k periods ahead, one finds thebest-fitting model by maximizing the correspondinglikelihood function:

(9)

In this forecasting exercise, the forecaster isassumed to know whether the economy is currentlyin recession when forecasting whether the economywill be in recession six months from now. Thisassumption is somewhat problematic when fore-casting from the early stages of a recession beforethe NBER has officially declared that the economyentered a recession. For example, when forecast-ing whether the economy would be in recession inOctober 2001, it was probably not clear that theeconomy was in recession in April 2001. The NBERdid not announce that the recession had started inMarch 2001 until November 26, 2001. On the otherhand, forecasts of the onsets of recessions are notlikely to be clouded by this assumption. In forecast-ing whether the economy would be in recession in

RS s R X

R S s X

RS s R X

R S s X

tt

T t t k t k

t t t k

t

t t k t k

t t t k

lnProb = ,

Prob ,

lnProb = ,

Prob ,

=

=

=

1

0

1

0

1

1

10

∑∑ ( )

= =( )

+ −( )∑ ( )

= =( )

− −

−

− −

−

s

s

.

GS R X

S R X

kt k t k t k

t k t k t k

Prob , ,

Prob ,,

− − −

− − −

=( )=( )

′0

1

Prob , ,

Prob ,

S R X

S R X

t

t

t k t k

t k t k

=( )=( )

′

=− −

− −

0

1

+ −( ) =( )− − −1 11 1 2q S R Xt t tProb ,

Prob ,

Prob ,

S R X

p S R X

t t t

t t t

=( )= =( )

− −

− − −

0

0

1 2

1 1 2


FEDERAL RESERVE BANK OF ST. LOUIS Dueker

March or April 2001, no one believed in Septemberor October 2000 that the economy was already inrecession. There was no confusion about the currentstate of the economy. Similarly, in January 1990everyone knew that the economy was still in anexpansion when forecasting whether a recessionwould start by July 1990. For this reason, one mightpay special attention to how the model predicts theonset dates of recessions.

FORECAST RESULTS

The Markov switching probit model was esti-mated with data from May 1960 to December 1989.

The explanatory variable, X, is the three-monthpercentage change in the leading indicators indexshown in Figure 1. The minimum value of the changein the leading indicators index is about –5. If we plugthis minimum value and the coefficient estimatesfrom Table 1 into equation (5), we see that a recessionis essentially never predicted in the state where S=1.Given the values of c0(S=1) and c1(S=1), a standardnormal shock, u, greater than 2.7 would have tooccur at the minimum value of the leading indicatorsfor a recession to occur in that state. In contrast,recessions are often implied in the regime whereS=0, as shown in Figure 2. (Note that in this andthe following charts, the coefficient estimates arealso applied to the out-of-sample data from January1990 to December 2001.) The unconditional prob-ability of the regime S=0, however, is only 0.25 andit is forecasted less often than regime S=1, as seenin Figure 3. Combining Figures 2 and 3, we see thatsix-month-ahead forecasts of a high probability ofS=0 amount to a forecast of recession. Figure 4 com-bines the two explicitly by plotting the probabilityof recession after summing across the two states:

(10)

Prob

Prob = , Prob , . =

R X

S s R X R S s X

t t k

t t k t k t t t ks

=( )= ∑ ( ) = =( )

−

− − −

1

10

1


Dueker R E V I E W

Coefficients from the Markov Switching Probit

Equation Coefficient value

c0 (S=0) 0.140 (0.233)

c1 (S=0) 1.415 (0.336)

c0 (S=1) 2.397 (0.730)

c1 (S=1) –0.062 (0.351)

p 0.950 (0.012)

q 0.984 (0.010)

NOTE: Standard errors are in parentheses.

Table 1

Probability of Recession Conditional onRegime S=0

1962 1968 1974 1980 1986 1992 19980

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

NOTE: Shaded bars indicate recessions.

Probability

Figure 2

Six-Month-Ahead Probability of Regime S=0

Out of SampleIn Sample

1962 1968 1974 1980 1986 1992 19980

0.2

0.4

0.6

0.8

1


Probability

Figure 3

A standard approach—which I call model 1—to deriving explicit recession signals from the fore-casted probability of recession, Prob (Rt=1|Xt–k ),is to choose a critical value, m, such that a recessionis signaled if

(11)

A key innovation in this paper is to recognizethat one can derive an alternative recession signal—called model 2—from regime-specific critical values,m0 and m1, such that a recession is signaled if

(12)

Alternatively, one can rewrite this recessionsignal as

(13)

Either way, two critical values are used, where theweight given to each depends on the regime proba-bilities. As shown in Figure 2, the probability of reces-sion is relatively high on average in the regime whereS=0, with an average probability of 0.34. In contrast,the average probability of recession in the regimewhere S=1 is not much above zero. Given the differ-ence between the average probabilities of recessionin the two regimes, it seems desirable to have sepa-rate critical probability levels for each regime, as inequation (12).

I chose critical probability levels based on thein-sample period through 1989 and examined howwell they work in the out-of-sample period. Thecriterion I used was the greatest number of correctsignals, where one point was given to a correct signalduring a recession and half a point to a correct signalduring an expansion. This point scheme puts greateremphasis on not missing recessions versus supply-ing false recession signals during expansions. Theimpetus for this asymmetry in the point scheme isthe belief that most firms would be more willing topay for recession insurance than for a contract thatwould indemnify them in the case where the econ-omy performed above expectations when a recessionwas forecast.

Prob >Prob ,

Prob ,

R X S R X m

S R X m

t t k t t k t k

t t k t k

=( ) =( )+ =( )

− − −

− −

1 0

1

0

1.

Prob ,

Prob ,

Prob ,

Prob , >

S R X

R S X m

S R X

R S X m

t t k t k

t t t k

t t k t k

t t t k

=( )= =( ) −( )

+ =( )= =( ) −( )

− −

−

− −

−

0

1 0

1

1 1 0

0

1 .

Prob >R X mt t k=( ) −−1 0 .

For the in-sample period through 1989, I foundthat values of m=0.28 (for model 1) and m0=0.43and m1=0.135 (for model 2) gave the greatest num-ber of correct signals. It makes sense that the optimalvalue of m would lie between optimal m0 and m1,since it is trying to fill both roles. The critical prob-ability level m0 lies above the average probability ofrecession conditional on S=0 (0.34), so that onepredicts a recession less than half of the time thatS=0.

Figure 5 shows the fit of the recession-signalingmodel, where the signal is based on model 2 withthe two regime-dependent critical values. With fore-casts from model 2, recessions are generally notmissed and the only notable false signal occurredin the 1966 slowdown. Figure 6, in turn, shows thefit of model 1—the signaling procedure that usesonly one critical value, as in equation (11). Here, someof the recessions are projected to start earlier andend later than they did and there are many morefalse recession signals during expansions.

Similarly, Figure 7 shows the fit of the signalfrom the simple probit model from equation (2). Theoptimal critical value, m, for the 1960-89 period is0.24. This approach, model 3, generated even moreand longer-lasting false recession signals than theMarkov switching model with one critical value(model 1). Based on these results, we do not look



Probability of Recession fromSix-Month-Ahead Forecasts

1962 1968 1974 1980 1986 1992 19980

0.2

0.4

0.6

0.8

1In Sample Out of Sample


Figure 4

further at the predictions from the simple probitmodel.

In comparing the two signals from the Markovswitching models, a closer look at the two out-of-sample recessions will help determine whether the

use of two critical values in model 2 as free param-eters to fit the in-sample data resulted in overfitting.Figure 8 zooms in on the 1990 recession and showsthat the recession signals from models 1 and 2 areidentical and they both miss the 1990 recession in


Dueker R E V I E W

1962 1968 1974 1980 1986 1992 1998

Fit of Recession-Signaling Model(Model 1: One Critical Value)

NOTE: Actual recessions (top) and six-month-ahead recessionsignals (bottom).

In Sample Out of Sample

Figure 6

1962 1968 1974 1980 1986 1992 1998

Fit of Recession Signal from Simple ProbitModel (Model 3)



Figure 7

Fit of Recession-Signaling Model(Model 2: Two Critical Values)

1962 1968 1974 1980 1986 1992 1998



Figure 5

Jan 1990 Jul 1990 Jan 1991 Jul 1991

1990-91 Recession and Signal(Signals from Models 1 and 2 Coincide: One or TwoCritical Values)

NOTE: Actual recession (top) and six-month-ahead recessionsignal (bottom).

Figure 8

the sense that the six-month-ahead signal does notkick in until at least six months too late. This resultconfirms previous findings that the 1990 recessionwas difficult to predict out of sample.

Fortunately, the performance of both signalapproaches is better in the 2001 recession. Figure 9shows that the signal from model 1 with one criticalvalue predicted a recession onset in April 2001 usinginformation through October 2000. Thus, this signaldid not miss the actual onset date of March 2001by much. The approach with two critical values—model 2—does slightly worse during the 2001 reces-sion. Figure 10 shows that this signal needed datathrough November 2000 to predict a recession onsetdate of May 2001. In addition, it incorrectly projectedAugust 2001 as an expansion month. Nevertheless,one has to keep Figures 5 and 6 in mind before con-cluding that the signal derived from one critical valueis better out of sample than the signal derived fromtwo critical values. Model 2 gave fewer false reces-sion signals than model 1 during the long economicexpansion of the 1990s, as seen by comparingFigures 5 and 6.

Looking out from the December 2001 data,both signaling approaches based on the Markovswitching model—with either one or two criticalvalues—predict that the recession would have endedby January 2002. Later, the NBER will officially datethe end of the recession and then the accuracy of

the model’s trough prediction will be known.

SUMMARY AND CONCLUSIONS

This article looks at forecasting the 1990 and2001 recessions out of sample and shows that 1990appears to be an anamoly in terms of being difficultto predict. Thus, one should not conclude based onthe 1990 recession that recession forecasting is afailed enterprise. This article also responds to theexhortation economists receive to provide unequivo-cal predications about whether or not the economywill be in recession in six months. To translate froma probability of recession to a yes/no recession signal,one compares the probability with a critical value.One innovation in recession signaling that I pursuehere is to have regime-specific critical values whenthe recession probability comes from a regime-switching model. This method of deriving a recessionsignal reduces the number of false recession signalsoutside of recession, without impairing the abilityto signal the recessions that occur.

REFERENCES

Birchenhall, Chris R.; Jessen, Hans; Osborn, Denise R. andSimpson, Paul. “Predicting U.S. Business-Cycle Regimes.”Journal of Business and Economic Statistics, July 1999,17(3), pp. 313-23.

Chin, Dan M.; Geweke, John F. and Miller, Preston J. “Predicting



Jan 2000 Jul 2000 Jan 2001 Jul 2001

2001 Recession and Signal(Model 1: One Critical Value)


Figure 9

Jan 2000 Jul 2000 Jan 2001 Jul 2001

2001 Recession and Signal(Model 2: Two Critical Values)


Figure 10

Turning Points.” Federal Reserve Bank of MinneapolisStaff Report No. 267, June 2000.

Del Negro, Marco. “Turn, Turn, Turn: Predicting TurningPoints in Economic Activity.” Federal Reserve Bank ofAtlanta Economic Review, Second Quarter 2001, 86(2),pp. 1-12.

Dueker, Michael. “Strengthening the Case for the Yield Curveas a Predictor of U.S. Recessions.” Federal Reserve Bankof St. Louis Review, March/April 1997, 79(2), pp. 41-51.

Estrella, Arturo and Mishkin, Frederic S. “Predicting U.S.Recessions: Financial Variables as Leading Indicators.”Review of Economics and Statistics, February 1998, 80(1),pp. 45-61.

Filardo, Andrew J. “How Reliable Are Recession PredictionModels?” Federal Reserve Bank of Kansas City EconomicReview, Second Quarter 1999, 84(2), pp. 35-55.

Friedman, Benjamin M. and Kuttner, Kenneth N. “IndicatorProperties of the Paper-Bill Spread: Lessons from RecentExperience.” Review of Economics and Statistics,February 1998, 80(1), pp. 34-44.

Hamilton, James D. “Analysis of Time Series Subject toChanges in Regime.” Journal of Econometrics, July/August1990, 45(1/2), pp. 39-70.


Dueker R E V I E W


Investment-SpecificTechnology Growth:Concepts and RecentEstimatesMichael R. Pakko

The rapid pace of productivity growth since themid-1990s has been attributed to improve-ments in technology, particularly in the areas

of information processing and communications.From e-mail and cell phones to inventory manage-ment and robotic manufacturing techniques, newways of doing business—facilitated by the use ofnew types of capital equipment—have transformedthe workplace.

However, traditional growth theory and growthaccounting techniques—which emphasize the roleof disembodied, neutral technological progress—aredeficient in explaining the phenomenon of produc-tivity growth driven by technology that is embodiedin new types of capital equipment. Consequently,models of “investment specific” technologicalprogress have gained prominence as a frameworkfor evaluating the role of capital-embodied growth.

This article outlines a general model of invest-ment-specific technological change, presents somenew estimates, and examines the role that this typeof technological progress has in explaining andpredicting recent and prospective productivitygrowth trends.

GROWTH THEORY WITH INVESTMENT-SPECIFIC TECHNOLOGY

The idea that technology can be manifested innew, more efficient types of capital equipment hasa long history in economics, dating at least to the“embodiment controversy” of Solow and Jorgensonin the 1960s.1 The rapid advancement of information-processing and communications technologies hasrenewed interest in the issue, inspiring the develop-ment of general equilibrium models that includeinvestment-specific technological progress.

In this section, I describe a simple neoclassicalgrowth framework—based on the model ofGreenwood, Hercowitz, and Krussell (1997)—thatincorporates this idea. In addition to balanced,neutral technological progress, the model includesa source of technological change that is associatedwith improvement in the quality of investment goodsthat becomes embodied in the productive capitalstock. The model differs slightly from Greenwood,Hercowitz, and Krussell in two respects: First, themodel in this paper treats equipment and nonresi-dential structures as two components of a single,composite capital good. In addition, the modeldescribed below includes a convex productionpossibilities frontier.

Our interest is in explaining economic growth—a sustained increase in economic activity per capita.Hence, attention will focus on “steady state” growthpaths in which all variables increase at constant(though possibly differing) rates.

A Growth Model with Two Types ofTechnological Change

A simple model that incorporates both types oftechnological change can be described as follows:The household sector is modeled as a representativeagent who directly controls the production technol-ogy and owns the capital stock. Households supplylabor inelastically to the production sector and makeconsumption-saving decisions by maximizing astream of discounted utility over consumption:

(1) ,

where β<1 is a constant discount factor and ct is(per capita) consumption. The momentary utilityfunction is assumed to be of the constant relativerisk aversion (CRRA) form u(ct)=ct

1–σ/1–σ.Technology is typically incorporated directly

into the production function: Output is producedusing capital, labor, and the current state of technol-ogy. The production function is Cobb-Douglas andtechnology is specified in labor-augmenting form2:

(2) .Y K z Lt t t t= −α α( )1

β t

ttu c

=

∞∑

0( )

1 See, for example, Solow (1960) and Jorgenson (1966). Hercowitz (1998)describes the 1960s controversy in the context of contemporarymodels of investment-specific technology like the one described here.

2 Note that with a simple transformation of variables, z̆=z1–α, the pro-duction function can be written in the alternative form Y=z̆·F(K,L),where z̆ is an index of total factor productivity.

Michael R. Pakko is a senior economist at the Federal Reserve Bankof St Louis. Rachel Mandal, Mrinalini Lhila, and Athena Theodorouprovided research assistance.


Here, zt is the technology index that directlyenhances the productivity of labor, Lt, and indirectlythat of capital, Kt, in the production of output, Yt.Note that equation (2) can be written in terms oflabor productivity as

(2′ ) ,

where the ratio of capital and output to labor arerepresented by lower case variables; that is, k=K/Lis the capital/labor ratio and y=Y/L is labor pro-ductivity.

Writing equation (2) in terms of log-differences,productivity growth can be expressed as

(3) ,

where gi , for example, denotes the growth rate ofvariable i. The “growth accounting” equation (3)shows that productivity growth can be decomposedinto components representing “total factor produc-tivity,” (1–α )gz, and “capital deepening,” α gz.3

Investment-specific technology enters the modelthrough the capital accumulation equation,

(4) ,

which states that the current productive capital stockconsists of undepreciated capital from the previousperiod plus net investment, qtxt.4 In equation (4)physical investment measured in consumption units,

k k q xt t t t= − +−( )1 1δ

g g gy z k= − +( )1 α α

y k zt t t= −α α1

xt, is enhanced by an index of the quality of newlyproduced capital goods, qt . The product qtxt repre-sents investment as measured in efficiency units.The improvement in the quality of capital goodsreflected in increasing values of qt is the driving forcebehind investment-specific technological change.

In the subsequent analysis of the growth proper-ties of these two types of technology, we will assumethat the economy’s opportunities for producingconsumption goods and investment goods is charac-terized by a nonlinear production possibilities fron-tier, H(ct,xt), that is concave and invariant to scale.5Figure 1 illustrates the tradeoff summarized by H(≅).For a given level of technology and existing capital,the economy is capable of producing any combina-tion of consumption and investment lying on orbelow the production possibilities frontier (PPF).Points that lie outside the frontier are not feasiblegiven the current state of technology, while pointsinside the frontier imply inefficient underutilizationof resources. The optimal production combinationwill therefore lie on the frontier itself. The slope ofthe PPF at any given point shows the trade-offbetween consumption goods and investment goods—that is, their relative price.

The durability of capital goods means thatinvestment produces a stream of consumption goodsinto the future. Hence, the location of the optimalpoint on the PPF will depend on household prefer-ences for substituting consumption between thepresent and the future (which, given the separableCRRA form of utility assumed, is time invariant inthis model).

This combination of consumption and invest-ment can be found from the representative agent’sproblem of maximizing utility (1) subject to the over-all resource contraint,

(5) ,

and to the capital accumulation equation (4).

k z H c xt t t tα α1− = ( , )


3 Using the notation from the previous footnote, the growth accountingequation (3) can be written explicitly in terms of the total factor pro-ductivity variable, gy=gz̆+αgk.

4 The accumulation equation is often written so that there is a one-periodtime to build; that is, capital at time t depends on investment at timet–1. The specification in this paper simplifies the exposition of capitalgrowth and emphasizes the flow concept of investment.

5 Formally, the H(·) function is assumed to be homothetic. The nonlinearPPF can be thought of as shorthand for a more detailed model in whichconsumption goods and investment goods are produced in separatesectors, with costly transfer of factors between sectors.

Pakko R E V I E W

Production Possibilities Frontier

x/c

A

Consumption

Investment

Figure 1

For a given level of technology, the representativehousehold’s maximization problem yields an opti-mal investment/consumption ratio, implying aspecific equilibrium such as that shown as point Ain Figure 1. The slope of the dotted line shows theprice of consumption goods relative to investmentgoods implied by this equilibrium.

Case 1: Neutral Technological Changeand Balanced Growth

Suppose that the sole source of technologygrowth is zt, the index of labor-augmenting techno-logical progress. For given quantities of labor andpreexisting capital, an increase in zt shifts the pro-duction possibilities frontier outward, as shown inFigure 2.

Because the expansion takes the form of aradial outward shift, both consumption and invest-ment expand at the same rate as total output; thatis, gc=gx=gy.6 Moreover, with q constant, the capitalaccumulation equation (4) implies that capital growsat the same rate as investment, gk=gx. Hence, thistype of growth is often referred to as “balanced,”based on “neutral” technological progress. Withinvestment and consumption growing at the samerate, the economy’s growth path will be characterizedby a constant x/c ratio, as shown by the growth pathrunning through points A and B in Figure 2. Alongthis growth path, the slope of the PPF, representingthe relative price of consumption and investmentgoods, is also constant.

From the growth accounting relationship (3),the shift in the PPF includes the direct effect of theincrease in zt (represented in Figure 2 as movementpoint A′ ) as well as a component associated withcapital growth (accounting for the remaining shiftto point B in Figure 2). However, the role of capitaldeepening for this type of technological expansionis distinctly secondary. The direct effect of technol-ogy growth is an expansion of investment, whichgives rise to a commensurate growth rate of capital.Indeed, substituting the relationship gk=gy into thegrowth accounting equation (3), we find that therate of output growth (as well as of consumption,investment, and capital growth) is equal to the rateof labor-augmenting technical progress. Althoughthe growth accounting decomposition shows a rolefor capital deepening, there is no sense in whichtechnological progress is “embodied” in capitalgrowth. Rather, the capital component represents apassive response to “disembodied” technological

progress and does not comprise a truly independentsource of economic expansion.7

Case 2: Investment-SpecificTechnological Change and Capital-Embodied Growth

Growth associated with investment-specifictechnological progress differs from neutral technol-ogy growth in several respects. First, note that qt doesnot appear directly in the economy’s resource con-straint, (5). Instead, the investment-specific tech-nology index appears in the capital-accumulationequation and therefore operates through the capital-deepening component of the growth accountingequation.

Investment-specific technological progress canbe illustrated using a modified PPF framework, asshown in Figure 3. The vertical axis now measures“effective” investment, qtxt, incorporating the notionof improvement in the quality of investment goods.In Figure 3, the direct effect of an increase in qt is


6 This outcome is ensured by the scale-invariance property that isimplied by the assumed homotheticity of utility and the PPF function.

7 Capital deepening does play an important role in the adjustmentdynamics of the model. That is, when the economy is not on its steady-state growth path (King and Rebelo, 1993) or is in the transitionbetween steady-state paths (Pakko, 2002b), capital deepening is themechanism that moves the economy toward long-run equilibrium.

FEDERAL RESERVE BANK OF ST. LOUIS Pakko

Balanced Growth

x/c

A

Consumption

A'

B

Investment

Figure 2

shown by the rotation of the PPF to the dashed linepassing through point A′ . This twist in the PPF repre-sents a change in the tradeoff between consumptionand capital accumulation. The movement frompoint A to point A′ represents no change in c or x(or their ratio), but is simply a measure of the growthof “effective” capital that is made possible by theincrease in q.

For this reason, the selection of an appropriatenumeraire is important. If output were to be mea-sured in terms of constant prices, the shift in thePPF attributable to the increase in q would implythat output had risen for fixed inputs of labor andcapital. Hence, growth accounting would incorrectlyattribute part of the change to an increase in neutraltechnology, z. This mismeasurement would be evenmore severe if total output were measured in unitsof investment goods.

When the consumption good is taken as numer-aire, total real income—as measured in consumptionunits along the horizontal axis—is left unchangedby the direct effect of growth in qt. Appropriatemeasurement of investment-specific versus neutraltechnology growth therefore requires that the databe expressed in consumption units. In practice, thismeans that for growth accounting in the presenceof investment-specific technical progress, nominaloutput and investment data should be deflated by aconsumption price index.8

From the accumulation equation, an increase

in qt has the effect of increasing the effective capitalstock. In fact, when improvement in the quality ofcapital goods is accounted for, the growth rate ofthe capital stock will be the sum of the growth ratesof physical investment and quality improvement,

(6) .

Hence, the indirect impact of investment-specifictechnology growth will be reflected in effectivecapital stock growth that shifts the PPF in Figure 3outward. As was the case for neutral technologicalprogress, the growth component of investment-specific technological progress will be representedby a radial outward shift of the PPF that is character-ized by a constant xt /ct ratio and a common growthtrend for output, consumption, and physicalinvestment.

Substituting equation (6) and the relationshipgy=gx into (3), we obtain a relationship betweenproductivity growth and the two sources of technol-ogy growth:

(7) .

In the presence of investment-specific technologi-cal progress, total economic growth will be equalto the rate of labor-augmenting technical changeplus a component reflecting improvement in thequality of capital goods. Hence, investment-specificgrowth represents a channel through which techno-logical progress is manifested through “embodiment”in productive capital.

Two features of the growth path passing throughpoints A and B in Figure 3 are important for evaluat-ing the role of investment-specific technology in thedata. First, investment—when properly measuredto include improvements in the quality of newcapital goods—is predicted to grow faster than con-sumption along a steady-state growth path. In addi-tion, the nature of the change in the tradeoff betweenconsumption and investment, represented by thetwist in the PPF, implies that the relative price ofinvestment goods should be falling over time relativeto consumption goods.

Figure 4 shows that these trends are, in fact, acharacteristic of the data in the National Income andProduct Accounts (NIPA).9 The ratio of investment

g g gy z q= +−α

α1

g g gk x q= +


8 Greenwood, Hercowitz, and Krusell (1997) emphasize this point.

9 In Figure 4, “investment” corresponds to total nonresidential fixedinvestment and “consumption” is measured as nondurables plus ser-vices less housing services.

Pakko R E V I E W

Investment-Specific Growth

Consumption

q . x/cq' . x/c

B

Effective Investment

AA

A

Figure 3

to consumption has risen persistently over the pasthalf-century and has appeared to accelerate sharplyin the past decade. Simultaneously, the price ofinvestment relative to consumption has followed aclear downward trend since at least the late 1950s,with the rate of decline increasing since the 1980s.

ESTIMATES OF INVESTMENT-SPECIFICTECHOLOGICAL CHANGE

The data presented in Figure 4 suggest thatinvestment-specific technology growth has beenan important feature of post-WWII trends in produc-tivity growth. In order to quantitatively evaluate therole of investment-specific technology, however, itis important to carefully examine the issue of qualityimprovement for investment goods.

The measurement of quality change has alwaysbeen important in the construction of the NIPA data.Quality characteristics of newly introduced goodsare routinely incorporated into the data using so-called “matching models” that compare the attributesof new and existing products. In recent years, theBEA has implemented several revisions to its method-ologies in order to account for the rapid rate of inno-vation in information processing, communications,and other high-tech sectors. In particular, so-called“hedonic regression techniques” have been appliedto construct quantity and price indices that adjustfor changes in quality over time. Among the moreimportant applications of this approach, the BEAincorporates hedonic indices for computer equip-ment and purchased software, telephone switchingequipment, cellular services, and video players,among others.10 Moreover, the BEA has even changedits aggregation methodology to more accuratelymeasure the contribution of quality change to GDPgrowth: the adoption in 1996 of a chain-weightingmethodology was intended to allow aggregates totrack quality improvement better over time.

Nevertheless, some economists contend that asignificant amount of quality change goes unmea-sured in the official statistics, particularly in caseswhere quality improvement is more incremental.In a seminal 1990 study, The Measurement of DurableGoods Prices, Robert Gordon undertook to quantifythe extent of this unmeasured quality change. Draw-ing data from a variety of sources, including specialindustry studies, Consumer Reports, and the Searscatalog, Gordon compiled a data set of more than25,000 price observations. He constructed quality-adjusted price indexes for 105 different productcategories, then aggregated the data to correspond

to the individual components of the BEA’s measureof spending on producers’ durable equipment. Foreach of 22 major categories of investment, Gordoncalculated “drift ratios” representing the cumulativedeviation of his adjusted price measures from theofficial data. The adjusted price components werethen used to deflate nominal series, with the result-ing real series aggregated to create a new quality-adjusted series for investment in durable equipment.

The bottom line of Gordon’s study was that theofficial NIPA data (as constructed at the time) under-stated the true growth rate of real investment spend-ing by nearly 3 percentage points per year over theperiod 1947-83. This quality adjustment for realinvestment spending is mirrored in the price deflator:the finding that quality-adjusted real investmentspending is undermeasured implies that increasesin the price of investment goods have been over-stated. Unfortunately, Gordon’s data set extendsonly through 1983.

Quality Improvement for Equipmentand Software Investment

Previous estimates of investment-specific tech-nology growth have been based on extrapolation of


10 Landefeld and Grimm (2000) report that 18 percent of GDP is estimatedusing hedonic methods.


NIPA Investment and Consumption:Relative Prices and Quantities

Quantity

1947 1953 1959 1965 1971 1977 1983 1989 1995 200140

60

80

100

120

140

160

180

Indices (1996=100)

Price

Figure 4

Gordon’s aggregate data for series producers’ durableequipment. For example, Greenwood, Hercowitz,and Krusell extended the Gordon data through 1990by adding 1.5 percent to the growth rates of realinvestment spending for all categories except com-puters. Hornstein (1999) invoked a similar procedureto extend the estimate through 1997.

As BEA definitions and methodologies areupdated and as relative shares of the componentsof equipment investment change over time, however,the simple extrapolation of Gordon’s aggregate databecomes less satisfactory. Ideally, one would like tohave extended data series at the disaggregated levelof Gordon’s original study. A less ambitious alter-native is to extrapolate the drift ratios for each ofGordon’s 22 major investment categories indepen-dently—accounting for changes in BEA definitionsand methodology—then aggregate the extrapolateddata to calculate a new, extended series.11

Two recent studies have followed variants ofthis procedure. Cummins and Violante (2002) esti-mate a simple time-series model that relates Gordon’squality-adjusted estimates and official BEA timeseries data for each of the individual investmentcategories.12 After estimating the coefficients ofthe model, Cummins and Violante extrapolate out-of-sample estimates of quality-adjusted price levelsfor the period 1984-2000.13 Pakko (2002a,b) uses asimpler extrapolation technique: recognizing thatthe measurement bias documented by Gordon is

larger in the earlier years of the sample period thanthe latter period, the Pakko estimates are based ona linear extrapolation of Gordon’s drift ratios for theperiod 1973-83. The drift ratios were then appliedto the official BEA price data to create extendedquality-adjusted series.

Both sets of estimates were then aggregated tocreate a quality-adjusted measure of equipmentinvestment for the period 1947-2000. Recent changesin BEA definitions and methodology complicate thisprocedure. One important innovation made in 1996was the inclusion of software as an investmentcomponent. Gordon’s data set did not include soft-ware, so both Pakko and Cummins and Violanteused the official BEA measure for this component.Similarly, the BEA has devoted considerable effortto accurately measuring quality change for com-puters and peripheral equipment; hence, both studiesassume that the bias found by Gordon in the vintagedata has been eliminated in contemporary timeseries estimates for that component.

Figure 5 shows annual growth rates of thesequality-adjusted series for aggregate equipment andsoftware investment, along with the correspondingBEA measure. The two adjusted series track eachother closely during the 1947-83 period, since bothare based on Gordon’s original data.14 The mainsource of divergence between the estimates overthis period is the difference in aggregation method-ologies: Cummins and Violante use the Törnqvistindex approach advocated by Gordon, while Pakkouses the Fisher-ideal chain-weighting approachthat has subsequently been adopted by the BEA.15

During the post-1983 period, the Cummins-Violante series displays more rapid growth than the


11 A disaggregated approach is preferable to a simple extrapolation ofthe aggregate trend for two reasons: First, several changes in the BEA’sdefinitions and methodology have, for some components, eliminatedor at least mitigated the measurement problems suggested by Gordon’sstudy. In addition, the procedure of re-aggregating the quality-adjustedcomponents using a chain-weighting methodology allows the role ofchanging expenditure shares over time to be appropriately accountedfor.

12 The model posits that the adjusted price index is a function of a con-stant, a time trend, current and lagged values of the BEA time series,a cyclical indicator (lagged GDP growth), and an error term.

13 Giovanni Violante was kind enough to provide the data from Cumminsand Violante (2002).

14 The growth rates of both adjusted measures exceed the official BEAgrowth rate by an average of about 2.75 percent per year over thisperiod.

15 The measures also differ in that the Pakko aggregate includes netsales of scrap equipment (excluding autos), as measured by the BEA.

Pakko R E V I E W

1948 1954 1960 1966 1972 1978 1984 1990 1996–15

–10

–5

0

5

10

15

20

25

BEA Pakko Cummins-Violante

Growth Rates for Equipmentand Software Investment

Figure 5

Pakko series—due largely to assumptions regardingquality change in communications equipment. In1997, the BEA introduced a quality-adjusted priceindex for telephone switching equipment and carriedback these revisions to 1985 in the 1999 comprehen-sive revision of the national accounts.16 Becausethis component (the largest single component inthe communications equipment category) was thepredominant source of quality bias in Gordon’s study,Pakko considers that the updated BEA data accuratelymeasure quality change in that sector. On the otherhand, Cummins and Violante note that the qualityof other types of telecommunications equipmenthas been improving rapidly, so they opt to use theirextrapolated estimate of quality bias from the Gordondata set (amounting to a drift ratio of nearly 7 per-cent). The two studies also differ somewhat in theirtreatment of automobiles, instruments and photo-copy equipment, and office equipment other thancomputers.17 The effect of these differences inassumptions and methodology is that, for the 1984-2000 period, the Cummins-Violante series displaysan average annual growth rate that is 2.7 percenthigher than the official BEA data, while the growthrate of the Pakko series exceeds the BEA measureby only 1.1 percent per year.18

Incorporating Quality Change forNonresidential Structures

In addition to equipment and software, anotherimportant component of the capital stock is thestructures component—accounting for approxi-mately 35 percent of nominal nonresidentialfixed investment in the period 1948-2001. Gort,Greenwood, and Rupert (1999) examined the mea-surement of quality improvement in nonresidential

structures and estimated that the official NIPA dataunderstates real, quality-adjusted growth by approxi-mately 1 percent per year.

To account for this source of investment-specifictechnology growth, I construct an adjusted measureof nonresidential structures by adding 1 percentagepoint to each year’s growth rate in real nonresidentialstructures over the sample period of 1947-2001(deducting 1 percent growth annually from its priceindex). The resulting real investment series and priceindex are then aggregated by chain-weighting withthe adjusted measures of equipment and softwarespending to produce quality-adjusted decompositionsfor total private nonresidential fixed investment (NFI).

Table 1 shows the growth rates for these esti-mates of quality-adjusted NFI, along with the contri-bution of equipment and software and nonresidentialstructures to total growth.19 Two measures of quality-adjusted data are included: The first corresponds tothe equipment and software data from Pakko. Thesecond measure uses the Cummins-Violante dataseries. Both measures incorporate the qualityimprovement in structures suggested by Gort,Greenwood, and Rupert.

For the period 1950-2001, equipment and soft-ware spending accounted for more than 80 percentof the growth in total nonresidential investment.The relative contributions to growth have not beenconstant over time, however. During the first half


16 Moulten and Seskin (1999).

17 For more detail, see the appendix to Pakko (2002a).

18 These growth spreads are used to extrapolate each of the adjustedseries for the final growth observation from 2000 to 2001 (which wasnot included in either of the original series).

19 The contributions to growth in Table 1 are calculated using the BEA’scurrent methodology, as described in Moulten and Seskin (1999).


Growth Rates and Contributions to Growth of Nonresidential Fixed Investment

Nonresidential Source of fixed investment Equipment and software Nonresidential structuresequipmentand software data 1950-2001 1950-1975 1976-2001 1950-2001 1950-1975 1976-2001 1950-2001 1950-1975 1976-2001

BEA 5.17 (100) 4.59 (100) 5.75 (100) 6.26 (80.3) 5.30 (70.9) 7.23 (87.8) 2.96 (19.7) 3.48 (29.1) 2.44 (12.2)

Adjusted 1 6.85 (100) 6.65 (100) 7.05 (100) 8.35 (80.0) 8.02 (74.0) 8.68 (85.7) 3.95 (20.0) 4.48 (26.0) 3.42 (14.3)

Adjusted 2 7.28 (100) 6.61 (100) 7.95 (100) 8.97 (81.2) 7.95 (73.8) 9.98 (87.3) 3.95 (18.8) 4.48 (26.2) 3.42 (12.7)

NOTE: Numbers in parentheses refer to percent contributions to NFI growth.

Table 1

of the sample period, equipment and softwareinvestment accounted for less than 75 percent oftotal NFI growth, but accounted for 85 to 90 percentduring the second half of the sample.

In previous literature, estimates of investment-specific technology growth have treated the equip-ment and structures components separately. Theestimates in this article use chain-weighted aggre-gates of both components, allowing flexibility toaccount for the shifting growth-shares, suggestedby the pattern of growth contributions shown inTable 1.

Growth Accounting with Investment-Specific Technical Progress

The data for quality-adjusted investment andassociated price indices form the basis for estimatingthe contribution of investment-specific technologyto productivity growth. The first step is to calculatethe index of investment-specific technology, q, asthe price of consumption goods relative to (quality-adjusted) investment goods:

(8) ,

where P∼i is a quality-adjusted price index for invest-ment and Pc is a consumption price index. Followingthe practice common in previous literature, the

q P Pt c i= / ˜

consumption price index used for this calculationcovers nondurables and non-housing services.20

Durable goods are excluded from the consumptionmeasure so as to avoid issues of quality improvementin that component.

Figure 6 shows this measure of q for each of thethree measures of investment prices constructedin the previous section. The data are indexed to abase year of 1950 in order to show their cumulativegrowth. The two quality-adjusted measures trackeach other closely through 1983, exceeding thegrowth rate of the unadjusted NIPA relative price byan average of 1.9 percent. For the period 1984-2001,the two adjusted series exceed the NIPA-based seriesby 1.0 percent (estimate 1, Pakko) and 2.0 percent(estimate 2, Cummins and Violante) per year.

The estimates of q, along with associated datafor real investment, x, can be used to constructadjusted measures of the capital stock that accountfor embodied technological progress. The modelsuggests that real physical investment correspondsto nominal investment deflated by the consumptionprice index, PiI/Pc. Effective investment, qx, is there-fore given by PiI/P

∼i. In the NIPA data, with Pi=P∼i, this

is simply the real investment series, so the BEA’s datafor private nonresidential fixed assets is an appro-priate measure of the capital stock. For each of theadjusted investment series, qx is the quality-adjustedreal component from which a quality-adjustedmeasure of the capital stock can be derived.

The procedure used to construct quality-adjustedcapital stock measures is as follows: First, I use theaccumulation equation (4) and the NIPA series forinvestment and capital to back out a series of implieddepreciation factors, (1–δt).21 These factors are thenused to construct synthetic capital-stock series usinga perpetual-inventory method—that is, by recon-structing the capital stock using equation (4) withthe quality-adjusted investment data. Starting valuesfor capital stocks in the base year used for thesecalculations, 1950, are initialized using the accumu-


20 The non-housing services data are constructed by chain-weightingPCE services with the additive inverse of the housing services com-ponent. The resulting series is then chain-weighted with nondurablesconsumption.

21 The BEA constructs measures of net stocks for individual components,then uses chain-weighted aggregation to build aggregates. The use ofthese annual depreciation factors approximately adjusts for changesin the composition of the capital stock and total depreciation that arisefrom this procedure. For more information about the construction ofthe BEA’s fixed-assets series, see Katz and Herman (1997).

Pakko R E V I E W

1950 1960 1970 1980 1990 2000–0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

BEA Adjusted 1 Adjusted 2

Investment-Specific Technology (q)

Log Indices (Base=1950)

Figure 6

lation equation (4) to relate investment/capital ratiosto the BEA data.22 The growth rates for these adjustedcapital stock series, shown in Figure 7, exceed theofficial BEA measure by about 2.2 percent (Adjusted1) and 2.5 percent (Adjusted 2) per year on averageover the entire sample period.

Completion of the growth accounting exerciserequires data for output and labor. In order for thedata to correspond to a broad measure of laborproductivity, output is taken to be gross domesticbusiness product.23 Business sector hours—fromthe BLS labor productivity accounts—is used tomeasure labor input. These data series, along withthe series for capital growth, can be used to backout measures of labor-augmenting technologicalchange from the growth accounting equation (3).

Figure 8 shows measures of “neutral” technol-ogy that are derived from this procedure, where theseries are expressed in log levels relative to a 1950base in order to illustrate cumulative growth. Eachof the series displays a clear decelleration beginningin the early 1970s, corresponding to the widely cited“productivity slowdown” that has prevailed for muchof the subsequent period. For the two measures ofz derived from quality-adjusted data, the slowdownis particularly distinctive. After growth associatedwith investment-specific technology has beenaccounted for, the indexes of neutral technologicalprogress have been nearly flat since 1970.

Estimates of Neutral and Investment-Specific Technology

Table 2 provides a summary of the two sourcesof growth, reporting the contributions to total poten-tial growth provided by neutral and investment-specific technological change as in equation (7).For comparability with previous studies, the datacoverage for this decomposition begins with 1954.Over the entire period from 1954 to 2001, the quality-adjusted measures show that the role of investment-specific technological change has been considerable,accounting for 60 to 68 percent of total potentialgrowth. Even the unadjusted NIPA data show a contri-bution of investment-specific technology that is 25percent of the implied total.

The relative contributions of the two types oftechnology growth have not been constant over


22 In particular, the accumulation equation implies that qx/k=(1–δ )/(1+gk ). The properties of the adjusted investment series andgrowth rates of investment-specific technology can therefore be usedto relate the initial adjusted capital stock levels to the BEA data. Thiscalculation yields initial values for the adjusted capital series of aboutone-third the level of the official data. Results are not very sensitiveto small changes in the assumptions generating this relationship,however.

23 In keeping with the model’s implications for price measurement,real output is calculated by deflating nominal business sector GDPusing the consumption price deflator.


1950 1960 1970 1980 1990 20000

1

2

3

4

5

6

7

8

9


Capital Stock Growth Rates

Percent

Figure 7

1950 1960 1970 1980 1990 20000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Neutral Technology (z)

Log Index (Base=1950)

Figure 8

the entire sample period, however. In the first halfof the sample, 1954-1977, the quality-adjustedmeasures show that investment-specific technologycontributes only about 38 percent to total potential.For the NIPA numbers, the measured contributionof investment-specific technology during this periodis negligible. During the second half of the sampleperiod, investment-specific technology overwhelm-ingly predominates. The second of these two adjustedmeasures (derived from Cummins and Violante)shows a negative contribution for neutral technologygrowth, while the first measure (based on Pakko)measures the contribution of neutral technology atonly about 6 percent.

Implications for Potential ProductivityGrowth

The final line in Table 2 shows the actual growthrate of output per worker for the relevant sampleperiods. Over the entire period from 1954 to 2001,all three measures of technology change overpredictactual growth. As demonstrated in the breakdownbetween the first half and the second half of thesample, however, the overprediction is attributableto the more recent span of years. For the period1954-77, the two adjusted measures slightly under-predict actual productivity growth. For the period1978-2001, all three overpredict actual growth.

Of course, the measures of potential growthderived from the estimated technology series areapproximated measures of long-run relationships,

so it is not surprising that they do not preciselyreplicate actual growth over any given finite sample.During the period from the mid-1970s to the present,however, the magnitude and prevalence of thediscrepancy suggest more than measurement orapproximation error.

Recent research on the economic effects ofintroducing new technologies help to explain theapparent gap between measures of technologygrowth and productivity growth. The data suggesta rather dramatic change in the pattern of technol-ogy trends: the period of slow productivity growthin the 1970s and 1980s is associated with a changein the composition of technological progress fromneutral to investment-specific technology.

Many economists have suggested that changesin trend technology growth— particularly for capital-embodied technologies—are associated with longtransition periods during which productivity lags therate of technological advance. Indeed, both Cumminsand Violante (2002) and Pakko (2002b) focus on theadjustment of productivity growth to technologicalinnovations. Cummins and Violante calculate thatthe “technology gap”—the gap between the produc-tivity of the best technology and average produc-tivity—rose from 15 percent in 1975 to 40 percentin 2000. This finding is in the spirit of “technologydiffusion” models (e.g., Hornstein and Krusell, 1996;Jovanovic and MacDonald, 1994; Greenwood andYorukoglu, 1997; Andolfatto and MacDonald, 1998;Hornstein, 1999), which posit that learning aboutthe full potential of new technologies can generatelong implementation lags as resources are channeledinto the process of adapting new technologies intoexisting production structures.24 Pakko (2002b)shows that even in the absence of explicit diffusionlags, the adjustment of the capital stock to changesin technology growth trends give rise to long lagsbetween technology and productivity—particularlywhen technology growth is investment-specific.These findings can be interpreted as suggesting thata great deal of the potential productivity improve-ment has yet to be fully incorporated into measuredactual productivity growth.

CONCLUSIONS

A great deal of attention has recently been paidto the notion that rapid technological innovation


24 Another class of general growth models addressing the adaptation of“general purpose technologies” (e.g., Helpman, 1998) suggests similarlags.

Pakko R E V I E W

Sources of Technological Progress, 1954-2001

1954-2001 1954-1977 1978-2001

BEANeutral 1.65 2.46 0.83Investment-specific 0.55 0.10 1.01Total 2.21 2.56 1.85

Adjusted 1Neutral 0.69 1.29 0.09Investment-specific 1.20 0.91 1.50Total 1.89 2.20 1.59

Adjusted 2Neutral 0.57 1.33 –0.18Investment-specific 1.39 0.93 1.86Total 1.97 2.26 1.67

Actual productivity 1.76 2.52 1.00growth

Table 2

has been the driving force behind recent gains inU.S. productivity growth. However, the nature ofthese technology advances—being embodied inentirely new types of high-tech capital equipment—is not well explained by classical growth theory.This paper has reviewed a class of economic modelsfeaturing “investment specific” growth that explicitlydescribe a process in which new technologies arecapital-embodied.

Recent estimates of the magnitude of this typeof technology growth reported in this article suggestthat over 60 percent of potential productivity growthover the past half-century can be attributed toinvestment-specific technology. Since at least themid-1970s, the estimates suggest that the importanceof investment-specific technology has increasedsharply, accounting for practically all of the impliedpotential productivity gains.

However, measured productivity growth hasfallen short of these estimates of potential. Recentresearch on the process of adapting new technologiesto existing production frameworks gives reason foroptimism about this finding. To the extent that rapidgrowth of investment-specific technological innova-tion has yet to be fully exploited, as the data suggest,technology-related gains in productivity should beexpected to continue well into the future.

REFERENCES

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Cummins, Jason G. and Violante, Giovanni L. “Investment-Specific Technical Change in the United States (1947-2000): Measurement and Applications.” Review of EconomicDynamics, April 2002, 5(2), pp. 243-84.

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___________ and Krusell, Per. “Can Technology ImprovementsCause Productivity Slowdowns?” NBER MacroeconomicsAnnual 1996. Cambridge, MA: MIT Press, 1996, pp. 209-59.

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Katz, Arnold J. and Herman, Shelby W. “Improved Estimatesof Fixed Reproducible Tangible Wealth, 1929-95.” Surveyof Current Business, July 1997, 77(5), pp. 69-92.

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Landefeld, J. Steven and Grimm, Bruce T. “A Note on theImpact of Hedonics and Computers on Real GDP.” Surveyof Current Business, December 2000, 80(12), pp. 17-22.

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Pakko, Michael R. “The High-Tech Investment Boom andEconomic Growth in the 1990s: Accounting for Quality.”Federal Reserve Bank of St. Louis Review, March/April2002a, 84(2), pp. 3-18.

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Pakko R E V I E W


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98-017A: Victor E. Li and Chia-Ying Chang, “Money,Credit, and the Cyclical Behavior of HouseholdInvestment.”

98-018A: Derek Laing, Victor E. Li, and Ping Wang,“Inflation and Economic Activity in a MultipleMatching Model of Money.”

98-019A: Victor E. Li, “Household Credit and theMonetary Transmission Mechanism.”

98-020A: Alison Butler and Michael R. Pakko,“R&D Spending and Cyclical Fluctuations: Puttingthe ‘Technology’ in Technology Shocks.”

98-021A: Ali M. Kutan and Josef C. Brada, “ThePersistence of Moderate Inflation in the CzechRepublic and the Koruna Crisis of May 1997.”PUBLISHED: Prague Economic Papers, December1999.

98-022A: Selahattin Dibooglu and Ali M. Kutan,“Sources of Real and Nominal Exchange RateFluctuations in Transition Economies.” PUBLISHED:Journal of Comparative Economics, as “Sources ofReal Exchange Rate Fluctuations in TransitionEconomies: The Case of Poland and Hungary,”June 2001.

98-023B: Rowena A. Pecchenino and Patricia S.Pollard, “The Effects of Aging and Myopia on thePay-As-You-Go Social Security Systems of the G7.”

1997 WORKING PAPERS

97-001A: Donald S. Allen, “Filtering PermanentCycles with Complex Unit Roots.”

97-002C: Christopher Neely and Paul Weller,“Technical Analysis and Central Bank Intervention.”PUBLISHED: Journal of International Money andFinance, November 2000.

97-003A: David C. Wheelock and Paul W. Wilson,“New Evidence on Returns to Scale and ProductMix Among U.S. Commercial Banks.” PUBLISHED:Journal of Monetary Economics, June 2001.

97-004A: Michael J. Dueker and Daniel L. Thornton,

“Do Bank Loan Rates Exhibit a CountercyclicalMark-Up?”

97-005B: Joseph A. Ritter and Lowell J. Taylor,“Valuable Jobs and Uncertainty.”

97-006A: Joseph A. Ritter and Lowell J. Taylor,“Economic Models of Employee Motivation.”PUBLISHED: Federal Reserve Bank of St. LouisReview, September/October 1997, 79(5), pp. 3-21.

97-007B: Michael R. Pakko, “The Cyclical Relation-ship between Output and Prices: An Analysis inthe Frequency Domain.” PUBLISHED: Journal ofMoney, Credit, and Banking, August 2000.

97-008A: Dennis L. Hoffman and Robert H. Rasche,“STLS/US-VECM 6.1: A Vector Error-CorrectionForecasting Model of the U.S. Economy.”

97-009A: William R. Emmons and Willi Mueller,“Conflict of Interest Between Borrowers andLenders in Credit Co-operatives: The Case ofGerman Co-operative Banks.”

97-010D: Christopher Neely and Paul Weller,“Predictability in International Asset Returns: AReexamination.” PUBLISHED: Journal of Financialand Quantitative Analysis, December 2000.

97-011A: David C. Wheelock, “Monetary Policy inthe Great Depression and Beyond: The Sources ofthe Fed’s Inflation Bias.” PUBLISHED: Mark Wheeler,ed., The Economics of the Great Depression. TheUpjohn Institute, 1998.

97-012B: James Bullard and Steven Russell, “HowCostly is Sustained Low Inflation for the U.S.Economy?”

97-013A: Donald S. Allen and Thomas S. Chan,“The Efficiency of Residential Mortgage GuaranteeInsurance Markets.”

97-014A: Sean Collins and Richard G. Anderson,“Modeling U.S. Household’s Demand for LiquidWealth in an Era of Financial Change.” PUBLISHED:Journal of Money, Credit, and Banking, February1998.

97-015C: Christopher Neely and Paul Weller,“Technical Trading Rules in the European MonetarySystem.” PUBLISHED: Journal of InternationalMoney and Finance, June 1999.


R E V I E W

97-016A: Peter S. Yoo, “Population Growth andAsset Prices.”

97-017A: Donald S. Allen and Meenakshi Pasupathy,“A State Space Forecasting Model with Fiscal andMonetary Control.”

97-018B: Cletus C. Coughlin and Eran Segev,“Location Determinants of New Foreign-OwnedManufacturing Plants.” PUBLISHED: Journal ofRegional Science, May 2000.

97-019A: Richard G. Anderson and Robert H. Rasche,“Construction of an Estimated Domestic MonetaryBase Using New Estimates of Foreign Holdings ofU.S. Currency.”

97-020A: Milka S. Kirova and Robert E. Lipsey,“Does the United States Invest Too Little?”

97-021A: Patricia S. Pollard, “The Role of the Euroas an International Currency.” PUBLISHED:Columbia Journal of International Law, Spring 1998.

97-022B: Patricia S. Pollard and Rowena A.Pecchenino, “The Transition from a Pay-As-You-Goto a Fully-Funded Social Security System: Is Therea Role for Social Insurance?”

97-023A: Michael Pakko, “Trade, Investment, andInternational Borrowing in Two-Country BusinessCycle Models.”




R E V I E W


Federal Reserve Bank of St. LouisReview, Annual Index, 2002

Emin M. Dinlersoz and Rubén Hernández-Murillo,“Did ‘Right-to-Work’ Work for Idaho?”

Christopher J. Neely and Paul A. Weller,“Predicting Exchange Rate Volatility: GeneticProgramming Versus GARCH and RiskMetrics™.”

JULY/AUGUST

Getting the Markets In Synch withMonetary Policy

Alex Cukierman, “Are Contemporary CentralBanks Transparent About Economic Models andObjectives and What Difference Does It Make?”

Stephen G. Cecchetti and Stefan Krause,“Central Bank Structure, Policy Efficiency, andMacroeconomic Performance: ExploringEmpirical Relationships.”

William Poole, Robert H. Rasche, and Daniel L.Thornton, “Market Anticipations of MonetaryPolicy Actions.”

Georgios Chortareas, David Stasavage, andGabriel Sterne, “Does It Pay To Be Transparent?International Evidence from Central BankForecasts.”

Manfred J.M. Neumann and Jürgen von Hagen,“Does Inflation Targeting Matter?”

Charles Freedman, Václav Klaus, and J. AlfredBroaddus Jr., “Transparency in the Practice ofMonetary Policy.”

Commentaries by:Carl E. WalshK. Alec ChrystalMark W. WatsonAdam S. PosenFrederic S. Mishkin

JANUARY/FEBRUARY

Cletus C. Coughlin, “The Controversy Over FreeTrade: The Gap Between Economists and theGeneral Public.”

Leora Friedberg and Michael T. Owyang, “NotYour Father’s Pension Plan: The Rise of 401Kand Other Defined Contribution Plans.”

Frank A. Schmid, “Voting Rights, Private Benefits,and Takeovers.”

R. Alton Gilbert, Andrew P. Meyer, and Mark D.Vaughan, “Could a CAMELS Downgrade ModelImprove Off-Site Surveillance?”

MARCH/APRIL

Michael R. Pakko, “The High-Tech InvestmentBoom and Economic Growth in the 1990s:Accounting for Quality.”

Hui Guo, “Why Are Stock Market ReturnsCorrelated with Future Economic Activities?”

James B. Bullard and Eric Schaling, “Why theFed Should Ignore the Stock Market.”

Michael J. Dueker, “The Monetary PolicyInnovation Paradox in VARs: A ‘Discrete’Explanation.”

MAY/JUNE

David C. Wheelock, “Conducting Monetary PolicyWithout Government Debt: The Fed’s EarlyYears.”

William T. Gavin and Kevin L. Kliesen,“Unemployment Insurance Claims andEconomic Activity.”


R E V I E W

SEPTEMBER/OCTOBER

William Poole, “Financial Stability.”

Cletus C. Coughlin, Jeffrey P. Cohen, and Sarosh R.Khan, “Aviation Security and Terrorism: A Reviewof the Economic Issues.”

Howard J. Wall, “Has Japan Been Left Out in theCold by Regional Integration?”

Robert H. Rasche and Daniel L. Thornton, “TheFOMC’s Balance-of-Risks Statement and MarketExpectations of Policy Actions.”

Christopher J. Neely and Lucio Sarno, “How WellDo Monetary Fundamentals Forecast ExchangeRates?”

Hui Guo, “Stock Market Returns, Volatility, andFuture Output.”

NOVEMBER/DECEMBER

William Poole and Robert H. Rasche, “Flation.”

Abbigail J. Chiodo and Michael T. Owyang, “A CaseStudy of a Currency Crisis: The Russian Defaultof 1998.”

William R. Emmons and Frank A. Schmid, “AssetMispricing, Arbitrage, and Volatility.”

Michael J. Dueker, “Regime-Dependent RecessionForecasts and the 2001 Recession.”

Michael R. Pakko, “Investment-SpecificTechnology Growth: Concepts and RecentEstimates.”

All nonproprietary and nonconfidential data and programs for the articles written by FederalReserve Bank of St. Louis staff and published in Review are available to our readers on our Web site:<research.stlouisfed.org/publications/review>. Also, you may request data and programs on eitherdisk or hard copy: Research Department, Federal Reserve Bank of St. Louis, P.O. Box 442, St. Louis, MO63166-0442. Please include the author, title, issue date, and page numbers with your request.

research.stlouisfed.org

Federal Reserve Bank of St. LouisP.O. Box 442St. Louis, MO 63166-0442

These data and programs are also available through Inter-university Consortium for Political andSocial Research (ICPSR). Member institutions may request data through the CDNet Order facility.Nonmembers may write to ICPSR, Institute for Social Research, P.O. Box 1248, Ann Arbor, MI48106-1248; call 734-998-9900; or e-mail <[email protected]>.

General data can be obtained through FRED (Federal Reserve Economic Data), a database providing U.S.economic and financial data and regional data for the Eighth Federal Reserve District. You may accessFRED through our Web site: <research.stlouisfed.org/fred>.

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