The Federal Reserve Bank of San Francisco’s Economic Review is published quarterly by the Bank’s Research and Public Information Department under the supervision of Michael W. Reran, Senior Vice President. The publication is edited by William Burke, with the assistance of Karen Rusk (editorial) and William Rosenthal (graphics). Opinions expressed in the Economic Review do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco, or of the Board of Governors of the Federal Reserve System.

For free copies of this and other Federal Reserve publications, write or phone the Public Infor­mation Section, Federal Reserve Bank of San Francisco, P.O. Box 7702, San Francisco, Califor­nia 94120. Phone (415) 544-2184.

2

Windfall Proffits? Interest !at©s and Index Numbers

I. Introduction and Summary 4

II. Crude Oil Price Controls and the Windfall Profit Tax: 6Deterrents to Production?

Yvonne Levy

. . . As a measure to reduce dependence on foreign-oil imports and improve the allocation of resources, decontrol is a step in the right direction — but the windfall profit tax is a step in the wrong direction.

III. Interest Rate Forecasts and Market Efficiency 29

Adrian W. Thro op

. . . By making use of the information contained in market professionals’ forecasts, an investor in Treasury bills could have improved his return.

IV. Indexes, Inflation, and Public Policy 44

Herbert Runyon

. ..T h e personal consumption “ deflator” has provided a more equitable choice recently than the consumer-price index for determining cost-of- living adjustments.

Editorial committee for this issue:John Scadding, Brian Motley, and Robert Jacobson

3

Production, investing, and household-buy­ing decisions have all come to be influenced bythe past decade's upsurge in inflation andincreased government participation in marketdecisions. This issue of the Economic Reviewpresents three studies of this type of influence.One article examines the production lossesattributable to price controls - and morerecently, windfall-profit taxes - on domesticpetroleum producers. Another article evalu­ates the ability of financial markets, in an infla­tionary era, to incorporate the best possibleinterest-rate forecast into current marketprices. And a third article analyzes the com­position of the price indexes which play such aprominent role, through indexation formulas,in determining household incomes andgovernment expenditures.

Yvonne Levy describes the nation's recentexperience with price controls ondomestically-produced crude oil, which heldthe average price of domestic crude below theworld market level for almost a decade. Con­trols thus tended to reduce production sub­stantially below the level that would otherwisehave prevailed, aggravating a decade-longdowntrend in U.S. production. "As a reSUlt,controls worked against the nation's goal ofenergy independence and led to an inefficientallocation of resources."

Crude-oil price controls were finally elimi­nated this past January, but producers still arenot realizing the world price - although theyare receiving considerably more than theywould have done with continued controls. Thereason is the windfall-profit tax, which hasbeen returning to the Federal Governmentmuch of the added revenue that otherwisewould have accrued to producers through

4

decontrol. "With the tax, future domestic pro­duction will be lower than it otherwise wouldhave been, although higher than with con­tinued controls. Similarly, imports will behigher than otherwise, and the misallocationof resources will continue."

Levy provides a number of scenarios of pro­duction possibilities with different long-runsupply elasticities and with different rates ofincrease of (uncontrolled) crude prices. All ofthe scenarios show that the decline in domesticproduction expected over the 1979-85 periodwith continued controls might have beenreversed without controls. But the windfall­profit tax would substantially reduce the posi­tive impact.

Levy thus concludes, "As a measure toreduce dependence on foreign-oil imports andimprove the allocation of resources, decontrolis a step in the right direction. But by the sametoken, the windfall-profit tax is a step in thewrong direction." She adds that policymakersshould consider altering the tax to make it atrue tax on profits, rather than an excise tax ona portion of the selling price. In that way, itwould not affect production decisions at themargin.

Adrian Throop points out that volatile finan­cial markets in this inflationary period haveput a premium on accurate forecasts of interestrates. Thus, he examines the degree to whichthe Treasury-bill market efficiently utilizes allavailable information so as to incorporate thebest possible interest-rate forecast into currentmarket prices. This involves an evaluation ofthe applicability of the "efficient market"hypothesis to the Treasury-bill market. Hencehe examines two types of independentforecasts - an autoregressive forecasting

equation based on the past history of the billrate, and the forecast of a selected panel ofmarket professionals.

If the market is not efficient, Throop argues,a group of investors could improve theirreturns by altering the maturity of their invest­ments in light of superior interest-rateforecasts. "Whether or not investors can profitby 'speculating' on interest rates, throughholding other than their normally preferredmaturities, depends critically on whetheravailable interest-rate forecasts are moreaccurate than the market's." If the market isefficient, the information in these forecastswould already be incorporated into the pricesof securities, and therefore nothing would begained. In that case, investors would be betteroff by simply "hedging" their positions withmaturities equal to their planned investmentperiods, thereby avoiding possible risk.

Throop shows that professional analysts'predictions of the Treasury- bill rate two quar­ters ahead are significantly more accurate thanmarket predictions. "This indicates that themarket does not efficiently utilize all availableinformation in making bill-rate forecasts." Theanalysts' ability to "beat the market" stemsfrom a better utilization of information aboutpast movements in the bill rate, and also froma more efficient utilization of other sorts ofinformation.

Herbert Runyon then turns to the questionof measuring inflation, which, as he emphas­izes, means an increase in the general pricelevel rather than separate price increases forindividual commodities. Since numerousgoods and services are bought and sold daily inthe markets, an index number represents theonly feasible way of describing the generalmovement of prices through time. But thisindex number should give an accurate repre­sentation of changes in living costs - at leastindirectly through market observations as con­sumers reveal their individual preferences by

5

purchasing certain goods and services. For thehousehold sector, the standard measuresinclude the Consumer Price Index (Cpn andthe Personal Consumption Expenditure(PCE) deflator.

The CPI - a "Laspeyres" index - usesquantities purchased in a certain base year as areference point from which to measurechanges in prices of a basic (presumablyunchanging) consumer-expenditure pattern.The PCE - a "Paasche" index - uses thecurrent-period expenditure pattern as areference base for comparison with expen­ditures in earlier periods. For structuralreasons, the cpr tends to overstate - and thePCE to understate - what consumers actuallypay for at the checkout counter. As a practicalmatter, the CPI and PCE were quite close intheir measurement of living costs from 1960through 1977. But in the 1978-80 period, theCPI rose at a much faster rate - not so muchbecause of the indexes' different statisticalcomposition as because of their different treat­ment of sharply rising homeownership costs.Among other things, the CPI's weightiDg ofhomeownership overstates its importance inthe cost of living.

Runyon argues that the use of the CPl inrecent years has overcompensated manyincome recipients - especially beneficiaries ofFederal programs, since the Federal govern­ment depends on the CPI as a standard indexin its efforts to offset the impact of inflation onsuch individuals. This overcompensation hasseveral public-policy consequences. "Over­compensation leads to unwarranted increasesin Federal expenditures and in the Treasurydeficit. Beyond that, it introduces inequitiesrelative to those individuals not receivingindexed benefits, and thus amounts to anunintended redistribution of income." Thus,he concludes, recent experience suggests thatthe PCE index is the more equitable choice fordetermining cost-of-living adjustments.

Yvonne levy'"

Throughout the decade of the 1970's, acomplex system of Federal controls governedprices for domestically-produced crude oil.Those controls held the average price ofdomestically-produced crude below the worldmarket level. Consequently, controls tendedto reduce production substantially below thelevel that would otherwise have prevailed,aggravating a decade-long downtrend in U.S.crude-oil production.

For any given level of refiners' crude-oildemand, the reduction in domestic productionraised imported-oil requirements by anequivalent amount. Moreover, that addedimport volume involved greater resource coststhan would have been required throughdomestic production. As a result, controlsworked against the nation's goal of energyindependence and led to an inefficient alloca­tion of resources.

Despite President Reagan's January 1981lifting of controls on domestically-producedcrude, producers still are not realizing theworld price. The Windfall Profit Tax - whichwent into effect March 1, 1980 and couldextend through 1990 - has been returning tothe Federal government much of the addedrevenue that otherwise would have accrued toproducers through decontrol. With the tax,producers are realizing less than the worldmarket price,. although of course more thanthey would have realized with continued con-

'Senior economist, Federal Reserve Bank of San Fran­cisco. Research assistance provided by Alane Sullivan andLloyd Dixon.

6

trois. Thus, with the tax, future domestic pro­duction will be lower than it otherwise wouldhave been with decontrol and no tax, althoughhigher than with continued controls. Similarly,imports will be higher than otherwise, and themisallocation of resources will continue.

Section I presents a simple model of the sup­ply of domestically-produced crude oil whichshows how supply responds positively to pricesreceived by producers. It also shows that, inthe absence of controls, domestic crude oilwould sell at approximately the world pricebecause domestic producers operate in a worldmarket. Section II describes the major featuresof the crude-oil price-control program con­tained in the recently terminated EnergyPolicy and Conservation Act of 1975. That sec­tion shows how that program held the averagedomestic price below the world market price,and thus kept domestic output below theamount that would have been produced in afree market. Section III outlines the major pro­visions of the Crude Oil Windfall Profit Tax of1980. It shows how the tax leaves the pricerealized by producers still below the world

, reducing the positive impact ofdecontrol on domestic crude-oil production.Finally, Section IV presents a range of esti­mates of the domestic production losses thatresulted from the Energy Policy and Conserva­tion Act, and that potentially could result overthe next decade from the Windfall Profit Tax.The recently-passed Reagan tax program con­tains minor changes in the windfall-profit tax,but these do not materially affect the conclu­sions of this paper.

I. Domestic Oil Production in a Free Market

Domestic crude-oil production can beincreased over the long-run in a number ofways, all of which are encouraged by a rise inselling price. "Long-run" means a planningperiod long enough to permit producers toinvest in new productive capacity to achievehigher production rates. In crude-oil produc­tion, new productive capacity can be installedeither at existing (i.e., already-producing) pro­perties or at entirely new sites. I

At existing properties, producers canincrease the rate of extraction by drilling moredevelopment wells. Alternatively, they caninvest in enhanced oil-recovery technologies.This involves drilling service wells throughwhich steam, chemicals, or gases may beinjected to increase well pressure, thus raisingthe recoverable proportion of the total reser­voir.

In addition, producers can expand produc­tion through the discovery of new properties- reservoirs in unproven regions as well asnear already-producing areas. But first, pro­ducers must do some exploratory drilling andidentify resources that are recoverable undercurrent economic and technological condi­tions.

The addition of development wells at knownreservoirs permits a higher rate of extractionfrom a given deposit, but it does not increasethe ultimate, or total cumulative, productionpotential. 2. This potential can be expandedonly through an increase in proved reserves,resulting from investments in enhanced oil­recovery technologies and the discovery ofnew economic resources. "Proved reserves"refer to the portion of the resource base thathas been identified and explored, and fromwhich crude oil can be recovered profitably atcurrent prices and with current technology. J

While the occurrence of oil is finite, beinggoverned by geology, a host of other factors ­economic, technological, environmental andpolitical - determine the rate at which oilresources are discovered, developed and trans­ferred to the category of reserves.

7

Long-run Supply ModelBuilding upon this foundation, we can

further clarify the concept of the long-run sup­ply schedule for representative individual pro­perties, the domestic crude-oil producingindustry and the combined domestic andimport sectors through Charts lA-C. Thecharts show, at a particular period in time, thequantity of crude oil that will be available tothe U.5. market from those various sources atvarious selling prices. We assume, throughoutthe analysis, that the crude-oil producingindustry is workably competitive, in line withthe bulk of the evidence presented in therecent academic literature. With regard tostructure, the producing and refining sectorsof the industry are clearly different. Thousandsof U.5. firms are engaged in the explorationand extraction of crude oil, with no firm domi­nant - in contrast to the oligopolistic refiningsector of the industry.4

Chart 1A shows the long-run supplyschedule for the representative individual pro­perty. This is the long-run marginal cost curve(MC) - the addition to total cost resultingfrom the last unit of output. The marginal costof producing additional barrels from a givenreservoir increases because firms must investin higher-cost recovery techniques, such asenhanced oil-recovery methods and deeperdevelopment wells, as more oil is extractedfrom a finite reservoir. In a competitivemarket, each firm maximizes profit by expand­ing output to the point where marginal costequals price. The schedule is upward sloping;as the price rises, it pays firms to thehigher-cost barrels that would have beenuneconomic to produce at a lower price.

Chart lB shows the long-run supplyschedule, 5 0 5 0 , for the entire domestic crude­oil producing industry. That schedule isderived by summing the amounts produced byall properties at each price - that is, summinghorizontally the marginal cost-output curvesfor all producing properties. Again, theschedule is upward sloping because, with

Chart 1

United States Crude Oil Supply Without Controls

A. INDIVIDUAL DOMESTIC PROPERTY

Price(dollars

per barrel)

Pw 1---------1

MC

qw

Quantity (barrels/unit of time)

Price(dollars

per barrel)

B. TOTAL DOMESTIC INDUSTRY

Pw t-------------.y

Qw Quantity(barrels/unit of time)

C. TOTAL U. S. CRUDE OIL MARKET

Price(dollars

per barrel)

Pwt------------,-~~------___S

Domestic + Imports

Qw QD QuantityDomestic Domestic (barrels/unit of time)

production consumption

8

increased production, firms must locate,develop and extract oil from less accessible andpoorer quality resources. 5

Chart lC shows the long-run supplyschedule for domestic and foreign oil availableto U.S. refiners at various selling prices. Atprices below the world price, Pw, the supplycomes entirely from domestic sources. Theprice cannot exceed Pw , since imports areavailable essentially without limit at that price.Hence, the total supply schedule is repre­sented by the kinked curve, So as. ScheduleDD meanwhile represents U.S. refiners'crude-oil demand schedule. In the absence ofprice controls, domestic crude oil would sell atapproximately the world price, P w - thelanded price for imported oil. 6 This is becauseU.S. producers operate in a world market. Atthe world price, domestic production cannotmeet the total quantity demanded by U.S.refiners. The price of imported crude thusrepresents the marginal cost of an additionalbarrel - and thus determines the marginaldomestic producer price. At the world price ­the domestic price that would prevail withoutcontrols - domestic producers would be will­ing to supply Qw barrels. Domestic demand

would be QD, and imports in the amount ofQD-Qwwould be required.

Efficiency in the allocation of resourcesrequires that the total cost of satisfying anygiven quantity demanded be as low as possi­ble. 1 When the alternative to domestic oil isimported oil purchased at the world price, effi­ciency requires that production from alldomestic properties be expanded to the pointwhere the marginal cost of the last unit of out­put is equal to the price of imported oil.Beyond that point, resources could be saved byreducing domestic output and replacing thatoutput with imported oil. But below that point,where the cost of the last barrel produced isless than the price of imported oil, resourcescould be saved by reducing imports andexpanding domestic production.

Since the supply schedule So So reflects themarginal cost of producing domestic oil, theuncontrolled market solution for domestic andforeign supply, So as, represents an efficientallocation of resources. In this allocation, themarginal cost of production for all domesticproducers is equal to the world price. There isno opportunity to reduce total cost by shiftingsupply between domestic and foreign sources.

II. Domestic Oil Production Under Price Controls

The Federal price-control programs of the1970's held the average selling price ofdomestically-produced crude below the worldmarket price, and thereby disturbed the effi­cient free-market solution. But governmentattempts to influence domestic prices firstdeveloped in the 1930's - although their pur­pose was to hold the producer price above(rather than below) the competitive level.During the 1930's, oil producing statesinstituted "conservation" programs - osten­sibly to prevent "wasteful" production prac­tices, but in reality, to keep prices high bylimiting production. 8 Those programs wereeffective until the mid-1950's, when increas-

9

ing quantities of foreign oil became available atprices well below the average domestic pro­ducer price. After trying (unsuccessfully) torestrict imports voluntarily, the Federalgovernment in 1959 introduced a program ofmandatory import quotas, using nationalsecurity as justification.

The early 1970's witnessed a fundamentalchange in the nation's demand for imports.Despite the quota system, domestic crude-oilproduction peaked by 1970, as import com­petition prevented the domestic price from ris­ing as fast as production costs. By 1973, U.S.petroleum consumption had outgrowndomestic production, and imported oil had

become the required source of marginal sup­plies. The marginal cost of imported oil, i.e.,the world price, thus became the determinantof the domestic producer price.

Energy Policy and Conservation ActThe Federal government first placed direct

controls on prices of domestic crude oil onAugust 15, 1971, when President Nixon frozewages and prices throughout the economy. 9 Amulti-tier pricing system evolved in Phase IVof the controls program, announced in August1973, and in the Emergency PetroleumAllocation Act, passed in November 1973 dur­ing the Arab oil embargo. These then led tothe Energy Policy and Conservation Act of1975 (EPCA). That legislation controlleddomestic producer selling prices on a property­by-property basis, with production above andbelow the "base production control level"(BPCL) -the 1975 average monthly produc­tion - subject to different price ceilings.Under the act, "lower-tier" oil referred to out­put at or below the BPCL, while "upper-tier"oil referred to output in excess of this baselevel or output from new properties broughtinto production after 1975.

The law stipulated a lower price ceiling forlower-tier oil than for upper-tier oil, and stipu­lated that ceilings would be set to achieve atarget average price for domestic oil. That pricecould rise to reflect inflation, but by no morethan 10 percent annually. 10 Initially, the lawclassified oil from "stripper" properties ­those producing ten barrels or less daily - asupper-tier oil. But in September 1976, theenergy agency decontrolled stripper oil and thuspermitted it to receive the world market price.

Policymakers designed the control programto hold the average price of domestically-pro­duced crude below the world market level, andthereby protect consumers from the fullimpact of sharply rising world prices - ineffect, transfering to consumers much of theadded income that would otherwise haveaccrued to producers. II At the same time,policymakers sought, through the multi-tiersystem, to accomplish that objective with theleast possible reduction in production incen-

10

tives.Since decisions to expand production aredetermined on the margin, they permitted pro­duction in excess of the base level to receive ahigher ceiling price. In that way, they hoped toprovide sufficient incentive to stimulate pro­duction. But they also wanted to preventowners of wells brought into production beforethe OPEC price run-up from receiving profitsfar higher than originally anticipated and soheld the lower-tier price not only below theworld level but the upper-tier price. In thisview, the removal of such unanticipated prof­its - "windfalls" - would have little impacton production decisions. (A similar philosophyapparently underlies the new Windfall ProfitTax.) Nevertheless, because controls generallyheld the price at the margin below the worldprice, production was less than it otherwisewould have been.

Charts 2A-C illustrate the price and produc­tion effects of the Energy Policy and Conserva­tion Act for representative properties and theentire domestic industry. 12 Properties in exis­tence before 1975 faced the marginal revenue(MR) schedule shown in Chart 2A. Output ofq, or less qualified as stripper oil and receivedthe uncontrolled (world) price, Pw. Outputfrom qs to the base production control level(BPCL) received the lower-tier price, PL' Out­put in excess of BPCL received the upper-tierprice, P u. "New" properties - those broughtinto production after 1975 - faced themarginal revenue schedule shown in Chart 2B,and except for small stripper properties,received the upper-tier price.

Existing stripper properties were unaffectedby this system of price controls. Those proper­ties received the world price, with or withoutcontrols, and hence production remained thesame at q,. But for other existing properties,production was lower than it would have beenwithout controls, since production was notpermitted to receive the world price. The mag­nitude of the impact depended on whethercosts had risen since the 1975 base year.

Producers not incurring higher costs wouldhave found it profitable to expand productionpast the base-period level, to earn the upper­tier price on the new production. Production

Prir.e(dollars

per barrel)

B. INDIVIDUAL DOMESTIC PROPERTY(New)

Chart 2

United States Crude Oil SupplyUnder Energy Policy and Conservation Act

A. INDIVIDUAL DOMESTIC PROPERTY(High-cost Existing)

Price(dollarsper barrel)

Pw

Pu MR

Pw

Pu

qs ql quqwBPCL

Quantity (barrels/unit of time) Quantity (barrels/unit of time)

Price(dollars

per barrel)

C. TOTAL U. S. CRUDE OIL MARKET

Pw ......------------r--ilIItf---------SDomestic + Imports

Pc 1-"""""""""""''''''''''''''''''''''''''''''''''''''''

So

0c QW QD QuantityDomestic Domestic (barrels/unit of time)production consumption

11

still would have been lower than it would havebeen without controls, however, since theupper-tier price was below P w. Producersincurring higher costs than faced in 1975would have fared less well. (See the marginalcost schedule, MC I> in Chart 2A.) For thoseproperties, producers would not have found itprofitable to expand production beyond thebase-period level. The new production up tothe base level would earn only P b the lower­tier price, which would be below the cost ofproducing it. The profit earned on upper-tieroil (shaded area) would not cover the lossincurred on lower-tier oil (striped area) andthe firm would choose to remain producing atql' Producers incurring an extremely sharp risein costs since 1975 would have an incentive toactually lower production to 10 barrels or lessper day, to qualify for the uncontrolled priceafforded strtpper properties.

Producers with new properties would havefound less incentive to produce than in a situa­tion with no controls (Chart 2B). When theworld price was Pw, output from new proper­ties was only qu because they were permittedto receive only the price P u. Had price controlsnot existed, output on new properties wouldhave been qw, because that output would havereceived the world price.

At the industry level, this system of con­trols, like its predecessors, tended to hold theaverage domestic producer price below theworld market level, thereby reducing produc­tion below the level that would otherwise haveprevailed (Chart 2C). When the world price isPw, controls hold the average domestic pro­ducer price at Pc. As a result, the domesticindustry produces only Qc instead of the quan­tity Qw produced in the absence of controls.For a given level of refinery crude oil demand,the consequent reduction in domestic outputis offset entirely by imports. Thus the controlprogram tended to increase the nation's de­pendence on foreign oil.

The control program also led to inefficiencyin the allocation of resources. Between outputlevels Qc and Qw, each additional barrel ofcrude could be produced domestically at a costbelow the world price, and thus at a smaller

12

expenditure of resources than for imported oil.Area dca, which equals the difference betweenthe world price and the domestic supplyschedule SoSo at each increment between Qcand Qw, represents resources wasted onexpenditures for imported oil because of con­trois. 1]

Intertemporal Production DecisionsThus far, we have considered the effects of

controls only in a static framework. We haveassumed that firms consider only the currentprice, without regard to price expectations,when making production decisions. Also, wehave ignored the potential effects of the cur­rent level of output on future production. Wehave assumed that firms could obtain optimalproduction and profit paths over time by pro­ducing at the point where marginal cost equalsprice in each planning period.

But there is an important difference betweenthe marginal-cost (supply) schedule of a typi­cal manufacturing firm and the schedule of afirm extracting an exhaustible resource such aspetroleum. As petroleum is removed from areservoir, the pressure of the reservoir de­clines, and so too does the total amount ofpetroleum available - a tendency known asthe "natural decline function." Because of theexhaustible nature of the resource, a barrel ofoil produced today will not be available insome future period. Petroleum producers thusface an additional cost of production not incur­red by manufacturers. That additional cost ­the user cost - is the opportunity cost or profitforegone of being unable to sell that unit ofoutput in the future.

In order for a producer to decide to producea barrel of oil in the present, the price of eachadditional barrel of oil produced today must besufficient to cover this opportunity cost as wellas normal production costs. Moreover, in viewof the effect of current output on future output- the "natural decline" problem - the firmcannot simply select the output level in eachperiod where marginal revenue equalsmarginal cost (defined to include user cost).Instead, the firm must maximize a stream ofprofits over time, which involves a discounting

procedure. This present-value analysis, ineffect, states that for production of an addi­tional barrel to take place, the present profitinvested at the market rate of interest must atleast be equal to the profit from selling thatbarrel any time in the future. 14

By affecting price expectations, the controlsprogram may have exerted still anotherrestraining effect on current production. If theexpected path of future prices rises, the usercost increases and producers can expand prof­its by deferring current production to thefuture. In this case, the present value of thefuture profit would exceed today's profit. Con-

troIs on current prices may have created justsuch an expectation of higher prices in thefuture when controls eventually might belifted. The expected price path in moving fromcontrol to decontrol would have been steeperthan had prices never been subject to controls.As a result, controls may have raised the usercost of controlled oil, thereby causing pro­ducers to restrict current production evenmore than they would have done because ofreceiving less than the world-market price.This was especially true of the 1978-80 period,when market participants widely expectedeventual decontrol. 15

III. Domestic Oil Production Under "Decontrol"

On June 1, 1979, the Energy Departmentbegan to implement a program, mandated byPresident Carter, for decontrolling domesticcrude-oil prices by October 1, 1981. Underthat program, production that previouslywould have been subject to the lower-tier pricewas permitted to move gradually to the upper­tier category. Then, beginning on January 1,1980, production previously classified asupper-tier oil, plus the lower-tier oil movinginto the upper-tier category, was permitted tomove to a free-market classification at a rate of4.6 percent per month.

The Energy Department decontrolled oildiscovered after January 1, 1979 on June 1 ofthat year, and it lifted controls on "heavy"crude on August 17,1979. Finally, the ReaganAdministration - in its first major economic­policy move - lifted all remaining price con­trols on domestically produced crude on Janu­ary 28, 1981.

In moving to decontrol domestic prices,both the Carter and Reagan Administrationshoped to encourage domestic production andto slow down the growth of U.S. petroleumconsumption. To the extent that the higherrefiner costs for domestic crude were reflectedin higher refined-product prices, consumptionshould be curtailed. 16

13

Windfall Profit TaxAt the same time, Congress was unwilling to

permit producers to realize all the addedrevenue that would accrue through decontrol,especially since that step could boost producerrevenues by about $1 trillion over the 1980-90period, according to estimates of the JointCommittee on Taxation. 17 As a result, Con­gress enacted the Crude Oil Windfall ProfitTax of 1980 to divert to the U.S. Treasurysome of the incremental revenues that wouldotherwise be received by producers throughdecontrol. The tax became effective March 1,1980.

The tax is perhaps the largest ever imposedon a single industry. Over the 1980-90 period,the tax could yield about $236 billion, in addi­tion to a $332-billion increase in corporateincome taxes resulting from decontrol. Thus,the U.S. Treasury could receive $568 billion ofthe projected $l-trillion additional industryrevenue received from decontrol. 18

Although called a tax on profit, the tax reallyis a Federal excise tax on a portion of the sell­ing price received from crude oil. The tax paidper barrel is determined by applying varioustax rates to the "windfall profit" - thedifference between the decontrolled producerprice and the price that would have prevailed

under continued controls (less state severancetax) .

The tax rates and base prices applicable tovarious properties vary according to type ofproduction and size of producer (Table 1). TheInternal Revenue Service established thesenew oil categories for tax purposes. Producersare classified either as "majors" or as "inde­pendents" (producers with gross annual salesof $5 million or less and with refiningcapacities of no more than 50,000 barrels aday). Identical tax rates are applied, except forthe first 1,000 barrels a day of Tier 1 and Tier 2production by independents. To providegreater incentive for certain investments, the

lowest tax rates apply to newly discovered andincremental tertiary oil, the latter being oilobtai ned throug h a qual ified tertiary(enhanced) recovery method, i.e., productionin excess of the projected decline rate for theproperty without the tertiary technique. 19

In computing the tax, producers firstsubtract a base price, adjusted for inflation,from the decontrolied producer price (Appen­dix A). Next they subtract a state severancetax - estimated to average about 5.4 percentof the selling price - to determine the"windfall profit." Then they apply theappropriate tax rate to determine the amountof tax to be paid.

Table 1Provisions of the Windfall Profit Tax 1

Tax RateIntegrated IndependentProducer Producer3

(percent)

Tier 1Controlled oil discovered before 19792 70 50

Tier 2Stripper well oil 60 30

National Petroleum reserve oil 60 30

Tier 3Newly discovered oil 30 30

Heavy oil 30 30

Incremental tertiary oil 4 30 30

ExemptS

Base Price($ per bbl.)

1281

15.20

15.20

16.55

16.55

16.55

Annual Adjustmentto Base Price

(percent)

Inflation6

Inflation

Inflation

Inflation + 2%

Inflation + 2%

Inflation + 2%

1 The Windfall Profit Tax of 1980 was enacted into law on April 2, 1980. But the tax was retroactive, i.e., applicable tocrude-oil production removed from properties after February 29, 1980.

2 For purposes of the windfall-profit tax, the pricing categories conform to those in effect in May 1979, before the processof gradual decontrol began.

3 The special reduced-tax rates afforded independent producers for Tiers I and 2 are applicable only to their first 1,000 bar­rels per day of production. Production in excess of 1,000 bid is taxed at the regular windfall-profit tax rates, i.e., the ratesapplicable to integrated producers.

4 Incremental tertrary oil is the amount of production from a property on which a producer uses a qualified tertiary(enhanced) recovery method in excess of the projected decline rate if a tertiary technique had not been used on that pro­perty.

5 Categories of crude exempt from the tax include: qualified governmental-interest oil, qualified charitable-interest oil, cer­tain Indian oil, Alaskan oil (other than from the Sadlerochit reservoir) north of the Alaska-Aleutian mountain range andover 75 miles from the pipeline, and tertiary oil from properties owned by independent producers.

6 Inflation is measured by the change in the Gross National Product deflator.

14

QC

Qr QW

Quantity of Crude Oil

Chart 3

Effects ofWindfall Profit Tax on

Domestic Crude Oil Production

Pwt--------~"Pr

Priceper barrel

Chart 3 shows how the windfall-profit taxwill affect U.S. crude oil production as long asthe uncontrolled domestic price remains abovethe adjusted base price. 20 So So represents thelong-run supply schedule for the domesticcrude-oil producing industry. Decontrol withno tax would provide producers with the max­imum incentive to increase production, andwould lead to an efficient ailocation ofresources in meeting with the nation'spetroleum requirements. In this situation, pro­ducers would realize the uncontrolled price,P w , and produce at output Qw. With con­tinued controls, producers would realize price,Pc, and supply Qc. The tax lowers the pricerealized by producers below the free-marketprice to an intermediate price, PT' Since theuncontrolled domestic selling price is deter­mined by the world price, producers mustabsorb the tax as a reduction in realized price,and cannot pass it on to consumers. The taxthus reduces the incentive to increase produc­tion provided by decontrol. At price, PT, quan­tity QT is produced - more than would be pro­duced with continued controls but less thanwould be produced with decontrol and no tax.Production is greater than with continued con­trols because only part of the so-called"windfall" is diverted to the U.S. Treasury.

IV. Estimating Production losses

Crude-oil production in the United Statesdropped 10 percent over the 1970-79 period,and would have dropped 24 percent except forthe addition of 1.4 million barrels per day ofAlaskan North Slope production. The decade­long decline in production resulted inevitablyfrom a decline in the amount of oil added toreserves annually, through new discoveriesand enhanced oil recovery, during the 1970-79period compared with the decade of the1960' s. Unless gross annual addi tions toreserves exceed the rate of extraction, the totalinventory of proved reserves declines. Pro­ducers were forced to lower production so asnot to experience an even greater run-down intheir proved reserve inventory. Even with a

cutback in production, proved reserves - theindustry's working "in the ground" inventory- declined steadily over the 1970-79 periodfrom 39.0 to 27.1 billion barrels.

Even with controls, the average wellheadprice for domestically-produced crude rosethree-fold over the 1973-79 period (TapIe 2).This price upsurge led to a reversal of theprolonged decline in exploration activity thathad occurred over the preceding decade and ahalf. Between 1973 and 1979, the total numberof oil wells drilled nearly doubled, rising at anaverage annual rate of 16 percent.

Nonetheless, additions to reserves stilldropped from an average annual rate of 2.7 bil­lion barrels during the 1965-69 period to 2.0

15

billion barrels during the 1971-74 period, andthen to only 1.3 billion barrels during the1975-78 period. The rate of reserve additionsonly began to pick up, to 2.2 billion barrels,with a new price upsurge during the period ofgradual decontrol in 1979.

Without price controls, drilling activitywould have increased somewhat faster, raisingannual additions to reserves as weB as produc­tion at existing properties through develop­ment drilling. How much did the controls ­more specifically the Energy Policy and Con-

servation Act - contribute to the decline inproduction?Energy Policy and Conservation Act

To answer that question, we could develop apetroleum-supply model to project productionunder an uncontrolled price assumption.Then, that outcome could be compared withactual production to estimate productionlosses. Given the fact that the oil-supply pro­cess involves several phases - exploration,reservoir development, and production - thedevelopment of such a model would be a vast

Table 2Relationship Between Controlled Domestic Crude Oil Prices

and World Price, 1972·80

AverageAverage Refiner Domestic Price:Acquisition Price Average Domestic Price at Wellhead Controlled as Percent

Year Imported Crude' Without Controls (est.) With Controls of Uncontrolled

1972 3.22 3.39" 3.39 100.0

1973 4.08 3.89" 3.89 100.0Old 011' New 011"

1974 12.52 11.66' 6.87 5.03 10.13 58.9

1975 13.93 12.95' 7.67 5.03 12.03 59.2Lower-Tier Upper-Tier Stripper

1976 13.48 12.56' 8.19 5.13 11.71 12.16 65.2Naval

Alaska PetroleumNorth Slope Reserves

1977 14.53 13.59" 8.57 5.19 11.22 13.59 6.35 12.34 63.1

1978 14.57 13.95" 9.00 5.46 12.15 13.95 5.22 12.85 64.5

1979 21.67 22.93" 12.64 5.95 13.20 22.93 10.57 19.40 55.1

1980 33.82 35.54'; 21.20 6.49 14.34 3554 14.14 3306 59.6

I Before entitlement benefit.

2 In 1972 and most of 1973, petroleum prices were subject to the Phase I-IV price provisions of the Economic StabilizationAct. The average domestic wellhead price during that period did not differ substantially from the refiner acquisition costof crude, so it can be assumed that controls had little effect on domestic crude-oil prices. As a reSUlt, prices are assumedto be the same with or without controls.

;i Estimated by excluding domestic producers' transportation costs from the refiner acquisition price for imported oil, theadjustment being based on the post-1976 price of stripper oil. This adjustment reflects the fact that the wellhead price forstripper oil-deconlrolled in September 1976-represents an uncontrolled price for domestic oil exclusive of transporta­tion costs.

4 Under the Emergency Petroleum Allocation Act in effect throughout 1974 and 1975, "old" oil in any given month wasdefined as the quantity produced from a given property in the corresponding month of 1972.

" Under the Emergency Petroleum Allocation Act, "new" oil was defined as any production in excess of output in thesame month of 1972.

" The post-1976 (decontrolled) stripper price was used as a proxy for the uncontrolled domestic producer price. Actually, in1979 and 1980, stripper oil sold at a substantial premium above comparable-quality imported oil, due to tight worldwidesupply conditions and the willingness of refiners to pay a premium for security of supply.

Source: Uncontrolled domestic price at the wellhead estimated by author as described in footnote 3 above. Actual prices aspublished by U.S. Department of Energy, Monthly Energy Review.

16

undertaking. Instead, we have relied uponalready existing models to obtain long-runprice elasticities of supply upon which to esti­mate the production losses resulting froJ1.l thatparticular control program. Elasticity of supplyis a measure of the responsiveness of the quan­tity supplied of a given product to an increasein its price.

In the absence of controls, the averagewellhead prices of lower and upper-tier crudeoil would have risen from their respective con­trolled levels to the world market level. 21 Atexisting properties - those in operationbefore 1975 - an increase in prices wouldhave: (1) raised investment in developmentwells, thereby raising the rate of extractionfrom proved reserves and (2) encouragedgreater investment in enhanced oil-recoverymethods, thereby increasing additions toreserves through improved technology. Anincrease in the upper-tier price also wouldhave encouraged more exploratory drilling,leading to the discovery of more new reservesat new properties.

This study attempts to estimate how muchextra production would have been forthcom­ing had prices been allowed to rise to the worldprice. It does so by drawing on outside esti­mates of the elasticity of supply, the percen­tage change in quantity supplied divided by thepercentage change in price. In estimating pro­duction losses as a result of controls, we calcu­lated the percentage difference between theuncontrolled and controlled prices in any givenyear, multiplied that by the supply elasticity toget the percentage change in output, and thenconverted that percentage change to anarithmetic change by multiplying it by theexisting quantity.

Recent studies by the Department of Energysuggest a long-run price elasticity of about .2for categories of production equivalent to"existing properties."22 Numerouseconometric studies are available for derivingelasticity estimates for new properties. Thesestudies relate price to additions to reservesfrom new discoveries. The authors thenassume that a given increase in new reservesleads to an equivalent percentage increase in

17

production, as we also have done here. 23 Thesestudies have yielded a wide range of elasticityestimates - ranging from .3 to .8 - with thevariation perhaps due in part to the differenttime periods involved. 24

Utilizing these published elasticity esti­mates, we estimated production losses for the1976-79 price-control period under severaldifferent elasticity assumptions (Table 3). Theelasticity of production from existing propertieswas assumed to be .2 in each scenario.However, the elasticities of production fromnew properties ranged from .3 (low), to .5(medium) to .8 (high assumption).

All these elasticity estimates pertain to thelong-run. They show how production even­tually might respond to a given change in priceafter time had elapsed for exploration anddevelopment. In our estimates, however, wehave recorded the response as if it shows upfully within a year. For example, the estimatedproduction losses for 1976 represent the addi­tional production that would be forthcoming inthe long-run as a result of closing the differen­tials between controlled and uncontrolledprices prevailing during that year. Althoughrecorded for that single year, in reality the pro­duction response would take considerablylonger.

The results indicate a substantial lowering ofproduction by controls in the 1976-79 period,under all scenarios (Table 3). The likeliest out­come would probably arise from the low­response assumption, because elasticity ofsupply would surely fall in the wake of a sub­stantial widening of price differentials. Underthis low-response scenario, production withuncontrolled prices eventually could havebeen 10 percent higher in 1976, and 29 percenthigher in 1979, than actual production withcontrolled prices. The comparable productionincreases under the high-response scenariowould have been 15 percent in 1976 and 73percent in 1979 - although the underlyingelasticity estimates in that scenario appear tobe unrealistically high. The limited nature ofthe resource base, and the further depletion inrecent years, would preclude the likelihood ofvery high supply elasticities - .8 for new dis-

Table 3Domestic Crude Oil Production Losses Under the Energy Policy and Conservation Act,

1976-79(Production in millions of barrels)

Domestic Crude 011 Prices'(Dollars per barrel)

Prices with Controlslower-Tier Upper-Tier

5.13 11.71

5.19 11.22

Year

1976

1977

1978

1979

5.46

5.95

12.15

13.20

Estimated PriceWithout Controls"

12.56

13.59

13.95

22.93

Domestic Crude Oil Production(Millions of barrels)

Total ProductionWith Controls

2968

3009

3178

3115

Domestic Crude Oil Production(Millions of barrels)

AdditionalLow Estimated Production Domestic ProductionResponse Losses Due to Controls" Estimated Total Without Controls as

Production a Percent of Produc-Year Existing Properties' New Properties' Without Controls tion with Controls

1976 267 18 3253 9.6

1n","J ~..,.., 1 A 1 '11"'''7 " "1'7'( £.// p" J ....L.I IJ.'7

1978 228 89 3495 10.0

1979 350 561 4026' 29.2

Medium ResponseYear

1976

1977

1978

1979

High Response

Year

1976

1977

1978

1979

267

277

228

350

267

277

228

350

115

330

244

1102

187

544

398

1924

3350

3616

3649

4566

3421

3830

3804

5389

12.9

20.2

14.8

46.6

15.3

27.3

19.7

73.0

, Annual averages; producer prices at the wellhead.

, For an explanation of the derivation of this price series, see Table 2.

:j Derived by the author on the basis of three separate assumptions regarding price elasticity of supply for new properties.Each response is assumed to become fully effective within a single year, although in reality, the production response toclosing any given differential between controlled and uncontrolled prices would take more than a year.

1 Elasticity figures for existing properties are .2 in all cases; for new properties .3 for low response, .5 for medium response,and .8 for high response.

Source: Price and actual production data: U.S. Department of Energy, Monthly Energy Review. Estimated production lossescomputed by author using methodology described in text.

18

coveries - included in that scenario. 25

In all scenarios, the potential losses withcontrols would have been very large on thebasis of 1979 prices because of the hugeincrease estimated for wellhead prices in theabsence of controls. The average refineracquisition price for imported crude (beforeentitlements) rose by only 41f2 percent over theentire 1975-78 period, but then jumped 49percent within 1979 alone (Table 2), as OPECmembers sharply boosted prices in the wake ofthe cutoff of Iranian exports and consequentworld-wide tightening of supplies. The averagedomestic wellhead price without controls ­the average refiner acquisition price forimported crude less transportation costs fromdomestic wells to refineries - thus wouldhave risen about 58 percent in 1979. Indeed,that wellhead price probably would have riseneven more, because of refiners' willingness topay a premium for domestic oil for security ofsupply. In this situation, controlled prices oflower-tier and upper-tier oil would have beenonly 17 and 39 percent, respectively, of theestimated uncontrolled price for domestic oil.

Windfall Profit TaxThe windfall profit tax will dilute the

stimulus to increased production provided bydecontrol. The amount of dilution will dependprincipally upon the future behavior of theuncontrolled domestic-producer selling price- and thus the after-tax realized price - andupon the production response to any givenincrease in realized price.

Based upon an assumed 2 percent "real"average annual increase in the uncontrolleddomestic producer selling price over the 1980­90 period, we have developed different sets ofestimates of the offsetting effect of thewindfall-profit tax on the positive productionresponse from decontrol for the years 1985and 1990. 26 This involves the development ofthree alternative policy assumptions: (l)Scenario I, continued price controls; (2)Scenario II, decontrol with no windfall-profittax, and (3) Scenario III, decontrol with awindfall-profit tax. As before, we haveassumed a lower elasticity of supply for oilfrom existing properties - Tiers 1 and 2, aswell as heavy oil and incremental tertiary oil -

Table 4Estimated Domestic Producer Oil Prices, 1985 and 1990,

Under Three Alternative Policy Assumptions!

Scenario I Scenario II Scenario IIIPrices with Decontrol but Prices with Decontrol and

Year Prices with Continued Controls' No Windfall Profit Tax" Windfall Profit Tax'Tier 1 Tier 2 Tier 3 Tier 1 Tier 2 Tier 3

1985 19.92 23.64 28.23 53.93 29.57 35.10 45.25

1990 26.78 31.77 41.92 79.84 41.84 49.96 67.03

I Annual averages in dollars per barrel; producer sales prices.

2 For 1985 and 1990, prices with controls were estimated by making inflation adjustments to the base prices for each tier (asdefined in the Crude Oil Windfall Profit Tax of 1980, Table O. For 1980, the adjustment factors were based on the GNPdeflator. Thereafter, the inflation rate was assumed to decline gradually, reaching a 6.0-percent annual rate by mid-1986and remaining at that rate through 1990. For Tier 3, the base price was adjusted upward by an additional 2 percent eachyear, as allowed in the law.

:J The author estimated that domestic oil, without controls, would have sold for $35/barrel in the fourth quarter of 1980.For 1985 and 1990, the decontrolled price was estimated by adjusting the 1980 price to reflect inflation plus an assumed 2­percent annual increase. The anticipated price of heavy oil in 1980 was assumed to be $7.50 less than the average domesticprice, reflecting the traditional price differential.

4 The author assumed that the windfall profit tax effectively reduces the selling price actually realized by domestic pro­ducers. For a description of the derivation of these prices, see Appendix A. The tiers represent the categories of domesticoil as defined by the Crude Oil Windfall Profit Tax of 1980, as shown in Table 1.

19

than for oil from newly discovered properties.And again, we have assumed a range ofelasticities for Tier 3 newly-discovered oil,ranging from .3 (low), to .5 (medium) to .8(high assumption).

To develop production estimates, we firstestimate producer prices under each of thethree scenarios (Table 4), making the esti­mates in nominal terms to conform with the

actual computation of the windfall-profit tax.The tax affects production in any given periodthrough its impact on the realized relativeprice of oil compared with what it would bewithout the tax.

Under continued controls (Scenario I), wecalculate prices for Tier 1 and Tier 2 oil for theyears 1985 and 1990 as equal to certain baseprices (defined by the Windfall Profit Tax)

Table 5: Estimated Domestic Crude Oil Production, 1985

Scenario I Scenario IIProduction with Production with Decontrol but

Continued No Windfall Profit Tax'Low Response' Price

Tier 1 Tier 2 Tier 3 Total" Tier 1 Tier 2 Tier 3 Total"

1979 (Actual) 2,046 500 97 3,113 U) U) (.3)

1985 730 536 907 2,748 891 632 1,530 3,628

1990 318 637 1,035 2,464 396 766 1,611 3,248

Medium Response'

1979 (Actual) 2,046 500 97 3,113 U) U) (.5)

1985 730 536 907 2,748 891 632 2,094 4,192

1990 318 637 1,035 2,464 396 766 2,112 3,749

High Response'

1979 (Actual) 2,046 500 97 3,113 U) U) (.8)

1985 730 536 907 2,748 891 632 2,993 5,091

1990 318 637 1,035 2,464 396 766 2,912 4,549

I In millions of barrels.

2 Low, medium and high responses refer to the assumed elasticities of supply for various categories of crude. Tier 1 andTier 2 oil, as well as heavy oil and incremental tertiary (enhanced) oil in Tier 3, were assumed to be oil from existing pro­perties. Oil from new properties appears in Tier 3. In each response case, oil from existing properties (Tiers 1 and 2 andheavy oil and incremental tertiary oil in Tier 3) was assumed to have a price elasticity of .2. The elasticity for new proper­ties (Tier 3) varied from .3 in the low case to .5 in the medium case and .8 in the high case.

:J Assumes domestic oil prices behave as described in Table 4, Scenario I, with a continuation of the controls embodied inthe Energy Policy and Conservation Act. Production estimates for the years 1985 and 1990 under this assumption arefrom the Congressional Budget Office study cited below, page 76. These production estimates were used to derive ourScenarios II and III.

4 Assumes domestic oil prices behave as described in Table 4, Scenario II. Production was estimated on the basis of thesame supply elasticities utilized in estimating producing losses under the Energy Policy and Conservation Act.

20

adjusted by inflation (measured by the GNPdeflator). The Tier 3 inflation adjustmentequals the inflation rate plus 2 percentannually, as specified in the law. 27 Thus, in thisscenario, prices for Tier 1 and Tier 2 produc­tion remain constant in real terms, while theprice for Tier 3 production rises at a 2-percentreal annual rate. Under decontrol (ScenarioII) , we estimate the free market price by apply-

ing the inflation-plus-2-percent adjustment tothe uncontrolled stripper price ($35 a barreDin the fourth quarter of 1980. (We use thesame methodology, although a higher baseprice, as the Joint Committee on Taxationuses in estimating windfall-profit taxrevenue.) 28 Under decontrol and the tax(Scenario III) we calculate the realized pro­ducer price by adding the extra after-tax

and 1990, Under Three Alternative Policy Assumptions]

Scenario III

Percent Increase Production with Decontrol and Percent Increase Inin Total Production Windfall Profit Tax' Total ProductionDue to DecontroF Tier 1 Tier 2 Tier 3 Total" After Tax'

(.2) (.2) (.3)

32.0 790 580 1,314 3,259 18.6

31.8 348 697 1,416 2,936 19.2

52.5

52.2

85.3

84.6

(.2)

790

348

(.2)

790

348

(.2)

580

697

(.2)

580

697

(.5)

1,690

1,752

(.8)

2,243

2,247

3,635

3,272

4,188

3,767

32.3

32.8

52.4

53.7

r, Assumes domestic oil prices behave as described in Table 4, Scenario Ill.

,; Alaskan oil from proved reserves has been included in the production totals, but not in any tier. The author estimatedthis Alaskan production at 575 and 475 million barrels in 1985 and 1990, respectively, compared with 471 million barrelsin 1979.

7 This refers to the amount (percent) by which total production without controls (Scenario ll) would exceed total produc­tion with continued controls (Scenario n during the years 1985 and 1990, respectively.

, This refers to the amount (percent) by which total production without price controls but with the windfall profits tax(Scenario III) would exceed total production with continued controls (Scenario I) during the years 1985 and 1990, respec­tively.

Source: Production estimates in Scenario I, Congressional Budget Office, The Windfall Profits Tax: A Comparative Ana(ysis ofTwo Bills. 1979. All other estimates by the author.

21

revenue per barrel to the estimated priceunder continued controls (Appendix A).

With these assumptions, the nominal freemarket price rises from $35/barrel during1980-IV to about $54/barrel by 1985 and $80/barrel by 1990 (Table 4). After-tax producerprices thus range (depending on tier) from 55to 84 percent of the uncontrolled price by1985, and from 52 to 84 percent of theuncontrolled price by 1990.

With these price estimates, and with Con­gressional Budget Office estimates of produc­tion under continued controls, we derive(Scenario II and III) production estimates byapplying appropriate supply elasticities, just aswe did in estimating the effects of the EnergyPolicy and Conservation Act. 29 The use ofnominal prices to estimate production losses isjustified by the use of the same inflationadjustment for both controlled anduncontrolled prices, so that inflation has aneutral effect.

As seen in Table 5, domestic oil productionundoubtedly would have continued to trenddownward over the 1979-90 period under con­tinued price controls. But all three sets of

Scenario I

assumed elasticities suggest that production islikely to rise substantially between 1979 and1985 under decontrol. The CongressionalBudget Office's forecast for production undercontinued controls shows total productiondropping from 3,113 million barrels in 1979 to2,748 million barrels by 1985, and then to2,464 million barrels by 1990. Under the "lowresponse" set of elasticities, total productionwith decontrol and no tax would reach 3,628million barrels in 1985 and 3,248 million bar­rels in 1990. With the tax, production couldstill reach 3,255 million barrels in 1985 and2,936 million barrels in 1990. Productionfigures in the "high response" case would beconsiderably higher, but as already indicated,the elasticity figures involved appear to beunrealistically high.

Here again, the production differentials be­tween the scenarios would not necessarilyoccur specifically in 1985 and 1990. Rather,the differentials represent the ultimate produc-tion responses to the estimated price differen­tials existing in those years as a result of thetax.

Domestic production probably would rise

Table 6: Estimated Domestic

Under Three Alternative Domestic Oil PriCE

Annual PercentIncrease in

the DecontrolledDomestic Pricein Real Terms

~2

o

+2

+5

+10

Total Productionwith Continued

Controls"2748

2748

2748

2748

2748

Total ProductionWith Decontrol but

No WindfallProfit Tax

3790

3962

4192

4555

5209

Percent Increasein Production

With Decontrol butNo WindfallProfit Tax

37.9

44.2

52.5

65.8

89.6

l Annual production in millions of barrels.

2 Medium response assumes an elasticity of.2 for tiers 1 and 2, respectively, and also for heavy oil and incremental tertiaryoil in tier 3. It assumes an elasticity of .5 for newly discovered oil in tier 3.

22

even faster than projected, under all three setsof elasticity assumptions, if the uncontrolledprice rose at more than 2 percent above theinflation rate (Table 6). For example, in themedium-response situation, an increase from2 percent to 5 percent in the real rate of oil­price rise would mean an increase, from 53percent to 66 percent, in the productiondifferential achieved by 1985 because of theshift from controlled to uncontrolled prices.With the windfall profit tax in effect, the pro­duction differential would be 32 percent with a2-percent real increase in prices, and 41 per­cent with a 5-percent real rate of increase.

The higher prices being realized by pro­ducers as a result of decontrol apparently arenow exerting a dramatic impact on explorationand development activity. Drilling activity seta new record in 1980, surpassing the previoushighs reached in the mid-1950's. During theyear, the industry drilled nearly 65,000 wells ofall types - about 26 percent more than during1979. For the year, total footage drilled rose 23percent, while the number of drilling rigs inoperation rose 32 percent. New records appearin prospect for 1981.

Crude Oil Production, 1985,Assumptions l and with Medium Response2

Unfortunately, increased drilling activity hasnot been translated into an increase in provedcrude-oil reserves. In 1979, total reserves con­tinued a decline that began in 1968. But grossadditions to reserves amounted to 2.2 billionbarrels, the highest figure since 1971. Thismeant a narrowing of the deficit between thegross volume added to reserves and theamount extracted. The inventory of provedreserves dropped by only 0.8 billion barrels ­the smallest amount since 1968.

In any event, our analysis indicates that thewindfall-profit tax would reduce the produc­tion response expected from decontrol. As ameasure to reduce dependence on foreign-oilimports and improve the allocation ofresources, decontrol is a step in the right direc­tion. But by the same token, the windfall-profittax is a step in the wrong direction. Perhapspolicymakers should alter the tax to make it atrue tax on profits rather than an excise tax ona portion of the selling price. In that way, itwould not affect production decisions at themargin.

. -'S=cc'-'e=nac.'-'rio~II"_I _

Total ProductionWith Decontrol and

WindfallProfit Tax

3379

3487

3635

3871

4301

Percent Increasein Production

With Decontrol andWindfall

Profit Tax23.0

26.9

32.3

40.9

56.5

Amount of ProductionLost Due to the Taxas a Percent of Pro­

duction with Decontroland No Windfall

Profit Tax'10.8

12.0

13.3

15.0

17.4

, Production under this scenario depends upon the controlled price, and therefore is not influenced by alternative assump­tions with regard to the decontrolled price.

4 Actual production losses under each price assumption are as follows: -2%, 411; 0%, 475; +2%, 557; + 5%, 684;

+10%,908.

23

24

V. Summary and ConclusionsFederal price-control programs in effect

throughout most of the 1970's held theaverage domestic producer price of crude oilbelow the world market level. By permittingrefiners to pay less than the world price fordomestic crude, Congress attempted to protectconsumers from bearing the full impact of ris­ing world prices. There is considerable debateas to whether that objective was achieved.Moreover, controls affected the supply side ofthe u.s. market, both by creating greater de­pendence on foreign oil and by leading to aninefficient allocation of resources. In thisrespect, the Energy Policy and ConservationAct - as well as earlier control programs ­reduced domestic production substantiallybelow the level that would have prevailedwithout controls.

Efficiency in satisfying any given level ofnational consumption requires that domesticproducers expand crude-oil production to thepoint where the cost of the last barrel equalsthe cost of acquiring an additional barrel of

foreign crude. Federal controls violated thatcondition, by holding the domestic sellingprice below the landed price of foreign oil, andthereby causing the industry to produce at lessthan the optimum production level. For everybarrel not produced, the nation's dependenceon foreign oil rose by an equivalent amount ata greater cost of resources.

The removal of price controls (January 28,1981) has forced refiners to pay free-marketprices for domestic crude oil. That meanshigher refined-product prices also, to theextent that refiners pass on those higher coststo consumers. But decontrol also should raisedomestic production above the level thatwould prevail under continued controls. Infact, decontrol may bring about at least a tem­porary reversal in the production decline of thepast decade.

But the windfall-profit tax will reduce thatpositive impact. For example, with supplyelasticities of .2 and .3 for existing and newproperties, respectively, and with a 2-percent

Appendix AWindfall Profit Tax (WPT) Calculation, 19851

Tier IIIHeavy All Other

Tier I Tier II Oil TIer III Oil

Estimated Decontrolled Price' 53.93 53.93 42.38 53.93

Less Adjusted 1979 Base Price' -19.92 -23.64 -28.23 -28.23

Windfall Profit Before Severance 34.01 30.29 14.15 25.70

Less Severance Tax' - 1.84 1.64 .76 - 1.39

Windfall Profit 32.17 28.65 13.39 24.31

Windfall Tax - 22.52 -1719 - 402 - 7.29

Amount of Windfall Retained by Producers 9.65 11.46 9.37 17.02

Producer Realized Prices with Decontrol after WPT

Adjusted Base Price 19.92 23.64 28.23 28.23

Amount of Windfall Retained by Producers + 965 + 11.46 + 9.37 +17.02

Producer Price with Decontrol after WPT 29.57 35.10 37.60 45.25

I All data in dollars per barrel.

, Price assumed to rise at the inflation rate plus 2 percent annually between 1980 and 1985.

1 Base price adjusted upward over the 1980-85 period according to the method described in the law.

, Tax imposed by the states, assumed 10 average 5.4 percent of the windfall profit before severance.

Source: All computations by aUlhor.

25

real rate of price increase, decontrolled pro­duction would be 31 percent higher in 1985,and 30 percent higher in 1990, than theamounts that might be produced with con­tinued controls. But the windfall-profit taxcould reduce that production differential to

about 18 percent in 1985 and 1990 respec­tively. And the more the decontrolled price ofoil rises relative to prices of other goods andservices, the greater will be the productionlosses attributable to the tax.

FOOTNOTES

1, The term Ilproperty" is used as defined in theEnergy Policy and Conservation Act to mean a sepa­rate and distinct producing reservoir. (U.S. Depart­ment of Energy, Monthly Energy Review, January1981, p. 99). A "reservoir" is a porous and permeableunderground formation containing an individual andseparate natural accumulation of producible oil or ofoil and natural gas. In most situations, reservoirs areclassified as oil or gas reservoirs by a regulatoryagency. See American Petroleum Institute (1976, p.7).

2. In this regard, crude-oil production differs frommost manufacturing processes. With the latter, weassume that increasing capacity, (i.e., rate of attaina­ble production), causes something like a proportio­nate increase in the amount of product that ultimatelywill be forthcoming. Crude-oil investments, however,must be described with reference to two dimensions:the rate of output to be achieved, and the total volumeof crude available for ultimate production. For a dis­cussion of this point, see Bradley (1967, p. 16).

3. American Petroieum Institute, American Gas As­sociation, Canadian Petroleum Association (1980, p.14).

4. See, for example, Duchesneau (1975) and Eppen(1975)

5. Herfindahl and Kneese (1974, p. 123).

6. This would be the average delivered price forimported oil at the refinery gate, including transporta­tion costs. In actuality, average domestic producerprices probably would not exactly equal the averagelanded cost of imported oil because of qualitydifferences. Crude oil is not a homogeneous com­modity; viscosity, sulfur content and other charac­teristics vary and affect its value. Nevertheless, theprice of imported oil would determine domestic pricesin the manner described in the text.

7. The requirements for efficiency on the supply anddemand sides of the U.S. crude-oil market, as well asthe inefficiencies created by price controls, are dis­cussed in detail by Arrow and Kalt (1979, pp. 9-27).Their work draws upon an earlier study by Roush(1976).

8. For a detailed discussion of these state programs,see McDonald (1971, pp. 29-55) and Bohi andRussell (1978, pp. 250-253).

9. For a description of the various Federal crude-oilprice-control programs of the late 1970's, seeMacAvoy (1977) and Montgomery (1977 and 1978).

10. In computing the average target price, the energyagency assigned stripper-well oil an upper-tier price

26

rather than the price actually paid. That approachmade it possible to increase the price of stripper-welloil without lowering the price of some other categoryof domestic oil. See Montgomery (1977, pp. 9 and 12)for a discussion of this point as well as the inflationadjustment.

11. Economists generally agree that controls heldthe average domestic producer selling price belowthe world market level. Economists also agree that theso-called entitlement program equalized the averagecost of crude oil to each refi ner regardless of the rela­tive amounts of imported, lower-tier, or upper-tierimputs used - and that refiners paid a commonaverage price for all oil that was below the worldmarket level. See, for example, Cox and Wright (1978,p. 4) and Montgomery (1977, p. 37). There iswidespread disagreement, however, about whethercrude-oil price controls reduced refined-productprices below the level that would have prevailed with­out controls. Montgomery has argued that the entitle­ment program utilized competitive market forces topass through the increased refiners' profits fromprice-controlled crude oil, from crude-oil producers torefined product consumers (1977, pp. 37 -40). Phelpsand Smith (1977) maintain that refined-productprices were not held down by the controls and entitle­ments, and profits were transferred from producers torefiners. They argue that world refined products aremade from world crude. The U.S. imports refined pro­ducts and therefore, U.S. refined product prices mustreflect world crude prices. This is basically an empiri­cal question, but with much conflicing evidence. See,for example, Deacon (1978).

12. The following analysis synthesizes an even moredetailed analysis of the production effects of theEnergy Policy and Conservation Act, made by Kall(1980, pp. 107-111). For an earlier discussion, seeRoush (1976, pp. 16-20).

13. There are also resources wasted on the demandside as a a result of controls. The entitlement programreduced the price of imported oil to a commonaverage price for domestic and imported oil (Pr) whichwas below the world market price (Pw)' In doing so, itraised the quantity of imported oil demanded abovethe quantity that would have been demanded hadrefiners been required to pay the world price for anincremental barrel of crude. The additional oildemanded had an incremental value to the economyof Pro But to realize that value, the nation paid theworld price, Pw' to foreign sellers of crude. Theresources consequently wasted on the demand-sideequalled the difference between the world price andthe average refiner acquisition price for both foreignand domestic oil, times the additional quantity

demanded as a result of the entitlement program. Weshould note that the average refiner acquisition pricefor domestic and imported oil, Pr, under the entitle­ment program was between the world price, Pw' andthe average controlled domestic price, Pc as shown inChart 2. That price was not shown on the chartbecause it did not affect the domestic production ofcrude oil.

14. If marginal profit is increasing like compoundinterest, an owner of a reservoir will be indifferent atthe margin between extracting and holding at everyinstant of time. Hotelling (1931) established theprofit-maximizing condition for a firm managing a de­pletable resource. For a further discussion of thepoint see Solow (1974, pp. 1-6).

15. Kalt found that, on balance, controls tended "toencourage later rather than earlier extraction." SeeKalt (1980, p. 132).

16. As indicated in footnote 11, some economistsargued that refined-product prices already reflectedworld oil prices. They maintained that decontrollingdomestic crude-oil prices would have no effect onrefined-product prices.

17. This estimate was based on the assumption thatthe uncontrolled domestic-producer price rose at theinflation rate plus 2 percent - i.e., at a 2 percent realannual rate - over the 1980-90 period. Reported in"U.S. Windfall Tax Bonanza Based on $75 Oil Price in1990" (1980, p. 3)

18. See assumption described in footnote 17.

19. The projected decline rate ("base level") is theaverage daily amount of oil removed from the propertyduring the six-month period ended March 31, 1979,reduced by one percent per month (after 1978) foreach month before the project-beginning date. SeePrice Waterhouse and Company (1980, p. 21).

20. There would be no "windfall" upon which to basethe tax unless this condition prevailed.

21. Since controls were imposed on the selling price,average selling prices of. lower- and upper-tier crudein the absence of controls would have risen toapproximately the landed price for imported oil(before entitlements). The uncontrolled wellheadprice would have roughly equalled the world priceminus the average transportation costs incurred bydomestic producers in supplying refiners. Forreasons of data availability we used actual and esti­mated uncontrolled wellhead prices, rather than seIl­ing prices, in calCUlating possible production lossesresulting from the Energy Policy and ConservationAct.

22. In its 1979 Annual Report to Congress, the U.S.Department of Energy forecast production for 1985and 1990 under several categories that would cor­respond to existing properties, the most responsivebeing production from enhanced oil-recovery tech­niques. The imputed price elasticity for existing pro­perties derivable from these forecasts is approx­imately.2.

23. That assumption was employed in most modelsof reserve additions. See Bohi and Russell (1978, p.237) for a discussion of this point.

24. For example, Fisher (1964) estimated aneillsticity for new-oil discoveries of .3 using data for1946-55, but Erickson and Spann (1971) obtained anestimate of .8 using 1946-59 data. The U.S. Depart­ment of Energy, in its 1979 Annual Report to Con­gress, developed forecasts for 1985 and 1990 whichimply an elasticity for new fields of around .3. For asurvey of these and other models, see Kimmel (1977).

25. Estimates vary widely concerning the totalamount of oil remaining to be discovered in the UnitedStates, both on and offshore. For example, one officialsource places the total undiscovered recoverableresource base at somewhere between 50 and 127billion barrels; see U.S. Geological Survey (1975,p. 4). Another recent assessment places the estimateat between 14 and 32 billion barrels; see Nehring(1981, p.175).

26. This is the same price assumption employed bythe Joint Committee on Taxation in developing its1979 estimates of the Federal revenues to be derivedfrom the windfall-profit tax. The price assumption isused to analyze the production effects of decontrol,with and without the windfall-profit tax. Note thatthere would be no tax unless the uncontrolleddomestic price remains above the adjusted baseprice.

27. Tier 3 encompases newly discovered oil, heavyoil and incremental tertiary oil. It receives preferentialtreatment in the law through a lower tax rate and anextra 2-percent annual increase in its base-adjustedprice.

28. Their estimate, made in early 1979, underesti­mated the actual increase in uncontrolled prices thatactually occurred by the latter part of that year.

29. For estimates of production under continuedcontrols, see U.S. Congress, Congressional BudgetOffice (1979, p. 76).

REFERENCES

American Petroleum Institute. Standard Definitionsfor Petroleum Statistics. Technical Report No.1.Washington, D.C.: American Petroleum Institute,1976.

American Petroleum Institute, American Gas Associ­ation, Canadian Petroleum Association.Reserves of Crude Oil, Natural Gas liquids and

27

Natural Gas in the United States and Canada asof December 31,1979. Washington, D.C.: Amer­ican Petroleum Institute, 1980.

Arrow, Kenneth J. and Kalt, Joseph P. PetroleumPrice Regulation: Should We Decontrol?Washington, D.C.: American Enterprise Institutefor Public Policy Research, 1979.

Bohi, Douglas R. and Russell, Milton. limiting OilImports. Baltimore: Johns Hopkins UniversityPress, 1978.

Bradley, Paul G. The Economics of Crude PetroleumProduction. Amsterdam: North-Holland Publish­ing Company, 1967.

Cox, James C. and Wright, Arthur W. "The Effects ofCrude Oil Price Controls, Entitlements and Taxeson Refined Product Prices and Energy Indepen­dence," land Economics. February 1978, pages1-15.

Deacon, Robert T. "An Economic Analysis of GasolinePrice Controls," Natural Resources Journal.October, 1978, pages 801-814.

Duchesneau, Thomas D. Competition in the U.S.Energy Industry. Cambridge: Ford Foundation,1975.

Eppen, Gary. Energy: The Policy Issues. Chicago:University of Chicago Press, 1975.

Erickson, Edward W. and Spann, Robert M. "SupplyResponse in a Regulated Industry: The Case ofNatural Gas," Bell Journal of Economics andManagement Science. Spring, 1971, pages 94­121.

Fisher, Franklin M. Supply and Costs in the U.S.Petroleum Industry. Baltimore: John HopkinsPress, 1964.

Haiimark, Fred O. Heavy Oil in California. Sacra­mento: California Division of Oil and Gas, 1980.(Unpublished memo)

Herfindahl, Orris C. and Kneese, Allan V. EconomicTheory of Natural Resources. Columbus:Charles E. Merrill Publishing Company, 1974.

Hotelling, Harold. "The Economics of ExhaustibleResources," Journal of Political Economy. April1931, pages 133-75.

Kalt, Joseph. Federal Regulation of PetroleumPrices: A Case Study of the Theory of Regula­tion. Unpublished Ph.D. dissertation, Universityof California, Los Angeles, 1980.

Kimmel, David. The Price-Responsiveness ofPetroleum Supply: A literature Review.Washington, D.C.: American Petroleum Institute,1977.

Lewin and Associates. Enhanced Oil RecoveryPotential in the United States. Study preparedfor the U.S. Congress, Office of TechnologyAssessment. Washington, D.C.; Office of Tech­nology Assessment, 1978.

MacAvoy, Paul W., ed. Federal Energy AdministrationRegulations. Report of the Presidential Task

28

Force. Washington, D.C.: American EnterpriseInstitute for Public Policy Research, 1977.

Montgomery, W. David. "A Case Study of RegulatoryPrograms of the Federal Energy Administration,"Study on Federal Regulations. U.S. Senate,Committee on Government Affairs. Washington,D.C.: U.S. Government Printing Office, 1978,pages 733-833.

__, The Transition to Uncontrolled Crude OilPrices. Prepared for the National Bureau of Eco­nomic Research Conference on Public Regula­tion, Washington, D.C., December 15-17, 1977.Pasadena: California Institute of Technology,1977.

Nehring, Richard and Van Driest, E. Reginald. TheDiscovery of Significant Oil and Gas Fields inthe United States. Santa Monica: Rand Corpora­tion, 1981.

Phelps, Charles E. and Smith, Rodney T. PetroleumRegulation: The False Dilemma of Decontrol.Santa Monica: Rand Corporation, 1977.

Price Waterhouse and Company. Windfall Profix Tax.New York: Price Waterhouse and Company,1980.

Roush, Calvin T., Jr. Effects of Federal Price andAllocations Regulations on the PetroleumIndustry. U.S. Federal Trade Commission StaffReport, 1976.

Solow, Robert M. "'The Economics of Resources orthe Resources of Economics," The AmericanEconomic Review, Papers and Proceedings. May1974, pages 1-14.

U.S. Congress, Congressional Budget Office. TheWindfall Profits Tax: A Comparative Analysis ofTwo Bills. Washington, D.C.: U.S. GovernmentPrinting Office, November 1979.

U.S. Department of Energy, Energy Information Ad­ministration. Amwal Report to Congress 1979,Volume 3. Washington, D.C.: U.S. GovernmentPrinting Office, 1980.

U.S. Department of Energy. Monthly Energy Review.Washington, D.C.: U.S. Government Printing Of­fice.

U.S. Geological Survey. Geological Estimates ofUndiscovered Recoverable Oil and GasResources in the United States. GeologicalSurvey Circular 725 Washington, D.C.; June1975.

"U.S. Windfall Tax Bonanza Based on $75 Oil Price in1990," Petroleum Intelligence Weekly. March10,1980, pages 3-6.

Adrian W. Throop*

Increasingly volatile financial markets haveput a premium on accurate forecasts of interestrates. To the extent, however, that market par­ticipants base their decisions on availableinterest-rate forecasts, the value of theseforecasts to an individual investor isdiminished because security prices wouldalready tend to reflect that information. At theextreme, the efficient-market hypothesisasserts that the market efficiently utilizes allavailable information in the pricing ofsecurities, so that market participantsgenerally can not profit from more accurateforecasts than those already incorporated insecurity prices.

This article examines the degree to whichthe Treasury-bill market efficiently utilizes allavailable information so as to incorporate thebest possible interest-rate forecast into currentmarket prices. In other words, it evaluates theapplicability of the efficient-market hypothesisto the Treasury-bill market. Two types of inde­pendent forecasts are examined: anautoregressive forecasting equation based onthe past history of the bill rate, and the forecastof a selected panel of market professionals.Statistical tests are used to determine whetherall useful information contained in these twoforecasts is efficiently incorporated into Treas­ury-bill market prices. The market's forecast isderived from the "forward rate" implied bythe term structure of yields.

If the market is not efficient, a group ofinvestors could improve their returns by alter­ing the maturity of their investments in light of

'Senior Economist. Federal Reserve Bank of San Fran­cisco. Coleman Kendall provided research assistance forthis article.

29

superior interest rate forecasts. Near a peak inthe business cycle, for example, if suchforecasts correctly foresee a larger decline ininterest rates than anticipated by the market,then investors should buy securities with amaturity longer than their expected invest­ment periods. The yields obtained by investorswould be greater than those obtainable if theyhad chosen maturities equal to their invest­ment period, due to larger capital gains thanhad been anticipated by the market. Alter­natively, if the available forecasts correctlyforesee interest rates falling more slowly thanthe market does, the investors utilizing thoseforecasts should buy maturities shorter thantheir investment periods. This is becauseinvestors would obtain a higher return from"rolling over" a series of short-term securitiesthan from buying maturities equal to theirinvestment periods.

Whether or not investors can profit by"speculating" on interest rates, through hold­ing other than their normally preferredmaturities, depends critically on whetheravailable interest-rate forecasts are moreaccurate than the market's. If the market isefficient, the information in these forecastswould already be incorporated into the pricesof securities, and therefore nothing would begained. In that case, investors would be betteroff by simply "hedging" their positions withmaturities equal to their planned investmentperiods, thereby avoiding possible risk.

This study shows that professional analysts'prediction of the Treasury-bill rate two quar­ters ahead are significantly more accurate thanmarket predictions. This indicates that themarket does not efficiently utilize all availableinformation in making bill-rate forecasts. By

making use of the information contained in theanalysts' forecast, an investor in Treasury billscould have improved his return. We show thatthe analysts' ability to "beat the market"stems from a better utilization of informationabout past movements in the bill rate, and alsofrom a more efficient utilization of other sortsof information.

Section I explains the concept of market effi­ciency and examines the forecasting accuracyof the market, compared with that of both apanel of professional analysts and a simpleautoregressive forecasting equation based onthe past history of the bill rate. In Section II,statistical tests for market inefficiency are de-

scribed and performed, on the basis of thesethree forecasts. Both the analysts' forecast andthe forecast from the autoregressive forecast­ing equation are found to contain useful infor­mation which is not fully incorporated into themarket's forecast indicating market ineffi­ciency. Section III shows that the analysts'forecast contains information similar to that inthe autoregressive forecasting equation, plusother useful information which is also not fullyincorporated into the market's forecast. Inves­tors could have traded on both types of infor­mation to improve their returns in the periodexamined. Section IV provides a summary andsome further conclusions.

I. The Concept of Market Efficiency

Efficient financial markets exist when theprices, or yields, of securities fully reflect allavailable information relevant for their valua­tion. In the case of riskless fixed-incomesecurities, such as Treasury bills, the relevantinformation consists of expectations about thefuture course of interest rates. Investors thenbid the prices of securities to the point whereexpected holding-period yields for securities ofdifferent maturities are roughly the same,given these expectations. For example, giventhe current six-month bill rate, the price andyield of a nine-month bill depends upon theexpected three-month rate for six monthsahead. New information can develop, butwhen it does it is rapidly reflected in revisedexpectations and in the prices of securities.Consequently, in an efficient Treasury-billmarket, investors would not have significantopportunities for making profits on the basis ofinformation about future interest rates l .

The hypothesis of an efficient market is anextreme one, and therefore could not beexpected to be literally true. Past tests of theefficient-market hypothesis have thusattempted to pinpoint the level of informationat which the hypothesis breaks down. In thesetests, all available information can be sepa­rated into three distinct types, or subsets, asshown in Table 1. The first subset consists of

30

the past history of rates of return, or prices. Atest of whether the market efficiently utilizesinformation in the past history of rates ofreturn, or prices, is called a test of weak-formefficiency. The second information subset con­sists of any other information publicly availa­ble at little or no cost - such as governmentstatistics on other relevant variables. Tests ofwhether the market efficiently utilizes thiskind of information in securities pricing arecalled semistrong-form tests. The third informa­tion subset consists of information that is priv­ileged or available only at significant cost.Tests of whether the market efficiently utilizesthis kind of information, so that profits cannotbe made from trading on it, are called strong­form tests.

Table 1Types of Market Efficiency

Subset of Information Test of WhetherParticular InformationSubset Is EfficientlyUtilized by Market

I. Past History of Prices,or Rates of Return Weak-Form Test

2. All Other PubliclyAvailable Information Semistrong-Form Test

3. Privileged or CostlyInformation Strong-Form Test

The three subsets of information exhaustthe universe of all available information; thus,a market is fully efficient only if it passes allthree kinds of tests. According to previousstudies, the Treasury-bill market tends to beefficient in the weak-form sense of utilizinginformation in the past behavior of the billrate. However, the evidence has generallybeen confined to very near-term marketforecasts of only up to a few months ahead. 2

Also, little evidence is available on the billmarket's efficiency in the strong or semi­strong form sense of incorporating otheravailable information useful for forecasting. 3

In this study, we consider two specific typesof information that the Treasury-bill marketcould utilize in formulating a two-quarterahead forecast of the 3-month bill rate. Thefirst is simply an autoregressive forecast basedon the past history of the bill rate. The yield onTreasury bills may vary somewhat predictablyover time, due to the business cycle and/orpredictable patterns in monetary policy. Topass the weak-form test, the market's forecastof the future bill rate needs to take intoaccount any systematic behavior evidenced byits past history. 4

For the period 1951-IV through 1969-III, weestimated a simple autoregressive forecast thatcould have been used by the market. In thisperiod, quarterly movements in the 3-monthbill rate followed a significant autoregressivepattern, ie., past movements of the rate weresignificantly related to future movements. Anequation explaining quarterly changes in thebill rate contained significant lags at 1, 2, 3,and 6 quarters, as well as a significant constantterm indicating a positive time trend. We thenobtained autoregressive forecasts for theperiod 1970-I through 1979-III by reestimatingthis equation on a growing sample of availableobservations and computing two quarter aheadforecasts from the latest coefficient estimatesat each point in time. 5 Whether or not themarket efficiently utilized the informationcontained in this autoregressive forecast is atest of weak-form efficiency.

The second type of information is theaverage interest-rate forecast made by a panel

31

of professional analysts, and compiled by theGoldsmith-Nagan Bond and Money Market Let­ter since Septem ber 1969. 6 The forecast periodis again 1970-1 through 1979-III. This periodbegins with the first Goldsmith-Nagan sam­pling of professional forecasts and continuesthrough the quarter just prior to the FederalReserve's October 1979 shift in operating pro­cedures, which emphasized controlling bankreserves for achieving its monetary objectives.We excluded later data on the ground thatboth the market and professional forecastershad to go through a learning experience whichreduced the forecasting accuracy of each byperhaps differing amounts.

Professional analysts use, either directly orindirectly, information in the past history ofthe bill rate - but they undoubtedly use othersources of information as well. So the analysts'forecasts contain information relevant to allthree kinds of efficiency. However, it is notpossible here to distinguish between a semi­strong and strong test of efficiency. Someinterest-rate forecasts in the Goldsmith-Nagansample are not widely circulated, being privi­leged or costly to obtain, while other informa­tion may be publicly available. Since we cannotdiscriminate between these two types of infor­mation in the analysts' forecasts, we simplyuse the term "strong-form efficiency" to referto efficient market use of all types of informa­tion besides the past history of the rate.

In order to test the bill market's efficiency,we need a measure of the market's forecast ofthe 3-month bill rate for two quarters ahead.This can be obtained from the term structureof Treasury bill rates - specifically, from themarket's two quarter ahead "forward rate."This is the interest rate on a 3-month Treasurybill two quarters ahead that would be requiredto equalize expected returns on 6- and 9­month bills over a 9-month holding period. 7

The forward rate also contains a "liquidity pre­mium," which compensates investors in thelonger-term security for their sacrifice ofliquidity. So an adjustment for that premiummust be made to provide an estimate of themarket's forecast of the bill rate. A n Appendixdevelops the concept of the forward rate more

precisely, and details the technique used toestimate the liquidity premium.

For a preliminary view of market efficiency,we can compare the market's two-quarterahead forecast of the 3-month Treasury billrate (F;':2) with the professional analysts'forecast (F~: 2) and the forecast from theautoregressive equation(F:l~2)' To measureforecast accuracy, we use the mean squarederror (MSE), the arithmetic average of thesquares of the forecast errors - or better still,the root mean squared error (RMSE), since itmeasures the error in the same units as theforecasted variable (Table 2) . As a baseline forcomparison, we use the RMSE for a forecast ofno change.

After extraction of the estimated liquiditypremium, the RMSE of the market's forecast,1.24 percentage points, is only slightly lowerthan that for the forecast of no change - butsubstantially higher than the RMSEs of theanalysts' and autoregressive forecasts.Moreover, the approach used to estimate themarket's liquidity premium is more likely tohave understated rather than overstated thetrue difference between forecast errors, asshown in the Appendix. These differencesthus suggest the possibility of market ineffi­ciency. If the estimated difference between theRMSEs of the autoregressive forecast and themarket's forecast were statistically significant,then the condition of weak-form efficiencywould not be met. An autoregressive forecastbased on available past information on the

Table 2Accuracy of Forecasts

Root Mean Squared Error (RMSE)19701 - 1979111

(percentage points)Forecast of No Change (it) 1.25

Market's Forecast (F:"t-2) 1.24(Adjusted for LiquidityPremium as Estimated fromAppendix Table 1, Eq. 3)

Analysts' Forecast (Fgn ) 1.10t+2Autoregressive Forecast (Ff't) .94

Treasury- bill yields would have provided ameans for investors to "beat the market". Byaltering the maturity of their holdings in lightof a superior forecast they could haveimproved their returns.

The RMSE of the analysts' forecast is lowerthan the market's, but higher than that of theautoregressive forecast. Thus, the analystsmay not have made full use of the availableautoregressive information. Still, the analysts'forecast could contain other informationbesides that embodied in the past history ofthe rate. If an investor could have profitedfrom trading on such other information, themarket would also be inefficient in a strong­form sense. 8 The next two sections providetests of forecast accuracy that distinguish be­tween strong-form and weak-form ineffi­ciency.

II. Evidence of Market Inefficiency

We first test for weak-form inefficiency byexamining whether the autoregressive forecastcould have been systematically used to reducethe error in the market's forecast. This couldhave been done if the market's forecast error isat least partially explained by the difference be­tween the autoregressive forecast and themarket's forecast. Symbolically, this relation­ship is:

i1+ 2- F:: 2 = B I (F;"t2 - F;':2)

+ e'+2' (1)

32

where i'+2 - FIl1

2= the market's forecasterror for two quartersahead;

F;t~2 = the autoregressiveforecast for quarter t+ 2made at time t;

F;': 2= the market's forecast forquarter t+ 2 made at timet;

e t+2= a random error term.

The value of B b at .664, is more than fourtimes its estimated standard error, given in theparentheses, indicating that it is indeed signifi­cantly different from zero. Thus, theautoregressive forecast could have been usedto reduce the market's forecast error, confirm­ing the existence of weak-form inefficiency. Infact, in an optimal forecast combining the two,the autoregressive forecast would be given alarger weight (.664) than the market's forecast(.336). Also note that the unexplained forecasterror has been reduced to .90 percentagepoints (equals the equation's standard error,S .EJ from 1.24 percentage points (equalsRMSE in Table 2).

Note that this equation can be rearranged togive the optimal forecast'of the interest rate asa weighted average of the two forecasts. Thatis, it implies:

i t +2= BIF:~2 + (l - B I ) F:: 2+ e t +2 (2)

In the case of weak-form inefficiency, theautoregressive forecast would receive a signifi­cant weight. On the other hand, if there wereno weak-form inefficiency, the weight of theautoregressive forecast would be insignifi­cantly different from zero, and the weight ofthe market's forecast would be close to one.Thus, the test of weak-form inefficiency is thatthe estimated value of B I be significantlydifferent from zero, so that the autoregressiveforecast could have been used to improveupon the accuracy of the market's forecast.

F:~2 and F~:2 are apt to be highly correlatedin equation (2), tending to increase the stan­dard errors of the estimated coefficients. So itis preferable to test for weak-form inefficiencyby estimating equation (1); in addition, thisequation constrains the weights to add up tounity. We estimated this and other equationsusing ordinary least squares, with a correctionfor a moving-average pattern of serial correla­tion in the error term. 9 The estimate of equa­tion (l) is:

S.E. = .862

S.E. = .893

i'+2 - F:~2 = .685 (F~:2 - F;1~2) (5)

(.187)

"R 2= .155

A second test for confirming the existenceof strong-form inefficiency involves generaliz-

The estimated value of the B I coefficient, at1.15, is over six times its estimated standarderror. It is clearly significantly different fromzero and also not significantly different fromone. Thus, only the analysts' forecast shouldbe used in an optimal forecast combining both,because the market's forecast provides little orno additional information.

This test also supports a finding of marketinefficiency. Whether it is of the weak orstrong form, or both, depends upon the type ofinformation embodied in the analysts'forecast. If the analysts' forecast were basedonly on the historical behavior of the bill rate,only weak-form inefficiency would be con­firmed. But if it contains other information aswell, both weak and strong-form inefficiencywould be indicated.

Two additional tests can be made for thepresence of strong-form inefficiency. The firstexplains the forecast error of the autoregres­sive equation by the difference between theanalysts' forecast and the autoregressiveforecast. The significant coefficient estimatedin this equation, given below, indicates thatthe analysts' forecast can be used to explain animportant part of the error in theautoregressive forecast. Therefore, thatforecast contains other useful informationbesides the past history of rates.

A similar test can be performed with theforecast of the Goldsmith-Nagan panel ofanalysts, F~:2' as the alternative to themarket's forecast. When the difference be­tween the analysts' and the market's forecast isused to explain the market's forecast error, weobtain the following estimated equation:

(3)

S.E. = .900

(.163)

"R 2 = .467

33

Both of the estimated coefficients are larger

(.337)

+ .321 (F:~2 - F:: 2)

(.201)

R2= .498 S.E. = .873

ing the form of the original test. That is, theerror in the market's forecast might be ex­plained by both the difference between theautoregressive and the market's forecast andthe difference between the analysts' and themarket's forecast. In symbols,

than their standard errors and significantlydifferent from zero at the 5-percent level,indicating both weak- and strong-form ineffi­ciency.

To summarize the results so far, we havefound that two different types of informationcould have been used to improve upon themarket's forecasts of the 3-month Treasurybill rate, as embodied in the forward rate. Thesignificance of an autoregressive equation inexplaining the market's forecast error indi­cates weak-form inefficiency, and the addedsignificance of the analysts' forecast confirmsstrong-form inefficiency. By incorporating theinformation contained in an autoregressivemodel and the analysts' forecast, the RMSEfor a two-quarter ahead forecast of the 3­month Treasury bill rate could have beenreduced from the market's 1.24 percentagepoints to .87 percentage points (equal to S.E.of equation (8). In fact, when all threeforecasts are combined into an optimalforecast, as in equation (7), the weightattached to the market's forecast is not signifi­cantly different from zero, at .022 (equals 1 ­.657 - .321). Thus, the autoregressiveforecast and the analysts' forecast contain allthe useful information embodied in themarket's forecast, plus other useful informa­tion as well.

(6)

(8)

i'+2 - F~:2 = B I (F;"1-2 - F~+2)-I- Q {pgn _ \ -'- ~

I U2 \.1·1+2 l+2JT C;t+2

or in the alternative form,

i'+2 = BIF;I~2 + B2F~:2 (7)

+ (l - B I - B2) F:: 2+ e'+2

If both B I and B2 were significantly differentfrom zero, then both weak and strong-forminefficiency would be confirmed. We againchoose the first form of the equation forestimation, in order to reduce the extent ofmulticollinearity among the independentvariables. The estimate of the equation in thisform is:

III. Information in the Analysts' Forecast

The previous section tested whether infor­mation from a simple autoregressive modelthat could have been used to predict the Treas­ury-bill rate was fully incorporated into themarket's forecast, as embodied in the forwardrate. However, some judgment enters into theconstruction of even such a simpleau toregressi ve forecast. Specifically, if thetime pattern of rate movements is unstable,the forecasting power of the estimatedautoregressive equation could depend impor­tantly upon the period of estimation chosen.Therefore, we should also test whether theautoregressive information that was actually

used by the professional analysts was efficientlyincorporated into the market's forecast. Infact, we shall see that the autoregressive infor­mation contained in the analysts' forecast issubstantially the same as that in ourautoregressive equation.

To approach this question, we decomposedboth the professional analysts' forecast and themarket's forecast into the portion related tocurrent and past bill rates (extrapolative com­ponent) and the remainder that is not sorelated (autonomous component) .10 Thedifference between the two forecasts can thenbe decomposed into the difference between

34

the extrapolative components of the twoforecasts plus the difference between theautonomous components. If the difference be­tween the extrapolative components werestatistically significant in explaining themarket's forecast error, this would indicatethat autoregressive information actually incor­porated into the analysts' forecasts was usefulin predicting the interest rate, but neverthelesswas not fully incorporated into the market'sforecast - an instance of weak-form ineffi­ciency. Similarly, if the difference between thetwo autonomous components were significantin explaining the market's forecast error, thiswould mean that the market did not incorpor­ate other useful information available to theanalysts - an instance of strong-form ineffi­ciency.

The extrapolative components of the twoforecasts were estimated by regressing thedifference between each forecast and the cur­rent rate on lagged quarterly differences in the

bill rate. Such components can be divided con­ceptually into three parts (Figure 1). The firstis simply the current interest rate, or a predic­tion of no change corresponding to a random­walk hypothesis. If there were no significantcoefficients in the above regression, the esti­mated extrapolative component would containonly the current interest rate. The secondcomponent is a time trend, or drift factor, indi­cated by a significant constant term in theabove regression. The third component is thepart of the forecast related to past changes inthe interest rate, indicated by any significantcoefficients on past changes in the rate.

The best equation explaining the differencebetween the market's two-quarter aheadforecast of the bill rate and the current rate,chosen on the basis of a minimum standarderror, contained no constant term and no lag­ged changes in the bill rate. Thus theextrapolative component of the market'sforecast, E:: 2, is estimated to be simply a

Figure 1

Elements of a Forecast

Realized interest rate ------....."'.

Forecast error

Autonomouscomponentof forecast

j'"Related to !

past changes." '"

Time-Trend [_ .1$!

Forecasted interest rate ---,-----_. ",'

Currentinterest rate

Forecast ofno change

Extrapol ativecomponentof forecast

1 1+2 Time

Note: For simplicity, this illustration assumes that elements of the forecast other than the current interest rate allcontribute to reducing the forecast error. Of course, this need not be true in general. For example, the time trend,the part of the extrapolative component related to past changes, and the autonomous component could all benegative, instead of positive as assumed here. In that case, and given the same realized interest rate, theforecasted interest rate would be below, rather than above, a forecast of no change; and the forecast error wouldbe increased, rather than reduced, by these three elements.

35

forecast of no change, equal to the currentinterest rate (i t). Symbolically,

The significant coefficient on the differencebetween the two extrapolative components

+ .323 (F:I~2- F~:2)

(.215)

R 2 = .484 S.E. = .885

+ .766 (E~:2 - E:2) + .323(.584)

The estimated standard error of the equationis reduced somewhat by the addition of thedifference between these two forecasts.However, neither the coefficient on this varia­ble nor the coefficient on the difference be­tween the extrapolative elements is signifi­cantly different from zero at the 5-percentlevel. Thus, neither the extrapolative compo­nent of the analysts' forecast nor theautoregressive equation's forecast significantlyreduces the market's forecasting error oncethe other is already being utilized. Both con­tain a positive time trend and extrapolate frompast changes in the bill rate, in contrast to theprediction of no change in the extrapolativeelement of the market's forecast. On the basisof this evidence, we conclude that the analystshave utilized the available information on pastmovements in the bill rate about as efficientlyas possible in their forecast.

shows that the analysts made better use ofinformation in the bill rate's past history thandid the market. In addition, the significantcoefficient on the difference between the twoautonomous elements indicates that theanalysts made superior use of other informa­tion besides past rates. The first finding con­firms weak-form inefficiency, and the secondsubstantiates a strong form of inefficiency.

A final point of interest is whether theextrapolative component of the analysts'forecast utilizes available information on pastbill-rate movements as efficiently as possible.To examine this question, we simply added tothe right-hand side of equation (12) thedifference between the forecast from theautoregressive equation and the market'sforecast, and then reestimated the equation.This gives

i t+2- F:: 2= .650 (A~:2 - A:) (13)

(.348)

(9)

(1 I)

S.E. = .902

(,377)

R2= .464

(.200)

F~:2 - 1+2 (A~:2+ E~:2)

- (A: 2 + E:: 2)

= (A~:2 - A::)

+ (E~:2 - E:':2)

To test for both weak and strong-form ineffi­ciency in terms of the autoregressive informa­tion actually used by analysts in theGoldsmith-Nagan survey, we substitute equa­tion (1 I) into equation (4) and reestimate thelatter. The resulting estimated equation is:

i t +2- F: 2= 1.10 (A~n~2 - A:: 2) (12)

tive element. It follows that the difference be­tween the analysts' forecast and the market'sforecast equals the sum of the differences be­tween the autonomous and extrapolative com­ponents; or

In contrast, the extrapolative component ofthe analysts' two quarter-ahead forecast, E~:2'

estimated in the same fashion, was found to bemore complex. It contains a constant term,indicating a positive time trend in the interestrate of 54 basis points per year, and significantlags at 1, 3, 4, and 5 quarters on past changesin the bill rate. Specifically, the extrapolativecomponent of the analysts' forecast is esti­mated to be:

E~:2 = it + .270 - .196(it - it-I) (10)

+ .186 (it-J - i H )

+ .306(iH - i t - s) + .200(i t •s - i H )

The autonomous components, denoted byA: 2 and A~:), are simply the difference be-tUloan tho t"ocn.a.r"ttUA fArAf"''lC'f -:lnrl ite' p-vtr<.:lnf\lo:l_L YV \,.1\,;1.1 Ll1'-' .l \,.IJP,",,"""-1 V ...... J. VI. ",,-,,"-,U'-'\. Ul.1. ..... l .. oJ .... ,.,,\.:1. ""'"p ....... ~~

36

These analysts also efficiently incorporatedother publicly available information into theautonomous component of their forecast. AsFriedman (1980) has shown, the forecastingerrors of the Goldsmith-Nagan panel for short­term interest rates have not been significantly

related to costlessly available information onpast values of macroeconomic series affectingthe interest-rate outlook. These series includethe unemployment rate, industrial production,price inflation, the money stock, and theFederal government's deficit.

IV. Summary and Conclusions

We have investigated whether the Treasury­bill market has efficiently utilized all availableinformation, so as to incorporate the bestpossible two-quarter ahead forecast of the 3­month interest rate into its pricing of Treasurybills. A forecast by a panel of professionalanalysts was used as a measure of all availableinformation, and the subset of informationrelating to the bill rate's past history was esti­mated by a simple autoregressive forecastingequation. We found that the analysts' forecastcontained a similar extrapolative component,based on past movements in the bill rate.Either this extrapolative component of theanalysts' forecast or a forecast from theautoregressive equation could have been usedto reduce the error in the market's forecast sig­nificantly; but both contained substantially thesame information. In addition, the analysts'forecast contained other useful information forexplaining a portion of the market's forecasterror. Altogether, the tests performed indicatethe existence of both weak-form and strong­form inefficiency.

Earlier studies of the efficiency of the Treas­ury-bill market, focusing on shorter terminterest-rate forecasts, have usually indicatedweak-form efficiency. In contrast, our resultsfor a two-quarter ahead forecast of the bill rateshow the existence of weak-form inefficiency.This reflects the positive time trend found inboth the extrapolative component of the

37

analysts' forecast and the autoregressiveforecasting equation, but not in the market'sforecast. The market's forecast of the 3-monthbill rate failed to incorporate the upward driftin the bill rate attributable to rising inflation inthe forecast period, even though this driftcould have been extrapolated from past data.

In testing for a stronger form of efficiency inthe bill market, earlier studies have generallyused current and past values of relevantmacroeconomic variables to measure otheravailable information besides the past historyof the bill rate. This study used the non­extrapolative component of the professionalanalysts' forecast for this purpose. We foundthat this component also contributed signifi­cantly to reducing the market's forecast error,indicating strong-form inefficiency as well.

The difference between the overall forecasterror of the analysts and the market in the1970's was a modest 14 basis points, asmeasured by the root mean square error.Nevertheless, our results indicate that theabove types of market inefficiency would haveallowed an investor to trade on the informa­tion contained in the analysts' forecast. Aninvestor could have improved his overallreturns with a strategy of shortening maturitieswhen the analysts' forecast was above themarket's and lengthening them when theopposite was true.

AppendixEstimation of the Market's Forecast

The market's two-quarter ahead forecast ofthe 3-month Treasury bill rate is embodied inthe differential between yields on 6- and 9­month bills. To see this, first consider thatmarket arbitrage makes the yield on a 9-monthTreasury bill equal to the expected return on a3-month Treasury bill that is rolled over twice,plus premiums for the sacrifice of liquidity. Inalgebraic terms, the yield on the 9-monthTreasury bill is:

are generally unbiased, so that the realizedinterest rate is approximately equal to the anti­cipated interest rate plus a random error:

(4)

The market's forward rate (f) is composed ofan anticipated interest rate and a liquidity pre­mium (p), or:

(5)

(6)

If the liquidity premium were constant, itcould be estimated simply from the averagedifference between the forward rate and subse­quently realized interest rate. But previousresearch indicates that liquidity premiums arein fact somewhat variable. Three not mutuallyexclusive hypotheses have received support inthe literature.

The first hypothesis views short-termsecurities as better substitutes for money bal­ances than longer-term securities. If this is so,the liquidity premium would tend to beaffected by the level of interest rates. Wheninterest rates are high, the foregone interestincome in holding money is larger; and so thepublic desires to exchange part of its moneybalances for securities. However, if the publicprefers short to long securities in thisexchange, prices of short-term securitieswould be bid up relative to those on longer­term ones. This would increase long-terminterest rates relative to returns on short-termsecurities, or in other words increase theliquidity premium. Thus, if short-termsecurities are better substitutes for money thanlonger-term ones, liquidity premiums wouldvary positively with the level of interest rates. II

Subtracting (4) from (5), we obtain theliquidity premium as equal to the differencebetween the market's forward rate and therealized interest rate plus the random (expec­tationaO error:

0)

Dividing (0 by (2) and rearranging terms, weobtain equation (3) for the market's two­quarter ahead "forward rate." This is theexpected return (including a liquidity pre­mium) on a 3-month Treasury bill two quar­ters ahead that is required to bring aboutequality between the expected returns on 6­and 9-month bills over a 9-month holdingperiod.

o + 3i ) 3

To evaluate the accuracy and informationcontent of the market's two-quarter aheadforecast, it is necessary to separate the liquiditypremium from the forward rate. The approachused here assumes that market expectations

where i = yield at a quarterly rate,p = liquidity premium,left subscript = maturity of security inquarters,right subscript = time at whichinvestment in security begins in quar­ters,superscript "e" = interest rateexpected by the market as of time t.

Similarly, for a 6-month Treasury bill:

0+ 2it)2= 0 + lit) 0 + li et+ l + IPt+l) (2)

38

Two other hypotheses relate to the fact thatliquidity premiums on longer-term securitiescompensate investors for exposure to the riskof capital losses. The greater the probablevariation in interest rates, the larger would besuch risk. If the variation in interest rates isexpected to approximate its recent variance,liquidity premiums ought to vary positivelywith that variance. Moreover, at times wheninterest rates are abnormally low (high), thereis a large likelihood of unanticipated increases(decreases) producing unanticipated capitallosses (gains) for holders of longer-termsecurities. Therefore, liquidity premiumsmight also be expected to vary inversely withthe height of interest rates relative to recentlyexperienced levels. 12

These three hypotheses were tested withavailable data for the period 1963-IV through1979-III (Table A.1). Following equation (6),we regressed the difference between the two­quarter ahead forward rate and the realizedTreasury-bill rate on the current level of theTreasury-bill rate (j), the difference betweenits current level and a weighted average of its

value over the previous 2 years (i - n, and thestandard deviation of the bill rate over thesame period (SD).13 When no explanatoryvariables other than a constant term areincluded, the constant term is significantlypQ$itive - indicating a positive averageliquidity premium of 54 basis points.

The level of the bill rate (reflecting theeffect of better substitutability between short­term securities and money) and the deviationof the bill rate from its recent trend (proxyingfor the probability of unanticipated capitalgains on long-term securities) show significanteffects when entered together. So also does themoving standard deviation of the bill ratewhen entered alone. But when all three varia­bles are entered together, only the standarddeviation retains at least marginal significance.Also the equation containing only the movingstandard deviation of the bill rate explainsmore of the variation in the liquidity premiumthan does the equation containing the othertwo variables. Mainly on statistical grounds,we chose this equation ((3) in Table A.1) forseparating out the liquidity premium from the

Table A.1.Estimation of liquidity Premium in the Two-Quarter Ahead

Three-Month Forward Rate (percentage points)19631V - 1979111

Constant i-i SO R2D.W. S.E.

(I) .540 .000 1.28* 117(.134)**

(2) -1.30 3.54 -.443 .148 1.67** 1.08(.565)** (I.03)** (.153)**

(3) -1.56 .162 .175 1.53** 1.07(.565)** (.0424)**

(4) -1.49 -.0909 .195 .169 1.62** 1.07(.578)** (.120) (.0611)**

(5) -1.49 -.0746 .158 .166 1.62** 1.07(.582)** (.126) (.0431)**

(6) -1.58 -.602 .545 .409 .165 1.51 ** 1.07(.560)*- (.645) (.675) (.273)

Note: Standard errors are in the parentheses.•• indicates a regression coefficient that is significantly differentfrom zero at the 1- percent level on the basis of a single-tailed test; and' indicates significance at the 5-percentlevel. The same symbols on the Durbin-Watson statistic (OW.) indicate the absence of significant serial correla­tion in the residuals at the 5- and 1-percent levels, respectively.

39

forward rate to arrive at an estimate of themarket's anticipated 3-month bill rate.Because this equation has a minimum standarderror, the resulting estimate of the market'sforecast has a minimum forecast error.However, equations incorporating the othervariables did about as well; and our results arenot particularly sensitive to this choice.

The procedure used to estimate the liquiditypremium assumes that the market's trueexpectational error, v, is uncorrelated with thevariables used to model the liquidity premium.To the extent that this condition is not met,the estimated liquidity premium captures aportion of the market's true expectationalerror. This portion of the true expectationalerror would then be removed from the esti­mate of the market's forecast when the esti­mated liquidity premium is subtracted fromthe forward rate. As a simple example of theproblem, if the true expectational error ispositively biased, the procedure would over­estimate the liquidity premium and correspon­dingly underestimate the size of the market'sforecast errors.

We investigated the seriousness of thisproblem by regressing the difference betweenthe forecast of the Goldsmith-Nagan panel andthe realized interest rate on the variables usedin Table A.l to explain the liquidity premium.The estimated coefficients from these regres­sions carried the same signs as those in TableA.l and were frequently statistically signifi­cant, indicating that these variables are capableof proxying for a portion of the analysts'forecast errors. However, the size of thesecoefficients was generally considerably smallerthan those in Table A.I., as would be true ifthese variables were also related to themarket's liquidity premium.

To minimize inclusion of expectationalerrors, we estimated the liquidity-premiummodels on all available data going back to1963, rather than just on the forecast period of1971-1 thru 1979-111. This reduced contamina­tion of the estimated liquidity premium with

40

expectational error, because the additionaldata allowed more of an ex ante estimation. Apurely ex post fit of the forward rate's forecast­ing error against the liquidity-premium varia­bles would have generated a larger bias.

Even so, our estimate of the liquidity pre­mium appears to have picked up some of thetrue expectational error in the market'sforecast. This was tested for by removing fromboth the analysts' forecast and the market's for­ward rate the portion of the respectiveforecast error associated with the moving stan­dard deviation of the bill rate (and a constantterm) during 1971-1 thru 1979-IIl. Theseadjusted forecasts were obtained by regressingeach forecast error on the moving standarddeviation of the bill rate, and subtracting thevalues predicted by these regression equationsfrom each respective forecast. The procedureremoves a similar amount of true expecta­tional error from both forecasts, but also anestimated liquidity premium from themarket's forward rate. The RMSE's of theseadjusted forecasts are 1.01 and 1.18 percentagepoints for the analysts' and the forward rate,respectively. The difference of 17 basis pointsexceeds the 14- basis-point difference betweenthe RMSE's of the analysts' forecast and ourestimate of the market's forecast, suggestingthat our estimate of the latter tends to under­estimate the size of its forecasting error.

The procedure used for estimating theliquidity premium has thus tended to under­state the difference in true expectational errorsbetween the market's forecast and the twoalternative forecasts. A possible bias in theopposite direction could result from the omis­sion of an important variable for explaining theliquidity premium. However, we have alreadyconsidered all such possibilities. On balance, itis more likely that our estimate of the market'sliquidity premium has understated, rather thanoverstated, the true difference between theforecast errors of the market and those of alter­native forecasts.

FOOTNOTES

Li t = .118 + .239 Li t_1 - .241 Li t_2(.055) (1.19) (.120)

market prices. Any systematic pattern in expectedshort-period returns would quickly be eliminated asinvestors bid prices up or down in attempts to profitfrom them. In contrast, the returns on 3-month Treas­ury bills held to maturity cannot be affected by thiskind of speculation, because the price at the end. ofthe 3 months is fixed contractually. Therefore, even afully anticipated time pattern in 3-month Treasury billyields is not likely to be arbitraged away in an effi­cient market. However, an efficient market would takesuch a pattern into account in its bill-rate forecasts.

5. The data sample was extended back to 1951-IVusing Salomon Brothers, An Analytical Record ofYields and Yield Spreads. The best autoregressiveequation for explaining quarterly changes in the 3­month Treasury bill rate during 1951-IV thru 1969-111was found to be:

Standard errors are indicated in the parenthesis. Atwo-step ahead forecast from this autoregressivemodel of quarterly changes was found to be con­siderably more accurate than a one-step aheadforecast from past changes over six-month periods.Hence, the two-step ahead forecast (with coefficientsreestimated at each point in time) was used for Ff~2'

6. The author wishes to thank Mr. Peter Nagan of TheGoldsmith-Nagan Bond and Money Market Letter forpermitting use of this data.

7. A large body of literature exists on the term struc­ture of interest rates. Useful surveys are contained inDodds and Ford (1974), Malkiel (1966), and VanHorne (1966).

An alternative measure of the market's forecast canbe obtained from yields on Treasury-bill futures con­tracts. We did not examine the accuracy of this typeof forecast because a futures market in 3-monthTreasury bills has existed only since January 1976,reducing the number of observations by more thanhalf. For a comparison of yields on futures contractsand implied forward rates, see Lang and Rasche(1978) and Poole (1978).

8. Prell (1973) compared the accuracy of forecastsby the Goldsmith-Nagan panel of analysts with thoseof no change and an autoregressive model for 1970through 1973. In that period, the RM8E of theGoldsmith-Nagan panel for a two-quarter ahead pre­diction of the 3-month bill rate was smaller than forboth of these forecasts. However, for long-term bondyields, the panel's forecasts were no more accuratethan a forecast of no change. Prell (1973) did notcompare the accuracy of the analysts' forecast withthose of the market implied by the forward rate, aswould be necessary for a test of bill-market effi-

- .126 Li t_3 - .338 Li t_6(.124) (.122)

DW.=1.93S.E. = .449f'l2=.193

1. Tests of market efficiency were first applied to thestock market. Surveys of this literature are containedin Cootner (1964), Fama (1970), and Lorie andHamilton (1973, Ch. 4).

2. The weak form of the efficient-market hypothesishas been tested in several ways in the bill market.One approach is to determine whether the forwardrate applicable to any particular point in time follows arandom walk. If the market's adjustment to new infor­mation is virtually instantaneous, as in an efficientmarket, successive changes in the forward rateapplicable to any particular period should be random.This approach is followed in Shiller (1973) and Roll(1970).

A second approach to testing the weak form is todetermine whether the market reacts appropriately toany autocorrelation in the bill rate. While changes inthe forward rate applicable to any particular timeperiod should be random, it does not follow that forweak-form efficiency the spot rate should also followa random walk. Indeed, in an efficient market the for­ward rate should extrapolate any systematic autocor­relation that tends to occur in the spot rate. Evidenceof weak-form efficiency in market forecasts of up to afew months ahead is contained in Hamburger andPlatt (1975), Fama (1975b), and Fildes and Fitzgerald(1980).

Fama (1975a, 1977) takes a rather differentapproach by focusing on the relationship between thenominal Treasury-bill rate and the subsequentlyobserved inflation rate. For weak-form efficiency, thenominal interest rate would summarize all the infor­mation about future inflation rates contained in thetime series of past inflation rates, so long as theexpected real return is constant. Fama argues thatthe Treasury-bill market is efficient in this sense, andalso that expected real returns on Treasury bills areapproximately constant. Carlson (1977), Joines(1977), and Nelson and Schwer! (1977) present con­trary evidence on the constancy of the expected realrate, but their evidence with respect to weak-formefficiency is not conclusive.

3. Hamburger and Platt (1975) conduct a test of thesemistrong form of efficiency by investigatingwhether variables other than current and past levelsof interest rates are better than forward rates in pre­dicting future bill rates. They considered such poten­tial predictors as the current and past values of per­sonal income and three alternative monetary aggre­gates, but found that the root-mean-squared error forsuch forecasts was no smaller than from a forecastusing the forward rate, consistent with the semistrongform of efficiency.

4. For the market to be considered efficient, theTreasury-bill rate does not necessarily have to followa random walk, in which the change in the return fromone period to the next is completely random. A ran­dom walk would be consistent with weak-form effi­ciency in stock and bond markets because short­period returns on these securities are dominated bycapital gains or losses resulting from changes in

41

ciency. For another study of the accuracy of interest- rate forecasts by market professionals, see Fraser (1977).

9. A moving-average pattern follows from overlap­ping of forecasts, i.e. quarterly observations on two- quarter ahead forecasts. The Chi Square statistic indicated an absence of significant serial correlation after this correction.

10. This approach is similar to one used in Diller (1969).

quarter. The two-quarter ahead 3-month forward rate, and the 3-month spot rate, were converted to a bond- equiva lent y ie ld (as was the forecast by the Goldsmith Nagan panel). The formula for converting the Treasury-bill rate on a discount basis into its bond-equivalent yield is:

~d m360 365

' _ 1 - d-H5___ m360

11. Cagan (1969), Conard (1966, Ch. 7), Friedman (1 979) and Kessel (1 965) find evidence of a positive relationship between estimated liquidity premiums and the level of interest rates, consistent with the the o ry th a t s h o rt- te rm s e c u r it ie s are c lo s e r substitutes for money than long-term securities.

12. Evidence for this is contained in Fildes and Fitzgerald (1980), Malkiel (1966, Ch. 7), Nelson (1972, Ch. 6) and Van Horne (1 966). Studies indicat­ing a positive relationship between liquidity premiums and recent variance in the interest rate include Fildes and Fitzgerald (1980), McElhattan (1975), Modigliani and Shiller (1973), and Talley (1979).

In estimating a liquidity premium for the Aaa corpor­ate bond rate, McElhattan (1975) found a response to the recent variation in inflation, in addition to the recent variance in the nominal interest rate. We experimented with this formulation, but could find no significant response of the liquidity premium in the two-quarter ahead forward rate to the variance in inflation, after allowing for the impact of the variance in the interest rate.

1 3. We obtained interest-rate data from daily closing quotations tabulated by the Federal Reserve Bank of New York. The original data from this source con­sisted of bid-side interest rates on a discount basis for 3, 6, and 9-month Treasury bills at the end of each

where i is the bill rate on a bond-equivalent yield basis, d is the bill rate on a discount basis, and m is the maturity of the bill in days. From equation (3) in the Appendix, the two-quarter ahead forward rate on a bond-equivalent basis is seen to be:

1 ft+2 “'3 ° t 273

360

1 - 2 dt 182360

36591

where 2dt and 3dt are the 6- and 9-month Treasury- bill rates on a discount basis.

Besides the current spot rate, the variables in the equation explaining the liquidity premium are the standard deviation of the spot rate about its mean over the past two years (SD), and the deviation of the current spot rate from a weighted average of its value over the same period (i - i). Weights in this average of past spot rates were declining, with:

( i - i ) t= i t- S n_ it-n n=1 28

REFERENCES

Cagan, Philip. “ A Study of Liquidity Premiums on Federal and Municipal Government Securities,” in Jack M. Guttentag and Phillip Cagan (eds.), Essays on Interest Rates, Volume. I. New York: National Bureau of Economic Research, 1969.

Carlson, John A. “ Short-Term Interest Rates as Pre­dictors of Inflation: Comment,” American Eco ­nomic Review, June 1977, pp. 469-75.

Conard, Joseph W. The Behavior of Interest Rates,National Bureau of Economic Research, 1966.

Cootner, Paul H., ed. The Random Character of Stock Market Prices. Cambridge: M.l.T. Press, 1964.

Diller, Stanley. “ Expectations in the Term Structure of Interest Rates,” in Jacob Mincer, (ed.), Economic Fo re casts and Expectations: A nalyses of Forecasting Behavior and Performance. NewYork: National Bureau of Economic Research, 1969, pp. 112-66.

Dodds, J.C. and Ford, J.L. Expectations, Uncertainty and the Term Structure of Interest Rates. NewYork: Barnes and Noble, 1974.

Fama, Eugene F. "E ffic ient Capital Markets: A Review of Theory and Empirical W ork,” The Journal of Finance, May 1 970, pp. 383-41 7.

____“ Short-Term interest Rates as Predictors ofInflation,” American Economic Review, June1975, pp. 269-82.

____“ Forward Rates as Predictors of Future SpotRates,” Journal of Financial Economics, July1976, pp. 361-77.

____“ Interest Rates and Inflation: The Message inthe Entrails,” American Economic Review, June1977, pp. 487-96.

Fraser, Donad R. “ On the Accuracy and Usefulness of Interest Rate Forecasts,” Business Economics, September 1977, pp. 38-44.

42

Fildes, Robert A. and Fitzgerald, M. Desmond. “ E ffi­ciency and Premiums in the Short-Term Money Market,” Journal of Money, Credit and Banking,November 1980 (Part I), pp. 615-29.

Friedman, Benjamin M. “ Interest Rate Expectations Versus Forward Rates: Evidence from an Expec­tations Survey,” The Journal of Finance, Sep­tember 1979, pp. 965-73.

____“ Survey Evidence on the Rationality of InterestRate Expectations,” Journal of Monetary Eco ­nomics, October 1980, pp. 453-65.

Hamburger, Michael J. and Platt, E lliott N. “ The Expectations Hypothesis and the Efficiency of the Treasury Bill Market,” Review of Economics and Statistics, May 1975, pp. 190-99.

Joines, Douglas. “ Short Term Interest Rates as Pre­dictors of Inflation: Comment,” American Eco ­nomic Review, June 1977, pp. 467-77.

Kessel, Reuben A. The C yclical Behavior o f The Term S tructure o f In terest Rates. New York: National Bureau of Economic Research, 1965.

Lang, Richard W. and Rasche, Robert H. “ A Com­parison of Yields on Futures Contracts and Implied Forward Rates,” Federal Reserve Bank of St. Louis, Review, December 1 978, pp. 21 -30.

Lorie, James H. and Hamilton, Mary T. The Stock Market: Theories and Evidence. C hicago: Richard D. Irwin, Inc., 1973.

Malkiel, Burton G. The Term S tructure of Interest Rates: Expectations and Behavior Patterns.Princeton: Princeton University Press, 1966.

McElhattan, Rose. “ The Term Structure of Interest Rates and In fla tio n U n ce rta in ty ,” Federal Reserve Bank of San Francisco, Economic Review, December 1975, pp. 27-35.

Modigliani, Franco and Shiller, Robert J. “ Inflation, Rational Expectations and the Term Structure of Interest Rates,” Economica, February 1973, pp. 1 2-42.

Nelson, Charles. The Term Structure of Interest Rates. New York: Basic Books, Inc., 1972.

___ and Schwert, G. William. "Short-Term InterestRates as Predictors of Inflation: On Testing the Hypothesis that the Real Rate of Interest is Con­stant,” American Economic Review, June 1977, pp. 478-86.

Poole, W illiam . “ Using T -B ill Futures to Gauge Interest Rate Expectations” , Federal Reserve Bank of San Francisco, Economic Review, Spring 1 978, pp. 7-1 9.

Prell, Michael J. “ How Well Do the Experts Forecast Interest Rates?” Federal Reserve Bank of Kan­sas City, Monthly Review, September-October 1973, pp. 3-13.

Roll, Richard. The Behavior of Interest Rates: An Application of the Efficient Market Model to U.S. Treasury Bills. New York: Basic Books, Inc., 1970.

Sargent, Thomas J. “ Rational Expectations and the Term Structure of Interest Rates,” Journal of Money, Credit, and Banking, February 1 972, pp. 74-97.

Shiller, Robert J. “ Rational Expectations and the Term Structure of Interest Rates: A Comment,” Journal of Money, Credit, and Banking, August 1973, pp. 856-60.

Talley, Ronald J. “ Market Interest Rate Expectations as Reflected in the Term Structure,” Business Economics, September 1979, pp. 10-18.

Van Horne, James C. “ Interest Rate Risk and the Term Structure of Interest Rates,” Journal of Po­litical Economy, December 1966, pp. 629-35.

____Financial Market Rates and Flows. New York:Prentice-Hall, 1 978.

43

Herbert Runyon*

In published polls, inflation generally topsthe list of problems troubling the public.Indeed, television news programs are full ofdiscussions of the rampancy of inflation. Butmost audiences remain unaware of how tomeasure inflation - or of what it really is.

Inflation may best be described as a substan­tial and continued rise in the general pricelevel. However, the general price level is hardto define in practice and presents a number ofmeasurement problems, since it is a singlenumber that represents the average behaviorof a great many prices during a given period oftime. In a system of freely functioningmarkets, there can be a great deal of disparityin the movement of prices of individual com­modities or services. This is to be expected, asshifting demand-and-supply conditions forspecific items become reflected in their prices.As demand presses against supply for somegoods, their market prices may rise relative toother goods. Of itself, this does not constituteinflation, because other prices may be falling- witness color television sets or hand-heldcalculators. Relative price movements of thissort are a normal manifestation of functioningmarkets for resources and final goods. Butinflation exists only if the prices of most goodsare rising, or if increases in the prices of somegoods consistently outweigh declines in theprices of other goods.

As inflation has accelerated - with con­sumer prices doubling over the last decade ­policymakers have attempted to offset itsimpact on living standards by indexingincomes to the cost of living. Workers, stillactive in the labor market, have tried tominimize their inflation-caused loss of eco­nomic welfare by negotiating cost-of-living

*Research Officer, Federal Reserve Bank of San Fran­cisco. Steve Kamin provided research assistance.

44

adjustments (COLAs) into bargaining agree­ments. Other groups now outside the labormarket - such as the retired or disabled ­instead have depended upon the political pro­cess to ensure income maintenance. These twomethods of adjusting to inflation - the marketprocess and the political process - are notnecessarily comparable, and consequentlyhave sometimes produced inequities.

The official measures of the cost of livingplaya significant role in public-policy decisionsaffecting both wages and income-maintenanceprograms. About two-fifths of the Federalbudget consists of expenditures that are tied,or "indexed," to some such measure. As aresult, inflation significantly affects theFederal budget. And in the private sector,COLAs are imbedded in most large unioncontracts. Altogether, about 80 million per­sons are affected by indexed payment of wagesor nonwage benefits. Thus, policymakers mustuse an index that accurately reflects change inthe price level of the pattern of consumerexpenditures.

The choice of a measure raises fiscal-policyquestions. If the growth of the official indexexceeds the increase in the actual expenditurepattern of individuals (designated here as thecost of living), real government expenditureswill rise and contribute to a Treasury deficit.Moreover, if the index is upwardly biased,many wage and benefit recipients will be over­compensated. However, many cost-of-livingadjustments (at least in government transferprograms) are based on the inflation rate insome earlier period (generally the precedingyear). Hence, when inflation accelerates, theamount of actual overcompensation may beless than might appear; on the other hand,when inflation decelerates, the overcompensa­tion increases.

I. Measuring Changes in the Cost of Living

Before attempting to measure the cost of liv­ing, we must first define what we wish tomeasure. The term "living" refers toindividuals' consumption patterns - the sumof commodities and services they consumewithin a given period. We assume that con­sumers plan their expenditure patterns so as toachieve the maximum amount of pleasure orwell-being within the income fixed for a giventime period. I Hence an increase in the cost ofliving may occur when the income needed tosecure a given level of satisfaction increasesfrom one period to another. In principle, then,the change in the cost of living between twoperiods may be represented as the ratio of twoincomes - with the denominator being theincome in the first or "base" period, and withthe numerator being the smallest incomerequired in the second period to buy the groupof commodities that affords the base period'slevel of satisfaction. 2

However, this concept is not amenable todirect measurement, since we cannot observedegrees of individual satisfaction, and hencecannot know whether an individual is main­taining the same level of satisfaction. 3 Thisbasic problem may be illustrated by consider­ing a simple situation in which a householdconsumes only two commodities with prices PIand P 2 (Figure 1) .

In this illustration, the point P representsthe level of prices of both commodities in the

Figure 1

P"

P

45

base period. Suppose now that the prices ofboth commodities double, as in the move fromP to P". In such a case we can say un­equivocally that the cost of living has doubled,since clearly the cost of obtaining the samelevel of satisfaction has exactly doubled.

But suppose the price of commodity 1 morethan doubles while that of commodity 2 lessthan doubles, as in the movement of pricesfrom P to P'. The total cost of the consumptionbundle may have exactly doubled, but thisdoes not mean a doubling of the cost of livingas we have defined it. In fact, it will probablyhave less than doubled. The consumer canprobably obtain his previous level of satisfac­tion at less than double the cost, by buying lessof commodity 1 (whose price has more thandoubled) and more of commodity 2 (whoseprice has less than doubled). However, wecannot tell precisely how much the cost of liv­ing has risen, because we do not know howmuch commodity-substitution the householdwill undertake in any particular price situation.

Statisticians have developed index numbersto deal with the problem of measuring changesin the cost of living. Their calculations utilizethe following quantities:

Po = price ofa given commodity in the con­sumer's expenditure pattern in timeperiod to

qo = quantity of the given commoditypurchased by the consumer in to

PI = price of the commodity purchased in t l

q I = quantity of the commodity purchasedin t l .

One approach - the Laspeyres index ­maintains a fixed composition of the pattern ofgoods and services consumed in the baseperiod. (The index was developed by the I9th­century French-German economist, EtienneLaspeyresJ Here, the amount spent on anindividual commodity is represented by theprice multiplied by the amount purchased, orPo qm for the base year. Hence total expen­ditures of consumers in the base period to aresimply the sum of these products of price andquantity, or Po qo. In order to compute a

Laspeyres index for a later period, t j, we mustcalculate the quantities purchased in the baseperiod to at the market prices prevailing in thelater period tj, or pj qo. Total consumer expen­ditures are then represented by PI qo and theentire index by

while the denominator represents how muchthat bundle would have cost in the past period.

How Do Indexes Differ?Because of their difference in approach, the

two indexes may differ considerably asmeasures of the cost of living. The base-periodconsumption bundle is fixed for the Laspeyresindex, so that it makes no allowance forsubstitution in the consumption pattern.However, as relative prices of goods change,consumers may find it advantageous to switchfrom higher-priced goods to lower-pricedsubstitutes. The classic example is the relativeprice of beef and chicken. As the price of beefrises, the consumer can substitute chicken inhis diet and expenditure pattern. But theLaspeyres index, being a base-period fixed­weight index, assumes that consumers willcontinue to buy the same quantity of beefpurchased in the base period rather than turnto chicken. With substitutions disallowed inthe base-year consumption pattern, expen­ditures for the more expensive beef receivetoo great a weight and chicken too small aweight. Hence the index overstates the trueincrease in the cost of living.

The Paasche index, being based upon the

P

L = :l,Pjqo:l,Poqo

In this formula, the denominator representsthe amount actually spent in the base period,while the numerator represents how much thatsame bundle of commodities would have costin the second period.

An alternative approach - the Paascheindex - weights prices at current-period quan­tities. (This index was developed byLaspeyres' German contemporary, HermannPaasche.) Where the Laspeyres index projectsthe consumer-expenditure pattern forwardfrom the base period til the Paasche indexprojects the pattern backward from the currentperiod t j to the past period to' The formula forthe Paasche index is therefore

= :l,Pjqj:l,Poqj

In this formula the numerator represents theamount actually spent in the current period,

Figure 2

Laspeyres (A)

Expenditures

Paasche (B)

Expenditures

E

Prices Prices

46

current consumption-expenditure pattern,does allow for substitution - but in a way thatunderstates the rise in the cost of living(Figure 2). Consumers may substitute on thebasis of changes in relative prices, and thusavoid part of the burden of generally risingprices, but in doing so they lose the satisfactionof consuming the more expensive good whichthey originally desired.

In each panel of Figure 2, the curve EE' isthe consumer's expenditure function, or theamount of money necessary to maintain thesame level of satisfaction in periods to and t I,assuming unchanged tastes. The consumerfaces different sets of relative prices in the twoperiods, with some prices rising, some fallingand others remaining unchanged. However,on average, in a period of inflation, the totalexpenditure necessary to maintain the samestandard of living (in terms of personalsatisfaction) will rise. The curve EE' traces outthe amount of expenditures needed atdifferent times to maintain a given standard ofliving.

In Panel A (Laspeyres index), the expen­diture line EE denotes the total cost of thebase-period consumer-expenditure pattern atthe prices prevailing in period to (as expressedby the vector PoqJ and at the prices prevailingin t l (as expressed by the vector PIqoJAlthough no allowance is made for substitu­tion, the consumer will adjust his expendituresif faced with a different set of relative prices inperiod t i . He will substitute lower-priced forhigher-priced goods in order to minimize totalexpenditures while maintaining the same levelof satisfaction, thereby moving along expen­diture function EE' rather than EE. The quan­tity PIq' I represents the expenditures requiredto maintain the same level of satisfaction inperiod t l as in period to'

In panel B (Paasche index), we project theconsumer-expenditure pattern PIqI in t l back­wards along EE to period to' where the expen­diture vector is PoqI' However, this ignores thesubstitutions made in the expenditure packagebetween to and t j, which would make the actualexpenditure line EE'. The quantity Poq' 0 repre­sents the expenditure which would have been

47

required in period to to achieve the same levelof satisfaction attained in period t I'

Substitution makes a considerabledifference between the measured and "real"change in the cost of living. In the case of theLaspeyres index, the measured change is theratio of p Iqo to poqo' If the consumer makessubstitutions in his consumption pattern onthe basis of changes in relative prices, the truechange in the cost of living will reflect thesesubstitutions and will be the ratio of PIqi toPoqo' The Laspeyres index thus overstates thechange in the cost of living. Empirical studiesof the substitution phenomenon suggest thatthe effect, while negligible with stable prices,becomes significant as the inflation rate rises. 4

The expenditure schedule in panel B can beconsidered in much the same way. Here again,the schedule EE' represents substitution in theexpenditure pattern between to and t i . ThePaasche index assumes that at base-periodprices Pm consumers would have made expen­ditures Poq I' which is not what consumerswould have spent given the change in relativeprices. The pattern of q I consumption isheavily weighted with goods and serviceswhich were relatively expensive in the earlierperiod. Thus, by overstating base-periodexpenditures, the Paasche index understatesthe rise in the cost of living. 5

Durable Goods and Price IndexesWhen dealing with price indexes, we assume

that the goods and services purchased during aspecified time period are consumed in thecourse of that period. This is obvious in thecase of nondurable goods and services, such asa hamburger, an opera performance or a hair­cut. None of these items can be used morethan once. But problems occur with durablegoods, such as houses, autos, furniture andappliances. By definition, such items provideservices for at least three years, and some ofthem much longer.

We expect a new house to provide shelterfor up to 80 years, and an auto to providetransportation for (say) ten years. 6 However,the full cost of a durable good is picked up inthe first period rather than being allocated over

its useful service life.The present value of an asset is determined

by the value of the stream of services that it isexpected to yield during its service life, as wellas by the market rate of interest. For thisreason, the inclusion of durable goods in acost-of-li ving index distorts the pattern ofactual consumption expenditures over a singleperiod. By including the actual market price ofsuch an asset in the index at the time ofpurchase, we greatly overstate the price of theservices of this durable good for that period.

Moreover, the value of durable goods

belongs in the consumer's balance sheet aspart of his stock of wealth, rather than in hisconsumption-expenditure pattern. To theextent that ownership of a home or other dura­ble good generates capital gains, it effectivelyreduces the cost of the services yielded by thatgood. The effectiveness of any price index thusdepends, to a great extent, upon the way thatstatisticians handle these two attributes ofdurable-goods purchases - the stream-of-ser­vices attribute and the investment-goodattribute.

II. Two Indexes of the Cost of Living

The most widely used price index, which hasofficial sanction in the indexing of labor agree­ments and retirement benefits, is the Con­sumer Price Index (Cpn. The Bureau of LaborStatistics (BLS) has compiled this index eversince World War I, when it was developed tohelp determine wage rates in the shipbuildingindustry. The other major index is the implicitprice deflator for the personal-consumptionexpenditure (PCE) sector of the national­income accounts. The two indexes differ inseveral respects, such as population coverage.The CPI covers the expenditures of urban con­sumers, and represents about 80 percent of thepopulation; the personal-consumption deflatorcovers "persons" as defined in the national­income accounts, chiefly individuals and non­profit institutions. The indexes also differ interms of items covered; the CPI regularlycovers a selected list of about 400 items, whilethe PCE includes all goods and services cur­rently consumed.

The CPI is a straightforward Laspeyresindex with the form

CPI, = P Q1972-73 (3)PoQ 1972-73

BLS chose the base-year weights on the basisof a 1972-73 survey of expenditures by about20,000 family units. For most durable-goodspurchases, the agency utilized interview panelsin which consumer units were interviewed

48

quarterly over a IS-month period. For lessexpensive day-to-day purchases, the agencyutilized diaries of actual expenditures keptover a t\:'1o-\veek period. These t\VO effortswere supplemented by a point-of-purchasesurvey conducted in 1974, and updated on aregular schedule. 7

BLS includes almost 400 categories of goodsand services in its statistical market basket,pricing them on a monthly basis. Interviewerscontact a sample of about 18,000 retail estab­lishments, such as supermarkets, cleaning es­tablishments, repair shops and professional of­fices. Questionnaires provide other data ­such as utility rates, transportation fares, andinformation not requiring personal visits ­and Federal agencies and private researchorganizations add further information.

The PCE deflator is widely used as a cost-of­living index, although it was not designed forthat purpose. It results from the procedureused to deflate personal-consumption expen­diture values into constant (I972) dollars, inorder to obtain a measure of change in thephysical volume of consumption. With its cur­rent-period reference weights, the PCE is aPaasche index 8 with the form:

PCE, = P,Q, (4)P 1972Q,

The two indexes differ in commodity com­position, as well as in population coverage and

statistical form. About three-quarters of thecomponents of the CPI and the PCE indexesare comparable, largely because of the deliber­ate use of CPI components in generating thePCE index. Most of the differences occur inthe components of homeownership, autos andallied services, and hospital charges and healthinsurance. The homeownership cost in the

PCE is based upon the imputed rental cost ofowner-occupied homes, whereas the compara­ble item in the CPI is based on home purchaseprices and new-home mortgage rates. Bothindexes treat durable goods on the basis of cur­rent purchase prices, rather than the cost ofthe stream of services which they yield. 9

III. Relative Performance of the CPI and peE

In comparing the historical performance ofthe CPI and PCE, we should keep in mind theconceptual differences of the indexes and themanner of their construction. Neither capturesthe "real" cost of living; rather they form anupper bound (Laspeyres) and a lower bound(Paasche) to the cost of living. This can beseen from an analysis of the past two decades,which contained one period of relative pricestability and two episodes of high inflation(Chart 3). For most of this period, the rate ofchange in the CPI, a Laspeyres index, was

above that of the PCE, a Paasche index. Forthe entire 20-year period, however, theaverage difference amounted to only 0.6 per­centage points (Table 1).

In the first half of the 1960's, when priceswere reasonably stable, the CPI perverselyshowed the lower rate of change - but thedifference was hardly significant, especially inview of the low rate of inflation prevailing atthat time. Again, the two indexes showedmuch less correlation than would normally beexpected, in view of the heavy use of CPI

Chart 1Consumer Price Movements

19801976

...Personal consumptionexpenditure deflator

19721968

Consumer price index~

1964

2

6

4

8

01960

10

12

AnnualChange (0/0)

14

49

prices in the generation of the PCE. The muchlower correlation in this period reflects the factthat both indexes moved within a fairly narrowrange,given the .low level of inflation. Becausethe general price level was comparatively sta­ble,. variations in relative. prices thus tended tocause greater variation about the general pricelevel.

The 1973-75 and 1978-80 episodes of infla­tion were different, bothother and in relation to the earlier period ofstability. As expected, the.CPImt>asured mOreinflation than thePCE in both episodes. Butmeanwhile, the inflation of 1973-75 was moregeneral in scope than the 1978-80 episode. In1973 -75, the U.S . economy felt the impact ofworld-wide inflation in the prices of interna­tionally traded commodities, partly food butespecially petroleum. Food and energy pricesalso contributed to the 1978-80 run-up ininflation, but the divergence in the twoindexes in this later period largely reflected asharp rise in the price of houses and in home­mortgage rates (Chart 4). The divergence thuscould be explained by the fact that the PCEincorporates only the CPI's relatively slow-ris­ing rental component, rather than the CPI'sfast-rising home-ownership component.

Weighting ProblemThe differences in the CPI and the PCE are

rooted in their basic conceptual natures,including differences in weights and the effectsof substitution in the consumer-expenditurepackage. To understand these differences, wecan compare the rates of change in CPI, thePCE, and also the fixed-weight PCE, whichhas some of the features of the other two

measures (Table 2). The fixed-weight PCEis aLaspeyres index - like the CPI but with a1972 expenditure pattern. At the same time,the fixed-weight PCE weights specific con­sumer items in the same way that the PCEdoes.

Consider the weight, or relative importance,of three major PCE components on a fixed­weight vs. a current-weight basis. In 1972,housing expenditures accounted for 15.3 per­cent of total consumer spending, but by 1980,this had increased to 17.6 percent of the totalin terms of 1972 dollars. At the Same time, theweight of food in the index dropped from 20.5percent in 1972 to 19.4 percent in 1980, whilegasoline and oil dropped from 3.4 percent to2.8 percent. Thus, in terms of 1980 consump­tion patterns, the fixed weight PCE under­represented housing and over-representedfood and gas and oil. Of course, the composi­tion of weights can change in a current­weighted index as changes occur in relativeprices, and in consumer tastes and income.

Weighting differences were not significantin 1977-78, but they began to tell in 1979.Home prices and mortgage costs began to soar,leading to a spread of 1.9 percentage points be­tween the CPI and the fixed-weight PCE(Table 2, line 4). But at the same time, a sig­nificant amount of substitution took place inthe consumer-expenditure package because ofsharp changes in relative prices, leading to aspread of 2.8 percentage points between thefixed-weight PCE and the current PCE (line5). An increase of more than 50 percent inretail gasoline prices resulted in a decline of 11percent in real purchases of gasoline andnearly 10 percent in real purchases of fuel oil

Table 1Average Annual Rates of Change in the CPI and PCE Indexes,

and Coefficients of Determination between the Indexes(change in percent)

CPI

PCE

19601-19801V

5.29

4.69

.975

19601-19651V

1.35

1.50

.667

50

19731-19751V

9.59

8.34

.969

19781-19801V

11.81

9.35

.947

and coal, not to mention an 11-percent cut in auto spending. As a result of these changes in current consumption, the composition of weights changed substantially between the current-weight and fixed-weight PCE.

In 1980, the situation was reversed, with the largest difference occurring between the CPI and the fixed-weight PCE (line 4), reflecting the different treatment of homeownership. The homeownership component of the CPI rose by nearly 17 percent from the end of 1979 to the end of 1980, as compared with a 9-per- cent increase in the PCE in the same period. Because of this difference, and the heavier weighting of homeownership in the CPI, that single component accounted for roughly half of the difference in the overall indexes in 1980. The much smaller difference between the two PCE indexes was due to declines in real expenditures for food and gas and oil,

which changed the composition of PCE expen­ditures for the year.

Homeownership ProblemBLS’ problem with measurement of home-

ownership costs stems partly from its treat­ment of home prices. The agency computes the weight from the purchase prices of homes bought in the survey year, minus the prices of homes sold in that year, plus transaction costs accompanying the purchase or sale. It then derives the index from price data supplied by the Federal Housing Administration. But the FHA sample is a small and unrepresentative segment of the market. The coverage is not geographically uniform; also, with its ceiling cutoff on mortgage loans, higher-priced homes are effectively eliminated from the sample.

Problems also occur with the computation of mortgage cost. BLS assumes that the mortgage

Chart 2

Rental, Homeownership and Mortgage CostsAnnual Change (%)

Year-over-year rate of change

51

borrower pays interest equal to the sum of allinterest payments over the first half of the lifeof the mortgage, each year's interest cost beingequal to the first. But this results in overcount­ing, in comparison with discounting the valueof future house services (as reflected in thefuture stream of interest payments) to theirpresent value. Moreover, many analysts sug­gest using the actual interest paid on allmortgages instead of the current new-homerate, to diminish the impact from sharp fluc­tuations in mortgage rates.

The CPI's weighting of homeownershipoverstates its importance in the cost of living.Homeownership accounts for nearly one­quarter of the total weight of the index. Withthat weight, it has five times the importance ofthe rental component. In the PCE, home­ownership is only about 21/2 times as importantas rental housing - a more reasonable figure,in view of the 2-to-l relationship of owners torenters in the national housing stock.

In response to these and other criticisms,BLS is currently publishing five experimentalmeasures of housing costs. These range from arental index to various "shelter" and "asset"concepts. Conceptually, much can be said for arental-equivalence measure, such as is used inthe PCE index, since it directly measures theprice of the services of a home. However, BLSdoes not at this time possess a true rental­equivalence sample; that is, one made up ofhousing units comparable in type and location

to its sample of owner-occupied units. Therental index, as presently constituted, is basedupon market observations of rental payments.The units involved in this sample generallydiffer from owner-occupied dwellings in suchterms as age and income of inhabitants, as wellas age, location and size of dwelling. Thus, thecurrent CPI rental index cannot serve as proxyfor the imputed rent of the different and largerpopulation of owner-occupied units. The con­centration of the rental sample in inner-cityareas - many of them rent-controlled - alsotends to bias the sample downwards, so thatthis is one of the slowest rising components ofthe entire CPI.

Other considerations must be kept in mindwhen choosing an inflation index. The CPI inits present form substantially overestimatesthe rate of inflation - even more than mightbe expected from a Laspeyres index. However,a Laspeyres (Cpn index number seldomneeds to be revised, being based on actualprices taken from primary sources and on fixedbase-year quantities. On the other hand, thePCE index depends heavily on estimation pro­cedures and thus is subject to frequent revi­sions, which can create problems of interpreta­tion for policymakers. However, over the1960-80 period, revised estimates of PCE ratesof change were only slightly higher than theinitial observations. Both the average rate ofchange and its variation were smaller for thePCE than for the CPI over this period.

Table 2Average Annual Rates of Change

in the CPI, PCE and Fixed-Weight PCE Indexes(percent)

mCPI

(2)PCE

(3)PCE (fixed weight)

(4)CPI - PCE (fw)

(5)PCE (fw) - PCE

1960-80 1977 1978 1979 1980

5.29 6.5 7.7 11.3 13.5

4.69 5.8 7.3 8.5 9.0

47] 63 7.6 9.4 9.6

0.58 0.2 0.1 1.9 39

0.02 0.5 0.3 2.8 06

52

IV. Indexes and Public Policy

Source: Office of Management and Budget, American Council of Life

[nsurance

Table 3Major Indexed Programs

The current Congressional debate on theFederal budget has focussed attention on therole of indexation in increasing expenditures.By linking increases in certain spendingcategories to changes in the cost of living,Congress originally sought to assure thatbenefit recipients would be able to cope withincreases in the cost of living without furtherCongressional action. In other words, reci­pients of Federal payments would almost auto­matically preserve their "real" income. Pay­ments to individuals account for more thanhalf of Federal-budget expenditures, and 90percent of such programs are indexed. Thus,nearly two-fifths of Federal budget outlays,whether paid out as wages or transfer pay­ments, are linked to a price index (Table 3).

In the case of social-security benefits, Con­gress ironically adopted an escalator approachas a means of capping the extraordinarilygenerous benefits it had adopted in the early1970s. (Between 1970 and 1974, benefits

Directly indexed

Social securitySupplemental security incomeRailroad retirementVeterans' pensionsCivilian retirement and disabilityMilitary retirementBlack lungFood and nutrition assistance

Subtotal, directly indexedAs percent of total outlays

Indirectly indexed

MedicareMedicaid

Subtotal, indirectly indexedAs percent of total outlays

Total indexed programs

Total outlays

As percent of total outlays

Outlays, FY 1980(billions)

$117.16.44.73.6

14.711.91.8

13.3

$173.529.9%

$ 35.014.0

$ 4908.5%

$222.5

38.4%

53

increased by about 70 percent - just aboutdouble the increase in the cost of living,) Thecurrent indexation formula essentially pro­vides for a benefit adjustment equal to theannual percentage increase in the CPI Cfirst­quarter to first-quarter). The benefit formulathus called for a 14.3-percent increase in July1980 and an 11.2-percent increase in July1981. If the cost of living had been measuredby the PCE instead, the increase in benefitswould have amounted to 8.2 percent in 1980and to about 9.4 percent in 1981.

Congress recently has begun to considerseveral less costly indexing alternatives. Oneof these would delay the first payment ofincreased benefits from July to October eachyear. This lag would lessen the extent of over­statement of the previous year's inflation rateif inflation were accelerating, but it wouldworsen the overstatement if inflation weredecelerating. Another suggestion would pegthe increase in benefits to the increase ineither the CPI or average wages, whichever issmaller. Still another suggestion would put an85-percent "cap" or ceiling on the increase inthe CPI.

According to the General Accounting Of­fice, inflation accounted for approximately halfof the increase in expenditures for indexedprograms over the 1970-77 period. The GAOstudy indicated that such spending increasesautomatically by $15-25 billion at a 10-percentinflation rate, and increases by $1.5 billion to$2.5 billion more for each additional percen­tage point of measured inflation. If the PCEindex had been used in place of the CPI duringthe 1970-77 period, roughly 11l/2 percent($12.5 billion) of the cumulative spendingincrease could have been saved.

A Congressional Budget Office study arguedthat CPI-based indexation could account forthree-fourths of a $200-billion increase inFederal payments to individuals projected forthe 1980-85 period. But again, a shift from theCPI to the PCE index could mean savings of$11 billion through 1986 for the social-securityprogram alone. 11

Compensation and EquityWhen indexation overcompensates for

inflation, questions of equity arise. Federalpayments to individuals are made only to cer­tain individuals, while taxpayers who pay for

benefits may be falling behind theincrease in living costs. The cost-of-livingadjustments (COLAs) that are written intomany labor contracts are generally capped, sothat workers fail to receive full compensationfor their higher living costs (Table 4). Andthose whose income is not indexed at all mayfall even further behind.

Increases in social-security benefits havebeen tied to the CPI since 1975. From 1976through 1978, the yearly increase in wagesexceeded the annual indexed increase insocial-sec uri ty benefi ts. However, thedifference was smaller than indicated, becausesocial-security benefits are not taxed as wagesare. When annual CPI increases ran ahead ofwage increases, from 1979 through 1981, the

Table 4Annual Change inthe CPI, PCE andAverage Wages*

(percent)

AverageCPI PCE Wages

1975/76 6.4 5.7 7.2

1976/77 5.8 5.3 7.7

1977/78 6.6 6.3 7.7

1978/79 9.8 9.1 8.3

1979/80 14.3 8.2 8.4

1980/81 11.2 9.4 9.8

Annual Average 9.0 7.3 8.2

*First quarter to first quarter

taxability factor made the gap even wider.In contrast, the PCE index - though risingmore slowly than wages - would have placedwage earners and benefit recipients on areasonably equitable basis during this period.

V. Summary and Conclusions

Many analysts have used the term "infla­tion" rather loosely in describing the recentsharp rises in prices of certain individual com­modities, such as oil and food. Nonetheless,the inflationary process primarily involves anincrease in the general price level. Within thiscontext, the relative prices of individual goodsmay rise or fall according to market forces.Since numerous goods and services are boughtand sold daily in the markets, an index numberrepresents the only feasible method of describ­ing the general movement of prices throughtime.

An index number designed to measureinflation should give an accurate representa­tion of changes in living costs. The true cost ofliving cannot be measured directly, since it is amatter of personal satisfaction or well-being.However, it can be measured indirectly bymarket observations as consumers reveal theirindividual preferences by purchasing certaingoods and services. The Consumer Price Index

54

has long been used as the common indicator ofthe cost of living, and thus as a basis for index­ing cost-of-living adjustments.

The CPI, as a Laspeyres index, uses quan­tities purchased in a certain base year as areference point from which to measurechanges in prices of a basic - presumablyunchanging - consumer-expenditure pattern.The unchanging nature of the base-year con­sumption pattern ignores substitution of lessexpensive for more expensive goods in thereference expenditure package as relativeprices change in succeeding periods. Thisimparts an upward bias to the index, as theincreasingly expensive base-period expen­diture pattern (in current prices) overstateswhat consumers actually bought and paid for atthe checkout counter.

The Personal Consumption Expenditureindex (or deflator) has come to be used as analternative index, although it was notspecifically designed to measure changes in the

cost of living. The PCE, as a Paasche index,uses the current-period expenditure pattern asa reference base for comparison with expen­ditures in earlier periods. This approach thusallows for substitution of goods in the expen­diture pattern. However, when that pattern isprojected backward from the current year, thesubstitute goods of the current period may nothave been as desirable to consumers in earlierperiods. The consumer's loss of satisfaction,relative to the current period, thus causes aPCE (Paasche) index to understate changes inthe "true" cost of living.

As a practical matter, the CPI and the PCEwere quite close in their measurement of livingcosts from 1960 through 1966. But from 1978through 1980, the CPI rose at a much fasterrate. This disparity resulted not so much fromthe indexes' different statistical composition asfrom their different treatment of sharply risinghomeownership costs. The housing compo­nent of the CPI overstated the inflation inhousing costs, because it included the full costof a house, which includes its value as an asset,rather than merely the cost of shelter services.The treatment of mortgage interest also con­tributed to this overweighting of the costs of

homeownership.The Federal government uses the CPI as a

standard index in its efforts to offset theimpact of inflation on benefits paid toindividuals. But since individuals receiveroughly half of all total budget outlays, andsince 90 percent of these payments contain acost-of-Iiving adjustment, the indexing for­mula should measure living costs as closely aspossible. In the past several years, most evi­dence has suggested that the use of the CPIovercompensates benefit recipients.

This overcompensation has raised two pub­lic-policy questions. First, overcompensationleads to unwarranted increases in Federalexpenditures and in the Treasury deficit.Beyond that, it introduces inequities relative tothose individuals not receiving indexedbenefits, and thus amounts to an unintendedredistribution of income. Indexed transfer pay­ments are not taxable, and this widens the gapbetween benefit increases and wage increases.A comparison of index movements since theFederal government's widespread adoption ofindexing suggests that the PCE index is a moreequitable choice for determining cost-of-livingadjustments.

FOOTNOTES

1. Allen. RD.G., Index Numbers in Theory and Prac­tice. Aldine: Chicago, 1975.

2. Klein, Lawrence and Rubin, Alan, "A ConstantUtility Index of the Cost of Living ", The Review ofEconomic Studies, 1949, p. 84.

3. Houthakker, Hendrik, "Compensated Changes inQuantities and Qualities Consumed", The Review ofEconomic Studies, Vol. XIX, no. 50, 1952-53, pp. 156­159.

4. Consumer substitution with a Laspeyres index canmake a difference between the measured cost of liv­ing and the "true" or perceived change in the cost ofliving as measured by an index. A number of studieshave indicated that the amount of error involvedincreases rapidly with the rate of inflation. Nicholas N.Noe and George von Furstenburg, ("The Upward Biasin the Consumer Price Index due to Substitution",Journal of Political Economy, November/December1972, Vol. 80, p. 1283) found the percentage error inthe CPI due to substitution to be "trivial" in the 1963-

55

70 period. However, an examination of their resultsindicates a steady increase in the percentage errorfrom 1964 (when the CPI rose by 1.2 percent) to1970 (when it rose by 6.1 percent). For a class of 20expenditure categories, the percentage errorincreased from 0.005 to 0.326, or at a compoundannual rate of 100.6 percent - more than three timesthe annual rate of increase in the CPI in this period.Steven D. Braitwait, ("The Substitution Bias of theLaspeyres Price Index", The American EconomicReview, March 1980, vol. 70, no. 1, p. 71), using1958-70 data, found that the substitution biasincreased significantly the longer that weights wereheld constant, rising from 0.36 for the 1958-63 periodto 1.5 for the 1958-73 period. As Braitwait points out,relative prices change more in a high-inflation periodthan in a low-inflation period. His index of relativeprice dispersion increased from .11 in 1956-63 to 3.5in 1958-73. And it is this dispersion of relative pricesthat affords greater opportunity for commoditysubstitution. Alan S. Blinder ("The Consumer PriceIndex and the Measurement of Recent Inflation",

Brookings Papers on [;conomicActivity, 1geO:2,p.545J,.found.thatdifferences in·weiQhting accountedforaboUlone-.half of the difference betweenthe CPIandPCEin.t979. Indeed,thepost-1978 period,analmostcontinuous perioqofhighinflation, "ha~b~enone .of •tho~erare .timesinwh.ich. sUbstantialdifferences. between the Laspeyres and Paascheindexe.sareexpected to arise."

5. Blinder, Alan $., "The Consumer Price Index andthe Measurement of Recenllnflation", BrookingsPapers on Economic Activity, NO.2,igeO, p. 542.

6.• "Fixeq Nonresidential and Residential Gapital inthe UnitedStates, 1925-1975", U.s. Depwtment ofCommerce, Washington, June 1976, pp. T-6, T-7.

7. U.S. Department of Labor, Bureau of LaborStatistics, Consumer Expenditure Survey: InterviewSurveY,1972-73,Bulietin 1997, u.S. GovernmentPrinting Office, 1978 and Consumer ExpenditureSurvey: Diary Survey, July 1972-June 1974, Govern­ment Printing Office, 1977.

8. The PCE is not a perfect Paasche index in thetheoretical sense, because it cannot follow everyprice for the commodities and services that it covers.The Bureau of Economic Affairs combines the 107personal-consumption categories, expressed innominal dollars, into 70 categories which are

P.O. Box 7702San Francisco. California lJ4120

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notberlavi,or to

n. period. Katz, Arnold J. andValue of Services Provided by theDurables, 1947-1977; An uooortumtvMeasure", Survey of Current Business,22

10. "An Analysis of the Effects of IndexinQ for Infla­tion on Federal Expenditures," General AccountingOffice, August 15, 1979, p. 40.

11. "Reducing the Federal Budget: Strategies andExamples", Congressional Budget Office, February1981, p.145.

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