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American Economic Association
Contract Duration and Relationship-Specific Investments: Empirical Evidence from Coal
Markets
Author(s): Paul L. Joskow
Reviewed work(s):
Source: The American Economic Review, Vol. 77, No. 1 (Mar., 1987), pp. 168-185
Published by: American Economic Association
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Contract Durationand Relationship-SpecificInvestments:
EmpiricalEvidence fromCoal Markets
By PAULL. JOSKOW*
in
This paper examines the importanceof specificrelationshipinvestments
negotiatedbetweencoal suppliersand
thedurationof coal contracts
determining
theanalysis.The
are usedtoperform
Data for277 coal contracts
electricutilities.
resultsprovidestrongsupportfor the viewthatbuyersand sellersmake longer
to thetermsoffuturetradeat thecontractexecutionstage,and rely
commitments
are more
investments
less on repeatedbargaining,when relationship-specific
important.
This paper seeks to test empiricallythe
investimportance of relationship-specific
ments in determiningthe durationof coal
contractsnegotiatedbetweencoal suppliers
and electricutilities.'The analysismakesuse
of informationfor a large sample of coal
contractsthatwerein forcein 1979. It takes
as a startingpoint OliverWilliamson's1983
definitionsand categorizationof relationship-specificinvestmentsand applies them
to the characteristicsof coal markettransactions. It also follows Williamson and
Benjamin Klein, Robert Crawford, and
ArmenAlchian (1978) and assumesthatrisk
factordeterminaversionis not an important
being the structureof verticalrelationships
tweencoal suppliersand electricutilities.2
*Departmentof Economics,MassachusettsInstitute
of Technology,Cambridge,MA 02139. The preliminary
whenI was a Fellow at
versionof thispaper was written
the CenterforAdvanced Studyin theBehavioralSciences. Leslie Sundt and Rafe Leeman provided valuable researchassistance.Keith Crocker,HenryFarber,
Oliver Hart, Scott Masten,JeanTirole,and OliverWilliamson read preliminaryversions and made useful
suggestions.Three anonymousrefereesprovidedhelpful
suggestions.I benefitedfromseminarpresentationsat
UCLA, Stanford,and MIT. SupportfromMIT and the
Center forAdvanced Studyin the BehavioralSciences
is gratefullyacknowledged.The views expressedhere
and anv remainin errors are mv sole resnonsibilitv
' Electric utilities account for over 80 percent of
domesticcoal consumption.
2Other empiricalworkin thistraditionincludesKirk
Monteverdeand David Teece (1982) and Scott Masten
(1984), which focus on vertical integration;Keith
Crocker and Scott Masten (1986), J. Harold Mulhem
to
are interesting
Coal markettransactions
focus on because thereis considerablevariaof vertical
tion in the durationand structure
relationshipsbetweenbuyersand sellers.I
observe spot market transactions,vertical
integration,and a wide varietyof longerterm contractual relationshipswith durations rangingfromone year to fiftyyears.3
My relatedwork (1985) suggeststhatassetspecificityconsiderationsmay be an important factor affectingthe structureof
vertical relationshipsin coal markets.The
empirical results reported below provide
strongsupportforthehypothesisthatbuyers
and sellers make longer ex ante commitmentsto the termsof futuretrade,and rely
overtime,when
less on repeatednegotiations
investmentsare more
relationship-specific
important.
and
Duration
I. Contract
Investments
Transaction-Specific
The reliance on relationship-specific
exinvestmentsto supportcost-minimizing
(1986), and Victor Goldbergand JohnErickson(1982)
which focus on long-termcontracts;my paper (1985)
and long-term
whichexaminesboth verticalintegration
contracts.
3About 15 percentof electricutilitycoal consumption is accounted for by transactionswith integrated
suppliers,15 percentis accountedfor by spot market
purchases, and about 70 percentis accounted for by
contractswith durationsof one to fiftyyears. See my
paper (1985, pp. 50-54).
168
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VOL. 77 NO. 1
JOSKOW: CONTRACTDURATION
change is frequentlyadvanced as an important factor explainingwhy we observe
the use of long-termcontractsthatestablish
the termsand conditionsof repeatedtransactions between two parties ex ante.4According to transactionscost theory,5when
in
exchangeinvolvessignificant
investments
relationship-specific
capital,an exchangerelationshipthatrelieson repeatedbargaining
is unattractive.Once the investmentsare
sunk in anticipationof performance,
"holdup" or "opportunism"incentivesare created
ex post which,if mechanismscannotbe designed to mitigatethe parties'abilityto act
on these incentives,could make a socially
cost-minimizingtransactionprivatelyunattractiveat the contractexecutionstage.6'7A
long-termcontractthat specifiesthe terms
and conditionsforsome set of futuretransactions ex ante,providesa vehicleforguarding againstex post performance
problems.8
A coal contractgenerallyspecifiesin advance a method for determiningthe price
that the buyeris obligatedto pay for each
4Williamson(1979, 1983), Klein et al., Oliver Hart
and Bengt Holmstrom(1986, pp. 1-2; 86-101). Other
reasons for the use of long-termcontractshave also
been suggested.These includeinformation
lags,income
effectsand risk aversion,and improvedmonitoringof
performance.
5I use the term"transactionscost theory"to refer
generallyto the workof Williamson(1979, 1983, 1985)
and Klein et al.
6As Klein et al. discuss,thesunkinvestments
createa
streamof quasi rentsthatgivesone partyor the other
(or both) some ex post bargainingpower.
7The presenceof thesecontracting
hazards and imperfectionsin the abilityof the transactingparties to
protectagainstthemdoes not mean thata deal will not
be made. It simplymeans thatthe costs of makingthe
transactions-the cost of thecoal in thispaper-will be
higherthan it would be if thesehazards could be fully
mitigated.Both parties have an interestin tryingto
structuretherelationshipso thata cost-minimizing
deal
can be struck.
8Asdiscussedin myearlierpaper(1985), reputational
considerationsmay providea naturalmarketconstraint
on "bad behavior" ex post. Reputationalconstraints
reduce the need to writeinflexiblelong-termcontracts
to supportcost-minimizing
exchangein the presenceof
asset specificity.Reputationalconstraintsare likelyto
be imperfectin coal markets,however.At the other
extreme,verticalintegration
maybe chosento deal with
ex post performanceproblemsif satisfactory
contractual solutionscannot be found.
169
delivery(generallya formulafordetermining
prices for deliveriesat each point in time),9
quantitiesthat the selleris obligatedto deliver and the buyeris obligatedto purchase
at each point in time (usually monthly),10
the qualityof the coal (Btu, sulfur,ash, and
chemicalcomposition),thesourceofthecoal,
and the period of timeover whichthe contractualprovisionsare to governthe terms
and conditionsof trade.11The primaryreadily quantifiablecharacteristics
of coal contracts that appear to vary widely from
contract to contract are the quantityand
characteristics
of thecoal contractedforand
the lengthof time thatthe partiesagree ex
ante to committhemselvesto the termsand
conditionsspecifiedin thecontract.It is this
length of time to which the parties agree
ex ante to abide by the termsof a contract
that I referto as the "duration"of the contract.12
My hypothesisis that the more importantare relationship-specific
the
investments,
longerwill be theperiodof time(or number
of discretetransactions)overwhichthe parties will establishthe termsof trade ex ante
by contract. I thereforeexpect to observe
that the variationin the agreed upon duration of contractualcommitments
is directly
related to variationsin the importanceof
relationship-specific
investments.13
9See myearlierpaper (1986).
l?The typical contractspecifiesa monthlyand annual deliveryschedule subject to minimumand maximum production and take obligations.The allowed
variationsfromthe contractedquantitiesin actual contractsthatI have reviewedis fairlysmall.
" There are manyotherprovisionsas well,including
arbitrationprovisions,force majeur provisions,resale
provisions,etc. These provisionsare fairlystandardin
long-termcoal contracts,however.
12The actual durationof a contractcould be longer
or shorterthanthis.Buyersand sellersfrequently
voluntarily negotiate an extensionof an existingcontract.
Contractsmay also be brokenthroughbreachor mutual
agreement.As faras I can tellfromthedata thatI have
reviewed,however,coal contractsare rarelyterminated
prematurelySee my paper (1986, p. 2).
13 To the extent that there are tradeoffsbetween
contract duration and the incidenceand structureof
other contractual"protective"provisions,such as the
method for determiningprice adjustmentand quantities, these other provisionsshould be included in the
analysis as well. As indicated above, however,there
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170
THE AMERICAN ECONOMIC REVIEW
Duration
andtheContractual
II. AssetSpecificity
ofCoal SupplyRelationships
Williamson (1983, p. 526) identifiesfour
investdistincttypes of transaction-specific
ments,threeof whichappear to be relevant
to differenttypes of coal supply relationships. The threetypesof relevanceto coal
markettransactionsare:"4
The buyerand seller
(a) Site Specificity:
are in a "cheek-by-jowl"relationshipwith
ex ante decisionsto
one another,reflecting
exminimizeinventoryand transportation
penses. Once sited theassetsin questionare
highlyimmobile.
Whenone
(b) PhysicalAssetSpecificity:
or both parties to the transactionmake investmentsin equipmentand machinerythat
specificto the
involvesdesigncharacteristics
transactionand whichhave lower values in
alternativeuses.
(c) Dedicated Assets: General investmentby a supplierthatwould not otherwise
be made but for the prospectof sellinga
amountof productto a particular
significant
customer.If the contractis terminatedprematurely,it would leave the supplierwith
significantexcess capacity. Although Williamsondoes not discussit,I thinkthatthere
is probably a "buyer" side analogy to the
dedicated asset storyas well. A buyerthat
relieson a singlesupplierfora largevolume
and costlyto
of an input may findit difficult
quickly replace these supplies if they are
appears to be relativelylittlevariationin these provisions in my data base, especiallyrelativeto the very
feelthat
largevariationin contractduration.I therefore
it is safe to assume forpurposesof thisanalysisthatwe
have a sample of contractsthatessentiallyholds these
otherprovisionsconstant.In any event,we can measure
the utilizationof theseotherprovisionsonlyfora small
fractionof the contractsin the data base and therefore
cannot examine such tradeoffs
directly.Note that the
coal marketis not subject to the kinds of price regulationdiscussedin Masten-Crocker
and Crocker-Masten
regardingnaturalgas contracts.
14The fourthis whatWilliamsoncalls "human asset
specificity"(1983, p. 526). JeanTirole has suggestedto
me that Williamson's four types of relationship-(or
inare simplydifferent
transaction)specificinvestment
stances of the same phenomenon.I believe thatthisis
correct.However, I find the distinctionsto be quite
usefulforempiricalapplications.
MARCH 1987
withterminatedsuddenly and effectively
drawn fromthe marketand, as a result,a
large unanticipated demand is suddenly
thrownon the market.
As discussed in more detail in the Appendix,I have put togethera data base that
includes informationfor nearly 300 contractsbetweenelectricutilitiesand coal suppliers that were in forcein 1979. The data
base includes informationof various kinds
of the individregardingthe characteristics
ual coal contracts,the suppliers,the buyers,
and the quality and quantityof the coal
contractedfor. The strategywas to use the
informationabout the individualcontracts
in the data base and to attemptto measure,
in the imporat least ordinally,differences
investmentsof
tance of transaction-specific
one or more of the typesidentifiedby Williamson foreach contract.
is
Williamson'snotionof "site specificity"
the easiest to capture explicitlyfor coal
supplyrelationships.For mostelectricgenerating plants, coal is purchased in one of
threemajor coal-producingregionsand then
transportedby rail, barge, and/or truck
(oftenat least two of thesetransportmodes
are involved) to the powerplant whereit is
burned.However,thereare a relativelysmall
numberof plants thathave been sited next
to specificminesin anticipationof takingall
fromthatmine.
or mostof theirrequirements
These "mine-mouth"plants are generally
developed simultaneouslywith the mines
themselves.This appears to be a classic case
of the cheek-by-jowlrelationshipthat Williamson has in mind when he discussessite
specificity.15The potentialfor ex post opportunismproblems arising if the parties
were to relyon repeatedbargainingappears
to be especiallygreatin thiscase.16I there-
15This is discussed in muchmoredetail in my 1985
paper.
16
Williamson(1983) states that commonownership
My work
is the predominantresponseto sitespecificity.
indicatesthat common
with coal supply arrangements
ownership(verticalintegration)is much morelikelyto
plantsthanothertypesofplants,
emergeformine-mouth
but thatcontractsare also used to governexchangefor
plantsconstructedsince
about half of the mine-mouth
1960. We would probablysee moreverticalintegration
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VOL. 77 NO. 1
JOSKOW: CONTRACTDURATION
fore expect that contractsfor supplies for
mine-mouthplantswillbe muchlongerthan
the average contractinvolvingsupplies to
othertypesof plants,otherthingsequal."7
Let us turnnextto physicalasset specificity.When coal-burningplantsare built,they
are designed to burn a specifictypeof coal
(see my 1985 paper; Richard Schmalensee
and myself,1986; mypaper withSchmalensee, 1985). By "type" of coal, I mean coal
with a specific Btu, sulfur,moisture,and
chemical content.The type of coal that a
generatingunitis designedto burnaffectsits
constructioncost and its design thermal
efficiency.Deviations from expected coal
in perforqualitycan lead to a deterioration
mance or requirecostlyretrofit
investments.
Thus when a plant is designed,the operator
becomes "locked in" to a particulartypeof
coal.18
The fact that a plant is locked in to a
particulartype of coal does not necessarily
implythatthebuyeris lockedin to a specific
supplier,however.Whetheror not the plant
lock in also leads
design/coal characteristic
to a lock in with the currentsupplierdeof thetransacpends on othercharacteristics
tion. In particular,it is likelythat the relationshipbetweenthistypeof asset-specificity
and ex post hold up or opportunismproblems is related to inter-and intraregional
formine-mouthplantsif stateand federalregulationof
electric utilitiesdid not discourageit. I have argued
elsewhere(1985) that to the extentthat electricutility
regulationbiases coal supplyarrangements
at all, it is
probably to make short-term
purchasesmore desirable
than theymightotherwisebe. Whileutilitiesmightalso
like to integratebackwardsintocoal productionto shift
profitsfroma regulatedto an unregulatedactivity,the
regulatoryprocesshas discouragedthis.
17j have includedtwoplantsin thiscategory
thatare
not technicallymine-mouthplants but have economic
characteristics
thatare identicalto thoseof mine-mouth
plants. For example, if a mine and a plant are connectedby a transportation
facility(a slurrypipelineor a
rail line) built and owned by the supplier or buyer
specificallyto transportcoal froma specificmine to a
specificplant, the associatedcoal contractwas grouped
withthe mine-mouthplants.
l8Exactly how locked in, is a variable of choice,
however.
171
variationsin coal quality,least cost supply
technology,and transportation
alternatives.19
The characteristics
of coal producedin the
amongthe
United States varysystematically
three major coal-producingregions. The
easterncoal-producingdistricts
producehigh
Btu coal of reasonablyuniformquality.The
midwesterncoal-producingdistrictsproduce
lower Btu coal thatgenerallyhas a veryhigh
sulfur content. Coal quality is also more
variable than that in the East. Finally,the
western coal-producing districtsgenerally
produce coal witha muchlowerBtu content
and a verylow sulfurcontent.The qualityof
the coal varies quite widelythroughoutthe
westernregion.20
In addition to variationsin coal quality
among the regions,thereare also systematic
variations in the least-costtechnologyfor
producing coal and in the transportation
alternativeavailable. In the East, relatively
small underground
minesare economicaland
the supply of easterncoal can be expanded
fairlyquickly.Relativelyabundanttransportation alternatives,combinedwithrelatively
shortaverage transportdistances,mean that
transportation
is not likelyto be a significant
barrier to a buyer's obtaining alternative
supplies. In the West, large surfacemines
that can be most economicallyexpanded in
large "lumps," are the least-costproduction
technology.Transportationalternativesare
poor, the average transportdistance quite
long, large unit trainshipmentsare themost
economical transportmethod,21and utilities
often must rely on one or two railroadsto
move the coal. The situationin the Midwest
lies somewherebetweenthesetwoextremes.22
19Transportationcosts are on average a large fraction of deliveredcosts and liningup efficient
transportation arrangementsforlargequantitiesof coal can be a
time-consuming
process.
20 The midwesternregion is sometimesbroken up
into two subregions(eastern and westerninterior)in
discussionsof coal supply.The westernregionis sometimes broken up into threeor more subregions.Texas,
where lignite coal is produced,is often considereda
separate producingregion.My data base has no contractsforTexas coal and I do not discussthatarea here.
21Unit train cars are oftenowned or leased by the
utilityratherthan by the railroad.
22See MartinZimmerman(1981, pp. 17-36).
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172
THE AMERICAN ECONOMIC RE VIEW
in
There are also systematicdifferences
the relativeand absoluteimportanceof spot
marketsin the three supply regions.23On
average,from1974 through1982 spotmarket
transactionsaccounted for roughly15 percent of total domestic coal purchases by
electricutilities.In 1982, spot marketsales
accounted forabout 10 percentof coal supplies or about 60 milliontons. However,in
the westernregion,less than2 percentof the
coal deliveredto electricutilitieswas purchased on the spot marketor less than 5
milliontons.24The spot marketis more active in the Midwest,accountingforabout 8
percent of deliveriesin 1982 or about 10
million tons per year. The spot marketis
most active in the East whereabout 18 percent of deliveries went throughthe spot
marketin 1982 or about 45 milliontons.25
These considerationsimplythe following:
Coal suppliersare likelyto be less able to
exploit the lock-in effectassociated with
boilers designed to burn coal with specific
in theEast thanin theWest.26
characteristics
contract
Thus the protectionof a long-term
is likelyto be moredesirablefromthebuyers'
23Spot marketsales also varyfromyearto year.Spot
markettransactionstend to be higherwhencoal miner
strikes are anticipated as utilitiesseek to build up
stockpiles or after coal miningstrikesare over and
stockpilesare replenished.The volume of spot market
transactionsalso varies in response to unanticipated
changesin coal supplyand demand.
24The aggregatevolumeof spot markettransactions
forwesterncoal is quite small comparedto the annual
quantity of westerncoal contractedfor in a typical
contract.The contractsforwesterncoal in mydata base
have a mean quantityof about 1.8 milliontonsper and
a maximumquantityof over 8 milliontonsper year.
251 believe that the wide variationin the importance
regionsis largelyrelated
of the spot marketin different
to the same economic considerationsthat lead me to
also
conclude that the importanceof asset specificity
varies fromregionto region.
26Generating plants located along the eastern seaboard that use coal essentiallyalwaysuse easterncoal.
These plantsare also morelikelyto have unitsthathave
multifuelcapabilitiesthanplantslocated elsewhereand
can switchback and forthbetweencoal and oil or gas
(often with some performancepenalty).See my paper
with FrederickMishkin (1977). Purchasersof eastern
coal with multifuelcapabilitieswill be less susceptible
to opportunismproblemswhen coal and oil pricesare
close together.
MARCH 1987
for
(and the sellers'-see below) perspective,
transactionsinvolvingwesterncoal relative
to transactionsinvolvingeasterncoal, with
midwesterncoal fallingsomewherein between.
Finally, let us turn to "dedicated asset"
in
considerations.The available information
the data base does not make it possible for
us to know specificallywhetherthe supplier
made general investmentsthat would not
have been made but for the prospect of
amountof productto a
selling a significant
particular customerand if the contractis
terminatedprematurelyit would leave the
supplier with excess capacity(see Williamson, 1983, p. 526).27 Williamson'sconceptualization of dedicatedassetsimpliesthatthe
coal
importanceof thisfactorin structuring
supply relationshipsshould vary with the
quantityof coal that is initiallycontracted
for,otherthingsequal. The largertheannual
quantityof coal that is contractedfor,the
it is likelyto be forthe seller
more difficult
to quicklydispose of unanticipatedsupplies
(if the buyer breaches) at a compensatory
will it be fora
price, and the more difficult
buyer to replace supplies at a comparable
price if the sellerwithdrawsthemfromthe
market. Thus, I expect that the greater
the annual quantityof coal contractedfor
the longerwill be the specifieddurationof
thecontract.
Because of systematicvariationsin the
optimal scale and capital intensityof coal
production across regions,dedicated asset
considerationsare also likely to be more
importantfor westerncoal than for eastern
in coal supcoal. The greaterheterogeneity
of obtainingsuitable
plies and the difficulties
transportationfor it in the West, suggest
thatdedicatedassetproblemsare likelyto be
more severein the westernthan the eastern
region. The very thin spot marketin the
27Indeed, as a practicalmatter,it is unclear to me
thisdirectly
how one could ever go about determining
given the data that are likelyto be available foranalysis. The coal contracts that I have reviewed do
sometimeshave language thatappears to recognizethe
dedicated asset notion directly,but the absence of an
explicit statementcannot be assumed to imply that
dedicated asset considerationsare not important.
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VOL. 77 NO. ]
JOSKOW: CONTRA CTDURA TION
western region should be especially problematical for both sellersand buyerswhen
are breached
large contractualcommitments
of the coal, the
because of the heterogeneity
characteristicsof least-costproductionand
alternatives.This
the limitedtransportation
all implies again that contractsforwestern
coal should have longer contractualdurations thancontractsforeasterncoal.
To summarize,if variationsin the impordo
investments
tance of relationship-specific
in fact lead to variationsin the extentto
whichthe partiesprecommitto the termsof
futuretrade ex ante,I expectto findthatthe
duration of contractualrelationshipsspecifiedat thecontractexecutionstagewillvary
withthreeprimaryobservable
systematically
characteristicsof coal supply transactions.
First,whetherthe plant takingthe coal is a
plant or not. I expectto observe
mine-mouth
longer-termcontractsnegotiatedfor minemouthplants. Second,withtheregionof the
countryin which the coal is produced. I
expectthewesternregionto have thelongest
contractsand the easternregiontheshortest
withthe midwesternregionhavingcontracts
with prespecifieddurationsthat lie in between. Third, with the annual quantityof
coal contractedfor.I expectthatcoal supply
arrangementsinvolvinglarge annual quanwillbe supportedby longer
titycommitments
involvcontractsthan supply arrangements
ing smallerquantitiesof coal.
III. ModelSpecification
andEstimation
I am primarilyinterestedin estimatinga
betweentheduraset of simplerelationships
tion of contractualcommitments(DURATION) specifiedby the partiesat the contract execution stage and, (a) the annual
quantity(QUANTITY)28 of coal contracted
173
for,(b) a dummyvariable(MINE-MOUTH)
that takes on a value of 1 fora mine-mouth
plant and zero otherwise,and (c) dummy
variablesthatindicatethecoal supplyregion
in whichthe supplieris located(MIDWEST
and WEST, so thatregionaleffectsare measured relativeto contractsforeasterncoal).
Additional variables are consideredin the
nextsection.
The data base thatI use includesinformation forapproximately300 coal supplycontractsbetween domesticcoal suppliersand
investor-ownedelectric utilities.The contractsincludedin thedata base werenegotiated in various years up through1979 and
were in forceat least forpart of 1979. The
data are discussed in more detail in the
Appendix. Informationon all variables of
primaryinterestfor this studyis available
for 277 of the contractsin the data base. I
present estimates using both the full 277
observationsample as well as a subsample
consistingof 169 contractsthat involvedeliveriesdedicated to a singlepower plant.29
Table 1 provides the mean, standarddeviation, minimumand maximumvalues,and a
briefdescriptionforall of thevariablesused
in this section and subsequentsectionsfor
both theprimarysampleand thesingle-plant
subsample. Table 2 is a correlationmatrix
for all of the variables in the two samples
with the correlationsfor the 277 observation sample below thediagonaland thosefor
the 169 observation subsample above the
diagonal.
I work firstwith three simple specifications of the contractdurationequation:
(1)
DURATIONJ= a0 + b1QUANTITYJ
+ b2QUANTITY7 + b3MINE-MOUTH1
+ b4MIDWESTJ + b5WEST1 + Ui
28
Quantitiesare expressedin termsof the thermal
(Btu) content of the coal. The basic resultsare not
affectedwhen quantitiesare expressedin tons. For the
extensions reported in the next section, normalizing
quantities for Btu contentmakes it possible to more
if any,of contractquanaccuratelyexamine theeffects,
tities relative to total plant and utilityutilizationof
coal.
29Severalpeople suggestedto me that relationshipspecificinvestmenteffectsare mostlikelyto be revealed
forcontractsthatdedicateall suppliesto a singleplant.
I also focus on this subsample to obtain the data
necessaryto exploreissuesdiscussedin thenextsection.
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174
THE AMERICAN ECONOMIC REVIEW
MARCH 1987
TABLE 1-SAMPLE STATISTICS
Observations
Variable
277
169
277
169
DURATION
QUANTITY
PLANT PROPORTION
169
UTILITY PROPORTION
169
PLANT QUANTITY
169
UTILITY QUANTITY
169
PLANT/UTILITY
169
MINE-MOUTH
277
169
277
169
277
169
277
169
277
169
277
169
277
169
WEST
MIDWEST
DA TE-71
DA TE-74
DA TE-78
YEAR
Mean
Description
Standard
Minimum Maximum Deviation
50
43
183.00
183.00
10.43
10.77
24.62
27.05
0.03
1.00
0.35
0.003
1.00
0.23
2.95
172.42
41.69
2.95
919.80
270.56
1.00
1.00
0.3696
0.3696
12.75
ContractDuration
14.18
(years)
20.45
AnnualContract
22.83
Quantity
(trillionBtu's)
0.44
Fractionof Total
PlantUse from
Contract
0.19
Fractionof Total
UtilityCoal Use
fromContract
51.47
Plant Utilizationof
Coal (trillionBtu's)
221.54
UtilityUtilization
of Coal (trillionBtu's)
0.455
PlantUse as Fraction
of Total UtilityUse
Mine-MouthPlant
DummyVariable
WesternRegion
SupplyDummy
MidwesternRegion
SupplyDummy
ContractsSigned
1971-73: Dummy
ContractsSigned
1974-77: Dummy
ContractsSigned
1978-79: Dummy
Year ContractExecuted 1974
1974
1.00
0.007
0.323
(D = 1; 14 Observations)
(D = 1; 14 Observations)
(D = 1; 54 Observations)
(D = 1; 44 Observations)
(D = 1; 68 Observations)
(D = 1; 47 Observations)
(D = 1; 43 Observations)
(D =1; 29 Observations)
(D = 1; 116 Observations)
(D =1; 71 Observations
(D = 1; 72 Observations)
(D =1; 38 Observations)
1979
4.49
1955
1979
4.64
1955
can be foundin the Appendix.
aData sourcesand variabledefinitions
b
The 169 observationsubsampleincludescontractsdedicatedto a singleplant.
TABLE 2-CORRELATION MATRIX
277
Observation
Sample
DURA TION
QUANTITY
LOG-QUANTITY
MINE-MOUTH
MIDWEST
WEST
YEAR
169 ObservationSample
DURATION
-
0.60
0.64
0.54
0.14
0.41
-0.63
LOG-
MINE-
QUANTITY
QUANTITY
MOUTH
0.60
0.80
0.38
0.08
0.25
-0.39
0.68
0.78
0.27
0.11
0.26
-0.44
0.64
0.42
0.30
-0.06
0.39
-0.31
MIDWEST
WEST
YEAR
0.004
0.51 -0.57
-0.04
0.28 -0.35
0.02
0.32 -0.44
0.41 -0.37
-0.09
-0.37 -0.20
-0.28
-0.10
-0.27
-0.08
PLANT
UTILITY
PLANT/
PLANT
UTILITY
PROP.
PROP.
UTILITY
QUANTITY
QUANTITY
0.58
0.48
0.57
0.46
0.04
0.46
-0.28
0.43
0.43
0.42
0.41
-0.04
0.40
-0.11
0.61
0.29
0.41
0.45
0.10
-0.03
-0.06
-0.16
-0.24
-0.08
0.15
0.02
0.11
0.17
-0.05
-0.15
0.16
-0.11
-0.14
-0.36
-0.57
0.05
0.05
0.02
0.05
-0.06
0.16
0.13
-0.11
0.66
PLANT PROP.
-
-
-
-
-
-
-
-
UTILITY
PROP.
-
-
-
-
-
-
-
-
-
PLANT/UTILITY
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
PLANT
QUANTITY
-
0.34
UTILITY
QUANTITY
-
Note: Figuresbelow thediagonalare forthe277 observationsample,thoseabove thediagonal are forthe 169 observationsubsample.
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VOL. 77 NO. I
(2)
JOSKOW: CONTRACT DURA TION
DURA TION1= a0
+ b1LOG-QUANTITY1
+ b3MINE-MOUTH,
+ b4MIDWEST1 + b5WEST1 + U1
(3)
log(D URA TION1) = a 0
+ b1LOG-QUANTITY1
+ b3MINE-MOUTHi
+ b4MID WEST, + b5WEST1+ log(ui)
wherei indexes contractsand ui is an error
termwhose characteristics
will be discussed
further
below.
I have allowed (QUANTITY) to enter
these relationshipsnonlinearlyby introducing a quadratic in quantity(QUANTITYSQUARED) in (1) and using the natural
logarithmof quantity(LOG-QUANTITY)
in equations (2) and (3). Since powerplants
have usefullives of roughlyfortyyearsand
the costs of breach are likelyto declineover
time as plants and minesage, I expectthat
the impact of quantityon contractualduration will diminishas quantityincreases.The
following pattern of coefficientestimates
for the three equations is implied by the
hypothesized relationship between asset
and contractduration:
specificity
(i) All of the b 's should be positive,
exceptforb2, whichcould be positive,negative, or zero (no nonlinearity),althoughI
expect thatit will be negative.
(ii) b4 should be smallerthan b5.
Equations (1), (2), and (3) are estimatedin
threedifferent
ways.First,I presentordinary
least squares (OLS) estimatesof each equation for both samples.Next, I presentOLS
estimates that introducedummy variables
which indicate the date that the contracts
were executed. Finally,I presentmaximum
likelihood estimatesbased on the assumption thatwe have a truncatedsample drawn
froma populationwitheithera normalor a
log-normaldensityfunction.I discuss the
rationaleand resultsforeach estimationapproach and also present OLS results for
175
contractswithsuppliersin each of the three
regions.
A. OLS Estimates
If we assume thatthe u1in (1), (2), and (3)
are independentlydistributedand drawn
froma normal distribution
withmean zero,
thenOLS will yieldan unbiasedestimatorof
the coefficientsof interest.I proceed first
with this assumptionand provideestimates
for alternativeassumptionsabout the error
structurebelow. The OLS resultsare presented in Table 3. The firstthreecolumns
are estimatesfor the threeequations using
the 277 observationsample. Columns 4, 5,
and 6 containestimatesforthe 169 observation subsample.
The OLS estimatesare, in all cases, consistent with the hypothesizedrelationship
betweenasset specificity
and contractduration.The effectsof annual contractquantity,
region,and mine-mouth
plantshave thepredicted signs and are estimatedquite precisely. A mine-mouthplant is predictedto
have a contractthatis about 16 yearslonger
than those of other plants. Contractswith
eastern producersare 3 to 5 years shorter
than those forwesternand midwestern
producers.Contractswithwesternproducersare
2 to 3 yearslongerthanthosewithmidwestern producers. The differencein duration
between the WEST and the MIDWEST is
generallynot significantat the 5 percent
level for the 277 observationsample,but is
significantfor the single-plantsubsample.
An increase in annual contractquantityof
22 trillion Btu's (roughly1 million tons)
yields about a 13-yearincreasein contract
duration.As a generalmatter,the estimates
are morepreciseforthesample of contracts
dedicated to a singleplant.
B. OLS Estimateswith
ContractDate Dummies
The desirable propertiesof the OLS estimates depend on the strong assumptions
made about the errorstructure.Since the
contractsin the data base were signed at
manydifferent
times,it is naturalto consider
the possibility that contractingpractices
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176
THE AMERICAN ECONOMIC REVIEW
MARCH 1987
TABLE3-CONTRACTDURATIONa
277 Sample
Independent
Variables
QUANTITY
QUANTITYSQUARED
LOG-QUANTITY
MINE-MOUTH
MIDWEST
WEST
169 Sample
LOG-
DURATION
LOG-
DURATION
DURATION
0.4289
(0.0373)
-
-
-0.0024
(0.00030)
-
-
16.3300
(2.0496)
3.4267
(0.9682)
5.3550
(1.357)
4.4206
(0.3742)
16.4317
(2.0045)
3.8795
(0.9821)
5.2033
(1.1641)
0.5057
(0.0425)
0.5104
(0.2279)
0.5154
(0.1116)
0.6142
(0.1323)
-
-
-
-
-
-
-
-
-
-
(1)
-
(2)
(3)
2SLS
DURATION
DURATION
DURATION
DURATION
0.4091
(0.0040)
-
-
-
-
-
-0.0020
(0.00003)
-
-
-
-
-
-
4.2080
(0.4069)
16.2300
(1.8421)
2.7843
(1.1032)
5.6108
(1.2586)
0.4942
(0.0453)
0.4616
(0.2050)
0.5785
(0.1228)
0.6844
(0.1401)
4.2022
(0.4617)
16.3432
(1.9426)
2.7317
(1.1295)
5.6456
(1.3406)
4.2057
(0.4084)
16.2284
(1.8477)
2.7848
(1.1065)
5.6391
(1.2751)
5.1066
(0.812)
15.4391
(1.968)
2.4268
(1.153)
4.8916
(1.394)
-
-
0.9729
(1.9806)
-
-
-
-
-
-2.0570
(2.5832)
-
-
-
-
(4)
15.9583
(1.9106)
2.7832
(1.0928)
5.9856
(1.2346)
(5)
(6)
PLANT
PROPORTION
UTILITY
PROPORTION
PLANT/
UTILITY
Constant
Corrected
R-squared
Observations
a
3.6770 -0.7902
(0.6586) (0.9579)
0.61
277
0.60
277
0.6014
(0.1089)
0.51
277
Estimate
Duration
(9)
DURATION
3.9334
(0.8109)
0.71
169
0.0155
(1.0917)
0.70
169
(7)
(8)
-0.2246
(1.4265)
0.6242 - 0.0146
0.1157 - 1.8922
(0.1215) (1.0978) (1.2665) (1.852)
-
-
0.61
169
0.70
169
0.70
169
-
169
OLS estimates.Standarderrorsof coefficient
estimatesare shownin parentheses.
changedover time.Not onlymighttheduration of a typicalcontracthave changedover
time,but such changesmayhave been correlated with changes in contractquantities,
supply location, and the developmentof
mine-mouthplants over time.Failing to include variablesindicatedcontractdatescould
thenlead to a correlationbetweenthe independent variables and the errorterm.The
OLS estimates would then be biased. To
check to see if the estimatesare sensitiveto
the presenceof a left-outvariablereflecting
the contractingdate, in Table 4, I report
estimatesof equations (1), (2), and (3) that
have contractdate dummiesincluded.These
contractdate dummyvariablesare DA TE-71,
whichis equal to one forall contractssigned
between1971 and 1973 inclusive,DA TE-74,
which equals one for contractssigned between 1974 and 1977, and DATE-78 which
equals one forcontractssignedin 1978 and
1979. Since a separatevariableforcontracts
signed prior to 1971 is not included, the
coefficientestimatesare all relativeto pre1971 contracts(i.e. the constantterm).This
aggregationof signingdates was made to
reflectmajor shocks to coal supply and/or
demand.30
30The 1971-73 periodis just afterthe Clean Air Act
Amendmentsof 1970 werepassed, the 1974-77 period
is the period afterthe Arab oil embargoand includes
the subsequent increases in fossil fuel prices. The
1978-79 period coincides with the beginningof a
slowdown in utilitycapacity additions.These periods
are discussed in more detail in my paper (1986). The
aggregationchosen is the same used to analyzepricing
behaviorin thatpaper. The resultsreportedhereare not
sensitiveto thisaggregation,
however.The same qualitative resultsare obtained if separate dummyvariables
are used foreach yearduringthe 1970's plus a separate
dummyvariable forpre-1965and 1966-70 contracts.
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VOL. 77 NO. I
177
JOSKOW: CONTRACT DURATION
TABLE4-CONTRACTDURATIONa
Independent
Variables
QUANTITY
QUANTITY-SQUARED
DURATION
DURATION
(1)
(2)
0.3120
(0.03547)
-0.0018
(0.00027)
-
LOG-QUANTITY
13.9437
(1.8482)
1.6814
(0.8785)
4.8429
(1.0301)
MINE-MOUTH
MIDWEST
WEST
LOGDURATION
(3)
-
-
-
-
3.0482
(0.3655)
13.6701
(1.8260)
1.9761
(0.8952)
4.8662
(1.0549)
0.3245
(0.0380)
0.3140
(0.1899)
0.3029
(0.0931)
0.4831
(0.1097)
DURATION
(4)
0.3355
(0.0406)
-0.0018
(0.00029)
14.6494
(1.8495)
1.4906
(1.0544)
5.1054
(1.2183)
DURATION
(5)
LOGDURATION
(6)
-
-
-
-
-
3.3485
(0.4330)
14.6907
(1.8347)
1.6405
(1.0786)
5.1731
(1.2524)
0.3631
(0.0446)
0.3792
(0.1891)
0.4083
(0.1112)
0.5137
(0.1291)
-
-
-
-
UTILITY PROPORTION
-
-
-
-
-
-
PLANT/UTILITY
-
-
-
-
-
-
9.2341
6.1982
1.4220
(1.5185)
(1.8612) (0.1918)
-0.7282
-0.9103
-0.0311
(1.4890)
(1.5057) (0.1552)
-3.9714
-4.3786
-0.3149
(1.3483) (1.3685) (0.1410)
-7.0789
-6.5193
-1.0317
(1.5540)
(1.6324) (0.1682)
0.74
0.69
0.73
169
169
169
3.2847
3.3461
(0.4923) (0.4344)
14.5742 14.6800
(1.9423) (1.8403)
1.5543
1.6323
(1.1083) (1.0821)
5.0697
5.1258
(1.3338) (1.2676)
0.9930
(1.8907)
-0.9138
(2.4975)
0.3785
(1.3660)
6.1583
6.0806
(1.8853) (1.9142)
-0.9389 -0.9455
(1.5290) (1.5154)
-4.3848 -4.4134
(1.3789) (1.3781)
-6.4995 -6.5749
(1.6702) (1.6493)
0.73
0.73
169
169
coefficient
estimatesare shownin parentheses.
While the introductionof these contract
date dummyvariablesmay help to control
for contract date related correlationsbetween the error termand the independent
variables,the estimatedcoefficients
of these
variables themselveshave no obvious economic meaning.This is because of the nature of the sample. Recall that I observe
contractsinforcein 1979. If we thinkof the
populationas consistingof contractswritten
for particularplants (i) in a particularyear
(t), we can observea contractonlyif
(4)
(8)
-
-
a OLS/Contract date dummies.Standarderrorsof
DURATION
-
-
12.0145
9.4184
1.7205
(1.2526) (1.5307) (0.1592)
DA TE- 71
-2.3734
-2.4564
-0.0988
(1.2715) (1.2876) (0.1339)
DA TE- 74
-6.6815
-7.3044
-0.5098
(1.2647) (1.1446) (0.1190)
DATE-78
-10.5052 -10.6151 -1.3926
(1.2647) (1.2976) (0.1349)
0.70
0.69
CorrectedR-squared
0.67
277
Observations
277
277
(7)
-
PLANT PROPOR TION
CONSTANT
DURATION
DURA TIONIt
2 (1979-Contract YEAR)
This means thatof thosecontractssignedin
1970, I can observe,in 1979,only thosethat
had durationsof at least 9 years,whileI will
observeshortercontractsthatweresignedin
lateryears.Even if therewereno changesin
contractingbehavior over time, we would
inevitablyfind that the coefficients
of the
contract date dummies indicate that the
average lengthof a contractin the sampleis
negativelycorrelatedwith the date of the
contract.31 The coefficients
of the contract
date variables can therefore
tell us nothing
directlyabout the changes in contracting
behaviorover time.
With theseconsiderations
in mind,we can
turnto the resultsreportedin Table 4, columns 1 through6. The resultsobtainedare
again consistentwith the hypothesizedrelationshipbetweenasset specificity
and contractduration.The coefficients
of the quantity, mine-mouth,and regional variables
In the sample, the simplecorrelationbetweencontract date (YEAR) and DURATION is about -0.60.
See Table 2.
31
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178
THE AMERICAN ECONOMIC REVIEW
MARCH 1987
The resultsare again quite consistentwith
the hypothesizedrelationshipbetween asset specificityand contractduration. The
of
signs and magnitudesof the coefficients
QUANTITY, MINE-MOUTH, WEST, and
MIDWEST are again as predicted.Withthe
exception of the mine-mouthdummy in
are estimated
equation (3), the coefficients
quite precisely.The magnitudesof the estiin thistableare difficult
to
matedcoefficients
C. MaximumLikelihoodEstimates
compare directlywiththosein Tables 3 and
A third alternativefor estimatingthese 4 because of the need to incorporatethe
truncationeffectsinto estimatesof contract
equations is to follow Keith Crocker and
duration.33Correctingfor the sample trunScott Masten (1986), who workwitha samcation, the estimatesreportedin Table 5,
ple of naturalgas contractswithsimilarsampling properties,and assume that the sam- column 5, for example,yield the following
pling procedurewhichchooses contractsin
expected durations: The expected duration
forcein a single year (1981 in theirpaper)
evaluated at the means of the independent
represents a classical sample truncation variablesis 10.5 years(comparedto a mean
problem as discussed by G. S. Maddala
of 14.2 years forthe truncated169 observa(1983, pp. 165-170). The populationof contion sample). Mine-mouthplants have contractsthenimplicitly
consistsof all contracts tractswithan expecteddurationthatis about
writtensince theearliestcontractin thedata
12 years longer than the averagenon-minebase. We obtain a truncatedsamplebecause
mouthplant.Midwesterncontractsare about
we observecontractsonlyif the durationof
3.5 years longer than eastern contracts.
the contractis greaterthan or equal to L1,
Westerncontractsare about 11 yearslonger
whereLi is equal to 1979 minusthecontract than eastern contractsand 6 years longer
than midwesterncontracts.The difference
date. In thiscase, OLS estimatesof (1), (2),
betweenwesternand midwestern
contractsis
and (3) would be biased, because the samat the5 percentlevel.34
plingprocessis likelyto inducea correlation generallysignificant
between the independentvariablesand the
errorterm.
D. EstimatesforIndividualCoal
We can obtain estimateswith desirable
SupplyRegions
thelikeasymptoticpropertiesby specifying
lihood functionof the sample, given the
Finally, in Table 6, I report estimates
natureof the samplingtruncation,
and then of the relationshipbetween DURA TION,
solve for the maximumlikelihoodestimates LOG-QUANTITY, and MINE-MOUTH for
of interest.Follow(MLE) of thecoefficients
ing Maddala (p. 166), I assume that the
population relationship between contract
of Li = (1979- YEAR)
thatrequiretakingthelogarithm
durationand theindependentvariableshas a
which is zero for contractswrittenin 1979. Since the
normallydistributederrorand thateach obshortestcontractin thedata base has a durationof one
servationis truncatedat Li. This leads to a
year,we can impose a lowerbound betweenzero and 1
standardmaximumlikelihoodestimator.
on Li to include all observations.Estimatesobtained
The maximumlikelihoodestimatesare reusing different
lower bounds does not change the results,so I simplyreportresultsforthesampleexcluding
ported in Table 5, columns 1 through6.32
continue to be of the predictedsigns and
difrelativemagnitudes.The onlyinteresting
ferencebetween these resultsand those rebeported in Table 3 is that the difference
tweenthe durationsof contractssignedwith
westernand midwesternproducersis now
at the5 pergenerallystatistically
significant
cent level forboth samples.
32
The estimateswereobtainedusingtheMLE routine
in the StatisticalSoftwareTools package (Version1.0 as
of October 1986) developed by Jeffrey
Dubin and
R. Douglas Rivers runningon an IBM XT. The estimates in cols. 3 and 6 assume thatthedensityfunction
is log-normal.Contractsexecutedin 1979 have been
dropped since the likelihood functionincludes terms
the small numberof contractsin thedata base executed
in 1979.
33See Maddala (p. 167).
34I have also produced maximumlikelihood estimates of equation (2) for subsamples consistingof
pre-1974contractsand thosesignedbetween1974 and
1979. The hypothesizedrelationshipbetweencontract
variationsin
duration and the variables representing
asset specificity
persistsforboth subsamples.
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VOL. 77 NO. I
JOSKOW: CONTRA CT DURA TION
179
DURATION a
TABLE 5-CONTRACT
2SLS
Independent
Variables
LOGLOGESTIMATE
DURATION DURATION DURATION DURATION DURATION DURATION DURATION DURATION DURATION
QUANTITY
(1)
(2)
(3)
0.7948
-
-
-
-
(0.1046)
QUANTITYSQUARED
-0.0043
(0.00061)
(4)
(5)
(6)
(7)
(8)
(9)
0.5807
-
-
-
-
-
-0.0030
-
-
-
-
-
(0.0717)
(0.0004)
-
11.8527 0.6733
(1.5663) (0.0897)
16.5557 13.8763 0.2407
(4.2407) (3.8955) (0.5714)
8.3650
8.1377
0.4493
(2.8188) (2.5461) (0.2281)
15.4864 13.8045
1.0500
(4.1940) (3.4244) (0.0500)
LOG-QUANTITY
-
MINE-MOUTH
MIDWEST
WEST
PLANT
PROPORTION
15.5484
(2.9435)
5.0050
(2.5018)
11.7771
(3.0124)
8.4367
(1.0816)
14.4004
(2.7764)
4.7042
(2.4511)
10.5506
(2.4220)
0.6340
(0.0816)
0.2736
(0.4614)
0.5376
(0.2763)
1.0522
(0.2416)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
2.4401
(3.7927)
UTILITY
PROPORTION
8.2709
8.4505
(1.0854) (1.0815)
14.1237 14.3841
(3.0120) (2.7650)
4.4397
4.7468
(2.5221) (2.4820)
10.1238 10.4730
(2.6136) (2.4095)
-2.0043
(3.1937)
PLANT/
UTILITY
Constant
Log-Likelihood
Observations
-
8.5737
(3.5777)
15.4508
(2.1083)
2.4404
(2.1797)
6.9477
(1.8329)
-
-
-
-
1.4626
-
(2.1945)
-18.4532 - 35.6427 - 0.4699 - 6.9510 -19.0118 - 0.2789 -19.0377 - 19.7755 - 36.1521
(5.7203) (7.2690) (0.26709) (3.4094) (4.6758) (0.2448) (4.8545) (4.8645) (6.9717)
- 781.08 - 769.29 -174.29 - 475.17 - 466.99 - 101.60 - 466.68 - 466.83 - 451.38
277
277
255
169
169
160
169
169
169
aMaximum likelihood
estimates.
Standard
ofcoefficient
errors
estimates
areshownin parentheses.
TABLE 6-CONTRACT
DURATION a
DependentVariable: DURATION
Independent
Variables
WEST
(1)
(2)
LOG-QUANTITY
3.4825
(1.0714)
18.4179
(2.5064)
6.819
(3.1610)
0.68
54
5.1249
(0.7646)
11.8107
(5.1695)
1.365
(5.1695)
0.42
68
MINE-MOUTH
Constant
CorrectedR-squared
Observations
MIDWEST
EAST
EAST
WEST
(3)
(4)
(5)
4.2362
(0.4668)
- 0.3975
(1.1159)
0.35
155
4.2968
3.5878
(0.4661) (1.0992)
17.6804 17.616
(3.7348) (2.3183)
- 0.5265
7.2101
(1.1151) (3.3441)
0.43
0.73
44
158
MIDWEST
EAST
EAST
(6)
(7)
(8)
4.0269
(0.6538)
12.8372
(3.6363)
3.4075
(1.8264)
0.52
47
4.408
(0.5798)
0.4106
(1.4256)
0.42
78
4.4992
(0.5786)
17.0887
(3.8111)
- 0.6028
(1.4246)
0.54
81
a OLS by region.Standarderrorsof coefficient
estimatesare shownin parentheses.
each coal supply region.35The samples do
not include any mine-mouthplants using
eastern coal, so columns 3 and 7 simply
report the estimated relationshipbetween
35I reportonly this variantof equation (2) to conservespace. It should be clear by now thatthe alternative specificationsof (1), (2), and (3) do not yield any
importantdifferences
in results.
DURATION and LOG-QUANTITY. There
are in fact a few mine-mouth
plants in the
East and I have some information
forthree
of them. These were not included in the
sample because the data base did not have
informationon annual contractquantities
for them. However, I was able to obtain
informationfordeliveredquantitiesforthree
eastern mine-mouthplants and have aug-
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180
THE AMERICAN ECONOMIC REVIEW
MARCH 1987
dependenton suppliesprovidedpursuantto
a specificcontract.These argumentsimply
thatvariablesmeasuringplantand/or utility
"dependence" on a specificcontractshould
be introducedinto the contractdurationrelationship.
To examine this possibility,I have included variables in (2)37 that measure the
(PLANT
fractionof a plant's requirements
PROPORTION) and thefractionof thetotal
coal requirementsof the utility(UTILITY
that operates the plant
PROPORTION)
whichare accountedforby a particularcontract. As an alternative,I also estimate(2)
introducinga variablethatis equal to total
plant utilization of coal divided by total
utilityutilization of coal (PLANT/UTILonly
ITY). I can estimatetheserelationships
for the contractsthat are for deliveryto a
singleplant because it is onlyfortheseconmeatractsthatI can constructa meaningful
sure of plant specificdependence(i.e., the
169 observationsubsamplemustbe used).
IV. Altemative
MeasureofAssetSpecificity
The resultsare reportedin columns7 and
8 of Tables 3, 4, and 5. The coefficient
Clearly,the hypothesizedrelationshipbeestimatesfor PLANT PROPORTION and
tween contract duration and the variables UTILITY PROPORTION are veryimprethat I have chosen to capturevariationsin
cise. They are of opposite signs and are
asset specificity
is quite robustto alternative neitherindividuallynorjointlysignificant
at
specifications,
samples,and estimatingtech- conventionalsignificancelevels. The coeffinique. Nevertheless,it is natural to ask
cient of PLANT/UTILITY is also veryimwhetherthere are alternativeor additional precise and varies in sign depending on
factorsthatmightexplaintheobservedvari- estimatingtechnique.Introducingthesevariations in contractduration.One argument ables has no effecton the estimatesfor the
thathas been suggestedto me is thatit is not
primaryvariables of interest.These results
only the size of the contractualcommitment implythat a plant or utilitythatrelieson a
that is likelyto be important,but also the
single supplier for a large fractionof its
fractionof a plant's,and perhapstheutility's, requirements,or that depends heavilyon a
requirementsobtained froma specificconspecificplant,does not encountersignificant
tract.The argumentis thatas a largerfrac- hold-up problemsper se. The lock-ineffect
tion of a plant's requirements
is associated
associated with designingplants to burn a
with a specific supplier, " physical asset
particulartype of coal becomes a potential
specificity"attributesare likelyto be more
contractualproblemonly to the extentthat
importantand lead to longer contracts.It
are
characteristics
the otherasset specificity
has also been suggestedto me that opactive.
portunismproblems are likely to be less
severeif the utilityas a whole is not heavily
mented the sample to include these plants,
using deliveredquantitiesratherthan contractquantitiesas thevalues forthe QUANTITY variable.These resultsare reportedin
columns4 and 8 of Table 6.36
The effectsof contractquantityand the
mine-mouthdummy on contractduration
are clearly not simplyassociated with the
contractingbehaviorforcoal froma particular region.The expectedeffectsare foundin
of
each of the threeregions.The coefficients
QUANTITY and MINE-MOUTH are of the
expectedsignsand are estimatedpreciselyin
in the
all cases. While thereare differences
of thesevarimagnitudesof the coefficients
ables betweenthe threeregions,theyare not
very large numericallyand equality of the
coefficientsof QUANTITY and MINEMOUTH across regionscannot be rejected
at standard significancelevels. Contracts
basically simplyget longeras we move from
East to West,otherthingsequal.
The inclusion of these three additional observations does not change the aggregateresults.
36
37Includingthese variablesin (1) and (3) does not
change the results and I reportthe resultsfor (2) in
order to conservespace.
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VOL. 77 NO. ]
JOSKOW: CONTRACT DURATION
Quantity
V. Contract
181
I estimatethe followingrelationshipusing
the single-plant(169 observation)subsample
to determineempiricallywhetherand how
these considerationsaffectannual contract
quantities.
Before concluding,it is usefulto explore
in determining
the role of asset specificity
annual contractquantitiessincethisvariable
plays such an importantrole in the contract
(5) QUANTITY = c0
duration equation. A relationshipbetween
potencontractquantityand asset specificity
+ d1PLANT-QUANTITY
tiallyemergesbecause of thepresenceof all
First,other
three types of asset specificity.
+ d2PLANT-QUANTITYI
things equal, physical asset specificity
considerationssuggestthata plant operator
+ d3UTILITY-QUANTITY,
would like to rely on a specific supplier
producing a particulartype of coal at a
+ d4UTILITY-QUANTITY7
particularlocationto thegreatestextentpossible.38This implies that contractquantity
+ d5MINE-MOUTHi
should vary directlywith the coal requirements of the individual plant (PLANT
+ d6MIDWEST, + d7WEST, + ViQUANTITY).39 On theotherhand,themore
I expect d1, d3, d5, d6, and d7 to be
a utilitycomes to relyon a single supplier
greaterthan zero, and d7 should be larger
the morecostlya breachof contractmaybe.
This suggeststhata utilitymaybe willingto
than d6.
Equation (5) is estimatedin two different
relymoreon a singlesupplierforan individual plant the largeris totalutilityutilization ways. First, OLS estimatesare presented.
of coal (UTILITY QUANTITY) given the
Second, OLS estimateswitha correctionto
reflectthe possibilitythatI have a censored
utilizationof a specificplant.
plants,
Second, in the case of mine-mouth
sample are also presented.The OLS estiof (5) maybe biased
the nature of the ex ante location/invest- matesof the coefficients
ment decision involvesthe mutualexpectaas a result of the samplingproceduredistion that all or most of a plant's require- cussed earlier.We observecontractquantity
ments will be taken from the proximate only if contractdurationis greaterthan or
equal to (1979-contract YEAR), so we have
supplier.This impliesthat the quantityper
plants,
a censored sample. This implies that the
contractwillbe largerformine-mouth
random error (v) in the contractquantity
otherthingsequal.
Finally,as discussedabove, whenutilities equation (5) may be correlatedwiththerandom error(u) in thecontractdurationequadesign plants to closely matchspecificcoal
quality attributestheywill have an interest tion. If thisis the case, therandomerror(v)
in relyingmoreheavilyon a specificsupplier in the contractquantityequation will be a
functionof the independentvariablesin the
who contractsto supply coal froma seam
This is likelyto be
contract duration equation. The OLS estiwiththesecharacteristics.
of the independent
mates of the coefficients
an especially importantconsiderationfor
variablesin the quantityequation (5) would
coal suppliesfromthewesternregion.
thenbe biased if theyare correlatedwiththe
independentvariablesin the durationequa38One mightalso argue that,other thingsequal, a
tion.In particular,independent
variablesthat
buyerwould ratherrelyon a singlesupplierto conserve
in
the
contract
duration
equation
appear
cost associated
on moretraditionaltypesof transactions
may appear to be significantwhen introwith negotiating,monitoring,and enforcingcontracts
withmultiplesupplies.
duced as independentvariablesin equation
39Ideally,we would like to look at specificgenerating
(8) when in fact theyare not.40Since three
plantswhereplants
unitsratherthanspecificgenerating
designcharacteristics.
have multipleunitswithdifferent
is not available
coal supplyinformation
Unfortunately,
at the generatingunitlevel.
40See George Judgeet al. (1985, pp. 610-13) and
JamesHeckman (1976, 1979).
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182
THE AMERICAN ECONOMIC REVIEW
MARCH 1987
TABLE 7-CONTRACT QUANTITYa
DependentVariable: QUANTITY
OLS
IndependentVariables
PLANT QUANTITY
PLANT QUA NTITYSQUA RED
UTILLITY QUANTITY
UTILITY QUA NTITYSQUA RED
MINE-MOUTH
WEST
MIDWEST
H
(2)
0.2501
(0.0441)
0.3133
(0.1573)
-
-0.00066
(0.0010)
0.0672
(0.0311)
0.00349
(0.00689)
Corrected
R-Squared
Observations
(3)
0.2128
0.0489
0.00393
(0.00685)
-
29.7932
(6.8591)
13.5416
(4.6437)
5.2469
(4.1841)
-0.00007
(0.00003)
27.1594
(6.8997)
13.5865
(4.6259)
4.856
(4.1708)
1.7329
(3.5879)
- 4.2283
(5.0515)
30.3261
(6.8251)
8.4955
(5.4680)
2.5832
(4.4371)
-66.7618
(38.7474)
10.6708
(6.2951)
0.33
169
0.34
169
0.34
169
-
-
Constant
OLS/H
(1)
-
(4)
0.2827
(0.1578)
-0.00067
(0.00099)
0.06376
(0.03104)
-0.000063
(0.000032)
27.775
(6.879)
8.9738
(5.4597)
2.4749
(4.4193)
-60.6647
(38.5749)
4.0997
(7.3027)
0.35
169
aStandard errorsof coefficient
estimatesare shownin parentheses.
variables that appear in the quantityequation (5) also appear in thedurationequation,
thisis a potentialproblemhere.
We can obtain consistentestimatesof the
coefficientsof (5) by obtainingmaximum
conlikelihcodestimatesof a reduced-form
tractdurationequation41to generatea sample selectioncorrectionH foreach observation,adding theestimatedvalues of H to (5)
and thenestimatingtheaugmentedequation
of H is thena
(5) usingOLS. The coefficient
consistentestimateof the covariance of u
and v.
The resultsare reportedin Table 7.42 The
OLS resultsappear in columns1 and 2 and
theOLS resultswithH introducedappear in
columns 3 and 4. In both cases, estimates
with and withoutquadratic termsin plant
and utilityquantitiesare reported.The re-
41QUANTITY and LOG-QUANTITY are treatedas
being endogenous.
42
The mean value forH is 0.076.
sults are generallyconsistentwithmyexpectations. Larger plants tend to place larger
ordersand utilitieswithlargeraggregaterequirementsdo so as well,althoughtheutility
effectis generallysmall. The quadratic in
different
plant quantityis not significantly
of themine-mouth
fromzero.The coefficients
dummyhas the predictedsign and is estimated fairlyprecisely.Mine-mouthplants
have contractsthat are nearly 1.5 million
tons larger (30 trillionBtu's) than other
in
plants ceterisparibus.Regionaldifferences
supply characteristicslead to larger contractswithwesternsuppliersthan withsuppliers elsewhere. The coefficientof the
correctionvariable H is negative,although
not quite significantat the 5 percentlevel
(two-tailedtest),implyingthatthe errorsin
the durationand quantityequationare negatively correlated.Including this correction
on theresults
does not have dramaticeffects
for the coefficients
of interest,
however.The
primaryeffectis to reduce the magnitude
of thecoefficient
of WEST.
and significance
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VOL. 77 NO. I
JOSKOW: CONTRACT DURA TION
Finally,for the record,I reporttwo-stage
least square (2SLS) estimatesof equation
(2) in Table 3, column9, and the equivalent
of two-stageleast squares forthe maximum
likelihoodestimatesof equation(2) in Table
5, column 9.43 The estimatesdo not change
in any importantway fromthose obtained
using the other estimatingtechniques in
eithercase.
VI. Conclusions
The purpose of this paper has been to
examine empiricallyhypothesesabout the
relationshipbetween the duration of coal
contractsand thepresenceof thethreetypes
of relationshipspecificinvestments
discussed
by Williamson
(1983).4
I arguethatas rela-
investmentsbecome more
tionship-specific
important,the partieswill findit advantageous to rely on longer-term
contractsthat
specifythe termsand conditionsof repeated
43I assumed that LOG-QUANTITY is endogenous,
and use the right-handside variablesin (5) as instrumentsforthe 2SLS resultsreportedin Table 3, col. 9.
Obtainingtheequivalentof 2SLS estimatesforthecase
in whichI treatthe sample as being truncatedand use
maximumlikelihood techniques,is more complicated.
Following Maddala (pp. 234-40) and L. S. Lee et al.
(1979), I proceeded in the followingway. First,I estimate a reduced-form
durationequationusingmaximum
likelihood techniques.This allows me to obtain consistentestimatesof Hi which can in turnbe used to
obtain consistentestimatesof a contractquantityequation as discussed above. I use these estimatesof the
quantityequation to obtainpredictedvalues of QUANTITY or LOG-QUANTITY which are then used in
place of QUANTITY and LOG-QUANTITY to estimate equations (1) and (2) using maximumlikelihood
techniques. The results reportedin Table 5, col. 9,
assume that log(PLANT QUANTITY), log(UTILITY
QUANTITY),
MIDWEST,
WEST, and MINEMOUTH are exogenousvariables.These variablesare
used to estimatea reduced-form
durationequationusing
themaximumlikelihoodtechniquedescribedearlier.An
equation forlog(QUANTITY) is thenestimatedusing
log( PLA NT QUANTITY), log(UTILITY QUA NTITY), MIDWEST, WEST, MINE-MOUTH, and H
(generatedfrom the reduced-form
durationestimates)
using OLS. The predictedvalues fromthisequationare
thenused insteadof LOG-QUANTITY in (2) to obtain
the maximumlikelihoodestimatesreported.Specification (1) has also been estimatedusing this approach.
The resultsappear to be robust.
"As well as similar considerationsof transactionspecificinvestments
identified
by Klein et al.
183
transactionsex ante, ratherthan relyingon
repeated bargaining.I make use of a large
sample of coal contractsto examine this
hypothesis.The empiricalresultsobtained
provide fairlystrongsupport for this hypothesis.They are quiterobustto alternative
model specifications,samples,and estimating techniques.The resultstherefore
provide
additional empirical support for the view
that the structureof verticalrelationships
between buyers and sellers is stronglyaffected by variationsin the importanceof
relationship-specific
investments.
APPENDIX
Here I discuss the sources of the data and the
constructionof the variables.The data base thatI rely
on was constructedfroma varietyof sourcesforuse in
a research project focusingon vertical relationships
betweenelectricutilitiesand coal suppliers.This is one
of threepapers thathas been producedso farfromthis
project.
The constructionof the data base began with the
choice of contractsto use in the analysis. Contracts
werechosenif theyappearedin boththe1981 and 1983
editionsof The Guideto Coal Contracts(Pasha Publications) and for which informationnecessary for the
project was reported.Contractshad to appear in both
thatwas desired
publicationsbecause some information
appeared in one or the otherpublication,but not both.
This also made it possible to check for errorsand
inconsistenciesin the contractcharacteristics
reported.
To appear in both publications,contractshad to be in
forcein 1979. In fivecases, actual contractswere used
to supplement the data available from the primary
sources.
This collectionprocedureresultedin a sampleof 296
contracts(of which 277 had enoughinformation
to be
used here) which generallyhad the followinginformation, some of whichis used in thispaper and some of
whichI am usingin relatedwork:
1. Informationrequired to calculate the agreed
upon durationof the contract(see discussionbelow).
2. Contractquantitiesfor1979,1980, and/or 1981
in tons.
3. The contractspecifications
forthe Btu content
and the sulfurcontentof thecoal.
4. The identityof thesellerand thelocationof the
mine.
5. The identityof thebuyerand thedestinationof
the coal.
6. The base price for the coal at the time the
contractwas signed.
7. The actual price for the coal in 1979, 1980,
and/or 1981.
8. Deliveredquantitiesin 1979,1980,and/or 1981.
9. Actual Btu and sulfurcontentof thecoal.
Once the contractsample was selected,individual
contractswere matchedwithspecificutilitiesand power
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184
THE AMERICAN ECONOMIC REVIEW
plants (wherepossible). Two publicationswere utilized
to obtain coal quantityand quality(Btu content)information by plant and utility:Cost and Qualityof Fuels
for the Electric UtilityIndustry(U.S. Departmentof
Energy,variousyears)and Steam ElectricPlant Factors
(National Coal Association,variousyears). For public
utility holding companies, coal utilization for subsidiaries was aggregated.Jointlyowned plants were
assignedto the operatingcompany.
The threecoal supplyregionsrepresentaggregations
of smallerU.S. Bureau of Mines (BOM) districts.The
West was definedas includingBOM districts16 through
23. The MidwestincludedBOM districts5, 9, 10, 11, 12,
14, and 15. BOM district15 includesTexas, but we have
no contracts for coal produced in Texas. The East
includes coal fromthe remainingBOM districts,pribetweenregions
marilyin Appalachia. The differences
is discussed in more detail in my 1985 paper. The
KeystoneCoal IndustryManual (Mining Information
Services)and an atlas wereused to help locate minesin
specificBOM districts.
and construction
are as folThe variable definitions
lows:
DURATION: Contract Duration: The contract
data base generallyprovidesinformationfor the date
the contractwas executed,the terminationdate and
(less frequently)the date of firstdeliveryof coal. A
specificmonth and year is oftenprovided,but sometimes the source specifiesonly years.Because the contractexecutiondate was available moreoftenthan the
date of firstdeliveryand because the two are generally
quite close together,contractdurationwas measuredas
contractterminationyear minusexecutionyear. Initial
withdurationmeasuredusingthe date
experimentation
of firstdeliveryor usingmonthand yearindicatedthat
the resultswere unaffected,
so the definitionthatpreservedthe largestnumberof contractswas used.
QUANTITY: Annual contractquantityin trillion
Btu's. The contractedtonnagereportedfor1980 (if that
was not available,or obviouslynot representative,
1979
1981 wereused instead)was multiplied
or, alternatively,
by the contractedBtu contentof the coal to arriveat
the contractquantityvariable.
MINE-MOUTH: Mine-mouthdummy variable
thatis equal to one if the plant is a mine-mouth
plant
in my 1985 paper
and zero otherwise.The information
in the
combined with the coal destinationinformation
Guide To Coal Contractswas used to constructthis
variable.The Navajo and Mohave plantswereincluded
in thiscategoryas well since theyhave economiccharacteristicsverymuchlike a mine-mouth
plant.
MID WEST: A regional dummy variable that
equals one if the coal is froma midwesternmine (as
definedabove) and zero otherwise.The contractdata
on minelocation.
base providesinformation
WEST: A regional dummyvariable that equals
one if coal is froma westernmine (as definedabove)
and zero otherwise.
PLANT QUANTITY: Annual plant utilizationof
coal. Coal utilizationby a plant to which a specific
contractis dedicated (at least 90 percentof the coal
deliveredto a single plant) for 1980 (1979 or 1981 if
necessaryto match QUANTITY) in trillionBtu's. Ob-
MARCH 1987
tained fromthe Departmentof Energyand National
Coal Associationpublicationsidentifiedabove.
UTILITY QUANTITY: Annual utilityutilization
of coal. Coal utilizationin 1980 (or 1979 or 1981 if
necessaryto matchotherdata) by theutilityoperatinga
plant to which a contractis dedicated.Obtained from
the Departmentof Energyand National Coal Association publicationsidentifiedabove.
PLANT PROPORTION:
Delivered contract
quantityin Btu's divided by plant utilizationin Btu's.
Delivered contractquantityin tonswas pulled fromthe
contractinformationfor1980 (or 1979 if 1980 was not
available), and multipliedby the deliveredBtu content
of the coal to obtain quantitiesdeliveredto a specific
plant under a contractdedicated to that plant. This
figurewas thendividedby PLANT QUANTITY.
UTILITY PROPORTION: Delivered contract
quantity in Btu's (as definedabove in definitionof
PLANT PROPORTION) dividedby utilityutilization
of coal in Btu's for1980 (1979 or 1981 otherwise).
PLANT/UTILITY: PLANT QUANTITY divided
by UTILITY QUANTITY.
YEAR: The yearspecifiedas theexecutiondate of
the contract.
DATE-71: A dummyvariablethatequals one for
contractssignedin 1971, 1972, and 1973.
DATE-74: A dummyvariablethatequals one for
contractssignedin 1974, 1975, 1976, and 1977.
DA TE-78: A dummyvariablethatequals one for
contractssignedin 1978 and 1979.
The mean, standarddeviation,minimum,and maximum values of these variablesforthe 277 observation
sample and the 169 observation(singledeliverypoint)
subsampleare containedin Tables 1 and 2. The data for
the variables used in this paper are available upon
request.
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