SYDNEY
LUDVIGSON
TheChannelof MonetaryTransmission
to Demand:
EvidencefromtheMarketforAutomobileCredit
In response to tight money, both consumerloans and consumptionfall. I ask whether
thereis any causalityrunningfrom loans to consumptionby focussing on how the composition of automobilefinancebetween bankand nonbanksourcesof creditchanges in
responseto unanticipatedinnovationsin monetarypolicy. The resultsindicatethatcontractionarymonetarypolicy producesa statisticallysignificantreductionin the relative
supply of bankconsumerloans, which in turnproducesa decline in real consumption.
The evidence thereforesupportsthe existence of a credit channel of monetarytransmission to aggregate consumption. Moreover, the nature of automobile finance is
uniquely suited to identifyingwhich of two possible subchannelsof the broadercredit
channelis relativelymoreimportant,and suggests the resultsare morelikely consistent
with a banklendingchannelthanwith a purebalance sheet channel.However,the findings also indicate that the quantitativeeffects of the lending channel on the aggregate
economy, thoughprecisely estimated,may be quite small.
Is THERE ANY CAUSAL LINK between the availabilityof consumercreditand consumerdemand?In bad times, does consumercreditdecline simply because demand is lower, or is the downturnexacerbatedby a reductionin the
supply of consumer loans to credit-dependenthouseholds who would otherwise
maintainconsumptionat higherlevels?
The relationof creditmarketconditionsto the productionandinvestmentdecisions
of firmshas recentlybeen a topic of extensive research(Bernankeand Blinder 1988
and 1992; Bernanke,Gertler,and Gilchrist 1995; Gertlerand Gilchrist 1993). In particular,the credit channel theoryof monetarytransmissionpostulatesthatrecessions
are worsened by the inability of credit-dependentfirms to borrowat the levels they
could in good times, eitherbecause banksdecreasethe supply of loans aftera monetarytightening,or because firms' credit-worthinessdeclines when net worthfalls. If
firmsdependon bankloans to maintainproductionandinvestmentactivitiesandto finance inventories,a monetarycontractionwill have a greaterimpact on real activity
The authoris gratefulto Ben Bernankefor his advice and many valuablediscussions, to John Y. Camp.bell, Mark Gertler, Kenneth Kuttner,MargaretMcConnell, Ilian Mihov, Don Morgan, Gabriel PerezQuiros, Mike Woodford, and to two anonymous referees for helpful comments and suggestions, to
Beethika Khanfor excellent researchassistance, and to the Alfred P. Sloan Foundationfor financialsupport.The views expressedin the paperare those of the authorand arenot necessarilyreflectiveof views at
the FederalResearchBank of New Yorkor the FederalReserve System.
SYDNEYLUDVIGSON
is an economist in the research departmentof the Federal Reserve
Bankof New York.
Journalof Money, Credit,and Banking,Vol. 30, No. 3 (August 1998, Part 1)
Copyright1998 by The Ohio StateUniversityPress
366
: MONEY,CREDIT,AND BANKING
than that predictedby the pure "money"view, accordingto which loan supply respondspassively to changes in the demandfor creditinducedby variationin the cost
of capital.
One can distinguishanalogously between two possible views of monetarytransmission to consumptionwith the following question:Does consumercredit simply
passively respondto consumptiondemand(itself respondingto variationin income or
the usercost of capital),or does a reductionin the supplyof consumercreditby banks
furtherretardconsumption?This questionis in fact twofold: (1) does a monetarycontractionreducethe supplyof consumerloans from banks,and (2) if so, does this lead
to lower consumption?
Althougha considerablebody of literaturehas developedto investigatethe mechanism by which monetarypolicy shocks affect the productionand investment decisions of Srms, much less work has been devoted to establishing whether credit
channelsof monetarytransmissionhave a significantinfluenceon the consumersector.One reasonit is importantto determinewhethera creditchannelis operativeto the
consumer sector is that doing so may help resolve the on-going disagreementover
how to interpretapparentempiricalfailuresof the permanentincome hypothesis.For
example, some have arguedthatthe predictabilityof aggregateconsumptiongrowth
may be explainedby the presenceof creditmarketimperfectionsin the form of borrowing restrictionson individualconsumers(Zeldes 1989; Deaton 1991; Ludvigson
1996; Jappelli,Pischke, and Souleles 1996). Otherscontend that precautionarymotives (for example, Carroll 1997), time nonseparablepreferences (for example,
Deaton 1992), myopia (for example, Campbelland Mankiw 1989), aggregationbias
(for example, AttanasioandWeber 1993; Pischke 1995), or nonlinearitiesin the consumer's intertemporalfirst-ordercondition (Ludvigson and Paxson 1997) may explainthe findings.Since the policy implicationsof creditconstraintsarevery different
from those of the otherexplanations,it is importantto determinewhethercreditmarket frictionsplay a role in propagatingpolicy shocks to aggregateconsumption.
The main problem with discriminatingbetween the money and credit channels
froma readingof the aggregatedatais thatthe two theoriesareobservationallyequivalent in the usual econometricapplications.For example, in either theory, loans contract after a monetary tightening, rendering it difficult to distinguish between
supply-versus-demand-inducedmovements in credit. Simple correlationsbetween
loans and various measures of real activity tell us something about the timing of
events, but little aboutthe directionof causality.
One approachto this problemis suggestedby Kashyap,Stein, and Wilcox (1993),
who study how the ratio of business bank loans to commercialpaper(a close substitute for bankcredit)is influencedby a shift in monetarypolicy. Theirinsight was, if a
monetarycontractionldadsto a decline in the ratio of bankloans to the sum of commercialpaperand bankloans, the supply of bankcreditmust be shrinking,since presumablythe demandfor both types of finance would fall in rough proportion.They
foundthata monetarycontractionwas associatedwith a decline in this ratioand concluded that tight money leads to a reduction in the supply of business loans from
banks,consistent with predictionsof the lending channel. Whereasthe money view
SYDNEYLUDVIGSON: 367
makes no explicit predictionaboutthe behaviorof the compositionof finance,an implicationof the creditchannelis thata monetarycontractionshould shift this composition away frombankloans andtowardnonintermediatedcredit.
This paperfollows a researchstrategyvery much in the spirit of Kashyap,Stein,
andWilcox (KSW) by investigatinghow the compositionof consumerloans between
bank and nonbanksources of finance changes after an unanticipateddisturbanceto
monetarypolicy. Consumerinstallmentcredit is issued by banks and by businesses
and is available as aggregatetime series data. Variationin the composition of consumer finance after a contractionarymonetary shock provides the needed econometric identificationfor sorting out supply and demand movements in household
credit:a fall in the ratioof bankloans to the sum of bankand nonbankloans indicates
a constrictionin the supply of bank credit. Before these empirical tests can be performed,however,two implementationissues need to be addressed.
The first implementationissue concerns the choice of household credit series. To
ensurethatbankand nonbankconsumerinstallmentcreditis issued for the same pool
of products,I focus my study on loans for a particularproduct automobiles.Automobile credit is extended by commercial banks and finance companies which are
largely subsidiariesof majorautomobilemanufacturers.
A second implementationissue concernsthe measurementof monetarypolicy. In
orderto trackhow the composition of consumercreditrespondsto an unanticipated
shift in monetarypolicy, a metricfor measuringthe stanceof monetarypolicy is needed. Romerand Romer( 1990) follow a descriptivestrategyby interpretingminutesof
Federal Open MarketCommitteemeetings to identify particularepisodes of tighter
policy. Alternatively,Bernankeand Blinder(1992) arguethatinnovationsin the federalfundsratearea good indicatorof centralbankpolicy becausethe FederalReserve
has directleverage over this ratein the shortterm.
Christiano,Eichenbaum,and Evans (1994) comparethese two ways of measuring
monetarypolicy. As they point out, an advantageof the Romerapproachis thatit requiresno formalspecificationof the FederalReserve's policy rule. Its primarydisadvantage,however, lies with the ad hoc identificationof exogenous policy episodes [of
which Romer and Romer (1990) locate six and Romer and Romer (1994) locate one
more] as deliberateandlargemovementsin policy-controlledvariables.Furthermore,
the federalfundsrateprovidesmore informationaboutthe stanceof monetarypolicy
because thereis a policy episode for each datapoint in the sample.Finally, the federal fundsrate,unlikethe Romerdates,furnishesa quantitativegauge of the intensityof
the policy action.Forthese reasons,I commit up frontto a particularfeedbackrule (or
reactionfunction) for monetarypolicy, and restrictmy empiricaltests to those that
measureunanticipatedpolicy as an innovationin the federalfundsrate.
The generalstrategyI employ is to use vector autoregressionsto evaluatewhether
shocks to monetary policy influence the composition of automobile credit, and
whethervariationin this composition in turnaffects consumerexpenditureon automobiles. In particular,I test (i) whether the composition of automobile finance
changes in response to an unanticipatedinnovationin federal funds rate (helping to
sort out supply and demandmovements in credit), and (ii) whethervariationin this
368 : MONEY,
CREDIT,
ANDBANKING
compositionaffects sales of new automobiles(providinginformationon how substitutablethese two forms of financeare).
In short,the results suggest thatcontractionarymonetarypolicy leads to a statistically significantreductionin the relativesupplyof bankconsumerloans, and thatthis
change in the composition of automobilefinance produces a statisticallysignificant
decline in real expenditure.Thus the results supportthe hypothesis that there exists
cyclical variationin the supply of loans to consumers, and that this variationinfluences spending.In principle,it is possible that changes in the financingcomposition
representshifts in the quality,mix of borrowersratherthan changes in the supply of
loans from banks.This alternateexplanationis discussed below.
The rest of this paperis organizedas follows. Section 1A discusses the empirical
procedurefor evaluatingthe hypothesisthata creditchannelto consumerdemandexists. Section 1B is devoted to documentinghow the compositionof consumerfinance
changes in responseto an innovationin the federalfunds rate, while section 1C asks
whether fluctuations in that composition influence real automobile expenditure.
Section 1D discusses alternativeexplanationsfor the findingspresentedhere;in particularit addressesthe issue of whether"hidden"price cuttingby automobilemanufacturersin the form of more generous financingterms is likely to explain cyclical
fluctuationin the compositionof automobilecreditdocumentedhere. Section 1E discusses which of two subchannelsof the broadcreditchannel,the banklendingchannel or the balancesheetchannel,better matches the findings in sections B and C.
Finally, section 1F asks whetherthe findings in sections B and C are quantitatively
significant.Section 2 concludes.l
1. EMPIRICALTESTS
A. EmpiricalProcedure
Following the terminologyused in KSW, I constructa variablecalled the "Mix"of
automobilefinance,equal to the ratio of bank automobilecredit to bank automobile
creditplus financecompany automobilecredit, both measuredas the stock of credit
outstanding.The Mix is includedin a set of vector autoregressive(VAR) models using monthlydatafrom 1965:1 to 1994:122andtwelve lags of each variable.3
As proposedby Bernankeand Blinder (1992), orthogonalizedinnovationsin the
federal funds rate representshocks to monetarypolicy. As Bernanke and Blinder
point out, in orderto identify the dynamic effects of unanticipatedpolicy shocks on
variablesin the system, withouthavingto identify the entiremodel structure,it is suf1. A previous version of this paperincluded a section describingsome institutionalaspects of finance
companies,includingtheirprimarysources of finance.The interestedreaderis referredto the FederalReserve Bankof New YorkResearchPaper version for moredetails.
2. Priorto 1965,the federalfundsmarketis generallythoughtto have been very thin (Strongin1995) and
thereforenot an useful indicatorof money marketconditions.Consequently innovationsin the funds rate
arenot typically used as an indicatorof the stanceof monetarypolicy in pre-i995 data.
3. The datafor the Mix variableareobtainedfromthe FederalReserveBoardof Governorsin theirG. 19
andG.20 statisticalreleases. The otherdataareobtainedfrom Citibasedatabank.
SYDNEY LUDVIGSON : 369
ficientto assumethatpolicy shocks do not influencethe othervariableswithinthe period.I makethis identifyingassumptionby placingthe fundsratelast in the VAR. The
last equationin the VAR thenrepresentsa reactionfunctionof the centralbank,so that
the innovationis the unanticipatedpolicy shock. The justificationfor this placement
of the federalfunds rate in the VAR is the idea that it can affect othervariablesonly
with a one-periodlag, while the rate itself can respondcontemporaneouslygiven the
FederalReserve's reactionfunction.
Two five-varxableVAR models are estimated,with quantityvariablesin logs. The
firstmodel investigateshow the compositionof financeis affected by monetarypolicy shocks, and includes the following variables:the log of industrialproduction,the
log of the consumerprice index (CPI),the log of the commoditypriceindex, the Mix,
andthe federalfundsrate,in thatorder.45 The second model investigateshow realretail passengercar sales reactto an innovationin eitherthe federalfunds rateor in the
Mix, and includes the log of industrialproduction,the log of the CPI, the log of the
commodity price index, the log of real car sales, and either the Mix, or the federal
funds rate,in thatorder.6The impulse responsesusing these VARs are analyzed.Finally, as an alternativetest of the incrementalexplanatorypower of the Mix for real
expenditure,I also addthe financecompositionto some standardautomobiledemand
andreduced-formequationsfor automobileexpenditure.
B. TheEffectofMonetaryPolicyon theComposition
ofAutomobile
Credit
I begin by analyzinghow the compositionof automobilefinancechangesfollowing
periodsof tightmoney. A contractionin the relativequantityof bankloans subsequent
to tight money indicatesa decreasein the supply of bankloans. Figure 1 plots the financing Mix (the ratio of bank automobilecredit to credit issued by automobilefinance companies plus bank credit) over time, where vertical lines indicate a date
documentedby Romer and Romer (1990 and 1994) as representinga period of tight
4. Adding the log of automobilesales andthe log of inventoriesto this VAR does not significantlyalter
the responseof Mix to a Fed fundsrateshock. Even thoughseveralof these variablesmay be nonstationary,
I do not differencethem since the hypothesistests basedon the VAR in levels will have standardasymptotic distributions;see Sims, Stock, and Watson(1990).
5. Christiano,Eichenbaum,and Evans (1994) [see also Sims (1992)] emphasize the importanceof including a commodityprice index in policy VARs to eliminatethe "pricepuzzle"thatcontractionaryshocks
to monetarypolicy lead to a sustainedincreasein the price level Includingthe commoditypriceindex controls for episodes such as the oil price shock of 1974, in which the subsequentrise in inflationwas preceded
by a rise in the federalfunds rate.
-6.I do not include the funds ratein the VAR when analyzinghow shocks to the Mix affect automobile
aggregates.If the Fed fundsrateindicatesmonetarypolicy, then in a VAR in which the fundsrateis also included, changes in the Mix marginallyreflectnonmonetaryeffects; thatis, the funds rateis a sufficientstatistic for policy-inducedchanges in real variables.If the credit channel is operative,and if the funds rate
indicates monetarypolicy, then the funds rate should cause the Mix, the funds rate should cause automobiles, and Mix shouldcause automobiles,but thereis no reasonto expect Mix to affect automobileswhen
the funds rate is also included in the VAR. [For furtherdiscussion of this issue, the readeris referredto
Bernanke( 1993).] Nevertheless,this makes it difficultto identifythe channelsof transmissionto real consumptionusing the VAR analysisif the Mix is a good proxyfor a quantityindicatorof conventionalpolicy.
Note, however, thatthe spreadbetween the interestrateson the two forms of financeis not subjectto such
identificationproblemsbecause conventionalchannels do not predicta correlationbetween spreadsand
ratesas they do betweencreditaggregatesandrates.The predictionsof the creditchannelfor movementsin
the spreadare discussedbelow.
370
: MONEY,CREDIT,AND BANKING
Bank Credit as a Fraction of Total
FIG. 1.
The Compositionof AutomobileFinance
monetarypolicy. As Figure 1 shows, there appearsto be relatively little trendin the
Mix, thoughtherearesome largelow-frequencymovements.The Mix declines shortly aftermost Romerdates.If the Romerdatesadequatelycaptureperiodsof tightmoney, the figure suggests contractionaryshifts in monetarypolicy generally lead to a
reductionin the relativesupplyof bankconsumercredit.
Figure 2 displays the responseof the composition of automobilefinanceto a onestandard-deviationincreasein the federalfunds rate, with two standarderrorbands.7
Thereis a significantdecline in the ratioof bankloans to total automobilecreditover
a periodof sixty monthsaftera positive innovationin the federalfunds rate.Impulse
responses(not shown) of each componentof the Mix variableto a positive innovation
in the federalfundsrateindicatethatthe financingMix dropsprimarilybecause bank
loans contract,while financecompanycreditremainsrelativelyflat.
C. TheEJfectsof Finance Compositionon Real Consumption
The evidence presentedin the last section suggests thatcommercialbanksdecrease
the supply of consumerloans in response to tight money. The constrictionin bank
loan supplymay not affect realconsumptionif bankandnonbankformsof financeare
highly substitutable.If the two forms of financeare not perfect substitutes,however,
7. Standarderrorbands are computedusing Monte Carlo simulationsand assumingthe errorsare normally distributed.
SYDNEY LUDVIGSON : 371
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FIG.2. Responseof Mix to Fed FundsRate Shock andTwo S.E. Bands
the overall supply of loans to credit-dependentindividualswill decline, leading to a
fall in consumption.In this section, I ask whethera decreasein the relative supplyof
bank loans is associated with a decline in real consumption.I take two approaches.
First,I investigatewhetherthe financingcompositionof consumercreditsignificantly affects automobileconsumptionin a VAR controllingfor income and the pricelevel. Next, I ask whether the Mix has any explanatory power in "off-the-shelfS'
automobiledemandor reduced-formequationsthatalreadyincludeincome andinterest rate (user cost) variablesas controls. Since the effects of conventionalchannels
shouldoperatethroughthe lattervariables,any incrementalexplanatorypower of the
Mix variablewould supportthe existence of an independentcreditchannel.
Using monthly data on retail sales of motor vehicles, seasonally adjustedand deflated,Figure3 shows thatreal automobileexpendituredeclines in responseto a federalfundsrateincrease;this correlationis an implicationof both the money andcredit
views. An implicationunique to the credit view is that the relative supply of bank
loans mattersfor consumption.Figure 4 plots the cumulativeresponse of the log of
automobilesales to a one-standarddeviationincreasein the Mix. Automobileexpenditurerises in response to an increase in bank loans as a fractionof total credit, approximatelysix monthsaftera positive innovationto the financingMix.
In principle,anotherway to test for an associationbetweenthe supplyof bankloans
andreal variablesis to investigatewhetherthe spread between the bankinterestrates
and financecompanyinterestratesrises in responseto tight money. The creditchannel theorypredictsthatthe spreadwill rise in responseto tight money because bankdependentborrowerscan not perfectlysubstitutebetweenbankandnonbankcredit.In
practice,analyzingchanges in the financingmix to infer the stateof bankloan supply
may be morereliablethananalyzingthe responseof the spread.The reasonis thatthe
spreadmay not rise even if the creditchannelis operative,thatis, even if banksdo decrease the supplyof loans and even when the two forms of financeare imperfectsub-
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FIG.3. Response of Auto Sales to Fed FundsRate Shock and Two S.E. Bands
stitutes.To see this, note thateven if banksrespondto a decreasein reservesby cutting
the overall supply of loans, they may also lend to a less-risky pool on average, and
these two factorshave offsetting effects on the spread.Note thatthese considerations
generallywork to bias tests based on the spreadagainst findingevidence in favor of
the creditview, so thatdetectingan increasein the spreadin responseto tight money
is arguablythat much more supportiveof the existence of a credit channel, whereas
findingno responseis simply uninformative.
Figure S displays how the bank-finance company loan rate spreadresponds to a
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SYDNEY LUDVIGSON : 373
Quarters
FIG.5. Response of Bank-FCRate Spreadto Fed FundsRate Shock andTwo S.E. Bands
one-standarddeviation shock in the federal funds rate.899A contractionaryshock to
monetarypolicy leads to a significantincreasein the spreadafterabouttwo months.lO
Moreover,Figure6 shows thata rise in the bank-financecompanyratespreadleads to
a significantdecline in automobilesales afteraboutfourquarters.11Both figuresindicate thatthe two forms of Snance arenot perfectsubstitutes.
Next, I considertwo aggregateautomobiledemandequationsand ask whetherthe
financingcompositionaddsany explanatorypower.The firstdemandequationcomes
from Chow (1957). Chow assumes thatindividualschoose automobileconsumption
by maximizingutilitysubjectto consumptionbeingproportionalto income.The desired
stock of automobilesis a linearfunctionof the relativeprice of new automobilesand
real disposable income, suggesting the following equationfor automobiledemand:
St = a + 131RPt
+ 132Yt
+ 133Xt_ls
(1)
where St iS sales of new automobiles,a is a constant,RPt is the relativeprice of new
automobilesin terms of consumption,Ytis real, disposable income, and Xt_l is the
consumer's lagged stock of automobiles.
Anotherdemandequationcomes from a study by Blanchardand Melino (1986).
8. The interestrate data are obtainedfrom the FederalReserve statisticalrelease G.19 and are annual
percentagerates. Interestrates at commercialbanks are simple unweightedaveragesof each bank's most
common rate charged for a forty-eight-monthnew car loan duringthe first calendarweek of the middle
monthof each quarter.Financecompanydataare from the subsidiariesof the threemajorU.S. automobile
manufacturersand are volume-weighted averages covering all loans for new cars purchasedduring the
month. Since the data for bank rates are of quarterlyfrequency,I average the monthly finance company
ratesover the quarterfor FiguresS and 6.
9. The VAR containsthe same variablesas those used to computethe responsesin Figure2, except that
the creditaggregateis replacedby the bank-financecompanyspread.
10. The sample mean of the spreadusing these rates (bankrate minus financecompany rate) is-0.84
percentusing datafrom 1972:02 to 1995:01.
11. The VAR used to computeFigure6 containsthe same variablesas those used to computeFigure 3,
replacingthe Mix with the bank-financecompanyratespread.
374
: MONEY,CREDIT,AND BANKING
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They examine a whole marketstructurederivingboth a demandand supply equation
for automobiles.Focusingon theirresultsfor the consumer's problem,one can extract
a demandequationfrom theirmodel of consumerbehavior.Consumersmaximize expected utility over consumptionand the stock of automobiles,subjectto a wealth accumulation constraintand an accumulationconstraintfor the stock of cars. After
linearizingthe first-orderconditions and making some assumptionsabout the informationset of the consumer,the following demandequationarises,which is very similarto (1): 12
St = a + 13lRPt+ 132Ct
+ 133Xt_
1
(2)
WhereCtis consumptionexcluding automobileservices.l3
Table 1 summarizesthe results of estimating equations (1) and (2).14 The table
shows thatthe relativeprice enterswith the "wrong"sign (discussedbelow), but otherwise producessome significantpatternswith respectto the compositionof automobile finance.The firstrow estimates(1) itself, andthe second row includes the current
period financingcomposition as an additionalexplanatoryvariable.The currentfinancingcompositionis stronglysignificant,as is disposableincome. Adding the current period mix to the regressionincreases the fractionof varianceexplained by 12
percent.
12. Once the first-orderconditionsarelinearized,the problemconsists of solving a linear,expectational
differenceequation.I make similarassumptionsto those of Blanchardand Melino, that C, follows a firstorderautoregressiveprocess, and use the methodof undeterminedcoefficients to solve for (2).
13. Following Blanchardand Melino, constantdollarpersonalconsumptionexpendituresare used as a
measureof C,, while the CPI componentfor new cars divided by the PCE deflatoris used to measureRP
To obtaina stock for consumerdurables,I assume the initial stock is the averageon expenditurefor, first;
fifteenmonths(67:1-68:3) dividedby the depreciationrate,andthatsubsequentvalues of the stock evolve
using a depreciationrateof 2 percentper quarter.
14. Johansentest statistics,the traceand"L-max"statistics,bothrejectedthe null of no cointegrationfor
the variablesin equations( 1)-(3) againstthe alternativeof multiplecointegratingrelationships.In general,
test resultsalso rejectedthe null of one cointegratingrelationshipin favorof multiplerelationships.
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If thereare costs to adjustingprices, lagged prices should be state variablesin the
model and includedin the regressions.Subsequentrows thereforeinclude lags of the
price variablesas well as lags of Ct in (2).15The thirdand otherrows include the rate
on the six-monthTreasurybill as a proxy for the consumer's user cost. The financing
compositionremainsa strongpredictorof automobilesales even thoughthe Treasury
bill addsexplanatorypower to the regression.Row 5 shows thatit makes little difference whetherconsumptionor disposableincome is used, indicatingthatthereis small
distinctionin practicebetween the specificationsin (1) and (2). Finally, the last two
rows includelags of the financingMix. The sum of the coefficientson the currentMix
and its lags has a positive sign and is stronglysignificantwith or withoutthe addition
of the T-bill rate,while controllingfor consumption,the relativeprice of new cars and
its lags, andthe lagged stock of cars.
Note thatthe relativepriceentersthe regressionwith a positive coefficient. One obvious reason for this apparentanomolous result is that the relative price is endogenous. Problems associated with price endogeneity might be resolved by using IV
estimationif one could find appropriateinstruments.However, it is hardto think of
what those instrumentsmight be; for example, labor strikedates and wages seem almost as likely as prices to be correlatedwith the errorterm.My strategyis insteadto
commit, up front, to a particularstructuralmodel of automobilesupply and demand
which explicitly recognizes this endogeneity,andthen use the reduced-formequation
for sales as my empiricalspecification.The Blanchardand Melino (BM) framework
provides a simultaneousmodel. I add the financingcomposition into Blanchardand
Melino's reduced-formequationfor automobilesales:
St
=
a + IXt_l + 2It_l + 133Zt_l+ ,,=ol34+iCt_i + ,,=ol38+iRt i,
(3)
where It is the producer-dealersinventoryof automobiles,Zt iS automobileproduction, Wtis the realwage, andthe othervariablesaredefinedas before.16 Blanchardand
Melino obtain the reduced-formequation above by simultancouslysolving the demandproblemalreadydiscussed, and a supply problemfor a representativeautomobile firm that makes both the productionand the sales decisions by maximizing the
expectedpresentvalue of cash flows subjectto an inventoryaccumulationconstraint.
Following Blanchardand Melino, dummyvariablesfor strikedates of the Big Three
automobilemanufacturersand a (quadratic)time trendare also includedin (3).
Table 2 summarizesthe resultsfrom estimating(3). The firstrow estimates (3) itself. The resultsare very similarto those obtainedby BM for all variablesexcept the
realwage. Herethe coefficient on the real wage is significantlypositive, whereasthey
foundit to be insignificantlydifferentfrom zero. The second row addsin the financing
compositionwhich againis a statisticallysignificantdeterminantof automobilesales;
15. Lags of C, are includedin case the first-orderautoregressivespecificationfor consumptionused to
solve (2) is too restrictive.
16.1, and Z, are obtained from the Citibase data bank, where the first is given as retail automobile
inventoriesof total new passengercars, and the second is measuredas the IndustrialProductionIndex for
automobiles.W,is obtainedfrom DatastreamInternationaland is the averagehourly wage for production
workersin the automotiveindustry.
TABLE 2
REDUCED-FORM
EQUATIONS
Dependent Vanable: S,, 1968:1 to 1994:11
FC
.3wFC_;
...
...
10.8
(0.96)
14.7
(12.4)
8.2
(1.2)
9.8
(12.3)
...
10.4
(0.98)
...
7.7
(1.2)
X_,
-0.04
(0.01)
0.00
(0.01)
0.00
(0.01)
-0.01
(0.01)
-0.02
(0.01)
1_,
0.00
(0.05)
-0.10
(0.05)
-0.09
(0.05)
-0.13
(0-05)
-0.12
(0-05)
Z_,
C
3=,C_;
W
0.11
(0.01)
0.10
(0.01)
0.09
(0.01)
0.09
(0.01)
0.09
(0.01)
6.9
(3.3)
10.6
(2.8)
9.9
(2.8)
8.5
(2.8)
7.7
(2.8)
1.5
(3.4)
-2.5
(2.9)
- 1.7
(2.9)
- 1.3
(2.9)
-0.42
(2.87)
0.22
(0.03)
0.21
(0.03)
0.22
(0.03)
0.20
(0.03)
0.20
(0.03)
NOTES:
OLS estimation,standarderrorsin parentheses.S is auto sales, FC is the financingcomposition,definedas bankloans divided by total. Yis real,
computedassuminga quarterlydepreciationrateof 2 percent.RP is the ConsumerPrice Index for Carsdivided by the implicit price deflatorfor persona
and r is the six-monthT-bill rate.The columns labeled "Adj.R2"and "p-value"give the adjustedR2 for the regression,andthe p-value on the Mix varia
dustrialproductionfor autos, C is personal consumptionexpenditures,Wis the averagehourlywage of productionworkersin the automobileindustry,a
[, t2 also included.
378
: MONEY,CREDlT, AND BANKING
the marginalsignificance level is better than 0.0001. The thirdrow includes the financingcomposition and its lags; the sign of the sum of all coefficients on this variable is positive and stronglysignificant.Finally, as a robustnesscheck, specifications
in the last two rows control for the price of fuel. Though the price of fuel adds explanatorypower to the regression, the Mix remains statistically significant at very
high marginallevels.
In summary,the evidence presentedsuggests that the credit channel has a strong
statisticallysignificanteffect on the consumersector. Below, I discuss whetherthese
effects are likely to be quantitativelyimportant.Before turningto the issue of quantitative significance,however, it may be useful to briefly discuss whetherthe findings
on the fluctuationsin the compositionof automobilefinancefound above can be plausibly explainedby cyclical changes in the automobileindustry's pricingstrategy,and
if not, whetherthe automobilecreditdatacan help sortout which of two subchannels
of the broadcreditchannelis more evident.
D. CreditChannelor HiddenPriceCutting?
One possible interpretationof the behaviorof the Mix variablereportedabove is
thatconsumerswillingly switch into financecompanycreditsubsequentto tightmoney because captive financecompanies offer betterfinancingterms when automobile
dealersarefacing decliningdemandandgrowinginventories.The "pricingstory"has
two parts. (i) First, a contractionaryshock to monetarypolicy reduces automobile
sales throughsome traditionalchannelof transmission(for example,because income
is lower or interestrates are higher). (ii) The Mix declines because, in bad times, finance companiesoffer more generousfinancingtermsas a form of hiddenprice cutting in an effort to reducerising inventories.
To evaluatethis alternativeexplanationfor the cyclical behaviorof the Mix, several considerationsbear noting. First, results in the previous section indicate that the
Mix has incrementalexplanatorypower not capturedin income and interest rates.
This findingis not consistentwith (i) which suggests thatautomobilesales decline in
responseto tight money due to the operationof conventionaltransmissionchannels.
Second, if automobilemanufacturersoffer moregenerousfinancingtermsin response
to contractionarymonetarypolicy, it seems reasonableto expect that the terms of
these loans should improve vis-a-vis finance companies' cost of funds. Short-term
fundsfor financecompaniesareprimarilyraisedin the commercialpapermarket.Figure 7 plots the responseof the spreadbetween rateschargedby financecompanieson
automobileloans and commercialpaperrates to a positive shock in the funds rate.17
The figuredemonstratesno significantdownwardshift in this spreadas would be expected if financing terms at finance companies improved following contractionary
monetarypolicy. Third,the pricing story also implies thathiddenprice cuttingin the
form of more generousfinancingtermscomes in responseto rising costs of carrying
higher inventories.It seems reasonableto expect that rising inventorieswould then
17. This impulseresponsecomes from a VAR with industrialproduction,consumerprices, commodity
prices, the interestratespread,andthe fundsrate,in thatorder.
SYDNEYLUDVIGSON: 379
0.3-
03*
W
b
' ' ' S' ' ' 'ld ' ' '16' ' 'fid' ' '26' ' 'Jd' ' 'St ' ' '4d' ' '46' ' 'Sd' ' 'St ' '
Months
FIG.7. Responseof FC-CPSpreadto Fed FundsRate Shock and Two S.E. Bands
precedea fall in the Mix. To investigatewhetherthe timing of events indicatedsuch a
pattern,I performedGrangercausalitytests:using twelve lags of inventoriesandMix,
inventoriesdid not Grangercause Mix (the p-valuefor a test thatthe coefficients on
inventorieswere jointly zero is 0.439), though the sum of coefficients on the lags of
inventorieshad the rightsign (negative).
In summary,this evidence takentogetheris at least suggestive that changes in the
financingMix cannotbe interpretedsimply as the resultof price cuttingof automobile
manufacturers(via financingcosts) designed to stimulatesagging sales and deplete
mountinginventories.18
E. BankLoanSupplyChannelorNet WorthChannel?
The statisticallysignificant shifts in the financingMix found here are consistent
with the predictionsof a broadcreditchannel.One interpretationof this channel,offered above, is thatbanks cut the supply of loans to borrowerswho cannot perfectly
substituteinto nonbankforms of credit.I referto this as a subchannelcalled the bank
lendingchannel.However, anotherpossibility, in principleconsistentwith the results
above, is thatshifts in the financingMix representa change in the qualitymix of borrowers,andnot a change in the supplyof bankloans. If tight money leads to a "flight
to quality,"high-gradeborrowersmay be betterable to satisfy theirdemandfor credit than lower-grade borrowers, so that the change in financing composition may
simply representa shift in lending away from low-gradeborrowersand towardhighgradeborrowers.I referto this subchannelas the balancesheetchannel.
In previousresearch,resultshavebeen ambiguousaboutwhich subchannelis opera18. It's worthnotingthatthe pricingstorybegs the questionof why the automobilemanufacturerswould
cut pricesby cuttingfinancingcosts, ratherthancuttingthe list price directly.One economic explanationis
thatthis strategyrepresentsa rationalresponseto a decreasein the supply of loans by banks:financecompanieswould benefit(at the margin)frompricediscriminatingbetweenconstrainedandunconstrainedconsumersif creditconditionsat bankswere tightening.
380 : MONEY,
CREDIT,
ANDBANKING
tive. For example, though KSW arguedthat their results were supportiveof a bank
lending channel, it is possible to interprettheir findings as equally consistent with a
pure balance sheet channelbecause only high-gradeborrowershave access to commercial paper.The shift in finance composition away from bank loans towardcommercialpaperthatthey findis thereforein princgiple
consistentwith eithera decline in
bankloan supply,or a shift in lending towardborrowerswith highercreditratings.19
Oliner and Rudebusch (1993) have emphasized the importanceof investigating
how financingpatternsdiffer across various groups of borrowersin interpretingthe
mechanismof credit channeltransmission.In this context, it is informativeto know
how the averagequalitygradeof bankborrowerscompareswith thatof financecompany borrowers.I obtaineddata on delinquencyrates for the automobilecredit contractsof commercialbanksand financecompanies.20The delinquencyrates on bank
loans are consistentlybelow rates on finance company loans: in monthly data from
1980:1 to 1994:12, the delinquencyrate at commercialbanks averaged 1.81 percent,
while thatof financecompaniesaveraged2.07 percent.The difficultywith analyzing
shifts in the compositionof business creditbetweenbankloans andcommercialpaper
is thata decline in the supplyof bankloans cannotbe distinguishedfrom the alternative explanationthat a greatershare of credit is going to betterborrowerswho have
nearexclusive access to the nonintermediatedcredit. In contrast,in the case of automobile credit,it is riskierborrowerswho are more likely to use the primarysourceof
nonbankfinance(financecompanyloans). The evidence is not consistentwith a pure
balance sheet channel because the behavior of the Mix (an increase in the relative
quantityof finance company loans after tight money) cannot be explained by an increase in creditgoing to higherqualityborrowers.The evidence insteadsuggests that
shifts in the bank-financecompany auto loan Mix are more likely consistent with a
banklendingchannelthana net worthchannel.
F. HowQuantitatively
Important
is theCreditChannel?
The evidence presentedabove suggests the existence of a bank lending channel
transmissionmechanismto automobileconsumptionthatis statisticallysignificant.A
remainingissue is whetherthese effects arequantitatively
significant.This section attemptsto quantifythe impactof funds rate innovationson the Mix, and to determine
how economically importantchanges in the finance composition are in influencing
real automobilesales.
Several authorshave arguedthata good metricfor determiningthe quantitativeeffect of one variableon anotherin a VAR system is a variancedecomposition,the use
of VAR residualsto decompose the forecast varianceof one variableinto the contributions by the other variables(see, for example, Sims 1980; Friedmanand Kuttner
1993). Table 3 reportsthe percentageof Mix innovationvariancethat can be attrib19. OlinerandRudebusch( 1993) arguethatthe KSW resultsaremoreconsistentwith the existence of a
broadercreditchannel,but not with a banklendingchannel.
20. Source:StatenandJohnson( 1995). The ratesare measuredas the percentof automobileloans thirty
or moredays past due, seasonallyadjusted.
SYDNEYLUDVIGSON: 381
TABLE 3
DECOMPOSITIONS
OFTHEINNOVATIONVARIANCEOFMIX
Percentageof Mix variance
Six monthsahead
Twelve monthsahead
Twenty-fourmonthsahead
Thirty-sixmonthsahead
FederalFundsRate
Mix
2.19
94.28
89.89
83.09
77.19
5.15
11.79
13.98
NOTES:
This table reportsthe percentageof the financecomposition(Mix) innovationvariancethatcan be attributedto the variablelisted in
the column head.The orthogonalizationorderis industrialproduction,consumerprice index, commodityprice index, Mix, federalfundsrate.
uted to the Fundsrate and to the Mix's own lags using the following orderingof five
variablesin the VAR: industrialproduction,consumerprice index, commodityprice
index, the Mix, and federalfunds rate. Several points are worthnoting. First,at forecast horizonsof six and twelve months,the contributionof funds rate innovationsto
Mix fluctuationsis negligible;the varianceof the fundsrateonly accountsfor about2
to 5 percentof the respectiveforecastvarianceof the Mix. Second, the contributionof
the funds rateis never very large even at longerhorizons, and reachesits peak thirtysix monthsout at about 14 percent.Third,the second columnin the table shows thatat
most forecasthorizons,the Mix is almostentirelydeterminedby its own innovations,
suggesting that the Mix is "Granger-causallyexogenous." In short, the numbersin
Table 3 suggest thatinnovationsin the funds rate,while having a statisticallysignificant impacton the compositionof automobilefinanceas evidenced from the impulse
responsefunction, have a small quantitativeimpactas measuredby the variancedecomposition.
To quantifythe impactof fluctuationsin the Mix on real automobilesales, I addthe
currentperiod Mix to the Blanchardand Melino reduced-formequation(3), putting
the quantityvariablesin logs. If the Mix adequatelyproxiesfor the incrementaleffects
of changesin relativebankloan supplyon automobileexpenditure,thenthe estimated
coefficient on the Mix in the BM reduced-formequationshould give some indication
of how quantitativelyimportantthe credit channel is for real activity. The resulting
pointestimateindicatesthe incrementaleffect of a 1 percentdecline in the currentperiod Mix is a 0.73 percent decline in automobilesales. This coefficient is very precisely estimated,with standarderrorequal to 0.0007.
The impulse response results indicate that a one-standard-deviation(0.5 percent)
increase in the funds rate leads to a maximal decline in the Mix equal to about0.32
percent.Defining a "typical"monetarycontractionas the averageof the positive orthogonalizedinnovationsto the funds rate (about0.3 percent),I can use the impulse
responses along with the point estimates reportedabove to calculate a "typical"reductionin real automobilesales resultingfrom a typicalpolicy-induceddecline in the
Mix. That is, the impulse responses indicate that a 0.3 percentincrease in the funds
rateleads to, at most, a 0.19 percentreductionin the Mix. A decline in the Mix of this
magnitudewould lead to a 0.14 percentdecline in real automobilesales accordingto
the reduced-formpoint estimate reportedabove. In comparison,using the VAR that
producedFigure3, the overallreductionin automobilesales in responseto a "typical"
382 : MONEY,CREDIT,
ANDBANKING
(0.3%)increasein the Fed Fundsrateis roughly 1.6 percent,or abouteleven times as
large. Althoughthe impactof the Mix on automobilesales is statisticallysignificant,
the estimatedeffects on the aggregateeconomy may be quantitativelyquite small.
2. CONCLUSIONS
The evidence presentedin this papersupportsthe existence of a banklendingchannel of monetary transmission to aggregate consumption demand. Unanticipated
shocks to monetarypolicy have a statisticallysignificanteffect on the composition of
automobilecredit,a findingconsistentwith the predictionsof a creditchanneltheory,
but not with conventionaltheories of monetarytransmission.Furthermore,innovations in the financing Mix have statistically significanteffects on automobile consumptionnot explainedby variationin conventionaldemandindicators.
The evidence is consistent with results in a numberof other preexisting studies
which have found supportfor the presenceof a creditchannelto the investmentsector.
One issue thatremainedunresolvedin this literatureis how quantitativelyimportant
the channells. The resultspresentedin this paperindicatethatthe incrementalimpact
of the credit channel on real consumptionis very precisely measuredand extremely
small. Thus, while the results supportthe existence of a bank lending channel, they
also suggest thatthe channel may be a quantitativelyinsignificantpartof the overall
monetarytransmissionmechanismto the aggregateeconomy.
21. This calculationcharacterizesthe incremental effect of the Mix on real automobilesales thatcan be
expectedfroma typicalcontractionarypolicy shock. It is possible thatthe overallimpactof the Mix on sales
is somewhatlargerif the Mix affects spendingthroughits influence on other variables.However, the impulse responseof automobilesales to a shock in eitherthe Mix or the bank-financecompanyratespreadis
also quite small, indicatingthat the effect on automobilespendingof a one-standard-deviationimpulse in
eitherof these indicatorsis at most 2.5 percent.
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