Financing Constraints, Product Market Competition, and Business Cycle Sensitivity

Business Cy le Sensitivity

Peter Pontu h

∗

This version: November 25,2011

Abstra t

Weanalyzetheintera tionsbetweennan ing onstraintsandprodu tmarket

ompe-tition. Finan ially onstrainedrmsfa erestri teda essto externalnan eduring

e o-nomi downturns,pre iselywhentheirinternalfundsde rease. Thisleadstovi ious ir le

dynami s. Wearguethatin ompetitiveindustries ash owsareparti ularlysensitiveto

aggregatesho ks,andtheadversedynami sareamplied. Wendsigni antsupportfor

thishypothesisin rms'operatingprotabilityandxedinvestment. Theadverseee ts

ofnan ing onstraintsarein reasinginthelevelofprodu tmarket ompetition. Market

valuationsdonottakeintoa ountthesedieren esin fundamental risk. Un onstrained

rmsin ompetitiveindustries earn positive abnormalreturns(on average24-40 bpper

month), espe ially following periods of ma roe onomi distress. Furthermore, nan ing

onstraintsae t ompetitiveme hanismswithinindustries. Theindustry-averagelevelof

nan ing onstraintstendstoredu e theintra-industrymean-reversionofrm

protabil-ity. Again,thisregularityisnotpri ed: highlyprotablermsearnalphasof20-29bpper

monthiftheyoperatein industrieswithmany onstrainedrms,but virtuallynoalphas

iftheirindustries havefew onstrainedrms.

Keywords: nan ing onstraints, produ t market ompetition, industry on entration,

business y le,protability,investment.

JELClassi ations: G32,L11.

∗

UniversitéParis-Dauphine-DRMFinan e-Pla eduMaré haldeLattredeTassigny-75775ParisCedex

16 -pontu hgmail. om. I am deeplygrateful to Gilles Chemla (my advisor) and Gordon Phillips. I also

thankMurilloCampello(dis ussantatFMAEuropeanConferen e2011),SandeepDahiya,Mi haelFaulkender,

DenisGromb,GerardHoberg,JayantKale,GaëlleLeFol,VojislavMaksimovi ,and‰ubo²Pástorforvaluable

suggestions. Ialsothankseminarparti ipantsatUniversitéParis-Dauphine,theUniverstyofMaryland,College

Park, GeorgetownUniversity,ESCP Europe,and INSEADDo toral workshop fortheir ommentsand ideas.

Allerrorsare myown. Igratefully a knowledgenan ialsupportfromtheFBFChairinCorporateFinan e,

It is widely a knowledged that orporate earnings are pro y li al and more volatile than the

overall e onomy due to their junior status relative to other laims. However, there is a high

degreeof heterogeneityinearningspro y li alityat the rmlevel. Some ofthis pro y li ality

isdriven byindustrydemand(Gomes,Kogan,andYogo(2009)). Butrelatively littleisknown

about other rm- and industry-level determinants of this heterogeneity, despite the obvious

relevan eof thisquestion.

Inthispaperwefo usontwosu hdeterminants,namelynan ing onstraintsandprodu t

market ompetition. We show that the ee ts of these two fa tors are interrelated. The

intuition behind this idea is simple. The literature on nan ing onstraints suggests that

(i) onstrained rms' investment is more dependent on internal funds (see Fazzari, Hubbard,

and Petersen (1986) and subsequent studies), and (ii) onstrained rms are more subje t

to a ight to quality in apital markets at the onset of an e onomi downturn (Bernanke,

Gertler, and Gil hrist (1996)). A vi ious ir le dynami s is at play: onstrained rms fa e

restri teda essto external nan eexa tlywheninternal fundstendto fall. Thisfall implies

insu ient nan ing for urrent operations and investment proje ts, whi h further redu es

available internal fundset .

In this paperwe arguethat theseverity ofthese adverse dynami s dependson the initial

sho kto ashows. We proposeto study the level of ompetition intheprodu t marketasa

fa torae tingtheinitialsho ktointernalfunds. Con entratedindustriestendtobemore

in-sulatedfromaggregatesho ks(RotembergandSaloner(1986),HouandRobinson(2006)). The

initialadversesho ktointernalfundsduetoworseningma ro onditionswilllikelybestronger

forrmsin ompetitive industries. Thevi ious ir ledynami sinnan ially onstrainedrms

should thereforebe strongest inhighly ompetitive industries.

It hasbeen re ently do umented thatnan ing onstraints have signi ant real ee tson

rms'poli iesduringadversee onomi onditions. Campello,Graham,andHarvey(2010)

per-forma CFOsurveyduring themost re ent nan ial turmoil andshowthat redit onstrained

sig- onstraints have more real ee ts on rms in highly ompetitive industries ompared to

on- entrated industries.

We rst showthat nan ing onstraints in rease thema ro-sensitivity of rms'operating

performan e. Compared to highly onstrained rms, un onstrained rms areabout 30% less

sensitiveto GDPandabout50to75%lesssensitiveto hangesinthebondspread. Consistent

withourhypothesiswe ndthattheseadverse ee tsofnan ing onstraintsaresigni antly

amplied by produ tmarket ompetition. Stated dierently, thedieren e of business y le

sensitivity between onstrained and un onstrained rms is in reasing inthelevel of

ompeti-tion.

Wealsoexaminetherealee tsofnan ing onstraints. Wendthatthesensitivityofxed

investment to aggregate e onomi onditions is in reasing in nan ing onstraints. Industry

ompetition furtheramplies this sensitivityinthehighly onstrained subsample,whereasno

su hsigni antee t ispresent inthe low onstraints subsample. Theseresultsare onsistent

withtheidea thatindustry ompetition alsoamplies thereal ee tsofnan ing onstraints.

We also analyze investments in net working apital and inventories: theee t of

ompe-tition has the same sign but is not signi ant. Lastly, employment growth's sensitivity to

aggregate sho ks is somewhat in reasing in nan ing onstraints, but the ee t of industry

on entration seemsto be ompletely absent.

Our asset pri ing tests reveal that market valuations do not ompletely in orporate the

above dieren es in fundamental risk. We nd that portfolios of un onstrained ompanies

in ompetitive industries earn abnormal positive returns. A moving window analysisreveals

that this abnormal return is mostly earned following periods of e onomi turmoil. On the

otherhand, onstrainedrmsearnnegativereturnsintheseperiodsandvirtuallynoabnormal

returnsinnormalperiods.

Finally, we study how nan ing onstraints ae t ompetitive me hanisms in industries.

We present a set of results about ross-se tional mean-reversion of rm protability. At the

industry by 50 per entage points redu es the mean reversion speed by about 12 per entage

points. This suggests that nan ially onstrained ompetitors are less aggressive. Abnormal

levelsof protability anbesustained fora longerperiod inindustries witha highper entage

of onstrained rivals. Again, this regularity is not fully pri ed: higly protable rms earn

signi ant alphas of 20-29 bp per month if they operate inindustries withmany onstrained

rms,but virtually no alphasiftheir industries have few onstrained rms.

Ourpaper ontributes totheexistingliteratureontherealee tsofnan ing onstraints.

We show that produ t market ompetition is an important determinant of the severity of

the real ee ts of nan ing onstraints. Companies' operating performan e and investment

behavioraremoreae tedbynan ial onstraintsifthermoperatesinahighly ompetitive

industry.

Ourresultshaveimpli ationsfornan ialanalysisatboththermlevel(e.g. forvaluation

and risk assessment) and the aggregate level (e.g. nan ial a elerator ee ts). Our study

also has impli ations for poli y design. It points to the fa t thatrms deemed as nan ially

onstrained andoperatinginhighly ompetitive industriesarethemostfragile elementof the

orporate universe inan e onomi downturn, espe iallyifitisa ompaniedbyaworseningof

overall redit onditions. Poli ies aimed at improving a ess to nan ing for SMEs during a

redit run h should fo usparti ularly on highly ompetitiveindustries.

Thearti le isorganizedasfollows. Inse tion2we present relatedliteratureandformulate

our resear h hypotheses. We then present our data and dis uss measures of nan ing

on-straintsand industry on entration inse tion 3. Ourresults onthe sensitivityofprotability

levelsarepresentedinse tion4. Weanalyzetherealee tsofnan ing onstraintsinse tion

5. Assetpri ingtestsareperformedinse tion6. Se tion 7 presents our ndingson theee t

The omplex relationships between ma roe onomi u tuations, markups and market

stru -turewerestudied byHall(1986),whosuggests thatalarge numberofU.S. industriesoperate

above ompetitive markup levels. This fa thas impli ations for the ma roe onomi s of

pro-du tivitysho ks: non ompetitive stru tures ouldbe asour e ofunderutilization ofresour es

and aggregate u tuations. Other ontributions in this eld in lude Domowitz, Hubbard,

and Petersen (1986), Bils (1987), Rotemberg and Saloner (1986), Rotemberg and Woodford

(1991), and Haskel, Martin and Small (1995). Oliveira Martinsand S arpetta (2002) give a

brief survey of the related theoreti al and empiri al literature. They note that there is no

onsensusonwhethermarkupsarepro y li alor ounter y li al, thelatterrequiringgenerally

some non- ompetitive elements inthemodel(e.g. monopolisti ompetitionwith pro- y li al

produ t variety, or oligopoly with ollusions). For instan e, Rotemberg and Saloner (1986)

developamodelof ounter y li almarkupsinanoligopolisti marketwithimpli it ollusions.

Thebasi ideaofthe modelisthatthe ollusionagreement ismu hlessstableundere onomi

expansions(wheredemand ishighand punishments hurtless), than ine onomi downturns.

Elements of nan ing fri tions were formalized in the ontext of markups by Chevalier

and S harfstein (1996) whodevelop and test amodel of ounter y li al markupswith apital

market imperfe tions where onsumers fa e a swit hing ost. Finan ially onstrained rms

nan e their operations indownturns by in reasing relative pri esand preservingshort-term

prots,whilesa ri inglong-termmarketsharesandprots. Hen etheirmarkupsare

ounter- y li al, makingtheir prots smoother.

Howshouldonere on ilethisresultwithamoretraditionalandintuitiveviewsummarized

in Bernanke, Gertler and Gil hrist (1996), namely that onstrained rms tend to suer in

re essions earlier and more than un onstrained rms? In fa t, we need to point out that

ChevalierandS harfsteinpresentasettingwithalimitednumberofrmsandwith onsumers

fa ing swit hing osts. These osts generate market power for rms whi h they an use to

survive low demandstates bytemporarily harging relatively higher pri es, at the expenseof

A seminal work by Fazzari, Hubbard and Petersen (1988, FHP) introdu ed the ash ow

sensitivity of investment as indi ator of nan ing onstraints. Hubbard (1998) summarizes

the main reasons why investment may respond more to hanges in net worth of onstrained

rms and surveys subsequent work on the subje t. A ri h body of literature was generated

following the ritique by Kaplan and Zingales (1997, KZ) who obje t to the idea that ash

owsensitivityof investment should be in reasing inthelevelof onstraints.

Inoneofthelaterextensionsintoassetpri ingLamont,PolkandSaá-Requejo(2001)study

a ommon fa torinsto kreturnsrelatedtonan ial onstraints(measured bytheKZindex),

buttwounexpe tedresultsshowup: thisfa torearnsonaverageanegativeriskpremiumand

the onstraints fa tor does not seem to respond to ma roe onomi variables. Moyen(2004)

triesto re on ileFHPandKZandproposesamodelofinvestment/ ashowsensitivitywhere

two spe ial ases are onsistent with either FHP or KZ. Whited and Wu (2006) propose an

alternative indexto theKZindex,usingastru tural modelofinvestment. Theyndeviden e

ofa ommonnan ial onstraintsfa torinsto kreturns,whi hearnsapositivebutstatisti ally

not signi ant premium. Interestingly, ontrolling for nan ing onstraints eliminatesthesize

premium.

Morere ently,Gomes,YaronandZhang(2006)proposeaninvestment assetpri ingmodel

wherenan ing onstraintsappearasa ommon fa torinthe ross-se tionofsto kretursand

wherethenegative onsequen esofnan ing onstraintsarepro- y li al. Livdan,Saprizaand

Zhang (2009) in orporate dynami debt into a produ tion asset pri ing model and provide

eviden e insimulated dataof a pri ed nan ing onstraints fa tor,giving a slight superiority

to theWW index overtheKZindex.

Produ tmarket ompetitionwasre ently studiedinnan einthe ontext ofthe ross

se -tionofsto kmarketreturnsbyHouandRobinson(2006),whoshowthatrmsin on entrated

industries earn lower returns even after ontrolling for standard risk fa tors. They attribute

this result to either a onservative innovation poli y, or to barriers against rival entry and

as opposed to systemati aggregate sho ks studied in our paper. Hoberg and Phillips (2010)

analyzeindustry dynami s related to valuationbooms in ompetitive and non ompetitive

in-dustries. They show that ompetitive pressure following a boom in ompetitive industries

tendsto redu e futureprotability,leading to asharp drop inse torvaluation.

Theintera tionbetweenprodu tmarket ompetitionandnan ing onstraintshasre eived

mu hlessattentionintheliterature. Koveno kandPhillips(1997)arguethatleverage-indu ed

onstraintsen ouragepassiveprodu tmarketbehaviorwhiletherivalsbe omemoreaggressive.

Campello (2003) onrms at the industry level the predi tion by Chevalier and S harfstein

(1996) of ounter y lial markups generated byleverage-indu ed onstraints. Campello (2003)

alsoshows at thermlevelthatduringe onomi downturns nan ial onstraintspenalizethe

marketsharegrowthofrmsoperatinginindustrieswithlowleveragelevels,whileithaslittle

ee t in industries with high leverage. Povel and Raith (2004) study the ase of a duopoly

withone rmnan ially onstrainedandshowthatthe onstrained rmtendsto havealower

marketsharedueto ostdisadvantages,whilebothrmsbenetfromahigherpri ethanunder

full ompetition. We should notethat their denitionof onstraints is loserto the notionof

nan ial dependen eon external funding.

Webuildontwomainideasexploredintheliteratureonnan ing onstraints. Therstone

is that internal ash ows signi antly ae t onstrained rms' investment a tivity (Fazzari,

Hubbard,and Petersen(1986)). A ording tothese ondviewdeveloped inma roe onomi s

asthenan ial a eleratorrms fa ing highagen y osts(i.e. nan ially onstrained rms)

aresubje ttoaight toqualityinthe apitalmarketsintheearlystagesofe onomi slumps,

implying an earlier and strongerredu tion of their e onomi a tivity (see Bernanke, Gertler,

and Gil hrist (1996)). The two views taken altogether impli itly point to a vi ious ir le

dynami s. Finan ially onstrained rms have more di ulties with raising external nan e

exa tly when internal ash ows are lowest. These low internal fundsprovide for insu ient

nan ing of urrent operations and investments, whi h implies further redu tion in a tivity

internal ash ows. We proposeto introdu e a fa tor thatae ts theinitial sho kto internal

funds: the level of ompetition in the produ t market. Hou and Robinson (2006) onsider

industry on entrationasapartialinsulationfromma rosho ksduetohigherbarriersofentry.

There are at least two other reasons why on entrated industries should be less exposed to

adversesho ks. First,if ollusion me hanismsworkbetterindownturns(asinRotembergand

Saloner(1986)),then on entratedindustrieswillenjoyhighermarkupsindownturnsthatwill

partially ompensate for the falling demand. Se ond, if onsumers fa ehigh swit hing osts,

rms in on entrated markets will be able to steal some of the onsumer surplus to support

their own ash ows in the short run (as in Chevalier and S harfstein (1996), and Campello

and Flu k (2006)).

Given that the initial fall in internal funds will be stronger in ompetitive industries (an

assumptionstronglysupportedinourdata),thesubsequentadversedynami sfollowingaight

to qualityin reditmarkets shouldbemore severe for onstrained rms inhighly ompetitive

industries. We thereforeaddress thefollowing resear h questions: (i) What aretheee ts of

nan ing onstraintsontheoperatingperforman eandtheinvestment behaviorofrms?,and

(ii)Arethese realee ts ae tedbyindustry on entration? Lastly,we will explorea reverse

relationship between the two fa tors: (iii) Do nan ing onstraints ae t the ompetitive

me hanisms inindustries?

Based on the related literature and e onomi reasoning, we formulate the following

hy-potheses:

H1: Firms in on entrated industries are better insulated fromaggregate sho ks.

Firmsenjoyingmarketpowerin on entrated industries anpasssomeoftheee tsofadverse

aggregatesho ksontotheir ustomers. Hen e,protabilityofrmsin on entrated industries

islessexposedtothebusiness y le,whiletheprotabilityofrmsin ompetitiveindustriesis

moresensitivetotheoveralle onomy. Arationaleforthishypothesisiseitherbarrierstoentry

ofpotential rivalsasinHouand Robinson (2006), better fun tioningof ollusion me hanisms

H2: Firms subje t to nan ing onstraints are more ae ted by aggregate sho ks

than un onstrained rms. Constrained rms aresubje t to a ight to quality in apital

markets at earlystages ofe onomi downturns asinBernanke,Gertler, and Gil hrist (1996).

Giventhatinternal ashowsaredepressedduringre essions,theyhavetoredu etheira tivity

more than un onstrained rmsdue to a vi ious ir le me hanism.

H3: The ee ts of nan ing onstraints in downturns are higher in ompetitive

industries than in on entrated industries. PerH1, rms operatingin ompetitive

in-dustries have their internal ash ows more sensitive to adverse aggregate sho ks. If a

on-strainedrmoperatingina ompetitiveindustry issubje tto aight toquality,asper H2,it

an nan e a lower part of its operations and investment internallythan ifit were operating

ina on entrated industry. Overallit redu es its a tivitymore than a rm ina on entrated

industry.

H4: Finan ing onstraintssigni antlyae tthemean-reversionofrm

protabil-ity. We expe t thata rm an sustain abnormallevels of protability for a longer timeifa

large proportion of its rivals are nan ially onstrained. We use a similar logi as Chevalier

and S harfstein (1996).

We will address H1-H3 by rst studying the business y le sensitivity of operating

prof-itability of rms. Asa se ond step we will analyze real ee ts in terms of xed investment,

networking apitaland inparti ular inventories, andemployment. WeaddressH4inthelast

3.1 Sample rms

We useall dataonU.S. publi ompanies overthe period1977 to 2009 fromtheCRSP

Com-pustat merged database (CCM). We drop nan ial ompanies (SIC between 6000 and 6999)

andutilities (SICbetween 4900 and4999)due toa spe i statusofthese twoindustries. We

drop rm-years with total assets below$10 millionor with ommon equitybelow $5 million,

asinFamaand Fren h (2000). Werequirevalid(and non-negative,where appli able) dataon

totalassets,totalliabilities,property,plant&equipment,sharesoutstanding,sharepri e,sales,

net in ome, in ome before extraordinary items, osts of goods sold, ommon and preferred

dividends.

Wedeneoperatingprotability(ROA)asearningsbeforeextraordinaryitemsandinterest

dividedbytotalassets. Bookequityistotalassetslesstotal liabilitieslesspreferredsto kplus

balan e sheet deferred taxes and tax redits (our denitions follow Fama and Fren h, 2000).

Markettobookratioisthemarketvalueofequity(sharesoutstandingtimesend-of-yearshare

pri e)plustotal bookassetslessbookequitydivided by totalbookassets.

We winsorize all ratio variables at the1% level. We have intotal 78,537 rm-years. Our

average ross-se tion is2,380 observations,thesmallest one being 1,350observations in1977,

thelargestone 3,362 in1998. We present thenumberofobservations inea h yearintable 1.

We present summarystatisti sof our sample intable 2. Themedian assetprotability is

6.7%,while the median ROEis somewhat higherat 8.4%. Themedian M/Bofassets is1.28,

whilethemedian real salesgrowthis about 4.8% forthemedian rm. Mediantotal payout is

only 11.7%. Medianinvestment rateisat 4.7% of assets.

3.2 Business y le variables

We use the following raw ma roe onomi data from the Federal Reserve data base 1

: real

annualGDP,the3-monthTreasurybillrate,10-yearTreasurynoterate,3-monthnon-nan ial

1

Commer ialpaperrate ,and yields onseasonedAaa and Baabonds ratedbyMoody's.

Themain business y levariableis realannualU.S.GDP growth(denoted GDPGR).Our

large sample overs manufa turing and non-manufa turing rms, whi h justies theuse of a

largeaggregateindi ator asopposedto amorenarrowindi atorlike industrialprodu tion. In

additionto our oin ident ma roe onomi indi ator we use three leading indi ators basedon

marketyields ofgovernment and privatedebt instruments.

First,weusethe hangeinthe averageannualspreadbetweenthe3-month ommer ial

pa-perrateandthe 3-month Treasurybillrate(DCPSPR),be auseitis onsideredasapredi tor

of future e onomi u tuations. Spe i ally, downturns are generally pre eded by a spike in

theCPspread andre overies byitstightening (seeFriedmanand Kuttner,1991). Arationale

for this property, besides default risk and monetary onditions, is ompanies' variability in

ash needs over the business y le. A ording to Friedman and Kuttner the CP spread an

beviewedasademand-drivenphenomenon,resultingfrom ompanies'operatingperforman e.

An alternative view isthat spikes inshort therm nan ing osts(related to rising redit risk

premiums) are also a signal of a general tightening of redit onditions, whi h for es

ompa-nies to restrain some of their operations and therefore redu e their protability (as notedby

Bernanke, GertlerandGil hrist,1996).

Foranalogous reasonswe in ludeinourvariablesthe hange inthebondspread(DBSPR)

al ulated asthe hange ofthe average annualspread between seasonedBaaand Aaa bonds.

The third and last variable is the hange inthe average annualterm spread between the

10-yearTreasury noterateandthe 3-month Treasury billrate (DTSPR),following studieson

theinformation ontent of the term spreadwith respe t to future real e onomi a tivity(see

Estrella and Hardouvelis(1991),and morere ently Hamilton andKim(2002)).

All spread variables are taken as hanges in the annual average of weekly values. GDP

growth is al ulated as the rate of hange of the real annual GDP. Given that these ma ro

variables are highly orrelated, we orthogonalize them in a triangular fashion. We start by

takingGDPGRwithout hanges. WethenregressDCPSPRonGDPGRanda onstantandwe

keep theresidualasour nextma ro variable. We thenregress DBSPRonGDPGR,DCPSPR

2

zeromean andunit varian e.

In the remainder of the paper the names GDPGR, DCPSPR, DBSPR, and DTSPR will

refer to these orthogonalized and standardized variables. We plot the GDP growth and the

unadjusted levelsof spreadvariables ongure1.

In gure 2 we plot the dieren ed spread variables beforeand after adjustment for

orre-lation. We notethatthere islittle hange beforeand afterthe adjustment intheCPand the

term spread variable. The bond spreadvariablekeepsthegeneral appearan e,but withsome

dis repan ies duringthe 1980s.

3.3 Measuring nan ing onstraints

We measurenan ial onstraintsusing thesyntheti WhitedandWu(2006)index(WW)and

four fundamentals-based proxies following Almeida, Campello and Weisba h (2004, ACW),

Faulkenderand Wang (2006),and Denisand Sibilkov(2010).

The index by Whited and Wu (2006) is dened as WW

= −0.091 ∗

CF

/

TA

− 0.062 ∗

Divpos

+ 0.021 ∗

LTD

/

TA

− 0.044 ∗

LNTA

+ 0.102 ∗

ISG

− 0.035

SGwhere CFisin omebefore

extraordinary itemsplusdepre iation expense,TAistotalassets,Divposisa dummyvariable

forpositive ashdividend,LTDistotallongtermdebt,LNTAisthenaturallogarithmoftotal

assets,ISGis3-digit level industrysalesgrowth,SG istheindividualrmsales growth. Note

thattheinitial index wasestimated usingquarterly data.

The fundamentals-based proxies ofFC are:

•

P/Oratiomeasuredas(Dividends+Repur hases)/Earningsbeforeextraordinaryitems and interest.

•

Size measuredasTotal assets.

•

LT rating measured as an indi ator of whether the rm has a S&P long-term issuer rating.

•

ST rating measured as an indi ator of whether the rm has a S&P short-term issuer rating.

WefollowACW indening rms withhighand lownan ial onstraints. For WW,P/O,

andsize we sortannuallyallrms basedon ea hof thethree variables. Finan ing onstraints

are in reasing in the WW index and de reasing inP/O and size. We dene as highand low

nan ing onstraints theappropriatetop/bottom three de ilesinea h annual ross-se tion.

For the rating variables we dene as onstrained rm-years without a valid non-default

grade rating and with positive debt. Un onstrained rms are those that have a valid

non-defaultrating andpositive debt. Itisnot obviouswhetherrmswithouta ratingandwithout

debt should behave as less onstrained than rms without a rating with positive debt. Not

havingadebt ouldbearesultofstri terinternalpoli ies. Ifthisisthe ase,thenthissituation

is equivalent to not being able to issuedebt, i.e. being onstrained. ACW brieydis uss the

fa tthatthese rmsmaybe either onstrained orun onstrained. Wheneverwedoregressions

on pooled observations we will therefore only usean indi ator variable for theun onstrained

rms, whi h isless disputable. This is equivalent to a more onservative assumption thatall

rms thatdo nothave a ratingare onstrained.

3.4 Measuring market ompetition

Themost ommonlyusedmeasureofthedegreeof produ tmarket ompetition(PMC) isthe

Herndahl-Hirs hmanindex(HHI)dened asthesumofthesquaredmarketsharesofrmsin

theindustry. Themost ommon alternative totheHHIisthesimple on entrationratio C(x)

(the ratio of the x largest rms' market share to the overall market sales). There is a wide

agreement that the HHI is an imperfe t measure of on entration. For example it does not

take into a ount regional on entration ee ts, and it annot apture more omplex market

stru tures su h asSta kelberg-like stru tures(see the dis ussionin Martin 1993, p. 167). In

ee t, intermediate HHI values of about 500 an be asso iated with very dierent levels of

ompetitive stru tures. 3

3

AlsonotethattheDepartmentofJusti e onsidersindustriesmoderately on entratedabovethevalueof

Hoberg and Phillips (2010) from their Web Data Library. 4

These data are onstru ted by

usingtheCensusHHIofmanufa turingindustriesandextendingthemto all3-digitindustries

using the HHI al ulated from publi rms' salesin Compustat. Thisapproa h is a

ompro-misebetween using only Census data(whi h are al ulated only with a5-yearfrequen y and

only overing manufa turingindustries) and al ulatingHHIfromCompustat dataonly,thus

ex luding all private rms (see the ritique by Ali, Klasa and Yeung, 2009). Our measure

of ompetition is PMC

= 1 −

HHI, that we standardize to zero mean and unit varian e. As

a se ondary measure, we dene indi ator variables for the top and bottom 30% observations

basedon thePMCvariable.

We also use as a robustness he k an individual measure of market power as in Peress

(2010),the relative Lerner index (orthe relative pri e- ost margin). The rawLerner index is

denedforea hrmasLerner

it

= (

Sales

it

−

COGS

it

−

SGA

it

)/

Sales

it

,whereCOGSistheCost

of goods sold and SGA isSelling, general and administrative expenses. We trun ate the raw

Lerner indexesat 0,asnegativevalues ofthe index have little theoreti alfoundationsand we

onsiderthemtoimplyzeromarketpower. Therelativeindexisobtainedbysubtra ting from

the raw value the rm's SIC3 industry sales-weighted average. The relative Lerner variable

hassigni antvariabilitythatmight beanoisymeasure. Asintheprevious asewe onstru t

indi ator variables for the top and bottom 3de iles and usethese inouranalysis.

To better understand the impli ations ofusing the HHI versus thealternative measureof

market power we inspe ted at the 3-digit SIC level the relationship between the tted HHI

valueandtheintraindustrydispersionofindividualrmmarketpower. Wemeasurethelatter

bytheindustrystandarddeviationofrms'rawLernerindexesinagivenyear. Asillustratedin

gure3,thereisrelativelylittledispersionofrmLernerindexesamongthehighly on entrated

industries. Onthe otherhand, the dispersionof rm Lerner indexes variesa lot at thelower

endof theHHI.Wenotethatsome ofthis patternisdue to alower number ofrms inhighly

on entrated industries. The table 4 presents for the year 2005 industries witha HHI below

January2010).

4

industrieswith alow dispersionof Lerner indexes(i.e. relatively equal distribution of market

power) are mostly standardized produ ts and servi es, with the ex eption of Newspapers.

Conversely, industries with dispersed Lerner indexes (uneven distribution of market power)

areindustrieswithlikelygeographi al, te hnologi al or variety-related on entration ee ts.

Given that our alternative measure of market power uses relative Lerner indexes, it will

mostlypi kupthevariabilityofmarketpowerwithintheselower on entration/highdispersion

industries, whereasindustrieswithlowdispersionofLerner indexeswill tendto generate rm

observations with relative Lerner indexes loser to zero. Therefore, when we later use the

relative Lernerindex tomeasuremarketpower, we willmostly he k thatour reasoningholds

inthese intermediate on entration industries.

Lastly,we notethatthedistribution ofnan ing onstraintsa rossthe orporate universe

isnotorthogonaltoindustry on entration. Infa t,mostmeasuresofnan ing onstraintsare

positively orrelatedwithrmsize(seedis ussioninBernanke,Gertler,andGil hrist(1996)).

Smallrmstendtobelesswellknown,lesstransparentandmoresubje ttoagen y osts. But

rmsinmore on entratedindustriestendtobelarger,bydenitionofindustry on entration.

We argue that nan ing onstraints an oexist with on entrated markets (i.e. not all

onstrained rms are in ompetitive industries). We present in table 4 average proportions

of rms withhigh nan ing onstraints inindustries with a Low, Medium and High

on en-tration. As expe ted, the proportion of onstrained rms seems to be negatively orrelated

withindustry on entrationforall onstraintsproxiesex eptSTratings. For example,forthe

WWindex 37%of rm-yearsaredeemedas onstrainedinlow on entrationindustries,while

only about 20%aredeemed as onstrained in on entrated industries. Although theseresults

onrm a lear orrelation between onstraints proxies and on entration, we are reassured

thatthere isstill asigni ant numberof onstrained rmsinhighly on entrated industries.

The most interesting ases of oexisten e of on entration with nan ing onstraints are

in moderate-to-high on entration industries. For example the Air rafts and Parts industry

very small and likely nan ially more onstrained rms su h as EDAC Te hnologies, CADE

Industries,and CPIAerostru tures,all withassetsbelow$35 million.

A se ond example is Household audio&video equipment (SIC3=365) where the HH was

1057 as of2005. The a tors in luded the large Harman International with assetsin ex essof

$2billion,medium-sizedUniversalele troni s($146million),aswellassmallerRo kfordCorp.

($52million) andKoss ($29million), both likely more onstrained.

4 Operating performan e and the business y le

4.1 Estimation method

We estimate a simple model inwhi h we regress rm protability ROA

t

(earnings before

ex-traordinaryitemsandinterestdividedbytotalassets)on(i)asetofma roe onomi variables,

(ii) their intera tion with measures of nan ing onstraints and industry ompetition (where

appli able), (iii)a setof rm ontrols, (iv)a lineartrendand a onstant.

All rm variables are lagged and they ontrol for the expe ted protability level. These

rm-level variables in lude similar variables as those used by Fama and Fren h (2000) for

estimating expe ted protability, namely the market-to-book ratio of assets (M/B Assets),

ommon dividends and repur hases divided by book equity ((D+Rep)/BE), and a dummy

variable indi ating nonzero ommon dividends during the previous year (DDUM). We also

addavariablemeasuring apitalintensity al ulatedasproperty,plantandequipmentdivided

by totalassets (K/A),and aninvestment intensityvariablemeasuredby apitalexpenditures

to totalassets(I/A). Wein lude alineartrendto apture the hanges inaverageprotability

inthe ross-se tionof listedrms.

We estimatethe following equationusing a xed rmee t estimatorwith rm- lustered

standard errors: ROA

it

= α +

4

X

j=1

β

j

Ma roVar

j

t

+

5

X

k=1

γ

k

FirmVar

k

it−1

+

Trend

t

+ ǫ

it

(1)

setin ludes the M/Bofassets,D/BE,DDUM,K/A andI/A.

This equationis rst estimatedwithout furtherintera tion terms, to serve asbaseline. In

subsequentse tionsweintera ttheMa roVarsetwithlaggedmeasuresofnan ing onstraints

and/or produ t market ompetition, asdened inse tions 3.3 and 3.4. This spe i ation is

similar to the redu ed form spe i ation used by Sharpe (1994) for analyzing rm

employ-ment growth. We will therefore be able to he k whether measures of onstraints and/or

on entration signi antly ae t thesensitivity tothebusiness y levariables.

4.2 Results on market ompetition

In this se tion we rst onrm one of the building blo ks of this paper, namely that that

rms in on entrated industries resist better to aggregate u tuations (hypothesis H1). We

will estimatethe following modi ationof equation (1)in luding thelevels of PMCasof the

previous year,and Ma roVar-PMC intera tions:

(1)

+

PMC

+

Ma roVar-PMC intera tions (2)

Astwo alternative spe i ations, wewill also estimateaversionthatin ludes lagged

indi- ator variables for high (Hi) andlow(Lo) produ t market ompetition/relative Lernerindex,

and theintera tions ofMa roVar withtheindi ator variables:

(1)

+

PMCindi ators

+

Ma roVar-PMC indi ator intera tions (3)

Therst olumn oftable5presents oe ientsonthema rovariablesinthebaseline ase

without on entration measures. The oe ients are strongly statisti ally signi ant ex ept

for DCPSPR. The GDP growth variable has a signi antly positive inuen e on operating

protability. Asa ruleofthumb,a onestandard deviation positive sho kinreal GDPgrowth

translatesinto0.36per entagepointsofadditionalprotability. The hangeinthebondspread

Lastly, the term spread also has a signi ant negative ee t on rm protability and is the

strongest in magnitude. A one standard deviation unexpe ted in rease inthe term spread is

asso iatedwith 0.61pointslessprotability.

Column (2)shows that intera ting the ma ro variables with the PMClevels signi antly

alters the pi ture. A one standard deviation in rease in the level of ompetition in reases

thesensitivity to the GDP growth by about one third. Moreover, PMCstrengthens also the

sensitivity to the bond spread (by more than one half per one standard deviation of PMC)

andtothetermspread(bymorethanonethirdperSD).Theseresultssupportourunderlying

hypothesisof insulation againstaggregatesho ks.

WepresentinColumn(3)intera tionswithdummyvariablesforhighandlowPMC.These

result suggest that the ee ts of the PMC levels presented in Column (2) seem to be driven

by the low end of the PMC variable (i.e. on entrated industries), as it is mostly the Lo

dummythathasasigni ant oe ient inthisspe i ation. Con entrated industriesseemto

bemore than one halflesssensitive to theGDP growth andtheterm spread,and seem to be

quasi-insensitive to thebond spread.

Column (4) presents results using intera tions with a dummy variable for high and low

relative Lerner index. As we dis ussed in se tion3.4, this measure of marketpower pi ks up

mostlythe variability inthe intermediately on entrated industries with a highdispersionof

individual rmLerner indexes. Therefore this approa h isa omplement to theprevious two

ases. The results are qualitatively similar to the previous ase. Firms with a high market

power are more than one half less sensitive to the GDP and about one quarter less sensitive

to theterm spread. Firms witha low marketpower aremore than twi eas mu h exposedto

thetermspread. There isasigni ant positive oe ient on theCPspread onbothhighand

lowintera tion oe ients.

The explained variabilityinall spe i ationsisabout 7to 8 per ent. We notethatthese

R-squaredex ludethe variability apturedbythermxedee ts(thexedee trepresents

Wesummarizethisse tionbystatingthatourresultsspeakinfavorofthehypothesisthat

imperfe tly ompetitive industriesbenetfromasigni ant insulation fromaggregate sho ks.

Firmsin on entrated industriesseemtobelessexposedtoboththe oin ident business y le

variable, aswellasto leading indi ators.

4.3 Results on nan ing onstraints

4.3.1 Standalone spe i ation

We rst pro eed to the analysis of the ee t of nan ing onstraints without ontrolling for

industry ompetition. The studied spe i ation will be analogous to the previous ase. We

estimatetheequationusinglaggedindi atorvariablesforhigh(Hi

FC

)andlow(Lo

FC

)nan ing

onstraints basedon theWWindex, thepayout ratio,size,and LTandST ratings, andtheir

intera tions withthe ma ro variables:

(1)

+

FCindi ators

+

Ma roVar-FCindi ator intera tions (4)

Intable6wesummarizeresultsforvedierentproxiesofnan ing onstraints(notethat

for the two ratings proxies there are only dummies for low onstrained rms due to reasons

dis ussed inthe last paragraphof se tion3.3).

ThesensitivitytoGDPissigni antly lowerforrmsfa inglow onstraints, forallproxies

ex ept the WW index whi h is only signi ant at the 10% level. Depending on the proxy,

un onstrainedrmsareabout30%to70%lesssensitivetoGDPgrowththantheaveragerm.

Constrainedrms have eitheran insigni ant negative intera tion oe ient (WWand size),

or apositive oe ient signi ant at the10%level(P/O ratio).

The results on thebond spread arethe most lear- ut. For all our proxies un onstrained

rms are lessexposedto unexpe ted hanges in thebond spread: on average about 50% less

than average rms. On the other hand, onstrained rms are signi antly more sensitive to

oe ientsareonly signi ant forone proxy,theP/Oratio.

Inall ases,the modelexplains about 6%to7%of thevarian e ofthedependentvariable,

ex luding the rmxed ee ts ontribution. The RMSE, not presented inthetable,isabout

0.09to 0.10 (notethattheSD of the dependent variableis about 0.14).

To sum up, using various proxies of nan ing onstraints we on lude that onstrained

rms'operationsaremore sensitiveto the overallbusiness y le,beingae tedmore by both

the oin ident indi ator as well as one leading indi ator, the bond spread. Our results are

apparently onsistentwiththeighttoqualityee tofBernanke,GertlerandGil hrist(1996)

where onstrained rms tend to suer early in e onomi slumps from a tightening of redit

onditions.

4.3.2 Spe i ation ontrolling for market ompetition

Wewillnowattempttoanalyzethe hypothesisH3,whi h laimsthatthenan ial

a elerator-like ee t do umented inthe previous se tion is strongest in ompetitive industries. We will

estimateanequationthat ontrolsfornan ing onstraints, produ tmarket ompetition, and

their intera tions. Wewill usethe PMClevels asameasure thelevelof ompetition.

(1)

+

FCindi ators

+

PMC

+

Ma roVar-PMC intera tions

+

Ma roVar-FC indi atorintera tions

+

Ma roVar-FC indi ator-PMC intera tions (5)

Table 7 shows results for this ombined spe i ation. We see that the rst eight rows

of thetable arequalitatively and quantitatively similarto thespe i ation with ompetition

intera tions only (presented intable 5, olumn (2) ): rms in on entrated industrieshave a

betteraggregate sho ks resistan e.

Simple intera tion termswithnan ing onstraints proxiesarelesssigni ant than inthe

survivesinthisspe i ation: allve oe ientsaresigni antlypositiveforthelow onstraints

dummy,andall oe ientsarepositive(onesigni antly)onthehigh onstraintsdummy. The

Term and CPspread oe ients showno lear patterns.

Theparameters ofinterestareon the rossintera tionterms ontainingboththe

ompeti-tionandthe onstraintsvariables. The rossintera tion termofLow onstraintswithPMCis

mostlypositiveandsigni antintwooutofve ases. The rosstermofHigh onstraintsand

PMCis negative and signi ant in two out ofthree ases. Thismeans that onstrained rms

tend to benet relatively more from the ma ro-prote tion ee t of industry on entration.

Conversely, onstrainedrms arethe most exposedto aggregate sho ksin highly ompetitive

industries. This pi ture is even more lear with the ross terms on the Bond spread. Low

onstraints rosstermsareallsigni antly negative at leastatthe10%level, whileHigh

on-straints terms are all positive, one signi ant and two just below the 10% signi an e level.

Again onstrained rms tend to benet more from theinsulation from aggregate sho ks due

to the on entration of their industry. Similar but less straightforward patterns an be seen

on theTermspread ross oe ients.

Inthesespe i ations,themodelexplains6to7%ofthevarian eofthedependentvariable,

ex luding the xed ee t's ontribution. The RMSE(not presented) is inthe interval

0.093-0.098, therefore about one-third of rm protabilty's standard deviation is aptured by the

model.

This se tion provided signi ant eviden e in favor of the hypothesis H3: nan ing

on-straints seem to have stronger real ee ts on the operating performan e of ompanies in the

most ompetitive industries. Put dierently, the ma ro-insulation benet of on entration

is higher for rms deemed as onstrained using a number of ommon proxies of nan ing

In this se tion we will inspe t whether thepreviously dis ussed results on erning operating

performan ehavetheir impa tonrmbehavior,inparti ular intermsof investment and

em-ployment. Inee t,themainimpli ationof thenan ial a eleratoraspresentedinBernanke

et al. (1996) is that nan ially onstrained rms subje t to a ight to quality are for ed to

restraintheir investment morethan otherrms,therebyfurther ontributingto theaggregate

e onomi slowdown andee tively ontributing to thedepthof there ession.

We will estimate an enri hed version of an investment model where we in lude a ma ro

variable and its intera tion with market on entration. As it is standard in the investment

literature we will estimatethis equation onsubsamples basedon various nan ial onstraints

proxies(see for exampleGil hristand Himmelberg(1995)). The estimatedspe i ation is:

Inv

it

= α + β + δ ×

PMC SIC3

t−1

×

GDPGR

t

+

6

X

k=1

γ

k

FirmVar

k

it−1

+ γ

7

∗

PMC SIC3

t−1

+ ǫ

it

(6)

In thefollowing subse tions Inv will be one of three investment rates (inphysi al apital,

innetworking apital, ininventories) or theemployment growth rate. The setof lagged rm

variablesFirmVarin ludestheM/Bratioofassets, ashowmeasuredastheoperatingin ome

beforedepre iationdividedbyassets, apitalintensitymeasuredasPPE/Assets,sizemeasured

asthelogof assets,adividend payer dummy,and previous year'sindustry salesgrowth.

5.1 Fixed investment

Werst present ourresultsfor equation6appliedto investmentinphysi al apital,dened as

the ratio of apitalexpenditures to netproperty,plant andequipment. We ex lude irrelevant

rms operating with verylowphysi al apital, i.e. whose PPE/Assets ratio islowerthan 5%.

The rst observation from table 8 is that the sensitivity of investment to GDP is learly

higher for rms with high onstraints. For un onstrained rms a one standard deviation

0.99-1.50 per entagepointin rease ininvestment.

Ase ondobservationisthattheintera tion ofGDPwithPMCispositiveinall ases,

sug-gestingthatindustry ompetition in reasesthema ro sensitivityofrminvestment. However

the oe ients are only signi ant in the high onstraints subsample, and only for the rst

three proxies. Judging bythe size of the onstrained subsample for the rating-based proxies,

this absen e ofsigni an e ould be due to the rudeness ofthese two onstraints proxies. In

thethree ases wherethe oe ient issigni ant, ithase onomi allysigni ant value. Aone

standard deviation in reaseinPMCin reases the GDPsensitivitybybetween 30%-50%.

We on ludethatwefoundpartialeviden eforanintera tionee tofnan ing onstraints

and industry on entrationinthe y li alsensitivity ofrm investment. Asinthese tionon

operating performan e, we nd that industry on entration benets onstrained rms more

than un onstrained rms.

5.2 Net working apital and inventories

Inthisse tionwe applyequation6to investment innetworking apitaldened asthe hange

innetworking apital dividedbyPPE. Networking apital(NWC)hasbeen analyzedinthe

ontextofrminvestmentbe auseitservesasabuerwhennan ingbe omess ar e(asshown

byFazzari and Petersen (1993)). In ee t, NWC just like anyother omponent of operating

assets has to be nan ed in the balan e sheet of the rm. When available nan e be omes

limited,parti ularlyforthemore onstrainedrms,rmsfa eatrade-owhenallo atingfunds

tovariousasset omponents. FazzariandPetersenarguethatrms anadjustNWCinvestment

at a lower ost than xed investment and therefore NWC is used as a liquidity buer. The

business y le variability of NWC ould thus reveal interesting patterns, omplementary to

those seenonxed investment inthe previous se tion.

We dene net working apital as urrent assets minus ash minus urrent liabilities. We

ex lude ash be ause ash plays an important role for investment poli y, in parti ular for

Table9 onrmsthattheNWCinvestmentsensitivitytoe onomi onditionsvariesstrongly

with the nan ing onstraints status. For low onstraints rms, the sensitivity is relatively

low and signi ant for only two out of ve proxies. For the high onstraints subsample, the

sensitivity to GDP is signi antly positive. A one standard deviation negative sho kon

e o-nomi a tivity redu es NWC investment by3.4-5.5 per entage points. The intera tion ee t

of GDPGR with PMC seems to be mostly positive, but it is only signi ant in one single

asehigh onstraint rms measuredbythe P/Oproxy.

Next, wefo usour attention on asubset ofNWC: inventories. Thisassethasa parti ular

role in ma roe onomi s, be ause hanges in inventories onstitute an important sour e of

aggregate e onomi growth variability. Gertler and Gil hrist (1994) point out dieren es in

the behavior of inventories in small manufa turing rms relative to large rms, the former

a ounting for alarge partof the variabilityof theaggregate inventory hanges.

Wealsofo usspe i allyoninvestmentsininventories,denedasthe hange ininventories

divided by PPE (results not presented, but available upon request). We note that inventory

investment ispro- y li alinallthreesubsamples,butthe ee tisstrongestinthe onstrained

subsample. Asinthe NWC ase, the oe ient on thePMCintera tion term is positive and

strongest inthe onstrained subsample,but issigni ant inonlyone ase.

Our results on NWC and inventories onrm their role of a buer asset: the sensitivity

ofthis itemto aggregatee onomi a tivityis onsiderably higher than thesensitivityof xed

investment. Moreover, onstrained rms'NWCinvestmentsseem to be more sensitive to the

aggregatesho kbyafa tor ofat least2 ompared to un onstrained rms. The intera tionof

onstraints withprodu tmarket ompetition isnot signi ant.

5.3 Employment

The last real ee t that we will analyze is employment growth. Sharpe (1994) shows that

the aggregate sensitivity of rm employment to the business y le is higher for small rms

market ompetition inadditionto onstraints.

We in lude in our analysis only rms whi h have had an asset growth rate within the

(-50%,+100%)range,tomitigatetheee tofrm-yearswithsigni ant orporateeventssu has

mergers andspin-os. Table 10 suggeststhat thereis ama ro-sensitivity dierential between

theHigh and Low onstraints subsamples. Low onstraint rms redu e their employment by

1.2%-2.5%for aone SDde reaseintheGDP,whereas onstrainedrms slashemployment by

2.8%-3.1%.

Intera tions withPMChave neitherasigni ant oe ient, norstablesigns. Constrained

rmsoperatingin on entrated industriesdonotseem have alower employment sensitivityto

theaggregate y le.

Wesummarizethisse tionbynotingthatnan ially onstrainedrms'investmentinxed

assets and working apital and their employment appear more sensitive to the business y le

ompared to un onstrained rms. Industry ompetition strengthens this ee t only on xed

investment.

6 Some asset pri ing results

Previousse tionsshowthatnan ially onstrainedrmsaremoreexposedtoaggregatesho ks.

Thisfundamental risk isparti ularly highifthey operateinhighly ompetitive industries. In

thisse tionweshowthatsto kpri esdonot ompletelyree tthesedieren esinfundamental

risk.

WestartbymergingourfundamentalsdatawithCRSPsto kreturns. Weuseallrmsthat

bothsatisfyour fundamentals datarequirements(see se tion3) andhave valid CRSPreturns

data. At the end of June of ea h year t we take the year t-1 sorts from the fundamentals

dataset and we onstru t portfolios based on PMCand FCtop/bottom ter iles. We usethis

portfolio stru ture to al ulate July t to June t+1 returns. We use three proxies for FC:

Weregress portfolio ex essreturns onthree Fama-Fren h fa torsand themomentum

fa -tor. 5

In table 11 we present time-series regression estimates on the period July 1977 to

De- ember2009.

InPanelA(WWindex)we observethatat lowandmediumlevels ofmarket ompetition,

un onstrained rmshave thelowest marketbeta, asexpe ted. For highlevels of ompetition,

however, marketbetadiers little fordierent levelsof FC. We also notethat market beta is

highest for medium FC rms at all levels of ompetition, although the dieren e is small for

mediumandhighPMClevels. Thislatterresultmightbesurprisingatrst. Butnotethatthe

dieren e inrisk between medium andhigh FCrms is aptured instead bythe SMB fa tor.

Indeed,highFCrms mapstrongly onthe size fa tor.

The most interesting observation inPanel A is that low onstraints rms inmedium and

highly ompetitiveindustriesearnasigni antpositiveabnormalreturn. TheHighPMC/Low

FC portfolio earns about 26bp monthly abnormal return (about 3.1% annualized). This is

surprising given our eviden e that these ompanies bear signi antly less fundamental risk

ompared to highly onstrained rms. Sto k pri es do not seem to ree t this fundamental

riskdierential.

Panel B(payout ratio) presents an even learer pi ture. Marketbetais generally

in reas-inginboth PMCand FC. Moreover, additionalrisk exposure ofhighly onstrained portfolios

is mapped on the SMB fa tor. Again, un onstrained rms in medium and highly

ompeti-tive industries earn a signi ant abnormalreturn of 19bp (2.3% annualized) and 40bp (4.9%

annualized), respe tively.

InPanelC(size)theoverallpi tureremainssimilartoPanelA.Marketbetaisnot

ne essar-ilymonotonous inFC,in parti ular inthe highly ompetitive portfolios. ButtheSMB fa tor

mappings apturealargepartofoverallriskofhighFCportfolios. Mostimportantly,theLow

FC rms earn again signi ant positive abnormalreturns in medium and highly ompetitive

industries(16bpand 24bp,respe tively).

We investigate further the abnormal returns patterns by running moving window

time-5

abnormal returns and their t-stats from 48-month moving window regressions of four orner

portfolios orrespondingto ombinations ofHigh/LowFC andHigh/LowPMC.

First, for all three FC proxies the High FC/High PMC portfolio earned mostly negative

abnormal returns over the se ond half of the 1980s, marked by the beginning of the savings

andloan risis andthesto kmarket rashof 1987. Theperiod 1994-95(Fedtightening, bond

market de line and rising yield volatility) through 1997 (Asian risis) was also a period of

mostly signi ant negative abnormal returns for this portfolio, but only based on the WW

and Size proxies. The rst half of the 2000s is the only period of positive abnormal returns,

signi ant only when using the WW and P/O proxies. The 2007-2009 risis brought again

some negative abnormalreturns.

TheHighFC/LowPMCportfoliohadalsonegativealphasduringtheseperiodsofdistress,

namely the se ond half of the 1990s and the 2007-09 risis. But there were also periods of

signi antly positive returns: therst halfofthe1980s andtheearly 1990s.

The Low FC/High PMC portfolios earned positive alphas during the 1997 Asian risis

period with a peak in early 1998 for all three FC proxies. Similarly, these ompanies earn

positive returns in the post-re ession periods of 2002-03, and 2009. The only instan e of

signi ant negative returnsisthe 1986 period.

Last, the Low FC/Low PMC portfolio abnormal returns are learly lower in magnitude

and only signi ant at times. For example, alphas were signi ant during the 2007-09 risis

period.

Themovingwindowanalysisshedssomelightonthe positiveabnormalreturnsoflow

on-straintsrmsinmediumandhighly ompetitiveindustriesobservedintable11. Un onstrained

rmsinhighly ompetitive industriestendto benetfrompost-downturn onditions when

ar-guably their onstrained rivals are still experien ing di ulties (as do umented in previous

se tions). However,this fa tisnot su iently ree ted invaluations andun onstrained rms

generatepositiveabnormalreturnsinthoseperiods. Thisndingisnewandis omplementary

Also,theabnormalnegativereturnsof onstrainedrms in risisperiodsindi ates thatat

normaltimestheirsto kpri esdonot ree ttheir nan ial`vulnerability'. Thisnding ould

partly explain why previous studies nd that a nan ing onstraints fa tor in sto k returns

earns a negative (Lamont, Polk, and Saá-Requejo 2001) or a positive but insigni ant risk

premium (Whitedand Wu 2006).

7 The ee tof nan ing onstraints on ompetitive me hanisms

7.1 A simple mean reversion model

Up to this point, we have studied how ex ante measures of ompetition intensity ae t the

real ee ts of nan ing onstraints. In this se tion we will look at how ex ante measures of

nan ing onstraintsat therm andindustry levelae t ompetitive me hanisms.

Toaddressthisquestionwewillanalyzehownan ing onstraintsae tthe ross-se tional

mean-reversionme hanismofrmprotability. FollowingFamaandFren h(2000,hereinafter

FF)weestimateasimple partialadjustment modelof mean-reversion ofprotability. Intheir

paperFFproposeatwostepmethod. Intherststeptheyestimateonea hyear's ross-se tion

theexpe tedprotability for ea h rm basedon three variables: M/B of assets,dividends to

bookequityand a dummyindi ating zero dividends. 6

Asase ond step, theauthors estimate

an equation where the hange of protability for a rm in year

t

is explained by the lagged

protability hangeandbythedieren ebetweenobserved andexpe tedprotabilityin

t − 1

.

The main FFspe i ation is somewhat disputable, given that expe ted protabilityis linked

to the M/B ratio of assets. In ee t, su h a spe i ation allows the expe ted protability in

subsequentyearsto revert to ee tiveprotabilityinsteadoftheseeing theee tive number

revertingto itsexpe tation(asweimpli itlyexpe tinthenotionofmean-reversion). Ifarm

earnsabnormallevelsofprotabilityinyeart,themarket aninterpretthissurpriseasthenew

normal: M/Bin reasesandthereforenextyear'sexpe tedprotabilityin reases aswell. This

6

Notethatwein ludeverysimilarvariablesasindividualrm ontrolsinourregressionsinse tion4,ex ept

offundamentalinformation bythemarkets. Theauthors nda rateofmean reversioninthis

spe i ation at about 38%a year.

FF in lude also a more robust spe i ation in whi h protability is assumed to

mean-revert to a ommon ross-se tional mean. In this setting the mean-reversion oe ient is

also signi ant, though lower, at about 30% a year. It seems normal that in this ase the

estimatedmean-reversionrateislower,be ausewe apturethepuremean-reversionofee tive

protability toa ommon mean.

We spe ify a model similar to the FF's se ond spe i ation, ex ept that we expli itly

in lude the rossse tional averageprotability inthe previous year. The baselinemodelis

ROA

it

−

ROA

it−1

= α + β

ROA

it−1

−

ROA agr

t−1

+ γ (

ROA

it−1

−

ROA

it−2

) + ǫ

it

, (7)

where ROA

it

is earnings beforeextraordinary items andinterest divided byassets and where

ROA agr

t

istheasset-weighted averageROAin the ross-se tionofall rms.

We then in lude intera tion mean-reversion terms ontrolling for nan ing onstraints at

the rmand industry level:

ROA

it

−

ROA

it−1

= α + β + δ

Lo

×

Lo FC

t−1

+ δ

Hi

×

Hi FC

t−1

+ δ

%Hi

×

%Hi FC

t−1

×

ROA

it−1

−

ROA agr

t−1

+ γ (

ROA

it−1

−

ROA

it−2

) + ǫ

it

, (8)

whereLoFC(HiFC)isanindi atorvariableofthebottom(top)30%observationsbasedonthe

onstraints proxyinthe previousyear, andwhere %Hi istheproportion ofHiFCrmswithin

the SIC3industry in theprevious year. Equation(8)is slightly modied for therating-based

proxies,where weonlyuseindi atorsfor low onstrained rms(seese tion3.3foradis ussion

of this hoi e).

Weestimatethese equations usingtheFamaandMa Beth (1973) method andwepresent

the results in table 12. The baseline mean-reversion oe ient of 26.9% per year in olumn

being orre ted for in our tables, whi h means that t-statisti s areoverestimated. If we had

a su iently long time series of these slope estimates, we would be able to estimate reliable

auto orrelationestimatesand al ulateadjustedSEsbytheNewey-Westte hnique. Sin ethis

isnot the ase, to infer signi an e we follow Famaand Fren h (2000)and require somewhat

higher t-statisti s for signi an e. FF estimate that with an auto orrelation of 0.5, the SEs

should be in reased by about 40%, therefore the threshold for t-stats should be at about 2.8

insteadof the ommonlyusedvalueof 1.96.

In olumns(2)to (6)weuseve dierentproxiesfornan ing onstraints. Wend partial

eviden ethatrm-level onstraintsae tthemean-reversionrateofrmprotability: fortwo

onstraints proxies (WW index and P/O ratio), the dieren e between onstrained and

un- onstrainedrms issigni antandsuggesting that onstrained rmsrevertto themeanfaster

than un onstrained rms. The eviden e is more lear- ut for the industry-level onstraints:

the per entage of onstrained rms signi antly redu es the mean reversion rate in all three

ases. In reasing the per entage of onstrained rms within an industry by 50 per entage

pointsredu es the mean reversionrate by about 12 per entagepoints. We notehowever that

theper entageofun onstrained rmsinthetworatings-based aseshastheexpe tednegative

oe ient only for LT ratings and the average t statisti is belowour in reased hurdle value

of 2.8inboth ases.

7.2 Two-level mean-reversion

Whenlookingatthespe i ationofequation(7)one ouldarguethattheimpli itassumption

of mean-reversion towards one ommon average protability for all rms is implausible. The

most obviousargument isthat one ommon expe ted protabilitydoesnot take into a ount

dieren es in risk a ross industries. A similar ritique an be made based on dieren es

in te hnology, the level of intangible assets (human apital, ta it knowledge, brand value,

reputation) et .

mean-mean. These ond level isthemean-reversionof theindustry meanto theaggregate mean. If

theabove ritiqueisjustied,weshouldonlyndeviden efortherstlevelofmeanreversion.

Spe i ally,we estimateinthebaseline asetheequation

ROA

it

−

ROA

it−1

= α + β

1

ROA

it−1

−

ROA SIC3

t−1

+ β

2

ROA SIC3

t−1

−

ROA agr

t−1

+ γ (

ROA

it−1

−

ROA

it−2

) + ǫ

it

, (9)

where ROA SIC3

t

is the asset-weighted average protability in the SIC3 industry. The hange

inrm protability an therefore be due to eitherthe previous year's dieren e between the

rm and its industry mean, or between the previous year's industry mean and the aggregate

mean.

Analogously,thetwo-level spe i ation ontrolling for nan ing onstraints is

ROA

it

−

ROA

it−1

= α + β + δ

Lo

×

Lo FC

t−1

+ δ

Hi

×

Hi FC

t−1

+ δ

%Hi

×

%Hi FC

t−1

×

ROA

it−1

−

ROA SIC3

t−1

+ λ + κ

Lo

×

Lo FC

t−1

+ κ

Hi

×

Hi FC

t−1

+ κ

%Hi

×

%Hi FC

t−1

×

ROA SIC3

t−1

−

ROA agr

t−1

+ γ (

ROA

it−1

−

ROA

it−2

) + ǫ

it

. (10)

Table 13 shows results based on the two-level spe i ation. The results at the rst level

(rmvs. industry)arequalitativelysimilartothesimple ase. Individualnan ing onstraints

status seems to be relevant only in some ases (WW and P/O). The signi ant negative

oe ientontheLoFCtermforLTratingsissurprisingasitsuggeststhatlow onstraintsrms

seem torevertfaster than otherrms. Theper entageof onstrained rmshasasigni antly

positive oe ient in all three ases (WW, P/O, size), and the per entage of un onstrained

rms is negative for LT ratings although the average t-stat is still below our onservative

thresholdof 2.8.

The pi ture is ompletelyreversed atthe se ondlevel(industryvs. aggregate): individual

per entage of onstrained rms in the SIC3 industry does not seem to be relevant as no

averaget-stat isabove our threshold of2.8.

Insummary,the two-level spe i ation onrmed thatprotabilitymean-reversionis

hap-peningat both theintra-industry andtheinter-industrylevels. Wefurther onrmedthatthe

proportion of onstrained/un onstrained rms within theindustry seems to be an important

determinant ofintra-industry protability mean-reversion.

Theseregularitiesarenottakenintoa ountbymarketvaluations. Intable14we onstru t

doublesortedportfolioson(i) therelative rmprotabilitywithrespe tto theprotabilityof

its SIC3 industry, and (ii) on theindustry per entage of nan ially onstrained rms within

the SIC3 industry. We that abnormally protable rms earn alphas of about 20-29 bp per

monthifahighper entageoftheirrivalsarenan ially onstrained. Thealphasaresmalland

not signi ant for abnormally protablerms iftheir industries have few onstrainedrms.

8 Robustness

Besides having used ve dierent proxies for nan ing onstraints previously employed in

the literature, we have also tried several robustness he ks for our results. First, we have

employed an alternative spe i ation for the intera tion ee t of nan ing onstraints and

market on entration on the business y le sensitivity of operating protability. Instead of

in luding rm level proxies of lagged protability we use lagged values of protability. We

usethreelaggedlevels ofROA.Dueto knownissueswithlaggeddependent variables inpanel

data, we estimate this equation following Arellano and Bond (1991) with a dieren e GMM

estimator (instrumenting with four lags of endogenous variables, ollapsed). The Ma roVar

set in ludes the same four ma ro variables asin se tion 4. The statisti al properties of this

spe i ation are improved when we in lude rm variables that likely ae t deviations from

a simple autoregressive spe i ation (we uselagged hanges inthe xed investment rate and

in advertising expense). We apture the ee t of nan ing onstraints by estimating the

and DBSPR for the LowFC subsample are relatively lowand sparselysigni ant. The same

intera tiontermsontheHighFCsubsamplearebothstatisti allyande onomi allysigni ant.

Sharpe (1994)notesthatusing anaggregate e onomi variableonrm-leveldata ouldbe

problemati due to dierent industryexposure to ma roe onomi onditions. Asarobustness

he kheproposestouseindustry-yearspe i dummyvariables. We onstru ttwelveindustry

groups based on the 12 Fama-Fren h portfolios and reate a dummy for ea h industry-year. 7

Theresults(notreported)onxedinvestment sensitivityareevenstrongerthan inthegeneral

ase: the intera tion variable ofPMC withGDPGR is signi ant for onstrained rms using

all ve nan ing onstraints proxies, while it is e onomi ally and statisti ally not signi ant

for mediumand low onstrained rms.

The underlying idea of of this paper statingthat nan ing onstraints should ae t more

rms in ompetitive industries relies strongly on the assumption that rm ash ows are

strongly orrelated with thebusiness y lein the rst pla e. Therefore, for industries whose

ashows ovarylittlewiththebusiness y le, theadditionalbenetofa on entrated market

(providing insulation against aggregate sho ks) should be less important. We follow Sharpe

(1994)andCampello(2003)inseparatingindustriesintohighly orrelated(durables),weakly

orrelated (non-durables), and others (non-manufa turing). Our results on thesensitivity

ofoperatingprotabilitytoGDPandbondspreadsho kssurvivequalitativelyinthedurables

subsample, with a loss of signi an e for some proxies. The results survive partially in the

non-manufa turing subsample. The non-durables subsample ompletely losessigni an e on

theGDP ross intera tion terms, as expe ted due to a low orrelation of ash ows with the

e onomi y le in these industries. On the other hand, theBond spread ee t survives even

inthenon-durables industries.

Lastly,we he kwhetherourresultsarenotdrivenbythere ent risis. Ourresultsremain

broadlyun hanged ifwe ex ludethe 2007-2009 period.

7

In this paper we studied the intera tions of produ t market ompetition and nan ing

on-straints. Thetworesear hpathsinthenan ing onstraints literature,namely thatof

invest-ment ash-owsensitivities and thatof nan ial a elerator, point to a vi ious ir leee t of

nan ing onstraints during e onomi downturns. Spe i ally, a onstrained rm must rely

on its internal fundsexa tlywhen ashows aredepressed, for ing itto a stronger redu tion

of operations and investment, further depressing internal fundset . We argue that

ompeti-tive industriesundergo the strongest sho k to ashows at theonset of a downturn, thereby

strengthening the adverse ee t of nan ing onstraintsduring theslowdown.

We nd signi ant support for our hypothesis in the analysis of operating protability

of rms. The ee ts of ma ro variables on the operating performan e of a onstrained rm

are stronger if the rm operates in a ompetitive industry. Stated dierently, nan ially

onstrained rms tend to benetrelatively more from the insulation ee t of a on entrated

produ tmarket.

We onrm that nan ing onstraints have signi ant real ee ts on the business y le

sensitivityof orporate investment andemployment tothebusiness y le. Inxedinvestment

data we nd an amplifying ee t of produ t market ompetition on the ee ts of nan ing

onstraints. On the other hand, investments innet working apital and employment growth

do not seemto besubje tto asimilar ampli ationee t.

In our asset pri ing tests we nd that sto k pri es do not ompletely take into a ount

thesedieren esinfundamentalrisk. Spe i ally,un onstrainedrmsoperatingin ompetitive

industriesearnabnormalpositivereturns,parti ularlyduringperiodsofe onomi andnan ial

distress.

Lastly,weshowthatnan ing onstraintsae t ompetitiveme hanismswithinindustries.

Wendsupportforthehypothesisthattheaveragelevelofnan ing onstraintsinanindustry

tends to redu e the intraindustry mean-reversion of rm protability. Sto k pri es do not

integratethis fa t. Firms withahighprotability relativeto their SIC3industryprotability