CompetitionandtheRealEffectsofUncertainty*
RajaPatnaik†
LondonBusinessSchool
November13,2015
JOBMARKETPAPER
Abstract
This paper investigates the impact of uncertainty on firm-level capital investment and
examineswhetherthiseffectdependsonthedegreeofcompetitionthatfirmsface.Iexploit
auniqueempiricalsettingtoconstructatime-varyinguncertaintymeasurethatisexogenous
to economic conditions and firm behavior. I show that higher uncertainty results in a
decreaseininvestmentforfirmsinmoreconcentratedindustries.Theeffectisstrongerfor
firmsthatfacehighercostsassociatedwithreversinginvestments.Thisfindingisinlinewith
irreversibleinvestmentmodelsthatpredictanegativerelationshipbetweenuncertaintyand
investment. In contrast, firms in highly competitive industries increase investment in
response to higher uncertainty, supporting the argument that competition can erode the
option value of deferring investment. In that case, other industry and firm characteristics
suchasoperationalflexibilitycanresultinincreasedinvestmentinresponsetoheightened
uncertainty. I also find economically significant effects of uncertainty on other types of
investmentsuchasR&Dspending,advertisingandinvestmentinhumancapital.Collectively,
my results illustrate that the degree of competition plays an important role in the link
betweenuncertaintyandinvestment.
JELclassification:G31,G32
Keywords:Uncertainty;Investment;Competition;RealOptions;Irreversibility
*Iamdeeplygratefultomyadvisor,FrancescaCornelli,andtoHowardKung,HenriServaesand
StefanLewellenfortheirguidanceandinvaluablesupport.IalsowanttothankTaylorBegley,João
Cocco,JamesDow,AlexEdmans,FranciscoGomes,ChristopherHennessy,BrandonJulio,RalphKoijen,
Ryan Lewis, Anna Pavlova, Tarun Ramadorai, Hélène Rey, Rui Silva, Vikrant Vig and seminar
participantsatLondonBusinessSchoolforhelpfulcommentsanddiscussion.Allerrorsaremyown.
†LondonBusinessSchool,Regent’sPark,NW14SA,London.Email:rpatnaik@london.edu
1
1. Introduction
Alargebodyofresearchintheirreversibleinvestmentliteraturepredictsanegativelink
between uncertainty and investment (e.g., Dixit and Pindyck, 1994). If firms face capital
adjustmentcostsandpartialinvestmentirreversibility,uncertaintycreatesanopportunity
costofinvestingtodayintheformofapositiveoptionvalueofwaiting.Thisleadsfirmsto
deferinvestmentuntilsomeoftheuncertaintyisresolved.Mostoftheempiricalevidenceis
consistentwiththispredictionandindicatesthatcapitalinvestmentonaveragedeclinesin
response to uncertainty (e.g., Bloom et al., 2007). However, this effect is likely not
homogenous across firms in different competitive environments. If firms face competitive
access to investment projects, the fear of preemption can render the option to defer
investmentlessvaluable.Thus,competitivepressurecanimpedeafirm’sabilitytowaitand
subsequently mitigate the negative effect of uncertainty predicted by the irreversible
investmentliterature(Aguerrevere,2003;Grenadier,2002).
Knowingwhetheruncertaintyhasauniformordifferentialeffectoncapitalinvestment
acrossindustriesiscentraltoourunderstandingofhowuncertaintyaffectstherealeconomy.
Surprisingly, the existing empirical evidence on the impact of uncertainty on investment
acrossdifferentindustriesisverylimited.Twokeychallengeshavehinderedprogress.First,
it is difficult to identify changes in uncertainty that occur independently from changes in
economic conditions. Such endogeneity issues usually impede the causal estimation of the
uncertainty effect on firm-level investment. Second, the lack of exogenous uncertainty
measuresthatarerelevantforawidevarietyofindustrieshaveledstudiestofocusonsingle
industriessuchasoilandgas(Kellogg,2014)ormanufacturing(Bloometal.,2007;Leahy
andWhited,1996).However,suchanapproachmakesitdifficulttotestwhethertheeffectof
uncertaintyonfirminvestmentdiffersacrossindustries.
In this paper, I use a unique empirical setting to construct a time-varying measure of
uncertaintythatisexogenoustoeconomicconditionsandfirmbehavior.Thisallowsmeto
providenewempiricalevidenceontheuncertainty-investmentlinkanditsinteractionwith
competition.Consistentwiththeoriesintheirreversibleinvestmentliterature,Ishowthat
firmsinconcentratedindustriesdecreaseinvestmentinresponsetoheighteneduncertainty.
However, I find that uncertainty leads to increased investment in highly competitive
industries,suggestingthatcompetitioncanattenuatethenegativeeffectofuncertaintyand
allowothermechanismsthatencourageinvestmentinthefaceofuncertaintytodominate.
2
Mycontributiontotheexistingliteratureistwo-fold.First,Iconstructanovelmeasureof
uncertainty that is exogenous to firm investment decisions and economic conditions.
Specifically,IexploituncertaintyaboutweatherchangesinthePacificassociatedwiththeEl
NiñoSouthernOscillation(ENSO)cyclethataffectavarietyofindustriesintheU.S.Industries
suchascommodity-producingsectorscanbedirectlyaffectedbychangesinweather,while
otherindustriesareimpactedindirectlythroughtherelationbetweencommoditypricesand
changesinENSOconditions1.AgrowingliteraturedocumentstheimpactofENSOeventssuch
asElNiñoweathershocksoneconomicactivityandcommodityprices(e.g.,Brunner,2002;
Cashin et al., 2014). While this literature generally examines the macroeconomic effect of
observedENSOshocks,tothebestofmyknowledge,thispaperisthefirststudytousefuture
ENSOuncertaintyasaninstrumentforeconomicuncertainty.
My empirical strategy is based on the following instrumental variables approach.
Consistent with existing studies, I use stock price volatility as a proxy for firm-level
uncertainty (e.g., Bloom et al., 2007). Due to the endogenous nature of this measure, I
constructaninstrument,whichcapturesindustry-relevantENSOuncertaintythatisplausibly
exogenous to economic conditions and firm behavior. The instrument is a combination of
industry-level sensitivities to changes in ENSO conditions, calculated using share price
returns, and ENSO forecast data 2. This identification strategy relies on the fact that ENSO
uncertainty varies across time and ENSO sensitivities vary substantially across industries.
IndustriescanhavenegativeandpositiveexposurestoENSOshockssuchasElNiñoepisodes.
Thus,anElNiñoeventresultsinachangeineconomicconditions(i.e.,achangeinthefirst
moment) that is favorable for industries with positive sensitivities and unfavorable for
industries with negative sensitivities. However, an increase in uncertainty about the
occurrence of an El Niño (i.e., a change in the second moment) represents an increase in
uncertaintyforbothtypesofindustriesandthisincreaseishigherforindustrieswithlarger
ENSO sensitivities. Hence, a first moment shock has opposing effects on industries with
positiveandnegativesensitivities,whileasecondmomentshockaffectstheseindustriesin
the same direction, and the latter effect only varies with the magnitudes of their ENSO
1ChangesinENSOconditionscanbemeasuredusingseasurfacetemperaturedatafromthePacific
Ocean.Large-scaleENSOeventssuchasElNiñoepisodesareusuallyassociatedwithincreasesinsea
surfacetemperaturethatcausemajorclimatologicalchangesaroundtheworld.
2Asdescribedinsubsequentsections,Iobtaindataontheprobabilitydistributionsof12-month
ENSO forecasts from the Climate Prediction Center. These forecasts generally follow normal
distributionsandcanthusbecharacterizedbytheirfirstandsecondmoments.Iwillspecificallyfocus
onthesecondmomentoftheseforecastprobabilitydistributionstoconstructmyinstrument.
3
sensitivities.Thisintuitionisusedtoconstructtheinstrumentandtodisentanglefirstfrom
secondmomenteffects3.
My second contribution is that I provide new evidence for a differential effect of
uncertainty on firm-level investment across industries. I examine a sample of more than
6,500publiclylistedU.S.firmsfrom1985to2014anddocumentthatcompetitionisamajor
determinantofthesignandmagnitudeoftheuncertainty-investmentrelationship.Iusethree
different proxies for industry concentration and show that firms in more concentrated
industriesdecreaseinvestmentinresponsetohigheruncertainty.Incontrast,firmsinhighly
competitiveindustriesincreaseinvestmentwhenuncertaintyrises.
Totestwhetherthenegativerelationinconcentratedindustriesisconsistentwithmodels
in the irreversible investment literature, I employ two different proxies for the degree of
investmentirreversibilitythatfirmsface,whicharebasedoninsightsfromKimandKung
(2014) and Zhang (2005). The negative effect is stronger for firms with less redeployable
assets and higher average adjustment costs, suggesting that the option to wait and defer
investmentismorevaluableforthem.Hence,thenegativeeffectofuncertaintyoninvestment
in concentrated industries is consistent with the predictions of real option models in the
irreversibleinvestmentliterature(e.g.,DixitandPindyck,1994).
Ialsoinvestigatealternativeexplanationsforthenegativeuncertaintyeffectobservedin
concentratedindustries.Forinstance,oneexplanationreliesontheargumentthatgreater
uncertaintymayincreaseborrowingcostsforfirmsinthepresenceoffinancialconstraints.
Gilchristetal.(2014)arguethatuncertaintyaffectsinvestmentindirectlythroughincreases
in credit spreads. As a result, firms reduce leverage and decrease investment spending.
Examining external financing decisions of the firms in my sample, I find no evidence
supportingthisprediction:forfirmsinmoreconcentratedindustries,Idocumentanegative
relationshipbetweenuncertaintyandnetequityissueandnoimpactofuncertaintyonnet
debt issue 4 . I also test whether firms offset the decrease in capital investment with
correspondingincreasesininvestmentinmoreflexibleproductionfactorssuchasworking
capitalandlabor(EberlyandVanMieghem,1997;Fischer,2013).Theresultsdonotprovide
3ThiseconometricapproachissimilartotheframeworkinSteinandStone(2013).Theyemploy
exposurestooilpriceandexchangeratefluctuationsasinstruments,butdonotaddresstheirpossible
endogeneity. My paper employs a distinctly different instrument that is based on changes in ENSO
uncertainty,whichareplausiblyexogenoustoinvestmentdynamicsandeconomicconditions.
4The overall debt-to-equity ratio increases in response to uncertainty for firms in concentrated
industries.
4
evidenceforsuchashiftininvestments.Uncertaintysimilarlyleadstodecreasesinworking
capitalandhiringinmoreconcentratedindustries.
Inhighlycompetitiveindustries,Ifindthatfirms,onaverage,increaseinvestmentinthe
faceofhigheruncertainty.Thisresultisconsistentwiththeargumentthatcompetitioncan
erodetheoptionvalueofdeferringinvestment,allowingothermechanismsthatencourage
investmentinresponsetoheighteneduncertaintytodominate.Forinstance,uncertaintycan
activelyencourageinvestmentwhenfirmshavehighoperationalflexibility(Mills,1984).If
firmscaneasilyexpandtoexploitgoodtimesandcontracttoinsureagainstbadoutcomes,
their profits are convex in input or output prices. A mean-preserving increase in cost or
demand uncertainty, by Jensen’s inequality, thus increases expected profits 5 . Similar
convexitiescanariseundersubstitutabilityofcapitalwithmoreflexibleproductionfactors
suchaslabor(Abel,1983;Hartman,1972).LeeandShin(2000)showthattheconvexityin
thefirm’sobjectivefunctionincreaseswiththeshareoftheflexibleproductionfactorinthe
production technology. Thus, the positive effect of uncertainty on investment should be
stronger for firms with higher labor-capital ratios. I test whether the positive effect of
uncertaintyinhighlycompetitiveindustriesresultsfromsuchconvexitiesinafirm’sprofit
function. Consistent with the above predictions, the positive effect of uncertainty is only
statistically significant for firms with higher operational flexibility (e.g., firms in
nonunionized industries) and for firms with higher labor-capital ratios where the flexible
productionfactorrepresentsalargershareoftheproductiontechnology.
The estimated effects of uncertainty are economically significant. In response to a ten
percentage point increase in uncertainty, firms in more concentrated industries decrease
investmentby10percent.Conversely,firmsinhighlycompetitiveindustriesraiseinvestment
ratesbyabout11percentinresponsetoasimilarincreaseinuncertainty.
The finding that competition plays a central role in the uncertainty-investment
relationshipisrobusttoarangeofadditionaltests6.Specifically,Iprovideevidencethatthe
differentialimpactofuncertaintyacrossindustrieswithvaryingdegreesofcompetitionisnot
5Themarginalprofitofcapitalineachperiodtcanbewrittenasmax[0,(p –c )].Ifthemarginal
t
t
unitofcapitaldoesnothavetobeutilizedinbadtimes,aunitofcapitalcanbeseenasanoptionon
futureproduction,whichismorevaluablethehigherthevarianceofptorct(Pindyck,1993a).
6 In robustness tests, I address the concern that my instrument might capture systematic
differencesinthelevelofcompetitionacrossindustries,whichcouldresultintheobserveddifferential
effectofuncertaintyoninvestmentincompetitiveandconcentratedindustries.Ialsodiscussindetail
theconcernthatENSOuncertaintycouldaffectthedegreeofcompetitioninanindustry,whichinturn
might impact investment, and why it is unlikely that such a causal chain would yield the results
reportedinthispaper.
5
auniquefeatureoftheparticularidentificationstrategyanduncertaintymeasureusedinthis
paper.Iemploymoreconventionalandaggregateproxiesforuncertaintysuchastheimplied
volatilityofS&P100optionstoshowthatcompetitivepressurecanattenuatethenegative
effectofuncertainty.Whilepossibleendogeneityconcernsmakeitdifficulttodrawinference
about the sign and the magnitude of the uncertainty-investment relationship in such a
specification, the results confirm that the level of competition firms face impacts their
investmentstrategiesinresponsetoheighteneduncertainty.
Whilemyfindingsprovideclearevidencethatfirmsindifferentcompetitiveenvironments
respond differently to uncertainty, the economic effect of uncertainty depends on the
persistenceofthesefirm-levelresponses.Iinvestigatethedynamicsofinvestmentfollowing
anuncertaintyshockinadifference-in-differencesframework.Theresultsshowasignificant
wedge in the response of investment spending by firms in competitive and concentrated
industriesafterbothalargeincreaseandalargedecreaseinuncertainty.Thiswedgeonly
persistsfortwoperiodsafteranincreaseinuncertaintyandforoneperiodafteradecrease
inuncertainty.ThesefindingsareconsistentwithsimulationsinBloometal.(2014)showing
that the negative effect of uncertainty on investment, prescribed by the real options
literature,generallyresultsinashortandsharpdropininvestmentratesfortwoperiods.
This is followed by a quick rebound of investment rates and a prolonged recovery of the
capitalstocktoitssteadystate.
Finally, I document the effect of uncertainty on other types of investment. Uncertainty
reducesR&DspendinginconcentratedindustriesandhasnoeffectonR&Dinvestmentfor
firmsincompetitiveindustries.ThemagnitudeofthenegativeeffectonR&Dissignificantly
smallercomparedtothenegativeeffectofuncertaintyoncapitalinvestmentinconcentrated
industries.ThisisconsistentwiththefactthatR&Dspendingismorepersistentacrosstime
thancapitalinvestment.Inlinewithdecreasesincapitalinvestment,firmsinconcentrated
industriesalsospendlessonadvertising.Iobservenoeffectonadvertisingforfirmsinhighly
competitive industries. Hiring on average declines across firms in response to uncertainty
indicating that uncertainty has a real impact on labor markets. However, this effect is not
significantforfirmsinhighlycompetitiveindustries.
6
1.1 RelatedLiterature
1.1.1
Theory
This paper builds on a large theoretical and empirical literature on investment under
uncertainty7.Theoreticalresearchhasexaminedmanydifferentsettingsinwhichuncertainty
mattersforfirminvestmentdecisions.Byfarthelargeststrandofliteratureemphasizesthe
realoptionsframeworktoidentifytheimpactofuncertaintyunderinvestmentirreversibility
(Abel and Eberly, 1994; Bernanke, 1983; Brennan and Schwartz, 1985; Dixit and Pindyck,
1994;McDonaldandSiegel,1987).Thecentralideaunderlyingthisargumentisbasedonthe
presenceofasymmetricadjustmentcosts,whichmakeitcostliertoreverseinvestmentthan
toincreaseit,effectivelyleadingtopartialorfullirreversibilityofinvestment.Undersuch
conditions, a mean-preserving rise in uncertainty increases a firm’s option to defer
investment until some of the uncertainty is resolved. This creates regions of inaction that
expand with uncertainty, leading to a negative link between uncertainty and investment.
Negative effects of uncertainty also arise out of managerial risk aversion and ambiguity
aversion(IlutandSchneider,2012;PanousiandPapanikolaou,2012).Higheruncertaintycan
leadtoincreaseddefaultprobabilitiesandtoinvestorsrequiringhigherriskpremia,which
in turn should increase borrowing costs (Bloom, 2014). Several models show that, in the
presence of financial constraints, uncertainty can raise the cost of finance for firms and
decreaseinvestmentspending(Arellanoetal.,2010;Christianoetal.,2014;Gilchristetal.,
2014).
Incontrast,othermodelsconsiderconvexitiesinthefirm’sprofitfunctionandpredicta
positivelinkbetweenuncertaintyandinvestment(Abel,1983;Hartman,1972;Oi,1961).The
basicintuitionbehindtheirresultsisthatuncertaintyraisesthevalueofthemarginalunitof
capitalifthefutureprofitabilityofthemarginalunitisaconvexfunctionoftheunderlying
stochastic variable such as input or output prices. Certain conditions such as constantreturns-to-scale and complementary production factors achieve such convexity in the
marginal revenue product of capital. Similar convexities can also arise due to operational
flexibilityandafirm’sabilitytoeasilyvaryoutputaccordingtoeconomicconditions(Mills,
1984; Pindyck, 1993a). In addition, investment lags can lead to a positive link between
uncertainty and investment. Many models of irreversible investment assume that
investmentsbecomeproductiveassoonasthedecisionismadeandcostsareincurred.In
7ForacomprehensivereviewofthisliteratureseeBloom(2014).
7
fact,manyinvestmentstaketimetocomplete,whichisoftenreferredtoas“time-to-build”or
investmentlags8.Bar-IlanandStrange(1996)illustratethatinthepresenceofinvestment
lags, the negative effect of price uncertainty on investment predicted by the irreversible
investmentliteraturecanbemitigatedorevenreversed.Pindyck(1993b)alsosuggeststhat
uncertaintyaboutthetechnicalfeasibilityofaprojectcanincreaseinvestment.
Many of the above models rely on specific assumptions about the competitive
environmentthatfirmsface.Inparticular,theliteratureonirreversibleinvestmentgenerally
assumesthatagentsmakeoptimalinvestmentdecisionsinisolation.Grenadier(2002)takes
intoaccountstrategicinteractionsamongfirmsandshowsthatincreasedcompetitioncan
erode the option value to defer investment, leading firms to revert to net present value
investing strategies. Similarly, Caballero (1991) highlights the lack of robustness of the
negativelinkbetweenuncertaintyandinvestmentunderirreversibleinvestmenttochanges
inthelevelofcompetition.Otherresearchsimilarlystudiestheeffectsofcompetitiononthe
uncertainty-investment relationship (Aguerrevere, 2003; Caballero and Pindyck, 1996;
Kogan, 2001; Leahy, 1993; Williams, 1993). Kulatilaka and Perotti (1998) further the
argument that strategic competition can encourage investment and illustrate that in the
presenceofgrowthoptions,strategicconsiderationstodiscourageentryorinvestmentsof
competitorscanprovidestrongincentivesforincreasedinvestment.
1.1.2
EmpiricalEvidence
Theambiguouspredictionsofthetheoreticalinvestmentliterature,particularlyintheface
ofvaryinglevelsofcompetition,showcasestheneedforempiricalanalysistodeterminethe
sign and the magnitude of the relationship between uncertainty and investment. The
empiricalliteratureoninvestmentunderuncertaintycanbroadlybeclassifiedintomacro-
andmicro-levelstudies.Themacroeconomicapproachconsistsofinvestigatingtheeffectof
uncertaintymainlyproxiedforbythevolatilityofmacroeconomicvariables(suchasinflation
rates,exchangerates,oilpricesandstockmarketreturns)onaggregatecapitalinvestment
(Eisfeldt and Rampini, 2006; Ferderer, 1993; Fernández-Villaverde et al., 2011; Goldberg,
1993; Huizinga, 1993; Servén, 2003). The general consensus in this literature is that
uncertainty,onaverage,reduceseconomy-widecapitalinvestment.
8MacRae(1989)observesthatprojectsintheelectricitysectorcantakeupto10yearstocomplete.
Similarly,Pindyck(1991)notesthatinvestmentsintheaerospaceandpharmaceuticalindustrieshave
comparabletimeframes.
8
Onthemicro-level,papershaveusedindustry-andfirm-specificdatatoprovideevidence
on the link between investment and uncertainty. To a large extent, this literature follows
macroeconomicstudiesintheconstructionofuncertaintymeasuressuchasthevolatilityof
stockreturns(Baumetal.,2008;Bloometal.,2007;Bulan,2005;Gilchristetal.,2014;Leahy
and Whited, 1996). Additional measures include the variation in output and input prices
(Ghosal and Loungani, 1996; Kellogg, 2014), currency fluctuations (Campa and Goldberg,
1995;Campa,1993)andproxiesforfirmprofitability(GhosalandLoungani,2000;Minton
and Schrand, 1999). A common challenge in the empirical literature on investment under
uncertaintyistodistinguishtheeffectofuncertaintyfromtheimpactofbusinessconditions.
Tomitigateendogeneityconcernsaboutuncertaintyproxiesbasedonreturnvolatility,many
of the above studies use “internal” instruments in the spirit of Arellano and Bond (1991),
which require strong identifying assumptions about the properties of the underlying
investment time series 9 . Stein and Stone (2013) try to address this issue by following a
differentidentificationstrategy.Theyuseindustryexposurestooilpriceandexchangerate
fluctuationsasinstrumentsforfirm-leveluncertaintymeasuredbyimpliedoptionvolatilities.
While their approach arguably presents an improvement to previous methods, it does not
accountfortheexistenceoflatentvariablesthatmightaffectinvestmentandexchangerates
oroilpricesatthesametime.Anotherapproachmodelschangesinuncertaintybyexploiting
naturalexperimentssuchaspoliticalregimechanges,terroristattacks,anddisasters(Baker
and Bloom, 2013; Julio and Yook, 2012; Kim and Kung, 2014). The challenge in these
frameworks is that these events might impact firm behavior directly rather than through
changes in uncertainty. While many of the above studies document suggestive empirical
supportforanegativeeffectofuncertainty,resultsareoftenverysensitivetotheinclusionof
first-momentcontrols(LeahyandWhited,1996).Thus,inconclusivemicro-levelevidenceon
the relationship between capital investment and uncertainty reflects the ambiguity in
theoretical predictions particularly in the cross-section 10 . As highlighted earlier, firm and
industrycharacteristicssuchasthecompetitiveenvironmentcanhaveasubstantialimpact
onthelinkbetweenuncertaintyandinvestment.Surprisinglyfewstudieshaveexaminedthe
differentialeffectofuncertaintyacrossindustries.Bulan(2005)andGuisoandParigi(1999)
9SeeAlmeidaetal.(2010)andEricksonandWhited(2012)forcomprehensivediscussionsofthe
advantages and disadvantages of using GMM versus OLS type instrumental variables estimators in
investmentregressions.
10 Abel and Eberly (1996) and Leahy and Whited (1996) provide in-depth discussions of the
possiblepositiveandnegativeeffectsofuncertaintyoncapitalinvestment.
9
examine a sample of manufacturing firms in the U.S. and Italy, respectively, and provide
evidence that increased competition can dampen the negative effect of uncertainty. In
contrast,GhosalandLoungani(1996)findtheoppositeresultusingindustry-leveldata.The
competing nature of these results might arise from studying very specific industries and
highlightstheneedforempiricalanalysisofabroadsampleoffirmsinavarietyofdifferent
industries.
Inthispaper,Iwillconstructanexogenous,time-varyingandforward-lookinguncertainty
measuretoinvestigatethedifferentialimpactofuncertaintyoncapitalinvestmentformore
than6,500U.S.firmsacrossalargenumberofindustries.
Theremainderofthepaperisorganizedasfollows:Section2providesdetailsaboutthe
El Niño Southern Oscillation cycle and discusses its impact on economic activity.The data
sources are described in Section 3. Section 4 outlines the empirical framework and
methodology used. Section 5 presents the main empirical results, while Section 6 covers
cross-sectional tests aimed at exploring the economic channels underlying the observed
effects.Section7and8investigatethedynamicsoftheuncertaintyeffectandtheimpactof
uncertaintyonothertypesofinvestment.Finally,Section8concludesthepaper.
2. ElNiñoSouthernOscillationCycle(ENSO)
I examine the effect of uncertainty on firm-level investment by exploiting exogenous
variationinuncertaintyinducedbytheElNiñoSouthernOscillation(ENSO)cycle.ENSOisa
prominent weather system in the tropical Pacific that heavily influences atmospheric
conditions in North and South America as well as Asia and Australia (Adams et al., 1995;
Changnon,1999;RopelewskiandHalpert,1987,1986;Rosenzweigetal.,2001;Wangetal.,
2000).
“Regular” seasons are characterized by a persistent high-pressure system off the west
coastofSouthAmerica,specificallyoverthecoastofPeru.Atthesametime,alow-pressure
system forms east of Australia. The resulting differential in atmospheric pressure leads to
prevailingsurfacewindsthatblowfromtheeasttothewest–oftenreferredtoas“easterlies”.
These trade winds carry warm surface water from the central and eastern regions of the
equatorialPacifictoAustraliaandAsia.Resultingprecipitationintheseregionsisextensively
usedforagriculturalandindustrialpurposes.AlongthewestcoastofSouthAmerica,coldand
nutrient-richwaterrisestothesurfaceboostingthefishingindustryintheseregions.
10
Thisweatherpatternisverysensitivetoanomalousdeviationsinatmosphericpressure
and sea surface temperature, which can cause serious disruptions to weather conditions
aroundthePacific.Occasionally,thehigh-andthelow-pressuresystemscanswitchpositions
causingtheeasterliestosubside.ThisphenomenonisoftenreferredtoasElNiño.LaNiña
events,ontheotherhand,arecharacterizedbyintensifiedpressuresystemsleadingtoadrop
in ocean temperatures and intensification of the trade winds. This periodical interaction
between atmospheric conditions and the Pacific Ocean is called the El Niño Southern
Oscillation(ENSO)cycle.WhileENSOeventsoccurinthePacificOcean,theycancausewidereachingchangesinglobalweatherpatternsthataffectavarietyofdifferentindustriessuch
ascommodity-producingsectors.Asanillustration,pastElNiñoepisodeshaveresultedin
extensive rainfall in South America hampering rice cultivation in Ecuador and flooding
copperminesinPeruandChile.Suchdirecteffectshavealsobeenobservedinlargepartsof
AustraliaandIndonesiawheredroughtconditionsassociatedwithElNiñoeventsresultedin
lower agricultural output and forest fires. In addition, ENSO has indirect effects on
commodityproductionandtransportation.ENSOrelateddroughtepisodesinIndonesia,the
world’slargestnickelproducer,havehistoricallycrippledtheminingindustrywhichrelies
heavilyonwaterwaysfortransportationandhydroelectricpower.IntheU.S.,ElNiñoevents
havefrequentlybeenassociatedwithunusuallywarmwintersboostingprofitsinindustries
engaged in and related to residential construction. Warmer than usual periods have
historicallyalsoledtochangesinenergypricesduetodecreasedheatingdemand(Brunner,
2002;Changnon,1999).
ThisanecdotalevidenceontheeconomiceffectsofENSOvariationhassparkedagrowing
body of research on the link between ENSO and economic activity 11 . Brunner (2002)
documentstheeffectofENSOvariationonworldrealnon-oilprimarycommoditypricesas
well as GDP growth in G-7 countries. The study shows that ENSO variability can explain
almost 20% of movements in commodity prices – in particular, prices of metals and
agriculturalproducts.Importantly,whiletheENSOeffectoncommoditypricesissignificant
in the short run, it is also pronounced over longer horizons 12 . Similar results have been
reported in a recent study by Cashin et al. (2015). They show that ENSO related weather
11Forcomprehensivereviewsoftheliteratureonthemoregeneralrelationshipbetweenclimate
andeconomicperformanceseeDelletal.(2014)andTol(2009).
12 The author examines both four-quarter- and 16-quarter-ahead forecast error variances in
commoditypricesandfindsthatENSOactivityexplainsalmost10%ofcommoditypricemovements
overtheshorterhorizonandupto20%overthelongerhorizon.
11
eventshaveheterogeneouseffectsoneconomicgrowthandinflationacrosscountriesaround
theworld.Inaddition,theydocumentthatENSOhasasignificantimpactonenergyandnonfuel commodity prices. Other research finds similar links between ENSO and economic
activityaswellascommodityprices(Adamsetal.,1999;Algieri,2014;BerryandOkuliczKozaryn,2008;Chenetal.,2001;Chimelietal.,2008;Chuetal.,2012;HandlerandHandler,
1983;Hansenetal.,1998;Hsiangetal.,2011;Nayloretal.,2001;Solowetal.,1998;Ubilava,
2012).
Inthispaper,Iwillbuildontheexistingresultsthatestablishalinkbetweeneconomic
activity and ENSO fluctuations. Previous studies focus specifically on the macroeconomic
effects of ENSO shocks such as El Niño events, which represent first moment shocks to
economicactivity.Incontrast,tothebestofmyknowledge,thisisthefirstpapertoutilize
ENSO prediction data to study the impact of variation in future ENSO uncertainty,
representingsecondmomentshocks,onrealeconomicoutcomes.Iwillexploitthefactthat
firmshavedifferentsensitivitiestochangesinENSOconditions13andconstructaforwardlookinguncertaintymeasureusingtimeseriesdataonENSOpredictions.PleaseseeAppendix
BformoredetailsontheElNiñoSouthernOscillationCycle.
3. Data
Formyempiricalanalysis,IexaminealargesampleofU.S.firmsintheperiodfrom1985
to2014.Thefollowingsectionprovidesabriefoverviewofthedatasources.
3.1 Firm-andIndustry-LevelData
ThefocusofthispaperisonU.S.firmsandIobtainfirm-levelfinancialinformationfrom
theCompustatdatabase.Databasedoncashflowstatements,incomestatementsandbalance
sheets is available for fiscal years from 1950 to 2014. Daily stock price information is
obtainedfromtheCRSPdatabase.
I collect industry-level data on union memberships from the Union Membership and
Coverage database (www.unionstats.com), described by Hirsch and Macpherson (2003).
13 The variation in ENSO exposure across industries is frequently being highlighted in popular
media.ArecentmarketreportbyMacquariestatesthat“companiesinNorthAmericaandAustralia/NZ
aremostlikelytobeimpacted,followedbycompaniesinAsia.Importantly,theimpactoncompanies
is not uniform within any given region. In Aus/NZ, 38% of highlighted companies are likely to be
negatively impacted with 62% expected to be positively impacted. In North America the
negative/positivesplitis45/55andinAsiathemixis70/30”(FT,2015).
12
Since this data uses the Census Industry Classification (CIC) to categorize industries, I use
matching information provided by the U.S. Census Bureau to map CIC codes to Standard
Industry Classification (SIC) and North American Industry Classification System (NAICS)
codes.ThisallowsmetomatchtheunionmembershipwithCompustatandCRSPdata.
3.2 ENSOData
The intensity of an ENSO event can be measured in various ways. One of the most
commonly used measurements relies on sea surface temperature (SST) anomalies in the
Pacific.Theseanomaliesrepresentdeviationsofseasurfacetemperatureinagivenregion
fromitshistoricalaverage.TheNationalOceanicandAtmosphericAdministration(NOAA)
providesthehistoricalmeasurementsofSSTanomaliesforseveralregionsinthePacific.This
paperusesSSTdataspecificallyforregion“Niño3.4”forseveralreasons.First,itisthemost
centralregioninthePacificforwhichSSTanomaliesarebeingrecorded.Second,theClimate
PredictionCenter(CPC)usesdatarecordedinthisregiontoforecastENSOepisodes.Finally,
thismeasurementisthefocusofseveralpreviousstudiessuchasBrunner(2002)andChuet
al.(2012).Whilethetimeseriesgoesbackasfarasthelate1800s,consistentmeasurements
areonlyavailablefrom1950tothepresent.Thedatacanbedownloadedfromthewebsiteof
the Climate Prediction Center (CPC, 2015a). Figure 1 illustrates the time series of SST
anomaliesinthecentralPacificregion“Niño3.4”.Largepositivevaluesareusuallyassociated
withunusuallywarmElNiñoperiods,whilenegativevaluesindicatecolderLaNiñaevents.
InadditiontohistoricaldataofobservedENSOevents,Iobtainprobabilisticconsolidation
forecastsofSSTforregion“Niño3.4”fromtheCPC14.Theseforecastsrepresentastatistical
combinationofvariousmodelsusedbytheCPCtopredictENSOanomaliesthroughoutthe
Pacific15.ThedatacontainsmonthlySSTforecastsfrom1985tothepresentforhorizonsof
uptotwelvemonths.InadditiontopointestimatesofSST,theentireprobabilitydistribution
for these forecasts is provided. The first and second moments of the twelve-month SST
forecastdistributions,whichgenerallyarenormaldistributions,aredepictedinFigure2.
CompustatandCRSPdataismergedwithENSOpredictiondata,whichlimitsthesample
periodto1985to2014.Thefinalsampleincludes12,017firmsand92,572observations.
14IthanktheCPCandNOAAforsharingthisforecastdatauponrequest.
15ThemodelsusedbytheCPCtoforecastENSOeventsincludecanonicalcorrelationanalysis(CCA),
Markovmodels,ConstructedAnalogmodelsandfullycoupled,one-tierocean-atmospheredynamical
models.TheCPCprovidesdetailedinformationabouttheconstructionofthesemodels(CPC,2015a).
13
4. EmpiricalFramework
I construct a panel data set of U.S. firms from the period between 1985 and 2014 to
investigatetherelationshipbetweenuncertaintyandinvestment.Myfocusonfirm-levelas
opposed to more aggregated data is motivated by several factors. First, it allows me to
investigate the differential effect of uncertainty across firms with different firm- and
industry-levelcharacteristics.Second,Icanaccountfortheeffectofmacroeconomicshocks
andthebusinesscyclethroughtimefixedeffects.
The following sections describe the empirical strategy in more detail and discuss the
centralquestionofhowImeasureuncertainty,andmyestimationmethodology.
4.1 EstimatingtheEffectofUncertainty:AnIVApproach
Theexistingempiricalliteratureoninvestmentunderuncertaintytakesmanydifferent
approaches to measuring uncertainty. While some studies build structural models, most
researchrelieseitheroneventsthatplausiblycreateunexpectedspikesinuncertainty16or
on time-series measures of aggregate and idiosyncratic uncertainty. The former research
design works well if uncertainty shocks are indeed unexpected, but is problematic if the
occurrenceofeventsiscorrelatedwithotherunobservedshocks.Studiesthatfallintothe
secondcategoryfrequentlyuseuncertaintymeasuresbasedonvolatilityindicessuchasthe
VIXfromtheChicagoBoardofOptionsExchangeoronfirm-levelstockreturnvolatility(e.g.,
Bloometal.,2007;LeahyandWhited,1996),whicharegenerallyrathernoisyproxiesfor
uncertainty.Changesinstockreturnsoftenreflectchangesinthefirstmomentofinvestment
opportunities rather than changes in uncertainty. In fact, a growing number of studies
documentthecounter-cyclicalnatureofreturnvolatility(Bloom,2009;Campbelletal.,2001;
Storeslettenetal.,2004).
In this paper, I take a different approach. To estimate the effect of uncertainty on firm
investment,Iconsiderasimplelinearinvestmentmodelofthefollowingform
!",$
%",$&'
= )* + ,- + ./ 01,2 + 34 ∙ 6*,- + 7*,- (1)
where )* represents firm fixed effects and ,- denotes year fixed effects. The dependent
variableiscapitalexpenditure,8*,- ,scaledbythepreviousperiod’scapitalstock,9*,-:4 .01,2 is
16 Some studies exploit uncertainty shocks caused by terrorist attacks, military action, natural
disastersorpoliticalshocks(BakerandBloom,2013;JulioandYook,2012;KimandKung,2014).
14
avectorofrelevantcontrolvariables(includingTobin’sq)and6*,- denotestheuncertainty
measure. Causal identification of the uncertainty effect requires 6*,- to be exogenously
determined.Thelackofsuchanexogenousmeasureleadsmuchoftheempiricalliterature
on investment under uncertainty to use realized stock return volatilities to proxy for
uncertainty.Duetotheendogenousnatureofthismeasure,moststudiesrelyon“internal”
instrumentssuchaslaggedlevelsanddifferencesoftheexplanatoryanddependentvariables
(Bloom et al., 2007; Bulan, 2005; Leahy and Whited, 1996). However, the validity of this
methodologyreliesonstrongassumptionsaboutthepropertiesoftheunderlyingdata,which
arenotrequiredfortheestimationinmypaper.
MyidentificationstrategybuildsonthemethodologyusedinSteinandStone(2013)and
intheliteratureoninvestmentundereconomicpolicyuncertainty(e.g.,BrogaardandDetzel,
2015).Inlinewiththeexistingempiricalliterature,Iemployfirm-levelstockreturnvolatility
as a proxy for firm-level uncertainty. However, I address concerns about the possible
endogenous nature of such a measure by constructing a novel instrument based on ENSO
uncertaintytoidentifyexogenousvariationinstockreturnvolatility.Iestimatetheeffectof
uncertaintyonfirminvestmentintwosteps.
4.1.1
Industry-LevelSensitivitiestoENSO
First,IidentifyindustriesthatareaffectedbychangesinENSOconditions.TheClimate
PredictionCenter(CPC)providesmonthlymeasurementsofENSOintensityintermsofsea
surface temperature (SST) anomalies (see Figure 1) 17. Positive deviations from long-term
regional means are usually associated with El Niño episodes, while negative deviations
indicate La Niña events. The climatology literature has established a strong association
betweenpositiveornegativeSSTanomaliesandchangesinweatherconditionsinthePacific
region (Adams et al., 1995; Ropelewski and Halpert, 1986; Wang et al., 2000)18. Thus, the
literatureontheeconomiceffectsofENSOfrequentlyusesthetimeseriesofSSTanomalies
to document the link between El Niño events, which represent the largest disruptions to
regular weather patterns, and economic activity (see discussion in Section 2). Figure 1
illustratesthemonthlytimeseriesvariationinSSTanomalies(indegreeCelsius)from1980
to 2014. The positive spikes in temperature indicate El Niño episodes. For instance, the
17TheNationalOceanicandAtmosphericAdministration(NOAA)definesanomalyasadeviationof
theseasurfacetemperatureinagivenregionfromitshistoricalaverage.Thismeasurewasinitially
developedinRasmussonandCarpenter(1982)andRopelewskiandHalpert(1987).
18SeeAppendixBformoredetailsontheglobaleffectsofENSOonweather.
15
positivespikeinthetimeseriesaround1997and1998reflectsthestrongElNiñoeventat
thetimethatinducedaprolongedperiodofdroughtinpartsoftheU.S.andledtoenormous
lossestoagriculturalindustries(WolterandTimlin,1998).
I use return data for firms in the CRSP-Compustat merged sample and monthly SST
measurements to identify industries whose operations are affected by changes in ENSO
conditions. To this end, I estimate the following regression for each year t (from 1985 to
2014)usingmonthlyreturndatafromthepreviousfiveyears(t-6tot-1)
CD?E
CD?E
;*,< = =>* + =* ∙ ;<?@A>> + =B,∙ F<
+ 7*,< (2)
where;*,< isthemonthlystockreturnforfirmi,;<?@A>> isthemonthlyreturnontheS&P500
CD?E
CD?E
index andF<
are the monthly observations of SST anomalies. The coefficient=B,in
each year t is constrained to be the same for all firms in industry k (based on 3-digit SIC
CD?E
codes19).Ineachyeart,=B,representstheaveragesensitivityofstockreturnsinindustry
k to anomalous ENSO activity over the previous five years. In other words, industry
sensitivitiesaretime-varyingandforanygivenyearttheindustry-specificsensitivitiesare
calculatedusingmonthlyreturnsandSSTdatafromthepreviousfiveyears.Giventhelong
sampleperiod(from1985to2014),thismethodologyaccountsforthefactthatindustries
andtheirexposurestoENSOmightchangeovertime.Icalculateindustry-levelinsteadoffirm
level sensitivities due to several reasons. First, the panel data set is highly unbalanced. In
addition,thismethodologymitigatesconcernsabouttheendogeneityofENSOsensitivities
withrespecttofirminvestment.Somefirmsmightchoosecertaininvestmentstoreducetheir
exposuretoENSO.However,itishighlyunlikelythatallfirmsinthesameindustryengagein
thesamediversificationeffortsthroughinvestment20.
For my subsequent analysis, I identify industries for which the estimated sensitivity
CD?E
coefficient,=B,,isstatisticallysignificantfromzero(atthe5%level).Firmsinindustries
thatarenotsignificantlyaffectedbyENSOwillbedroppedfrommysample,sinceIdonot
expectthesefirmstoreacttoENSOuncertainty21.
19Theresultspresentedthroughoutthispaperarerobusttobasingtheindustryclassificationon
2-digitSICcodes.
20InAppendixD,Ishowthatthemainresultsofthepaperarerobusttocalculatingtimeinvariant
ENSOsensitivitiesusingreturndatainyears1980to1984,whichispriortomymainestimationperiod
of1985to2014.
21Someindustrieshavelargesensitivitycoefficientswithsimilarlylargestandarderrorsrendering
thecoefficientestimatenotstatisticallysignificantfromzero.Includingsuchobservationsthroughout
16
4.1.2
InstrumentalVariablesEstimation
Tomitigateanyendogeneityconcernswithrespecttostockreturnvolatility,Iestimate
model(1)usinganinstrumentalvariablesapproach.Iconstructaninstrumentthatidentifies
exogenous variation in firm-level stock returns by combining the estimated ENSO
CD?E
sensitivities,=B,,withENSOpredictiondata.
The CPC uses various statistical models to provide forecasts of ENSO anomalies for
horizonsofuptotwelvemonths22.TheseforecastsusetrendsinmonthlySSTmeasurements
topredictfutureENSOactivitymakingtheunderlyingstochasticprocessplausiblyexogenous
to economic conditions. The prediction data obtained from the CPC includes information
about the probability distribution of these forecasts including its mean and standard
deviation. Since the forecasts are generally normally distributed, the first and second
momentsaresufficientstatisticstocharacterizetheforecastprobabilitydistributions.Figure
2 depicts the first and second moments of the probability distributions for the 12-month
forward-lookingENSOforecasts23.Asillustrated,thereissubstantialvariationinbothtime
series.TheidentificationstrategyinthispaperreliesontheargumentthatENSOuncertainty
is higher, the higher the standard deviation of the 12-month ENSO forecast. Figure 3
illustrates this idea. Using predicted rather than observed variation in ENSO activity, I
constructthefollowingforward-lookingmeasurethatcapturesindustry-leveluncertaintyfor
firms affected by changes in ENSO conditions (through uncertainty about weather and
commoditypricesorotherchannels)
CD?E
=B,∙ 6-CD?E CD?E
where=B,is the estimated ENSO sensitivity for industry k in year t and6-CD?E is the
standarddeviation(secondmoment)oftheprobabilitydistributionforthe12-monthENSO
forecast,whichwasissuedinyeart24.Asafirstmomentcontrol,Iconstruct
subsequentanalysismayleadtoanoverestimationoftheeffectofENSOonthesefirmsandthusto
misleadingresults.SeeSection5.3andAppendixDforanadditionaldiscussionofthisissue.
22 Starting in the early 1980s, the CPC started publishing monthly and weekly bulletins that
summarizeinformationoncurrentand futureexpectedENSOconditions(seeAppendixBformore
details).
23Thegraphillustratesthefirstandsecondmomentoftheforecastdistributions.Iremovealinear
trendinthetimeseriesforthesecondmomenttomitigateconcernsaboutpossibletimetrendsinthe
expectedaccuracyofforecasts.Theresultsofthepaperarerobusttoomittingthisstep.Thefirstand
secondmomentsofmonthlyforecastsareaveragedovereachyeartoarriveatannualmeasurements.
24 The construction of the uncertainty measure is in line with the following simple
intuition:6 =BCD?E ∙ H-CD?E = =BCD?E ∙ 6 H-CD?E = =BCD?E ∙ 6-CD?E .
17
CD?E
=B,∙ H-CD?E whereH-CD?E denotesthefirstmomentoftheforecastprobabilitydistribution.
In line with two-stage least squares methodology, the first stage of the instrumental
variablesapproachtoestimatemodel(1)takesthefollowingform
CD?E
CD?E
6*,- = I* + J- + K/ 01,2 + )4 ∙ =B,∙ 6-CD?E + =B,∙ H-CD?E + L*,- (3)
where I* and J- are firm and time fixed effects, and 01,2 is a vector of relevant control
variables.
Finally,substitutingthepredictedvaluesof6*,- fromregression(3)intomodel(1)yields
thefollowingspecification
!",$
%",$&'
CD?E
= )* + ,- + ./ 01,2 + 34 ∙ 6*,- + 3M ∙ =B,∙ H-CD?E + 7*,- (4)
where)* represents firm fixed effects and,- denotes year fixed effects.01,2 is a vector of
relevant control variables and6*,- denotes the predicted values for stock return volatility
CD?E
fromestimatingthefirststageregression(3).=B,∙ H-CD?E capturestheeffectthatthe12-
monthENSOlevelforecast,H-CD?E ,hasonfirminvestmentinindustrykinyeart.
8*,- inequation(4)iscapitalexpenditurebyfirmiinperiodt,whichisscaledbythecapital
stockattheendofthepreviousperiod9*,-:4 .Sincefinancialstatementsreportcapitalstock
at book rather than replacement value, 9*,- is calculated recursively using a perpetualinventorymethod.ThiscalculationisdiscussedinmoredetailinAppendixA.
Inmanytheoreticalmodels,uncertaintyaffectsfirminvestmentpartlyorentirelythrough
marginalTobin’sq25.Therefore,itisstandardpracticetoincludemeasuresofTobin’sqas
controls in investment models. Empirically, it is challenging to measure a firm’s marginal
25Mostnotably,AbelandEberly(1994)introduceirreversibilityintothemodelofAbel(1983)and
showthatinvestmentdependsonlyonthecapitalstockandmarginalq.Therefore,theyarguethat
uncertaintyimpactsinvestmentonlytotheextentthatitaffectsmarginalq.DixitandPindyck(1994)
advance a similar argument and conclude that uncertainty changes the threshold value of q above
whichfirmsarewillingtoinvest.
18
Tobin’sq,definedastheratioofthemarketvalueandthereplacementcostofanadditional
unitofcapital.Thus,IincludethefollowingmeasureofaverageTobin’sq26
N*,- =
OPQBR-SPT*-PU*VP-*WX",$ YZR[-",$ :S\QQRX-]^^R-^".$
%",$ Y!X`RX-WQa",$ Y!X-PXb*[UR^",$ Y!X`R^-<RX-^PXcPc`PXdR^",$
(5)
which represents the ratio between the market value of a firm’s capital stock and its
replacementcost.Othercontrolvariablesincludedarecashflowscaledbypreviousyear’s
capitalstock,debt-to-equityratio,totalassetsandsales.
Identificationoftheuncertaintyeffectoninvestmentinmodel(4)isachievedthroughthe
combined effect of two sources of variation: First, SST forecasts are time-varying and
generatedbyanunderlyingstochasticprocessthatisexogenoustothebusinesscycleand
economic conditions. Second, the sensitivity to changes in ENSO conditions varies across
industries. The first- and second-moment shocks are separated through the specific
constructionofthesensitivities.Specifically,whileachangeinthefirstmomentofENSOhas
opposing effects on industries with positive and negative ENSO sensitivities, an increase
(decrease) in ENSO uncertainty, the second moment, represents an increase (decrease) in
uncertainty for both types of industries. In addition, the first moment of the SST forecast
probabilitydistribution,H-CD?E ,doesnotco-varywithitssecondmoment,6-CD?E ,inthetime
series.
5. CompetitionandInvestmentunderUncertainty
5.1 IVEstimation–ENSOUncertainty
Inthissection,Iexaminetheimpactofuncertaintyonfirminvestmentandtestwhether
thiseffectdiffersdependingonthelevelofcompetitionthatfirmsfaceintheirindustries.For
thatpurpose,Iconstructthreecommonlyusedproxiesforcompetitionthatarebasedonthe
Herfindahl-Hirschmanindex(HHI)andprice-costmargins.First,IcalculatetheHHIbasedon
26 Average and marginal Tobin’s q to be equal requires, among other conditions, perfect
competitionandconstantreturnstoscale(AbelandEberly,1994;Hayashi,1982).Itisunlikelythat
theseconditionsaregenerallymetacrossindustriesandfirms.Therefore,evenundertheassumption
thatuncertaintyonlyaffectsinvestmentthroughmarginalq,itisreasonabletobelievethattheimpact
ofuncertaintyoninvestmentisnotinitsentiretycapturedbyaverageTobin’sq.
19
publicly listed firms using accounting data from Compustat (Compustat HHI)27 . Following
Campello(2006)andByounandXu(2012),IconsideranindustryascompetitiveiftheHHI
(expressed as fraction of the total market) is below 0.1. Due to concerns about the
representativenessofacompetitionmeasurebasedentirelyonpublicfirms(Alietal.,2009),
IalsoutilizetheindexconstructedbyHobergandPhillips(2010).Theirmeasure(HPHHI)
captures industry concentration by using data for both private and public firms and is
availablefrom1984to2005.IcategorizeindustriesashighlycompetitiveiftheirHPHHIfalls
belowthesample33rdpercentile.Third,Iproxyforcompetitionusingtheprice-costmargin
(PCM)orLernerindex,constructedasthefirm’soperatingprofitmargin(salesminuscosts
dividedbysales)28.FollowingPeress(2010),Isubtractthevalue-weightedaverageindustry
(basedon3-digitSICcodes)price-costmargintocontrolforsystematicdifferencesinthis
measure across industries which are unrelated to the level of competition. Higher values
indicatestrongermarketpowerandweakercompetition.Companiesareassumedtobein
competitiveindustriesiftheirPCMsfallbelowthesample33rdpercentile.
Prior to estimating the effect of uncertainty on investment using the instrumental
variables approach described in Section 4.1.2, I estimate model (1) by simple OLS. I use
annual,firm-levelreturnvolatilityasanuncertaintyproxywithoutaddressingitspossible
endogeneity.TheresultsofsuchanaïveOLSestimationarepresentedincolumn1inPanelA
ofTable2.Inlinewithpreviousstudiesusingreturnvolatilityasanuncertaintyproxy,the
coefficient suggests a significantly negative relationship between uncertainty and capital
investment.Asexpected,firmswithhigherTobin’sqratioshavehigherinvestmentspending.
Inthenextstep,Isplitthesampleoffirmsaccordingtothedegreeofcompetitionintheir
respective industries using the Compustat HHI measure. Columns 2 and 3 reveal that the
negativeeffectofuncertaintyoninvestmentisobservedforbothsubsamples.However,due
to the possible endogeneity of stock return volatility, it is difficult to infer any causal
relationshipsusingasimpleOLSestimation.
27 This measure has been widely employed in the literature and I construct it following the
methodologyusedinpreviousstudiessuchasAlietal.(2009),Baranchuketal.(2014)andHouand
Robinson (2006). The Compustat based HHI measure is calculated for each year using the market
shares of all firms in an industry and is averaged over the past three years. Industries are defined
accordingto3-digitSICcodes.
28 The price-cost margin has been extensively used as a proxy for market power in empirical
studies,particularlyintheindustrialorganizationliterature(Aghionetal.,2005;ByounandXu,2012;
GasparandMassa,2006;LindenbergandRoss,1981;Nickell,1996;Peress,2010).
20
Toaddressthisissue,IfollowtheinstrumentalvariablesapproachdescribedinSection
4.1.2.First,Icalculateindustry-levelsensitivitiestoENSOactivitybyestimatingequation(2).
IremoveanyfirmsfrommysamplethatbelongtoindustriesforwhichtheENSOsensitivity
coefficients are not statistically significant (at the 5% level). Figure 4 illustrates the
distribution of these sensitivities for the full (left) and the restricted sample (right).
IndustrieswiththehighestsensitivitiestochangesinENSOconditionsareindustriesthatare
either engaged in the production of commodities such as agricultural products and metal,
industries that are affected by abnormal changes in weather conditions (e.g., construction
andrelatedsectors)orindustriesthatrelyoncommoditypricesintermsoftheirinputsor
outputs29.TestsusingthefullsampleoffirmsarediscussedintheSection5.3andinAppendix
D.Theresultingsampleincludesmorethan6,500firmsand51,000observations.
Inordertoexaminetheeffectofuncertaintyoncapitalinvestment,Iestimatemodel(4)
byinstrumentingfirm-levelstockreturnvolatilitiesusingindustry-levelENSOsensitivities
and ENSO prediction data. All of the following specifications include firm and year fixed
effects to account for systematic differences in investment responses to heightened
uncertaintyacrossfirmsandforshockstoinvestmentthataffectallofthefirmsatthesame
time.Standarderrorsareclusteredatthefirmlevelinallspecificationsthroughoutthepaper.
Theresults,basedontwo-stageleastsquaresestimation(2SLS),arepresentedincolumn1
inPanelBofTable2.Thecoefficientfortheinstrumentedstockreturnvolatilityisnegative
but not statistically significant.While I observe no average effect of uncertainty on capital
investment,theoreticalpredictionspointtoadifferentialeffectofuncertaintyoninvestment
across firms facing different degrees of competitive pressure. In the next step, I test these
predictionsbysplittingmysamplebasedonindustryconcentrationusingthesamethreshold
levelfortheCompustatHHImeasureasbefore.
Resultsofthisanalysisarepresentedincolumns2and3inPanelBofTable2.Ifindthat
firms in highly competitive industries increase capital investment in response to higher
uncertainty. This is in line with arguments that competition can erode the option value of
deferring investment in the face of uncertainty allowing other channels through which
uncertaintycanencourageinvestmenttodominate.Incontrast,firmsinmoreconcentrated
industries tend to decrease investment in response to heightened uncertainty, which is
29Theseresultsareinlinewithconclusionsdrawninpreviousstudies.Brunner(2002)suggests
thatresidentialconstructionandrelatedindustriesarestronglyaffectedbyENSOactivity,sincetheir
operations depend heavily on weather conditions. In addition, Cashin et al. (2015) show a strong
relationshipbetweenENSOvariationandcommoditypricesincludingenergyprices.
21
consistentwiththeargumentthatforsuchfirmsthepositiveoptionvalueofwaitingleads
firmstodeferinvestmentspending.
ThesignificantdifferencebetweentheOLSandIVestimateshighlightsendogeneityissues
that bias the OLS estimates in the subsample of competitive industries more than in
concentrated industries (see discussion in Appendix C). One finding of note is that the
magnitudeofthecoefficientontheuncertaintymeasureislargerthanthemagnitudeofthe
correspondingOLSestimateinmoreconcentratedindustries.Apotentialexplanationforthis
finding is based on possible measurement errors in the uncertainty measures. Since stock
return volatility is a very noisy proxy for uncertainty, the OLS estimates are likely biased
towardzero.Suchabiasiscommonlyobservedinthecontextofmeasurementerrorsinthe
endogenous explanatory variable and is usually eliminated by the instrumental variable
approach(Almeidaetal.,2010;AngristandKrueger,2001;Card,1993;Gujarati,2003;Theil,
1971).Iinvestigatethemeasurementerrorexplanationbycomparingthevarianceofstock
returnvolatilitywiththevarianceofthevaluespredictedbythefirststageregression.The
variance of the raw stock return volatility variable is significantly larger than that of the
predictedcomponent.Thissignificantdifferenceisconsistentwiththelargermagnitudeof
thecoefficientinthe2SLSestimationandsupportstheerrors-in-variableexplanation.
In addition to being statistically significant, the effect of uncertainty on investment
illustrated in Panel B of Table 2 is also economically significant. A ten percentage point
increaseinuncertaintyasmeasuredbystockreturnvolatilityleadstoa2.4percentagepoint
decrease in the investment rate for firms in concentrated industries and a 2.8 percentage
point increase for firms in highly competitive industries. Given the average sample
investment rate of 25 percent these numbers approximately correspond to a 10 percent
decreaseandan11percentincreaseinfirminvestmentrates,respectively.
PanelCandDofTable2showresultsofsimilarestimationsusingtheHobergandPhillips
(2010) HHI variable and price-cost margins to measure the degree of product market
competition.Thesample-splitisbasedonthe33rdpercentileofeachmeasuretocapturethe
differencebetweenhighlycompetitiveandmoreconcentratedindustries.Theresultsofthese
estimationsarequalitativelysimilar.ComparedtotheestimatesinPanelB,themagnitudes
ofthecoefficientsarelargerwhenusingtheHPHHImeasureandlowerwhensplittingthe
sampleaccordingtoprice-costmargins.However,thedirectionoftheuncertaintyeffectis
the same for each of the two subsamples regardless of the competition proxy used to
categorize firms. Since the HP HHI time series ends in 2005, I will mostly focus on the
22
CompustatHHImeasureinsubsequenttests.However,themainconclusionspresentedinthe
nextsectionsholdregardlessofthecompetitionmeasureemployed.
TheaboveresultspresentedinTable2arebasedonseparateestimatesoftheimpactof
uncertaintyoninvestmentforfirmsinhigh-andlow-competitionindustries.Ifocusonthe
split-sample methodology for several reasons. First, empirical evidence shows that firms
have different investment dynamics depending on the level of competition they face
(Bustamante, 2015). Firm investment might change differently in response to changes of
relevantcontrolvariablesintheinvestmentmodel,whichwarrantsaseparateestimationof
theseparametersinthedifferentsubsamples.Second,theeffectofcompetitiononthelink
betweenuncertaintyandinvestmentislikelynotlinear.Whileanincrementalchangeinthe
degreeofcompetitionmighthavearathersmalleffectontheuncertainty-investmentlinkfor
firmsinveryconcentratedindustries,achangeincompetitionofsimilarmagnitudemight
havelargeeffectsaroundcertainthresholdlevelswhenanindustrytransitionsfrombeing
moderatelyconcentratedtobeingcompetitive30.
However, to demonstrate the robustness of the results, we can relax these two
assumptions. Assuming that the effect of competition on the uncertainty-investment
relationshipislinear,wecanestimateitbyincludingthecontinuouscompetitionmeasureas
aregressorinmodel(4)togetherwithaninteractiontermbetweenthecompetitionproxy
and return volatility. The results of estimating this specification are presented in Table 3.
Compdenotesthe competitionproxy andVolxCompisthe interactionbetweenfirm-level
return volatility and the competition measure 31 . The column headers in Table 3 indicate
which competition measure is used. Columns 1 and 2 employ the Compustat HHI and the
Hoberg and Phillips HHI measure, respectively, and illustrate that firms in concentrated
industriesdecreaseinvestmentmorerelativetofirmsincompetitiveindustriesinresponse
toheighteneduncertainty.Thecoefficientontheinteractionbetweenreturnvolatilityand
price-costmarginsincolumn3isalsonegativealbeitnotstatisticallysignificant.However,
the low F-statistic of the first stage regression for the interaction term indicates that this
specificationincolumn3maysufferfromaweakinstrumentproblem.Themagnitudeofthe
30 In their Horizontal Merger Guidelines, the U.S. Department of Justice and the Federal Trade
Commissionclassifyindustriesaseitherunconcentrated(competitive),moderatelyconcentratedor
highlyconcentrateddependingontheirHHIvalues(DoJ,2010).
31Duetotheendogenousnatureoffirmreturnvolatility,theinteractiontermisinstrumentedas
well.Theinteractiontermisconstructedbymultiplyingtheendogenousvariablereturnvolatilitywith
the competition measure. In addition to using =BCD?E ∙ 6-CD?E as an instrument for stock return
volatility,Iconstruct =BCD?E ∙ 6-CD?E ×fghiasaninstrumentfortheinteractionterm.
23
effectsincolumns1and2ofTable3areagaineconomicallysignificantandcanbeinterpreted
asfollows.Asanillustration,firmsinindustrieswithHHIvaluesequaltothe5thpercentile
plausiblyfacehighlevelsofcompetitivepressure,whilefirmsinindustrieswithHHIvalues
equaltothe95thpercentilefacelowerlevelsofcompetition.Consideragainatenpercentage
pointincreaseinuncertainty.Thecoefficientsincolumns1and2indicatethatinresponseto
such a change in uncertainty, firms in the 5th percentile increase while firms in the 95th
percentile decrease their investment rates. In column 1 the difference in the change in
investment rates is 3 percentage points and in column 2 this difference is 12 percentage
points.TheresultsinbothTable2andTable3providestrongevidencethatcompetitionisa
majordeterminantofthesignandthemagnitudeoftheuncertainty-investmentrelationship.
5.2 ConventionalUncertaintyMeasures
Oneconcernmightbethattheeffectofcompetitionontheuncertainty-investmentlink
documentedinprevioussectionsisspecifictomyuniqueuncertaintymeasureandempirical
identificationstrategy.Forthisreason,inthissection,Ishowthatthedifferentialeffectof
uncertaintyacrossindustrieswithdifferentdegreesofcompetitioncansimilarlybeobserved
whenusingmoreconventionaluncertaintymeasures.
Aggregate and idiosyncratic uncertainty measures based on return volatility are often
unsuitabletodeterminethesignandmagnitudeoftheuncertainty-investmentrelationship
due to their possibly endogenous nature. However, they can still be useful to investigate
whether firms adopt different investment strategies in response to increased uncertainty
based on certain industry characteristics. I will exploit heterogeneity in the structure of
product markets across industries to determine whether firms in competitive industries
reactdifferentlytoheightenedaggregateuncertaintycomparedtotheircounterpartsinmore
concentratedindustries.Inlinewithpreviousstudies,IemploytheS&P100VolatilityIndex
(VXO) 32 as a proxy for aggregate uncertainty. Simple correlations between firm-level
investment rates for the Compustat-CRSP merged sample and the VXO are presented in
Figure5.Thelinesrepresentlinearregressionlinesforthefullsampleoffirmsandforthe
sample of firms in competitive and concentrated firms (based on the Compustat HHI
thresholdvalueof0.1).TherightgraphinFigure5clearlydepictsthedifferentslopesforthe
threeregressionlines.Allfirms,onaverage,decreaseinvestmentinresponsetohigherVXO
32TheChicagoBoardofOptionsExchangeprovidesdatafortheS&P100volatilityindex(VXO)from
1986onwards,whilecoveragefortheVIXonlystartsin1989.UsingtheVIXindexyieldssimilarresults.
24
values.Thisrelationismorepronouncedforfirmsinconcentratedindustries,butreversed
for firms in competitive industries, which seemingly increase investment when aggregate
volatilityishigher.
I test this difference in slopes more formally by estimating a specification of model (1)
wherelaggedvaluesoftheVXOareusedastheuncertaintymeasure.Firmfixedeffectsand
controlvariablessuchasTobin’sq,totalassets,cashflowscaledbycapitalstockandthedebtto-equityratioareincluded.TheresultsarepresentedinTable4.NotethatIamunableto
include year fixed effects in this first specification, since the VXO index only varies across
time. Column 1 indicates that an increase in aggregate volatility has a negative effect on
investmentacrossallfirms.Thespecificationsincolumns2and3ofTable4includeadummy
variableCompindicatingwhetherafirmbelongstoacompetitiveindustryaccordingtothe
CompustatHHI(thresholdvalueof0.1asinprevioussections).VXOxCompisaninteraction
termbetweenCompandtheVXOindex.Theresultsincolumns2and3illustratethatwhile
anincreaseinaggregateuncertaintygenerallydecreasesinvestment,thedecreaseislessfor
competitive firms. However, the effect of competition on the uncertainty-investment
relationshipseemstobenon-linear.Column4showsthatthecoefficientontheinteraction
betweentheHHIindex,whichisacontinuousmeasurethatvariesacrosstimeandindustries,
and the VXO index is not statistically significant. Thus, a marginal increase or decrease in
competition does not seem to affect the investment-uncertainty relationship unless this
changeoccursaroundtheHHIthresholdlevelof0.1.
Collectively,thefindingsinTable4providestrongevidencethatincreasesinaggregate
uncertainty,onaverage,leadfirmstoreducecapitalinvestment.However,thiseffectdepends
on the degree of product market competition that firms face. The negative effect of
uncertaintyonfirminvestmentismuchlesspronouncedforfirmsincompetitivemarkets.
5.3 Robustness
In this section, I briefly discuss additional empirical evidence to demonstrate the
robustnessofmymainresults.PleaseseeAppendixDforamoredetaileddiscussionandthe
resultsforthefollowingaswellasadditionalrobustnesstests.
FullSampleofFirms
In Section 5.1, I estimate the effect of uncertainty on firm investment using the
instrumental variables approach outlined in Section 4.1.2. The first step consists of
identifyingindustriesthatareaffectedbychangesinENSOconditionsandIusestockreturn
25
datatoestimateindustry-levelsensitivitiestoENSO.Subsequently,Iremoveanyfirmsfrom
myanalysissampleinindustriesforwhichtheestimatedENSOsensitivityisnotstatistically
significant (at the 5 percent level). This step was taken to increase the precision of the
subsequent instrumental variables estimates. If industries are not affected by changes in
ENSOconditions,theirestimatedsensitivitiesshouldbezero.Subsequently,therewillbeno
variationintheuncertaintymeasureforthesefirmsthatcanbeusedtoidentifytheeffectof
uncertaintyonfirminvestment.However,someindustrieshavelargesensitivitycoefficients
with similarly large standard errors rendering the coefficient estimate not statistically
significantfromzero.Includingsuchobservationsthroughoutsubsequentanalysismaylead
tomeasurementerrorintheENSObaseduncertaintymeasure.Nonetheless,Icanestimate
model(4)usingthefullsampleoffirmswithoutexcludingindustrieswithENSOsensitivities
that are not statistically significant. The results of the estimation (see Appendix D.2) are
similartotheonespresentedinPanelBofTable2.However,themagnitudeoftheuncertainty
effect is smaller, suggesting that measurement error in the ENSO based uncertainty proxy
mightbiasthoseestimatestowardszero.Firms,onaverage,reduceinvestmentandthiseffect
is stronger for firms in concentrated industries. In contrast, firms facing high levels of
competitionincreaseinvestment.
CompetitionasaRiskFactorandthePossibleEndogeneityofCompetition
One concern might be that industry-level ENSO sensitivities calculated by estimating
regression(2)capturethedegreeofcompetitioninanindustry.Inthatcase,thedifferential
effect of uncertainty in competitive versus concentrated industries might be a result of
systematicdifferencesintheENSOsensitivitiesbetweenthetwosubsamples.Iaddressthis
concernbyincludingcompetitionasariskfactorintheestimationofspecification(2).Using
theENSOsensitivitiesestimatedthiswayyieldsasimilardifferentialeffectofuncertaintyon
investment as presented in Panel B of Table 2 (see Appendix D.5). Firms in competitive
industriesincreaseinvestmentandfirmsinconcentratedindustriesdecreaseinvestmentin
responsetoheighteneduncertainty.
Another concern relates to the causal links among uncertainty, investment, and
competition. One might argue that uncertainty could affect the level of competition in an
industrywhichinturnaffectsinvestmentandleadstothedifferentialeffectofuncertainty
observedinPanelBofTable2.Fromatheoryperspective,itisunclearwhethersuchacausal
linkbetweenuncertaintyandcompetitionexists,sincemodelsintheliteratureoninvestment
underuncertaintygenerallydonotmodelitexplicitly.However,itisunlikelythatacausal
26
chainamonguncertainty,competitionandinvestmentcanexplaintheresultspresentedin
this paper for several reasons. The unreported coefficients in the first stage, model (3), of
CD?E
estimatingmodel(4)usinginstrumentsarepositivefor =B,∙ 6-CD?E forallsubsamplesof
firms, irrespective of the level of competition in an industry. This indicates that ENSO
uncertainty has a positive effect on firm-level return volatility. Under the assumption that
uncertaintyaffectsproductmarketcompetition,theresultswouldsuggestthatuncertainty
increasesthelevelofcompetition,sinceithasbeendocumentedthathigherproductmarket
competitionleadstoincreasedreturnvolatility(GasparandMassa,2006;IrvineandPontiff,
2009;Peress,2010).Foranincreaseincompetitiontoexplainthedifferentialresponseof
investment,firmsinhighlycompetitiveindustrieswouldhavetoincreaseinvestment,while
firmsinconcentratedindustrieswouldhavetodecreaseinvestmentinresponsetohigher
competition. However, empirically, the opposite effect has been documented. Frésard and
Valta(2013)examinetheeffectoftariffreductionsandtheassociatedincreaseincompetition
from foreign rivals on firm-level investment. Their main result is that the effect of higher
competitiondependsstronglyontheex-antecompetitivestructureoftheproductmarket.
While an increase in competition leads to significant decreases in investment in highly
competitive industries, the same increase in competition has no significant effect on firmlevelinvestmentinconcentratedindustries.Giventheseresults,itisunlikelythattheresults
in this paper can be explained by an indirect effect of uncertainty on investment through
changesincompetition.
AdditionalTests
TheresultspresentedinPanelBofTable2arealsorobusttocalculatingENSOsensitivities
based on a four-factor model (see Appendix D.3), calculating ENSO sensitivities using an
industryclassificationbasedontwo-digitSICcodes(seeAppendixD.6),andcalculatingtime
invariantENSOsensitivitiesusingreturnsinyears1980to1984,whichispriortothemain
estimation period (see Appendix D.4). In addition, the results are robust to using the
unpredictablecomponentofreturnvolatilityastheuncertaintyproxy,6*,- ,inmodel(4)(see
AppendixD.7).
6. TheEffectofUncertainty:UnderlyingMechanisms
Theevidencepresentedintheprevioussectionsuggeststhattheeffectofuncertaintyon
firm investment depends heavily on the competitive environment of firms. Firms in less
27
competitive industries decrease investment, while firms in highly competitive industries
increase investment in response to higher uncertainty. In this section, I explore the
underlyingmechanismsthatdrivethenegativeandpositiveeffects.
6.1 NegativeEffect:IrreversibleInvestmentunderUncertainty
WhiletheresultsinPanelBofTable2illustratethatfirmsinlesscompetitiveindustries
reduce investment under uncertainty, it remains unclear whether the negative effect of
uncertainty (as observed in column 3) is in fact a result of the real option mechanism
predictedbytheirreversibleinvestmentliterature.Toanswerthisquestion,Iwillonlyfocus
onfirmsinconcentratedindustriesforthefollowingtestsinthissection.
In models such as Abel and Eberly (1996) or Dixit and Pindyck (1994), the increased
option value of waiting and deferring investment in uncertain times arises out of specific
assumptionsaboutthedegreeofinvestmentreversibility.Inparticular,iffirmsfacepartial
orfullirreversibilityofinvestment,anincreaseinuncertaintyexpandstheregionofinaction
wherefirmsprefertowaitanddeferinvestment,creatinganegativelinkbetweenuncertainty
andinvestment.Thiseffectislikelytobestrongerforfirmsfacinglessreversibleinvestment
decisionsandhighercapitaladjustmentcosts.Iexaminethesepredictionsusingtwodifferent
approachesthatidentifyheterogeneityintheabilityoffirmstoreverseinvestmentsandin
theadjustmentcoststheyface.
6.1.1
Assetredeployability
First, I test whether costly reversibility of investment leads to a negative investmentuncertainty relationship by exploiting variation in the type of capital that firms invest in.
Consideringcapitalinvestmentasanacquisitionofoneormoreassets,firmscanreversesuch
investmentdecisionsbyresellingthesecapitalgoodsonthesecondarymarketforcorporate
assets. Empirical evidence is consistent with the idea that firms heavily engage in such
activity(Gavazza,2011;Warusawitharana,2008).However,afirm’sabilitytoresellassetson
thesecondarymarketheavilydependsonthetypeofcapitalgoodstheyinvestin.Frictions
such as search costs for buyers and sellers as well as financial constraints can make such
transactions more or less costly depending on the asset type (Kim and Kung, 2014).
Irreversibilitygenerallyariseseitherduetoalackofsecondarymarketsforcertainassetsor
ifassetsareindustry-orfirm-specificandcannoteasilybeusedbyotherfirmsinthesameor
otherindustries.Assetsthatarewidelyusedwithinandacrossindustriesareplausiblyless
costlytoresellonthesecondarymarket.Consequently,investmentinsuchassetsislikelyto
28
bemorereversiblethaninvestmentinassetswithalownumberofalternativeusers.Based
onthislogic,KimandKung(2014)constructameasureofassetredeployabilityusingBureau
ofEconomicAnalysis(BEA)capitalflowdata.Theassetredeployabilityscoreforanindustry
depends on the usability of the assets employed by that industry and is higher the more
industriesusethesameassets.Therefore,theauthorsarguethatfirmsinindustrieswithlow
redeployabilityscoresfacemoreasymmetricadjustmentcoststhanfirmsinindustrieswith
higherscores,sinceitiscostlierforthemtoreversetheirinvestmentsbysellingtheirassets
on the secondary market. I split my sample of firms in concentrated industries into two
groups based on whether their respective industry has a redeployability score above or
belowthesamplemedianandestimatespecification(4)foreachsubsample.PanelAofTable
5illustratestheregressionresults.Inlinewiththepredictionsoftheliteratureonirreversible
investment, firms with less redeployable assets decrease investment significantly more in
response to higher uncertainty than their counterparts with more redeployable assets as
indicatedincolumns1and2.Theseresultsprovidestrongevidencethatcostlyreversibility
ofinvestmentisamajordriverofthenegativeuncertainty-investmentrelationshipobserved
inlesscompetitiveindustries.
6.1.2
Valuevs.GrowthFirms
Second, I use insights from Zhang (2005) to further test whether heterogeneity in the
capital adjustment costs faced by firms can explain the negative effect of uncertainty on
investment observed in more concentrated industries. Zhang (2005) argues that costly
reversibilityofcapitalandcountercyclicalpriceofriskmakeitmoredifficultforvaluefirms
to reduce capital, making them riskier than growth firms, particularly in bad times. The
authorusesthislogictoexplainthedifferenceinexpectedreturnsbetweenvalueandgrowth
firms.Theintuitionbehindtheargumentissimple.Valuefirmsderivemoreoftheirfirmvalue
fromassetsinplaceandfromtheirrelativelylargercapitalstock,whilethemaindriversof
valueforgrowthfirmsaregrowthoptions.Thus,inbadtimesvaluefirmswanttoreducetheir
capital stock more sharply than growth firms, since they are faced with relatively more
unproductive capital. In contrast, in good times growth firms increase their capital stock
more than value firms, for which most of their unproductive capital becomes productive
again.Sinceitiscostliertodecreasethanitistoincreasecapitalstock,ameanpreserving
increaseinuncertaintyaboutfuturestatesoftheworld,onaverage,leadstohigherexpected
adjustmentcostsforvaluefirms.Accordingtotherealoptionargument,valuefirmsshould
thusdecreaseinvestmentinresponsetoheighteneduncertaintymoresharplythangrowth
29
firms to avoid relatively higher costs of reversing investment in bad times. To test this
prediction empirically, I split my sample of firms into two groups based on whether their
book-to-marketratioishigherorlowerthanthesamplemedian.Estimatingmodel(4)for
eachofthesubsamplesyieldsresultspresentedinPanelAofTable5.Estimationsincolumns
3 and 4 indicate that the negative effect of uncertainty on investment in less competitive
industriesisstrongerandstatisticallysignificantonlyforvaluefirms.Thedifferencebetween
coefficientsforeachsubsamplebecomeslargerandstatisticallysignificantwhensplittingthe
sample according to the 25th percentile of book-to-market ratios. These results are in line
withpredictionsoftheliteratureonirreversibleinvestment.
6.1.3
TestingAlternativeExplanationsfortheNegativeUncertaintyEffect
Onealternativeexplanationforthenegativeeffectofuncertaintyisbasedontheargument
thatfirmsmayshiftinvestmentfromcapitalstocktowardsmoreflexibleandpotentiallyless
productiveproductionfactors(EberlyandVanMieghem,1997).Fischer(2013)examinesa
micro-level data set of firms in the Dominican Republic and finds that higher inflation
uncertaintydecreasescapitalinvestmentandincreasesinvestmentinworkingcapital,which
isoftenassumedtobeamoreflexibleandlessproductivefactorofproduction33.Itestthis
ideabyestimatingtheeffectofuncertaintyonworkingcapital(scaledbythecapitalstock).
Resultsofthisestimationarepresentedincolumn5inPanelAofTable5andillustratethat
uncertaintyaffectsworkingcapitalinthesamedirectionascapitalinvestment.Firmsinmore
concentrated industries decrease working capital in response to higher uncertainty. The
sameinferencecanbedrawnundertheassumptionthatlaborisamoreflexibleproduction
factorthancapital(Hartman,1972).Column6demonstratesthatuncertaintyalsonegatively
affects hiring in concentrated industries. These findings do not support the idea that
uncertaintydistortsinvestmentandshiftsitfromfixedassetstowardsmoreflexiblefactors
suchasworkingcapitalorlabor.
Anotherchannelthroughwhichuncertaintycanhaveadampeningeffectoninvestmentis
based on the presence of financial market frictions. Gilchrist et al. (2014) argue that
uncertainty has an indirect effect on investment. Increases in uncertainty raise default
probabilitiesandthusincreasecreditspreadstocompensatebondholdersforhigherdefault
risk. In response, firms decrease their leverage and reduce investment rates. I test this
33Forinstance,FazzariandPeterson(1993)discusstheroleofworkingcapitalasaninputfactor
andinsmoothingfixedinvestmentsunderfinancingconstraintsduetolowercostsassociatedwith
adjustingworkingcapital.
30
predictionbyexaminingtheexternalfinancingdecisionsoffirmsinconcentratedindustries.
Columns7and8inPanelAofTable5illustratetheeffectofuncertaintyonnetdebtandnet
equity issue, respectively 34 . The results contrast the predictions of the risk premium
argument.Forfirmsinconcentratedindustries,uncertaintyhasnoeffectonnetdebtissue
andanegativeimpactonequityissuewhichlikelyleadstohigherleverage.Infact,additional
results presented in Appendix D.8 illustrate that the debt-to-equity ratio of firms in
concentratedindustriesincreasesinresponsetoheighteneduncertainty.Thisfindinglends
furthersupporttotheideathattheobservednegativeeffectofuncertaintyoninvestmentin
concentrated industries is consistent with a real option as opposed to a risk premium
argument.
TheresultspresentedinPanelAofTable5indicatethatinvestmentirreversibilityand
higher adjustment costs can strengthen the negative link between uncertainty and
investmentinconcentratedindustries.Thesefindingsareconsistentwiththeargumentthat
theoptionvaluetodeferinvestmentincreaseswiththedegreeofinvestmentirreversibility
andthelevelofcapitaladjustmentcosts.Whilecompetitivepressurecandecreasethisoption
value(asshowninTable2),thedecreaseshouldbemorepronouncedforfirmsthatcanmore
easilyreverseinvestmentsandforfirmsthatfaceloweradjustmentcosts.PanelBofTable5
illustratesresultsofthetestsperformedinthissectionforthesampleofhighlycompetitive
firmsthat,onaverage,increaseinvestmentinresponsetohigheruncertainty.Columns1and
2indicatethattheaveragepositiveuncertaintyeffectobservedinTable2isonlysignificant
for firms with more redeployable assets. Similarly, only growth firms, whose expected
adjustment costs are lower in uncertain times compared to those of value firms, increase
investment in the face of heightened uncertainty. These findings suggest that even under
higherlevelsofcompetition,therealoptionmechanism,onaverage,counteractsanypositive
effects of uncertainty on investment for firms with less reversible investment and higher
averageadjustmentcosts.
6.2 PositiveEffect:ConvexitiesintheFirm’sProfitFunction
ResultsinTable2showthatfirmsinhighlycompetitiveindustries,onaverage,increase
theirinvestmentspendinginresponsetouncertainty.Thesefindingsareconsistentwiththe
idea that competition can erode the real option of waiting and deferring investment
34 Net
debt and net equity issue are calculated according to Frank and Goyal (2003) using
Compustatdata(seeAppendixA).
31
(Grenadier, 2002), allowing other channels through which uncertainty can encourage
investmenttodominate.
Theoretically,uncertaintycanhaveapositiveeffectoninvestmentifitincreasesthevalue
of the marginal unit of capital. This idea is based on a simple Jensen’s argument. If future
profits generated by the marginal unit of capital are a convex function of the underlying
stochasticvariable(e.g.,inputoroutputprices),then,byJensen’sinequality,anincreasein
uncertaintyraisesexpectedfutureprofits.Suchconvexitiesintheprofitfunctioncanarisein
avarietyofdifferentsettings.
6.2.1
Labor-CapitalRatios
InmodelssuchasOi(1961),Hartman(1972)andAbel(1983),themarginalproductof
capitalisaconvexfunctionofoutputpricesandwageratesiflaborisaflexibleproduction
factorthatcaneasilybeadjustedinresponsetopricechanges.Aspriceschange,firmsalso
adjusttheirlabor-capitalratios,causingthemarginalrevenueproductofcapitaltochangeby
morethanjustthefluctuationinprices.LeeandShin(2000)expandonthatresultandshow
thattheconvexityintheprofitfunctionishigher,thegreatertheproportionoftheflexible
production factor such as labor. I test this prediction by splitting firms in competitive
industries into two subsamples based on whether their labor-capital ratios are above or
belowthesamplemedian.Resultsofestimatingmodel(4)foreachsubsamplearepresented
in columns 1 and 2 in Panel A of Table 6. Consistent with the discussion above, the
significantlypositiveeffectofuncertaintyisonlyobservedforfirmswithhighlabor-capital
ratios. The coefficients on return volatility for the two subsamples are not statistically
significant from each other. However, splitting firms into two subsamples using the 25th
percentileofthesamplelabor-capitalratiosyieldscoefficientsthatarestatisticallydifferent
fromeachother,asshownincolumns3and4inPanelAofTable6.
6.2.2
OperationalFlexibility
Evenwithfixedproportionsoftheproductionfactors,convexitiesintheprofitfunction
canresultfromoperationalflexibilityandafirm’sabilitytovaryoutputaccordingtomarket
conditions(MarschakandNelson,1962;Oi,1961).Iffirmscaneasilyexpandtoexploitgood
outcomes and easily contract in response to bad outcomes, they might desire a meanpreservingincreaseinuncertainty.Inotherwords,iffirmsdonotneedtoutilizethemarginal
unitofcapitalinbadtimes,aunitofcapitalcanbeseenasanoptiononfutureproduction.
Thevalueofthisoptionwillincreaseinthevarianceofinputoroutputprices,leadingtoa
32
positive relationship between uncertainty and capital investment. Mills (1984) formalizes
thisintuitionandshowsthattheexpectedprofitofacompetitivefirmfacingstochasticprices
increasesinitsoperationalflexibility
j k i
=k j i
+
lm n
M
,
wherek i istheprofitgivenpricepandpricevariance6 M .Theparameter=representsthe
degree of operational flexibility a firm has and varies inversely with the cost of changing
output in response to price fluctuations. Following Grullon et el. (2012), I measure
operational flexibility using industry-level labor union membership. Intuitively, firms face
moreconstraintswhenadjustinglaborinputinresponsetochangesineconomicconditions
(particularly if it involves reductions in labor in economic downturns) if the workforce is
heavilyunionized(AbrahamandMedoff,1984;Chenetal.,2011;GrammandSchnell,2001).
Thus,Itreatunionmembershipasapercentageoftheworkforceinanindustryasaninverse
proxyforoperationalflexibility.Isplitthesampleofcompetitivefirmsintotwosubsamples
usingthesamplemedianofthismeasureandestimatemodel(4)foreachofthesesubsamples
separately.Theresultsarepresentedincolumns5and6inPanelAofTable6.Thecoefficient
oninstrumentedreturnvolatilityispositiveforfirmswithlowunionmembershipratesand
negative for firms with high union membership rates. Both estimates are statistically
significant.Thesefindingssupporttheideathatoperationalflexibilitycombinedwithhigh
levelsofcompetitioncanencourageinvestmentinresponsetoheighteneduncertainty.
PanelBofTable6presentsresultsofthetestsinthissectioninthesampleoffirmsinmore
concentratedindustries.Columns2and4illustratethatfirmswithhighlabor-capitalratios
inconcentratedindustriesdecreaseinvestmentinresponsetouncertainty.Theuncertainty
effectisnotstatisticallysignificantforfirmswithlowlabor-capitalratios.However,theFstatisticforthefirststageregressionindicatesthatthesespecificationsmightsufferfroma
weak instrument problem. Columns 5 and 6 indicate that union membership does not
significantlychangetheimpactofuncertaintyoninvestmentinconcentratedindustries.This
result suggests that convexities in the firm’s profit function associated with operational
flexibility cannot mitigate the negative effect of uncertainty arising from the real option
mechanisminconcentratedindustries.
33
7. LargeChangesinUncertaintyandtheDynamicsofInvestment
Section5illustratesthattheeffectofuncertaintyonfirminvestmentheavilydependson
the level of competition that firms face in their industries. The results show a significant
wedge in the response of investment spending to increased uncertainty between firms in
competitiveandconcentratedindustries.Idocumentthatthiswedgeispartlycausedbya
decline in investment in concentrated industries, which is consistent with real options
argumentsintheirreversibleinvestmentliterature.
Sofar,weknowlittleabouthowpersistentthiseffectofuncertaintyisacrosstime.Bloom
etal.(2014)showthatthenegativeeffectofuncertaintyoninvestmentprescribedbythereal
options literature generally results in a short and sharp drop in investment. In their
simulations,thisdropisobservedintheperiodinwhichtheuncertaintyshockoccursandin
thefollowingperiod.Onceuncertaintydecreases,investmentratesrecovertolevelsslightly
above the ones observed prior to the uncertainty shock. The authors conclude that
uncertainty shocks lead to a short and sharp drop in investment, which results ina lower
capitalstocksincecapitalcontinuestodepreciate.Thisisfollowedbyaprolongedrecovery
ofthecapitalstocktoitssteadystate.Basedontheseresults,thewedgeintheresponseof
investment to heightened uncertainty between firms in competitive and concentrated
industriesshouldbesimilarlyshort-livedifitismainlyaresultoftherealoptionsmechanism.
Inthissection,Itestthesepredictionsbyfocusingonlargechangesinuncertainty,which
I define as the five largest yearly increases and the five largest yearly decreases in my
CD?E
measureofENSOuncertainty,6B,,overtheentireperiodof1985to2015.Iinvestigatethe
dynamicsofinvestmentfollowinganuncertaintyshockbyusingadifference-in-differences
approachandbyestimatingthefollowingmodel
8*,= )* + o- + 3M f*,- + 3p q-:M f*,- + 3p q-:4 f*,- +3p q-> f*,- + 3p q-Y4 f*,- + 3p q-YM f*,- + rs*,- + 7*,- 9*,-:4
wheref*,- isadummyindicatingwhetherfirmiisinahighlycompetitiveindustry(basedon
the threshold value of 0.1 for the Compustat HHI).q-YM ,q-Y4 ,q-> ,q-:4 , andq-:M are dummy
variablesindicatingwhetheralargechangeinuncertaintytookplaceinyeart-2,t-1,t,t+1,
andt+2,respectively.s*,- denotesthesamevectorofcontrolvariablesasinsection5.Aswith
allpreviousspecifications,Iincludefirmandtimefixedeffectsandclusterstandarderrorsat
thefirmlevel.
34
First,Iexaminethefiveyearsinwhich6-CD?E increasedthemostandestimatetheabove
specification.Resultsarepresentedincolumns1and2ofTable7.Column2revealsthatthere
arenosignificantchangesininvestmentpriortoanuncertaintyshockandhighlightsthatthe
differential effect of uncertainty between competitive and concentrated industries is only
significant in the year of the uncertainty shock and in the subsequent year. In these two
periods,firmsinmorecompetitiveindustriesinvestmoreinresponse touncertaintythan
their counterparts in more concentrated industries. This wedge in investment rates is not
statisticallysignificantinyear2aftertheuncertaintyshock.Incolumns3and4ofTable7,I
investigate the response of firm investment to a decrease in uncertainty. The average
magnitudeofthefivelargestdecreasesinuncertaintyissimilartotheaveragemagnitudeof
thefivelargestincreasesinuncertaintyexaminedincolumns1and235.Column4indicates
that in response to decreases in uncertainty, firms in concentrated industries invest more
than firms in competitive industries. This significant effect is only observed in the year in
whichuncertaintydecreases.Thedifferentialeffectofadeclineinuncertaintyoninvestment
betweenfirmsincompetitiveandconcentratedindustriesissmallerinmagnitude.Following
anincreaseinuncertainty,firmsincompetitiveindustrieshave2.8and4.3percentagepoints
higherinvestmentratesthantheircounterpartsinconcentratedindustriesinthesameyear
of the uncertainty shock and in the following year, respectively. In contrast, firms in
concentrated industries have 1.9 percentage points higher investment rates than firms in
competitive industries in the year of an uncertainty decrease. This suggests that the
differential effect of uncertainty on investment rates after an uncertainty increase is not
immediatelyoffsetonceuncertaintydecreasestoasimilarextent.
ThefindingspresentedinTable7arehighlyconsistentwiththeresultsinBloometal.
(2014)andprovidefurtherevidencefortheargumentthatthedifferenceininvestmentrates
followinganuncertaintyshockbetweenfirmsincompetitiveandconcentratedindustriesis
infactaresultoftherealoptionsmechanismintheirreversibleinvestmentliterature.
35Theaverageincreasein6 CD?E duringtheyearswiththehighestspikesinENSOuncertaintyis
-
0.069andtheaveragedecreasein6-CD?E duringtheyearswiththehighestdeclinesinuncertaintyis0.057.However,thedifferencebetweentheabsolutevaluesofthesetwonumbersisnotstatistically
significant(p-valueof0.63).
35
8. UncertaintyandInvestmentinOtherFactors
Whileuncertaintyhasasignificantimpactoncapitalinvestment,itislessclearwhether
the effect is similar for other types of investment such as R&D, advertising spending or
investment in labor through hiring. I aim to address these empirical questions by first
estimating model (4) using R&D spending scaled by the knowledge stock as a dependent
variable36.Theresultsofthisanalysisarepresentedincolumns1to3ofTable8.Uncertainty
hasnosignificanteffectoninvestmentforfirmsinhighlycompetitiveindustries.Incontrast,
firms in more concentrated industries reduce R&D spending in response to heightened
uncertainty.Columns4to6showsimilarresultswhenusinganalternativeproxyforR&D
investment opportunities instead of Tobin’s Q. R&D Q is constructed using the same
nominator as in equation (5) but using the knowledge stock as the denominator. These
findingssuggestthatsimilarmechanismsareatworkforbothcapitalandR&Dinvestment
underuncertainty.However,themagnitudeoftheuncertaintyeffectonR&Dissmaller(about
halfthesizeofthecoefficientsforcapitalinvestment),whichisconsistentwiththeempirical
regularitythatR&Dspendingishighlypersistentinthetimeseries(Bloom,2007).
Table9showssimilarresultsforadvertising(scaledbyadvertisingcapitalstock37).Firms,
on average reduce their advertising spending in response to higher uncertainty. However,
thiseffectisnotstatisticallysignificantforfirmsinhighlycompetitiveindustries.
Finally,Ireportresultsforhiringasmeasuredbythenumberofemployeesscaledbytotal
assetsinTable10.Onaverage,Ifindanegativerelationshipbetweenuncertaintyandhiring
forallfirms.However,thiseffectseemstobedrivenbyfirmsinmoreconcentratedindustries
asillustratedincolumns2and3whereIsplitthesamplebasedonindustrycompetition.The
negativeeffectofuncertaintyonhiringisinlinewithpreviousstudiesthatrelateuncertainty
associatedwithrecessionstoanaggregatefallinhiring(SteinandStone,2013).
9. Conclusion
Themaincontributionofthispaperistwo-fold:First,Iexploitauniqueempiricalsetting
to construct an uncertainty measure that is exogenous to economic conditions and firm
behavior. This allows me to estimate the causal effect of uncertainty on firm investment.
Second, I examine a broad data set of more than 6,500 U.S. firms in a variety of different
36Thecalculationoftheknowledgestockfollowstheperpetual-inventorymethodusedinHalland
Mairesse(1995).SeeAppendixAformoredetails.
37SeeAppendixAfordetailsaboutthecalculationoftheadvertisingcapitalstock.
36
industries and document that the uncertainty effect differs depending on industry
characteristicssuchasproductmarketcompetition.
Theresultsinthispaperchallengethecommonviewamongacademicsandpolicymakers
that increased uncertainty leads to a decline in capital investment effectively dampening
economicgrowth.Ishowthatthecompetitiveenvironmentplaysalargeroleindetermining
the sign and magnitude of the uncertainty and investment relationship. Firms in highly
competitive industries tend to increase capital investment, while firms in less competitive
industriesreduceinvestmentinmoreuncertaintimes.
Ifindsubstantialevidencesupportingtheargumentthatthenegativeeffectofuncertainty
inmoreconcentratedindustriesisaresultoftherealoptionsmechanismintheirreversible
investment literature. Firms with less redeployable capital and firms incurring higher
expectedadjustmentcostsinthefaceofuncertaintydecreaseinvestmentinuncertaintimes.
This reduction in investment is not offset by corresponding changes in more flexible
production factors such as working capital or labor. Similarly, I find no support for the
argumentthatuncertaintyaffectsfirminvestmentthroughborrowingcosts.Incontrastto
predictionsinGilchristetal.(2014),uncertaintyhasnoeffectonnetdebtissueandanegative
effectonnetequityissueforfirmsinconcentratedindustries.
Thepositiveeffectofuncertaintyincompetitiveindustriessuggeststhatcompetitioncan
counteractthenegativeimpactofuncertaintyoninvestmentpredictedbytherealoptions
literature.Instead,uncertaintycanactivelyencourageinvestmentincompetitiveindustries
iffirmshaveoperationalflexibility.Inlinewiththisprediction,Ifindthatthepositiveeffect
is only significant for firms in competitive industries that have operational flexibility (e.g.,
firms with low union membership rates). Similarly, firms where labor, which is often
assumedtobeamoreflexibleproductionfactorthancapital,representsalargershareofthe
productiontechnologyincreaseinvestmentinresponsetohigheruncertainty.Ifurthershow
thatuncertaintyhasaneconomicallysignificanteffectonothertypesofinvestmentsuchas
R&Dspending,advertising,andhiring.
The findings in this paper have important policy implications. My results provide new
empirical evidence and corroborate existing theoretical predictions on the importance of
competitioninthelinkbetweenuncertaintyandfirminvestment.Policymakersaimingto
attenuate the negative effects of uncertainty on the real economy would need to put
substantialattentiononindustry-levelcharacteristicsinthedesignofoptimalpolicies.
37
TablesandFigures
Figure1
Seasurfacetemperatureanomaliesin“Niño3.4”region
Thisgraphdepictsthetimeseriesofseasurfacetemperature(SST)anomaliesinthecentralPacificregion
“Niño3.4”from1980to2014.TheNationalOceanicandAtmosphericAdministration(NOAA)definesanomalies
asseasurfacetemperaturedeviationsinaregionfromitshistoricalmean.MonthlySSTdatacanbedownloaded
fromthewebsiteoftheClimatePredictionCenter(CPC)attheNOAA.
4
2
1
0
-1
-2
-3
-4
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
3
SSTanomalies
Figure2
Seasurfacetemperatureforecastsin“Niño3.4”region
This graph shows the time series of the 12-month sea surface temperature (SST) predictions and their
standarddeviationsforthePacificregion“Niño3.4”.ThesepredictionsarebasedonestimatesbytheClimate
Prediction Center (CPC) at the National Oceanic and Atmospheric Administration (NOAA), which kindly
providedthedataforthisstudy.
1.1
1
0.9
0.8
0.7
0.6
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
SSTforecastdistributionmeanH-CD?E
SSTforecastdistributionSD6-CD?E
38
Figure3
Illustrationoflowandhighuncertaintystate
Thisgraphdepictsexampleprobabilitydistributionsfor12-monthENSOforecasts(bluecurve).Uncertainty
islowerinyearswhenthestandarddeviationofthedistributionissmaller(leftgraph).Incontrast,uncertainty
ishigherwhenthestandarddeviationislarger(rightgraph). tuvwx
2
yuvwx
2
Figure4
IndustrysensitivitiestoENSO
This graph illustrates the distribution of ENSO sensitivities across industries calculated using regression
model(2).Forillustrationpurposes,thesesensitivitieswereestimatedbasedontheentiresampleoffirmsforthe
periodfrom1985to2014andarenottime-varying.Intheestimationofmodel(4),time-varyingsensitivitiesare
usedbasedonthecalculationsdescribedinSection4.1.1.Theleftfigureshowssensitivitiesfortheentiresample
offirms,whiletherightfigureillustratesENSOindustrysensitivitiesthatarestatisticallysignificantata5%level.
CD?E
=zB,
CD?E
=zB,
39
Figure5
Correlationbetweenvolatilityindex(VXO)andinvestmentrates
ThisgraphillustratesthecorrelationbetweenthevolatilityindexVXOprovidedbytheChicagoBoardof
OptionsExchange andfirm-levelinvestmentrates.The VXO is basedonimpliedvolatilitiesofthe S&P 100
options.Thelinesrepresentsimplelinearregressionlinesforthefullsampleoffirmsandthesampleoffirms
incompetitiveandconcentratedindustries.
40
Table1
Summarystatistics
ThistableshowssummarystatisticsandthedistributionoffirmcharacteristicsinthefullsampleofCompustat
firmsandtheanalysissample,whichonlyincludesfirmsinindustrieswithstatisticallysignificantsensitivitiesto
changesinENSOconditions.
Compustat
Mean
Median
Std.dev.
Sales($M)
1689
82
9598
TotalAssets($M)
2197
95
Investment($M)
143
Capitalstock($M)
It/Kt-1
Analysissample
Mean
Median
Std.dev.
1574
67
9464
12883
2128
85
12321
4
922
145
4
982
1048
28
6772
1033
25
7058
24%
13%
27%
25%
14%
28%
R&D($M)
70
2
436
75
3
447
R&Dstock($M)
322
13
2185
338
16
2217
Rt/Gt-1
27%
22%
20%
27%
22%
21%
Cashholdings($M)
183
7
1298
191
7
1276
CEt/TAt-1
19%
8%
20%
9%
25%
92,341
Firms
12,017
N
24%
51,203
6,695
41
Table2
Theeffectofuncertaintyoncapitalinvestmentunderdifferinglevelsofproductmarketcompetition
This table presents results of various regression specifications to investigate the impact of uncertainty on
capitalinvestment(investmentscaledbythepreviousyear’scapitalstock).PanelAillustratesresultsofasimple
OLSestimationofmodel(1).PanelsB,CandDpresentresultsoftheinstrumentalvariableestimationdescribed
inSection4.1.2usingthreedifferentmeasuresofcompetitiontosplitthesampleintofirmsinhighlycompetitive
industries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).CompustatHHI,HPHHI
and PCM denote a Compustat based Herfindahl-Hirschman Index (HHI), the HHI measure used in Hoberg and
Philips(2010)andaprice-costmarginmeasure,respectively.Controlsincludetotalassets,debt-to-equityratio,
cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthe
firmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
PanelA:OLS-CompustatHHI
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
Tobin'sQ
-0.0760***
-0.0606***
-0.0877***
(0.01)
(0.01)
(0.01)
0.0162***
0.0123***
(0.00)
0.0176***
(0.00)
(0.00)
N
51,203
24,663
26,540
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
PanelB:IV-CompustatHHI
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0471
F-Stat.-1stStage
(0.05)
0.0172***
(0.00)
N
-0.2384***
(0.07)
0.0197***
0.2792***
(0.03)
Tobin'sQ
0.0192***
(0.00)
(0.00)
51,203
24,663
26,540
229.12
87.99
117.93
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
42
Table2continued
PanelC:IV–HPHHI
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0466
F-Stat.-1stStage
(0.08)
0.0213***
(0.00)
N
-0.3786***
(0.04)
0.0197***
0.3726***
(0.03)
Tobin'sQ
AllFirms
0.0157***
(0.00)
(0.00)
34,168
11,390
22,778
325.82
473.45
104.82
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
PanelD:IV-PCM
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0506
F-Stat.-1stStage
(0.04)
0.0177***
(0.00)
N
-0.1114**
(0.06)
0.0197***
0.1157**
(0.03)
Tobin'sQ
0.0224***
(0.00)
(0.00)
49,750
16,583
33,167
262.95
97.37
153.65
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
43
Table3
The effect of competition on the investment-uncertainty relationship using interaction terms and IV
estimates
ThistablepresentsresultsofIVestimatesformodel(4)wherethedependentvariableisinvestmentscaled
bythepreviousyear’scapitalstock.CompindicatesthecompetitionproxyandVolxCompisaninteractionterm
betweenthecompetitionmeasureandreturnvolatility.Thecolumnheadersindicatewhichcompetitionmeasure
isused.CompustatHHI,HPHHIandPCMdenoteaCompustatbasedHerfindahl-HirschmanIndex(HHI),theHHI
measureusedinHobergandPhilips(2010)andaprice-costmarginmeasure,respectively.Controlsincludetotal
assets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standard
errorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
Returnvolatility
CompustatHHI
HPHHI
PCM
(1)
(2)
(3)
0.3071***
0.0003
(1.75)
-0.9048***
(0.00)
12.9676***
(0.17)
VolxComp
0.0213***
(0.00)
0.6929***
(0.04)
0.0216***
(0.00)
Comp
0.1627***
(0.15)
0.0206***
1.2789***
(0.06)
Tobin'sQ
(0.53)
-18.6904***
(0.32)
-0.0006
(2.73)
(0.00)
N
51,203
34,168
49,750
F(Vol)
121.25
179.8
114.8
43.98
170.21
0.48
F(VolxComp)
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
44
Table4
Theeffectofaggregateuncertaintyonfirm-levelinvestmentincompetitiveandconcentratedindustries
This table presents results of estimating model (1) using OLS where the dependent variable isinvestment
scaled by the previous year’s capital stock. Comp is a dummy variable indicating whether a firm is in a highly
competitiveindustry(basedonCompustatHHIthresholdvalueof0.1).VolxCompisaninteractiontermbetween
Comp and the volatility index VXO. HHI is the Compustat HHI measure and VXO x HHI is an interaction term
between HHI and the volatility index. Controls include total assets, debt-to-equity ratio, cash flows scaled by
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and
*indicate1%,5%,and10%significance,respectively.
(1)
VXO
Tobin'sQ
(2)
-0.0024***
(0.00)
(0.00)
0.0235***
(0.00)
Comp.
-0.0021***
0.0235***
(3)
VXOxComp.
0.0213***
(0.00)
(0.00)
-0.0377***
-0.0188***
(0.01)
(0.01)
(0.00)
0.0006***
(0.00)
HHI
0.0213***
0.0009***
(4)
(0.00)
0.0790***
(0.02)
VXOxHHI
0.0004
(0.00)
N
119,989
119,989
119,989
119,989
Controls
Yes
Yes
Yes
Yes
YearFE
No
No
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
45
Table5
Testingtherealoptionmechanismintheirreversibleinvestmentliteratureasthedriverofthenegativeeffectofuncertaintyoninvestmentinconcentrated
industries
Thistablepresentsresultsofestimatingmodel(4)usinginstrumentalvariables.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)isusedto
split the sample into firms in less competitive industries (Panel A) and firms in highly competitive industries (Panel B). The dependent variable in columns 1 to 4 is
investment scaled by the previous year’s capital stock. In columns 1 and 2, the sample is split into firms with more or less redeployable assets based on the asset
redeployabilityindex(RI)constructedbyKimandKung(2014).Incolumns3and4,thesampleissplitintogrowthandvaluefirmsbasedonwhetherthebook-to-market
valueexceedsorisbelowthesamplemedian.Thedependentvariableforcolumns5,6,7and8isworkingcapital,employeesscaledbytotalassets,netdebtissue,andnet
equityissue,respectively.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrors
areclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
PanelA:LessCompetitive(MoreConcentrated)Industries
Redeployability
Valuevs.Growth
FlexibleFactors
ExternalFinancing
LowRI
HighRI
(1)
(2)
Returnvol.
-0.8117***
-0.1805**
(0.18)
(0.08)
Tobin'sQ
0.0117***
(3)
(4)
-0.1634
-0.2934***
(0.13)
(0.08)
0.0172***
(0.00)
Value
0.0208***
(0.00)
Growth
(0.01)
(5)
(6)
NDI
NEI
(7)
(8)
-0.1962**
-0.0247*
(0.09)
(0.01)
(0.03)
-0.0001
(0.00)
0.0296***
Labor
0.0360***
(0.00)
WC
0.0009*
(0.00)
(0.00)
0.0042
-0.1298***
(0.05)
0.0149***
(0.00)
N
F-Stat.-1st
Stage
9,517
9,397
10,802
14,192
15,105
25,587
24,739
24,307
44.13
76.35
56.66
76.22
60.25
143.84
139.43
126.46
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
46
Table5continued
PanelB:HighlyCompetitiveIndustries
Redeployability
Valuevs.Growth
FlexibleFactors
ExternalFinancing
LowRI
HighRI
(1)
(2)
Returnvol.
0.0798
(0.16)
0.0139***
0.0230*** (0.00)
(0.00)
Growth
Value
(3)
(4)
0.3720**
(0.11)
Tobin'sQ
0.4990***
(0.13)
0.0164***
(0.00)
WC
Labor
(5)
(6)
-0.0096
(0.15)
0.0342*** (0.00)
0.2773**
(0.00)
0.0288***
0.0000
(0.00)
NDI
NEI
(7)
(8)
0.0034
(0.13)
(0.00)
-0.0120
(0.05)
0.3337***
(0.10)
0.0003
0.0209***
(0.00)
(0.00)
N
F-Stat.-1st
Stage
9,766
9,864
12,649
9,582
12,660
22,644
22,351
20,532
84.50
20.50
90.66
17.37
122.33
82.28
79.63
51.39
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
47
Table6
Driversofthepositiveeffectofuncertaintyoninvestmentinhighlycompetitiveindustries
Thistablepresentsresultsofestimatingmodel(4)usinginstrumentalvariables.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)isusedto
splitthesampleintofirmsinhighlycompetitiveindustries(PanelA)andfirmsinlesscompetitiveindustries(PanelB).Thedependentvariableinallcolumnsisinvestment
scaledbythepreviousyear’scapitalstock.Incolumns1to4,firmsarecategorizedaccordingtotheirlabor-capitalratios.Incolumns5and6,thesampleissplitintofirms
inindustrieswithlowandhighlevelsofunionmembershipratesbasedonthesamplemedian.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledby
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
PanelA:HighlyCompetitiveIndustries
Labor-CapitalRatio
LaborUnionMembership
LowL/K(<median)
HighL/K(>median)
LowL/K(<25thpctile)
(1)
(2)
(3)
Returnvol.
0.1287
0.1535**
(0.12)
Tobin'sQ
0.2969***
(0.17)
0.0143***
(4)
-0.1242
(0.07)
0.0127***
HighL/K(>25thpctile)
(0.07)
0.0166***
0.0152***
(0.00)
(0.00)
LowLUM
HighLUM
(5)
(6)
0.5730*
-0.3144**
(0.30)
(0.13)
0.0166***
0.0136***
(0.00)
(0.00)
(0.00)
(0.00)
10,661
11,235
4,456
17,734
8,691
7,191
44.91
137.37
22.78
157.84
20.71
51.99
N
F-Stat.-1stStage
Controls
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
Yes
48
Table6continued
PanelB:LessCompetitive(MoreConcentrated)Industries
Labor-CapitalRatio
LaborUnionMembership
LowL/K(<median)
HighL/K(>median)
LowL/K(<25thpctile)
(1)
(2)
(3)
Returnvol.
0.0960
(0.11)
Tobin'sQ
0.0219***
(4)
-0.3494***
-0.8705
-0.2709***
(0.05)
(0.92)
(0.04)
0.0151***
HighL/K(>25thpctile)
0.0000
HighLUM
(5)
(6)
0.0190***
(0.02)
LowLUM
-0.3945***
-0.2305***
(0.09)
(0.06)
0.0172***
(0.00)
0.0179***
(0.00)
(0.00)
(0.00)
(0.00)
10,025
14,818
4,314
20,828
8,493
13,701
12.31
235.87
1.19
148.95
62.66
106.73
N
F-Stat.-1stStage
Controls
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
Yes
49
Table7
Largechangesinuncertaintyandthedynamicsofinvestment
Thistablepresentsresultsofestimatingthepersistenceofthedifferentialeffectofuncertaintyoninvestment
between competitive and concentrated industries using the difference-in-differences approach presented in
Section7.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%
significance,respectively.
LargeIncreaseinUncertainty
LargeDecreaseinUncertainty
(1)
Tobin'sQ
C
!×#
$%
(2)
0.0149***
!×#
(0.00)
-0.0152***
-0.0237***
-0.0107**
-0.0103*
(0.00)
(0.01)
(0.00)
(0.01)
-0.0078
(0.01)
(0.01)
-0.0080
(0.01)
0.0034
0.0152***
0.0284***
(0.01)
!×#
(&
(0.01)
0.0433***
(0.01)
!×#
(%
0.0149***
(0.00)
(0.01)
!×# 0.0149***
'
(0.00)
(4)
(0.00)
$&
(3)
0.0149***
0.0030
0.0074
-0.0181***
-0.0185***
(0.00)
(0.01)
-0.0049
(0.01)
(0.00)
0.0041
(0.01)
N
51,203
51,203
51,203
51,203
Controls
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
50
Table8
TheeffectofuncertaintyonR&Dinvestmentundervaryinglevelsofcompetition
This table presents results of estimating model (4) using an instrumental variables approach where the
dependent variable is R&D spending scaled by the previous year’s knowledge stock. The Compustat based
measure of the Herfindahl-Hirschman Index (HHI) is used to split the sample into firms in highly competitive
industries(HighComp.)andfirmsinlesscompetitiveindustries(LowComp.).R&DQisanalternativeproxyfor
R&Drelatedinvestmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusing
theknowledgestockasthedenominator.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledby
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and
*indicate1%,5%,and10%significance,respectively.
AllFirms
HighComp.
LowComp.
AllFirms
HighComp.
LowComp.
(1)
(2)
(3)
(4)
(5)
(6)
Returnvol.
-0.0500
(0.03)
Tobin'sQ
0.0075***
0.0945
(0.06)
0.0062***
(0.00)
(0.00)
-0.0941**
-0.0209
0.1823***
-0.1277***
(0.04)
(0.03)
(0.05)
(0.04)
0.0081***
(0.00)
R&DQ
0.0001*
(0.00)
0.0001
0.0000
(0.00)
(0.00)
N
24,834
14,110
10,724
31,588
17,391
14,197
F-Stat.-1stStage
181.45
103.63
65.90
262.56
176.58
100.70
Controls
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
Yes
51
Table9
Theeffectofuncertaintyonadvertisingspendingundervaryinglevelsofcompetition
This table presents results of estimating model (4) using an instrumental variables approach where the
dependent variable is advertising spending scaled by total assets. The Compustat based measure of the
Herfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(High
Comp.)andfirmsinlesscompetitiveindustries(LowComp.).AdvertisingQisanalternativeproxyforadvertising
relatedinvestmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusingthe
advertising stock as the denominator. Controls include total assets, debt-to-equity ratio, cash flows scaled by
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and
*indicate1%,5%,and10%significance,respectively.
AllFirms
HighComp.
LowComp.
AllFirms
HighComp.
LowComp.
(1)
(2)
(3)
(4)
(5)
(6)
Returnvol.
-0.3603***
-0.0836
-0.5022*** -0.3454***
-0.0596
-0.4779***
(0.05)
(0.09)
(0.08)
(0.07)
(0.06)
Tobin'sQ
0.0118***
0.0119***
(0.00)
AdvertisingQ
(0.04)
0.0048**
(0.00)
0.0000
(0.00)
0.0000**
(0.00)
-0.0000
(0.00)
(0.00)
N
F-Stat.-1st
Stage
13,400
6,096
7,304
17,351
7,654
9,697
153.31
52.94
80.14
260.14
93.79
158.40
Controls
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
Yes
52
Table10
Theeffectofuncertaintyonhiringundervaryinglevelsofcompetition
This table presents results of estimating model (4) using an instrumental variables approach where the
dependent variable is the number of employees scaled by total assets. The Compustat based measure of the
Herfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(High
Comp.) and firms in less competitive industries (Low Comp.). Labor Q is an alternative proxy for labor related
investmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusingthenumberof
employeesasthedenominator.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapital
stock, sales and the 12-month SST level forecast. Standard errors are clustered at the firm level. ***, ** and *
indicate1%,5%,and10%significance,respectively.
AllFirms
(1)
(2)
(3)
Returnvol.
-0.0164*
(0.01)
0.0034
0.0003*
AllFirms
Low
Comp.
(4)
(5)
(6)
-0.0116**
0.0032*
(0.01)
0.0009*
(0.00)
High
Comp.
(0.01)
0.0000
-0.0247*
(0.00)
(0.00)
LaborQ
Low
Comp.
Tobin'sQ
High
Comp.
-0.0213**
(0.00)
(0.01)
-0.0000
-0.0000
-0.0000
(0.00)
(0.00)
(0.00)
(0.00)
N
48,231
22,644
25,587
60,935
28,094
32,841
F-Stat.-1stStage
264.42
82.28
143.84
325.65
249.05
159.62
Controls
Yes
Yes
Yes
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
Yes
53
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Appendix
A DataandVariableConstruction
Thissectiondescribesthedatathatisusedforthemainanalysisandtheconstructionof
relevantvariablesinmoredetail.
Iusefirm-levelfinancialdataobtainedfromtheCompustatdatabase,whichIsupplement
withreturndatafromCRSP.Thisdatasetismergedwithseasurfacetemperature(SST)and
SST prediction data from the Climate Prediction Center (CPC) at the National Oceanic and
Atmospheric Administration. This results in a data sample of more than 12,000 firms and
90,000firm-yearobservation.Thissampleisfurtherrestrictedbyexcludingindustriesfor
-./0
whichtheindustry-levelsensitivitytoENSO,)*,,
,estimatedinmodel(2)isnotstatistically
differentfromzero(atthefivepercentlevel).Thefinalanalysissamplecomprises6,695firms
and51,203firm-yearobservations.
The construction of variables included in the estimation of model (4) follows standard
approaches in the existing literature.12,, in equation (4) is capital expenditure by firm i in
periodt,whichisscaledbythecapitalstockattheendofthepreviousperiod32,,$& .Since
financial statements report capital stock at book rather than replacement value, 32,, is
calculatedrecursivelyusingaperpetual-inventorymethod.Ibroadlyfollowthemethodology
utilized in Salinger and Summers (1983) and Stein and Stone (2013) and set the first
observation for each firm spell equal to the book value of property, plant and equipment
(PPE):
32,' = 5562,' Inthenextstep,Irecursivelycalculatethereplacementvalueofcapitalstockforeachfirmin
eachperiodtasfollows
32,, =
551,7
1 − : 7 32,,$& + 12,, 7
551,$&
62
where551,7 is the Producer Price Index for Finished Capital Equipment Goods,12,, is the
capitalexpenditureforfirmiinperiodtand: 7 isthedepreciationrate(assumedtobe10%
asinSteinandStone(2013)38).
I follow the same methodology to calculate the “stock” variable used to scale R&D
spending.Duetotheunavailabilityofabookvalueforthisvariable(equivalenttoproperty,
plantandequipmentforcapitalinvestment),foreachfirmspellthevariableisinitializedas
<=>2,' =
<=2,'
+ A?@/
: ?@/
where<=2,' isR&Dspendinginthefirstyearoffirm’sispellofdata.AsinHallandMairesse
(1995),IuseadepreciationrateforR&Dstock,: ?@/ ,of15%andagrowthrate,A?@/ ,of5%
(netofdepreciation).TheR&Dstockissubsequentlydeflatedusingtheaggregatepriceindex
forprivatebusinessR&Dspending,whichisobtainedfromtheBEA.Thestockvariablefor
advertisingspendingissimilarlycalculatedusingagrowthrateof5%andadepreciationrate
of50%,sinceestimateddepreciationratesforadvertisingrangefrom15%to80%(Bagwell,
2007).Investmentinlaborisconstructedasthenumberofemployeesscaledbythefirm’s
totalassets.
Finally,thecapitalinvestmentrate,theR&Dspendingrate,andtheadvertisinginvestment
ratearewinsorizedtoliebetween-0.1and1,0and1,and0and1.5,respectively.
ItisstandardpracticetoincludemeasuresofTobin’sqascontrolsininvestmentmodels.
Empirically,itischallengingtomeasureafirm’smarginalTobin’sq,definedastheratioof
themarketvalueandthereplacementcostofanadditionalunitofcapital.Thus,Iincludethe
followingmeasureofaverageTobin’sq:
B2,, =
CDEFGH!DJKHDLKMDHKNO2,, + =GPH2,, − !QEEGOHRSSGHS2.,
32,, + 1OUGOHNEV2,, + 1OHDOAKPLGS2,, + 1OUGSHWGOHSDOXDXUDOYGS2,,
which represents the ratio between the market value of a firm’s capital stock and its
replacementcost.Othercontrolvariablesincludedintheestimationsthroughoutthepaper
38Theresultspresentedinthepaperarerobusttoassumingadepreciationrateof8%asinBloom
etal.(2007).
63
are cash flow scaled by previous year’s capital stock, debt-to-equity ratio, total assets and
sales.
Changeinnetworkingcapital,netdebtandnetequityissuearecalculatedaccordingto
FrankandGoyal(2003).Intheconstructionofworkingcapital,Idistinguishbetweenfirms
withreportingformatcode1,2,3and7.Itemnumbersdenotethenumbersassociatedwith
eachCompustatvariable.
Reportingformatcode1:ZNetWorkingcapital=Item236+Item274+Item301
Reportingformatcode2,3:ZNetWorkingcapital=-Item236+Item274-Item301
Reportingformatcode7:ZNetWorkingcapital=-Item302-Item303-Item304-Item305
-Item307+Item274-Item312-Item301
NetdebtandnetequityissuesarecalculatedusingthefollowingCompustatdataitems.Net
debtissuesequalsItem111minusItem114andnetequityissuesequalsItem108minus
Item115.
64
B ElNiñoSouthernOscillation(ENSO)–AdditionalDetails
ChangesinENSOconditionscanhavealarge-scaleimpactonweatheraroundtheworld.
FigureB.1illustratestheclimatologicaleffectsofwarm(ElNiño)andcoldepisodes(LaNiña).
Thesechangesinweatherpatternshavepronouncedeffectsoncommodity-producingand
constructionindustriesaswellasglobalandnationalcommodityprices(Cashinetal.,2015).
Asanillustration,FigureB.1showsthatwarmepisodescanresultinunusuallywarmwinters
in large parts of the U.S., while cold episodes have the opposite effect. These changes can
significantly impact energy demand in the U.S. and thus the price of commodities such as
naturalgasandheatingoil.Amoredetaileddiscussionoftheimpactoftheseverestandmost
recentElNiñoeventondifferentindustriesintheU.S.andtheoverallU.S.economycanbe
foundinChangnon(1999).
TheintensityofENSOeventscanbemeasuredusingseasurfacetemperature(SST)and
sea-level air pressure in the Pacific Ocean. The most commonly used measure for ENSO
activity in the academic literature is based on sea surface temperature measurements for
Pacificregion“Niño3.4”(see,e.g.,Brunner,2002)andcanbedownloadedfromthewebsite
oftheClimatePredictionCenter,whichispartoftheU.S.NationalOceanicandAtmospheric
Administration (CPC, 2015a). This data is also used by the CPC to predict future ENSO
conditions.
B.1 CPCWeeklyandMonthlyBulletins
Starting in the early 1980s, the Climate Prediction Center (CPC) has been summarizing
current and future expected ENSO activity in weekly and monthly bulletins, which are
availableontheirwebsite.Theyprovideaplethoraofinformationaboutcurrentconditions
suchashistoricaldeviationsofseasurfacetemperaturefromhistorical,regionalmeans.As
anexample,FigureB.2,whichwaspartoftheweeklyENSOupdatepublishedonNovember
9,2015,illustratesSSTdeviationsintheTropicalPacificintheperiodfromOctober11,2015
toNovember7,2015.Thepositivedeviationsillustratedinthatfigureareconsistentwiththe
strongElNiñoeventexperiencedintheyear2015.
Along with data about current ENSO conditions, the weekly and monthly bulletins also
contain extensive information about expected future ENSO activity in the form of
probabilistic forecasts of ENSO events (see Figure B.3) and forecasts of future sea surface
temperature (see Figure B.4). Figure B.4 illustrates consolidation forecasts of sea surface
65
temperature that are based on several statistical models. The graph also indicates the
standarddeviationsfortheseforecasts.
The graphs illustrating ENSO forecasts are also accompanied by comprehensive
discussions of current and future expected ENSO conditions. In particular, the degree of
uncertaintyaboutfutureENSOconditionsisusuallyclearlystated.Thisisillustratedinrecent
reports issued by the Climate Prediction Center. As an example, consider the “El
Niño/Southern Oscillation (ENSO) Diagnostic Discussion” published by the Climate
PredictionCenter,theNationalCentersforEnvironmentalPrediction(NCEP),theNational
WeatherService(NWS),andtheInternationalResearchInstituteforClimateandSocietyat
Columbia University on June 5, 2014. The discussion states that over “the last month, the
chance of El Niño and its ultimate strength weakened slightly in the models (Fig. 6).
Regardless,theforecastersremainjustasconfidentthatElNiñoislikelytoemerge.IfElNiño
forms, the forecasters and most dynamical models, such as NCEP CFSv2, slightly favor a
moderate-strengtheventduringtheNorthernHemispherefallorwinter(3-monthvaluesof
theNiñ o-3.4indexbetween1.0°Cand1.4°C).However,significantuncertaintyaccompanies
thisprediction,whichremainsinclusiveofaweakerorstrongereventduetothespreadofthe
models and their skill at these lead times.” (CPC, 2014). In particular, the last sentence
indicatesthatthepredictionwasaccompaniedbysubstantialuncertaintyaboutthestrength
ofthepredictedElNiñoevent.Incontrast,amorerecentreportissuedonOctober8,2015
shows that the ENSO forecast at that time was considered to be associated with less
uncertainty:“AllmodelssurveyedpredictElNiñotocontinueintotheNorthernHemisphere
spring2016,andallmulti-modelaveragespredictapeakinlatefall/earlywinter(Fig.6).The
forecaster consensus unanimously favors a strong El Niño, with peak 3-month SST
departuresintheNiño3.4regionnearorexceeding+2.0°C.Overall,thereisanapproximately
95% chance that El Niño will continue through Northern Hemisphere winter 2015-16,
graduallyweakeningthroughspring2016(clickCPC/IRIconsensusforecastforthechanceof
eachoutcomeforeach3-monthperiod).”(CPC,2015b).
These examples illustrate that there is significant variation in the uncertainty that
accompanies the predictions of ENSO conditions. And this uncertainty is clearly
communicated in the weekly and monthly bulletins published by the CPC in the form of
quantitativegraphsandcomprehensivediscussions.
66
FigureB.1
GlobaleffectsofENSO
ThisfigureillustratestheglobalimpactoffluctuationsinElNiñoSouthernOscillation(ENSO)activityon
theclimateandweatherconditionsaroundtheworld.Source:ClimatePredictionCenter,NationalOceanicand
AtmosphericAdministration(NOAA).
FigureB.2
SeasurfacetemperaturedeviationsintheTropicalPacific
This figure illustrates the monthly departures of sea surface temperatures from historical averages
throughout the Tropical Pacific for the period between October 11, 2015 and November 7, 2015. This
informationisprovidedonaweeklybasisbytheClimatePredictionCenter.Source:ClimatePredictionCenter,
NationalOceanicandAtmosphericAdministration(NOAA).
67
FigureB.3
ProbabilisticforecastofENSOevents
ThisfigureillustratestheprobabilisticforecastsofENSOeventsfortheperiodbetweenSeptember,2015
and July, 2016. This information is provided on a weekly basis by the Climate Prediction Center. Source:
ClimatePredictionCenter,NationalOceanicandAtmosphericAdministration(NOAA).
FigureB.4
ProbabilisticforecastofseasurfacetemperatureanomaliesinthePacific
ThisfigureillustratesconsolidationforecastsofseasurfacetemperatureinthePacificOceanfortheperiod
from November, 2015 to January, 2017. This information is provided on a monthly basis by the Climate
PredictionCenter.Source:NationalOceanicandAtmosphericAdministration(NOAA).
68
C
Return-basedUncertaintyMeasuresandPotentialBiases
Theexistingempiricalliteratureoninvestmentunderuncertaintytakesmanydifferent
approachestomeasuringuncertainty.Themostcommonlyuseduncertaintymeasuresare
basedonvolatilityindicessuchastheVIXfromtheChicagoBoardofOptionsExchangeoron
firm-levelstockreturnvolatility(see,e.g.,Bloometal.,2007;KimandKung,2014;Leahyand
Whited,1996).Thesearegenerallyrathernoisyproxiesforuncertaintyandoftencapture
changesinthefirstmomentofinvestmentopportunitiesratherthanchangesinuncertainty.
Without using an exogenous measure of uncertainty or appropriate instruments for
returnvolatility,itisdifficulttocontrolforfactorsthatarecorrelatedwithreturnvolatility
andatthesametimeaffectfirminvestment39.Therearemanyexamplesthatcanbeusedto
emphasize the potential endogeneity of return volatility as an uncertainty proxy in an
investment regression. However, to illustrate this point, I will specifically focus on two
possible confounding factors that will lead to biased OLS estimates when using return
volatilityasanuncertaintyproxy.Inparticular,thesebiaseswillbedifferentdependingon
thelevelofproductcompetitionthatafirmfaces.
A growing body of empirical research has linked product market competition to stock
returnbehaviorandvolatility.Variousempiricalstudiesdocumentthatidiosyncraticreturn
volatilityusuallyincreasesinresponsetohigherproductmarketcompetition (Gasparand
Massa,2006;IrvineandPontiff,2009;Peress,2010).Inaddition,FrésardandValta(2013)
illustrate the effect of tariff reductions and the associated increase in competition from
foreign rivals on firm-level investment and financing choices. They find that higher
competition is associated with substantial decreases in capital expenditure and R&D. This
effect is economically large and heterogeneous depending on the ex-ante competitive
structure of the product market. While more competition decreases investment in
competitive industries, similar changes in more concentrated industries have virtually no
effect on investment. Combined, these results present several empirical challenges to
identifyingacausallinkbetweenuncertaintyandinvestmentwhenusingidiosyncraticstock
returnvolatilityasanuncertaintyproxy.Ifreturnvolatilityispositivelycorrelatedwiththe
degreeofproductmarketcompetitionandproductmarketcompetitionleadstodecreasesin
39Itcaneasilybeshownthatgivenatruemodel,V
= [& \&2, + [% \%2, + Q2, ,omittingvariable\%2, willleadtoabiasedOLSestimate,J lim [& = [& + J lim : [% ,if[% ≠ 0and!NU(\& , \% ) ≠ 0.
2,
.→a
.→a
69
investment for firms in highly competitive industries, OLS estimates of the coefficient on
returnvolatilitywillhaveastrongdownwardbiasincompetitiveindustries.UsingOLSin
such a setting may lead to the inference that firms in competitive industries reduce
investmentmoresharplyinresponsetoanincreaseinuncertaintythanisactuallythecase.
Moreover,HobergandPhilips(2010)relatestockreturnsandoperatingperformancein
industryboomsandbuststoindustry-levelcompetition.Thebasicintuitionbehindtheiridea
relates to information gathering costs. It is costlier to collect information about a large
number of competitors compared to few rivals and therefore firms in highly competitive
industries rely more on signals conveyed by industry-level stock returns when taking
investmentdecisions.Inconcentratedindustries,monopolypowerdecreasesthedispersion
ofearningsforecasts(GasparandMassa,2006)andallowsforfasterincorporationofprivate
informationintostockprices(Peress,2010).Thesefirmswillthusrelymoreonfirm-level
information.Theauthorsfindthatfirmsincompetitiveindustriesoverinvestaboveoptimal
levels in response to positive industry-level shocks and experience sharp decreases in
operating cash flow and stock returns in the following periods. This relationship is much
weakerandmostlyinsignificantformoreconcentratedindustries.Inasimilarargumentto
above,ifreturnvolatilitycapturesthecyclicalnatureofindustryboomsandbustsandiffirms
incompetitiveindustriesfacelargerdecreasesinoperatingperformanceinbustperiodsthan
theircounterpartsinmoreconcentratedindustries,theOLSestimateoftheuncertaintyeffect
willagainbedownwardbiasedforcompetitivefirms.
Itisthereforeessentialtoidentifyexogenousvariationinuncertaintytodrawanycausal
inferencesabouttheinvestment-uncertaintyrelationshipanditsinteractionwithindustry
competition. In this paper, I exploit the unique empirical setting of the El Niño Southern
Oscillation(ENSO)cycletoidentifyexogenouschangesinuncertaintythatarerelevantfora
wide array of firms in different industries. See Section 4 for a detailed description of the
specificidentificationstrategyfollowedinthisstudy.
70
D AdditionalResultsandRobustnessTests
D.1 AdditionalSpecificationsofModel(4)
Oneofthekeyresultsofthispaperisthatuncertainty,onaverage,hasapositiveeffecton
investmentforfirmsincompetitiveindustriesandanegativeimpactoninvestmentforfirms
inconcentratedindustries.TheseresultsarediscussedinSection5.1andpresentedinPanel
BofTable2.Theestimationsarebasedonspecificationsofmodel(4)thatincludeanumber
ofdifferentcontrolvariables,andfirmandyearfixedeffects.
In order to confirm the robustness of these findings, Table D.1 presents results for
estimationsofsimplerspecificationswithoutcontrolvariables.Columns1to3,columns4to
6,andcolumns7to9showestimatesforallfirmsintheanalysissample,forthesubsample
offirmsincompetitiveindustries,andforthesubsampleoffirmsinconcentratedindustries,
respectively.Thecoefficientsincolumns3,6and9representthesameestimatesillustrated
in Panel B of Table 2. The findings in Table D.1 confirm that excluding control variables
(columns1,4and7)oraverageTobin’sq(columns2,5and8)hasnosignificantimpacton
thecoefficientestimatesfortheuncertaintymeasure.
D.2 FullSampleofCompustatFirms
In Section 5, I document the effect of uncertainty on investment using a multi-step
procedure. Before estimating the instrumental variables specification, I identify firms that
-./0
are sensitive to ENSO by estimating the industry specific coefficient)*,,
in model (2).
Subsequently, I drop all industries from my sample for which this coefficient is not
statistically significant (at the 5 percent level). This step is meant to reduce possible
measurementerrorintheinstrument.IndustriesnotsensitivetoENSOfluctuationsshould
theoretically have a sensitivity of zero, which effectively eliminates all the variation in
uncertaintyforthesefirms.Therefore,identificationoftheuncertaintycoefficientintheIV
estimation will only come from firms with non-constant uncertainty values. However, in
practice,smallsamplesizesincertainindustriesthatarenotsensitivetoENSOmayleadto
animpreciseestimationofthesensitivitycoefficientsinmodel(2).Whilethecoefficientfor
such an industry is likely not statistically significant, the point estimate might still be
substantially positive or negative if standard errors are large. This would introduce
measurement error in the instrument and would lead to biased IV estimates of the
71
uncertaintycoefficient.Nevertheless,forrobustnesspurposesTableD.2presentstheresults
oftheIVestimatesforcapitalinvestmentusingthefullsampleoffirmswithoutdiscarding
industries that are not sensitive to changes in ENSO conditions. Firms on average reduce
investmentandthiseffectisstrongerforfirmsinconcentratedindustries.Incontrast,firms
facing substantial product market competition increase investment. The findings are
qualitativelyconsistentwiththeresultspresentedinPanelBofTable2,butthemagnitudeof
theuncertaintyeffectissomewhatsmaller.ThesmallercoefficientsinTableD.2areinline
withanerrors-in-variablesargumentsuggestingthatmeasurementerrorintheENSObased
uncertaintyproxymightbiasthoseestimatestowardszero.
D.3 ENSOSensitivityCalculationBasedonFour-FactorModel
Icalculateindustry-levelsensitivitiestochangesinENSOconditionsbyestimatingmodel
(2).Thisinvolvesregressingmonthlyfirm-levelreturnsonreturnsoftheS&P500indexand
-./0
monthlyseasurfacetemperaturemeasurements.Theresultingsensitivityestimates,)*,,
,
arekeyingredientstotheconstructionofmyinstruments.
Totesttheirrobustness,Ialsoestimatethefollowingversionofmodel(2)inthesameway
asdescribedinSection4.1.1
-./0
-./0
E2,f = )'2 + gih jk + )*,,
∙ Df
+ m2,f (D.1)
wherejk isavectorofobservableriskfactors.Iemployafour-factormodelusingthethree
factors of Fama and French (1992), which I augment with the momentum risk factor in
-./0
Carhart(1997).Usingtheresultingestimates)*,,
intheconstructionofmyinstruments
describedinSection4andestimatingmodel(4)yieldstheresultspresentedinTableD.3.The
coefficients throughout this table are not statistically significantly different from the
coefficientsinPanelBofTable2.Thus,themainfindingsofthepaperarerobusttousing
model(D.1)toestimateindustryENSOsensitivities:inresponsetoheighteneduncertainty,
firms in competitive industries increase investment and firms in concentrated industries
decreaseinvestment.
D.4 TimeInvariantENSOSensitivities
-./0
Theindustry-levelsensitivities,)*,,
,arecalculatedbyestimatingmodel(2)onafive-./0
yearrollingbasis(asdescribedinSection4.1.1).Foreachyeart,)*,,
iscalculatedbyusing
72
monthlyreturnsofthepreviousfiveyears(fromt-6tot-1).Oneconcernmightbethatfirms
inagivenindustrysystematicallychooseinvestmentdecisionstochangetheirindustry-level
sensitivitiestoENSO.Tomitigatethisconcern,inanadditionalrobustnesstest,Icalculate
industry-levelENSOsensitivitiesbyestimatingmodel(2)usingmonthlyreturndatainthe
years 1980 to 1984, which is prior to my main estimation period (1985 to 2014). The
resulting time invariant sensitivities,)*-./0 , are then used to construct the instruments as
describedinSection4andestimatemodel(4).TheresultsarepresentedinTableD.4.The
main conclusion that firms in competitive industries increase investment and firms in
concentrated industries decrease investment in response to heightened uncertainty still
holds.
D.5 CompetitionasanAdditionalRiskFactorintheCalculationofENSO
Sensitivities
Icalculateindustry-levelsensitivitiestochangesinENSOconditionsbyestimatingmodel
(2) and using stock return data for firms and the S&P 500 as well as monthly sea surface
temperaturemeasurements.Oneconcerncouldbethatanindustry’ssensitivitytoachange
in ENSO conditions might systematically capture the level of competition in that industry.
Thus,thedifferentialeffectofuncertaintyacrossfirmsinindustrieswithvaryingdegreesof
competitioncouldbearesultofsystematicdifferencesinthesensitivitiesoftheseindustries
to changes in ENSO conditions. To mitigate this concern, I include the Compustat HHI
measureinmodel(2)asanadditionalriskfactor.Estimatingmodel(4)usingtheresulting
-./0
sensitivities,)*,,
,intheconstructionoftheinstrumentsyieldsresultspresentedinTable
D.5.Column2showsthatuncertainty,onaverage,hasapositiveeffectonfirminvestmentin
competitive industries, while it has a negative effect on investment in concentrated
industries.ThesefindingsareconsistentwiththeresultsinPanelBofTable2.However,the
magnitudesoftheeffectsaresmaller.
D.6 Industry-LevelENSOSensitivitiesBasedonTwo-DigitSICCodes
Section4.1.1describeshowIestimatemodel(2)tocalculatetime-varying,industry-level
sensitivitiestochangesinENSOconditions.Theindustryclassificationisbasedonthree-digit
SICcodes.However,forrobustnesspurposes,Icalculatethesesensitivitiesforeachindustry
based on two-digit SIC codes. The results are presented in Table D.6. The signs of the
coefficientsareconsistentwiththeestimatesinPanelBofTable2.However,thenegative
73
effectofuncertaintyinconcentratedindustryisstronger,whiletheaveragepositiveeffectof
uncertaintyoninvestmentincompetitiveindustriesisslightlysmaller.
D.7 UnpredictableComponentofReturnVolatilityasUncertaintyProxy
In model (4), I use firm-level stock return volatility as an uncertainty proxy. Due to its
endogenous nature, I construct instruments based on ENSO prediction data to identify
exogenous variation in return volatility. Instead of examining all of the variability in firmlevelstockreturns,Icanremovetheforecastablevariationbyestimatingthefollowinglinear
factormodel(Gilchristetal.,2014)
o
E2,n − En = p2 + qih jr + Q2,n o
whereE2,n aredailystockreturnsandEn istherisk-freerate.jr isavectorincludingthethree
riskfactorsinFamaandFrench(1992)aswellasthemomentumfactorsuggestedbyCarhart
(1997).Thevolatilityoftheunpredictablepartofthefirm-levelstockreturnreturnisthen
calculatedas
s2,, =
1
=,
@t
nu&
%
Q2,n − Q2,, wherethesamplemeanofdailyfirm-levelreturnsinyeartisdefinedasQ2,, =
&
@t
@t
nu& Q2,n .
Theabovemeasure,s2,, ,representstheannualfirm-levelstockreturnvolatilityforfirmiin
yeartvoidoftheforecastablevariationinexpectedreturns40.Instrumentingthismeasure
insteadoftherawstockreturnvolatilityinmodel(4)yieldstheresultspresentedinTable
D.7.ThecoefficientsshownarenotsignificantlydifferentfromtheonespresentedinPanelB
ofTable2.
D.8 TheEffectofUncertaintyonLeverage(Debt-EquityRatio)
In Section 6.1.3, I test alternative explanations for the negative effect of uncertainty on
investmentobservedinconcentratedindustries.Oneoftheseexplanationsisbasedonthe
argument that under financing frictions, uncertainty might affect investment through
borrowing costs. More specifically, Gilchrist et al. (2014) argue that a rise in uncertainty
40Toannualizethismeasure,theaveragedailyreturnvolatilityineachyearismultipliedbythe
squarerootoftradingdaysinthatyear.
74
increasescreditspreadsandleadsfirmstodecreasetheirleverageandinvestmentspending.
Examining external financing decisions of firms in concentrated industries in response to
heighteneduncertainty,Ifindnoevidenceforthischannel.Columns7and8inPanelAof
Table5illustratethatuncertaintyhasnoeffectonnetdebtissueandresultsindecreasednet
equityissuesforsuchfirms.Asanadditionaltest,Iexaminetheeffectofuncertaintyonthe
overall debt-to-equity ratio by estimating model (4) with the debt-to-equity ratio as a
dependent variable. I define this ratio as the book value of long-term debt plus current
liabilities divided by the market value of common equity plus the liquidating value of
preferredequity.TheresultsforthistestarepresentedinTableD.8.Thecoefficientsreveal
thatuncertaintyhasapositiveeffectonleverageinconcentratedindustriesandnoeffectin
competitiveindustries.
75
TableD.1
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–additionalspecificationsofmodel(4)
Thistablepresentsresultsofestimatingmodel(4)usinganinstrumentalvariablesapproachwherethedependentvariableisinvestmentscaledbythepreviousyear’s
capitalstock.ThesampleonlyincludesfirmsinindustriesthataresensitivetoENSOfluctuations.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)
isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotal
assets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*
indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
Returnvol.
(2)
0.0330
(0.03)
Tobin'sQ
(3)
-0.0438
-0.0439
(0.03)
(0.03)
0.0198***
(4)
(0.00)
(5)
0.3005***
(6)
0.3267***
(0.05)
(7)
0.2557***
(0.06)
(0.06)
-0.2268***
0.0172***
(8)
(9)
-0.2842***
(0.04)
-0.2360***
(0.05)
(0.05)
(0.00)
0.0192***
(0.00)
N
F-Stat.-1st
Stage
51,203
51,203
51,203
24,663
24,663
24,663
26,540
26,540
26,540
483.69
421.11
325.36
254.13
257.87
170.94
196.51
199.15
144.78
Controls
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
YearFE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
Yes
Yes
76
TableD.2
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–withoutexcludingindustriesthat
arenotsensitivetoENSO
This table presents results of estimating model (4) using an instrumental variables approach where the
dependent variable is investment scaled by the previous year’s capital stock. The sample includes firms in all
industriesregardlessofwhethertheyaresensitivetoENSOfluctuations.TheCompustatbasedmeasureofthe
Herfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(High
Competition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-toequityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsare
clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0682**
F-Stat.-1stStage
(0.04)
0.0169***
(0.00)
N
-0.1023**
(0.07)
0.0197***
0.1377**
(0.03)
Tobin'sQ
0.0200***
(0.00)
(0.00)
85,095
32,456
52,639
176.63
101.54
109.92
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
77
TableD.3
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–ENSOsensitivitiescalculated
using4-factormodel
This table presents results of estimating model (4) using an instrumental variables approach where the
dependentvariableisinvestmentscaledbythepreviousyear’scapitalstock.Thesampleincludesonlyfirmsin
industries which are significantly affected by changes in ENSO conditions. These sensitivities are calculated
accordingtomodel(D.1).TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)isusedtosplit
thesampleintofirmsinhighlycompetitiveindustries(HighCompetition)andfirmsinlesscompetitiveindustries
(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesand
the12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and
10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
0.0349
N
F-Stat.-1stStage
-0.2634**
(0.06)
0.0191***
0.2712***
(0.04)
Tobin'sQ
(0.12)
0.0194***
0.0171***
(0.00)
(0.00)
(0.00)
51,203
24,663
26,540
321.63
144.29
32.02
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
78
TableD.4
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–timeinvariantENSOsensitivities
calculatedusingmonthlyreturndatafrom1980to1984
This table presents results of estimating model (4) using an instrumental variables approach where the
dependentvariableisinvestmentscaledbythepreviousyear’scapitalstock.Thesampleincludesonlyfirmsin
industries which are significantly affected by changes in ENSO conditions. These sensitivities are calculated
accordingtomodel(2)usingmonthlyfirm-levelreturndatafrom1980to1984.TheCompustatbasedmeasure
oftheHerfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries
(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debtto-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsare
clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-2.4350*
N
F-Stat.-1stStage
-0.7865***
(0.40)
0.0019
2.6832***
(1.35)
Tobin'sQ
(0.30)
0.0293***
0.0112***
(0.01)
(0.00)
(0.00)
51,203
24,663
26,540
3.44
42.81
6.81
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
79
TableD.5
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–ENSOsensitivitiescalculatedby
includingcompetitionmeasureasanadditionalriskfactorinmodel(2)
This table presents results of estimating model (4) using an instrumental variables approach where the
dependentvariableisinvestmentscaledbythepreviousyear’scapitalstock.Thesampleincludesonlyfirmsin
industrieswhicharesignificantlyaffectedbychangesinENSOconditions.Thesesensitivitiesarecalculatedby
estimatingmodel(2)augmentedwithameasureofcompetitionasanadditionalriskfactor.TheCompustatbased
measure of the Herfindahl-Hirschman Index (HHI) is used to split the sample into firms in highly competitive
industries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotal
assets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standard
errorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0272
F-Stat.-1stStage
(0.06)
0.0169***
(0.00)
N
-0.1059*
(0.06)
0.0191***
0.1852***
(0.04)
Tobin'sQ
0.0205***
(0.00)
(0.00)
51,203
24,663
26,540
54.09
225.49
35.90
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
80
TableD.6
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–industry-levelENSOsensitivities
basedontwo-digitSICcodes
This table presents results of estimating model (4) using an instrumental variables approach where the
dependentvariableisinvestmentscaledbythepreviousyear’scapitalstock.Thesampleincludesonlyfirmsin
industrieswhicharesignificantlyaffectedbychangesinENSOconditions.Thesesensitivitiesarecalculatedby
estimatingmodel(2).Industryclassificationisbasedon2-digitSICcodes.TheCompustatbasedmeasureofthe
Herfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(High
Competition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-toequityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsare
clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0540
N
F-Stat.-1stStage
-0.3504***
(0.07)
0.0184***
0.1892***
(0.06)
Tobin'sQ
(0.11)
0.0154***
0.0180***
(0.00)
(0.00)
(0.00)
51,203
24,663
26,540
35.31
134.83
17.46
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
81
TableD.7
Theeffectofcompetitionontheinvestment-uncertaintyrelationship–removingforecastablevariation
inexpectedreturnsfromfirm-levelstockreturnvolatility
This table presents results of estimating model (4) using an instrumental variables approach where the
dependent variable is investment scaled by the previous year’s capital stock. Return volatility indicates the
measuredescribedinAppendixSectionD.7.Thesampleincludesonlyfirmsinindustrieswhicharesignificantly
affectedbychangesinENSOconditions.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)
is used to split the sample into firms in highly competitive industries (High Competition) and firms in less
competitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledby
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and
*indicate1%,5%,and10%significance,respectively.
AllFirms
HighCompetition
LowCompetition
(1)
(2)
(3)
Returnvolatility
-0.0484
F-Stat.-1stStage
(0.05)
0.0171***
(0.00)
N
-0.2368***
(0.06)
0.0197***
0.2404***
(0.03)
Tobin'sQ
0.0191***
(0.00)
(0.00)
51,203
24,663
26,540
269.43
197.63
120.03
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
82
TableD.8
Theeffectofuncertaintyonleverage(debt-equityratio)
This table presents results of estimating model (4) using an instrumental variables approach where the
dependentvariableisthedebt-to-equityratio.Thesampleincludesonlyfirmsinindustrieswhicharesignificantly
affectedbychangesinENSOconditions.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)
is used to split the sample into firms in highly competitive industries (High Competition) and firms in less
competitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledby
capitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and
*indicate1%,5%,and10%significance,respectively.
Returnvolatility
HighCompetition
LowCompetition
(1)
(2)
(3)
0.0114
F-Stat.-1stStage
AllFirms
N
-0.0146
0.0263**
(0.01)
(0.01)
(0.01)
51,203
24,663
26,540
262.98
172.66
117.25
Controls
Yes
Yes
Yes
YearFE
Yes
Yes
Yes
FirmFE
Yes
Yes
Yes
83