1

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

optionvalueofdeferring 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

2

1. IntroductionAlargebodyofresearchintheirreversibleinvestmentliteraturepredictsanegativelink

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

homogenousacross firms indifferent competitiveenvironments. 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).

Knowingwhetheruncertaintyhasauniformordifferentialeffectoncapital investment

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

economicconditions.Suchendogeneity issuesusually impedethecausalestimationof 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-varyingmeasure of

uncertaintythatisexogenoustoeconomicconditionsandfirmbehavior.Thisallowsmeto

providenewempiricalevidenceontheuncertainty-investmentlinkanditsinteractionwith

competition.Consistentwiththeoriesintheirreversibleinvestmentliterature,Ishowthat

firmsinconcentratedindustriesdecreaseinvestmentinresponsetoheighteneduncertainty.

However, I find that uncertainty leads to increased investment in highly competitive

industries,suggestingthatcompetitioncanattenuatethenegativeeffectofuncertaintyand

allowothermechanismsthatencourageinvestmentinthefaceofuncertaintytodominate.

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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;

Cashinet al., 2014).While this literaturegenerally examines themacroeconomic 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 toeconomic conditionsand firmbehavior.The instrument is a combinationof

industry-level sensitivities to changes in ENSO conditions, calculated using share price

returns, andENSO forecastdata2.This identification strategy relieson the fact thatENSO

uncertaintyvariesacross timeandENSOsensitivitiesvarysubstantiallyacross 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 anElNiño (i.e., a change in the secondmoment) 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.

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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 ofmore 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 twodifferent proxies for the degree of

investment irreversibility that firmsface,whicharebasedon insights fromKimandKung

(2014)andZhang(2005).Thenegativeeffect is stronger for firmswith less redeployable

assets and higher average adjustment costs, suggesting that the option towait and defer

investmentismorevaluableforthem.Hence,thenegativeeffectofuncertaintyoninvestment

in concentrated industries is consistentwith the predictions of real optionmodels in the

irreversibleinvestmentliterature(e.g.,DixitandPindyck,1994).

Ialsoinvestigatealternativeexplanationsforthenegativeuncertaintyeffectobservedin

concentrated industries.For instance,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.Mypaperemploysadistinctlydifferent instrument that isbasedonchanges inENSO

uncertainty,whichareplausiblyexogenoustoinvestmentdynamicsandeconomicconditions.4Theoveralldebt-to-equityratio increases inresponse touncertainty for firms inconcentrated

industries.

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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. Amean-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 firmswithhigher labor-capital ratioswhere the flexible

productionfactorrepresentsalargershareoftheproductiontechnology.

The estimated effects of uncertainty are economically significant. In response to a ten

percentage point increase in uncertainty, firms inmore 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,(pt–ct)].Ifthemarginal

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.

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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,generallyresults inashortandsharpdropin investmentratesfortwoperiods.

This is followedby a quick reboundof investment rates and aprolonged recovery of the

capitalstocktoitssteadystate.

Finally, I document the effect of uncertaintyonother typesof investment.Uncertainty

reducesR&DspendinginconcentratedindustriesandhasnoeffectonR&Dinvestmentfor

firmsincompetitiveindustries.ThemagnitudeofthenegativeeffectonR&Dissignificantly

smallercomparedtothenegativeeffectofuncertaintyoncapitalinvestmentinconcentrated

industries.ThisisconsistentwiththefactthatR&Dspendingismorepersistentacrosstime

thancapitalinvestment.Inlinewithdecreasesincapitalinvestment,firmsinconcentrated

industriesalsospendlessonadvertising.Iobservenoeffectonadvertisingforfirmsinhighly

competitive industries.Hiringonaveragedeclinesacross firms inresponsetouncertainty

indicating thatuncertaintyhasa real impacton labormarkets.However, thiseffect isnot

significantforfirmsinhighlycompetitiveindustries.

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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

(AbelandEberly,1994;Bernanke,1983;BrennanandSchwartz,1985;DixitandPindyck,

1994;McDonaldandSiegel,1987).Thecentralideaunderlyingthisargumentisbasedonthe

presenceofasymmetricadjustmentcosts,whichmakeitcostliertoreverseinvestmentthan

to increase it,effectively leadingtopartialor full irreversibilityof investment.Undersuch

conditions, a mean-preserving rise in uncertainty increases a firm’s option to defer

investmentuntil someof theuncertainty is resolved.This createsregionsof inaction that

expandwith 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). Severalmodels 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 constant-

returns-to-scale and complementary production factors achieve such convexity in the

marginal revenueproduct of capital. Similar convexities can also arisedue tooperational

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).

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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

intoaccountstrategic interactionsamongfirmsandshowsthat increasedcompetitioncan

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 andRampini, 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.

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Onthemicro-level,papershaveusedindustry-andfirm-specificdatatoprovideevidence

on the linkbetween investment anduncertainty. To a large extent, this literature follows

macroeconomicstudiesintheconstructionofuncertaintymeasuressuchasthevolatilityof

stockreturns(Baumetal.,2008;Bloometal.,2007;Bulan,2005;Gilchristetal.,2014;Leahy

andWhited, 1996). Additionalmeasures 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

andSchrand,1999).A commonchallenge in theempirical literatureon investmentunder

uncertaintyistodistinguishtheeffectofuncertaintyfromtheimpactofbusinessconditions.

Tomitigateendogeneityconcernsaboutuncertaintyproxiesbasedonreturnvolatility,many

of theabovestudiesuse “internal” instruments in thespiritofArellanoandBond (1991),

which require strong identifying assumptions about the properties of the underlying

investment time series9. Stein and Stone (2013) try to address this issue by following a

differentidentificationstrategy.Theyuseindustryexposurestooilpriceandexchangerate

fluctuationsasinstrumentsforfirm-leveluncertaintymeasuredbyimpliedoptionvolatilities.

While theirapproacharguablypresentsan improvementtopreviousmethods, itdoesnot

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 eventsmight impact firmbehavior 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-section10. As highlighted earlier, firm and

industrycharacteristicssuchasthecompetitiveenvironmentcanhaveasubstantialimpact

onthelinkbetweenuncertaintyandinvestment.Surprisinglyfewstudieshaveexaminedthe

differentialeffectofuncertaintyacrossindustries.Bulan(2005)andGuisoandParigi(1999)

9SeeAlmeidaetal.(2010)andEricksonandWhited(2012)forcomprehensivediscussionsofthe

advantages anddisadvantagesofusingGMMversusOLS type instrumental variables estimators in

investmentregressions.10 Abel and Eberly (1996) and Leahy and Whited (1996) provide in-depth discussions of the

possiblepositiveandnegativeeffectsofuncertaintyoncapitalinvestment.

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examine a sample ofmanufacturing 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 resultsmight arise from studying very specific industries and

highlightstheneedforempiricalanalysisofabroadsampleoffirmsinavarietyofdifferent

industries.

Inthispaper,Iwillconstructanexogenous,time-varyingandforward-lookinguncertainty

measuretoinvestigatethedifferentialimpactofuncertaintyoncapitalinvestmentformore

than6,500U.S.firmsacrossalargenumberofindustries.

Theremainderofthepaperisorganizedasfollows:Section2providesdetailsaboutthe

ElNiñoSouthernOscillationcycleanddiscusses its impactoneconomicactivity.Thedata

sources are described in Section 3. Section 4 outlines the empirical framework and

methodology used. Section 5 presents themain 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 inNorth andSouthAmerica aswell asAsia andAustralia (Adamset al., 1995;

Changnon,1999;RopelewskiandHalpert,1987,1986;Rosenzweigetal.,2001;Wangetal.,

2000).

“Regular” seasons are characterized by a persistent high-pressure system off thewest

coastofSouthAmerica,specificallyoverthecoastofPeru.Atthesametime,alow-pressure

systemformseastofAustralia.Theresultingdifferential inatmosphericpressure leads to

prevailingsurfacewindsthatblowfromtheeasttothewest–oftenreferredtoas“easterlies”.

These tradewinds carrywarm surfacewater from the central and eastern regionsof the

equatorialPacifictoAustraliaandAsia.Resultingprecipitationintheseregionsisextensively

usedforagriculturalandindustrialpurposes.AlongthewestcoastofSouthAmerica,coldand

nutrient-richwaterrisestothesurfaceboostingthefishingindustryintheseregions.

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Thisweatherpatternisverysensitivetoanomalousdeviationsinatmosphericpressure

and sea surface temperature, which can cause serious disruptions toweather 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,theycancausewide-

reachingchangesinglobalweatherpatternsthataffectavarietyofdifferentindustriessuch

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 horizons12. Similar results have been

reported in a recent studybyCashinet al. (2015).They show thatENSO relatedweather

11Forcomprehensivereviewsoftheliteratureonthemoregeneralrelationshipbetweenclimate

andeconomicperformanceseeDelletal.(2014)andTol(2009).12 The author examines both four-quarter- and 16-quarter-ahead forecast error variances in

commoditypricesandfindsthatENSOactivityexplainsalmost10%ofcommoditypricemovements

overtheshorterhorizonandupto20%overthelongerhorizon.

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eventshaveheterogeneouseffectsoneconomicgrowthandinflationacrosscountriesaround

theworld.Inaddition,theydocumentthatENSOhasasignificantimpactonenergyandnon-

fuel commodity prices. Other research finds similar links between ENSO and economic

activityaswellascommodityprices(Adamsetal.,1999;Algieri,2014;BerryandOkulicz-

Kozaryn,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 themacroeconomic

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

firmshavedifferentsensitivitiestochangesinENSOconditions13andconstructaforward-

lookinguncertaintymeasureusingtimeseriesdataonENSOpredictions.PleaseseeAppendix

BformoredetailsontheElNiñoSouthernOscillationCycle.

3. DataFormyempiricalanalysis,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).

13The variation in ENSO exposure across industries is frequently being highlighted in popular

media.ArecentmarketreportbyMacquariestatesthat“companiesinNorthAmericaandAustralia/NZ

aremostlikelytobeimpacted,followedbycompaniesinAsia.Importantly,theimpactoncompanies

is not uniformwithin any given region. InAus/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).

13

Since thisdatauses theCensus IndustryClassification(CIC) tocategorize industries, Iuse

matching information provided by the U.S. Census Bureau tomap 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 usedmeasurements 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).

14

4. EmpiricalFrameworkI 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

Theexistingempirical literatureon investmentunderuncertaintytakesmanydifferent

approaches to measuring uncertainty. While some studies build structural models, most

researchrelieseitheroneventsthatplausiblycreateunexpectedspikesinuncertainty16or

on time-seriesmeasures of aggregate and idiosyncratic uncertainty. The former research

designworkswell if uncertainty shocks are indeed unexpected, but is problematic if the

occurrenceofevents iscorrelatedwithotherunobservedshocks.Studiesthat fall intothe

secondcategoryfrequentlyuseuncertaintymeasuresbasedonvolatilityindicessuchasthe

VIXfromtheChicagoBoardofOptionsExchangeoronfirm-levelstockreturnvolatility(e.g.,

Bloometal.,2007;LeahyandWhited,1996),whicharegenerallyrathernoisyproxies for

uncertainty.Changesinstockreturnsoftenreflectchangesinthefirstmomentofinvestment

opportunities rather than changes in uncertainty. In fact, a growing number of studies

documentthecounter-cyclicalnatureofreturnvolatility(Bloom,2009;Campbelletal.,2001;

Storeslettenetal.,2004).

In thispaper, I takeadifferentapproach.Toestimate theeffectofuncertaintyon firm

investment,Iconsiderasimplelinearinvestmentmodelofthefollowingform

!",$%",$&'

= )* + ,- + ./01,2 + 34 ∙ 6*,- + 7*,- (1)

where)* represents firm fixed effects and,- denotes year fixed effects. The dependentvariableiscapitalexpenditure,8*,- ,scaledbythepreviousperiod’scapitalstock,9*,-:4.01,2is

16 Some studies exploit uncertainty shocks caused by terrorist attacks, military action, natural

disastersorpoliticalshocks(BakerandBloom,2013;JulioandYook,2012;KimandKung,2014).

15

avectorofrelevantcontrolvariables(includingTobin’sq)and6*,-denotestheuncertaintymeasure. Causal identification of the uncertainty effect requires 6*,- to be exogenouslydetermined.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 andWhited, 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

endogenousnatureof suchameasurebyconstructinganovel instrumentbasedonENSO

uncertaintytoidentifyexogenousvariationinstockreturnvolatility.Iestimatetheeffectof

uncertaintyonfirminvestmentintwosteps.

4.1.1 Industry-LevelSensitivitiestoENSO

First, I identify industriesthatareaffectedbychanges inENSOconditions.TheClimate

PredictionCenter(CPC)providesmonthlymeasurementsofENSOintensityintermsofsea

surface temperature (SST) anomalies (seeFigure1)17. Positivedeviations 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 (Adamsetal.,1995;RopelewskiandHalpert,1986;Wangetal.,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.

16

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 (from1985 to

2014)usingmonthlyreturndatafromthepreviousfiveyears(t-6tot-1)

;*,< = =>* + =* ∙ ;<?@A>> + =B,-CD?E ∙ F<CD?E + 7*,< (2)

where;*,<isthemonthlystockreturnforfirmi,;<?@A>>isthemonthlyreturnontheS&P500index andF<CD?E are themonthly observations of SST anomalies. The coefficient=B,-CD?E ineachyear t is constrained tobe the same for all firms in industryk (basedon3-digit SIC

codes19).Ineachyeart,=B,-CD?Erepresentstheaveragesensitivityofstockreturnsinindustryk 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 sensitivitiesdue to several reasons.First, thepaneldata set ishighlyunbalanced. In

addition,thismethodologymitigatesconcernsabouttheendogeneityofENSOsensitivities

withrespecttofirminvestment.Somefirmsmightchoosecertaininvestmentstoreducetheir

exposuretoENSO.However,itishighlyunlikelythatallfirmsinthesameindustryengagein

thesamediversificationeffortsthroughinvestment20.

For my subsequent analysis, I identify industries for which the estimated sensitivity

coefficient,=B,-CD?E ,isstatisticallysignificantfromzero(atthe5%level).FirmsinindustriesthatarenotsignificantlyaffectedbyENSOwillbedroppedfrommysample,sinceIdonot

expectthesefirmstoreacttoENSOuncertainty21.

19Theresultspresentedthroughoutthispaperarerobusttobasingtheindustryclassificationon

2-digitSICcodes.20InAppendixD,Ishowthatthemainresultsofthepaperarerobusttocalculatingtimeinvariant

ENSOsensitivitiesusingreturndatainyears1980to1984,whichispriortomymainestimationperiod

of1985to2014.21Someindustrieshavelargesensitivitycoefficientswithsimilarlylargestandarderrorsrendering

thecoefficientestimatenotstatisticallysignificantfromzero.Includingsuchobservationsthroughout

17

4.1.2 InstrumentalVariablesEstimation

Tomitigateanyendogeneityconcernswithrespecttostockreturnvolatility, Iestimate

model(1)usinganinstrumentalvariablesapproach.Iconstructaninstrumentthatidentifies

exogenous variation in firm-level stock returns by combining the estimated ENSO

sensitivities,=B,-CD?E ,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 secondmoments 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)

=B,-CD?E ∙ 6-CD?E

where=B,-CD?E is the estimated ENSO sensitivity for industry k in year t and6-CD?E is thestandarddeviation(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

summarize informationoncurrentand futureexpectedENSOconditions(seeAppendixB formore

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 .

18

=B,-CD?E ∙ H-CD?E

whereH-CD?Edenotesthefirstmomentoftheforecastprobabilitydistribution.In line with two-stage least squares methodology, the first stage of the instrumental

variablesapproachtoestimatemodel(1)takesthefollowingform

6*,- = I* + J- + K/01,2 + )4 ∙ =B,-CD?E ∙ 6-CD?E + =B,-CD?E ∙ H-CD?E + L*,- (3)

whereI* andJ- are firm and time fixed effects, and01,2 is a vector of relevant controlvariables.

Finally,substitutingthepredictedvaluesof6*,-fromregression(3)intomodel(1)yieldsthefollowingspecification

!",$%",$&'

= )* + ,- + ./01,2 + 34 ∙ 6*,- + 3M ∙ =B,-CD?E ∙ H-CD?E + 7*,-(4)

where)* represents firm fixed effects and,- denotes year fixed effects.01,2 is a vector ofrelevant control variables and6*,- denotes the predicted values for stock return volatilityfromestimatingthefirststageregression(3).=B,-CD?E ∙ H-CD?Ecapturestheeffectthatthe12-

monthENSOlevelforecast,H-CD?E ,hasonfirminvestmentinindustrykinyeart.8*,-inequation(4)iscapitalexpenditurebyfirmiinperiodt,whichisscaledbythecapital

stockattheendofthepreviousperiod9*,-:4.Sincefinancialstatementsreportcapitalstockat book rather than replacement value,9*,- is calculated recursively using a perpetual-inventorymethod.ThiscalculationisdiscussedinmoredetailinAppendixA.

Inmanytheoreticalmodels,uncertaintyaffectsfirminvestmentpartlyorentirelythrough

marginalTobin’sq25.Therefore, it isstandardpracticetoincludemeasuresofTobin’sqas

controls in investmentmodels. Empirically, it is challenging tomeasure a firm’smarginal

25Mostnotably,AbelandEberly(1994)introduceirreversibilityintothemodelofAbel(1983)and

showthat investmentdependsonlyonthecapitalstockandmarginalq.Therefore, theyarguethatuncertaintyimpactsinvestmentonlytotheextentthatitaffectsmarginalq.DixitandPindyck(1994)advance a similar argument and conclude thatuncertainty changes the thresholdvalueofq abovewhichfirmsarewillingtoinvest.

19

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

generatedbyanunderlyingstochasticprocessthat isexogenoustothebusinesscycleand

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) inENSOuncertainty, thesecondmoment, representsan increase (decrease) in

uncertainty for both types of industries. In addition, the firstmoment of the SST forecast

probabilitydistribution,H-CD?E ,doesnotco-varywithitssecondmoment,6-CD?E ,inthetimeseries.

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,itisreasonabletobelievethattheimpactofuncertaintyoninvestmentisnotinitsentiretycapturedbyaverageTobin’sq.

20

publicly listed firmsusing accountingdata fromCompustat (CompustatHHI)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-costmargintocontrol forsystematicdifferences inthis

measure across industrieswhich 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 estimatemodel (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 industriesusing theCompustatHHImeasure. Columns2 and3 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). TheCompustat basedHHImeasure is calculated for each year using themarket

sharesof all firms inan industryand is averagedover thepast threeyears. Industriesaredefined

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).

21

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

eitherengaged in theproductionof commoditiessuchasagriculturalproductsandmetal,

industries thatareaffectedbyabnormalchanges inweatherconditions (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

butnotstatisticallysignificant.While Iobservenoaverageeffectofuncertaintyoncapital

investment,theoreticalpredictionspointtoadifferentialeffectofuncertaintyoninvestment

across firms facingdifferentdegreesofcompetitivepressure. In thenextstep, I test these

predictionsbysplittingmysamplebasedonindustryconcentrationusingthesamethreshold

levelfortheCompustatHHImeasureasbefore.

Resultsofthisanalysisarepresentedincolumns2and3inPanelBofTable2.Ifindthat

firms in highly competitive industries increase capital investment in response to higher

uncertainty.This is in linewitharguments thatcompetitioncanerodetheoptionvalueof

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 onweather conditions. In addition, Cashin et al. (2015) show a strong

relationshipbetweenENSOvariationandcommoditypricesincludingenergyprices.

22

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 isbasedonpossiblemeasurementerrors in theuncertaintymeasures.Sincestock

returnvolatility is a verynoisyproxy foruncertainty, theOLSestimates are likelybiased

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 industriesanda2.8percentage

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,thedirectionoftheuncertaintyeffect is

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

23

CompustatHHImeasureinsubsequenttests.However,themainconclusionspresentedinthe

nextsectionsholdregardlessofthecompetitionmeasureemployed.

TheaboveresultspresentedinTable2arebasedonseparateestimatesoftheimpactof

uncertaintyoninvestmentforfirmsinhigh-andlow-competitionindustries.Ifocusonthe

split-samplemethodology for several reasons. First, empirical evidence shows that firms

have different investment dynamics depending on the level of competition they face

(Bustamante, 2015). Firm investmentmight change differently in response to changes of

relevantcontrolvariablesintheinvestmentmodel,whichwarrantsaseparateestimationof

theseparametersinthedifferentsubsamples.Second,theeffectofcompetitiononthelink

betweenuncertaintyandinvestmentislikelynotlinear.Whileanincrementalchangeinthe

degreeofcompetitionmighthavearathersmalleffectontheuncertainty-investmentlinkfor

firmsinveryconcentrated industries,achange incompetitionofsimilarmagnitudemight

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 returnvolatility.The resultsof estimating this specificationarepresented inTable3.

CompdenotesthecompetitionproxyandVolxComp is the interactionbetweenfirm-level

return volatility and the competitionmeasure31. The column headers in Table 3 indicate

whichcompetitionmeasure isused.Columns1and2employ theCompustatHHIand the

Hoberg and Phillips HHI measure, respectively, and illustrate that firms in concentrated

industriesdecreaseinvestmentmorerelativetofirmsincompetitiveindustriesinresponse

toheighteneduncertainty.Thecoefficientontheinteractionbetweenreturnvolatilityand

price-costmarginsincolumn3isalsonegativealbeitnotstatisticallysignificant.However,

the lowF-statistic of the first stage regression for the interaction term indicates that this

specificationincolumn3maysufferfromaweakinstrumentproblem.Themagnitudeofthe

30In their Horizontal Merger Guidelines, the U.S. Department of Justice and the Federal Trade

Commissionclassify industriesaseitherunconcentrated(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 returnvolatility,Iconstruct =BCD?E ∙ 6-CD?E×fghiasaninstrumentfortheinteractionterm.

24

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 column2 this difference is 12 percentage

points.TheresultsinbothTable2andTable3providestrongevidencethatcompetitionisa

majordeterminantofthesignandthemagnitudeoftheuncertainty-investmentrelationship.

5.2 ConventionalUncertaintyMeasures

Oneconcernmightbethattheeffectofcompetitionontheuncertainty-investment link

documentedinprevioussectionsisspecifictomyuniqueuncertaintymeasureandempirical

identificationstrategy.Forthisreason, inthissection, Ishowthatthedifferentialeffectof

uncertaintyacrossindustrieswithdifferentdegreesofcompetitioncansimilarlybeobserved

whenusingmoreconventionaluncertaintymeasures.

Aggregate and idiosyncratic uncertaintymeasures 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.

25

values.Thisrelationismorepronouncedforfirmsinconcentratedindustries,butreversed

for firms in competitive industries,which seemingly increase investmentwhen aggregate

volatilityishigher.

I test thisdifference inslopesmore formallybyestimatingaspecificationofmodel (1)

wherelaggedvaluesoftheVXOareusedastheuncertaintymeasure.Firmfixedeffectsand

controlvariablessuchasTobin’sq,totalassets,cashflowscaledbycapitalstockandthedebt-

to-equityratioareincluded.TheresultsarepresentedinTable4.NotethatIamunableto

includeyear fixedeffects in this first specification, since theVXO indexonlyvariesacross

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 theVXO index isnot statistically significant.Thus, amarginal increaseordecrease 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

26

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 issmaller, suggesting thatmeasurementerror in theENSObaseduncertaintyproxy

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. Onemight argue that uncertainty could affect the level of competition in an

industrywhichinturnaffectsinvestmentandleadstothedifferentialeffectofuncertainty

observedinPanelBofTable2.Fromatheoryperspective,itisunclearwhethersuchacausal

linkbetweenuncertaintyandcompetitionexists,sincemodelsintheliteratureoninvestment

underuncertaintygenerallydonotmodelitexplicitly.However,it isunlikelythatacausal

27

chainamonguncertainty,competitionandinvestmentcanexplaintheresultspresentedin

thispaper forseveralreasons.Theunreportedcoefficients in the firststage,model (3),of

estimatingmodel(4)usinginstrumentsarepositivefor =B,-CD?E ∙ 6-CD?Eforallsubsamplesoffirms, irrespective of the level of competition in an industry. This indicates that ENSO

uncertaintyhasapositiveeffecton firm-levelreturnvolatility.Under theassumption that

uncertaintyaffectsproductmarketcompetition,theresultswouldsuggestthatuncertainty

increasesthelevelofcompetition,sinceithasbeendocumentedthathigherproductmarket

competitionleadstoincreasedreturnvolatility(GasparandMassa,2006;IrvineandPontiff,

2009;Peress,2010).Foran increase incompetitiontoexplainthedifferentialresponseof

investment,firmsinhighlycompetitiveindustrieswouldhavetoincreaseinvestment,while

firmsinconcentratedindustrieswouldhavetodecreaseinvestment inresponsetohigher

competition.However, empirically, theoppositeeffecthasbeendocumented.Frésardand

Valta(2013)examinetheeffectoftariffreductionsandtheassociatedincreaseincompetition

from foreign rivalson firm-level investment.Theirmain result is that the effect ofhigher

competitiondependsstronglyontheex-antecompetitivestructureoftheproductmarket.

While an increase in competition leads to significant decreases in investment in highly

competitive industries, thesame increase incompetitionhasnosignificanteffecton firm-

levelinvestmentinconcentratedindustries.Giventheseresults,itisunlikelythattheresults

in thispaper canbe explainedbyan indirect effect ofuncertaintyon investment through

changesincompetition.

AdditionalTests

TheresultspresentedinPanelBofTable2arealsorobusttocalculatingENSOsensitivities

based on a four-factormodel (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)(seeAppendixD.7).

6. TheEffectofUncertainty:UnderlyingMechanismsTheevidencepresentedintheprevioussectionsuggeststhattheeffectofuncertaintyon

firm investment depends heavily on the competitive environment of firms. Firms in less

28

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.

Inmodels such as Abel and Eberly (1996) or Dixit and Pindyck (1994), the increased

optionvalueofwaiting anddeferring investment inuncertain times arisesout 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 investment-

uncertainty 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

suchas searchcosts forbuyersandsellersaswell as financial constraints canmakesuch

transactions more or less costly depending on the asset type (Kim and Kung, 2014).

Irreversibilitygenerallyariseseitherduetoalackofsecondarymarketsforcertainassetsor

ifassetsareindustry-orfirm-specificandcannoteasilybeusedbyotherfirmsinthesameor

otherindustries.Assetsthatarewidelyusedwithinandacrossindustriesareplausiblyless

costlytoresellonthesecondarymarket.Consequently,investmentinsuchassetsislikelyto

29

bemorereversiblethaninvestmentinassetswithalownumberofalternativeusers.Based

onthislogic,KimandKung(2014)constructameasureofassetredeployabilityusingBureau

ofEconomicAnalysis(BEA)capitalflowdata.Theassetredeployabilityscoreforanindustry

depends on the usability of the assets employed by that industry and is higher themore

industriesusethesameassets.Therefore,theauthorsarguethatfirmsinindustrieswithlow

redeployabilityscoresfacemoreasymmetricadjustmentcoststhanfirmsinindustrieswith

higherscores,sinceitiscostlierforthemtoreversetheirinvestmentsbysellingtheirassets

on the secondarymarket. I splitmy 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, firmswith less redeployableassetsdecrease investment significantlymore in

response to higher uncertainty than their counterpartswithmore redeployable assets as

indicatedincolumns1and2.Theseresultsprovidestrongevidencethatcostlyreversibility

ofinvestmentisamajordriverofthenegativeuncertainty-investmentrelationshipobserved

inlesscompetitiveindustries.

6.1.2 Valuevs.GrowthFirms

Second, I use insights fromZhang (2005) to further testwhether 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, forwhichmost of their unproductive capital becomes productive

again.Sinceit iscostliertodecreasethanit istoincreasecapitalstock,ameanpreserving

increaseinuncertaintyaboutfuturestatesoftheworld,onaverage,leadstohigherexpected

adjustmentcostsforvaluefirms.Accordingtotherealoptionargument,valuefirmsshould

thusdecreaseinvestmentinresponsetoheighteneduncertaintymoresharplythangrowth

30

firms to avoid relatively higher costs of reversing investment in bad times. To test this

predictionempirically, I splitmysampleof firms into twogroupsbasedonwhether their

book-to-marketratioishigheror lowerthanthesamplemedian.Estimatingmodel(4)for

eachofthesubsamplesyieldsresultspresentedinPanelAofTable5.Estimationsincolumns

3 and4 indicate that thenegative effect of uncertainty on investment in less competitive

industriesisstrongerandstatisticallysignificantonlyforvaluefirms.Thedifferencebetween

coefficientsforeachsubsamplebecomeslargerandstatisticallysignificantwhensplittingthe

sampleaccording to the25thpercentileofbook-to-market ratios.Theseresultsare 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

and insmoothingfixed investmentsunder financingconstraintsdueto lowercostsassociatedwith

adjustingworkingcapital.

31

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.

Theresultspresented inPanelAofTable5 indicatethat investment irreversibilityand

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 inuncertain times compared to thoseof value firms, increase

investment in the face of heighteneduncertainty. These findings suggest that evenunder

higherlevelsofcompetition,therealoptionmechanism,onaverage,counteractsanypositive

effects of uncertainty on investment for firmswith 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).

32

(Grenadier, 2002), allowing other channels through which uncertainty can encourage

investmenttodominate.

Theoretically,uncertaintycanhaveapositiveeffectoninvestmentifitincreasesthevalue

of themarginalunitof capital.This idea isbasedonasimple Jensen’sargument. If future

profits generated by themarginal 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 onwhether 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 mean-

preservingincreaseinuncertainty.Inotherwords,iffirmsdonotneedtoutilizethemarginal

unitofcapitalinbadtimes,aunitofcapitalcanbeseenasanoptiononfutureproduction.

Thevalueofthisoptionwillincreaseinthevarianceofinputoroutputprices,leadingtoa

33

positive relationshipbetweenuncertainty and capital investment.Mills (1984) formalizes

thisintuitionandshowsthattheexpectedprofitofacompetitivefirmfacingstochasticprices

increasesinitsoperationalflexibility

j k i = k j i + lmnM ,

wherek i istheprofitgivenpricepandpricevariance6M.Theparameter=representsthedegree of operational flexibility a firmhas and varies inverselywith the cost of changing

output in response to price fluctuations. Following Grullon et el. (2012), I measure

operational flexibilityusing industry-level laborunionmembership. Intuitively, firms face

moreconstraintswhenadjustinglaborinputinresponsetochangesineconomicconditions

(particularly if it involves reductions in labor ineconomicdownturns) if theworkforce 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

effect isnotstatisticallysignificantforfirmswithlowlabor-capitalratios.However,theF-

statisticforthefirststageregressionindicatesthatthesespecificationsmightsufferfroma

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.

34

7. LargeChangesinUncertaintyandtheDynamicsofInvestmentSection5illustratesthattheeffectofuncertaintyonfirminvestmentheavilydependson

the level of competition that firms face in their industries. The results show a significant

wedge in the responseof investment spending to increaseduncertaintybetween 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

uncertaintyshocks leadtoashortandsharpdrop in investment,whichresults 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

measureofENSOuncertainty,6B,-CD?E ,overtheentireperiodof1985to2015.Iinvestigatethedynamicsofinvestmentfollowinganuncertaintyshockbyusingadifference-in-differences

approachandbyestimatingthefollowingmodel

8*,-9*,-:4

= )* + o- + 3Mf*,- + 3pq-:Mf*,- + 3pq-:4f*,-+3pq->f*,- + 3pq-Y4f*,- + 3pq-YMf*,- + rs*,- + 7*,-

wheref*,-isadummyindicatingwhetherfirmiisinahighlycompetitiveindustry(basedonthe threshold valueof 0.1 for theCompustatHHI).q-YM ,q-Y4 ,q-> ,q-:4 , andq-:M aredummyvariablesindicatingwhetheralargechangeinuncertaintytookplaceinyeart-2,t-1,t,t+1,

andt+2,respectively.s*,-denotesthesamevectorofcontrolvariablesasinsection5.Aswithallpreviousspecifications,Iincludefirmandtimefixedeffectsandclusterstandarderrorsat

thefirmlevel.

35

First,Iexaminethefiveyearsinwhich6-CD?Eincreasedthemostandestimatetheabovespecification.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, firms inmorecompetitive industries investmore inresponse touncertaintythan

theircounterparts inmoreconcentrated industries.Thiswedge in investmentrates isnot

statisticallysignificantinyear2aftertheuncertaintyshock.Incolumns3and4ofTable7,I

investigate the response of firm investment to a decrease in uncertainty. The average

magnitudeofthefivelargestdecreasesinuncertaintyissimilartotheaveragemagnitudeof

thefivelargestincreasesinuncertaintyexaminedincolumns1and235.Column4indicates

that inresponse todecreases inuncertainty, firms inconcentrated industries investmore

than firms incompetitive industries.Thissignificanteffect isonlyobserved in theyear 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 industrieshave1.9percentagepointshigher 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.

ThefindingspresentedinTable7arehighlyconsistentwiththeresults inBloometal.

(2014)andprovidefurtherevidencefortheargumentthatthedifferenceininvestmentrates

followinganuncertaintyshockbetweenfirmsincompetitiveandconcentratedindustriesis

infactaresultoftherealoptionsmechanismintheirreversibleinvestmentliterature.

35Theaverageincreasein6-CD?EduringtheyearswiththehighestspikesinENSOuncertaintyis

0.069andtheaveragedecreasein6-CD?Eduringtheyearswiththehighestdeclinesinuncertaintyis-0.057.However,thedifferencebetweentheabsolutevaluesofthesetwonumbersisnotstatistically

significant(p-valueof0.63).

36

8. UncertaintyandInvestmentinOtherFactorsWhileuncertaintyhasasignificantimpactoncapitalinvestment,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

estimatingmodel (4) usingR&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,

onaveragereducetheiradvertisingspending inresponsetohigheruncertainty.However,

thiseffectisnotstatisticallysignificantforfirmsinhighlycompetitiveindustries.

Finally,Ireportresultsforhiringasmeasuredbythenumberofemployeesscaledbytotal

assetsinTable10.Onaverage,Ifindanegativerelationshipbetweenuncertaintyandhiring

forallfirms.However,thiseffectseemstobedrivenbyfirmsinmoreconcentratedindustries

asillustratedincolumns2and3whereIsplitthesamplebasedonindustrycompetition.The

negativeeffectofuncertaintyonhiringisinlinewithpreviousstudiesthatrelateuncertainty

associatedwithrecessionstoanaggregatefallinhiring(SteinandStone,2013).

9. ConclusionThemaincontributionofthispaperistwo-fold:First,Iexploitauniqueempiricalsetting

to construct an uncertainty measure that is exogenous to economic conditions and firm

behavior. This allowsme to estimate the causal effect of uncertainty on firm investment.

Second, I examineabroaddata set ofmore than6,500U.S. firms in a varietyofdifferent

36Thecalculationoftheknowledgestockfollowstheperpetual-inventorymethodusedinHalland

Mairesse(1995).SeeAppendixAformoredetails.37SeeAppendixAfordetailsaboutthecalculationoftheadvertisingcapitalstock.

37

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 tendto increasecapital investment,while firms in lesscompetitive

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

argumentthatuncertaintyaffects firminvestmentthroughborrowingcosts. Incontrastto

predictionsinGilchristetal.(2014),uncertaintyhasnoeffectonnetdebtissueandanegative

effectonnetequityissueforfirmsinconcentratedindustries.

Thepositiveeffectofuncertaintyincompetitiveindustriessuggeststhatcompetitioncan

counteractthenegativeimpactofuncertaintyoninvestmentpredictedbytherealoptions

literature.Instead,uncertaintycanactivelyencourageinvestmentincompetitiveindustries

iffirmshaveoperationalflexibility.Inlinewiththisprediction,Ifindthatthepositiveeffect

isonlysignificant for firms incompetitive industries thathaveoperational 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 paperhave important policy implications.My results providenew

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.

38

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TablesandFigures

Figure2

Seasurfacetemperatureforecastsin“Niño3.4”regionThis 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 kindlyprovidedthedataforthisstudy.

Figure1

Seasurfacetemperatureanomaliesin“Niño3.4”regionThisgraphdepictsthetimeseriesofseasurfacetemperature(SST)anomaliesinthecentralPacificregion

“Niño3.4”from1980to2014.TheNationalOceanicandAtmosphericAdministration(NOAA)definesanomalies

asseasurfacetemperaturedeviationsinaregionfromitshistoricalmean.MonthlySSTdatacanbedownloadedfromthewebsiteoftheClimatePredictionCenter(CPC)attheNOAA.

SSTforecastdistributionmeanH-CD?E SSTforecastdistributionSD6-CD?E

39

Figure4

IndustrysensitivitiestoENSO Thisgraph illustrates thedistributionofENSOsensitivitiesacross industriescalculatedusingregression

model(2).Forillustrationpurposes,thesesensitivitieswereestimatedbasedontheentiresampleoffirmsforthe

periodfrom1985to2014andarenottime-varying.Intheestimationofmodel(4),time-varyingsensitivitiesare

usedbasedonthecalculationsdescribedinSection4.1.1.Theleftfigureshowssensitivitiesfortheentiresample

offirms,whiletherightfigureillustratesENSOindustrysensitivitiesthatarestatisticallysignificantata5%level.

Figure3

IllustrationoflowandhighuncertaintystateThisgraphdepictsexampleprobabilitydistributionsfor12-monthENSOforecasts(bluecurve).Uncertainty

islowerinyearswhenthestandarddeviationofthedistributionissmaller(leftgraph).Incontrast,uncertainty

ishigherwhenthestandarddeviationislarger(rightgraph).

t2uvwx

y2uvwx

=zB,-CD?E =zB,-CD?E

40

Figure5

Correlationbetweenvolatilityindex(VXO)andinvestmentratesThisgraphillustratesthecorrelationbetweenthevolatilityindexVXOprovidedbytheChicagoBoardof

OptionsExchangeandfirm-levelinvestmentrates.TheVXO isbasedonimpliedvolatilitiesoftheS&P100

options.Thelinesrepresentsimplelinearregressionlinesforthefullsampleoffirmsandthesampleoffirms

incompetitiveandconcentratedindustries.

41

Table1

SummarystatisticsThistableshowssummarystatisticsandthedistributionoffirmcharacteristicsinthefullsampleofCompustat

firmsandtheanalysissample,whichonlyincludesfirmsinindustrieswithstatisticallysignificantsensitivitiesto

changesinENSOconditions.

Compustat Analysissample

Mean Median Std.dev. Mean Median Std.dev.

Sales($M) 1689 82 9598 1574 67 9464TotalAssets($M) 2197 95 12883 2128 85 12321Investment($M) 143 4 922 145 4 982

Capitalstock($M) 1048 28 6772 1033 25 7058

It/Kt-1 24% 13% 27% 25% 14% 28%

R&D($M) 70 2 436 75 3 447R&Dstock($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% 24% 20% 9% 25%

N 92,341 51,203 Firms 12,017 6,695

42

Table2

TheeffectofuncertaintyoncapitalinvestmentunderdifferinglevelsofproductmarketcompetitionThis tablepresents resultsofvarious regression specifications to investigate the impactofuncertaintyon

capitalinvestment(investmentscaledbythepreviousyear’scapitalstock).PanelAillustratesresultsofasimple

OLSestimationofmodel(1).PanelsB,CandDpresentresultsoftheinstrumentalvariableestimationdescribed

inSection4.1.2usingthreedifferentmeasuresofcompetitiontosplitthesampleintofirmsinhighlycompetitive

industries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).CompustatHHI,HPHHIandPCMdenoteaCompustatbasedHerfindahl-Hirschman Index(HHI), theHHImeasureused inHobergandPhilips(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 -0.0760*** -0.0606*** -0.0877***

(0.01) (0.01) (0.01)

Tobin'sQ 0.0162*** 0.0123*** 0.0176***

(0.00) (0.00) (0.00) N 51,203 24,663 26,540

Controls Yes Yes Yes

YearFE Yes Yes Yes

FirmFE Yes Yes Yes

PanelB:IV-CompustatHHI

AllFirms HighCompetition LowCompetition (1) (2) (3)

Returnvolatility -0.0471 0.2792*** -0.2384***

(0.03) (0.07) (0.05)

Tobin'sQ 0.0197*** 0.0172*** 0.0192***

(0.00) (0.00) (0.00) N 51,203 24,663 26,540

F-Stat.-1stStage 229.12 87.99 117.93

Controls Yes Yes Yes

YearFE Yes Yes Yes

FirmFE Yes Yes Yes

43

Table2continued

PanelC:IV–HPHHI

AllFirms HighCompetition LowCompetition (1) (2) (3)

Returnvolatility -0.0466 0.3726*** -0.3786***

(0.03) (0.04) (0.08)

Tobin'sQ 0.0197*** 0.0213*** 0.0157***

(0.00) (0.00) (0.00) N 34,168 11,390 22,778

F-Stat.-1stStage 325.82 473.45 104.82

Controls Yes Yes Yes

YearFE Yes Yes Yes

FirmFE Yes Yes Yes

PanelD:IV-PCM

AllFirms HighCompetition LowCompetition (1) (2) (3)

Returnvolatility -0.0506 0.1157** -0.1114**

(0.03) (0.06) (0.04)

Tobin'sQ 0.0197*** 0.0177*** 0.0224***

(0.00) (0.00) (0.00) N 49,750 16,583 33,167

F-Stat.-1stStage 262.95 97.37 153.65

Controls Yes Yes Yes

YearFE Yes Yes Yes

FirmFE Yes Yes Yes

44

Table3

The effect of competition on the investment-uncertainty relationship using interaction terms and IVestimates

ThistablepresentsresultsofIVestimatesformodel(4)wherethedependentvariableisinvestmentscaled

bythepreviousyear’scapitalstock.CompindicatesthecompetitionproxyandVolxCompisaninteractiontermbetweenthecompetitionmeasureandreturnvolatility.Thecolumnheadersindicatewhichcompetitionmeasure

isused.CompustatHHI,HPHHIandPCMdenoteaCompustatbasedHerfindahl-HirschmanIndex(HHI),theHHImeasureusedinHobergandPhilips(2010)andaprice-costmarginmeasure,respectively.Controlsincludetotal

assets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standard

errorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

CompustatHHI HPHHI PCM (1) (2) (3)

Returnvolatility 0.3071*** 1.2789*** 0.1627***

(0.06) (0.15) (0.04)

Tobin'sQ 0.0206*** 0.0216*** 0.0213***

(0.00) (0.00) (0.00)

Comp 0.6929*** 12.9676*** 0.0003

(0.17) (1.75) (0.53)

VolxComp -0.9048*** -18.6904*** -0.0006

(0.32) (2.73) (0.00) N 51,203 34,168 49,750

F(Vol) 121.25 179.8 114.8

F(VolxComp) 43.98 170.21 0.48

Controls Yes Yes Yes

YearFE Yes Yes Yes

FirmFE Yes Yes Yes

45

Table4

Theeffectofaggregateuncertaintyonfirm-levelinvestmentincompetitiveandconcentratedindustriesThis tablepresentsresultsofestimatingmodel(1)usingOLSwhere thedependentvariable is investment

scaledby thepreviousyear’scapitalstock.Comp isadummyvariable indicatingwhethera firm is inahighlycompetitiveindustry(basedonCompustatHHIthresholdvalueof0.1).VolxCompisaninteractiontermbetweenComp and the volatility indexVXO.HHI is the CompustatHHImeasure andVXO xHHI is an interaction termbetweenHHI and the volatility index. Controls include total assets, debt-to-equity ratio, cash flows scaled bycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and

*indicate1%,5%,and10%significance,respectively.

(1) (2) (3) (4)

VXO -0.0021*** -0.0024*** (0.00) (0.00) Tobin'sQ 0.0235*** 0.0235*** 0.0213*** 0.0213***

(0.00) (0.00) (0.00) (0.00)

Comp. -0.0377*** -0.0188***

(0.01) (0.01)

VXOxComp. 0.0009*** 0.0006***

(0.00) (0.00)

HHI 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

46

Table5

Testingtherealoptionmechanismintheirreversibleinvestmentliteratureasthedriverofthenegativeeffectofuncertaintyoninvestmentinconcentratedindustries

Thistablepresentsresultsofestimatingmodel(4)usinginstrumentalvariables.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)isusedto

split thesample into firms in lesscompetitive industries(PanelA)and firms inhighlycompetitive industries(PanelB).Thedependentvariable incolumns1 to4 is

investment scaled by the previous year’s capital stock. In columns 1 and 2, the sample is split into firmswithmore 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 Growth Value WC Labor NDI NEI

(1) (2) (3) (4) (5) (6) (7) (8) Returnvol. -0.8117*** -0.1805** -0.1634 -0.2934*** -0.1962** -0.0247* 0.0042 -0.1298*** (0.18) (0.08) (0.13) (0.08) (0.09) (0.01) (0.03) (0.05)Tobin'sQ 0.0117*** 0.0208*** 0.0172*** 0.0360*** 0.0296*** 0.0009* -0.0001 0.0149*** (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) N 9,517 9,397 10,802 14,192 15,105 25,587 24,739 24,307F-Stat.-1stStage 44.13 76.35 56.66 76.22 60.25 143.84 139.43 126.46Controls Yes Yes Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes Yes Yes

47

Table5continued

PanelB:HighlyCompetitiveIndustries

Redeployability Valuevs.Growth FlexibleFactors ExternalFinancing

LowRI HighRI Growth Value WC Labor NDI NEI

(1) (2) (3) (4) (5) (6) (7) (8) Returnvol. 0.0798 0.3720** 0.4990*** -0.0096 0.2773** 0.0034 -0.0120 0.3337*** (0.11) (0.16) (0.13) (0.15) (0.13) (0.00) (0.05) (0.10)Tobin'sQ 0.0139*** 0.0230*** 0.0164*** 0.0342*** 0.0288*** 0.0000 0.0003 0.0209*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) N 9,766 9,864 12,649 9,582 12,660 22,644 22,351 20,532F-Stat.-1stStage 84.50 20.50 90.66 17.37 122.33 82.28 79.63 51.39Controls Yes Yes Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes Yes Yes

48

Table6

DriversofthepositiveeffectofuncertaintyoninvestmentinhighlycompetitiveindustriesThistablepresentsresultsofestimatingmodel(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) HighL/K(>25thpctile) LowLUM HighLUM

(1) (2) (3) (4) (5) (6) Returnvol. 0.1287 0.1535** -0.1242 0.2969*** 0.5730* -0.3144** (0.12) (0.07) (0.17) (0.07) (0.30) (0.13)Tobin'sQ 0.0127*** 0.0143*** 0.0166*** 0.0152*** 0.0166*** 0.0136*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) N 10,661 11,235 4,456 17,734 8,691 7,191F-Stat.-1stStage 44.91 137.37 22.78 157.84 20.71 51.99Controls Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes

49

Table6continued

PanelB:LessCompetitive(MoreConcentrated)Industries

Labor-CapitalRatio LaborUnionMembership

LowL/K(<median) HighL/K(>median) LowL/K(<25thpctile) HighL/K(>25thpctile) LowLUM HighLUM

(1) (2) (3) (4) (5) (6) Returnvol. 0.0960 -0.3494*** -0.8705 -0.2709*** -0.3945*** -0.2305*** (0.11) (0.05) (0.92) (0.04) (0.09) (0.06)Tobin'sQ 0.0219*** 0.0151*** 0.0000 0.0190*** 0.0172*** 0.0179*** (0.00) (0.00) (0.02) (0.00) (0.00) (0.00) N 10,025 14,818 4,314 20,828 8,493 13,701F-Stat.-1stStage 12.31 235.87 1.19 148.95 62.66 106.73Controls Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes

50

Table7

LargechangesinuncertaintyandthedynamicsofinvestmentThistablepresentsresultsofestimatingthepersistenceofthedifferentialeffectofuncertaintyoninvestment

between competitive and concentrated industries using the difference-in-differences approach presented inSection7.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

LargeIncreaseinUncertainty LargeDecreaseinUncertainty

(1) (2) (3) (4)

Tobin'sQ 0.0149*** 0.0149*** 0.0149*** 0.0149*** (0.00) (0.00) (0.00) (0.00)C -0.0152*** -0.0237*** -0.0107** -0.0103* (0.00) (0.01) (0.00) (0.01)

!×#$% -0.0078 0.0074 (0.01) (0.01)

!×#$& 0.0034 -0.0080 (0.01) (0.01)

!×#' 0.0152*** 0.0284*** -0.0181*** -0.0185*** (0.01) (0.01) (0.00) (0.01)

!×#(& 0.0433*** -0.0049 (0.01) (0.01)

!×#(% 0.0030 0.0041 (0.00) (0.01) N 51,203 51,203 51,203 51,203Controls Yes Yes Yes YesYearFE Yes Yes Yes YesFirmFE Yes Yes Yes Yes

51

Table8

TheeffectofuncertaintyonR&DinvestmentundervaryinglevelsofcompetitionThis 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 basedmeasureof theHerfindahl-Hirschman Index(HHI) isused tosplit thesample into firms inhighlycompetitiveindustries(HighComp.)andfirmsinlesscompetitiveindustries(LowComp.).R&DQisanalternativeproxyforR&Drelatedinvestmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusingtheknowledgestockasthedenominator.Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighComp. LowComp. AllFirms HighComp. LowComp.

(1) (2) (3) (4) (5) (6) Returnvol. -0.0500 0.0945 -0.0941** -0.0209 0.1823*** -0.1277*** (0.03) (0.06) (0.04) (0.03) (0.05) (0.04)Tobin'sQ 0.0075*** 0.0062*** 0.0081*** (0.00) (0.00) (0.00) R&DQ 0.0001* 0.0001 0.0000 (0.00) (0.00) (0.00) N 24,834 14,110 10,724 31,588 17,391 14,197F-Stat.-1stStage 181.45 103.63 65.90 262.56 176.58 100.70Controls Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes

52

Table9

TheeffectofuncertaintyonadvertisingspendingundervaryinglevelsofcompetitionThis 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 theHerfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(HighComp.)andfirmsinlesscompetitiveindustries(LowComp.).AdvertisingQisanalternativeproxyforadvertisingrelatedinvestmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusingtheadvertising stock as the denominator. Controls include total assets, debt-to-equity ratio, cash flows scaled bycapitalstock,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) (0.04) (0.07) (0.06)Tobin'sQ 0.0118*** 0.0119*** 0.0048** (0.00) (0.00) (0.00) AdvertisingQ 0.0000 0.0000** -0.0000 (0.00) (0.00) (0.00) N 13,400 6,096 7,304 17,351 7,654 9,697F-Stat.-1stStage 153.31 52.94 80.14 260.14 93.79 158.40Controls Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes

53

Table10

TheeffectofuncertaintyonhiringundervaryinglevelsofcompetitionThis 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 basedmeasure of theHerfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(HighComp.)and firms in lesscompetitive industries (LowComp.).LaborQ isanalternativeproxy for laborrelatedinvestmentopportunitiesandcalculatedbyusingthesamenumeratorasinequation(5)butusingthenumberofemployeesasthedenominator. Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock, salesand the12-monthSST level forecast. Standarderrorsare clusteredat the firm level. ***, ** and *indicate1%,5%,and10%significance,respectively.

AllFirmsHighComp.

LowComp. AllFirms

HighComp.

LowComp.

(1) (2) (3) (4) (5) (6)

Returnvol. -0.0164* 0.0034 -0.0247* -0.0116** 0.0032* -0.0213** (0.01) (0.00) (0.01) (0.01) (0.00) (0.01)Tobin'sQ 0.0003* 0.0000 0.0009* (0.00) (0.00) (0.00) LaborQ -0.0000 -0.0000 -0.0000 (0.00) (0.00) (0.00) N 48,231 22,644 25,587 60,935 28,094 32,841F-Stat.-1stStage 264.42 82.28 143.84 325.65 249.05 159.62Controls Yes Yes Yes Yes Yes YesYearFE Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes

54

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Appendix

A DataandVariableConstruction

Thissectiondescribesthedatathatisusedforthemainanalysisandtheconstructionof

relevantvariablesinmoredetail.

Iusefirm-levelfinancialdataobtainedfromtheCompustatdatabase,whichIsupplement

withreturndatafromCRSP.Thisdatasetismergedwithseasurfacetemperature(SST)and

SSTpredictiondata fromtheClimatePredictionCenter(CPC)at theNationalOceanicand

AtmosphericAdministration.Thisresults inadatasampleofmore than12,000 firmsand

90,000firm-yearobservation.Thissampleisfurtherrestrictedbyexcludingindustriesfor

whichtheindustry-levelsensitivitytoENSO,)*,,-./0 ,estimatedinmodel(2)isnotstatistically

differentfromzero(atthefivepercentlevel).Thefinalanalysissamplecomprises6,695firms

and51,203firm-yearobservations.

Theconstructionofvariables included in theestimationofmodel (4) followsstandard

approaches in theexisting literature.12,,inequation (4) is capitalexpenditureby 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 thebookvalueof property, plant and equipment

(PPE):

32,' = 5562,'

Inthenextstep,Irecursivelycalculatethereplacementvalueofcapitalstockforeachfirmin

eachperiodtasfollows

32,, =551,

7

551,$&7 1 − :7 32,,$& + 12,,

63

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).

64

arecash flowscaledbypreviousyear’scapitalstock,debt-to-equityratio, totalassetsand

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

debt issuesequals Item111minusItem114andnetequity issuesequals Item108minus

Item115.

65

B ElNiñoSouthernOscillation(ENSO)–AdditionalDetails

ChangesinENSOconditionscanhavealarge-scaleimpactonweatheraroundtheworld.

FigureB.1illustratestheclimatologicaleffectsofwarm(ElNiño)andcoldepisodes(LaNiña).

Thesechangesinweatherpatternshavepronouncedeffectsoncommodity-producingand

constructionindustriesaswellasglobalandnationalcommodityprices(Cashinetal.,2015).

Asanillustration,FigureB.1showsthatwarmepisodescanresultinunusuallywarmwinters

in largepartsof theU.S.,while coldepisodeshave theoppositeeffect.These changes can

significantly impactenergydemand in theU.S. and thus thepriceof commodities suchas

naturalgasandheatingoil.Amoredetaileddiscussionoftheimpactoftheseverestandmost

recentElNiñoeventondifferentindustriesintheU.S.andtheoverallU.S.economycanbe

foundinChangnon(1999).

TheintensityofENSOeventscanbemeasuredusingseasurfacetemperature(SST)and

sea-level air pressure in the Pacific Ocean. The most commonly usedmeasure for ENSO

activity in theacademic literature isbasedonsea surface temperaturemeasurements 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 theearly1980s, theClimatePredictionCenter(CPC)hasbeensummarizing

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.

AlongwithdataaboutcurrentENSOconditions, theweeklyandmonthlybulletinsalso

contain extensive information about expected future ENSO activity in the form of

probabilistic forecastsofENSOevents (seeFigureB.3)and forecastsof futureseasurface

temperature (see Figure B.4). Figure B.4 illustrates consolidation forecasts of sea surface

66

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

ColumbiaUniversityon June5,2014.Thediscussionstates thatover “the lastmonth, the

chance of El Niño and its ultimate strength weakened slightly in the models (Fig. 6).

Regardless,theforecastersremainjustasconfidentthatElNiñoislikelytoemerge.IfElNiño

forms, the forecasters andmost dynamicalmodels, such as NCEP CFSv2, slightly favor a

moderate-strengtheventduringtheNorthernHemispherefallorwinter(3-monthvaluesof

theNino-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 theweekly andmonthly bulletins published by the CPC in the form of

quantitativegraphsandcomprehensivediscussions.

67

FigureB.1

GlobaleffectsofENSOThisfigureillustratestheglobalimpactoffluctuationsinElNiñoSouthernOscillation(ENSO)activityon

theclimateandweatherconditionsaroundtheworld.Source:ClimatePredictionCenter,NationalOceanicandAtmosphericAdministration(NOAA).

FigureB.2

SeasurfacetemperaturedeviationsintheTropicalPacificThis 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. ThisinformationisprovidedonaweeklybasisbytheClimatePredictionCenter.Source:ClimatePredictionCenter,NationalOceanicandAtmosphericAdministration(NOAA).

68

FigureB.3

ProbabilisticforecastofENSOeventsThisfigureillustratestheprobabilisticforecastsofENSOeventsfortheperiodbetweenSeptember,2015

and July, 2016.This information is providedon aweeklybasis by theClimate PredictionCenter. Source:ClimatePredictionCenter,NationalOceanicandAtmosphericAdministration(NOAA).

FigureB.4

ProbabilisticforecastofseasurfacetemperatureanomaliesinthePacificThisfigureillustratesconsolidationforecastsofseasurfacetemperatureinthePacificOceanfortheperiod

from November, 2015 to January, 2017. This information is provided on amonthly basis by the ClimatePredictionCenter.Source:NationalOceanicandAtmosphericAdministration(NOAA).

69

C Return-basedUncertaintyMeasuresandPotentialBiases

Theexistingempirical literatureoninvestmentunderuncertaintytakesmanydifferent

approachestomeasuringuncertainty.Themostcommonlyuseduncertaintymeasuresare

basedonvolatilityindicessuchastheVIXfromtheChicagoBoardofOptionsExchangeoron

firm-levelstockreturnvolatility(see,e.g.,Bloometal.,2007;KimandKung,2014;Leahyand

Whited,1996).Thesearegenerallyrathernoisyproxies foruncertaintyandoftencapture

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 growingbodyof empirical researchhas linkedproductmarket competition to stock

returnbehaviorandvolatility.Variousempiricalstudiesdocumentthatidiosyncraticreturn

volatilityusually increases inresponsetohigherproductmarketcompetition(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 isassociatedwithsubstantialdecreases incapitalexpenditureandR&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 inmore concentrated industrieshave virtuallyno

effect on investment. Combined, these results present several empirical challenges to

identifyingacausallinkbetweenuncertaintyandinvestmentwhenusingidiosyncraticstock

returnvolatilityasanuncertaintyproxy.Ifreturnvolatilityispositivelycorrelatedwiththe

degreeofproductmarketcompetitionandproductmarketcompetitionleadstodecreasesin

39Itcaneasilybeshownthatgivenatruemodel,V2, = [&\&2, + [%\%2, + Q2, ,omittingvariable\%2,

willleadtoabiasedOLSestimate,J lim.→a

[& = [& + J lim.→a

: [%,if[% ≠ 0and!NU(\&, \%) ≠ 0.

70

investment for firms in highly competitive industries, OLS estimates of the coefficient on

returnvolatilitywillhaveastrongdownwardbias incompetitive industries.UsingOLS in

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 flowand stock returns in the followingperiods. This relationship ismuch

weakerandmostlyinsignificantformoreconcentratedindustries.Inasimilarargumentto

above,ifreturnvolatilitycapturesthecyclicalnatureofindustryboomsandbustsandiffirms

incompetitiveindustriesfacelargerdecreasesinoperatingperformanceinbustperiodsthan

theircounterpartsinmoreconcentratedindustries,theOLSestimateoftheuncertaintyeffect

willagainbedownwardbiasedforcompetitivefirms.

Itisthereforeessentialtoidentifyexogenousvariationinuncertaintytodrawanycausal

inferencesaboutthe investment-uncertaintyrelationshipandits interactionwith industry

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.

71

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.Beforeestimating the instrumentalvariablesspecification, I identify firms that

are sensitive to ENSO by estimating the industry specific coefficient)*,,-./0 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

estimationwill only come from firmswith 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

72

uncertaintycoefficient.Nevertheless,forrobustnesspurposesTableD.2presentstheresults

oftheIVestimatesforcapitalinvestmentusingthefullsampleoffirmswithoutdiscarding

industries that arenot sensitive to changes inENSO conditions. Firmson 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

monthlyseasurfacetemperaturemeasurements.Theresultingsensitivityestimates,)*,,-./0 ,

arekeyingredientstotheconstructionofmyinstruments.

Totesttheirrobustness,Ialsoestimatethefollowingversionofmodel(2)inthesameway

asdescribedinSection4.1.1

E2,f = )'2 + ghijk + )*,,

-./0 ∙ Df-./0 + m2,f (D.1)

wherejkisavectorofobservableriskfactors.Iemployafour-factormodelusingthethree

factors of Fama and French (1992), which I augment with themomentum risk factor in

Carhart(1997).Usingtheresultingestimates)*,,-./0intheconstructionofmyinstruments

describedinSection4andestimatingmodel(4)yieldstheresultspresentedinTableD.3.The

coefficients throughout this table are not statistically significantly different from the

coefficients inPanelBofTable2.Thus,themainfindingsofthepaperarerobusttousing

model(D.1)toestimateindustryENSOsensitivities:inresponsetoheighteneduncertainty,

firms in competitive industries increase investment and firms in concentrated industries

decreaseinvestment.

D.4 TimeInvariantENSOSensitivities

Theindustry-levelsensitivities,)*,,-./0 ,arecalculatedbyestimatingmodel(2)onafive-

yearrollingbasis(asdescribedinSection4.1.1).Foreachyeart,)*,,-./0iscalculatedbyusing

73

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 invariantsensitivities,)*-./0 , are thenused toconstruct the instrumentsas

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)andusingstockreturndata for firmsand theS&P500aswellasmonthlyseasurface

temperaturemeasurements.Oneconcerncouldbethatanindustry’ssensitivitytoachange

inENSOconditionsmightsystematicallycapture the levelofcompetition 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

sensitivities,)*,,-./0 ,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

coefficientsareconsistentwiththeestimates inPanelBofTable2.However,thenegative

74

effectofuncertaintyinconcentratedindustryisstronger,whiletheaveragepositiveeffectof

uncertaintyoninvestmentincompetitiveindustriesisslightlysmaller.

D.7 UnpredictableComponentofReturnVolatilityasUncertaintyProxy

Inmodel (4), Iuse firm-level stockreturnvolatilityasanuncertaintyproxy.Due to its

endogenous nature, I construct instruments based on ENSO prediction data to identify

exogenousvariation inreturnvolatility. Insteadofexaminingallof thevariability in firm-

levelstockreturns,Icanremovetheforecastablevariationbyestimatingthefollowinglinear

factormodel(Gilchristetal.,2014)

E2,n − Eno = p2 + qh

ijr + Q2,n

whereE2,n aredailystockreturnsandEno istherisk-freerate.jrisavectorincludingthethree

riskfactorsinFamaandFrench(1992)aswellasthemomentumfactorsuggestedbyCarhart

(1997).Thevolatilityoftheunpredictablepartofthefirm-levelstockreturnreturnisthen

calculatedas

s2,, =1

=,Q2,n − Q2,,

%@t

nu&

wherethesamplemeanofdailyfirm-levelreturnsinyeartisdefinedasQ2,, =&

@tQ2,n

@tnu& .

Theabovemeasure,s2,, ,representstheannualfirm-levelstockreturnvolatilityforfirmiin

yeartvoidoftheforecastablevariationinexpectedreturns40. Instrumentingthismeasure

insteadoftherawstockreturnvolatilityinmodel(4)yieldstheresultspresentedinTable

D.7.ThecoefficientsshownarenotsignificantlydifferentfromtheonespresentedinPanelB

ofTable2.

D.8 TheEffectofUncertaintyonLeverage(Debt-EquityRatio)

InSection6.1.3, I testalternativeexplanations for thenegativeeffectofuncertaintyon

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.

75

increasescreditspreadsandleadsfirmstodecreasetheirleverageandinvestmentspending.

Examining external financingdecisionsof firms in concentrated industries in response to

heighteneduncertainty, I findnoevidenceforthischannel.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.

76

TableD.1

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–additionalspecificationsofmodel(4)Thistablepresentsresultsofestimatingmodel(4)usinganinstrumentalvariablesapproachwherethedependentvariableisinvestmentscaledbythepreviousyear’s

capitalstock.ThesampleonlyincludesfirmsinindustriesthataresensitivetoENSOfluctuations.TheCompustatbasedmeasureoftheHerfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevel forecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Returnvol. 0.0330 -0.0438 -0.0439 0.3005*** 0.3267*** 0.2557*** -0.2268*** -0.2842*** -0.2360*** (0.03) (0.03) (0.03) (0.05) (0.06) (0.06) (0.04) (0.05) (0.05)Tobin'sQ 0.0198*** 0.0172*** 0.0192*** (0.00) (0.00) (0.00) N 51,203 51,203 51,203 24,663 24,663 24,663 26,540 26,540 26,540F-Stat.-1stStage 483.69 421.11 325.36 254.13 257.87 170.94 196.51 199.15 144.78Controls No Yes Yes No Yes Yes No Yes YesYearFE Yes Yes Yes Yes Yes Yes Yes Yes YesFirmFE Yes Yes Yes Yes Yes Yes Yes Yes Yes

77

TableD.2

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–withoutexcludingindustriesthatarenotsensitivetoENSO

This table presents results of estimating model (4) using an instrumental variables approach where the

dependentvariable is investment scaledby thepreviousyear’s capital stock.Thesample includes firms inall

industriesregardlessofwhethertheyaresensitivetoENSOfluctuations.TheCompustatbasedmeasureofthe

Herfindahl-HirschmanIndex(HHI)isusedtosplitthesampleintofirmsinhighlycompetitiveindustries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controls includetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevel forecast.Standarderrorsare

clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3)

Returnvolatility -0.0682** 0.1377** -0.1023** (0.03) (0.07) (0.04)Tobin'sQ 0.0197*** 0.0169*** 0.0200*** (0.00) (0.00) (0.00) N 85,095 32,456 52,639F-Stat.-1stStage 176.63 101.54 109.92Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

78

TableD.3

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–ENSOsensitivitiescalculatedusing4-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,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and

10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3) Returnvolatility 0.0349 0.2712*** -0.2634** (0.04) (0.06) (0.12)Tobin'sQ 0.0191*** 0.0194*** 0.0171*** (0.00) (0.00) (0.00) N 51,203 24,663 26,540F-Stat.-1stStage 321.63 144.29 32.02Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

79

TableD.4

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–timeinvariantENSOsensitivitiescalculatedusingmonthlyreturndatafrom1980to1984

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,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsare

clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3) Returnvolatility -2.4350* 2.6832*** -0.7865*** (1.35) (0.40) (0.30)Tobin'sQ 0.0019 0.0293*** 0.0112*** (0.01) (0.00) (0.00) N 51,203 24,663 26,540F-Stat.-1stStage 3.44 42.81 6.81Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

80

TableD.5

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–ENSOsensitivitiescalculatedbyincludingcompetitionmeasureasanadditionalriskfactorinmodel(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

measureof theHerfindahl-Hirschman Index(HHI) isused tosplit thesample into firms inhighlycompetitive

industries(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standard

errorsareclusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3)

Returnvolatility -0.0272 0.1852*** -0.1059* (0.04) (0.06) (0.06)Tobin'sQ 0.0191*** 0.0169*** 0.0205*** (0.00) (0.00) (0.00) N 51,203 24,663 26,540F-Stat.-1stStage 54.09 225.49 35.90Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

81

TableD.6

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–industry-levelENSOsensitivitiesbasedontwo-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(HighCompetition)andfirmsinlesscompetitiveindustries(LowCompetition).Controls includetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevel forecast.Standarderrorsare

clusteredatthefirmlevel.***,**and*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3) Returnvolatility -0.0540 0.1892*** -0.3504*** (0.06) (0.07) (0.11)Tobin'sQ 0.0184*** 0.0154*** 0.0180*** (0.00) (0.00) (0.00) N 51,203 24,663 26,540F-Stat.-1stStage 35.31 134.83 17.46Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

82

TableD.7

Theeffectofcompetitionontheinvestment-uncertaintyrelationship–removingforecastablevariationinexpectedreturnsfromfirm-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 lesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and

*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3) Returnvolatility -0.0484 0.2404*** -0.2368*** (0.03) (0.06) (0.05)Tobin'sQ 0.0197*** 0.0171*** 0.0191*** (0.00) (0.00) (0.00) N 51,203 24,663 26,540F-Stat.-1stStage 269.43 197.63 120.03Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes

83

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 lesscompetitiveindustries(LowCompetition).Controlsincludetotalassets,debt-to-equityratio,cashflowsscaledbycapitalstock,salesandthe12-monthSSTlevelforecast.Standarderrorsareclusteredatthefirmlevel.***,**and

*indicate1%,5%,and10%significance,respectively.

AllFirms HighCompetition LowCompetition

(1) (2) (3)

Returnvolatility 0.0114 -0.0146 0.0263** (0.01) (0.01) (0.01) N 51,203 24,663 26,540F-Stat.-1stStage 262.98 172.66 117.25Controls Yes Yes YesYearFE Yes Yes YesFirmFE Yes Yes Yes