TheEffectofMergersandAcquisitionsonMarketPowerandEfficiency

BruceA.Blonigen* JustinR.Pierce# UniversityofOregon FederalReserveBoard

NationalBureauofEconomicResearch

August2015PreliminaryandIncomplete

Abstract:Afundamentalquestionintheanalysisofmergersandacquisitions(M&As)isthe potential tradeoff between increased market power and efficiency gains. Theevidenceonthistradeoffintheliterature,however,isfarfromconclusive.CasestudiesofM&Afindmixedevidence,andestimationinmoregeneralsettingsishamperedbythedifficultyofseparatelyidentifyingproductivityfrommarkups.Inthispaper,weusenewtechniquestoseparatelyestimateproductivityandmarkupsacrossheterogeneousindustries using firm‐ or plant‐level data, and then employ these estimates in adifferences‐in‐differencesframeworktoexaminetheaverageimpactofM&Aactivityonproductivityandmark‐ups.WeemploydataonM&AtransactionscombinedwithU.S.Censusplant‐leveldatafortheentireU.S.manufacturingsectoroverthe1997to2007period.Robusttoavarietyofspecifications,wefindsignificantevidenceofincreasedmarkupsafterM&A,butnoevidenceofanyaverageimpactonplant‐levelproductivity.Moreover,wefindthattheincreaseinmarkupsgeneratesoverstatedeffectsofmergersontraditionalrevenueproductivitymeasures.Keywords:Markups,productivity,mergers.JELCodes:D24,G34,L41Acknowledgements:ThispaperhasbenefittedfromconversationswithPeterSchottandNicholasSly,aswellasparticipantsofaseminarattheLondonSchoolofEconomics.We thank Brian Hand and Dominic Smith for outstanding research assistance. Anyopinions and conclusions expressed herein are those of the authors and do notnecessarilyrepresenttheviewsoftheU.S.CensusBureau,theBoardofGovernorsoritsresearchstaff.Allresultshavebeenreviewedtoensurethatnoconfidentialinformationisdisclosed.*DepartmentofEconomics,1285UniversityofOregon,Eugene,OR,97403‐1285;541‐346‐4680;bruceb@uoregon.edu#BoardofGovernorsoftheFederalReserveSystem,20thandCSt.NW,Washington,DC20551,202‐452‐2980;justin.r.pierce@frb.gov

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1.Introduction

Merger and acquisition (M&A) activity by firms is an important phenomenon in the

globaleconomy,involvingseveraltrillionsofdollarsofworldwideassetsannually.1

Asa result there is significant change inownershipof assetsannually, especially for

advancedeconomies.Forexample,MaksimovicandPhillips(2001)findthatabout4%

oflargemanufacturingplantsintheUnitedStateschangeownershipeveryyear.

Relatedly,cross‐borderM&Aactivityisaprimarymodebywhichmultinationalfirms

engageinforeigndirectinvestment(FDI).2

Fundamental questions in finance and industrial organization concern the

motivationforandeffectsofM&Aactivity.Andperhapsthemostfundamentalquestion

isthepotentialtradeoffbetweenincreasedmarketpowerversusefficiencygainsinthe

wakeofaM&Atransaction.Increasedmarketpowercreatesnetwelfarelosses,while

efficiency gains lower costs and create net welfare gains. While the theory is

straightforward,estimatingtheseeffectsempiricallyisdifficult.Yet,understandingthe

actualeffectsofM&Aactivity inoureconomyisnotonly important fortheacademic

literature,butalsoakeyconcernforsocialwelfare.

Therehavebeenanumberofapproachestoestimatethesepotentialeffectsof

M&Aactivity,eachassociatedwiththeirownstrengthsandweaknesses.Inthe1980s

and 1990s, a finance literature developed that used stock market event studies to

examine the impact of a variety of phenomena on firms’ profitability, including the

1ArecentForbesarticlevaluedM&Adealsin2014ataround$3.5trillion.(http://fortune.com/2015/01/05/2014‐was‐a‐huge‐year‐for‐ma‐and‐private‐equity/)2SeetheWorldInvestmentReportpublishedannuallybytheUnitedNation’sConferenceonTradeandDevelopment.

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impactofM&Aactivity.Thesestudiesexaminechangesinreturnstofirmshareprices

after an announced M&A, generally finding that M&A activity leads to greater firm

profitability,withthebulkoftheprofitgainsaccruingtoshareholdersofthetargetfirm.

(seeRavenscraftandScherer,1987, foranoverview) Themethodologyofempirical

event studies is simple to implement consistently across a wide range of settings.

However,thisapproachisunabletoidentifywhetherthesourceofprofitabilitychanges

fromM&Aactivityisduetochangesinmarketpower,costefficiencies,orsomeother

factor.

Giventheseconcerns,morerecentanalysesoftheeffectsofM&Aactivityhave

taken primarily a case study approach,where the researcher can explicitly examine

morecloselytheparticularfeaturesofthefirmsandmarketwheretheM&Atakesplace.

Ashenfelter, Hosken and Weinberg (2014) document 49 such studies, which have

mainly focused on a few key sectors (airlines, banking, hospitals, and petroleum),

primarilybecausethesearethesectorswhereresearcherscanfinddetailedfirm‐and

product‐levelpricedata.3Mostofthesestudiesfocusonpriceandmarketsharechanges

toinfermarketpowereffects,typicallyfindingevidenceforincreasedmarketpowerby

the firms involved in the M&A activity with the exception of M&A activity in the

petroleumsector. While thesestudiescontributetoourunderstandingoftheeffects

M&Aactivity,theydosoinanincrementalfashion.Theytypicallyfocusonhighprofile

acquisitions, making it more likely that their results suffer selection bias and are

3Someprominentexamplesincludeairlines(Borenstein,1990;KimandSingal,1993,KwokaandShumilkina,2010),appliances(Ashenfelteretal.,2013),banking(FocarelliandPanetta,2003),andcottonspinning(Braguinskyetal.2013).

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thereforenotgenerallyrepresentativeofM&Aeffectsonmarketpower.Additionally,as

mentioned, their data and analyses are specific to the particularmarket they study.

Finally,withonlyacoupleexceptions,theydonothavethedatatoestimateefficiency

effectsofM&Aactivity.4Asaresult,wedonothaveageneralpictureoftheeffectsof

M&Aactivityonmarketpowerandefficiencyintheeconomyfromthispriorliterature

usingacasestudyapproach.

Incontrast,therehavebeenafewanalysesoftheaverageeffectsofM&Aactivity

onproductivityandmarketpowerusingmicro‐leveldataforabroadsetoffirms(or

plants)acrosstheeconomy,includingMcGuckinandNguyen(1995),Maksimovicand

Phillips (2001), Gugler (2003), and Bertrand (2008). With the exception of Gugler

(2003),thesepapersusedetailedplant‐orfirm‐leveldataonthemanufacturingsector

toestimatetheeffectofM&Aactivityontotal factor(or labor)revenueproductivity,

findingthatM&Aactivitypositivelyimpactstheseproductivitymeasures.Onechallenge

facedinthesestudies,whichwefindtobeimportantinoursettingaswell,arisesfrom

theuseofrevenueasaproxyforoutput. Inparticular,whenestimatingtheeffectof

M&Aontraditionalrevenueproductivitymeasures,themarketpowereffect–operating

through output prices–makes it impossible to identifywhether changes in observed

revenueproductivityareduetochangesintrueproductiveefficiencyormarketpower.5

Gugler et al. (2003) examinesM&A transactionsworldwide using COMPUSTAT firm

data.Theytakeareducedformapproachandconductadifference‐in‐difference(DID)

4TheexceptionsofwhichweareawareareJaumandreu(2004)whichfoundsomeefficiencyeffectsfromM&AactivityintheSpanishbankingindustryandBraguinskyetal.2013.Kulick(2015)findspositiveeffectsonbothpricesandefficiencyresultingfromhorizontalmergersinthecementindustry,usingoutputdatameasuredinphysicalunitsofquantity.5BertrandandZitouna(2008)alsoexaminetheimpactofM&Aactivityonprofits,butuseaccountingdataonearnings,whichcanbeaffectedbychangesinaccountingpracticesafteranM&Atransaction.

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analysisoftheextenttowhichsalesand/orprofitschangesignificantlyforafirmafter

anacquisition.Perhapsduetodatalimitations,theygenerallyfindmixedresults.

Thispaperprovidesanumberofnewcontributionstothisliteratureaddressing

theimportantquestionofM&Aeffectsonefficiencyandmarketpower.Thefirstisthe

applicationofrecenttechniquesdevelopedbyDeLoeckerandWarzynski(2012),where

one can separately estimate productivity and market power effects for firms with

minimalstructuralassumptions–onlycostminimizingbehaviorbyfirmsisneeded.We

useU.S.Censusplant‐leveldataforthemanufacturingsectorandmatchthatwithdata

onM&AactivityfromtheSDCPlatinumdatabasemaintainedbyThomsonFinancialto

examinetheeffectsattheplantlevelafteranacquisitionoverthe1997to2007period.6

Thisapproachhasimportantadvantagesoverthepriorstudies.Unlikethecasestudy

evidence,wecanexamineM&Aeffectsacrossabroadrangeofindustriesinaconsistent

framework. Unlike the prior studies estimating general effects ofM&A on revenue‐

basedmeasureofTFP,wecan identify theseparateM&Aeffectsonproductivityand

marketpowerinourestimates.

Asecondimportantcontributionofourpaperisthat,unlikemanystudiesinthe

prior literature, we address the difficult issue of endogenous selection due to the

possibilitythatthedecisiontoengageinM&Aactivitymayberelatedtocurrentand

expectedchangesinproductivityandmarketpower.ThiswouldbiasestimatesinaDID

frameworkthatuses“allotherfirms”asthecontrolgroup.First,oneadvantageofour

datainthisrespectisthatitisplant‐level,whereasM&Adecisionsaretypicallyatthe

6Around50%ofallU.S.M&Asareinthemanufacturingsectoroverrecentdecades,includingoursampleperiodfrom1997to2007.UseofCensusdataalsomeansthatoursampleincludebothprivateandpublicfirms.

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firm‐level. Many of the firms in our sample aremulti‐plant,whichmakes theM&A

decisionmore independent of the performance of any one plant. While some prior

studieshaveemployedplant‐leveldata,mosthavenot.Beyondthis,weexploreother

strategiestoconstructaplausiblecontrolgroup.

Themainalternativeweuseinthispreliminarydraftistouseplantswherean

acquisitionisannounced,butisnevercompleted,asourcontrolgroup.Suchplantshave

alltheattributesnecessarytoleadtoanannouncedacquisition,eliminatingaportionof

potentialsampleselectionbias.Thus,theidentifyingassumptionisthatthereasonfor

non‐completionoftheM&Ais independentof futureproductivityandmarketpower.

Belowweprovidefurtherdiscussionforwhythismaybeareasonableassumption.In

future drafts, we also plan to examine the robustness of our results when using

propensityscorematchingtechniquestoidentifyacontrolgroup. Whilethisisnota

novelstrategy,ithasnotoftenbeenemployedinstudiesofM&Aeffects,especiallywhen

examining effects of M&A on productivity and market power.7 Finally, we plan to

examine another more‐novel strategy in future drafts – using plants that will be

acquiredinsubsequentyearsinoursampleasacontrolgroupforcurrentlyacquired

plants. This isavalidstrategy if theattributesthatmakeaplantmore likely tobea

targetoftenexistforafewyearsbeforeasuccessfulmatchwithanacquirerismade.

Likepriorstudies,wefindthatM&Aactivitysignificantlyincreasestraditional

productivitymeasuresthatuserevenue‐relateddata.However,usingourestimatesthat

separatelyidentifytruetechnicalproductivityfrommarkupsusingthemethodsofDe

Loecker andWarzynski (2012), we find new evidence for an important divergence.

7ExceptionsincludeFresard,HobergandPhillips(2015),amongothers.

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M&Assignificantly increasemarkupsonaverage,buthaveno statistically significant

effectonproductivity.Themagnitudeofthemarkupincreaseiseconomicallysignificant

aswell,rangingfroma16%to85%increaseoftheaveragemarkupinthesample.These

resultsarerobusttowhetherwedefinethecontrolgroupasallothernon‐M&Aplants

orthemuchsmallergroupofplantsthatwereinvolvedinanannouncedM&Athatwas

ultimatelywithdrawn. They are robust to2SLS estimates,wherewe instrument for

M&Aselection,aswellastheinclusionofanumberoffixedeffects,includingplantfixed

effects.Theyarealsorobusttowhetherweexaminefive‐yearintervals,usingthefull

censusofU.S.manufacturingplants,orestimateusingannualsurveydataofonlythe

largestplantsfromtheAmericanSurveyofManufactures. Wealsopresentaplacebo

testtoverifythatsecular(pre‐)trendsarenotdrivingourresults,whichisacommon

concernwithDIDestimation.

On a final note, our focus is on the effect ofM&Aonplant‐level productivity,

consistentwithmanyprior studies ofM&A. Thereare, however, additionalways in

which M&A can affect overall firm‐level efficiency. First, an acquiring firm may

reallocate resources across the firm tomore efficient plants and/or shut down less‐

efficientplants. Inthisway,anM&Acouldincreaseefficiencyacrossthefirmevenif

plant‐levelproductivitywasunaffected.Weplantoexaminethisinfuturedrafts,but

havenotaddressedthissourceofefficiencygainsinthisversionofthepaper.Asecond

sourcemayberealizationofeconomiesofscalewithheadquarterservicesofthefirm

(management,marketing,advertising,etc.)afteranM&A.

Therestofthepaperproceedsasfollows.Inthenextsection,wediscussthedata

weusetoexaminetheeffectsofM&Aonmanufacturingplants.Wethendescribeour

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two‐stageestimationprocessthatbeginswithestimationofplant‐levelproductivityand

markupmeasuresandisthenfollowedbyemployingtheseasdependentvariablesina

DIDframework.Wethenpresentanddiscussourresultsbeforeconcluding.

2.Data

Wemake use of two datasets for this analysis. First, to calculate productivity and

markupsforU.S.manufacturingfirms,weemployconfidentialdatafromtheU.S.Census

Bureau’sCensusofManufactures(CM).TheCMcollectsplant‐leveldataforeveryU.S.

manufacturer including, for example, total value of shipments, value added, cost of

materials,employment,investmentandthebookvalueofcapital.Thisanalysisemploys

datafromthe1997and2007CMs,focusingonlongdifferencesinplant‐levelmeasures

of productivity and markups. 8 Lastly, because the CM contains data for all U.S.

manufacturers,weareabletocalculateseveralusefulcontrolvariables includingthe

Herfindahlindexforeachindustry,aswellaseachfirm’sshareofindustryoutput.

Ourseconddataset,ThomsonFinancial’sSDCPlatinumdatabase(SDC),contains

informationonmergerandacquisitiontransactionsinvolvingbothpublicly‐tradedand

privatefirms.Foreachtransaction,SDCprovidesdataforthetargetandacquiringfirms

includingthename,addressandmajorindustryofboth,withadditionalinformationfor

the firms’ corporate parents if applicable. SDC also reports a variety of detailed

informationaboutthetransaction,includingthedatesthemergerwasannouncedand

completed, the share of the target that was purchased and the share owned after

completionofthemerger.Moreover,SDCcontainsinformationformergersthatwere

8TheCMiscollectedeveryfiveyears,inyearsendingin2and7.

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announcedbutlaterwithdrawn.Weusethesetoffirmsinvolvedinthesewithdrawn

mergersasacontrolgroupinsomeportionsoftheanalysis.

Forpurposesofthispaper,wefocusonthesetofmergertransactionsinwhicha

U.S.manufactureristhetarget,sincethesearethefirmsforwhichCMproductionand

inputdataareavailable.Intermsoftimeperiod,weusemergertransactionsthatwere

completedorwithdrawnfrom1998to2006.Thistimeframeensuresthatweareable

toobserveeachtargetfirmbothbeforeandaftertheyareacquired.Itisalsoaperiod

that includes both periods of high M&A activity in the late 1990s and less activity

connectedwith a general slowdown of theworld economy in the early 2000s. The

sample period ends just before the start of the Great Recession. We also limit our

analysis tomerger transactions inwhich the entire target firm is purchased by the

acquirer. Withoutthisrestriction, itwouldbeimpossibletodetermineintheCensus

datawhichportionofthetargetfirmwasacquiredinthemerger.

Themergertransactiondata inSDCare linkedtotheCMdataviaanameand

addressmatchingprocedure. Suchmatchingisfarfromperfect,butwefindthatour

resultsarerobusttoanumberalternativematchingcriteriasuchaslimitingthesample

toonlytheobservationswhereboththefirmnamesandaddressesmatchtosamples

wherewerequireamatchinonlyoneofthesedimensions.

3.EmpiricalFramework

Our empirical analysis proceeds in two major steps. We first estimate plant‐level

markupsandproductivityfollowingthemethodsofDeLoeckerandWarzynski(2012),

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whichwebrieflydescribebelow. Wethenusetheseestimatesinasecond‐stageDID

frameworktoassesstheimpactofM&Aonplant‐levelmarkupsandproductivity.

3.1.Estimationofmarkupsandproductivity

Weclosely followthe frameworkdevelopedbyDeLoeckerandWarzynski (2012) to

separately identifyaplant’smarkupfromitsproductivity.9 Usingtheirnotation,we

beginwithaproductionfunction

,..., , , ,(1)

where , … , aretheVvariable inputchoicesbyplant i intimeperiodt; is the

plant’scapitalstock;and isaproductivityparameter.Assumingcostminimization,

wecanwritetheassociatedLagrangian,

,..., , , , ∑ . ,(2)

where , … , and arethevariableinputpricesandcostofcapital,respectively.

Thefirst‐orderconditionforanygivenvariableinput(V)is

.

0.(3)

Rearrangingthefirstconditions,wecanwrite:

. (4)

9Thiscontrastswithliteraturethatmakesmorespecificassumptionsonconsumerpreferencesandmarketstructuretostudyaparticularindustry.Perhapsthemostwell‐knownexampleistheseminalworkofBerryetal.(1995)andGoldberg(1995)tomodelandestimatestructuralparametersofmarketbehaviorintheautomobilemarket.

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Define the markup as ≡ ⁄ , where is the marginal cost of production.

Substitutingintheexpressionforthemarkup,yields

. (5)

Theleft‐handsideofequation(5)istheelasticityofoutputwithrespecttoavariable

input(whichwedenoteas ),while theratioon therighthandside is theshareof

expendituresonthevariableinputintotalsalesofthefirm(whichwedenoteas ).As

aresult,wecanexpresstheplant’smarkupasasurprisinglysimplyfunctionofthese

twoelements:

(6)

In order to obtain consistent estimates of the production functionparameters,DLW

followthemethodsproposedbyAckerberg,Caves,andFrazier(2006).Fortractability,

werestrictattentiontoproductionfunctionswithaHicks‐neutralscalarproductivity

termandassumecommontechnologyparametersforplants(withinthesameNAICS3‐

digitindustry):

,..., , ; exp .(7)

Takinglogsandassumingarandomerrorterm,wecanexpresstheproductionfunction

as:

, ; .(8)

Productivityandshockstoproductivityareunobservedtotheeconometrician,but

maybeendogenouswithinputchoicesmadebytheplant.Thisishandledthrougha

controlfunctionapproach.Inparticular,weassumethattheplant’scurrentchoiceof

materialsdependsonthecurrentlevelofanydynamicvariables(here,capitalstock),

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productivity,andanyotherobservablevariablesthatcouldaffectoptimalmaterial

demand: , , .Invertingthisfunctionweobtain,

, , ,(9)

whichservesasaproxyindexingaplant’sproductivity,providedthatmaterialdemand

is monotonic in productivity after conditioning on a plant’s capital stock and other

observablesinthevector, . 10

Wenowassumethatproductivityfollowsasimplelawofmotion:

.(10)

Usinglaborasourvariableinputandassumingatranslogproductionfunction,wecan

derivecurrentproductivityasafunctionofourparametersviaequation(8):

.(11)

Using this,we can derive an expression for the unobserved productivity shock as a

functionofourproductionfunctionparameters.Lastperiod’sinputdecisionsshouldbe

highlycorrelated,butindependent,ofthisperiod’sinputdecisions.Thus,wecanuse

theseasinstrumentsandformthefollowingmoments

0.(12)

and then use General Method of Moment (GMM) estimation techniques to recover

consistentestimatesofourproductionfunctionparameters.Withtheseinhand,wecan

constructconsistentestimatesofourindexofproductivityandmarkups.

10ThemonotonicityofmaterialdemandinproductivityisaconditionthatholdsforabroadclassofmodelsofimperfectcompetitionasdiscussedinDLW.

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Afewobservationsareworthnoting.First,were‐iteratethatthesemethodsare

generalenough toapplyacrossabroadrangeofheterogeneous industries. Weonly

require assumptions of cost minimization and some basic functional forms for the

productionfunction.However,followingpreviousstudiesusingthistechnique,wedo

notassumecommonproductionfunctionparametersacrossallplantsinoursample,but

estimateseparateparameters foreachNAICS3‐digit sector. Themethodabovealso

assumesproductionofa singleproduct. A recentpaperbyDeLoeckeretal. (2012)

highlights the complications when applying these techniques to multiproduct firm

becauseoneoftenhasonlyinformationonafirm’stotalinputusage,notinputusageby

eachproductitproduces.Here,wehaveplant‐level,ratherthanfirm‐leveldata,which

allowsustolargelyavoidtheissue.Thevastmajorityofplantsaresingle‐productor

veryclose,andourresultsarerobusttowhetherweexcludethesmallshareofplants

thathavesubstantialproductioninmorethanoneproduct.Afinalnoteisthatourmain

samplehasdataat five‐year intervals,whichcould lead toaweakcorrelationofour

(lagged)instruments.However,asdescribedbelow,wealsofindourresultstoberobust

tousingasmallersamplewherewehaveannualobservations.

3.2.DIDestimationofmarkupandproductivityeffectsofM&A

Inoursecondstageofestimation,weidentifytheeffectsofM&Aactivityonplant‐

levelproductivityandmarkupmeasuresthroughaDIDstrategy.Thereareanumberof

featuresofourdatathatwemustaddressinhowwedeviseourparticularDIDstrategy.

Themainaspectisthatourdataarerepeatedobservationsonplantsatthreefive‐year

intervals:1997,2002and2007.Inordertoensurethatwehaveatleastoneobservation

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of acquired plants before the acquisition and one after the acquisition,we focus on

acquisitionsthatoccurredbetween1998and2006.Asaresult,thelengthoftimesince

the acquisition has occurred can vary across observations. We show an example in

Figure1,whereanacquisitionoccursintheyear2000.Plantsinvolvedintheacquisition

willbecodedasnotsubjecttoanM&Ainthefirstyearofoursample1997,butthen

codedassubjecttoM&Ain2002and2007.Asonecansee,thisstructuremeansthatwe

areestimatingM&Aeffectsfortheaverageperiodsincetheplantwasacquired.11

WeestimateourDIDspecificationwithtwocontrolgroups.Inordertoprovide

abaseline, the first issimplyallotherplants in thesamplethatarenotacquired. Of

course, there is the real concernof sample selectionbiaswith this relativelygeneric

controlgroup.Asmentioned,usingplantdatamaysubstantiallymitigateselectionbias

becauseM&Adecisionsaremadeatthe(multi‐plant)firm‐level,nottheplantlevel.Past

studies of M&A effects have often controlled for this sample selection bias using

propensityscoremethods.12Weleavethisanalysisforfuturedraftsofthispaper,but

alsonotethatthisstrategyiseffectiveonlytotheextentthatonecaneliminatesuchbias

throughobservables.

Instead,weestimateM&Aeffectsusinganovelcontrolgroup–plantsthatwere

partofanannouncedM&Adealthatwasnotultimatelycompleted.Suchplantsshare

allthesameattributes–observedandunobserved–thatleadthemtobetargetedforan

M&Atransaction,arelativelyinfrequentevent. Andthenumberoffaileddealsisnot

11Wehaveexperimentedwithestimatingdifferentlaglengths,butdonotfindmuchvariationineffectsovertimesubsequenttotheacquisition.12SuchstudiesincludeHeyman,Sjöholm,andTingvall(2007),BertrandandZitouna(2008),ArnoldandJavorcik(2009),BandickandGörg(2010),andFresard,Hoberg,andPhillips(2013).

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trivial.Forexample,BranchandYang(2003)showthatabout11%oftheover1000U.S.

mergerstheyevaluateoverthe1991‐2000periodfailtocomplete.Akeyworrywith

thiscontrolgroupstrategyisthattherearealsofactorsunobservedtousthatbecome

observedtotheinvolvedfirms(especiallywithrespecttothetargetfirm)thatleadtoa

M&A deal failure. There is a small literature that evaluates failure of M&A deal

completion.13Mostofthecovariateswithexplanatorypoweraresolelyrelatedtothe

acquiring firm, including the type of financing it uses to fund the deal, its size, and

measures of their attitude toward completing the deal, which Baker and Savasoglu

(2002)indicateisthebestsinglepredictorofdealcompletion.Thereisn’tanyobvious

correlationbetweenthesefactorsrelatedtotheacquiringfirmandthefuturemarket

powerand/orproductivityof the targetplants,which is the focusofourstudy. The

media often reports that disputes betweenmanagers of the two firms–often termed

“socialissues”–canleadtofailedM&Adeals.14

4.Results

4.1.First‐stageestimatesofmarkupsandproductivity

WebeginbyusingthemethodsinDLWasdescribedinsection3.1toestimateamarkup

andameasureofproductivityforeachofthe187,100manufacturingplantsinourfull

sample.Table1providesthemeanandstandarddeviationoftheseDLWmeasuresof

markupandproductivity.ThemeasureofDLWproductivityissimplyanindexfroma

13AdditionalstudiesbeyondthoselistedinthetextincludesMitchellandPulvino(2001),Officer(2007),andBranch,Wang,andYang(2008)14TheinherentproblemisthattherearetwosetsofallseniormanagerscomingintoanM&Aandthisduplicationmustbeeliminated.Willis,A.(2001)“’Socialissue’maybekeytobankmergers,”TheGlobeandMail(Canada),August28,p.B17isanexamplearticleinthebusinesspressonthisissue.

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translogproductionfunction.Thus,itslevelisnotmeaningfulperse,butchangesinthe

indexwillreflectpercentagechangesintheplant’sproductivity.Asonecansee,there

issubstantialvariationinthisproductivitymeasureacrossplants.

Themarkupsmeasurehowmuchmoreisthepricechargedbythefirmthanits

marginalcost.Wefindquitehighmarkups,withanaveragemarkup(theratioofprice

tomarginalcost)ofaround5.5withastandarddeviationover7. Therangeofthese

markups ismuchhigherthanwhatDLWfindforSlovenianfirms,andabouttwiceas

highonaverageasthatfoundbyDeLoeckeretal.(2012)forIndianfirms.However,an

importantdifferenceisthatweestimatethesemarkupsattheplant‐level,notthefirm‐

level.Whilethepriceiswhatthefirmcancharge,the(marginal)costsarespecifictothe

plant. Inputs used and costs incurred by the firm for headquarter services, such as

advertising,distribution,centralmanagementandR&D,willtypicallynotbeaccounted

forintheseproductionplants.Inotherwords,weshouldexpectlargermarkupsatthe

plant‐level because the price has to only cover the plant’s costs, but the firm’s non‐

productioncostsaswell.Therefore,likeourDLWproductivitymeasure,wearenotas

interestedinthelevelperse,buthowthemarkupchangeswithanM&A.

For comparison purposes,wewill be examining the impact ofM&A onmore

traditional revenue productivity measures; specifically, the log of total factor

productivity,whichwecalculateusing thesamemethodologyasFoster,Haltiwanger

and Syverson (2008), and a simplemeasure of log labor productivity. Table 1 also

providesthemeanandstandarddeviationofthesemeasures.

Forthepurposesofcontrollingforpotentialsampleselectionbias,weemploya

sampleofonlytheplantswhereaM&Adealwasannounced.Thisreducesoursample

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substantiallyto4,200plants. ThelasttwocolumnsofTable1providethemeanand

standarddeviationofourmarkupandproductivityparametersforthissampleofplants.

There are not large differences in these descriptive statistics between this reduced

sample and the full sample, though both averagemarkup and all three productivity

measuresareslightlyhigherinthesmallersampleofplantssubjecttoanannounced

M&Adeal,suggestingthatthereistargetingoffirmswithplantsthathavehigherthan

averagemarkupsandproductivity.

4.2.Second‐stageDIDestimatesofM&Aeffects:Baselineestimates

Wenowtakeourfirst‐stageestimatesanduseaDIDspecificationtoexaminetheimpact

ofM&Asonthese threemeasuresofproductivityandtheDLWestimateofmarkups.

FollowingastandardDID,ourindependentvariableofinterestisaninteractionbetween

anindicatorvariablethataplanthasbeen“treated”bybeingsubjecttoanM&Aandan

indicatorthatitistheperiodaftertreatmenthastakenplace.Welabelthisvariableas

the“TargetFirminthePost‐M&APeriod.”Wealsoincludetheindicatorvariableforthe

“Post M&A Period” to control for any secular trends, year fixed effects to capture

macroeconomic shocks affecting all plants identically in a particular year, and plant

fixed‐effects,whichcontrolforanytimeinvariantfactors(observedorunobserved)that

affectanindividualplant’sproductivityormarkup.

Table2providesresultsforourfullsampleofplants.Forthefullsample,weonly

lookatthelongdifferenceofoursample, includingonlytheyears1997and2007,to

avoid concerns about ambiguity of definingwhen a control plant is in a “postM&A

period.”ThefirsttwocolumnsofTable2provideestimatesoftheimpactofM&Aon

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traditional revenue‐based measures of productivity, log revenue TFP and log labor

productivity. Weestimateapositive,butstatistically insignificant,M&Aeffecton log

revenueTFP,butapositiveandstatisticallysignificantM&Aeffectonourmeasureof

labor productivity. As mentioned, these traditional revenue‐based measures can

confoundchangesinmarketpowerwithchangesintrueproductivity.

To address this, the second two columnsofTable2provide resultswhenwe

apply our DID exercise to our first‐stage estimates where we estimate separate

estimatesofameasureofproductivityandmarkupforeachplant.Nowweseeasharp

divergence. There is a negative, but statistically insignificant, effect on productivity

whenaplanthasbeenacquired,butastatisticallysignificantincreaseinmarkup.The

magnitudeofthemarkupeffectissizeableaswell,withthe1.167effectcorresponding

toroughlya20%increaserelativetothemeanmarkup.

4.3.Second‐stageDIDestimatesofM&Aeffects:AnnouncedM&Asample

Our baseline estimates do not control for sample selection issues, other than

through inclusion of plant‐level fixed effects, which capture time‐invariant

characteristics. However, plants of firms targeted for M&A may have unobserved

attributes that will affect changes in future markups and productivity that could

spuriously show up as M&A effects. To address this potential for selection on

unobservablesweconstructanovelcontrolgroup.

Specifically, this alternate control group consists of plants in firms thatwere

announcedastargetsofM&A,butforwhichthemergerwasultimatelywithdrawn.By

definition,theseplantspossesstheattributesnecessarytoleadtoanannouncedmerger,

18

eliminatingaportionofpotentialsampleselectionbias.Oneconsequenceofrestricting

the control group to the setof firmswithwithdrawnmergers is that it substantially

reducesoursamplesizefromover187,000observationstojust4,200observations.

Estimates obtained with this alternate control group are qualitatively very

similartothoseobtainedwiththebaselinesample.Asshowninthefirsttwocolumns

ofTable3,ourrevenue‐basedproductivitymeasurescontinuetoshowapositiveM&A

effect.Coefficientestimatesforthemergereffectvariablearestatisticallysignificantfor

bothrevenue‐basedmeasures,thoughtheeffectsareoffairlymodestmagnitudes–less

than5%oftheaverageproductivity.Moreover,weobtainthesamequalitativeresults

fortheDLWmeasuresonproductivityandmarkupswiththisannouncedM&Asample

andthefullsample–thereisnodiscernibleM&Aeffectontheproductivityofacquired

plants,butsubstantialM&Aeffectsontheirmarkups. Here, thecoefficientestimates

suggestamuchlargermarkupeffectofsomethingaround60%oftheaveragemarkup

in the sample. Moreover, the results indicate that even after our initial control for

sampleselection,wecontinuetofindthattheeffectofmergersonrevenueproductivity

measures isoverstated for theaverageplant in themanufacturingsector,due to the

increaseinmarkups.15

Weconductseveraladditionalrobustnessexerciseswiththissampleofplants

thataresubjecttoanannouncedM&Adeal. First,toconfirmthatourresultsarenot

driven by spurious matches between the Thomson Financial and Census data, we

construct a sample composed only of firms with an exact match between the two

15Kulick’s(2015)examinationofhorizontalmergersinthecementindustryalsofindsevidenceofover‐estimatedeffectsofmergersonrevenueproductivity,usinganentirelydifferentestimationprocedure.

19

databases(i.e.matchingonfirmname,address,cityandstate).Requiringthestricter

criterionofanexactmatchgivesmorecertaintyofthematchquality,butalsoreduces

oursamplebyafairamount.InTable4weshowresultswhenwelimitourannounced

M&Asampletoonlythoseobservationsthatmeetastrictmatchcriterion.Thislimits

thenumberofobservationsevenfurther from4,200to just1,900. Nevertheless, the

estimatesarequalitativelyidenticaltothoseinTable3withthelessstrictmatchcriteria.

Thesecondrobustnesscheckinvolvesasecondtechniquetocontrolforsample

sectionbiasviaa2SLSstrategythatthatinstrumentsforwhetheranannounceddealis

completedornot.Inparticular,weinstrumentforM&Acompletionbyinteractingthe

“PostM&APeriod” variablewith indicator variables for the year theM&A dealwas

announcedfortheplant. Theideaisthatseculartrends,suchasbusinesscyclesand

changesinantitrustenforcementintheyearthattheM&Adealisannouncedcouldaffect

whetherthedealiscompleted.Theresultsofthese2SLSestimates,reportedinTable5,

arequalitativelyverysimilartotheOLSestimatedM&Aeffects.

In Table 6, we explore a possible source of heterogeneity in our results. In

particular,onemightthinkthattheM&Aeffectonmarkupswouldbegreaterfortargets

thatalreadyhavesignificantmarketsharefortheproducttheyproduce.Weexamine

thisby interactingthetargetfirm’smarketshareintheplant’sproductwithourDID

regressors. As Table 6 shows, there is an estimated positive coefficient on the

interactionof“TargetFirminPostM&APeriod”with“FirmMarketShare”inthemarkup

equation, but it is not statistically significant. These interactions are statistically

insignificantintheotherequationsexceptforthelogrevenueTFPequation,wherethis

20

samefocusinteractiondoeshaveapositiveandstatisticallysignificantcoefficient.The

weakstatisticalresultsmaybeduetolowpowerfromarelativelysmallsamplesize.

AfinalrobustnesscheckisexaminationofonlytheplantsintheannouncedM&A

sample for which we have a balanced panel of annual data. While the Census of

Manufacturesdataisgatheredonfive‐yearintervals,theAnnualSurveyofManufactures

(ASM)gathersdataonlargerplantsannually.UseofannualdatamaybehelpfulforDLW

estimationmethods, as noted above, and also allows formore precise coding of the

timing ofM&A announcements in our data. Because the sample frame for theASM

changeseveryfiveyears,werestrictattentiontothesetofplantsthatarepresentin

everyyear,toavoidspuriousinstancesofentryandexit.Thisrestrictionreducesthe

announcedM&Asamplefrom4,200to3,300.Table7providesresultsforthissample

ofannualobservationsonlargerplants. Onceagain,weobtainqualitativelyidentical

estimates.

In summary, our results are robust to a number of alternative specifications

meant to address various concerns onemay have with our estimates. They give a

consistentmessage that acquiredplantsdonot see statistically significant effects on

productivity,butdoexperiencepositivesignificanteffectsonmarkups.Wethinkthese

resultsaresurprisinglyrobustgiventhesmallnumberofobservationsinourannounced

M&A sample. Future drafts will explore a couple other alternative control‐group

strategiestoprovidefurtherrobustnessexercises.

4.4.Second‐stageDIDestimatesofM&Aeffects:Aplacebotest

21

Afinalconcernwithouranalysisistheworrythatpre‐existingseculartrendsfor

the treatment and control group could be driving spurious correlations for our

estimatedM&Aeffects.Toaddressthisconcernweconstructthefollowingplacebotest.

WeadddataforCensusyears1987and1992toourannouncedM&Asampleandthen

addindicatorvariablesthatflagourtargetedplantsin1992and1997.Theseareyears

when these targeted plants were not yet acquired; hence, these are the placebo

treatments. Iftherearesignificantcoefficientsontheseplacebotreatments,itwould

castsubstantialdoubtonthevalidityoftheestimateeffectweareobtainingforthetrue

treatmentvariable. Relatedly, if significant, thesevariableswould indicatepre‐trend

differences in our treated and control groups,which is amajor concern for anyDID

analysis. In other words, this particular placebo test is also a test for pre‐trend

differences.

AsresultsinTable8show,ourestimateseasilypassthisplaceboandpre‐trend

test. Thefirstfourcolumnsshowtheresultswhenweincludetheplacebovariables,

“Target Firm in 1992” and “Target Firm in 1997.” Estimated coefficients on these

variablesare statistically insignificant forall threemeasuresofproductivityandour

markupmeasure.Thissampleofobservationsissomewhatdifferentfromourprevious

samplesdiscussedaboveduetotheadditionalyearsof1987and1992.Socolumns5

through 8 of Table 8 verify that our base specification yields qualitatively identical

results to our previous analysis showing no M&A effects on DLW productivity, but

significant,positiveM&Aeffectsonmarkups.

5.Conclusion

22

This paper estimates the effects of mergers and acquisitions on productivity and

markupsofplantsacrossallU.S.manufacturingindustries.Ouranalysismakesuseof

high‐qualityU.S.CensusBureaudatacoveringtheuniverseofU.S.manufacturingplants,

whicharematchedtothesetofprivateandpublicmergersandacquisitionstrackedby

ThomsonFinancialintheirSDCPlatinumdatabase.Usinganoveltechniquedeveloped

byDeLoeckerandWarzynski(2012),wefindthatcontrollingformarketpowereffects

isimportantwhenestimatingtheeffectofM&Aonfirmandplant‐levelproductivity.In

particular,wefindthatwhileM&Aisassociatedwithastatisticallysignificantincrease

inrevenue‐basedproductivitymeasures,thereisalsopositiveandsignificanteffecton

markups. We findnoeffectofM&Aonaproductivitymeasure thatcontrols for this

changeinmarketpower.

23

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FIGURE1:DataandCodingofM&ATreatment

1997 2002 2007

Acquisition(Year=2000)

CensusYears

Pre‐AcquisitionObservation

Post‐AcquisitionObservations

27

Table1:First‐stagemeasuresofmarkupsandproductivity

FullSample

SampleWithOnlyAnnouncedM&A

Deals

Variable MeanStandardDeviation Mean

StandardDeviation

Logrevenuetotalfactorproductivity 4.07 0.60 4.38 0.63Loglaborproductivity 4.33 0.65 4.69 0.69DLWproductivitymeasure ‐0.74 1.87 ‐1.67 2.57DLWmarkup 5.49 7.97 7.20 11.27Notes:Wecalculatetotalfactorproductivityatthe3‐digitNAICSlevelusingthesamemethodologyasinFoster,Haltiwanger,andSyverson(2008).TheDLWmarkupandproductivitymeasuresareestimatedatthe3‐digitNAICSlevelusingthetechniquesinDeLoeckerandWarzynski(2012).Thereare187,100observationsinthefullsampleand4,200observationsinthesamplethatonlyincludesplantsthatwerepartofannouncedM&Adeals.

28

Table2:BaselineResultswithFullSampleofPlants

Variables

LogRevenueTFP

LogLaborTFP

DLWProductivity

DLWMarkup

TargetFirminPostM&APeriod 0.022 0.044* ‐0.063 1.167*** (0.016) (0.024) (0.060) (0.215)Post‐M&APeriod 0.067*** 0.161*** ‐0.322*** 0.557*** (0.002) (0.002) (0.004) (0.025) Constant 4.031*** 4.235*** ‐0.556*** 5.154*** (0.001) (0.002) (0.003) (0.018) Observations 187,100 187,100 187,100 187,100R‐squared 0.89 0.82 0.93 0.88Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

29

Table3:BaselineResultsWhereControlPlantsarefromWithdrawnM&ADeals(ReducedSample)

VariablesLogRevenue

TFPLogLabor

TFPDLW

ProductivityDLW

Markup

TargetFirminPostM&APeriod 0.150*** 0.123* ‐0.078 4.638** (0.057) (0.070) (0.112) (2.320)Post‐M&APeriod ‐0.158*** ‐0.125* 0.156 ‐4.237* (0.058) (0.071) (0.121) (2.314) Constant 4.358*** 4.590*** ‐1.477*** 6.442*** (0.009) (0.013) (0.028) (0.156) Observations 4,200 4,200 4,200 4,200R‐squared 0.85 0.73 0.90 0.84Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

30

Table4:ReducedSamplewithStricterMatchCriteria

VARIABLESLogRevenue

TFPLogLabor

TFPDLW

ProductivityDLW

Markup

TargetFirminPostM&APeriod 0.283*** 0.181* ‐0.011 2.696*** (0.076) (0.097) (0.153) (0.626)Post‐M&APeriod ‐0.320*** ‐0.265*** 0.108 ‐1.928*** (0.078) (0.101) (0.163) (0.684) Constant 4.364*** 4.562*** ‐1.474*** 5.752*** 0.014 0.019 0.037 0.146 Observations 1,900 1,900 1,900 1,900R‐squared 0.83 0.66 0.90 0.79Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

31

Table5:2SLSEstimateswithReducedSample

VariablesLogRevenue

TFPLogLabor

TFPDLW

ProductivityDLW

Markup

TargetFirminPostM&APeriod ‐0.079 0.292 ‐0.183 6.222** (0.164) (0.241) (0.558) (2.996)Post‐M&APeriod 0.054 ‐0.281 0.252 ‐5.699** (0.153) (0.225) (0.520) (2.793) Constant 4.357*** 4.591*** ‐1.478*** 6.452*** (0.009) (0.012) (0.029) (0.155) Observations 4,200 4,200 4,200 4,200Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

32

Table6:InteractionswithFirmMarketShare

VariablesLog

RevenueTFPLogLabor

TFPDLW

ProductivityDLW

Markup

TargetFirminPostM&APeriod 0.115* 0.095 ‐0.138 4.515* (0.061) (0.075) (0.119) (2.392)Post‐M&APeriod ‐0.127** ‐0.100 0.195 ‐4.153* (0.062) (0.076) (0.127) (2.383)TargetFirminPostM&APeriod×FirmMarketShare 1.283*** 1.031 2.229 4.611 (0.461) (0.715) (1.762) (10.479)Post‐M&APeriod×FirmMarketShare ‐1.123** ‐0.94 ‐1.425 ‐3.051 (0.439) (0.671) (1.688) (10.298) Constant 4.358*** 4.590*** ‐1.477*** 6.443*** (0.009) (0.013) (0.028) (0.156) Observations 4,200 4,200 4,200 4,200R‐squared 0.85 0.73 0.90 0.84Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

33

Table7:ResultsfromReducedSamplewithAnnualData

Variables

LogRevenueTFP

LogLaborTFP

DLWProductivity

DLWMarkup

TargetFirminPostM&APeriod ‐0.047 ‐0.084 ‐0.432*** 2.815*** (0.070) (0.077) (0.118) (1.091)Post‐M&APeriod 0.046 0.053 0.377*** ‐2.408** (0.069) (0.075) (0.134) (1.078) Constant 4.554*** 4.887*** 3.881*** 4.969*** (0.015) (0.023) (0.125) (0.194) Observations 3,300 3,300 3,300 3,300R‐squared 0.90 0.83 0.99 0.76

Notes:Robuststandarderrorsinparentheses.Estimatesforplantandyearfixedeffectsaresuppressed.P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.

33

Table8:PlaceboRegressionsUsingDatafrom1987through2007

Variables

LogRevenueTFP

LogLaborTFP

DLWProducti‐vity

DLWMarkup

LogRevenueTFP

LogLaborTFP

DLWProducti‐vity

DLWMarkup

TargetFirminPostM&APeriod 0.102* 0.114 ‐0.196** 2.249** 0.103* 0.102 ‐0.262*** 2.425** (0.062) (0.076) (0.092) (1.122) (0.057) (0.071) (0.082) (0.988)PostM&APeriod ‐0.137** ‐0.097 0.220** ‐1.680 ‐0.138** ‐0.088 0.276*** ‐1.822* (0.062) (0.077) (0.098) (1.106) (0.058) (0.072) (0.095) (0.984)TargetFirminYear1992 0.052 0.062 0.124 ‐1.397 (0.061) (0.086) (0.091) (0.996)TargetFirminYear1997 ‐0.044 ‐0.010 0.125 0.541 (0.052) (0.070) (0.104) (0.933)Constant 4.205*** 4.115*** ‐0.416*** 4.013*** 4.205*** 4.115*** ‐0.416*** 4.017*** (0.011) (0.016) (0.032) (0.105) (0.011) (0.016) (0.032) (0.105) 6,200 6,200 6,200 6,200 6,200 6,200 6,200 6,200 0.81 0.71 0.89 0.75 0.81 0.71 0.89 0.75Notes:Robuststandarderrorsinparentheses. Estimatesforplantandyearfixedeffectsaresuppressed. P‐valuesof0.01,0.05,and0.10aredenotedby***,**,and*respectively.