BecomingaSuccessfulResearcher1. Energy,Time,Smarts,Ambition,Perseverance,

Instructors,FellowStudents,…2. ToolsandSkills

– Mathtools(e.g.,dynamicoptimization)– Numericalanalysistechniques– Constructingandanalyzingeconomicmodels– StatisticsandEconometrics– theoryandmethods– **Datasources,datahandlingskills,measurement– **Empiricaldesigns– *Usingstatisticsandeconometricsinresearch– **Communicationskills– oralandwritten 1

Professor Steven Davis, University of Chicago, January 2018

3. **Knowledgeofthefieldincourse-relatedtopics:– Tobuildonwhatcamebefore– Toadvancethefrontierratherthanreinventthewheel– Forinspiration,andforexamplesofhow(not)todoresearch– Toconnectwithotherresearchers,andtopersuadethenthat

yourworkisinterestingandworthyoftheirattention

4. **Howtodevelopgoodideasfor(empirical)research,andhowtodeviseaneffectiveresearchstrategy:new,feasibleandwithhighpotentialtocastlightonimportanttopics,questionsandmodels

5. **CriticalJudgment– Assessingstrengthsandweaknessesinworkbyothersand

yourself– Identifying&droppingweakorimpracticalresearchideas– Refiningandpursuingproming/goodresearchideas 2

Forthemostpart,thiscoursefocusesonapplyingtoolsandtechniquesyou’velearnedelsewhereinyourstudies.Twopartialexceptions:1. Youwilllearnsomebasictext-basedmethods,apply(someof)

theminthehomeworkassignmentsandseeseveralexamplesoftext-basedmethodsineconomicsresearch.Examplesinclude:

– MarcoSammon’s tutorial,AnIntroductiontoPythonforTextAnalysis.– HomeworkAssignments1and2,whichaskyoutoconstructtext-based

measuresofriskexposuresfrom10-Kfilingsandusetheminresearchthatcastslightonfirm-levelequityreturnsandotheroutcomes.

– “MeasuringEconomicPolicyUncertainty,”mypaperwithBakerandBloomthatusesscaledfrequencycountsofnewspaperarticlestoquantifypolicy-relateduncertaintyandassessitseffectsonfirm-levelandaggregateperformance.

– SpeciallecturebyTarekHassan:Heappliesmachine-learningmethodstotranscriptsofconferencecallsaboutquarterlyearningsreportsforpubliclylistedfirms.Heusesthesemethodstoquantifyfirm-levelpolicyrisksandassesseffectsoninvestment,hiring,lobbyingandmore. 3

Theuseoftextasdataandtext-basedmethodsineconomicsresearchisexplodingrapidly.Seetherecentandexcellentsurveyof“TextasData”ineconomicsresearchbyGentzkow,KellyandTaddy (2017).Formoreonmachine-learningmethodsappliedtotextualdata,youmightstartbyperusingthematerialsandreferencesonMattTaddy’s website:http://taddylab.com.2. Youwillhaveampleopportunitytopracticeyouroralandwritten

communicationskillsinthiscourse.– MostPhDstudentsinEconomicsinvesttoolittleindevelopingand

polishingtheircommunicationskills,inmyview.– Ifyoucan’texpressyourideasclearly,it’sasignyouhaven’tthoughthard

enoughaboutyourideasandhowtoexplainthemtoothers.– Poorcommunicationskillsslowyourintellectualdevelopment.Ifyoucan’t

expressyourideasclearly,it’shardforotherstoprovideusefulfeedback.– Forthesamereason,youwillmakefasterprogressonyourdissertation

andyourjobmarketpaperifyouwriteandspeakclearly.– Seethecoursesyllabusformoreontheimportanceofcommunication.– GetStrunk&White’sTheElementsofStyle.Readit.Practiceitsprinciples

andrules.– Read“AnEconomist’sGuidetoVisualizingData,”bySchwabish intheJ.of

EconomicPerspectives,28,no1.Applyitslessonsandprinciples.4

• Governments routinely collect data on the employment, wages, revenues, industry and location of nearly all businesses, often at both the establishment and firm levels.– Data include taxpayer IDs and other identifiers

• Governments use these data for tax compliance purposes, as inputs to NIPA tables, as sampling frames for business surveys, and as raw material in the creation of widely used economic statistics.

Professor Steven Davis, January 2018

5

Large Business Databases • With (much!) work, these raw data can be turned

into core longitudinal research databases covering the universe of business firms and establishments– Establishments linked to parent firms– Both followed longitudinally (harder for firms)– In some cases, employers are also linked to their

employees, and both are followed longitudinally. – In some cases, businesses are also linked to their

owners, and both are followed longitudinally.6

Large Business Databases, 2 • Data from other sources – one-off and recurring

surveys, other administrative records, and proprietary data – are often merged with these core databases to investigate a specific issue.

• Advances in data storage and computing power greatly increase the scope for building these databases and using them as research tools.

• The next slide lists two core longitudinal U.S. business databases that emerge from different administrative record systems.

7

Two Large U.S. Databases 1. LBD:LongitudinalBusinessDatabase(Census)

– Firms&Establishments,linkedandtrackedovertime– Universalcoverage,annualobservationssince1976– Corevariables:employment,payroll,sales/revenue

(since1994),industry,location,companyname,IDs2. BED:BusinessEmploymentDynamics(BLS)

– Firms(intra-state)&Establishments,linkedandtrackedovertime

– Universalcoverage,quarterlysince1990– Corevariables:employment,industry,location,IDsTheLBDandBEDcoverbusinesseswith1+employees8

Other Business Databases • Other longitudinal business databases

derive from panel surveys conducted by statistical agencies, proprietary sources created for commercial purposes, etc.

• Some of the most exciting research ideas on the margin combine core business micro datasets like the LBD and BED with smaller, targeted sources that contain rich information about particular aspects of business behavior and outcomes. 9

Selected Business Databases3. COMPUSTAT: Rich source of data on firms

listed on major exchanges or traded OTC.– Heavily used in economics and finance research.– Considered in more detail later in this lecture– You will use in Homework Assignments #1 and #2

4. 10-K filings: Text documents that publicly listed firms file with the U.S. Securities & Exchange Commission:– Available for download through EDGAR.– You will use 10-K filings for text-based analysis in

HWs 1&2. 10

5. LRD:Alarge,rotatingpanelofmanufacturingplantswithannualcoveragebackto1972andcoverageinbusinesscensusyearsbeforethen– Large,richsetofmeasures– DrawsonAnnualSurveyofManufacturesandonce-

every-fiveyearsCensusofManufactures– Heavilyexploitedineconomicsresearch,e.g.,my

bookonJobCreationandDestruction.6. ManufacturingEnergyConsumptionSurvey

– Highlydetaileddataonenergy-relatedinputs,expenditures,technologiesandpracticesforroughly15,000manufacturingplants

– Every3or4yearssince1985– SeeDavisetal.(RESTAT,October2013)forinfo.

11

7. AnnualSurveysofBusinessforRetail,WholesaleandManufacturing(ASM).

8. Manufacturing&OrganizationsPracticeSurvey(MOPS)– asupplementtotheASM.– SeveralpapersinSectionsIIandVIIIofthereadinglist.– IamdeeplyinvolvedindesigningthepartofMOPSthat

dealswithelicitingsubjectiveprobabilitydistributionsoverfutureplant-leveloutcomes

9. VariousCensusesofBusiness– Extensivecoverageofestablishmentsandfirmsaboutonce

everyfiveyears,largesetofmeasures– CoverageofConstruction,FIRE,Retail,Wholesale,

Manufacturing,Mining,Servicesandmore.OnlytheManufacturingdatahavebeenheavilyworked.

– Firstavailableyearvariesbyindustrysector12

10.ILBD:LBD+revenue,industryandownershipforbusinesseswithnoemployees.(Davisetal,2009).

11.JobOpeningsandLaborTurnoverSurvey(JOLTS):BLSestablishment-levelsurveyofjobvacanciesandworkerflows(quits,layoffs,hires,etc.)

12.CapitalIQ:Commercialplatform,extensivedataonfinancialcharacteristicsofbusinessesandtheirsale(changesinownership,capitalstructure,etc.)www.capitaliq.com/Main3/ourproducts_platform.asp

13.Dealogic:Anothercommercialplatformwithdataonfinancialcharacteristicsofbusinesses,businesssales,etc.www.dealogic.com/ 13

• Datasets1and5-10areproductsoftheU.S.CensusBureau.Theysharecommonbusinessidentifiers,makingitrelativelyeasytomergetheestablishment-levelandfirm-leveldataacrossdatabases.TogetafullersenseoftherangeofbusinessmicrodatasetsthatresidewithintheU.S.Censussystem,perusewww.census.gov/ces/dataproducts/economicdata.html

• Likewise,datasets2and11areproductsoftheU.S.BureauofLaborStatistics,anditisreasonablystraightforwardtomergethem.

14

• Whenbusinessdatasetsdonotsharecommonidentifiers,theycanbemergedonthebasisofbusinessnameandaddress,industry,sizeandothercharacteristics.Thistypeofmergingprocesscaninvolveagreatdealofwork,andittypicallyyieldsmatchratesacrossdatasetswellbelow100percent.

• Fortunately,youcanoftenbuildonpreviouseffortsbyothers.Forexample,Davisetal.(2007)buildonMcCueandJarmin (2005)tomatchCompustat datatofirm-levelrecordsintheLBDthrough2005.

15

SelectedLongitudinalEmployer-WorkerDatasets14. LongitudinalEmployerHouseholdDynamics– LEHDmicrodata:quarterlyearningsforworkers,linked

toemployersandbothfollowedlongitudinally.– Datastartin1990forselectedstates,withmorestates

availableinmorerecentyears– Seewww.census.gov/ces/dataproducts/lehddata.html

15. SocialSecurityAdministration(SSA)longitudinalemployerworkerdata

– Annuallongitudinaldataontheearningsofprivatesectoremployeesfrom1974onwards,linkedtoemployerswhoarealsofollowedlongitudinally

16

SelectedLongitudinalEmployer-WorkerDatasets16. GermanSocialSecurityData– SimilartoLEHD

andU.S.SSAdata,butbetterintworespects:First,theyallowforarelativelycleandistinctionbetweenearningsandwages.Second,theyallowforamuchsharperdatingofjob-losseventsandotheremploymenttransitions.Strongrecentapplicationsinclude:

– Jarosch (2015)inananalysisoftheearningslossesofdisplacedworkers.

• Jarosch graduatedfromChicago,andthiswashisjobmarketpaper.It’snowR&RatEconometrica!

• Hispaperhasitsrootsinworkhedidforthiscourse! 17

18

A.Newmeasurementthatinformsourthinkingaboutfundamentalaspectsofeconomicbehavior.Examples:1. DavisandHaltiwanger(1992)andthebook byDavis,

HaltiwangerandSchuh(1996)developempiricalunderpinningsfortheflowapproachtolabormarkets.Slide20andSectionIIIinthecoursereadinglist.

2. In“VolatilityandDispersioninBusinessGrowthRates,”Davisetal.(2007)showthattrendsinbusinessvolatilitydiffereddrasticallybetweenpubliclylistedandprivatelyheldfirmsinthedecadesleadingupto2001.Coveredlaterinthislecture.

19

3. DavisandHaltiwanger(2014)documentasecularfallinthe“fluidity” ofU.S.labormarketsanddevelopevidenceonitsimplicationsforemployment.Laterinthecourse.

4. “FirmingupInequality”bySongetal.(2016)exploitslongitudinalworker-employerlinkeddatafromtheSSAtocastnewlightontheevolutionofU.S.earningsinequality.

5. “Capitalistsinthe21st Century”bySmith,YaganandZidar (2017)exploitslongitudinalfirm-ownerlinkeddatatodebunkthenotionthatpassiverentiershavereplacedtheworkingrichatthetopoftheU.S.incomedistribution.

Empirical Regularities Uncovered by the Flow Approach to Labor Markets

• Grossjobflowsacrossemployersareremarkablylarge– ingoodeconomictimesandbad,ineverymarketeconomy,invirtuallyeveryindustryandsector

• Workerflowsarelargeryet• Between-sectoremploymentshiftsaccountforasmallshareofjobflows.Idiosyncraticfactorspredominate.

• Jobandworkerflowsexhibitpronouncedcyclicalmovements.

• Workerandjobflowsaretightlylinkedinthecrosssectionandovertime.(Davis,Faberman andHaltiwanger,2012,J.ofMonetaryEconomics).

• AllstudentsofthiscourseshouldreadDavis,FabermanandHaltiwanger(2006).Thatwillgiveyoutheminimalbackgroundyouneed.Ifyouwanttodigdeeper,seethereadingsinSectionIII. 20

21

B.Uncoveringempiricalregularitiesthatchallengestandardtheoryandguidenewresearch.Example:Davis,Faberman andHaltiwanger(2013)useJOLTSmicrodatatostudyvacancies,hiresandvacancyyieldsinalargesampleofU.S.employers.• TheyshowthattheC-Sbehaviorofvacancyyieldsisinconsistentwithstandardequilibriumsearchtheories.

• Theydevelopcompellingevidencethatemployersuseotherinstruments,inadditiontovacancynumbers,tovarytheirpaceofhiring.

• Theirevidenceleadsthemtoformulateageneralizedmatchingfunctionthataccommodatesaroleforrecruitingintensity(pervacancy)andoutperformsthestandardmatchingfunction,H=M(U,V),inmanyways.

22

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-5.0 -4.0 -3.0 -2.0 -1.0

Log Daily Fill Rate

Log Hires Rate

Hires-Weighted Least SquaresSlope (s.e.) = 0.820 (0.006)R-squared = 0.993

Data points correspond to growth rate bins

This empirical relation should be flat according to Mortensen-Pissarides models!

• Wewillcoverthispaperon“TheEstablishment-LevelBehavioronVacanciesandHiring”indetaillaterinthecourse.

• See SectionVIinthecoursereadinglistforrelatedempiricalstudiesandandseveralpapersthatadvancetheoreticalexplanationsfortheempiricalrelationshiponthepreviousslideandotherevidenceinthepaper.

• Wewillalsoconsidersomework-in-progressthatexploitsproprietarydatathatlinksmillionsofjobseekers,applicationsandvacancypostings.

C.Identifyingandcorrectingsampledesignflawstoproducebetteraggregatestatistics.Subtleflawsinsampledesigncanleadtomajorerrorsinmeasurementandinference.•Remark:Asampledesignsuitableforestimatinglevelscanbeunsuitableforestimatingchangesorflows.•Example:TheoriginalsampledesignfortheJobOpeningsandLaborTurnoverSurvey(JOLTS)underweightedtailmassinthecross-sectionalgrowthratedensity.Asaresult,publishedJOLTSstatisticsunderstatedworkerflowsandjobopenings,andmisstatedtherelativecyclicalityofhiresvs.separationsandquitsvs.layoffs•Solution:ReweightthesampletoensurethatitreplicatestheemploymentgrowthratedensityintheBED. 24

Rates of Hires, Quits, Layoffs and Job Openings in the Cross Sectionof Establishment Growth Rates, JOLTS Micro Data, 2001-2006

Reproduced from Davis, Faberman, Haltiwanger and Rucker, 2010.

25

Reproduced from Davis, Faberman, Haltiwanger and Rucker, “Adjusted Estimates of Worker Flows and Job Openings In JOLTS,” NBER volume, 2010.

26

• Ouradjustedstatisticsforhiresandseparationsexceedthepublishedstatisticsbyaboutonethird.

• Ouradjustedlayoffrateismorethan60percentgreaterthanthepublishedlayoffrate.

• Ouradjustmentssignificantlyaltertime-seriespropertiesaswell.– Aggregatehiresare50percentmorevariablethanseparationsin(old)publishedJOLTSstatistics,asmeasuredbythevarianceofquarterlyrates,but20percentlessvariableaccordingtoouradjustedstatistics.

– Quarterlyquitratesaremorethantwiceasvariableaslayoffsinthe(old)publishedstatisticsbutequallyvariableaccordingtoouradjustedstatistics.

27

• BLSreviseditspublishedJOLTSstatisticsafterwecirculatedourpaper.Theirnewstatisticsaremuchclosertoour“adjustedestimates”.

• Dootherimportanteconomicstatisticssufferfromsimilarmeasurementproblemsthatdistortestimatedlevelsandtime-seriesproperties?– Investmentexpenditures,businessborrowing?

• Perhaps,butit’sunclear(tome).– Theerrorsintroducedbyflawedandill-suitedsampledesignscanbecorrectedexpostbyadjustingsurvey-basedestimatestomatchbenchmarkquantities.

– However,suitablebenchmarksarenotalwaysavailableor,whenavailable,notalwaysusedwell. 28

• Foraveryshortandeasy-to-digestdiscussionoftheweaknessesintheJOLTSsampledesign,whytheyledtoseriousproblemsinthepublishedJOLTSstatistics,andhowtocorrectforthesampledesignproblems,readDavis(2010).

• Whilethebasicideabehindthecorrectionisquitesimple,variouschallengescomplicatetheactualimplementation.Forthegorydetails,seeDavis,Faberman,HaltiwangerandRucker(2010).

29

D.ExploitingCross-SectionalRelationstoConstructSyntheticTime-SeriesData¨Statisticalmodelofcross-sectionalrelations+completequarterlydataonthecross-sectionaldistributionofestablishment-levelgrowthratesà syntheticdataforaggregateworkerflows

¤BEDdataonf+model-basedŵ for1990-2001¤BEDdataonf+JOLTS-basedwfor2001-2010.Thismethodrequiresagoodstatisticalmodelofhoww(workerflowrate)relatestog(employmentgrowthrate)inthecrosssection!SeeDavis,Faberman andHaltiwanger(J.ofMonetaryEconomics,2012)fordetails.30

Wt = ft (g)wt (g)g∑

¨ Layoffsmovewithjobdestruction.Quitsmoveoppositetoboth.

4.0

5.0

6.0

7.0

8.0

9.0

10.0

4.0

5.0

6.0

7.0

8.0

9.0

10.01990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

PercentofEmployment

JobDestruction

Layoffs

Quits

Synthetic JOLTS EstimatesActual JOLTS Estimates

This chart is reproduced from “Labor Market Flows in the CrossSection and Over Time,” by Davis, Faberman and Haltiwanger, Journal of MonetaryEconomics, January 2012.

E.Combiningquasi-experimentalvariationandlongitudinalbusinessdatatotesttheoreticalpredictionsandestimateresponsemagnitudes.Thereareincreasinglymanyexamplesofthissort,andtheydifferwidelyincharacter.Thisapproachrequiressomeingenuityinrecognizingausefulquasiexperimentandingatheringorconstructingdatatomergeintoexistinglongitudinalbusinessdatabases.Ifestimationofcausaleffectsisthegoal,thensuccessalsoturnspartlyonwhethertheidentificationstrategyispersuasiveandyieldsenoughpowertorecoverreasonablypreciseestimates oftheeffectsofinterest. 32

Examples• Estimatingtheeffectsofprivateequitybuyoutsonemployment,jobreallocation,productivityandcompensationperworker.– Davisetal.(2014)advancetheliteratureontheeffectsofPEbuyoutsinseveralrespects,butperhapstheirmostimportantinnovationinvolvesthesimultaneoususeoffirmandestablishmentdatatoestimatewithin-firmreallocationeffects.Coveredinnextlecture.

• Identifyingandquantifyingspatialspilloversontheproductivityofmanufacturingplants.– Greenstoneetal.(2010)compareoutcomesatincumbent

plantsincountiesthatwoncontestsfortheopeningofmajornewplantstooutcomesincountiesthatlost. 33

• Greenstone,ListandSyverson (2012)estimatetheeffectsofairqualityregulationsonTFPatU.S.manufacturingplants.Theirquasi-experimentalvariation:airqualityregulationsaremorestringentinsomecounties(andforsometypesofplants) than others.

• How large were the employment effects of credit market disruptions in the wake of the Lehman bankruptcy?– Chodorow-Reich (2014) investigates this question

using differences in lender health after the Lehman crisis as a source of variation in the availability of credit to borrowers. Another very successful JMP!34

• Howdoshockstoinvestmentopportunitiesatoneplantaffectinvestmentoutcomesatother plantsinthesamefirm?Doestheanswerturnonwhetherthefirmisfinanciallyconstrained?– GiroudandMueller(2015)addressthesequestions.TheyusetheLRDtomeasureproductivity,ownership,plantlocation,etc.

– Theyusenewairlineroutesthatreducetraveltimebetweenthefirm’sHQandcertainofitsplantstoobtainexogenousvariationinplant-levelinvestmentopportunities.“[A]reductionintraveltimemakesiteasierforHQtomonitoraplant,giveadvice,shareknowledge,etc.,raisingtheplant’smarginalproductivitythusmakinginvestmentinthe(treated)plantmoreappealing.”

– Cleverrecognitionofaninteresting,non-obviousquasi-experimentinthedata.

35

• Howsensitiveisstate-levelemploymenttostate-leveltaxesonbusinessincome?Howmuchoftheemploymentlossinonestate(inresponsetoaahikeinitstaxrateonbusinessincome)isoffsetbyemploymentgainsinotherstates?

• Giroud andRauh (2017)addressthesequestionsIn“StateTaxationandtheReallocationofBusinessActivity:EvidencefromEstablishment-LevelData,”usingtheLBDandfocusingonfirmsthatoperateinmultiplestates.

• Theymarshalandexploitmuchknowledgeofbusinesstaxationandthenatureofvariationinbusinesstaxratesacrossstates,timeandformofbusinessorganizations.Theyusethisknowledgetoisolateseveralsourcesofstate-specifictimevariationintheeffectivetaxrateoncorporateincome(forbusinessesorganizedas“C” corporations)andinthepersonaltaxrate(forunincorporatedenterprisesandbusinessesorganizedaspass-throughentities).36

F.Applicationsofemployer-workerandbusiness-ownerlinkedlongitudinaldatasets.Examplesincludeitems4and5onSlide19above.Perhapstheleadingapplicationsoflongitudinalemployer-workerlinkeddatasetsinvolvethestudyofearningslossesassociatedwithjobdisplacement.Jacobson,LalondeandSullivan(1993)provideanearlyandinfluentialstudythatexploitedemployer-workerdatasetsandappliedempiricaltechniquespreviouslydevelopedinresearchonprogramevaluation.MorerecentstudiesinthesamemoldincludeCouchandPlaczek (2010),VonWachter,SongandManchester(2009),andDavisandvonWachter (2011). 37

DavisandvonWachter (DvW)alsoshowthatworkhorsesearchandmatchingmodelsfailtoexplainthesizeandcyclicalityofthepresentvalueearningslossesassociatedwithjobloss.Observedlossesareseveraltimeslargerthanpredictedbystandardtheory.RecentpapersbyDopelt (2016),JungandKuhn(2016),Krolikowski(2017),Huckfeldt (2016),andJarosch (2015)considervariousmodificationstosearchandmatchingmodelstoaddressthisissue.WewillcovertheDvW papercarefullyanddiscusssomeoftherelatedliteraturelaterinthecourse.

38

NBERMacroeconomicsAnnual,2006

ByStevenJ.Davis,JohnHaltiwanger,RonJarmin andJavierMiranda

39

Startwithsomebackgroundandmotivationtoexplainwhywechosetoworkonthistopic:

• APuzzle:Twoprominentparallelliteratureshadproducedseeminglycontradictoryevidence.

• Thefactsatissuearerelevantforimportanttheoriesofreallocationandgrowth,frictionalunemployment,andeffectsofrisksharing,inputvarietyandcompetitiononproducervolatility.

• Comparedtopreviousworkontrendsinbusinessvolatility,wehadbetterdatasourcesandabetterempiricalstrategyforestablishingthefacts. 40

• Risingvolatilityofgrowthratesinsalesandemploymentamongpubliclytradedfirms– CominandMulani(2003),CominandPhilippon(2005)

• Risingvarianceintheidiosyncraticcomponentoffirm-levelequityreturns– Campbelletal.(2001),FamaandFrench(2004),manyothers

• Decliningexcessjobreallocationratesanddecliningratesofbusinessentryandexit– Davis,FabermanandHaltiwanger(2006),Faberman(2006),Jarmin,KlimekandMiranda(2003,2005),andFoster(2003)

For the full references, see “Volatility and Dispersion in Business Growth Rates.” 41

1

1( ) / 2it it

itit it

x xx x

g -

-

-=

+ 1/ 25

2,

4

1 ( )10it i t itt

t

s g g+=-

é ù= -ê úë ûå

42

Figure 1(a): Volatility, Publicly Traded Firms

Average Volatility of Sales Growth Rates

0.05

0.10

0.15

0.20

0.25

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

Simple Mean Sales-weighted mean

Based on COMPUSTAT Data

43

Average Volatility of Employment Growth Rates

0.06

0.10

0.14

0.18

0.22

0.26

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Simple Mean Employment-weighted mean

Based on COMPUSTAT Data

44

7.0

8.0

9.0

10.0

11.0

12.0

13.0

14.0

15.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

Excess Reallocation Rate H-P Trend

LRD data spliced to other sources.45

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Based on data from the BLS Business Employment Dynamics 46

• Reallocationandgrowth– Schumpeteriantheories(Aghion-Howitt,1998,Caballero,2006,manyothers)

– Industry-levelproductivitystudies(FHK,2001,etc.)• Theoriesoffrictionalunemployment (MP,etc.)• Effectsofdiversificationopportunitiesonrisktaking,investment,growth(Obstfeld,1994,etc.)

• Decliningaggregatevolatilityanditsconnectiontobusiness-levelvolatility– Financialmarketdevelopment(Thesmar-Thoenig,2004,Acemoglu,2005)

– Greatercompetitioningoodsmarkets(Philippon,2003,CominandPhilippon,2005,CominandMulani,2006)

– Expandinginputvariety(KorenandTenreyo,2006)

47

1, 1,2,... .

K

it ik kt itk

Z i Ng b e=

= + =å Agg. Growth Rate = α it

i=1

n

∑ γ it ,

where α i is firm i's share of activity

A Simple Reason to Anticipate that Aggregate andBusiness-Level Volatility Measures Trend in theSame Direction: Write the growth rate of firm i as a linear function of k mutually uncorrelated common shocks and an idiosyncratic shock:

48

2 2 2

1 1 1Firm Volatility =

n n K

it t it ik kti i k

ea s a b s= = =

é ù+ ê úë û

å å å

2 2 2 2 2 2

1 12

n n K n K

it it it ik kt it jt ik jk kti i k j i ka s a b s a a b b s

= > =

é ù é ù+ +ê ú ê úë û ë û

å å å å å

Then we can write the weighted mean of the firm-level growth rate volatility as:

And the volatility of the aggregate growth rate as:

Positive co-movements over time in the cross section imply that the last cross-product above is positive.

49

Reproduced from DavisAnd Kahn, 2008 JEP

50

Reproduced from DavisAnd Kahn, 2008 JEP

51

Anempiricalstudyoftrendsinthevolatilityanddispersionofbusinessgrowthrates.

Keythemes• Publiclytradedversusprivatelyheld

– Resolutionofpuzzleturnsonthisdistinction.• Majorshiftintheeconomicselectionprocessgoverningtherisk/volatilityprofileoffirmsthatgopublic

• Roleofchangesinbusinessage,size,cohortandindustrydistributions 52

• Annualobservations,1976to2001(nowthrough2014or2015)

• All establishmentsandfirmswithemployees• Employment,payroll,andothervariables• Employmentconcept: countofworkerssubjecttoU.S.payrolltaxesinpayperiodcovering12thofMarch

• SeeJarmin andMiranda(2002)onLBD.• Welimitanalysistononfarmsector

53

• Publiclytraded,listedfirms• Annualdatafrom1950to2004(sinceupdated)• EmploymentConcept: Numberofcompanyworkersreportedtoshareholders– Annualaverageorend-of-yearfigure– Includesemployeesofconsolidatedsubsidiaries,domesticorforeign

– Missingdata,measurementerror• WeuseCOMPUSTATforsomeexercisesand tosupplementtheLBDwithinformationonwhetherfirmsarepubliclytraded

54

Employment-weighted correlation = 0.83

Weighted MeanAbsolute Difference = 30%

55

Weighted Corr. = 0.54

56

Average Volatility of Firm Employment Growth Rates:COMPUSTAT and COMPUSTAT-LBD Bridge Compared

0.07

0.11

0.15

0.19

0.23

1954

1959

1964

1969

1974

1979

1984

1989

1994

1999

0.07

0.09

0.11

0.13

0.15

COMPUSTAT, unweighted Bridge, unweightedCOMPUSTAT, weighted Bridge, weighted

57

Average Volatility of Firm Employment Growth Rates:COMPUSTAT and COMPUSTAT-LBD Bridge Compared

0.07

0.11

0.15

0.19

0.23

1954

1959

1964

1969

1974

1979

1984

1989

1994

1999

0.07

0.09

0.11

0.13

0.15

COMPUSTAT, unweighted Bridge, unweightedCOMPUSTAT, weighted Bridge, weighted

à Restricting attention to publicly traded firms we canidentify in the LBD has no material effect on measuredfirm-level volatility.

58

Publicly Traded Firms, Unweighted

0.060.100.140.180.220.26

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

0.10.150.20.250.30.35

Firm Level Volatility Cross Sectional Dispersion (right axis)

Dispersion measured as Cross-Sectional Standard Deviation of Annual Employment Growth Rates

Using LBD employment data and COMPUSTAT to identifypublicly traded firms

59

Publicly Traded Firms, Employment Weighted

0.07

0.09

0.11

0.13

0.15

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

0.05

0.1

0.15

0.2

0.25

Firm Level Volatility Cross Sectional Dispersion (right axis)60

• Largeseculardeclineinthecross-sectionaldispersionofemploymentgrowthratesandinthemagnitudeoffirm-levelvolatility– 40%dropinfirm-levelvolatilitysince1982– Patternholdsforallmajorindustrygroups

• Hugetrenddifferencesbetweenpubliclytradedandprivatelyheldfirms– LBDconfirmsrisingvolatilityanddispersionforpubliclytradedfirms

– Butoverwhelmedbydeclinesforprivatelyheld– “Volatilityconvergence” inallindustrygroups

61

Employment-Weighted Dispersion of Firm Growth Rates, Three-Year Moving Averages

0.150.250.350.450.550.650.750.85

1977 1980 1983 1986 1989 1992 1995 1998 2001Year

Stan

dard

Dev

iatio

n

Total Privately Held Publicly Traded 62

Employment-Weighted Volatility of Firm Growth Rates

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1977 1980 1983 1986 1989 1992 1995 1998 2001

Year

Ave

rage

Firm

Vol

atili

ty

Total Economy Privately Held Publicly Traded 63

σ it =zi ,t+τPit −1

⎛

⎝⎜⎞

⎠⎟(γ i ,t+τ −γ it

w

τ =−4

5

∑ )2⎡

⎣⎢⎢

⎤

⎦⎥⎥

1/2

,

64

1.5( )it it itz x x -= +5

,4

/ ,it it i tK P z tt

+=-

= å

zi,t+τ = Pit

τ =−4

5

∑

zit = Kit zit

Correcting a typoin the paper

Fix the scalingquantity overthe windowof the firm-level volatilitymeasure. By construction, the

sum of the rescaledweights add up to P.

# of years from t-4 to t+5for which zit > 0.

65

Modified Volatility, Employment Weighted

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Year

Ave

rage

Firm

Vol

atili

ty

Total Economy Privately Held Publicly Traded 66

67

Industry 1982 1996 % ChangeMinerals 0.24 0.15 -39Const. 0.45 0.22 -52Manuf. 0.13 0.08 -38TPU 0.18 0.10 -44Wholesale 0.24 0.11 -53Retail 0.19 0.10 -46FIRE 0.18 0.14 -23Services 0.29 0.14 -53 68

Volatility Ratio: Privately Held to Publicly Traded

Industry 1982 1996 ChangeMinerals 4.5 1.4 -3.1Const. 6.4 2.8 -3.6Manuf. 3.7 1.7 -2.0TPU 3.9 2.0 -1.9Wholesale 2.9 1.5 -1.4Retail 3.3 2.1 -1.3FIRE 3.2 1.2 -2.1Services 1.7 1.0 -0.7 69

Employment-Weighted Dispersion of Establishment Growth Rates, Three-Year Moving Averages

0.5

0.55

0.6

0.65

0.7

0.75

0.8

1977 1980 1983 1986 1989 1992 1995 1998 2001Year

Stan

dard

Dev

iatio

n

Total Privately Held Publicly Traded 70

Modified Establishment Volatility, Employment Weighted

0.35

0.45

0.55

0.65

0.75

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Total Economy Privately Held Publicly Traded71

Volatility and dispersion differ by industry and especially by business size and age. To investigate whether shifts in activity across categories accounts for the volatility trends, we use a cell-based shift-share methodology were we compute the modified volatility for 448 age, size and industry cells: Age: entrants, 1, 2, 3, 4, 5, 6+ years of age (oldest Establishment), eight size categories, and eight industry groups-- Fixing the industry distribution of employment at 1982 cuts the 21% rise in modifiedvolatility among publicly traded by half. Fixing the age distribution of employment at 1982 accounts for 27 percent of volatility fall among privately held. This figure probably understates role of age distribution shift – see Table 4. 72

Employment Shares Among Publicly Traded Firms, Selected Industries

0

0.1

0.2

0.3

0.4

0.5

0.6

1975 1980 1985 1990 1995 2000 2005

Manufacturing Retail Trade FIRE Services 73

74

• Hugeupsurgeofnewlylistedfirms(Fama-French,2004)– #newlists(mostlyIPOs)jumpsfrom156peryearin1973-1979to549per

yearfrom1980-2001– 10%oflistedfirmsareneweachyearfrom1980to2001

• Newlistsbecameincreasinglyrisky(FF,andFinketal.)– Newlistsbecameincreasinglyriskyrelativetoseasonedfirmsasmeasured

byprofitabilityandreturns– FirmageatIPOfellfrom40yearsintheearly1960stolessthan5yearsby

thelate1990s.(Jovanovic-Rousseau2001data)• Upsurgeofnewlistsexplainsmostorallofriseinvolatilityof

idiosyncraticcomponentofequityreturns(FF,Finketal.)• Pointstoadeclineinthecostofequityforriskyfirmsandfirms

withdistantpayoffs

75

Figure 11. Modified Firm Volatility By Cohort, 1951-2004

0.05

0.1

0.15

0.2

0.25

1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003

1950s 1960s 1970s 1980s 1990s Left-Censored76

Figure 12. Share of Employment By Cohort, 1950-2004

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

19501954

19581962

19661970

19741978

19821986

19901994

19982002

1950s 1960s 1970s 1980s 1990s Left-Censored77

Modified Volatility among Publicly Traded Firms: The Role of Size, Age, Industry and Cohort Effects

0.08

0.1

0.12

0.14

0.16

0.18

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

No Controls Size Size, Age & Industry Cohort 78

79

1. Averagefirm-levelvolatilitydownsharply§ 23%declinefrom1978to2001usingourpreferredvolatility

measure(Figure6),29%since1987§ Patternholdsforallindustrygroups

2. Hugetrenddifferencesbetweenpubliclytradedandprivatelyheldfirms

§ Volatilityconvergenceinallindustrygroups3. Shifttoolderfirmsaccountsformuchofvolatility

declineamongprivatelyheldfirms4. Largeupsurgeandincreasinglyriskynatureofnewly

publicfirmsaccountsformostofvolatilityrise§ Theprocessgoverningselectionintothesetofpubliclytraded

firmsshiftedmarkedlyafter1979§ Casualempiricismsuggestsitshiftedagainafterearly2000s,

perhapsduetoSarbanes-Oxley.80

Simple selection story doesn’t fit the facts. It implies, contrary tothe evidence, a volatility rise in

both groups of firms and a rising share of employment at publicly traded firms. Neither occurred.

81

Our results also present a challenge to Schumpeterian theories of growth – the large decline in firm-level and establishment-level volatility that we document coincided with a period of impressive productivity gains.

-- This coincidence belies any close and simple positive relationship between productivity growth and the intensity of creative destruction, at least as measured by firm-based or establishment-based measures of volatility in employment growth rates.-- Perhaps there has been a big increase in pace of restructuring, experimentation and adjustment within firms.-- Perhaps more intense creative destruction among publicly traded firms, partly facilitated by easier access to public equity by high-risk firms, has been sufficient to generate the commercial innovations that fueled productivity gains throughout the economy. 82

1. Formoreonthechangingnatureofselectionintothesetofpubliclytradedfirms,readFama-French(2004)andBrownandKapadia(J.ofFinancialEconomics,2007)

2. Foraninterestingtheoreticalandempiricalefforttoexplainthedivergingtrendsofpubliclytradedandprivatelyheldfirms,see“ContrastingTrendsinFirmVolatility” byMathiasThoenig andDavidThesmar,AEJMacro,October2011.

3. OnSchumpeteriantheoriesofgrowththroughcreativedestruction,seeAghion andHowitt(1998)andCaballero(2006).Chapter2ofCaballero’sbookhasaverynicereviewoftheempiricalworkontheroleoffactorreallocationinproductivitygrowth.See,alsoBartlesman andDoms (2000)andFosteretal.(2001). 83

4. FormoreontheGreatModeration,seeDavisandKahn(2008)andreferencestherein.

5. ForastudythatdrawsonsimilardatasourcestoprovideanupdatedcharacterizationoftrendsinU.S.businessvolatility,see”TheSecularDeclineofbusinessDynamismintheUnitedStates”byDecker,Haltiwanger,Jarmin andMiranda.Theyshowthatbusinessvolatilityhastrendeddownwardsincetheearly2000sforprivatelyheldand publiclylistedfirms.

6. Wewillconsiderthelabormarketconsequencesofdecliningbusinessvolatilitylaterinthecourseinourdiscussionof“LaborMarketFluidityandEconomicPerformance."

84