Labor Intensity and Expected Stock Returns

GillesChemla Peter Pontu h

∗

February15, 2012

Abstra t

This paper analyses the ee ts of labor intensity on a rm's operating risk and its

expe ted sto kreturns. Weisolateapure laborintensity ee t byusing arelative

mea-surewith respe tto the three-digitindustrymedian level. Weshowthat laborintensity

is positively asso iated with operating leverage,at least in the small and medium-sized

rms subsample. Sto k and portfolio returns of small and, to a lesser extent, mid ap

rms are positively asso iated with labor intensity after ontrolling for traditional risk

fa tors. Inparti ular,thelabor-indu edoperatingleveragedoesnotseemtobeexplained

bythebook-to-marketfa tor. Therelationshipbetweenlaborintensityandsto kreturns

isstrongerin lowwageindustriesandatmediumlevelsofnan ialleverage.

Keywords: labor intensity, operating leverage, expe ted sto k returns, ross-se tion of

sto kreturns.

JELClassi ations: G31,D24,G12.

∗

Chemla: ImperialCollegeBusinessS hool,DRM/CNRSandCEPR.Pontu h:UniversitéParisDauphine

-DRMFinan e. WethankChristinaAtanasova,LaurentCalvet,DenisGromb,GaëlleLeFol,OguzhanKarakas,

Gordon Phillips,and seminarparti ipantsatthe UniverstyofMaryland, College Park, inparti ular Mi hael

Faulkender and Gerard Hoberg, and Université Paris-Dauphine. All errorsare our own. Pontu hgratefully

a knowledges nan ialsupportfromthe FBFChairinCorporate Finan e,as wellas kindhospitality ofthe

Firmsfa easigni antlevelofdis retioninde idinghowtoorganizetheirprodu tionpro ess.

Foragivensetofte hnologi al onstraintsarm ana hievea ertainlevelofprodu tionusing

dierentproportionsofinputs(e.g. labor, apital, basi resour es,intermediategoods). These

hoi esae t therm'sstru tureof ostsand laims,andthushavelikely impli ationsforthe

riskinessofits ashows. Theleveloflaborintensityisaprominent exampleofsu ha hoi e.

Laborexpensesareknowntoberelativelystableovertimeandareseniorto investors' laims.

Theyae tme hani allythevariabilityofresidual ashowsandshouldthereforeberee ted

inrmvaluations.

The nan ial ee ts of labor have re ently been studied with respe t to their quasi-xed

nature (e.g. Danthine and Donaldson (2002), Merz and Yashiv (2007)), the inexibility due

to unionization (Chen, Ka per zyk, and Ortiz-Molina (2011)), and the impli ations of labor

mobility (Donangelo (2011)). In all of these studies labor exposure generates some kind of

operatinginexibilityand thereby in reasesa rm'srisk.

It is empiri ally hallenging, however, to attribute observed regularities to a pure labor

ee t. Infa t, the inter-industry variability inlabor intensity ishigh(see Donangelo (2011)).

Portfolio sorts based on labor intensity are likely to on entrate rms from apital intensive

industries(sayPrimaryMetal Industries)at oneend,andfromlabor intensive industries(like

BusinessServi es) atthe otherend. The sensitivity ofthese industriesto aggregate fa tors is

likely to be very dierent. Any observed ee t in su h sorted portfolios may not ne essarily

besolely relatedto the labor exposureper se.

The obje tive ofthis paperis to isolate a pure ee t of labor intensity on theriskinessof

rms' operations and on their sto k returns. We propose to measure labor intensity relative

to an industry normal (the SIC3 median). We dene two relative labor intensity (RLI)

measures using assets per employee and operating assets per employee. Sorting based on a

relativemeasureoflaborintensitygivesallindustriesalmostequal han esofbeingrepresented

at both ends of the distribution of labor intensity. Our approa h is similar in spirit to an

in presen e of high labor adjustment osts the labor expenses are quasi-xed and produ e

an operating leverage ee t. Se ond, if labor produ tivity volatility is mu h higher than the

volatility of unit wages, thenlabor exposure reates also an operating risk. Note thatthis is

true even ifadjustment ostsaremoderate. Third, labor intensive rms simplyfa ea higher

human apital turnover risk. They are relatively more vulnerable to losses of human apital

due to layos.

We show that relative labor intensity is positively related to operating leverage, at least

in small and medium-sized rms. This higher operating leverage is ree ted in individual

sto kreturns. Smallandmid-sizedrmshaveapositiveasso iationbetween returnsandlabor

intensity, even ifwe ontrol for othertraditional rm-leveldeterminantsof returns. Asimilar

on lusionispresentinsortedportfolios'averagereturns: astrongee t ofRLIinsmallrms

portfolios,and anee t of RLIon equallyweightedreturns ofmid- ap portfolios.

Time seriesregressionsofdoublesorted portfolios onrm previousresults,but alsoreveal

an interesting link between mappings on the B/M fa tor (HML) and abnormal returns of

RLI-sorted portfolios. We nd positive abnormal returns on Large rms with low RLI, and

theyappeara ompaniedwithaparti ularlystrongnegativemappingontheHMLfa tor. We

showthattheseabnormalreturnsonLargerms o urred duringthelate1990s' periodofthe

high-te h boom.

In previous literature the B/M fa tor was suggested to apture the ee ts of operating

leverage(see e.g. Carlson, Fisher,andGiammarino (2004)). One ofthemain messagesofour

paperis thatthe B/Mfa tor doespi k up a partof therisks indu edby labor intensity, but

some abnormalreturnspatternssurviveeven ina 4-fa torsetting.

Tojudge the three alternative me hanisms throughwhi h labor intensityae ts rmrisk,

we split our sample based on the level of industry wages. The latter is a proxy for both the

importan e of human apital and the level of adjustment osts. Our ndings do not seem

to be onsistent with either an adjustment osts story, or a human apital turnover story.

Lastly,weshowthatnan ial leverageae tsthe relationshipbetweenlaborintensityand

expe tedsto kreturns. Weshowthatsto kreturnsareatleastmoderatelyin reasing inlabor

intensityfor rms having amedium-level ofdebt.

Thepaperisorganizedasfollows. Werstpresentrelatedliteratureandthenformulateour

hypothesesinse tion2. We onstru t our proxiesforlaborintensityinse tion3. We analyze

the ee ts of labor intensity on operating leverage in se tion 5. We study individual sto k

returnsin se tion6.2. Average portfolio returns areanalyzed inse tion6.3, whiletime-series

regressions ofportfolioreturnsarepresentedinse tion 6.4.

2 Ba kground and problem formulation

Resear honoperatingleverageandreturnsgoesba katleasttoLev(1974)whoshowsina

sim-pleanalyti al framework thatoperatingleveragein reases the systemati riskof a ompany's

sto k. MankelderandRhee (1984)formalize therelationshipbetweenasto k'ssystemati risk

and the operating and nan ial leverage of the ompany's fundamentals. The authors nd

thatthese two omponentsexplain a large part of ompanies' betaand they nd supportfor

thehypothesisof atrade-o between thetwosour es.

Inamorere entstudyPenman,Ri hardsonandTuna(2007)de omposetheB/Mratiointo

aB/M omponentofassets( apturingoperatingleverage)andanan ialleverage omponent.

Theauthorsndthat onditionallyontheassetB/Mratio,nan ialleverageearnsasurprising

negativepremium. GomesandS hmid(2009)usetheideaof hangingrmriskover therm's

life y le to explain these empiri al puzzles of nan ial leverage and sto k returns. Their

investment-based assetpri ingmodelexpli itlytakesintoa ounttheendogeneityofnan ing

and investment de isions. Spe i ally, highly levered rms tend to be large and with lower

underlying asset risk. On the other hand, small rms are, within their general model, more

subje ttooperatingleverageandtheyfa e(relatively)higherxed ostsofbankrupt y(p.487).

Operating leverage plays an important role in the real options literature. In parti ular,

leverage. If the xed osts are proportional to the apital level, a B/M ee t arises from

the intera tion of these xed ost ommitments and the variability of the aggregate demand

onditions.

Theroleoflaborindu edoperatingleverageisthefo usofDanthineandDonaldson(2002).

Theymotivatetheirmodelbytheobservationthattheaggregate laborshareishighlyvariable

over the business y le, suggesting that risk transfers o ur between providers of labor and

apital. Theseniorityof laborexpensesgeneratesoperatingriskforequityholderswhi h,ifit

remains uninsurable, in reases therisk premium on equity laims. This result is obtained at

theaggregatelevelbyseparatingagentsintoworkerswithnoparti ipationinnan ialmarkets

and investorswithno labor in ome.

Merz and Yashiv(2007) adapt theadjustment ost framework from the orporate

invest-ment literature and apply it to both investment and hiring de isions. They show that in

this settingrm value isdetermined byboththe apital sto kand thetotal employment ofa

ompany. Thisresultobtainsduetoapositiveshadowpri eofemploymentinthissetting

gen-eratedbyadjustment osts. Thereisno linkbetween theriskinessof earningsandthelevelof

employmentinthis model. Bazdres h,Beloand Lin(2008)extend thisreasoning andprovide

empiri aland theoreti al eviden ethat both investment and hiring de isions helpexplain the

rossse tionof returns. Inparti ular, their forward-looking nature makesthem a goodproxy

for the onditional beta oftherm(idea alreadyformulated byMerzand Yashiv, 2007).

Ourpaperis loselyrelatedtoGourio(2007). Hestartswiththestylizedfa tthatwagesdo

notfullyadjustto hangesinlaborprodu tivitydueto rigidities. Labor intensivermshavea

longpositioninthevolatilelaborprodu tivityandashortpositioninlessvolatilewages. This

me hanism isakin to operatingleverage andresults inhigher pro- y li alityof earnings. The

higher the labor share of a ompany (or, equivalently, the lower the apital share) the more

pro- y li alaretheearnings. Inhisownwordsrmswhi hhavehighlabor osts`leverage'the

smoothnessofwages Gourio(2007,p. 8). Givenaprodu tionte hnologyarmwithahigher

orporate earnings isin reasing inlabor leverage, i.e. systemati risk is alsoin reasing. This

nding is usedto develop a two-fa tor asset pri ingmodel based on aggregate real wage and

total fa tor produ tivity. High B/M rms have higher betas on the produ tivity fa tor and

lower betaon the wage fa tor.

Donangelo (2011) develops a similar idea based on workers' interindustry mobility. He

argues that highly mobile workers prevent rms from adjusting wages downwards following

adverse industry-spe i sho ks. Thisin reasesearnings' exposureto thesesho ks and

gener-atesanoperatingleverageee t. Hedevelopsanindustrymeasureoflabormobilityandshows

thatitispositivelyasso iatedwithoperatingleverageandexpe tedsto kreturns. One aveat

regarding theempiri al resultsis that themeasureof labor mobility seemsto pi kup mostly

manufa turing industriesas highlymobile. The observed returns dierentials ould therefore

berelatedto a se toral storyinstead.

The obje tive of this study is to isolate a pure ee t of labor intensity. In parti ular, we

wishto study an ee t thatis orthogonal to industryee ts ( y li ality ofdemand,

ompeti-tive me hanisms,te hnology life- y le). Our empiri alapproa h istherefore to measurelabor

intensity of rms relative to their industry normal level. By onstru tion, ea h industry

on-tributes to both the highand the lowend of thedistribution of therelative measureof labor

intensity. We addresstwo resear h questions. Doesrelative labor intensity ae t ompanies'

operatingleverage? Ifyes,isthis additionalrisk apturedbytraditionalriskfa torsordoesit

generate abnormalreturns?

Based on the previousdis ussion ofthe literature we formulate two resear h hypotheses:

H1: Labor intensive rms have a higher level of fundamental risk. There are at

leastthree possible me hanisms throughwhi h labor intensity ouldae t rmrisk.

First, if there are signi ant adjustment osts on labor (as in Merz and Yashiv (2007),

Bazdres h etal. (2008)),labor adjustmentsarelumpyandrmswill ommonlyoperateabove

orbelowoptimumlevels. Inthis aselaborexpenseisaquasi-xed ostandgeneratesoperating

intensivermswillhavemoreleveragedearnings(Gourio(2007),Donangelo(2011)). Notethat

this me hanism will holdeven if adjustment osts are low, sin e the argument is formulated

in terms of wage and produ tivity per unit of labor. Indownturns, output per unit of labor

de reases more than the per unit ost of labor dragging downoperating margins. This ee t

will be strongerinlabor intensive rms.

Third,laborintensivermsfa eahigherlaborturnoverrisk. Investmentsinhuman apital

are ompletelylostindownsizing,asopposedtoapartialre overyof ostsinthe aseofdisposal

ofphysi al apital. Labor intensive rms ouldbelesswillingtoredu es ale indownturns,at

leastinhuman apitalintensiveindustries. Again,thiswouldleadtoahigherlevelofoperating

leverage.

H2-Duetoriskierfundamentals,laborintensivermsearnhigheraveragereturns.

Operatingleverageamplies thevariabilityofearningsfollowingsho kstosales. Thesesho ks

aneitherree tthestateofthee onomy,orberm-spe i . Atleastapartoftheadditional

risk borne by labor intensive rms is systemati and should be ree ted in higher betas. In

parti ular, ifthe B/Mfa tor(HML) apturestheee tsof operatingleverage, thenitshould

pi kup theadditional systemati riskof labor intensive rms.

3 Measuring labor intensity

Theidealmeasureof laborintensitywouldbederivedfromtheCompustat itemStaexpense

(eld 42, xlr). s aled by some measure of the size of operations like sales. However, the

expense eldisonly available for lessthan 10%ofrm-yearsin theCompustat database.

We therefore use another labor-related item, one that is provided almost systemati ally,

Numberofemployees (eld29, emp). We take into a ount dieren esinsize bytakingthe

workfor e size relative to a referen eassetgroup. Throughout thepaperwe will a tually use

the inverse,i.e. assetsperemployee variables. We usetwo referen eassetgroups to onstru t

•

APE -total assets peremployee,

•

OAPE - operatingassets (assets minus ash&S-T instruments) per employee.

There are at least two problems with using su h raw measures of labor intensity. First,

labor isnot ahomogeneous fa tora ross industriesanddierentindustries requirea dierent

blend of skilled vs. non-skilled labor (see Bazdres h et al., 2008). Therefore 100 employees

in the mining industry are not dire tly omparable to the same number of employees in the

medi al instruments industry.

Se ond, produ tion te hnologies are very dierent a ross industries (and arguably even

a rossrms), so dierent industries mayrequire very dierent levels of labor intensity at the

optimum. Sortingbasedontheserawmeasureswouldleadtoportfolios on entratedinspe i

industries. Resultsobserveda rosssu hsortedportfolios ouldaswellbedrivenbysomeother

latentvariable andnot dire tlyrelated to laborintensityper se.

Ouranswertotheseissuesistouseameasureofrelativelaborintensity. Wedenerelative

labor intensity as the raw measure of labor intensity of rm

i

divided by the median of its

3-digit SICindustry,minus one. For example, relative labor intensity measuredbyassetsper

employee isdened for rm

i

at date

t

as

APE rel

it

=

APE

it

APE SIC3

it

−

1

.

Dened inthis fashion, our measureof relative labor is bounded below by

−

1

. This measure

issimilarinspirittothete hnologi al naturalhedgebyMa KayandPhillips (2005). Without

furtherpre isionthea ronymsAPEandOAPEwillhereinafterrefertotheserelativemeasures

of labor intensity.

4 Data

We use data on U.S. in orporated publi ompanies from the Compustat CRSP merged

database. We ex lude nan ial ompanies (SIC odes between 6000 and 6999) and utilities

pri e,sales,netin ome,operatingin ome, depre iation, ostsofgoodssold, ommonand

pre-ferred dividends. We ex lude rm-years with book assetsbelow $10 million or with ommon

equitybelow$5 million, both in onstant 1994 dollars.

Book equity is total assets minus total liabilities minus preferred sto k (standard F

ama-Fren h orderinthe denitionofpreferredsto k)plusbalan e sheetdeferred taxesand

invest-ment tax redits. B/Mofequityis dened asbookequitydividedbyshare pri e timesshares

outstanding. Wewinsorizeratio variablesat the1%leveltominimizetheinuen eofextreme

variables in regressions. When sorting portfolios on measures of relative labor leverage we

use labor exposure data from s al year t-1. Our sample period on annual nan ial data is

1975-2009.

Wepresentsummarystatisti sintable1. Sample rmshave amedian sizeof approx. 170

million1994 dollars and1,300 employees. MedianROA isabout 6.7%,median B/Mof assets

1.3andmedianrealsalesgrowth5.8%. Firmsinvestabout4.8% oftheirassets. Wepresent in

panels BthroughD some des riptive statisti sfor the1st, 3rd and 5th quintilegroups sorted

on a proxy of relative labor intensity (inversely sorted on assets per employee minus SIC3

median). The most labor intensive rms are smaller and slightly more protable. Theyalso

havea lowerand lessvolatile salesgrowth and ahigher investment rate.

Our monthly returns data from CRSP over the period July 1976 to De ember 2009 (we

needarstyearoffundamentalsdatainordertosort portfolios). Whenmergingreturnsdata

with Compustat variables we only keep primary joiner issues. We perform portfolio sorts at

theend of June ofea h year t. We useB/Mequityratiosasof De ember ofyeart-1, market

apitalizationdataasofJuneofyeart,anda ountingdataaswellasrelativelabor exposures

from s al year t-1 ensuring that all sorts are performed using available data as of the sort

date. Themonthlyfa torreturnsaswell astherisk-freerate omefromKennethFren h'sweb

page.

We adjust average returns following the methodology by Daniel, Grinblatt, Titman, and

Wermers (1997)and Wermers (2004) and useRussWermers's ben hmark data. 1

1

In this part we establish the ee t of labor intensity on the operating leverage of rms. To

this end we independently sort rms into

3 × 5

groups ea h yearusing previous year'ssize (3

groups) and relative labor intensity (5 groups). The size groups are dened using the 20th

and 50th NYSE apitalization per entiles. Relative labor intensity groups are dened based

on previousyear'sdistribution quintiles.

Thedegreeofoperatingleverageisoriginallydened astheper entage hange ofoperating

earningsinresponsetoaone per ent hange inunits sold. Ingeneral rms sellmore thanone

type of produ tand therefore a feasible spe i ation is to usesalesinstead of units sold (see

for example Lev(1974)). The basi spe i ation thatweestimate is:

∆

ln

(

OI

it

) = α

k

+ β

k

∆

ln

(

Sales

it

) + ǫ

it

(1)

We run a separate estimation for ea h group

k

(one of the 15 size-RLI groups) on all rms

i ∈ k

in year

t

. To deal with theproblem of orporate events (mergers, spinos, large asset disposals) that ould seriously bias the year-over-year growth rates of either sales or OI, we

ex luderm-yearswhi h havean assetgrowth above

+100%

or below

−

50%

.

Weestimate the equation usinga rm-wise Fama-Ma Beth pro edure following Skoulakis

(2008). We start by time-demeaning the data for ea h size-RLI group. Then for ea h rm

ina given groupwe run a time-series regression. We require at least 5 observations per rm

within that given group. We thenaverage the oe ient estimates over therms within the

group to obtain point estimates. We estimate the oe ient SEs based on the dispersion of

rmestimates.

Wepresentintable2theresultsofourestimation. InpanelAweuseassetsperemployeeas

measure ofRLI. Insmall rms, the dieren e between the most and theleastlabor intensive

groups is about 0.6 points. Operating leverage in reases slightly from H to RLI3 but the

dieren e remains low. TheR-squared indi ates that there ismore residual variability inthe

monotoni . Operating leverage is learly de reasing from RLI4 to L.The H group, however,

has only a slightly higher oe ient than the L group. In large rms there is no apparent

relationship between operating leverage and RLI. The mid-RLI group is the most operating

leveraged one. Lastly,theR-squareds arein general higher in thebigsize groups, suggesting

thatresidual volatility islowest inbigrms.

InpanelBtheresultsusingoperatingassetsperemployeearebroadlysimilar. Insmallrms

theH−Ldieren e,at0.37points, issmallerthaninpanelAbutstille onomi allysigni ant.

The oe ients in RLI4 and RLI3 are higher than in theprevious ase. There is however a

lear positive dieren e between the left and the right end of thesmall rms oe ients. In

medium-sized rms, the pattern is less lear ut than in panel A. The de reasing patter is

broadlypreserved inthe RLI4 to L groups, but the H oe ient is learly thelowest. Again,

a non-monotoni pattern appears withthe RLI3group having thehighest oe ient.

Theanalysisinthispartyieldedpartialeviden einfavorofour H1hypothesis. Operating

leverage isin reasing inrelative labor intensityin small rms. There was some eviden e ofa

similaree t inmedium-sized rms. Largerms do notshow anysimilar relationship.

6 Expe ted sto k returns

6.1 Sorting methodology

We now turnto sto k returnsdata to inspe twhether therisk patterns thatwe do umented

intheprevious se tionhave any impli ationsintermsof sto kreturns. Inse tion 6.2dealing

with individual sto k returnswe use the levels of theRLI proxiesalong withother variables

do umentedto berm-level determinants ofsto k returns(size,B/M, momentum).

In se tions 6.3 and 6.4 we onstru t portfolio sortsbased on the two measures of relative

labor intensity, APE and OAPE. We also perform independent double sorts based on size

andRLImeasures. Our sortingmethodology respe ts thestandard approa h (seefor instan e

quintiles of the RLI distribution that ex ludes small rms (to avoid their over-inuen e due

to their high number). We keep this portfolio stru ture onstant when al ulating July

t

to

June

t

+ 1

returns. We dene three size groups based on the 20th and 50th NYSE market

apitalization per entiles in June t. We sort rms into ve groups based on the year

t −

1

RLI distribution quintiles. The H portfolio (L portfolio) is the 20% of observations with the

highest(lowest) RLIi.e. theleast(most) assetsperemployee asofJune

t

.

6.2 Individual sto k returns

In table 3 we run regressions of individual sto kreturns on rm-level determinants. We use

thetime-wiseFama-Ma Beth methodology,asthistimewehavealarger numberofdates(402

monthly observations). Everymonth we run a ross-se tional regressionof returnsona setof

rm-leveldeterminants. Wethentaketheaverageoftheestimated oe ientsoverallmonths.

We estimatethe standard error witha Newey-West4 lags orre tionand provide at-statisti

for oe ients. We alsoreportaverage R-squaredfrom the ross-se tional regressions.

Panel A,based on the relative APE measure, onrms that there is a signi ant positive

asso iation between labor intensity and expe ted sto k returns. In the standalone

spe i a-tion, the oe ient is signi ant (about 4 standard deviations from zero) but the explained

variability is very low (R-squared of 0.1%). When used alongside Size, the APE oe ient

remains signi ant while size's oe ient is below the 10% signi an e threshold.

Interest-ingly,theR-squaredimprovesmore thantentimes. TheAPEsigni an e surviveswhenused

alongsidetheB/Mandthepreviousreturnmomentumvariables. Lastly,whenallvariablesare

used altogether, the APE oe ient remains about 3.6 standard deviations from zero, while

the size oe ient isfurther redu edand not signi ant.

Panel B provides an identi al pi ture. In all spe i ations the relative OAPE variable

retains a signi ant oe ient, at least 3.7standard deviationsfrom zero. Size isagain lose

tobutbelowthe10%signi an elevelinthespe i ationwithAPE,butlosesitssigni an e

(i.e. its APE is double the industry APE) earns on average a lower return byabout 1%per

year. The lowR-squaredobtained bytheAPE/OAPE variablesisworthbeing dis ussed. On

theone hand,itisnotsurprisinggiventhatweregressmonthlyreturnsofindividualsto kson

a rm hara teristi that isrelatively stableovertime and hanges only on e a year. On the

otherhand,itaddsa aveatto ourresults. Ataminimum,inordertobenetfromthereturn

dierential through a long-short position an investor has to onstru t a very well diversied

portfoliothatredu es thevariabilityof individual sto kreturns.

Given the results from se tion 5 we further inspe t whether these abnormal returns in

individualsto kreturnspersistinallsize groups(ourapproa h issimilartoFamaand Fren h

(2008)). Table4 presents results using theAPE proxy. In thesmall rms subsample (<20th

NYSE per entile) the oe ient ofthe laborintensity proxy isnegativeand signi ant,about

4 standard deviations below zero. The medium-sized subsample (between the 20th and 50th

NYSE per entiles) shows a lower oe ient in absolute terms that is still signi ant (about

1.7 standard deviations belowzero). The large subsample (>50th NYSE per entile) also has

a negative oe ient butit is learly not signi ant (0.72 standard deviationsbelowzero).

Insummary,individualsto kreturnsshowastatisti allysigni ant ee t ofthetwo labor

intensity proxies but the variability explained by this variable is very small. The ee t is

mostlydue to small andmedium-sized rms. The oe ient inthelarge ap subsample does

havethe samenegative sign,but is not signi ant.

6.3 Average returns of sorted portfolios

Wenowuseoursortingpro eduredetailedinse tion6.1toanalyzeaveragereturnsofsize/RLI

sorted portfolios. Our results for the two RLI proxies are presented in tables 5 (APE) and

6 (OAPE). In a rst step we al ulate value weighted and equally weighted average returns

for ea h month in our sample period from July 1976 to De ember 2009. We then al ulate

simple averages over all 402 months. We al ulate ex ess returns against the risk-free rate

Wermers's data).

Table5againsuggeststhatthereissomerelationshipbetweenlaborintensity(APEproxy)

andaveragereturns. InPanelAusingallrmsweobservethatequally-weightedaverageex ess

returnsarede reasinginAPE,while value-weightedreturnsaresomewhat de reasing aswell.

The dieren e H−L is about 34 bp for E-W returns and about 9 bp for V-W ex ess returns,

but only theformer issigni ant. In adjustedreturnsthe dieren eis 25 bpfor E-W returns

and lessthan 5bp forV-W returns. Againonly theE-Wdieren e is signi ant.

Wealso note inpanelA thatthe average size of rms isin reasing as wemove fromH to

L labor intensity portfolio. Therefore we provide average returns persize ategory in panels

Bthrough D. After sorting by size, average ME indi ates that we have ee tively ontrolled

for almost all the variability in average size within Small and Medium ompanies. There is

however some residual variability in Large ompanies, suggesting that the largest ompanies

tendto on entrate towards the Low end oflabor intensity.

In panel B we observe that average ex ess returns and adjusted returns are strongly

de- reasing in labor intensity for both V-W and E-W portfolios. The H−L return dierential

is in all ases in the range 30-35 bp per month (3.6% to 4.3% annual), signi ant in all four

instan es. InpanelC,mid-sized ompanies'ex essreturnsandadjustedreturnsarede reasing

only in the E-W ase. The H-L dierential is 14 and 12.5 bp per month respe tively (about

1.7% and 1.5% annual). In panel D, the H−L adjusted returns dierential is 3.5 bp/month

both(EW andVW), i.e. positive but statisti allyand e onomi allynot signi ant.

Results using the OAPE proxy in table 6 onrm these patterns. Without size sorting

(PanelA)therelationshipbetweenRLIandaveragereturnsispresent,positiveandsigni ant

inE-Wportfolios. TheequallyweightedDGTW-adjustedH−L returndierential isabout30

bp/month (3.6%annual).

In small rms (Panel B) ex ess returns and adjusted returns are in reasing in RLI. The

H−L dierentials are even stronger than in the APE ase: e.g. for adjusted returns the

H−L dieren esaresmalland not signi ant.

To sum up, the portfolio average returns onrm previous nidings. RLI does have a

positiveasso iationwithexpe tedsto kreturns,butonlyinsmallrmsand,toalesserextent,

in medium-sized rms. In large rms, the average return dierentials between high and low

labor intensity portfolios arepositivebut not signi ant (statisti ally or e onomi ally).

6.4 Time series asset pri ing tests

6.4.1 Long-term analysis

In this se tion we will ontinue our analysis on sorted portfolios byrunning time series asset

pri ingtests. Wewillusea standard4-fa torFama-Fren h-Carhart assetpri ingmodelbased

onfa tordatafromKennethFren h'swebpage. Forea hdoublesortedportfolioweregressthe

V-W and E-W ex ess returns on the market ex ess return, the SMB fa tor, the HML fa tor

andthe MOM fa tor. Wethenanalyzethe signi an e oftheinter eptfromtheseregressions

using Newey-Westadjusted SEs (4 lags).

Table7presentstheestimated oe ientsfromtheseregressions. We seeontheleft panel

(E-Wreturns)thatintheSmallsubsampletheinter eptispositiveandstatisti allysigni ant

(61bp) at thehigh endof RLI andthat itde reases withRLI down to theL portfolio where

it is not signi ant and lower inmagnitude (24 bp). In the Medium subsample the highend

of RLI earns a signi ant alpha of about 17 bp and it de reases down to a not signi ant 1

bp for the L portfolio. Large E-W portfolios have also some signi ant alphas but with no

dis ernible monotoni pattern.

There seems to be an interesting link between thesigni an e of thealphas and the

sig-ni an e of the HML oe ient. In ee t, for small and medium-sized rms the oe ients'

signi an e either appears for both, or for neither. Also, for all size groups the High RLI

end seemsto mapmore positively ontheHML fa tor, onsistent withtheideathatB/Misa

ontrol for operatingleverage (seeCarlson etal. (2004)).

de reasingpatterninthePanelA(Smallsubsample),wheretheH−Lalphadieren eamounts

toabout34bp. AgaintheHMLmappingsarein reasinginRLI.Asomewhatsimilarde reasing

patternofthe inter eptispresent inPanel B(Medium sizedrms),butitismu hweakerand

none of the oe ientsaresigni ant. Still within the mid ap subsample,theHML mapping

is again in reasing inRLI. Lastly, in Panel C (Large rms) on the two lowest RLI portfolios

weseeasurprisingsigni antpositivealphaaswellasastrongnegative mappingontheHML

fa tor. NotethattheHML fa tor isnot signi ant inother portfolios of theLargegroup.

Resultsintable8usingtheOAPEproxyare onsistentwithourAPEndings. IntheE-W

ase, Small rms have a signi ant inter ept in reasing inRLI. Asimilar pattern is observed

in the Mid sample, although with lower t-statisti s. The Big sample does not have a lear

pattern of alphas. IntheV-W ase, Smallrms have aH−L alphadieren e of about 29 bp.

However, noneof theinter eptsissigni antintheSmallandMid samples. Again, theLarge

subset earns a surprising positive alpha inthe two lowRLI portfolios,again a ompanied by

strong negative HML mappings.

6.4.2 Moving window tests

Wenowtrytoidentify theperiods atwhi htheseabnormalreturnsareearned. Pinningdown

theseperiods shouldshedsomelighton theme hanisms atplay. For example,ifthesereturns

areequallyspread outoverthesampleperiodhasadierent meaning thanifthesealphas are

surprisereturns earnedat some parti ular pointsintime(e.g. during downturns).

Onemethodthatallowsustoobtainsomeanswerstoourquestionsistorunmovingwindow

regressions of value-weighted returns. We keep our annual sorting and portfolio updating

frequen y as inthe previous ases, but we redu e the length of thetime series regressions to

a moving window. We hose a four year window in order to ensure a su ient pre ision of

estimations. Wepresent ingure1thealphas and orrespondingt-stats fromour experien e,

usingtheAPE measureasproxy forrelative labor intensity. Forea h ofthethree sizegroups

the lowest, mostlynegative, abnormalreturns for most of our sample period. These negative

returns were parti ularly signi ant during the expansionary periods of the late 1980s, late

1990sandthe2ndhalfof2000s. Theonlyex eptionswithpositivereturnsarepost-re essionary

periods of the early1980s and of the2000s. At the otherextreme, small size/high RLI rms

earnedmostlypositivereturns,withmostsigni antperiodsduringtheexpansionarylate1990s

and through the mid 2000s. The low labor intensity rms seem to have negative premiums

due to theirper eived lowerrisks. Thehighlaborrms earned theirpremiums mostlyduring

the 1990sand 2000s.

We also he ked whether these patterns do not ree t a nan ing onstraints story. In

ee t, small size/low RLI rms ouldbe seenas apitalintensive rms withmore pledgeable

ollateral. Our results (presentedin theprevious hapter) using several nan ing onstraints

proxies show ompletely dierent patterns of abnormal returns over time. Thisex ludes the

hypothesisof RLIpi king up nan ing onstraints.

There are some similarities between the mid-sized rms in panel B and the small rms

panelA.ThelowRLIportfolioinparti ular seemstohavesimilarnegativespikesor abnormal

returns in normal periods, well in advan e of re essions. The high labor intensity portfolio

os illates between positiveand negative abnormalreturns,whi h explainswhyon averagethe

return waszerooverthe wholeperiod.

Large rmsinpanelChave hadmu hsmalleralphas overall. TheHighRLI group earned

zero or slightly positive alphas over most of the sample period, withonly one negative spike

inthelate 1990s. The Low RLIgroup(high assetsperemployee) hashad asimilar abnormal

returnpatternintheearly1980sastheLowRLIgroupsofSmallandMidsto ks. The

dis on-ne tion o urred in the mid-1990s, where these rms started to earn mostly positive alphas

all theway throughthe early2000s. Thetiming ofthis break ouldindi ate thatitis related

withtheappearan eofastrongpopulationofhigh-te hrmsinthe1990s. Duringthatperiod

investors required a higher returns from traditional high asset per employee rms, in ex ess

There are several important points to be made from the time series analyses of portfolio

returns. First, we have do umented further support for H2 in the Small and, in part, the

Mid-sized groups (espe ially with the E-W returns). The additional returns of highly labor

intensivesmallrmswereearned ontinuouslyoverthe sampleperiod,and notat aparti ular

point in time. They remain signi ant even after ontrolling for the Fama-Fren h-Carhart

fa tors. Se ond, the mappings on theHML book-to-market fa tor are in reasing with labor

intensity, but they donot ompletely remove theabnormal returnsinE-W portfolios. Third,

thereseems tobe alinkbetween themappingson theHMLfa tor and thesigni an e of the

abnormal returns. Spe i ally, in several ases positive abnormal returns in regressions are

a ompanied bya strikinglydierent mappingon theHML fa tor. Inshort, portfolios sorted

onRLIseem tomapdierentlyon theHMLfa tor,aspredi tedontheoreti algrounds

(Carl-son et al. (2004)). But after ontrolling for this HML fa tor mapping, a return dis repan y

appearsina 4-fa tormodel.

6.5 Industry wage levels and the ee ts of labor intensity

We dis ussed inse tion 2 three me hanisms through whi h labor intensitypotentially ae ts

rmrisk(seehypothesisH1). Wenowinvestigatewhi h oftheseme hanisms ismostlikelyat

playbyseparatingindustries basedon their wage level.

Ever sin e Grili hes(1969) itiswidelya epted that apitaland unskilledlabor aremore

substitutable,whereas apitalandskilledlabor show omplementarity ee ts(formorere ent

eviden e see Bergström and Panas (1992) and Duy et al. (2004)). Furthermore Krusell et

al. (2000) showthat apital-skill omplementarity is entral inexplainingtheevolution ofthe

wage premium of skilled labor withrespe t to unskilled labor over the last de ades. For our

purposes we will assumethat theindustry wage levelis a proxy for theimportan e of skilled

labor andhuman apital withinan industry. It isalso likely thatlabor adjustment osts(e.g.

We use data from the U.S. Census Lo al Employment Dynami s program to separate

high and low wage industries. The Quarterly Workfor e Indi ators (QWI) provide quarterly

dataon average monthly workerearningsperstate andtwo-digit SICindustry. We aggregate

thesestate-leveldatato obtaina rankingofindustries. For agivenstate andquarter werank

industriesbasedonaverageworkerearnings. Wethenaverageranksa rossquartersanda ross

states. 2

We split our return data in two halves by omparing the industry wage rank to the

me-dian rank of all observations. High wage industries (ex luding the nan ial servi es and

utilities) in lude 28 Chemi als, 35 Industrial ma hinery&equipment, 36 Ele troni &ele tri

equipment,and 38 Instruments. Thelow workerearnings industriesin lude among others 20

Food&kindred,34 Fabri atedmetalprodu ts, 33 Primary metals,and 73 Businessservi es. 3

We present in table 9 average DGTW-adjusted returns of sorted portfolios, separating

high and low wage industries. The rst observation is that the ee t of relative labor

inten-sity is stronger in low wage industries. Using the APE proxy and looking at all rms, the

value-weightedreturn dierential is

17.5

bp/monthfor lowwage industries, whileitisslightly

negative at

−

6.3

bp for high wage industries. When using the OAPE proxy, the return

dif-ferentials are respe tively

19.5

bp and

−

2.4

bp. We note that the low-wage H−L return is

statisti allysigni ant only inthe OAPE ase.

A further inspe tion oftable 9 reveals that theabove ee t of wage level ismostlydriven

by theLargesubsample (>50th NYSE per entile). For theAPE proxy thereturn dierential

is

18

bp per month in low wage industries, ompared to

−

8

bp in high wage industries. For

the OAPE proxy the dierentials arerespe tively

21

bp and

−

4.6

bp. The dieren e between

the two H−L returns, though e onomi ally relevant (about 3.1% annual), is in neither ase

statisti ally signi ant (

t

-stats of 1.24 for APE and 1.27 for OAPE). Industry wages do also

ae tthe H−LreturnintheSmallsample. Thisee tdisappearshowever(a tuallyitslightly

2

Weuseasample omprisingtenlargeststatesthathavesu ienthistori al SIC2workerearningsdatain

theQWIdatabase: California,Florida,Georgia,Illinois,Mi higan,NewJersey,NorthCarolina,Pennsylvania,

Virginia,andTexas.

3

Our results are not sensitive (in fa t, they are slightly improved) if we ex lude the very heterogeneous

Our results, though statisti ally weak, tend to suggest that labor intensity matters

rela-tively more in industries that use more low-skilled labor and that are more likely to fa e a

substitutability trade-o between labor and apital. More importantly, we nd no eviden e

indi atingthatlaborintensityrisksmattermoreinhighwageindustries. Therefore,ourresults

ast doubt on the idea that itis labor adjustment oststhatgenerate operatingrisk inlabor

intensiverms. Indeed,adjustment ostsaremorelikelyto be higherinhighwage industries.

Similarly, the human apital turnover risk is equally unlikely to be the me hanism at play,

given that high-wage industries are very likely those that use more skilled labor. The only

foundation of H1 that is not at odds with these results is the wedgebetween a volatile labor

produ tivityand arelatively sti ky unitwage.

6.6 Finan ial leverage and labor intensity

One obje tion to our results ould be that operating and nan ing de isions are jointly

de-termined (see Ma Kay and Phillips (2005)). If rms target an overall levelof risk through a

ombination of operating risk and nan ial risk, empiri al estimation of the marginal ee ts

is hallenging. Gomes andS hmid (2010) dis usswhyempiri al studiesof theee t of

nan- ial leverage on expe ted sto k returnswere in on lusive. As one omponent of risk hanges

(exogenously or not), rms are likely to adjust the other omponents and the overall ee t

is ambiguous. For example, Simintzi, Vig and Volpin (2010) show that rms redu e their

nan ial riskfollowing in reases the bargaining powerof labor.

We address this issue in table 10 by additionally sorting the size-RLI portfolios on book

nan ial leverage. We dene bookleverage asthe ratio of urrent and long-term debt (elds

dl  anddltt)tobookassets(eldat). Wedenethreeleveragegroupsusingthe30thand

the70th per entile. Asin previous ases,we setthe uto per entiles annually at theend of

June from the distribution of all but Small sto ks. We only present results using the assets

peremployee proxy of RLI,butresults using operating assets arevery similar.

lever-aged small rms and theH−L return is signi antly positive. The relationship is ambiguous

inhighlyleveragedmediumand largerms. At mediumlevels ofleverage thepi tureis mu h

learer. Both ex ess and adjusted returns are broadly in reasing with labor intensity.

Al-thoughtheH−L returnis signi ant onlyfor Smallrms, itispositive alsofor mediumrms

(a modest 4.6 bp/month) and large rms (almost 13 bp/month). Moreover, theL portfolios

earna negative adjusted return for allsizes at mediumlevels ofleverage. Lastly,theee t of

labor intensity is the least present in low leverage rms. The H−L return is only positive in

thesmallsubsample,anditisnotsigni ant. InthelargesubsampletheH−Ladjustedreturn

isalmost

−

23

bp/month (witha

t

-statof

−

1.45

).

In summary,relative labor intensity seemsto ae t returnspositively atmedium levelsof

nan ial leverage. Thisee t is statisti allysigni ant only intheSmallsubsample. At high

levelsofleveragetheee toflaborintensityonadjustedreturnsislowintheMedium-sizeand

Large subsample. The measure ofrelative labor intensity hasan even more ambiguous ee t

atlowlevelsofleverage. Arguably,rmsthattakeonverylittledebt oulddosobe ausethey

fa esomeotherspe i businessrisk. Our measureoflabor intensityfails apturetheseother

risks.

7 Con lusion

In this paper we attempt to isolate a pure ee t of labor intensity on rmrisk and on sto k

returns. Weproposetomeasurelaborintensityusingtwoassets-per-employeevariablesrelative

to an industry normal level (measured by the SIC3 median). Based on previous literature

we provide three alternative rationales for the ee t of labor intensity on rm risk. First,

labor adjustment ostspotentiallymakethelabor ostsquasi-xed, resultinginlaborindu ed

operating leverage. Se ond, given a higher volatility of labor produ tivity ompared to unit

wages, labor intensive rms fa e a higher operating risk even if adjustment osts are small.

Third, labor intensive rms fa e a relatively higher worker turnover risk, exposing them to

losses ofhuman apitalinvestments during downsizing or voluntary departures.

returns, these are positively related to labor intensity, even after ontrolling for other rm

hara teristi slikesize,B/Mofequity,andpreviousreturns. Thisrelationshipisagainpresent

insmall andmedium rms.

Portfolio returns onrm a strong ee t of RLI for small rms, and an ee t of RLI on

equallyweightedreturnsofmid- aprms. Intime seriesregressionsofdoublesorted portfolio

returns,weagainndeviden eforRLIee tsinSmallrmsandMediumrms. Aninteresting

asso iationappearsbetween HMLmappingsandex essreturns: positiveabnormalreturnson

Largerms area ompanied witha parti ularly strong mapping on theHMLfa tor. Moving

windowanalysisrevealsthattheSmallandMid- apabnormalreturnswerespreadoutoverthe

sample period,while theabnormal returnson Large rms wereearned during thelate 1990s'

period ofte hnologi al hanges. Oneof the main ndingsof our paper isthattheHML B/M

fa tor seems to pi k up some of the RLI dieren es, but still leaves some abnormal returns

patternsina 4-fa torsetting.

Our furtherinspe tion ofthe ee t ofindustrywage levelsreveals thatRLI hasa stronger

inuen e in low-wage industries. This does not speak in favor of the adjustment osts and

thelaborturnoverrationalesoftheee tofRLI.Themostplausible rationaleisthereforethe

volatile produ tivity/sti ky wagesstory.

Lastly, we ontrol for therms' likely trade-o between operating and nan ial risk. At

medium levels of nan ial leverage we nd a positive ee t of RLI on expe ted returns. On

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Chen, H., M. Ka per zyk, and H. Ortiz-Molina (2011): Labor Unions, Operating

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Daniel, K., M. Grinblatt, S. Titman, and R. Wermers (1997): Measuring Mutual

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Donangelo,A.(2011): LaborMobilityandtheCross-Se tionofExpe tedReturn,Working

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Duffy, J., C. Papageorgiou, and F. Perez-Sebastian (2004): Capital-Skill

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Wermers, R. (2004): Is Money Really Smart? New Eviden e on the Relation Between

Des riptive statisti s of U.S.rms fromthe merged Compustat-CRSP database that satisfy our sele tion

riteria. Assetsandmarket apitalizationareinmillionsof onstant1994dollars. WeusetheCPItodeate

dollarseries. ROAisearnings before extraordinary itemsandinterestover total assets. Employees arein

thousands. Panel Apresents allrm-years over 1975-2009. PanelsB,C, and Dpresent statisti sfor the

top,middleandbottomquintilegroupsofrm-yearssortedonrelativelaborintensity. Weusetheinverseof

previousyear's assetsperemployeeminusthe SIC3medianasproxy ofRLI:laborintensivermshavelow

assetsperemployeerelativetotheSIC3median.

Mean SD 25th% 50th% 75th%

PanelA:Allrms(81953 obs.)

Assets( onst. $mil.)

1606.2

9649.3

54.8

169.0

640.7

Market ap. ( onst. $mil.)

1654.6

9180.2

42.1

150.2

641.6

ROA

0.030

0.149

0.015

0.067

0.104

M/Bassets

1.707

1.235

0.991

1.300

1.927

Realsalesgrowth

0.131

0.389

−0.041

0.058

0.197

Capex/Assets

0.069

0.073

0.024

0.048

0.087

Employees(000s)

9.2

38.8

0.4

1.3

5.0

Panel B:Labor intensity sorted,Lowestquintile group

Assets( onst. $mil.)

2589.2

17200.6

62.2

193.5

743.3

ROA

0.007

0.176

−0.017

0.057

0.101

Realsalesgrowth

0.208

0.525

−0.049

0.086

0.300

Capex/Assets

0.065

0.082

0.015

0.036

0.080

Panel C:Labor intensity sorted,3rd quintile group

Assets( onst. $mil.)

1679.1

7774.9

71.7

228.6

899.5

ROA

0.040

0.136

0.027

0.071

0.104

Realsalesgrowth

0.109

0.335

−0.036

0.053

0.172

Capex/Assets

0.068

0.066

0.027

0.050

0.087

PanelD:Laborintensity sorted,Highestquintile group

Assets( onst. $mil.)

661.6

3294.3

36.0

91.5

296.6

ROA

0.042

0.129

0.021

0.069

0.106

Realsalesgrowth

0.108

0.329

−0.042

0.054

0.181

Weregress rms'annual hange oflogoperatingearningsafterdepre iationon the hangeoflog salesand

a onstant (not presented). We independently sort rms based on previous year's sizeand relative labor

intensity(RLI).Sizegroupsaredenedusingthe20thand50thNYSEmarket apitalizationper entiles. RLI

isproxiedinpanelAbyassetsperemployeerelativetotheSIC3median(Rel.APE

=

APE

/

APE SIC3

− 1

). PanelBusesoperatingassetsper employeeinstead. WesortrmsintoveRLIgroupsbased onprevious

year'squintiles. H(L)isthe20%ofrm-yearswiththehighest(lowest)RLIi.e. theleast(most)assetsper

employeeinyear

t − 1

.Forea hsize-RLIgroupwerunaseparateFama-Ma Bethpro edure(runninga ross rms). WeuseCompustatnan ialdata overing theperiod1975to2009.

H RLI4 RLI3 RLI2 L

PanelA:APE Smallrms

∆

ln(Sales)

2.247

∗∗∗

_2.280

∗∗∗

_2.381

∗∗∗

_1.999

∗∗∗

_1.646

∗∗∗

(15.42)

(14.22)

(15.09)

(11.76)

(7.93)

Avg.

R

2

_0.152

_0.157

_0.174

_0.216

_0.258

Medium-sizedrms

∆

ln(Sales)

1.843

∗∗∗

_2.152

∗∗∗

_1.680

∗∗∗

_1.721

∗∗∗

_1.785

∗∗∗

(12.26)

(9.54)

(9.09)

(12.26)

(12.68)

Avg.

R

2

_0.202

_0.212

_0.160

_0.230

_0.274

Big rms

∆

ln(Sales)

1.349

∗∗∗

_1.721

∗∗∗

_1.904

∗∗∗

_1.856

∗∗∗

_1.654

∗∗∗

(8.45)

(17.19)

(16.23)

(18.79)

(17.01)

Avg.

R

2

_0.227

_0.253

_0.230

_0.285

_0.306

Panel B:OAPE Smallrms

∆

ln(Sales)

2.104

∗∗∗

_2.405

∗∗∗

_2.495

∗∗∗

_2.083

∗∗∗

_1.733

∗∗∗

(13.58)

(15.98)

(14.93)

(11.60)

(12.64)

R

2

0.152

0.177

0.190

0.198

0.256

Medium-sizedrms

∆

ln(Sales)

1.571

∗∗∗

_2.193

∗∗∗

_1.672

∗∗∗

_1.717

∗∗∗

_1.836

∗∗∗

(11.19)

(12.19)

(10.49)

(11.81)

(12.19)

R

2

0.197

0.223

0.143

0.246

0.256

Big rms

∆

ln(Sales)

1.195

∗∗∗

_1.790

∗∗∗

_1.884

∗∗∗

_1.730

∗∗∗

_1.779

∗∗∗

(6.60)

(13.28)

(15.10)

(14.49)

(19.27)

R

2

0.197

0.247

0.233

0.269

0.301

t statisti sinparentheses

∗

_{p <}

_0.10

,

∗∗

_{p <}

_0.05

,

∗∗∗

_{p <}

_0.01

We run monthly Fama-Ma Beth regressions of sto k returns on rm-level hara teristi s. Relative labor

intensityisproxiedbytheinverseofassetsperemployee(PanelA)andoperatingassetsperemployee(PanelB),

measuredrelativetotheSIC3median(Rel. APE

=

APE

/

APE SIC3

− 1

). Highlaborintensity orrespondsto lowAPE/OAPE.Sizeisthelogofmarket apitalization,B/MisthelogoftheDe emberbooktomarketequity,

Mom2-12ismomentumi.e. the umulative ontinuous returnoverthemonths

t − 12

to

t − 2

. Observations ofRHSvariablesforJuly

t

toJune

t + 1

aretakenasofJune

t

,ex eptMom2-12whi hismeasuredmonthly. Allvariablesarewinsorizedattheupper andlower1%.Sto kreturns arefromtheCRSPMergeddatabase.

RegressionsareestimatedontheperiodJuly1976toDe ember 2009,withanaverage ross-se tionof1,754

rms. Wepresentaverageestimatesandtheirt-statisti sbasedonNewey-WestadjustedSEs(4lags).

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

Panel A:Assetsper employee

Rel. APE

−0.112

∗∗∗

−0.101

∗∗∗

−0.103

∗∗∗

−0.098

∗∗∗

−0.089

∗∗∗

(−4.14)

(−3.96)

(−3.96)

(−3.93)

(−3.61)

Size

−0.064

−0.037

(−1.57)

(−0.84)

B/M

0.324

∗∗∗

_0.281

∗∗∗

(3.40)

(2.94)

Mom2-12

0.011

∗∗∗

_0.010

∗∗∗

(5.48)

(5.15)

Constant

1.290

∗∗∗

_1.585

∗∗∗

_1.384

∗∗∗

_1.131

∗∗∗

_1.406

∗∗∗

(4.21)

(3.49)

(4.36)

(3.94)

(3.25)

Avg

R

2

_0.001

_0.015

_0.011

_0.017

_0.036

PanelA:Operatingassets peremployee

Rel. OAPE

−0.117

∗∗∗

−0.106

∗∗∗

−0.112

∗∗∗

−0.098

∗∗∗

−0.093

∗∗∗

(−4.15)

(−4.05)

(−4.13)

(−3.82)

(−3.71)

Size

−0.064

−0.036

(−1.56)

(−0.82)

B/M

0.326

∗∗∗

_0.284

∗∗∗

(3.42)

(2.97)

Mom2-12

0.011

∗∗∗

_0.010

∗∗∗

(5.48)

(5.14)

Constant

1.292

∗∗∗

_1.586

∗∗∗

_1.388

∗∗∗

_1.133

∗∗∗

_1.405

∗∗∗

(4.21)

(3.50)

(4.37)

(3.94)

(3.25)

Avg

R

2

_0.001

_0.015

_0.011

_0.017

_0.036

tstatisti sinparentheses(usingNewey-WestSEs,4lags)

∗

_{p <}

_0.10

,

∗∗

_{p <}

_0.05

,

WerunmonthlyFama-Ma Bethregressionsofsto kreturnsonrm-leveldeterminantsseparatelyforSmall,

MediumandBig rms. Atthe endof Juneof year

t

wedene three sizegroupsusingthe 20th and 50th NYSEper entileand keeptheseassignmentsforJuly

t

toJune

t + 1

. Relativelaborintensityisproxiedby assetsperemployeemeasuredrelativetotheSIC3median(Rel.APE

=

APE

/

APE

SIC3

− 1

),Sizeisthelogof market apitalization,B/MisthelogoftheDe emberbooktomarketequity,Mom2-12ismomentumi.e. the

umulative ontinuousreturnoverthemonths

t − 12

to

t − 2

.ObservationsofRHSvariablesforJuly

t

toJune

t + 1

aretakenasofJune

t

,ex eptMom2-12whi hismeasuredmonthly. Allvariablesarewinsorizedatthe upper andlower 1%. Sto kreturns arefromtheCRSPMergeddatabase. Regressionsareestimatedonthe

periodJuly1976toDe ember2009. Wepresentaverageestimatesandtheirt-statisti sbasedonNewey-West

adjustedSEs(4lags).

(1) (2) (3)

Small Medium Large

Rel. APE

−0.113

∗∗∗

−0.073

∗

−0.028

(−3.95)

(−1.71)

(−0.72)

Size

−0.067

0.020

−0.053

(−1.00)

(0.24)

(−1.05)

B/M

0.296

∗∗∗

_0.272

∗∗

_0.139

(2.88)

(2.44)

(1.39)

Mom2-12

0.011

∗∗∗

_0.010

∗∗∗

_0.007

∗∗

(6.25)

(3.76)

(2.49)

Constant

1.549

∗∗∗

_1.077

∗

_1.399

∗∗

(3.54)

(1.80)

(2.52)

Avgobs. 895 409 450 Avg

R

2

_0.027

_0.042

_0.068

t statisti sinparentheses

∗

_{p <}

_0.10

,

∗∗

_{p <}

_0.05

,

∗∗∗

_{p <}

_0.01

(APE)

We onstru tportfoliosattheendofJuneofyeartbyindependentlysortingrmsbymarket apitalization

andbyrelativelaborintensity(RLI).Weusethesesortsto al ulateJuly

t

toJune

t + 1

returns. Sizegroups aredenedusingthe20thand50thNYSEmarket apitalizationper entiles.Relativelaborintensityisproxied

byassetsperemployee,measuredrelativetotheSIC3median(Rel. APE

=

APE

/

APE SIC3

− 1

). Wesortrms intovegroupsbasedontheyear

t − 1

RLIdistributionquintilesex ludingsmallrms. H(L)isthe20%of observationswiththehighest(lowest)RLIi.e. theleast(most)assetsperemployeeinyear

t − 1

.Thet-stats arebasedonNewey-West SEsusing4lags. We al ulate ex essreturnsagainst therisk-free rate(Kenneth

Fren hdata) andDGTW-adjustedreturns(followingDanieletal. (1997),usingRussWermersdata). Sto k

returns arefromtheCompustat CRSPMerged database, overing the periodJuly1976 toDe ember2009

(402monthlyobservations).

Ex essreturns DGTW-adj. returns Avg. Avg. Avg.

VW EW VW EW #rms ME BE/ME PanelA:Allrms H

0.558

1.087

0.039

0.317

555

865.5

0.497

RLI4

0.601

1.025

0.038

0.257

400

1430.5

0.562

RLI3

0.416

0.938

−0.075

0.196

345

1990.1

0.587

RLI2

0.551

0.861

0.030

0.126

347

2533.1

0.555

L

0.468

0.749

−0.007

0.066

354

2872.7

0.504

H

−

L

0.089

0.338

0.046

0.252

t

-stat

(0.623)

(3.735)

(0.520)

(3.058)

Panel B:Smallsize

H

1.009

1.289

0.190

0.423

368

65.8

0.766

RLI4

1.050

1.302

0.198

0.435

207

77.0

0.837

RLI3

1.024

1.177

0.162

0.288

162

79.4

0.867

RLI2

0.936

1.072

0.112

0.211

159

81.9

0.864

L

0.651

0.949

−0.134

0.130

168

83.2

0.880

H

−

L

0.358

0.340

0.324

0.293

t

-stat

(3.203)

(3.410)

(2.961)

(2.957)

Panel C:Mediumsize

H

0.771

0.863

0.032

0.134

106

425.3

0.747

RLI4

0.841

0.856

0.045

0.071

99

435.8

0.632

RLI3

0.877

0.918

0.147

0.183

86

440.3

0.643

RLI2

0.743

0.793

−0.004

0.034

81

440.1

0.657

L

0.795

0.721

0.074

0.009

87

442.4

0.614

H

−

L

−0.024

0.142

−0.042

0.125

t

-stat

(−0.164)

(1.042)

(−0.330)

(1.067)

Panel D:Largesize

H

0.540

0.682

0.034

0.087

81

5312.7

0.459

RLI4

0.576

0.683

0.034

0.061

94

5558.9

0.551

RLI3

0.395

0.645

−0.088

0.052

96

6694.2

0.579

RLI2

0.546

0.680

0.032

0.079

107

7770.7

0.547

L

0.474

0.649

−0.001

0.052

99

9425.2

0.496

H

−

L

0.066

0.033

0.035

0.035

t

-stat

(0.429)

(0.277)

(0.366)

(0.349)

(OAPE)

We onstru tportfoliosattheendofJuneofyeartbyindependentlysortingrmsbymarket apitalizationand

byrelativelaborintensity(RLI).Weusethesesortsto al ulateJuly

t

toJune

t + 1

returns. Sizegroupsare denedusingthe20thand50thNYSEmarket apitalizationper entiles.Relativelaborintensityisproxiedby

operatingassetsperemployee,measuredrelativetotheSIC3median(Rel.OAPE

=

OAPE

/

OAPE SIC3

− 1

). Wesortrmsintovegroupsbasedontheyear

t − 1

RLIdistributionquintiles. H(L)isthe20%ofrm-years withthehighest(lowest)RLIi.e. theleast(most)op. assetsperemployeeinyear

t − 1

.Thet-statsarebased onNewey-WestSEsusing4lags.We al ulateex essreturnsagainsttherisk-freerate(KennethFren hdata)

andDGTW-adjustedreturns (followingDanieletal. (1997),usingRuss Wermersdata). Sto kreturns are

fromtheCompustatCRSPMergeddatabase, overingtheperiodJuly1976toDe ember2009(402monthly

observations).

Ex essreturns DGTWadj. returns Avg. Avg. Avg.

VW EW VW EW #rms ME BE/ME Panel A:Allrms H

0.521

1.078

0.021

0.318

568

639.8

0.526

RLI4

0.560

1.016

0.042

0.254

403

1187.7

0.526

RLI3

0.516

0.937

−0.007

0.195

349

1747.2

0.637

RLI2

0.556

0.916

0.002

0.175

337

2840.4

0.554

L

0.434

0.718

−0.036

0.021

344

3714.2

0.484

H

−

L

0.087

0.360

0.057

0.298

t

-stat

0.696

4.426

0.707

4.265

.

PanelB:Smallsize

H

1.018

1.291

0.209

0.434

380

68.1

0.748

RLI4

1.061

1.264

0.212

0.386

213

76.6

0.830

RLI3

0.932

1.161

0.072

0.281

164

78.0

0.884

RLI2

0.930

1.166

0.108

0.312

149

83.2

0.883

L

0.690

0.913

−0.124

0.075

158

81.3

0.903

H

−

L

0.329

0.378

0.333

0.359

t

-stat

2.676

3.852

2.831

3.710

.

Panel C:Mediumsize

H

0.745

0.827

0.025

0.118

112

417.0

0.726

RLI4

0.872

0.910

0.084

0.131

99

438.0

0.628

RLI3

0.891

0.920

0.158

0.177

89

445.8

0.637

RLI2

0.804

0.821

0.017

0.039

79

443.5

0.660

L

0.708

0.648

−0.001

−0.061

80

444.6

0.639

H

−

L

0.036

0.178

0.026

0.179

t

-stat

0.282

1.388

0.226

1.571

.

PanelD:Large size

H

0.484

0.633

0.012

0.029

75

3972.2

0.484

RLI4

0.527

0.666

0.031

0.080

91

4594.3

0.507

RLI3

0.497

0.665

−0.020

0.066

96

5922.8

0.635

RLI2

0.551

0.732

0.003

0.119

109

8224.7

0.547

L

0.431

0.622

−0.034

0.021

106

10887.5

0.475

H

−

L

0.053

0.011

0.046

0.008

t

-stat

0.405

0.120

0.488

0.091

.