manufacturing and job Offshoring stability: Evidence fromItalian Change Structural and Economic Dynamics

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ContentslistsavailableatSciVerseScienceDirect

Structural

Change

and

Economic

Dynamics

j o ur na l ho me p a g e:w w w . e l s e v i e r . c o m / l o c a t e / s c e d

Offshoring

and

job

stability:

Evidence

from

Italian

manufacturing

Alessia

Lo

Turco

a

_,

_Daniela

_Maggioni

a,∗

_,

_Matteo

_Picchio

a,b,c,d

a_Marche_Polytechnic_University,_Department_of_Economics_and_Social_Sciences,_Italy b_Tilburg_University,_CentER,_The_Netherlands

c_Ghent_University,_Sherppa,_Belgium d_IZA,_Germany

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: ReceivedMay2012

ReceivedinrevisedformFebruary2013 AcceptedApril2013 Available online xxx JELclassiﬁcation: C41 F14 F16 J62 Keywords: Offshoring Jobstability Manufacturing Durationanalysis Proportionalhazard

a

b

s

t

r

a

c

t

Westudytherelationshipbetweenoffshoringand jobstabilityinItaly intheperiod 1995–2001byusinganadministrativedatasetonmanufacturingworkers.Weﬁndthatthe internationalfragmentationofproductionnegativelyaffectsjobstability.Serviceoffshoring andmaterialpurchasesfromdevelopedcountriesfosterjob-to-jobtransitionswithin man-ufacturingofallworkersandwhitecollars,respectively.However,themostdetrimental effectsforjobstabilitycomefrommaterialoffshoringtolowincomecountrieswhichdrives bluecollarworkersoutofmanufacturing.Therefore,policyinterventionsshouldespecially focusonthislattercategoryofworkersmoreexposedtofragmentationprocessesand foreigncompetition

1. Introduction

In the last decades low labour cost countries have gained a growing role in the process of international fragmentationofproduction.Atthesametime,therapid spreadofICTsacrosstheworldhasfavouredthetradability ofsomeserviceactivities.Thesephenomenahaveraised concernsaboutjobsecurity,especiallyoflowskillworkers andemployeesperformingroutinelyandsimpletasks,as theymightbemoreexposedtotheprocessofinternational

∗ Correspondingauthorat:MarchePolytechnicUniversity,Department ofEconomicsandSocialSciences,PiazzaleMartelli8,60121Ancona,Italy. Tel.:+390712207250;fax:+392207102.

E-mailaddresses:a.loturco@univpm.it(A.LoTurco),

d.maggioni@univpm.it(D.Maggioni),m.picchio@univpm.it(M.Picchio).

fragmentation of production. Asa consequence, a large strandofthetheoreticalandempiricalliteratureontrade andlabourhastriedtounderstandtheimpactofoffshoring ofmaterialsandservicesontheequilibriumemployment, theskillupgrading, and thewage differentials between highskillandlowskillworkers.1

Although the short run dynamics generated by off-shoring might be extremely policy relevant and their analysismighthelptodampentheassociatedadjustment costs, the theoretical literature has not devoted much attentiontothem.Theoffshoringofproductionphasesor tasksmayresultincostsaving,productivityimprovements,

1 _See_Feenstra_and_Hanson_(1996,₁₉₉₉₎_and_Amiti_and_Wei₍₂₀₀₄₎_for

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andexpansionoftheoutputandoftherelative demand ofthefactormoreintensivelyusedintheoffshoring sec-tor(Arndt,1997; Grossman and Rossi-Hansberg, 2008). However,theseproductivitygainsfromoffshoringarenot alwayscomparedtotheshortrunwelfarelossesgenerated bythepossibleriseinunemploymentandjobtransitions. Theshort runeffects, modelled bymeansof low or no inter-sectormobilityintwosectormodels,highlightthe theoreticalpossibility of increased unemployment from offshoringintheoffshoringsector(MitraandRanjan,2007, 2010). It is essentially anempirical matter toascertain whether and to what extent an increase in offshoring intensitycausesanincrease in job dismissalsand, con-sequently,areducedjobstability.Thisisarelativelyless researchedareaconsistingofveryrecentstudiesproviding evidenceabouttheeffectsofforeigncompetitivepressures onemployees’probabilityof retaining theirjobs(Egger etal.,2007;Geishecker,2008;Baumgarten,2009;Munch, 2010;BachmannandBraun,2011).

Withinthisframework,thispaperisaimedat empiri-callyexploringtheeffectofoffshoringonjobstability.We matchsectorlevel measuresofoffshoringwith employ-ees’information onjob durations and we test whether offshoringofmaterialsandknowledgeintensivebusiness services(KIBS) affects thejob stability inItalian manu-facturingsectors.Administrativedataonjobmatchesare informativeaboutemployees’characteristicsand destina-tionstatesincaseofjobseparation.Weexploitthisrich pieceof information tounderstandwhethertheimpact ofoffshoringis heterogeneous betweenwhite andblue collarworkersand whethertransitions outof manufac-turingandtransitionstodifferentmanufacturingjobsare differentlyaffectedbyoffshoring.Thissecondpartofthe analysis also shedslight on inter sectoral reallocations of workers. As high adjustment costs are often associ-atedwithsuchreallocations,weprovidepolicyadviceto designinterventionswhichareeffectiveincushioningsuch costs.

Afurthercontributionofourstudyconsistsin under-standingwhethertheeffectofoffshoringonjobstability dependsontheorigincountryofintermediates.Whereas offshoringtolowlabourcost countriesmayrepresenta costsavingstrategyinvolvingtherelocationofthemore labourintensivephasesofproduction,offshoringtohigh incomeeconomiesmayactuallyhidethesearchfor tech-nologyimprovementswhichmayturnintoanimportant complementforthedomesticlabour.Also,theskill inten-sityofimportsincreaseswiththehumancapitalabundance and,thus,withtheincome levelof theorigincountries (Fitzgeraldand Hallak, 2004;Schott, 2003).Asa conse-quence,adifferentpatternofsubstitutabilitymayfollow accordingtothe inputorigin,withwhite and blue col-larsbeingmorethreatenedbyimportsfromhighandlow incomecountriesrespectively.

Weﬁnd,indeed,thatpurchasesofforeignintermediates fromdeveloping countriesreduceemployees’job stabil-ity.Material offshoringtolow income economiesraises bluecollars’probabilityofexperiencingatransitionoutof manufacturing.Intermediatepurchasesfromhighincome countriesfosterinsteadwhitecollarworkers’job-to-job transitionswithinmanufacturing.OffshoringofKIBShas

asimilarpositiveeffectonthejob-to-jobtransitionsofall workers.

Thisarticleisorganizedasfollows.Section2reviewsthe literaturedealingwiththelabourmarketimpactoftrade opennessandoffshoring.Section3presentsthedata,the sampleandsomedescriptiveevidenceofjobexitratesand offshoringinItalianmanufacturing.Section4describesthe econometricmodelforanalysingtheimpactofoffshoring onjobstability.Theestimationresultsarepresentedand commentedinSection5.Section6concludes.

2. Literaturereview

Thetheoreticalandempiricalliteratureonoffshoring andthelabourmarkethasmainlyfocusedontheeffects ofoffshoringontherelativewageofhighandlowskilled workers(FeenstraandHanson,1996,1999;Arndt,1997; EggerandFalkinger,2001).However,moreattentionhas beendevotedinrecenttimetotheunemployment-trade nexusinmodelswithlabourmarketfrictions(Davisand Harrigan, 2007; Egger and Kreickemeier, 2009, 2010; Felbermayretal.,2008;HelpmanandItskhoki,2010;Dutt et al.,2009).Inthe longrun,thesemodels predictthat theequilibriumunemploymentmightbeeitherpositively ornegativelyaffectedbytradeliberalisation.Thespecific role ofoffshoringintheshortrun istakenintoaccount inageneralequilibriumframeworkbyMitraandRanjan (2007,2010).Undermixedoffshoringequilibrium–some firms offshoreand others do not– theypredicta posi-tivelinkbetweenoffshoringandunemploymentwhenthe labourforceisimperfectlymobileacrosssectors:despite itspositiveimpactonproductivity,offshoringcausescost savingand, thereby,a pricereduction inthefinal good, sothatmoreresourcesaredirectedtotherelativelymore rewardingnonoffshoringsectorandunemploymentrises in the offshoringsector. Thus, thetheoretical literature suggests that, at least in theshort run, offshoringmay positivelyimpactjobseparationratesintheoffshoring sec-tor.Weaimattestingsuchhypothesisinareducedform modelwithinapartialequilibriumframework.Wedonot thereforeaddressaformaltestoftheabovementioned gen-eralequilibriumframeworkandoftheresultingchannels throughwhichoffshoringaffectsunemployment,like rel-ativepriceschangesandproductivityimprovements.We onlyprovideevidenceontheshortrunneteffect.

Some empirical studies close to our research line adoptthesamepartialequilibriumapproach.Theyexploit employee level databases to understand the relation-shipbetweentradeandtheindividualprobabilityofjob separation.2 _For _the_US _the_evidence _on_{manufacturing} workerssuggeststhatindustryimportexposurematters, but themagnitude of itseffects ontheﬁring rates and

2_Some _further _{contributions}_have_instead _investigated_the

conse-quencesofopennessonjobcreationanddestructionattheindustryor ﬁrm-level(DavisandHaltiwanger,1999;Kletzer,2000;Kleinetal.,2002; DavidsonandMatusz,2005;NucciandPozzolo,2010).Althoughthese analysesprovideageneralinsightintothepotentialrestructuringeffects ofopenness,theydonotfullyidentifytheimpactofopennessonthe prob-abilityofjobseparationatemployeelevel.Studiesbasedonemployee leveldataaremorereliableforthispurpose.

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the duration of workers’ joblessness is not big (Kruse, 1988;Hungerford,1995).Moreover,employmentstability isdecreasingintheappreciationoftheimportexchange rate(Goldbergetal.,1999).Thespeciﬁcroleofoffshoring practiceshasnotbeenexploredbythispieceofresearch.

ForEurope,thisliteratureismorerecentandmadeup offewcontributions.Eggeretal.(2007)estimate employ-menttransitionprobabilitiesbetweensectorsbymeansof adynamicfixed-effectsmultinomiallogitapproach.They find thatan increase in theimport share of intermedi-ate goods negatively affects the probability of Austrian workers to stay in or change into the manufacturing sector,especially for industrieswitha comparative dis-advantage. Munch (2010)finds that in Denmark in the period 1992–2001 offshoring marginally increases the jobchangehazardrate,thejobseparationrate,andthe unemploymentriskoflow-skilledworkers.Threestudies usingGerman dataconveysomehowconflictingresults. Geishecker(2008)estimatesadurationmodelexploiting monthlyinformationonjobspellsfrom1991until2000.He findsthatoffshoring,definedinthenarrowsense(Feenstra andHanson,1996),significantlyraisestheindividualrisk ofleavingemploymentandishomogeneousacross educa-tionalattainments.ThisevidencecontrastswithBachmann andBraun’s(2011)findings.Usingadifferent administra-tivedatasetonindividualworkers’employmenthistories recordedonadailybasis,theyfindthatinthe manufactur-ingsectortheprobabilityofmovingtonon-employment rises withoffshoringonlyformedium-skilled andolder workers. Moreover, their findings corroborate the evi-denceofalimitedimpactofoffshoringontheoveralljob stability inthemanufacturingsectorandshowthat off-shoringincreasesjobstabilityintheservicesector.Finally, Baumgarten(2009)analysestherelationshipbetween off-shoringandjobtasksandfindsthatinthemanufacturing sectortheadverseeffectofoffshoringissmallerfor non-routineandinteractivetasks.

Our study is in linewiththe latter group of works. In ordertoestimatea partialequilibriumreduced form empirical modelfor theoverall impactofoffshoringon jobstability,weexploitmicrodataonindividualjobspells matchedwithsectorlevelmeasuresofoffshoringretrieved fromtheinput–output(IO)tables.Wehaveinformation onjob durations onmonthlybasis asin Munch (2010) andGeishecker (2008).Like them,wewillalsoconsider a broad measure ofmaterial offshoring, which includes allintermediate importsandnot onlyimportsfromthe samemanufacturingsector.Thisbroadmeasureallowsfor awidersetofmaterialinput-laboursubstitution possibili-ties.Wefocusonbothmaterialandserviceoffshoringas in Baumgarten(2009),evenif,as faras serviceimports areconsidered,ourfocuswillbeonoffshoringofKIBS:we explicitlytakeintoaccountthepossiblenegativeimpactof importsofhighskillintensiveservicesonwhitecollarjobs and,moregenerally,ontheinternalorganizationofﬁrm production.

Finally,animportantcontributionofourworkconsists insplittingthematerialimportsbyorigincountry.None ofthepreviousstudiestookintoaccountthattheeffectof offshoringonjobstabilitymightdependontheorigin coun-tryoftheimportﬂow.Nevertheless,whereasoffshoringto

lowlabourcostcountriesmayrepresentacostsaving strat-egyinvolvingtherelocationofthemorelabourintensive phasesofproduction,offshoringtohighincomeeconomies may actually hide the search for technology improve-mentswhichmayturnintoanimportantcomplementfor thedomestic labour.Also, theskill intensityof imports increaseswith thehumancapital abundanceand, thus, withtheincomeleveloftheorigincountries(Fitzgerald andHallak,2004;Schott,2003).Asaconsequence,a dif-ferentpatternofsubstitutabilitymayfollowaccordingto theinputorigin,withwhiteandbluecollarsbeingmore threatenedbyimportsfromhighandlowincome coun-triesrespectively.Somerecentliteraturedealingwiththe effectsofoffshoringonthefirmlabourdemandsupports theimportanceofsuchdistinctionintheoriginofforeign inputs.HarrisonandMcMillan(2007)showthatimports fromforeignaffiliateslocatedinlow incomeeconomies reduce home employment in US multinationals, while importsfromaffiliateslocatedinhighincomecountries positivelyaffectit.Outoftheevidenceonmultinational firms,LoTurcoandMaggioni(2012),atthefirmlevelfor Italy, and Cadarso et al.(2008) and Falk and Wolfmayr (2008),attheindustrylevelforSpainandtheEU respec-tively,showasimilarfindingonimportsfromlowincome economies.Thisevidencemotivatesourexpectationson thepossibilityofdifferentoffshoringeffectsonthejobexit ratestemmingfromdifferentmotivationsforimports,i.e. costsavingversustechnologysearch(OECD,2007). 3. Thedata

3.1. Thedatasourcesandthesample

Toanalysetheimpactofoffshoringonjobsecurityinthe Italianlabourmarket,wecombinemicrodataonjob dura-tionsand workers’characteristicswithsectorlevel data onoffshoring,import penetration,technologicalchange, efﬁciency,andregionalproxiesforthelabourmarket con-ditions.

Microdataarefromalongitudinaldatasetprovidedby theInstitutefortheDevelopmentofVocationalTraining (ISFOL)andbasedontheadministrativerecordscollected bytheItalianInstituteforNationalSocialSecurity(INPS). INPScollectsdataonallItalianworkersoftheprivatesector throughanadministrativeprocedurebasedonﬁrms’ dec-larations.Thedurationsofallthejobspellsarecollectedon amonthlybasis.However,duetotheadministrativenature ofthedataanditscollectiondesign,whenindividualsexit thedataset,wedonotknowwhethertheyexittothepublic sector,toself-employment,tounemployment,oroutofthe labourforce.Wethink,however,thatthislackof informa-tiondoesnotrepresentalimitationforourwork,sincewe focusontheimpactofoffshoringonjobtransitionswithin andoutofthemanufacturingindustry,regardlessofthe workers’destinationsaftertheyexitmanufacturing.3

3 _Furthermore,_we_are_not_able_to_distinguish_between_job_spells_ended

duetotheﬁrm/plantclosurefromthoseendedforotherreasons.Although thedatacontainvariablesabouttheﬁrmstartingandendingdates,they containseveralmissingvaluesandwepreferrednottousethem.

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FromalltheINPSrecords,ISFOLcollectsinformationon everyworkerbornonthe10thofMarch,June,September andDecemberofeachyear.Thus,aboutoneworkeroutof 91isincludedinthesample.Thewholedatasetiscomposed bymorethan2,470,000observations,whichcorresponds toabout963,000jobspellsforabout310,000workersin theyears1985–2002.4

From this database, we select a sample of fresh job matches which started between January 1995 and December1998andwefollowthemonamonthlybasis untiltheendof2001.Wekeeponlymanufacturing work-ersagedbetween20and50.Foreachworkerweretainonly theﬁrstjobspellintheﬁrstyeartheworkerappearsinthe database.Duetotheendingoftheobservationperiodin December2001,wetreatasright-censoredthejobspells whicharenotcompletedyetbythen.5

Therestrictionofoursampletojobsstartedintheperiod 1995–1998isduetotworeasons.First,wecannotuseolder jobspellssincedataonourmainexplanatoryvariable, off-shoring,arenotavailablebefore1995.Second,weprefer nottousejobspellsstartedlaterthan1998,asthe Ital-ianlabourmarketwent throughaseriesofinstitutional changes,mainlyintroducingatypicalformsofjob arrange-ments.Thisrestrictionis,therefore,aimedatavoidingjob heterogeneitydrivenbyinstitutionalchangesinthelabour market.

Inouranalysiswealsouseothervariablesatworker level in modelling job duration distributions: the daily grosswage,theindividualage,workexperiencecalculated asthetotalworkexperiencesince 1985anduntil sam-pleentry,thenumberofpreviousjobssince1985,anda setofindicatorsforgender,whitecollar,nationality,ﬁrm size,regionalarea,andsector.6_These_variables_are time-constantandtheirvalueisﬁxedatthemomentofentry intooursample.

Concerningthesectoraloffshoring,therelative indica-torsareretrievedfromtheNationalimport-useIOtables providedbytheItalianInstituteofStatistics(ISTAT).They canbecomputedonlyona2-digitNACERev.1sectorand onyearlybasis.Tomeasurematerialoffshoringintensity, weuseanarrowindicatordeﬁned,inlinewiththeprevious literature(FeenstraandHanson,1996,1999),as

OFFnarrow jt ≡

IMjjt

TIjt ×

100 forj=1,...,mand

t=1995,...,2001, (1)

whereIMjjt is, foreach jmanufacturingsector,thecost

ofintermediateinputsfromtheforeignsectorjattimet andTIjtisthetotalofdomesticandimportednonenergy

inputsusedinsectorj.Inwords,thisisameasureofwithin

4 _See_Centra_and_Rustichelli₍₂₀₀₅₎_for_a_detailed_description_of_the

dataset.Atthetimeofwriting,weusedthemostrecentversionofthe ISFOLdatabase.Recently,ISFOLreleasedadatabaseupdateaddingone yeartothelongitudinaldimension.

5 _Given_the_small_number_of_observations_with_a_complete_spell_longer

than60months, weright-censorobservations lastingmorethan60 monthsinordertoreducethecomputationaltimeinmodelestimation.

6 _Given_the_{administrative}_nature_of_the_data,_information_on_education,

familycomposition,andfamilybackgroundarenotavailable.

industryintermediateinputssubstitution,sinceit repre-sentstheshareofintermediatepurchaseswhichisshifted tothesameindustryabroad.

Theprocessofinputsubstitutionmayhoweverinvolve intermediatesfromotherindustries,previouslyproduced within the boundaries of the ﬁrm or purchased from domestic suppliers. We therefore compare the perfor-manceofthenarrowmeasureofoffshoringtoabroader one,whichtakesintoaccountthedegreeofbothintraand inter-industrysubstitution:

OFFbroad jt≡

m

i=1IMjit

TI_jt ×100 forj=1,...,m. (2) Thisindicatorcapturestheroleofimportsofsectorjfrom allmanufacturingsectors.

Finally,intheempiricalanalysiswewillalsotestthe roleoftheoffshoringofKIBS,7_which_we_deﬁne_as OFFKibsjt≡

n

i=m+1IMjit

TIjt ×

100 forj=1,...,m, (3) wheretheKIBSsectorsaretheonesindexedbym+1ton intheeconomy.

Thesemeasuresarejustproxiesoftheoffshoring phe-nomenon, asthe imported intermediate inputintensity doesnotexactlyandexclusivelycapturethedelocalisation ofﬁrmproductionphases.Nevertheless,fromwidespread anecdotalevidence8_and_from_tailored_survey_data₍_OECD, 2007), it issensible toassume that offshoringpractices mayhaveimportantlyaffectedtherecentimportdynamics, especiallyfromlowlabourcostcountries.Intheabsence ofbettersectorlevelindicators,wefollowtheliterature (FeenstraandHanson,1996,1999;OECD,2007)andstick totheuseofthesemeasures.

Inordertotakeintoaccountthedifferenttype, qual-ity,andtechnologylevelofinputspurchasedfromdifferent tradingpartners,wecomputethemeasuresofmaterial off-shoringbyincomeleveloftheorigincountries.Wefollow thetraditionalwaytoconstructoffshoringindicatorssplit byoriginwhentheoriginofforeignintermediatescannot bedetectedfromtheIOtables.Then,wecombineIOtables withtheintermediateimportsharebyorigincountryfor eachsector.Theresultingoffshoringmeasurestohighand lowincomecountriesaredeﬁnedas

OFFc_narrow_jt≡IMjit∗(IM

c it/IMit) TIjt × 100 fori=j OFFc_broad_jt≡

i

IMjit∗(IMcit/IMit)

TI_jt

×100.

IMjicomesfromtheIOtablesandmeasurestheimported

intermediatesfromsectoriusedinsectorj.IMiandIMcit

areinsteadretrievedfromtheWITS-COMTRADEdatabase andtheyrespectivelymeasurethetotalimportsof inter-mediates ofsector iand theintermediate importsfrom

7_According_to_the_deﬁnition_of_the_EU_Economic_Commission_(2009),

KIBSareservicesbelongingtoNACERev.1sectors72–74.

8_Several_issues_of_the_“Il_Sole₂₄_ore”_witness_the_offshoring_process_for

Italianﬁrms.Asarecentexample,seethe8thofOctober2012newspaper issueonItalianﬁrms’offshoringtoSerbia.

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countryc,withc={High,Low}indexingtheincomelevelof theexportingcountries.9_{Unfortunately,}_we_are_not_able_to splitKIBSimportsbyorigin,duetothedifﬁcultytoretrieve dataonimportedservicesoutoftheIOtables. Neverthe-less,itissensibletopresumethatthebulkoftheseimports originatesfromhighincomeeconomies.

Ourbaselinespecificationincludesfurthercontrolsto account for sectorand geographic time-varying hetero-geneity that might affect the job exit rate, other than offshoring. At the sectorlevel, we make useof:(i) the extentof ICTsectorcapital deepening,measuredasthe logarithmofthesectoralcapitalstockinofficemachines, telecommunicationapparatus,andsoftwareovertotal out-put;(ii)thesectorallabourproductivity,measuredasthe logofsectoralvalueaddedoverthetotalemployment;(iii) anoverallmeasureofsectoralimportpenetration calcu-latedasthepercentageshareofsectorimportsoverthe sumofsectoroutputandimportsminussectorexports. ThesevariablesaregatheredfromISTATNationalAccounts, apart fromtradeflowswhich, togetherwiththe defini-tionofhighandlowincomecountries,areretrievedfrom theWITS-COMTRADEdatabase.Finally,weusetheannual regionalunemploymentrategatheredfromISTATto con-trolforthestateofthelabourmarketatregionallevel: TableA.1reportsthelistofallthevariablesusedinthe analysiswiththeirdefinition.TableA.2andA.3displays summarystatisticsofthetime-costantvariablesand time-varyingcovariatesatthesampleentry,respectively.Finally, TableA.4showsthepairwisecorrelationsofvariablesat sectoralandregionallevel.

3.2. Descriptiveanalysis

Table1displaysthetransitionsoutofthecurrentjob andthejob-to-jobtransitionsbydestinationsector.Most ofthejobseparationsintheprivatesectorendwitha tran-sitionoutofemploymentintheprivatesector(65%ofthe total exits).Focusingonthespellsendingwitha transi-tionintoanewjob,itismorelikelythattheworkerwill beemployedinthesame2-digitsector:about58%of job-to-jobtransitionsarewithinthesamesectorofactivity.

9_The_deﬁnition_of_high_and_low_income_countries_directly_comes_from

theWITSdatabaseandisbasedontheWorldBankcountryclassification. Inordertoidentifytheintermediateimports(totalandbycountrygroup) ofeachNACEsectori,weretainedtheHarmonisedSystem(HS)import flowsrepresentingflowsofintermediatesaccordingtoBroadEconomic Category(BEC)classificationandmatchedthemwithNACEsectorsby meansoftheHS/NACEcorrespondencetableavailableinRAMON Euro-stat.TheBECcodesidentifyingintermediatesarethefollowing:121,122, 22,42,and53.Analternativeoptionistousethetotalsectoriimports(of whichimportsofintermediatesrepresentonlyonecomponent),retrieved fromWITS-COMTRADEdatabase,inordertocomputetheoffshoring mea-suressplitbyorigincountries(Cadarsoetal.,2008;FalkandWolfmayr, 2008).Inthiscase,weassumethattheweightofeachcountrygroup inimportsisthesameforintermediatesandothergoods.Theresults obtainedusingtheselattermeasuresareavailablefromtheauthorsupon request.Finally,Schott(2004)proposedanotherapproach:withinasector imports,intermediatesarethoseproductscontainingtheword“part”or “component”ortheirabbreviationsintheirdescription.Wetriedtouse

Schott’s(2004)approach.However,usingthe6-digitHS,8-digitCN,or 5-digitSITCclassiﬁcations,weendedupwithaverysmallshareofgoods (lessthan8%)identiﬁedasintermediates.Wepreferredthereforenotto followSchott’s(2004)approach.

Table1

Transitionsoutofemploymentandjob-to-jobtransitions.

Transitions Absolute frequency Relative frequency(%) Outofemployment 36,075 65.46 Fromprimary 4947 8.97 Frommanufacturing 13,365 24.25 Fromservices 17,763 32.23 Toanother2digitNACEsector 7935 14.40 Fromprimarytomanufacturing 520 0.94 Fromprimarytoservices 1355 2.46 Frommanufacturingtoprimary 233 0.42 Frommanufacturingtoservices 1292 2.34 Fromservicestoprimary 282 0.51 Fromservicestomanufacturing 1020 1.85

Withinprimary 10 0.02

Withinmanufacturing 1714 3.11

Withinservices 1509 2.74

Tothesame2digitNACEsector 11,101 20.14

Total 55,111 100.00

Transitionsacrosssectorsmightbedifﬁcultand require

importanttrainingcostsforworkersinordertoacquire

theabilitiesandskillswhichareneededtoperformajob

inadifferentsector.Nevertheless,Table1alsoshowsthat

transitionstoadifferent2-digitsectorarenotsorareand theinvolvedworkersoftenmoveacrosstheprimary,the secondary,andthetertiarysectors.Thesetransitionsmay reflect thestructural change of anindustrial developed economytowardsmoreadvanced,skill,andtechnological intensiveactivities–especiallyservices–thatgoeswiththe industrialgrowthandinternationalaffirmationof emerg-ingcountries.Thisprocessoftertiarisationoftheeconomy, whichcouldbepushedandspeededupbythe internation-alisationofproduction, findssomeempiricalsupportin Table1:whenachangeoccursinthemainsectorofactivity themostimportantdestinationsectoristheservicesector. Animportantsourceofheterogeneitythatmayaffect thejob stability ofworkers is theskilllevel ofthe job, regardlessofthesectorwheretheindividualisemployed. Wesplittheworkersbetweenwhiteandbluecollars.For manufacturing, representing the focus of ourempirical analysis,Fig.1showsemployees’probabilityofjob surviv-ingandjobexitratebyoccupation.Whitecollarworkers aremuchmorelikelytopreservetheirjobpositionthan bluecollarworkers.Thisisconsistentwiththeideathat lowskillintensiveworkersaremoreexposedtoforeign competition,economicslowdown,technologicalprogress, andotherexternalpressuresthatmaydriveindividualsout oftheiroccupations.

Inwhatfollows,ouraimistounderstandwhetherthe processofproductionfragmentationacrosscountrieshas significantlycontributedtotheabovedescriptivechanges injobstability.In thelast decadesdevelopingcountries havegained agrowingrole inworldtrade ofthe inter-mediates.Therehasalsobeenafurtherintegrationamong developedcountriesstemmingfromthedeepeningofthe existingrelationshipswithforeignsuppliersandcustomers andtheincreasedimportanceoftheintra-firmtradeflows withinmultinationalgroups.Asaconsequence,Italyhas experienced a growthin the sharesof imported inputs

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Fig.1.Kaplan–Meiersurvivorandhazardfunctionsbyoccupationinmanufacturingsectors.

andouraimistoinfertheeffectofthisexpansionin off-shoringactivitiesontheevolutionofthejobstability.As wecan seefromFig.A.1,in theperiod 1995–2004 off-shoringofmaterialsincreasedinmostsectors,althoughnot monotonicallyandwithsomeheterogeneity.Forexample, insectorsPaperandpaperproducts(NACEsector21)and Editingandprinting(NACEsector22)materialoffshoring hasbeencharacterizedbyalternatephasesofgrowthand drop.Instead,thepurchasesofintermediatesfromabroad signiﬁcantlyraisedinsectorsTextiles,ApparelandLeather productsandfootwear(NACEsectors17–19).Thepicture is moreclearcut ifone looks at KIBS.Even in activities wheretheimports ofmaterial intermediateshave been decliningorstable,thepurchaseofKIBSfromabroadhas beenexpanding.Thisisstrictlyrelatedtotheworldwide rapidadvancesandexpansionofICTs,whichhavefostered thetradabilityof services,especially ofthose whichare moreintensiveinknowledgeandskills,andhavedrivento within-ﬁrmreorganizationsofproductionprocesses. How-ever,Fig.A.1showsthatmaterialoffshoringisstillmore importantthanknowledgeintensiveserviceoffshoringin termsofmagnitudeoftheshares.

Somefurtherinsightscanbegatheredbysplittingthe materialoffshoringaccordingtotheoriginofinputs.Input purchases from developing countries have signiﬁcantly increased in levels and with respect to the offshoring sharestodevelopedcountries.Nonetheless,highincome countriesarestill themainpartners ofItaly in tradeof intermediates,withsharesthataregreatlylargerthanthe onesofdevelopingcountries.Onlyinsomelowskilland traditionalsectors,especiallyApparelandLeatherproducts andfootwear,lowincomecountriesarethemostimportant sourcesofmaterials(seeFig.A.2).

4. Econometricframework

4.1. Mixedproportionalhazardjobseparationrates Inordertodetecttheimpactofoffshoringonjob separa-tionrates,weestimatemixedproportionalhazard(MPH) modelswithtime-varyingvariables.Asweonlyobserve thelabourmarketstateoccupiedattheendofeachmonth, theobserveddurationsaremeasuredindiscretetime.We model the discrete time process as if it was generated byagroupedcontinuous-timemodelasinvandenBerg andvanderKlaauw(2001).Bydoingso,theparameters donotdependonthetimeunitofobservation(Flinnand Heckman,1982).

Jobdurationisdeﬁnedasthetimeuntilthejobis ter-minated,eitherbecauseofatransitiontoanotherjobor becauseofa transitionoutofemployment.Letxdenote thevectorofexplanatoryvariableswhichareconstantover time and zthesetoftime-varyingcovariates. The vari-ablet(witht∈N0)denotesthejobdurationasmeasured

fromthemomentofjobinﬂow,whilethevariable(with ∈N0)denotescalendartime.Thejobseparationrateofa

spellstartedattimeandaftertmonthsisspeciﬁedinthe followingMPHform

[t|x,z(+t),

v

]=exp[˛(t)+ˇx+ız(+t)]

v

, (4) where

• exp[˛(t)]isthepiecewiseconstantbaselinehazard cap-turingthedurationdependence.Thetimeaxisofeachjob spellisdividedintoQintervalsIq=[hq,hq+1)withq=1,

(7)

...,Q,h1<h2<...<hQ,h1=1,andhQ=∞.10Thebaseline

hazardfunctioncanberewrittenas exp[˛(t)]₌exp[

Q

q=1

˛qdq(t)], (5)

wheredq(t)isadummyindicatorequaltooneifthejob

separationoccursduringintervalIqand˛qisthe

corre-spondingintensityparameter.11

• xisaKdimensionalvectoroftime-invariantcovariates controllingforobservedheterogeneity.

• z(+t)isaJdimensionalvectoroftime-variant covari-ates,amongwhichoffshoringindexesandasetoffurther variables controllingfor time-variantheterogeneity at thetransitionmonth(+t).

• ˇ and ı are the parameter vectors associated (and conformable) to the time-variant and time-invariant covariates,respectively.

•

v

isthenon-negativetime-invariantindividual hetero-geneitywhichisassumedtobeindependentonxand z.

Inordertoavoidstrictassumptionsonthedistributionof theunobservedheterogeneity,weassumethat

v

hasa dis-cretedistributionlikeinHeckmanandSinger(1984).We choosethenumber ofpointsofsupportonthebasis of informationcriteria(Hannan–QuinnandAkaike informa-tioncriteria),assuggestedbyBakerandMelino’s(2000) and Gaure et al.’s (2007) Monte Carlosimulations. We alwaysendupwithchoosingthemodelwithonepointof support,i.e.thereisevidenceofnounobserved heterogene-ity.Moreindetail,wefollowedBakerandMelino(2000) andGaureetal.(2007)insearchingfornewmasspoints whenmovingfromthemodelwithoutunobserved hetero-geneitytothemodelwithunobservedheterogeneity.The starting valuesofthelocationofthenewsupportpoint werechosenbylookingforpotentialimprovementsinthe likelihoodfunctionbymaximisingtheGateauxderivative. Welimitedthesearchofthestartingvalueofthenew sup-port point over theinterval [exp(−5),exp(2)].Once we founda starting value forthe locationof thenew sup-portpoint,wesetitsstartingprobabilitymassto0.0001 andwemaximizedthelikelihoodfunctionusinganalytical derivatives.Wewerenotabletoincreasethelikelihoodany furtherwhenaddingnewsupportingpointstothediscrete speciﬁcationoftheunobservedheterogeneitydistribution. Inordertoensurethatthisresultwasnotgeneratedbybad initialvaluesandconvergencetoalocalmaximum,we fol-lowedGaure etal.’s(2007)suggestionandwerestarted severaltimesthefullmaximizationprocessusingrandom numbersasinitialvaluesforthenewsupportpointandits probabilitymass.Sincewewereneverabletoincreaseany furtherthelikelihood,westoppedand,accordingtothe informationcriteria,wechosethemodelwithonesingle constant,i.e.withoutunobservedheterogeneity.

10_We_split_the_time_axis_into₉_intervals_at_3,_6,_9,_12,_18,_24,_30,_and₃₆

months.

11_˛

1isnormalizedto0.Thisnormalisationisinnocuousasthescaleof

thejobseparationrateiscapturedbyv.

4.2. Thelikelihoodfunction

Inoursampleweobservebothcomplete and incom-plete job spells and the data duration is measured in discretetime.Weassumethatthediscretetimeprocess isgeneratedbysomeunderlyingcontinuoustimeprocess. Sincewehavemonthlydata,wedonotexactlyknowwhen thejobexitoccurswithin twoconsecutivemonths.We thereforeassumethatthejobhazardrateisconstantwithin twoconsecutivemonths.Underthisassumption,itcanbe shownthatthecontributiontothelikelihoodfunctionof acompletejobspellstartedatcalendartimeand termi-natedaftertmonthstakesthefollowingform

L(t|x,z,

v

;)= t−1

r=1 exp{−[r|x,z(+r),

v

]} − t

v

]} = t−1

r=1 exp{−exp[˛(r)+ˇx+ız(+r)]

v

} − t

v

} ≡S(t−1|x,z,

v

)−S(t|x,z,

v

), (6) whereisthesetofparameterstobeestimated.Aswe specifythediscretetime-processasifitwasgeneratedby agroupedcontinuous-timemodel,thecontributiontothe likelihoodfunctionofexitingajobspellaftertmonthsis givenbythedifferencebetweentheprobabilityofjob sur-vivingfort−1monthsandtheprobabilityofsurvivingfor tmonths.

Thecontributiontothelikelihoodfunctionofajobspell startedatcalendartimeandincompleteaftertmonths becauserightcensoredattheendoftheobservationperiod isgivenbythesurvivorfunctionevaluatedattmonths:

Lc_(t_|x,_z,

_v

_;₎ _≡_S(t_|x,_z,

_v

₎₌ t

v

]} = t

v

}. (7) Letcnbeanindicatorvariableequaltoonewhenthejob

spellofindividualnisrightcensoredand0ifcompleted. Undertheassumptionthatthedistributionofthe unob-servedheterogeneityisdiscrete,wecanintegrateitout whenconstructingthelikelihoodfunctionofindividualn withjobdurationtn:

Ln(tn|xn,zn;)= M

m=1 pm[Lnc(tn|xn,zn,

v

m;)]cn ×[Ln(tn|xn,zn,

v

m;)](1−cn). (8)

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The log-likelihood function sums the logarithm of Eq. (8) over all the individuals in the sample, i.e. _L₌

N

n=1Ln(tn|xn,zn;).

4.3. Identiﬁcation

Indurationmodels,thefailuretocontrolfor selectiv-ity issues due tounobserved heterogeneity can lead to substantialbiasesintheestimationofthestructural param-etersofthehazardfunction.Wecontrolfortheselection onunobservablesonthebasis ofa discrete distribution withanunknownnumberofpointsofsupport,unknown probabilitymasses,and unknownlocationof thepoints ofsupport.ElbersandRidder(1982)showedthatunder theMPHassumption,exogenoustime-invariantregressor variation,andanauxiliaryassumptiononthefirstmoment oftheunobservedheterogeneitydistribution,themodel componentsarenon-parametrically identified.If exoge-nousinformationfromtime-varyingvariablesisavailable, likeinthisstudy,theMPHassumptionisnotnecessaryfor identificationandtheimpactofthecovariatesonthe haz-ardfunctioncanbeallowedtobeheterogeneousovertime (Brinch,2007).

Afurtherconcernincrediblyidentifyingtheimpactof offshoringonjobstabilityistime-varyingheterogeneity. Theremightindeedbeothertime-varyingdeterminantsof jobstabilitywhich,ifleftoutofthemodelspecification, couldgiverisetospuriouseffects.Inordertoaddressthis potentialproblem,weincludeinthemodelspecificationa richsetoftime-varyingvariablesatnational,regional,and sectorallevelswhichmightexplainthejobduration distri-bution.Moreindetail,weinclude:(i)timedummiestotake intoaccountidiosyncraticchanges,likethosedetermined bylegislationchanges;(ii)theregionalunemploymentrate tocontrolforthestateofthelabourmarket;(iii)thesectoral ICToveroutput;(iv)thesectorallabourproductivitywhich isaproxyfortheefficiencyandevolutioninthesector;(v) theimportpenetrationratiowhichcapturesthe competi-tivepressurefromforeignfirmsinthesamesectorandmay alsoreflectthegeneraltradeopennessofthesector.

Finally,thecombinationofmicrodataaboutthe dura-tionofindividualjobspellsandsectorallevelindicators foroffshoring helpsin mitigating endogeneityconcerns relatedtoreversecausality.Itisindeedunlikelythatthe individualbehaviourisabletoaffectthesectoral perfor-manceintermsofforeignintermediatepurchases. 5. Estimationresults

Table2reportstheestimationresultsofthejob haz-ardfunctiondescribed intheprevioussection.Thefirst two columnspresent the analysisfor the sample of all employees.Consistentlywithourexpectations,the sec-toralpurchasesofforeignintermediateinputssignificantly increasetheworker’sprobabilityofexperiencingajob sep-aration.Thispositiveeffectonthejobexitrateisrobust tothe definition of the offshoringmeasure (narrow or broad).12_Concerning_the_magnitude_of_the_effect,_we_find

12 _This_ﬁnding_contrasts_with_Geishecker₍₂₀₀₈₎_,_who_ﬁnds_no_support

forasigniﬁcanteffectofthebroadlydeﬁnedoffshoring.

thata1percentagepointsincreaseinthenarrow(broad) offshoringincreasesthejobexitrateby3.4%(3.2%). More-over,thepurchasesofKIBSabroadhasafurthernegative effectonthejobstabilityinmanufacturing.13

Hence,thegeneralprocessoffragmentationof produc-tionacrosscountriesseemstosigniﬁcantlyaffecttheﬁrm laboursavingorganization choices.The resultinghigher dynamisminthelabourmarketmaygenerateimportant adjustmentcostsintermsofincreasedunemploymentand needforworkers’re-training.Itmightnonetheless repre-sentanopportunityfortheeconomicsystemtoundergo structuralchanges thatmayimproveand strengthenits competitiveness.

Asmentionedabove,inordertodisentanglethetrue effectofoffshoringfromthespuriousonedeterminedby furthertime-varyingheterogeneity, weincluded among thecovariates a setof time-varyingcontrolsatsectoral and geographical level. The sectoral import penetration is aimed at controlling for the growing international integration among countries and the resulting stronger competitivepressures.Wefindthattougherinternational competitionpositivelyandsignificantlyaffectsthejob haz-ardrate.Thus,thegeneralprocessofglobalizationseemsto increasethejobinstabilityduebothtothefragmentation strategiesinwhichthedomesticfirmsmayengageandto thegrowingflowsofforeigngoodsenteringthedomestic market.Anotherrelevantphenomenonwhichmay poten-tiallyaffectthelabourmarketdynamicsistechnological change.Contrarytosomepreviousevidence(Geishecker, 2008),theadvancementsintechnology,measuredbythe sectoralICTcapitalstock,donotexplainthejobexitrate. According to expectations, the regional unemployment rateispositivelyrelatedtotheprobabilityofjobseparation. Theestimatedcoefficientsofthetime-invariant covari-ates arebroadlyinlinewiththose previouslyfoundfor otherindustrializedcountries. Whitecollarworkersand employeeswithItaliannationalityhaveasignificantlower probabilityofexperiencingajobseparation.Boththewage andpreviousworkingexperiencearepositivelyassociated withjobdurations.AsinMunch(2010),olderworkersare lesslikelytoexitthejob.ThiscontrastswithGeishecker (2008)whoinsteadfindsjobstabilitydecreasingwithage, evenifnotlinearly.Firmsizemattersandthelargerthe firm,thelowerthejobexitrate.Biggerfirmsmightbeless sensitivetothebusinesscycleandshocksinthemarket. Differentlyfromtheresultsforothercountries(Geishecker, 2008;BachmannandBraun,2011;Baumgarten,2009),we findthatmenandwomenhavethesamejobexitrate.

Sofar,wehaveconsideredoffshoringtohavea homo-geneousimpactonjobstabilityregardlessofthetypeof employees’tasksandactivities.Thisishoweverastrong assumption since workers witha higher skill level and committedwithknowledgeandtechnologyintensivetasks may beless substitutableby foreigninputs than work-ersperformingsimpleandroutinelyjobs.Theincreasing internationalintegrationmightaffectmorethelowskilled

13_A₁_percentage_points_increase_in_KIBS_increases_the_job_hazard_rate

ofabout62.0%–54.2%,dependingonthebroadornarrowdeﬁnitionof materialoffshoring.

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Table2

Estimationresultsofthesystematicpartofthejobhazardfunction.

Allemployees Whitecollars Bluecollars

(1) (2) (3) (4) (5) (6) Constant −1.941* _−2.844** _−2.768 _−3.542* _−0.769 _−1.544 [1.120] [1.106] [2.024] [1.908] [1.395] [1.397] Female −0.017 −0.017 −0.078** _−0.079** _0.011 _0.011 [0.020] [0.020] [0.040] [0.040] [0.024] [0.024] Age −0.134*** _−0.134*** _−0.056** _−0.055** _−0.152*** _−0.152*** [0.011] [0.011] [0.026] [0.026] [0.012] [0.012] WhiteCollar −0.195*** _−0.195*** [0.021] [0.021] Italian −0.219*** _−0.219*** _−0.348*** _−0.347*** _−0.210*** _−0.210*** [0.044] [0.044] [0.128] [0.129] [0.047] [0.047] Wage −0.080*** _−0.080*** _−0.083* _−0.085* _−0.108*** _−0.107*** [0.023] [0.023] [0.046] [0.046] [0.027] [0.027] WorkExp −0.417*** _−0.417*** _−0.439*** _−0.437*** _−0.421*** _−0.419*** [0.051] [0.051] [0.092] [0.093] [0.061] [0.061] PrevJobs −0.560*** _−0.560*** _−0.811* _−0.831* _−0.485** _−0.489** [0.207] [0.207] [0.428] [0.430] [0.238] [0.239] Quarter2 0.199*** _0.199*** _0.197*** _0.198*** _0.196*** _0.196*** [0.021] [0.021] [0.046] [0.046] [0.023] [0.023] Quarter3 0.320*** _0.320*** _0.366*** _0.365*** _0.304*** _0.304*** [0.023] [0.023] [0.052] [0.052] [0.026] [0.026] Quarter4 0.281*** _0.281*** _0.300*** _0.298*** _0.275*** _0.275*** [0.023] [0.023] [0.049] [0.049] [0.026] [0.026] Size2 −0.088*** _−0.088*** _−0.140** _−0.139** _−0.071*** _−0.072*** [0.024] [0.024] [0.059] [0.059] [0.026] [0.026] Size3 −0.096*** _−0.096*** _−0.127** _−0.126** _−0.087*** _−0.087*** [0.023] [0.023] [0.053] [0.053] [0.026] [0.026] Size4 −0.169*** _−0.169*** _−0.181** _−0.180** _−0.170*** _−0.170*** [0.038] [0.038] [0.072] [0.072] [0.046] [0.046] Size5 −0.230*** _−0.230*** _−0.217*** _−0.216*** _−0.239*** _−0.239*** [0.025] [0.025] [0.055] [0.055] [0.028] [0.028] Centre 0.036 0.036 −0.100* _−0.101* _0.071*** _0.071*** [0.023] [0.023] [0.054] [0.054] [0.026] [0.026] South −0.02 −0.021 −0.191* _−0.200* _0.014 _0.014 [0.046] [0.046] [0.110] [0.110] [0.051] [0.051] OFFKibs 0.620*** 0.542*** 0.691*** 0.467** 0.351** 0.336** [0.128] [0.123] [0.213] [0.186] [0.169] [0.171] OFFnarrow 0.034*** 0.015 0.042*** [0.012] [0.022] [0.014] OFFbroad 0.032*** 0.050*** 0.023** [0.009] [0.016] [0.011] ImpPenj 0.004*** 0.004*** 0.005*** 0.004** 0.003*** 0.003*** [0.001] [0.001] [0.002] [0.002] [0.001] [0.001] Unempreg _1.373*** _1.375*** _1.535** _1.583** _1.305*** _1.310*** [0.336] [0.336] [0.760] [0.761] [0.375] [0.376] LPj −0.216 −0.143 −0.223 −0.116 −0.225 −0.156 [0.156] [0.157] [0.268] [0.270] [0.209] [0.210] ICTj 0.079 0.071 0.043 0.088 0.148 0.135 [0.100] [0.100] [0.205] [0.197] [0.116] [0.116] ln(v) −1.941* _−2.844*** _−2.768 _−3.542* _−0.769 _−1.544 [1.045] [1.030] [1.979] [1.831] [1.295] [1.302] NT 511,919 511,919 146,218 146,218 365,701 365,701 N 19,259 19,259 4,589 4,589 14,670 14,670 Log-likelihood −66,198.2 −66,196.1 −14,638.6 −14,634.0 −51,458.9 −51,461.2

Standarderrorsareinbrackets.Dummyindicatorsforyearsandsectorsareincludedinallestimationsbutnotreportedforthesakeofbrevity.Thereference employeeisItalian,male,workinginﬁrmssmallerthan20employeesinthesectorofFurnitureandothermanufacturingindustries,enteringthesamplein theﬁrstquarteroftheyear,andlivingintheNorthofItaly.

*_Significant_at_10%_level. **_Significant_at_5%_level. ***_Significant_at_1%_level.

thanthehighskilledbecauseofboththeirrelativescarcity inadvancedcountriesandthegrowingroleoflowskilled labourabundantcountriesinworldtradeﬂows.In partic-ular,offshoringpracticesareoftenmeantmoretosaveon labourintensivefragmentsofproductionthantoacquire newtechnologiesfromabroad(OECD,2007).

Inordertotestwhetheroffshoringdifferentlyaffects thejobsecurityofworkersaccordingtotheirskills,we dis-tinguishbetweenblueandwhitecollarworkers.Columns (3)–(6)ofTable2reporttheestimationresultsofoursingle riskmodelsplitbyskilllevel.Accordingtobothmeasures ofoffshoring,thepurchasesofforeigninputsdecreasethe

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Table3

Thejobbaselinehazardproﬁleoftheestimatedmodels.

Baselinecoefﬁcients Allemployees Whitecollars BlueCollars

(1) (2) (3) (4) (5) (6) ˛2:[3,6)months −0.359*** _−0.359*** _−0.286*** _−0.287*** _−0.369*** _−0.369*** [0.036] [0.036] [0.096] [0.096] [0.038] [0.038] ˛3:[6,9)months −0.478*** −0.478*** −0.272*** −0.274*** −0.512*** −0.512*** [0.038] [0.038] [0.097] [0.097] [0.042] [0.042] ˛4:[9,12)months −0.062* −0.063* 0.327*** 0.325*** −0.141*** −0.141*** [0.035] [0.032] [0.086] [0.086] [0.039] [0.039] ˛5:[12,18)months −0.070** −0.070** 0.224*** 0.221*** −0.123*** −0.123*** [0.032] [0.032] [0.080] [0.080] [0.035] [0.035] ˛6:[18,24)months 0.101*** 0.101*** 0.610*** 0.608*** −0.018 −0.018 [0.033] [0.033] [0.078] [0.078] [0.036] [0.036] ˛7:[24,30)months −0.354*** −0.354*** 0.068 0.066 −0.445*** −0.448*** [0.039] [0.039] [0.090] [0.090] [0.044] [0.044] ˛8:[30,36)months −0.091** −0.092** 0.263*** 0.259*** −0.157*** −0.157*** [0.038] [0.038] [0.090] [0.090] [0.043] [0.043] ˛9:[36,60)months −0.140*** −0.140*** 0.200** 0.203** −0.199*** −0.198 [0.038] [0.038] [0.088] [0.088] [0.043] [0.043]

Standarderrorsareinbrackets.Thebaselinehazardisidentiﬁeduptoaconstant,so˛1isnormalizedto0. * _Signiﬁcant_at_10%_level.

** _Signiﬁcant_at_5%_level. ***_Signiﬁcant_at_1%_level.

jobstabilityofblueandwhitecollarworkers.14_Services purchased abroad signiﬁcantly increase the job hazard functionofbothgroupsofworkersandthemagnitudeof theeffectislargerforwhitecollars.Thismightbedueto thefactthatKIBSarecharacterizedbyveryhighknowledge requirementsandneedspeciﬁctechnicalabilities.Hence, KIBSareusuallyperformedbyhighskillworkersandtheir delocalisationaffectsmorethewhitecollarworkers’job stability.Evenifwesplitthesampleinwhiteandblue col-larworkers,furtherheterogeneitymightcharacterizethe impactofoffshoringonjobstability.Thisis whyin the nextsubsection,wecontrolforanotherpotentialsource ofheterogeneity,thatistheoriginoftheoffshored inter-mediates.

Table3 displaystheprofileofthebaselinehazardof themodelspresentedinTable2.Thejobbaselinehazards display non-monotonic profiles, with similar patterns forwhiteandbluecollarworkers.Theprobabilityofjob separationisdecreasinginthefirstthreequarters.Itjumps upattheend ofthefirstyear andit isincreasinguntil theend of the second year, when a spike is observed. Then,itbouncesdowninthefirstsemesterofthethird yearand,finally,itstabilizesfromthatmomentonward.15 Thedeclineofthejobseparationratewithtenureinthe initialpartof the job relationship and afterthe end of thesecondyearisconsistentwiththecentralfactsabout working mobility (e.g. Topel and Ward, 1992; Farber, 1999). The spikes at the end of the first and second yearsof tenuremight berelated tothenon-renewalof temporarycontractsandtothedismissalofunproductive

14 _The_effect_of_narrow_offshoring_of_white_collar_workers’_job_exit_rate

is,althoughpositive,notsigniﬁcantatthe5%signiﬁcancelevel.

15 _In_order_to_quantify_the_job_baseline_hazard_in_a_given_month_for_the

referenceindividual,onehastotaketheexponentialofthesumbetween theconstantreportedinTable2andthebaselinecoefﬁcientofthe corre-spondingmonth.Sincethebaselinehazardisidentiﬁeduptoaconstant, ˛1isnormalizedto0.

jobmatches.InaframeworkàlaJovanovic(1979)where theproductivityofaparticularworker-ﬁrmmatchisnot observableex-ante,butisrevealedafterawhile,wemight indeedobservespikesinthejobseparationrate.16

5.1. Theoriginofimports

Emergingandlowlabourcostcountrieshave experi-encedastrongexpansionduringthelastdecades,bothin termsofeconomicgrowthandtradeﬂowsin intermedi-ates.Theirincreasedroleintheglobaleconomyhasrisen worries about the job stability of workers in advanced economies, even if most of the foreign inputs in high incomecountriesarestillimportedfromotherdeveloped partners.

Therearedifferentreasonswhytheimpactofoffshoring on job stability might depend onthe origincountry of theinputﬂows.Asmentionedabove,imports fromlow labourcostcountriesarelikelytohidecostsavingreasons, whereasimportsfromhighincomeeconomiesmaystem fromthesearchforabettertechnology.Moreover,theskill intensityofthetwotypesofimportsisfoundtobedifferent, thusimplyingaheterogeneousimpactonworkers accord-ingtotheirskilllevel(FitzgeraldandHallak,2004;Schott, 2003).Awareofthis,wetakeintoaccounttheimportance ofthecountrywheretheproductionisoffshoredtoand, especially,wecrossheterogeneousimportoriginswiththe differentoccupation skills.We expect offshoringtolow incomecountriestoplaythemajorroleontherecentlabour marketevolution,duetoitsrecentgrowthinmagnitude andtoitsgenerallaboursavingpurpose.Inwhatfollows, we onlydisplaytheresultsfor ouroffshoringmeasures

16_Cockx_and_abd_Picchio₍₂₀₁₂₎_report_similar_spikes_in_the_job

sepa-rationratesattheendoftheﬁrstyearoftenureforyoungworkersin Belgium.

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Table4

Estimationresultsofoffshoringbyskilllevelandorigincountries.

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

OFFKibs 0.628*** _0.548*** _0.688*** _0.472** _0.362** _0.351**

[0.129] [0.124] [0.212] [0.184] [0.171] [0.176]

OFFHighnarrow 0.024* 0.023 0.024

[0.013] [0.024] [0.016] OFFLow narrow 0.079*** −0.034 0.109*** [0.030] [0.078] [0.033] OFFHigh broad 0.024 ** _0.061*** _−0.002 [0.011] [0.018] [0.014]

OFFLowbroad 0.070** −0.048 0.110***

[0.029] [0.076] [0.031] ImpPenj 0.004*** 0.004*** 0.005*** 0.004** 0.003*** 0.003*** [0.001] [0.001] [0.002] [0.002] [0.001] [0.001] Unempreg _1.373*** _1.375*** _1.537** _1.582** _1.306*** _1.314*** [0.336] [0.336] [0.760] [0.761] [0.375] [0.375] LPj −0.2 −0.126 −0.237 −0.134 −0.201 −0.101 [0.156] [0.157] [0.267] [0.270] [0.209] [0.210] ICTj 0.049 0.053 0.071 0.123 0.105 0.088 [0.102] [0.101] [0.210] [0.198] [0.118] [0.118] NT 511,919 511,919 146,218 146,218 365,701 365,701 N 19,259 19,259 4,589 4,589 14,670 14,670 Log-likelihood −66,197.1 −66,195.3 −14,638.4 −14,633.1 −51,456.8 −51,457.3

Standarderrorsareinbrackets.

*_Significant_at_10%_level. **_Significant_at_5%_level. ***_Significant_at_1%_level.

andothersectoralandregionalvariablesforthesakeof brevity.17

Usingboththebroadandthenarrowmeasuresof off-shoring,Table4showsthat,focusingonthesampleofall workers,amongthemeasuresofmaterialimportsthemain negativeimpactforthejobstabilityisdisplayedforthe pro-cessofproductionfragmentationtodevelopingcountries. Asamatteroffact,althoughtheimpactofinputpurchases fromhighincomecountriesonthejobhazardrateis pos-itive,thecoefﬁcientissmallerandthesigniﬁcance level islower.OffshoringofKIBSstillcontributestoreducethe probabilityofworkerstopreservetheirjobs.

Themostinterestinginsights,however,aredelivered whenwetakesimultaneouslyintoaccountthetwo het-erogeneity sources, worker skills and origin of inputs. Materialoffshoringtolowincomecountriesrepresentsa detrimentalfactorofthejobstabilityonlyofbluecollar workers. A 1 percentage points increase in the narrow orbroadoffshoringshareincreasesthemonthlyjobexit rateofbluecollarworkers byabout11%.Thisisinline with previous empirical evidence and also supported byLoTurcoand Maggioni(2012),whoﬁndthatinItaly offshoring affects the ﬁrm labour demand only if it is towardslowincomecountries.18_In_contrast,_material off-shoringtohighincomecountriesincreasestheprobability

17_The_full_set_of_estimation_results_are_available_from_the_authors_upon

request.

18_The_negative_effect_of_offshoring_to_developing_countries_on_the_ﬁrm

labourdemanddisplayedinLoTurcoandMaggioni(2012)isespecially importantintraditionalsectors,deﬁnedastheonesbelongingtothegroup “Supplier-Dominated”sectorsaccordingtoPavitt’staxonomy,wherethe shareoflowskilledworkersisusuallyhigher.

ofwhitecollarsofexperiencingajobseparationonlywhen computedaccordingtothebroaddefinition.Ifpurchases from advanced economies consist of more knowledge intensivegoods,thentheymaywellsubstituteforwhite collars, especially in the case of material imports not directlyrelatedtothecorebusinessofthefirm.Asamatter of fact, taking a vehicle manufacturer as an example, imports of computers may substitute for the work of some of the firm administrative employees,as well as importing advanced technology electronic devices may turnengineersanddesignersredundant.Bothimportsare notincluded inthenarrow definitionofoffshoring, but theybelongtothebroadone.

5.2. Assessingthemagnitudeoftheoffshoringeffect Tohave a betterunderstanding ofthe magnitudeof theeffectof offshoringonthejob exitrate,we predict jobsurvivor functionsunder threedifferent counterfac-tual scenarios and compare them to the job survivor functionpredictedusingtheactualdata.Thethree coun-terfactualscenariosarecharacterizedbydifferentlevelsof offshoring:(i)weincreasetheactuallevelbyonestandard deviation;(ii)wesetittothemaximumvaluerecordedin theperiodunderanalysis;(iii)wesetittozero.Whenwe setthecounterfactualoffshoringtothemaximumvalue, wemimicwhatthejobexitratewouldbeiftherewere aneconomicwidemovementtowardsthesectorwiththe largest offshoring. Whenwe setoffshoring to zero,we insteadpredictthejobexitrateinasortofautarchic eco-nomicsystem.

We run this exercise on thebasis of theestimation resultspresentedinTable4withthebroaddeﬁnitionof

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Fig.2.Predictedjobsurvivorfunctionsunderdifferentscenariosofmaterialoffshoringtolowincomecountries.

Fig.3.Predictedjobsurvivorfunctionsunderdifferentscenariosofserviceoffshoring.

materialoffshoring.Wereplicateittwice:ﬁrst,weﬁx ser-viceoffshoring(OFFKibs)atthelevelobservedintheactual

dataandweletbroadmaterialoffshoringtolowincome countries (OFFLow_broad) vary according to different scenar-ios;second,weletserviceoffshoringvarywhilematerial offshoringtolowincomecountriesisﬁxedattheactual value.Sincethematerialoffshoringeffectlooksstronger forbluecollarworkersandwhentheorigincountriesare lowincome,wedecidetofocusonOFFLow_broad,whilealways keepingthematerialoffshoringtohighincomecountries (OFFHigh_broad)ﬁxedatthelevelobservedinthedata.

Fig.2reportsthepredictedjobsurvivorfunctionsunder thedifferentscenarios describedabovefor material off-shoring tolow income countries, for allthe employees (graph(a)),forwhitecollarworkers(graph(b)),andfor bluecollarworkers(graph(c)).Fig.2suggeststhat mate-rialoffshoringtolowincomecountrieshasasizableimpact

onthejobsurvivorprobability.Ifmaterial offshoringto low income countries increasesby one standard devia-tion,theprobabilitythata jobmatchlastsmore than2 yearsdecreasesfrom49.9%to45.8%.Thiseffectistotally driven bybluecollarworkers.Whenwe limitthe sam-pletobluecollarworkers,theeffectis indeedlargerin relativeandabsolutesize,movingfrom45.8%to38.8%.If therewereaneconomywidemovementtothesectorwith thehighestoffshoringtolowincomecountries,blue col-larworkers’probabilityofsurvivinginajobfor24months woulddecreaseto16.4%.19_Fig.₂_suggests_therefore_that materialoffshoringtolowincomeeconomiesimportantly affectstheexitratefromamanufacturingjob.

19_We_get_similar_ﬁgures_if_we_run_this_analysis_by_playing_with_OFFLow narrow

insteadofOFFLow broad.

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Fig.3 focuses,instead, onthepredictedjobsurvivor functions under the different scenarios for service off-shoring.Likematerialoffshoring,serviceoffshoringhasa sizable impacton thejobsurvivor function.It is worth mentioningthat, differentlyfrom materialoffshoringto lowincomecountries,bluecollarworkersarelessaffected by service offshoring than white collar workers. If ser-viceoffshoringincreasesbyone standarddeviation,the probabilitythatajobrelationshiplastsmorethan2years decreasesfrom59.7%to40.2%forwhitecollarworkersand from45.8%to38.8%forbluecollarworkers.Itseems, there-fore,thatpurchasesofservicesfromabroadpresentalower degreeofsubstitutabilitywithbluecollarworkers. 5.3. Competingrisks

Sofar,wehavestudiedthejobstabilityinasinglerisk framework,withoutdistinguishingbetweendifferent des-tinations incase of job separation. In whatfollows, we re-estimatethedurationmodelinacompetingrisks frame-workwithtworisksofjobexit:transitiontoanotherjobin themanufacturingsectorandtransitionoutofthe man-ufacturing sector.Like inthesinglerisk framework, we estimated competing risks modelsencompassing unob-served heterogeneitywith a discrete distribution. More indetail,wespecifiedabivariatediscretedistributionof theunobserveddeterminantsofeachtransitionintensity withfreecorrelationmatrix.However,asinthesinglerisk case,wewereneverabletoimprovethelikelihoodwith respecttothemodelwithoutunobservedheterogeneity. Theprocedureweusedtomovefromthemodelwithout unobservedheterogeneitytothemodelwithunobserved heterogeneityisthesameastheoneforsingleriskmodels described in Section4.1.We decidetofocus on job-to-job transitionswithin manufacturingandontransitions outof manufacturingsincethewelfareconsequences of thesetransitionsmaybeverydifferent.Transitionsoutof manufacturingemploymenthaveimmediatedetrimental effectsfortheeconomyintermsofdeteriorationof sec-toralspecific humancapital and,thereby, higherrisk of futureunemployment,skillobsolescence,andcostsrelated tore-trainingprogrammes.Instead,job-to-jobtransitions within manufacturing might not represent a real dam-age,as theymight putan end tobad job matches and moveemployeestowardsmoretechnologyandknowledge intensivefirms,whicharealsolessexposedtointernational competition.

Table5displaystheestimationresultsofthe compet-ingrisksproportionalhazardmodelwiththeindicatorof offshoringsplitbycountrygroups.Theupperandbottom panelsdisplaytheeffectsforoutofmanufacturing tran-sitionsandjob-to-jobtransitions,respectively.Theinput purchasesfrom low income countriesonly signiﬁcantly increasethetransitionsoutofmanufacturingforthetotal sampleofemployees.Offshoringtodevelopedcountries displays insteadnorole. However, when we separately considerwhiteand bluecollarworkers,thedetrimental effect ofoffshoringto low incomecountries onthejob stabilityonlyconcernsbluecollarworkers.A1 percent-agepointincreaseinthenarrow(broad)offshoringshare increasesbluecollars’jobexitrateoutofmanufacturingby

18.9%(17.3%).Thus,consistentlywithourexpectations,the processofdelocalisationofproductiontowardsdeveloping countriesthrowsonlybluecollarsout ofmanufacturing sector.

Offshoringtodevelopingcountrieshasnoeffecton job-to-jobtransitionseitherforwhitecollarsorforbluecollars. Onlyamildandbarelysigniﬁcantimpactisfoundforblue collarworkerswhenthebroadmeasureisused.

Movingonoffshoringtohighincomecountries,weﬁnd apositiveandsigniﬁcanteffectonthewhitecollars’ prob-abilityofexperiencingajobchange.Consistentlywiththe evidencedisplayedbythesingleriskmodel,this roleis detectedonlybythebroadmeasureofoffshoring.

TheﬂowsofKIBSfromabroaddonotcontributetothe workers’exits frommanufacturing sector.They instead affectthejob-to-jobtransitionsofallworkers,evenif,asin thesingleriskmodel,ahighermagnitudeoftheirimpact is recordedfor whitecollars’ transitions. Thus, the pro-cessofdelocalisationofservicesseemstocontributetothe structuralchangeintheeconomicsystem,sinceitdrives workersawayfromthesectorsmoreexposedtoforeign pressuresand, possibly, towardshigher technologyand moreknowledgeintensivesectors.

Finally,theresultsconcerningtheothersectoraland regionalvariablesshowthatthesectoralimport penetra-tionseemstoleadtojob-to-jobtransitionsand todrive bluecollarsonlyoutofmanufacturing.Thereforeblue col-larworkersaremoreexposedtothegrowinginternational integrationacrosscountries.Also,regionalunemployment, asexpected,increasestheprobabilityofworkers, regard-lessoftheirskilllevel,toexitthemanufacturingsector.

Summing up, the overall analysis shows that it is mainly the purchase of inputs from low labour cost economiestoincrease thejobseparations.Thiseffectis howeverrestrictedtolowskilledworkers.Therefore,while bluecollarsaredrivenoutofmanufacturingbytheﬁrm delocalisationprocesstodevelopingcountries,white col-larsexperiencetransitionstoanothermanufacturingjob followingtheexpansionofoffshoringtohighincome coun-tries.Thus,themainfocusofpolicyinterventionshouldbe onlowskilledworkers,whoarethosemostlyaffectedby theprocessoffragmentationofproductionacrosscountries and,moreingeneral,bythedeeperanddeeper integra-tionofcountries,asalsoshownbytheindicatorofimport penetration.

5.4. Furthersensitivitychecks

We ran some sensitivity checks to test the robust-nessoftheestimationsresultspresentedabove.First,we relaxedtheimposedproportionalityofoffshoringvariables andtestedwhetherfreshlyhiredworkersaredifferently affectedbyanincreaseinoffshoringactivitiesthan work-erswithhigherjobseniority.Weﬁndthattheoffshoring effectdisplayssomeheterogeneityaccordingtoworkers’ tenure.Thenegativeimpactofoffshoringonjobstability isstrongerforworkerswhohavebeenworkingformore thanoneyearifweusethebroadmeasureofoffshoring. Whenweusethenarrowindicator,wegethomogeneous effectsoverthejobtenure.

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Table5

Estimationresultsofthecompetingrisksmodels.

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

Outofmanufacturing

OFFKibs 0.439* _0.395 _0.559 _0.449 _−0.127 _−0.154

[0.257] [0.271] [0.417] [0.456] [0.362] [0.368]

OFFHighnarrow −0.007 −0.033 0.016

[0.024] [0.049] [0.028]

OFFLow

narrow 0.167*** 0.074 0.189***

[0.053] [0.143] [0.058]

OFFHigh_broad −0.010 −0.002 −0.015

[0.018] [0.031] [0.025]

OFFLowbroad 0.159*** 0.110 0.173***

[0.051] [0.136] [0.056] ImpPenj 0.004** 0.004** 0.004 0.004 0.004* 0.004* [0.002] [0.002] [0.004] [0.004] [0.002] [0.002] Unempreg _3.876*** _3.902*** _5.731*** _5.779*** _3.373*** _3.389*** [0.655] [0.656] [1.418] [1.419] [0.750] [0.749] LPj 1.486 1.366 5.616 5.413 −1.216 −0.951 [4.009] [3.932] [6.890] [6.599] [5.131] [5.089] ICTj 1.344 1.590 4.710 5.402* 1.211 1.345 [1.826] [1.805] [3.222] [3.101] [2.319] [2.311]

Jobtojobwithinmanufacturing

OFFKibs 0.705*** 0.646*** 0.774*** 0.503* 0.492** 0.465**

[0.175] [0.180] [0.288] [0.304] [0.224] [0.228]

OFFHigh

narrow 0.039* 0.057 0.023

[0.020] [0.039] [0.024]

OFFLownarrow 0.043 −0.081 0.068

[0.047] [0.126] [0.051]

OFFHigh_broad 0.036** _0.103*** _0.002

[0.014] [0.026] [0.019] OFFLow broad 0.037 −0.128 0.082* [0.042] [0.113] [0.046] ImpPenj 0.003*** 0.003*** 0.003 0.002 0.003** 0.003** [0.001] [0.001] [0.002] [0.002] [0.001] [0.001] Unempreg _0.413 _0.415 _−0.459 _−0.400 _0.564 _0.575 [0.410] [0.410] [1.009] [1.006] [0.454] [0.454] LPj −3.682 −3.852 −7.987 −6.577 −1.501 −1.488 [2.812] [2.780] [5.727] [5.516] [3.262] [3.255] ICTj 0.363 0.612 −2.224 −1.421 1.536 1.626 [1.344] [1.321] [2.812] [2.722] [1.559] [1.543] NT 511,919 511,919 146,218 146,218 365,701 365,701 N 19,259 19,259 4,589 4,589 14,670 14,670 Log-likelihood −74,435.8 −74,433.8 −16,395.6 −16,387.8 −57,898.4 −57899.4

Standarderrorsareinbrackets.

* _Significant_at_10%_level. ** _Significant_at_5%_level. ***_Significant_at_1%_level.

Second,wetestedwhetherheterogeneouseffectscould bedetectedondifferentlyagedworkers.Differentlyfrom BachmannandBraun(2011),weﬁndnosuchevidence.

Third,wesubstitutedoutputfornonenergy intermedi-atesinthedenominatorofouroffshoringmeasuresandall theestimationresultsofinterestareverymuchinlinewith thosepresentedabove.20_The_full_set_of_estimation_results

20 _The_need_for_this_check_stems_from_the_fact_that,_if_in_a_sector_some

intermediateproductionisoutsourcedtodomesticsuppliers,the off-shoringindicatornormalized onsectoralintermediateswilldecrease becauseoftheincreasedpurchasesofdomesticintermediateinputs,on thecontrarytheoffshoringindicatornormalizedonsectoraloutputwill notexperienceanychange.Theconfirmationofourresultsbymaking useofthelatterindicator,then,letsusconfidentthatourfindingsabout theimpactonworkers’jobstabilityareactuallydrivenbytheoffshoring phenomenonandarenotrelatedtotheprocessofdomesticoutsourcing.

ofthesesensitivitychecksarenotreportedforthesakeof brevity,butareavailableintheWebAppendix.

Finally,were-estimatedthemodelassumingthatthe monthly probability of job separation hasa logit form. In a logit framework, it is, indeed, easy to allow the unobserved heterogeneity components to be arbitrarily related to thesetof covariates. We can thereforerelax therandomeffectsassumptionofastandardhazard func-tion approach.Table 6reports theestimation resultsof the variables ofprimary interest of a fixedeffects logit model for the monthly rate of job separation. Qualita-tively,weobtainthesamefindingsasinthebenchmark model (Table 2). Since in a fixed effects logit model it is not possible to quantify the impact of covariates and the parameters depend onthe time unit of obser-vation (Flinn and Heckman, 1982), we prefer to stick

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Table6

Fixedeffectslogitestimationresults.

(1) (2) (3) (4) (5) (6) OFFKibs 39.598*** _39.289*** _26.469*** _26.753*** _55.596*** _55.330*** [1.392] [1.309] [2.003] [2.020] [2.003] [1.931] OFFnarrow 0.948*** 0.909*** 1.122*** [0.063] [0.124] [0.087] OFFbroad 0.432*** 0.116*** 0.713*** [0.043] [0.069] [0.060] NT 299,175 299,175 71,307 71,307 227,868 227,868 N 14,554 14,554 3,125 3,125 11,429 11,429 Log-likelihood −27,442.1 −27,530.4 −6,121.2 −6,153.8 −21,091.9 −21,124.6 *Significantat10%level. **Significantat5%level. ***_Significant_at_1%_level.

Standarderrorsareinbrackets.Thetime-varyingvariablesincludedinthebenchmarkhazardfunctionmodelsarealsoincludedintheﬁxedeffectslogit modelsbutnotreportedforthesakeofbrevity.Thenumberofobservationsislowersinceindividualforwhomtheoutcomevariableisalways0or10 (rightcensoredobservations)donotcontributetothelikelihoodfunctioninaﬁxedeffectslogitapproach.

to the hazard function speciﬁcation of the benchmark model.

6. Conclusions

Theconsequencesofoffshoringactivitiesinadvanced countries depend on the time horizon. The theoretical possibilityof increasedjobexitratesfromoffshoringin theshortrunisoffsetbythelongrunproductivitygains accruingtoalltheworkersinvolvedinmanufacturing pro-duction.Nevertheless,itmaywelltakealongtimebefore thefirmmayreapthegainsfromincreasedspecialization andsucceedinincreasingitscompetitiveness.Meanwhile, the adjustment process may produce long-lasting eco-nomicandsocialcosts.Regardlessofthepotentiallongrun benefitsofdelocalisation,theshortrunconsequencesof offshoringarearelevantissuefromthepolicyviewpoint, sinceanypolicyinterventionshouldbefirstlyconcerned withrestrainingtheimmediatewelfarecostsandwith eas-ingthetransitiontoa newequilibrium.Forthis reason, thefocusofourworkisontheimpactof offshoringon employees’jobstability.

Intheempiricalanalysis,weestimatedMPHduration models to understand the impact of offshoring on the jobhazardfunction.Ourfindingssuggestthattheprocess ofinternationalfragmentationofproductionsignificantly reducesthejobstabilityintheItalianmanufacturingsector. Theeffectofoffshoringishoweverheterogeneousacross skillgroupsanddependsontheorigincountryofinputs.As amatteroffact,importsofintermediatesfromlowlabour costcountriesappeartosignificantlyand morestrongly reducethejobstabilityofworkersandthemagnitudeof this effectis quitelarge. Offshoring todeveloped coun-tries, instead, increasesthewhite collars’ probability of experiencingawithinmanufacturingjob-to-jobtransition

andoffshoringofKIBSfavoursalltheworkers’transition toanothermanufacturingjob,theinputﬂowsfromlow incomeeconomiespushbluecollarworkersoutof manu-facturing.Bluecollarworkersarethereforeaffectedmost by offshoring. We provide moreover evidence that the competitivepressurefromforeigncountriesonthe domes-ticmarkets,measuredbythesectoralimportpenetration, increasestheprobabilityofunskilledworkerstoexitthe manufacturingsector.Thissuggeststhattheinternational integrationprocess, capturedby both the expansionin offshoring activities and the increased import penetra-tion,isdrivingthedismantlingofmanufacturingactivities, at leastof those activities characterizedby less knowl-edge/technologyintensityandbymoreroutinelytasks.

As a consequence, policy makers should especially devotetheirattentiontolowskilledworkersandshould easetheirre-trainingandtheirskillupgrading,inorderto fostertheirtransitiontomoreknowledgeintensivejobs, whicharelessaffectedbytheinternationalcompetition. Acknowledgments

Alessia Lo Turco acknowledges ﬁnancial support by MIURthroughthe2009ResearchProjectofNational Rele-vance(PRIN)onStructuralChangeledbyNeriSalvadori. Matteo Picchio acknowledges ﬁnancial support by the Research Foundation – Flanders (FWO), Belgium, dur-ingtheperiodatSherppaand theDepartment ofSocial EconomicsofGhentUniversity,fromOctober2011until October2012.WewishtothankRosarioCrinòandLucia Tajolifortheirusefulcommentsandsuggestions.Weare alsogratefultotheparticipantstotheXBrucchiLuchino LabourEconomicsWorkshop(Rome,December2011)and theItalianTradeStudyGroupWorkshop(Rome,February 2012).

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AppendixA.

Fig.A.1.Evolutionofmaterialandserviceoffshoringby2digitNACEmanufacturingsector.Notes:Thegraphspresenttwoscales,theoneonthevertical axisontheleftformaterialoffshoringandtheoneontherightforoffshoringofKIBS.SectornamesarereportedinTableA.2.

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Fig.A.2.Offshoringevolutionsplitbyoriginofmaterialintermediatesand2digitNACEmanufacturingsector.Notes:Thegraphspresenttwoscales,the oneontheverticalaxisontheleftformaterialoffshoringtohighincomecountriesandtheoneontherightforoffshoringtolowincomecountries.Sector namesarereportedinTableA.2.