Study: Who Will Benefit in Germany from a TTIP?

States, Branches of Industry and Education Levels

Who will Benefit in Germany from a Transatlantic Trade and Investment Partnership (TTIP)?

Final Report, Part 2: Microeconomic Effects in Germany

States, Branches of industry, and Education Levels

Who Will Benefit in Germany from a Transatlantic Trade and Investment Partnership (TTIP)?

Final Report, Part 2: Microeconomic Effects in Germany

Prof. Gabriel Felbermayr, Ph. D.Sybille LehwaldDr. Ulrich SchoofMirko Ronge

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Table of Contents

Table of Contents

1. Introduction 6

2. Commentsontheinvestigationalmethod 7

2.1 Sectortradeeffects 7

2.2 Tradeeffectsforoccupationalgroups,educationlevelsandstates 8

2.3 Effectsonrealwagesandwagedisparities 8

3. Dataandtrends 10

3.1 Tradedata 10

3.2 Input-OutputDataset 11

3.3 RegionalData 12

3.4 CompanyData 12

3.5 Wagedata 12

4. Howarethetradeeffectsdistributedacrossindustries? 15

4.1 TradecreationandNTBquantification 15

4.2 Indirecteffectsontheservicesectors 17

4.3 Valuecreationandemploymenteffects 20

5. Impactofeducationandoccupationalgroupsandimpactofregions 22

5.1 Descriptiveanalysisoftheeducationgroups 22

5.2 Impactofeducationgroups 25

5.3 Descriptiveanalysisoftheoccupationalgroups 25

5.4 Impactonoccupationalgroups 26

5.5 Descriptiveanalysisoftheregions 26

5.6 Impactonregions 28

5.7 Valuecreationandemploymenteffectsintheregions 29

5.8 Employmentandeducationintheregions 30

6. EffectsofaTTIPonincomeandtheincomerisk 32

6.1 Effectsonrealincome 32

6.2 Effectsonincomerisk 34

7. Summary 36

A.Annex 37

A.1 Gravitationmodels,estimationmethodsandresults 37

Literature 48

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1. Introduction

1. Introduction

Inthefirstpartofthisstudyweexaminedthemacroeconomiceffectsofatransatlantictradeand

investmentpartnership(TTIP)betweentheEuropeanUnionandtheUSA1.Themainfocuswason

changesintradestructure,realincomeandemploymentprovokedbytheTTIP.Usingageneral

equilibriummodel,aggregatedeffectswereexamined formore than120countries.Adaptation

oftheaggregatedpriceindexinallthesecountriesandthefeedbackeffectsongrossdomestic

productswereconsideredindoingso,aswasthefullmatrixoftradeeffects(eventhosebetween

countriesonly indirectlyaffected)and thusallworldwide tradediversioneffects.However, the

broadgeographicscaleandthefocusonmacro-economicresultsmadeitimpossibletodrawmore

preciseconclusionswithinindividualcountries.Thesecondpartofthestudyisintendedtoclose

thatgapforGermany.

Thisstudysegmentisdevotedtothemicroeconomiceffectsofatransatlantictradeandinvestment

partnership.ItconsistsofzoominginonGermany,whereweexaminethedisaggregatedeffects

ofanagreementonGermany’ssectorsandregions.Thisframeworkmakesitpossibletoclarify

whichsectorsandregionswouldbemoreimpactedbyapotentialtradeagreementthanothers.

Furthermore,wecananalyzetheeffectsofaTTIPondifferenteducationlevelsandoccupational

categories.UnlikePart1ofourstudy,wedonotapplyageneralequilibriummodel.Insteadwe

baseouranalysisonapartialanalyticalapproachofagravitationequation.Thismeansthatthe

effectsofthegeneralequilibrium,suchastradediversioneffects,areleftout.Thiscircumstance

isimportantwheninterpretingtheresultsandhasobviousimplicationsforanycomparisonwith

theresultsofthemacrostudy.

OuranalysismakesitpossibletoidentifydifferencesforGermanyinhowtheimpactsofaTTIP

arefeltinparticularindustries,occupationsandeducationcategoriesorregions.Whileourstudy

makesuseofquantitativemethods, itspartialanalyticnaturestudystronglysuggests that the

resultsbeinterpretedmainlyqualitatively.Foradiscussionoftheeffectsonthewholeeconomy,

wereferthereadertothemacrostudycitedabove.

Thismicrostudyisorganizedasfollows:Insection2weexplainourmethodologicalapproachand

then,insection3,webrieflypresentthedataweused.Insection4,weidentifythesectortrade

effectsanddiscusswhichindustriesweanticipatewillexperiencegreatereconomicvitalitythan

others.Moreover,inthissectionweuseanewapproachtoquantifytheextentofnon-tariffbarriers

(NTBs)attheindustrylevel.Beyondthat,wecalculatethevaluecreationandemploymenteffects

atthesectorlevel.Insection5,welookatboththeeffectsofaTTIPonGermany’sjobmarketas

wellasonGermany’sregions.Weexplainwhichoccupationandeducationcategoriesandwhich

regionswouldbemostimpactedbysuchanagreement.Insection6welookattheeffectsofaTTIP

onrealincomeandtheincomerisk.Attheendofthestudy,wesummarizeourresults.

1 See“TheTransatlanticTradeandInvestmentPartnership(TTIP).WhoBenefitsfromaTransatlanticFreeTradeAgreement?Part1:MacroeconomicEffects.“

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2. Comments on the investigational method


Our analysis is divided into three major steps. First we estimate the effect of a transatlantic

agreement on trade between the USA and Germany in 16 different economic sectors on the

basisofgravitationalequations.Fortheseeconomicareas,wehaverobustdataandcanreadily

estimatetheprojectedinducedtradeeffects.2Thisgivesusindicatorsthatreflecttheimpacton

specificsectorsofaTTIP.Inthesecondstep,weapplythesesectorimpactlevelsto88different

occupationalgroups,3educationlevelsand16regionswithinGermany’sfederalstates.Inathird

step,weusetheseresultstoestimatetheeffectsofaTTIPonrealwagesinindividualoccupational

groupsandeducationlevels.WealsothencalculatetheeffectoftheTTIPonwagedistribution.

2.1 Sector trade effects

Thestartingpoint forthefirststepis toestimatetheexpectedtradeeffectsat thesector level.

Aswedidinthefirstpartofthestudy,weassumethatatransatlanticagreementwillresultin

trade-creationeffectsthataresimilartothoseforwhichdataalreadyexists.Themaindistinction

fromthefirstpartofthisstudyisthesectordisaggregation:Weestimatetheextenttowhichthe

freetradeagreementhasledtotradecreationwithintheaffectedcountrypairsfor16different

industriesandthenusetheresultasthemostcredibleestimatorfortheeffectsofatransatlantic

agreement.3

Thisapproachhasthemajoradvantageofallowingasimplequantificationofthepotentialeffects

oftheagreementonnon-tariffbarriers.Inthatway,besidestheeliminationofimportduties,which

wecantakeforgranted,wecanincludeallimportantcategoriesoftradecostswhosedeclinewould

resultinstimulatingtradebetweentheUSAandtheEU.Specifically,ourapproachconsidersall

coststhatlimitinternationaltradebetweentwocountriesbutdonotfallinthecategoryofimport

duties.Non-tariff barriers are regulatorymeasureswithprotectionist effects that disadvantage

foreign suppliers compared to domestic ones. They can be politically induced or result from

geographicandhistoricalcircumstances.

This economic analysis based on the gravitation model provides us with benchmarks for the

increaseintradebetweenGermanyandtheUSAexpectedfromeliminatingcustomsbarriersand

non-tarifftradecosts.Intermsofmethodology,weuseacompletelysaturatedfixedeffectmodelon

paneldataforannualindustrydatafrom1998to2007andtherebyexcludetherecentcrisisyears.4

Weareespecially interested intheaverageeffectsof freetradeagreementsontrade,5because

2 The16sectorsconsistof14inthemanufacturingsectorplusagricultureandmining.Theyaredescribedbelowasmanufacturingsectors.Fortheservicesector,wecalculateindirecteffectsthroughinter-andintra-sectorlinks.

3 Anotherdifferenceconsistsintheunderlyingmodelframeworks.UnlikePart1,wedonotuseageneralequilibriummodelbutapartialanalyticmodel.

4 SuchasaturatedfixedeffectmodelhasbeenusedbyFelbermayrandYalcin (2013), forexample.See thereferences to theliteraturecitedthere.InAnnexA.1tothisstudy,weexplaintheestimationmethodingreaterdetail.

5 Thereisaspecialchallengetoempiricalmodellingintryingtoprovidecoveragethatisascompleteaspossibleofallpotentialtradeflowdeterminants.Onlywhenthatisassuredcantheeffectsofafreetradeageementbeisolatedandtreatedascausal.

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theycanbeusedtodrawconclusionsaboutchangesintradecostsatthesectorlevel.Moreover,

because the average customs duties applied are known, the significance of non-tariff barriers

(moreexactly:theexpectedextentoftheirreduction)canbequantified.

Based on quantified trade potentials in the manufacturing industries and effects that can be

interpolatedfromthemfortheservicesector,weshowwherethelargestvaluecreationeffectscan

beexpectedandinwhichindustriesemploymentwillbemostaffectedbyaTTIP.

2.2 Trade effects for occupational groups, education levels and states

Webeginbyestimating theexpected tradeeffectsof aTTIPat the industry level.For thatwe

useofficial tradestatistics,asdescribedingreater lengthbelow.Wetranslatethetradeshocks

identifiedinthisway,usingdatafromtheInstituteforEmploymentResearch(IAB)inNuremberg,

intoshocksforindividualoccupationalgroupsandeducationlevels.

The approach is as follows: We know from the IAB dataset how various occupational groups

are distributed among the individual segments of the economy, or what share of employment

isheldwithinspecificindustrialsectorsbymembersofvariousoccupationalgroups.Thesame

applies to the different levels of education (university degrees, high school diplomas and/or

vocationaltrainingorless).Fromtheinteractionofthetradeshocksidentifiedinsteponewith

theemploymentdistributionsdescribed,wecanconvertthemintoshocksspecifictooccupational

groupsandeducation levels.Wehaveselectedanappropriateap-proach toquantify theshock

atthestatelevelusingregionalforeigntradestatisticsfromtheFederalStatisticalOffice,which

providesinformationaboutexportandtradeactivitiesintheindividualsectorsthroughoutevery

stateinGermany.Thisenablesustodrawconclusionsaboutregionalindustryeffects.

2.3 Effects on real wages and wage disparities

In the final step, we use the shocks for occupational groups and education levels described

above inMincerwage equations. Such equationsmodelworkers’wages as a function of their

characteristics.Toconductouranalysis,wehaveexpandedtheclassicmodeltoincludeemployer

characteristics.Whatinterestsusinparticularistheextenttowhichtheestablishmentinwhicha

workerworksisaffectedbyinternationaltrade(exports,imports).Theliteraturetypicallyreports

thatcompanieswithamorepronouncedinternationalpresencepayhigherwagesthanthosethat

arelessinternationallyorientedorareactiveonlyonthedomesticmarket.6

6 Felbermayr, Hauptmann and Schmerer (2013) discuss methodological aspects of the estimates of such equations and theclassificationoftheresultsintheliteratureontradeandlabormarkettheory.

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WecannowusetheshockscalculatedbytheestimatedMincerequationsinthefirstandsecond

stepsofouranalysistoprojectaveragechangesinrealwagesresultingfromaTTIP.Thisanalysis

canbeexecutedonsamplessothattherealwageeffectscanbeidentifiedinindividualsegments

oftheGermanlabormarket(occupationalgroupsandeducationlevels).

Similarly,usingthemeansdescribedabove,itispossibletoforecastchangesinwagedisparities

amongindividualsegmentsoftheGermanlabormarket.Todothat,theindividualdatamustbe

aggregated,however.Thisisdonebycalculatingawagedisparitybenchmarkforthelabormarket

segmentunder consideration.Asusual in the literature,weuse the standarddeviation of the

logarithmofwages.Thisbenchmarkdoesnotdependonscalingthewagevariablesandisrelated

monotonicallytothewell-knownGiniIndexofincomeinequality.Accordingly,ahigherstandard

deviationindicatesahigherdegreeofinequality.

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3. Data and trends

3. Data and trends

3.1 Trade data

Our analysis is based on bilateral trade data at the industry level. We use the BACI dataset

developedbyCEPIIwithUNCOMTRADEdataandthus including trade informationonallUN

countries.7Unfortunately,therearenarrowlimitstodisaggregationbysector.Thisisdueonthe

onehandtothefactthatwemustensurethatoursectorsarerecognizedinthesystemusedfor

theinput-outputtables,andontheother,thatoursectorclassificationiscompatiblewiththeIAB

datasets.Oursectorclassificationisbasedonthestandardclassificationofeconomicactivitiesin

theEuropeanUnion(NACERev.1.1)definedtotwoplaces.Forouranalysisofthetradeeffects

inducedbyaTTIP,weexamine16manufacturingsectors.Theseaccountforabout87percentof

Germanforeigntrade,withonly13percentincludedintheservicesector.8Thesectorsweexamine

thuscoverthegreatermajorityofallofGermany’sdirecttraderelationships.Thisfact,combined

with the poor data situation for trade in services in general, justifies concentration on the 16

sectorsinouranalysisofthetradeeffectsinducedbyaTTIP.

Figure2showstheaverageannualrateofchangeoftradebetweenGermanyand,respectively,the

EU(definedastheEU27),theUSA,theBRICScountries(Brazil,Russia,India,ChinaandSouth

Africa)andthewholeworldforthesectorsweexamined.Itbecomesclearthatineverysector

exceptpetroleum, therewas less change inGerman tradewith theUSA thanwith theBRICS

countriesorwiththewholeworld.ThismaybeduetothefactthattheleveloftradewiththeUSA

isalreadysubstantiallyhigherthanwiththeemergingcountries,wherethereisneedtocatchup

withtheUSA.However,tradewiththeUSAhasincreasedin12ofthe16sectorslessthanithas

withtheEU,duetothenumberofnewcountriesjoiningthecustomsunionandtheextensionof

thedomesticmarketprogram.Nevertheless,thisfindingclearlyshowsthatthereisapotential

formoretradebetweentheEUandtheUSAfromeliminatingcustomsandregulatorybarriersto

marketentry.

7 Formoreinformation,see:http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=1.

8 ThebasisforthesecalculationsisdatafromtheWorldInputOutputTablesfrom2007.Thiswasthelastyearbeforethesharpdeclineinworldtradeasaresultofthe2008and2009financialmarketcrisis.

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3. Data and trends

3.2 Input-Output Dataset

The16manufacturingsectorswestudiedaccount formorethan80percentofGermanforeign

trade,butaccount foronlysome20percentof totalvaluecreationandabout20percentofall

employees.9Inordertobeabletoestimatetheeffectsofatransatlanticagreementforthewhole

economy,weadditionallycalculatetheindirectlyinducedeffectsfortheservicesector.Todothis,

weusetheinter-andintra-sectorinterconnectionsoftheinput-outputanalysis,basedontheWorld

Input-OutputTables(WIOD).10

Inthetextthatfollows,wethereforedistinguishbetweenthedirecteffectsofaTTIP,whichresult

inchangesinthetradevolumeinthe16manufacturingsectors,andtheindirecteffectsofaTTIP,

inducedbyinterconnectingrelationships.

9 ThesecalculationsarebasedonthedataintheWorldInput-OutputTablesform2007.Seealsofootnote19.AccordingtothemostrecentdatafromtheGermanFederalStatisticsOfficefrom2012,manufacturinggenerates31.6percentofallvaluecreationand26.6percentofallemployees.

10 Formoreinformation,seehttp://www.wiod.org/index.htm.

Source: Calculations by the ifo Institute based on the BACI dataset.

Figure 1: Changes in trade in manufacturing Sector designation GER-WOLRDGER-BRICSGER-USA GER-EU

Manufacture of furniture, recycling

Manufacture of motor vehicles

Manufacture of office machinery

Machinery and Equipment

Metal production and processing

Glass, ceramics

Rubber and Plastics

Chemical products

Coking, petroleum processing

Paper, publishing and printing

Wood and wood products

Leather and leather products

Textiles and wearing apparel

Food products and tobacco processing

Mining and quarrying

Agriculture and forestry, fishing 5.4 1.0 5.4 4.8

9.8 12.0 12.4 12.9

7.3 5.2 6.3 7.1

1.1 2.3 11.8 2.8

2.7 0.1 11.9 4.7

6.0 5.4 13.5 6.6

6.6 3.4 12.5 6.7

15.3 17.5 17.8 15.5

9.6 8.2 12.8 9.5

8.1 7.4 15.5 8.6

4.4 5.4 14.6 5.4

10.1 8.0 17.4 10.3

8.0 5.1 16.1 8.7

6.7 4.4 18.9 7.6

8.4 6.4 18.6 8.6

6.0 3.3 13.7 6.8

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3.3 Regional Data

Fortheanalysisattheregionallevel,weutilizedatafromtheGermanFederalStatisticalOffice.Here

weuseeachstate’sdatasegmentedbyeconomicsectorsforexportstotheUnitedStatesfromthe

year201211,aswellasemploymentfiguresfromthemanufacturingindustryfromtheyear200812.

Thisdataenablesustoderiveconclusionsonsector-basedtradeandvaluecreationeffectsinevery

state.

3.4 Company Data

The interconnectionof tradeeffectsandemployment informationnecessary forouranalysis is

made through the Linked-Employer-Employee Dataset (LIAB) of the IAB. More specifically, we

usetheLIABCross-sectionDataset2,inwhichinformationonallemployeesregisteredforsocial

securitywasadded toasampleofestablishmentsbetween1993and2010.13Thisdataset thus

includesnotonlydetailedinformationaboutpersonalcharacteristicsofindividualemployees,but

alsoimportantinformationaboutthecompany.Ofparticularinterestforouranalysisisinformation

on the levelofacompany’s internationalactivity.This linkingof companyandpersonneldata

allowsustoestimatetheeffectsofapotentialtradeagreementonwagesandtheincomeriskfacing

employees.TheLIABdata,however,areastratifiedsampleofcompanies.Theuseofweighting

factors enables us to make representative statements about the distribution of companies in

Germany.

3.5 Wage data

To be able to make representative statements about the distribution of education levels and

occupationalgroups in thespecificsectors,weuse theSIABdataset (sampleof the integrated

labormarketbiographies)of theIAB.14Thisdataset isarepresentative2percentsampleofall

employmentsubjecttosocialsecurityobligationsintheFederalRepublicofGermany.Itcontains

themostimportanthumancapitalcharacteristicsofemployeesandmakesitpossibletocalculate

(daily)wages.15Thesewages,inflation-correctedbytheConsumerPriceIndex,arethebasisofour

11 Statitisches Bundesamt (https://www-gene-sis.destatis.de/genesis/online/data;jsessionid=83BF49DC2CDF812C2DE72CE8956C3355.tomcat_GO_1_2?operation=abruftabelleAbrufen&selectionname=51000-0036&levelindex=1&levelid=1379954988568&index=5)

12 Statitisches Bundesamt (https://www-gene-sis.destatis.de/genesis/online/data;jsessionid=83BF49DC2CDF812C2DE72CE8956C3355.tomcat_GO_1_2?operation=previous&levelindex=3&levelid=1379955209838&levelid=1379955194721&step=2)

13 Thedatabasiscomesfromthecross-sectionmodel(Version2,1993–2010)oftheLinked-Employer-EmployeedataoftheIAB.AccesstothedatawasachievedduringvisitstotheResearchDataCenteroftheFederalAgencyforLaborattheInstituteforLaborMarketandOccupationalResearch(FDZ).Formoreinformation,seeHeining,Scholz,Seth(2013).

14 Formoreinformation,seeBerge,König,Seth(2013).

15 Thedatasethasseveralwell-knownweaknesses.Themost important is that the incomevariable isonlyfilled if thepersoninvolvedhasanincomefromemploymentthatisbelowtheupperlimitforsocialsecuritycontributions.Forthoseemploymentrelationshipswherethisdoesnotapply,therearealgorithmsforimputingitthathaveproventhemselvesintheliterature.

3. Data and trends

13

3. Data and trends

calculationsoftherealwageeffectsofaTTIP.Wealsousewagedatatodrawconclusionsabout

effectsofaTTIPonincomedisparity.Figure2illustrateschangesinwagedisparityinGermany

inselectedsegmentsoftheeconomy,representedbythestandarddeviationofthelogarithmof

wages.Asalreadyexplainedabove,thisisanappropriatemeasureoftheextentoftheunequal

distributionofwageincome.

Theeconomicsectorsshownofferonlyslightdifferencesastotheextentofthemeasureddisparity.

Thedisparityishigherinthefoodindustrythaninmachineryorautomotivemanufacturing,but

intheperiodaverageofthesectors,themeasurementsgenerallyscatteraround0.35,whichalso

roughlyappliesfortheGermaneconomyasawhole.16Acrossalargeportionofthesectors,the

trendisrising:inequalityrosesignificantlyfrom1998to2007.

16 SeeforexampleDustmannetal.(2009),Cardetal.(2012)andBaumgarten(2012).

Source: Calculations by the ifo Institute on the basis of the SIAB dataset of the IAB Nürnberg.

Figure 2: Wage disparity in Germany

Recycling

Motor vehicles manufacturing Office machineryMachinery & equipment

Metal production and processingGlass, ceramics

Rubber and plasticsChemicals



Textiles and wearing apparelAgriculture and forestry, fishing

0.25

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20072004200119980.25

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20072004200119980.25

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2007200420011998

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3. Data and trends

Figure3showstheextentofthewagedisparitythatcannotbeexplainedbythecharacteristicsof

theemployee,suchaseducation,workexperience,gender,immigrantstatus,etc.Thatistosay,

thisisaresidualofaMincerequation.17Thisresidualcanalsobeinterpretedasthesignificance

of factors that are not directly under the employee’s influence. These include which company

theemployeeworksfor,butthereisalsothesimpleinfluenceofchance(e.g.,thatthepersonnel

managergivesasalarybonusforreasonsthatanexternaluserofthedatacannotaccountfor).

Figure3makesitclearthatmostofthelevelofinequalityandchangesshowninFigure2aredue

totheresidualportionofthewageinequality.Inotherwords,itisnotchanginghumancapital

characteristics that explain Figure 2. This makes it clear that changes in the implicit price of

humancapitalorparticipationofemployees incompanysuccessmay lagbehinddevelopment.

Thetheoreticalandempiricalliteratureshowsveryclearlythatinternationaltradeimpactsincome

distributionspecificallythroughthesechannels.18

17 SeparateMincerequationswerecalculatedforeachsector;withinthesectors,thedatawaspooledacrosstheyears,however.

18 SeeFelbermayretal.(2013)andBaumgarten(2012).

Source: Calculations of the ifo Institute based on the SIAB dataset from IAB Nürnberg.

Figure 3: Residual wage disparity in Germany

Recycling

Motor vehicles manufacturing Office machineryMachinery & equipment

Metal production and processingGlass, ceramics

Rubber and plasticsChemicals



Textiles and wearing apparelAgriculture and forestry, fishing

0.15

0.25

0.35

20072004200119980.15

0.25

0.35

20072004200119980.15

0.25

0.35

2007200420011998

0.15

0.25

0.35

20072004200119980.15

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20072004200119980.15

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2007200420011998

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20072004200119980.15

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20072004200119980.15

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2007200420011998

0.15

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20072004200119980.15

0.25

0.35

20072004200119980.15

0.25

0.35

2007200420011998

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4. How are the trade effects distributed across industries?


4.1 Trade creation and NTB quantification

Wearenowreadytoconductthefirststepof theresearchapproachsketchedinsection2.We

start byquantifying theexpected tradeeffects in the16manufacturing industries.Asalready

mentioned inPart1 of our study, the trade effect anticipated fromaTTIP is the one that can

actuallybeobserved in thedata fromexisting tradeagreements.Thiseffect,aswewill see in

greaterdetailbelow,comesnotfromtheeliminationofcustomsdutiesbutmainlyfromlowering

non-tariff barriers (NTBs). We assume symmetry, i.e., that imports and exports are similarly

affectedintermsofhowtheychange.Theeffectsdocumentedare,asalreadyemphasizedinthe

introduction,partiallyanalyticalinnature.Theyarerelatedonlytotheexpectedchangeintrade

betweenGermanyandtheUSAandrepresentitslowerlimitsbecausetheendogenousadaptation

ofgrossdomesticproducts(whichinturnhasatrade-increasingeffect)hasnotbeentakeninto

consideration.19

The third column in Table 1 shows the changes in bilateral trade expected from a possible

transatlantic tradeand investmentpartnership. It isevident thatbesides the foodand tobacco

processingindustries,themetalindustrywouldprofitfromsuchanagreement.Thereweexpect

tradegrowthofmorethan50percent.Asimilarlystrongincreaseinthetradeflowofjustunder

50percent canalsobe seen for agricultureand forestproducts.Alsoevident is that for these

sectors inparticular, thereductionofnon-tariffbarrierswillplayadecisiverole.Likewise, the

manufactureofofficemachineryanddataprocessingequipmentwillclearlybenefitfromatrade

agreementbetweentheEUandUSA.Theretheexpectedincreasesareinthe40percentrange.

Forthechemicalindustryaswellasforthefurnituremanufacturingsector,theexpectedtrade

growthisaround20–26percent.

19 Inparticular,thereportedeffectsdonotnecessarilyadduptotheeffectsoccurringinthewholeeconomicequilibrium;seePart1ofthestudy.

16


Moderate,butdefinitelypositive tradecreationeffectsaremoreover tobeexpected in leather,

paperandprinting,glassandmachinery.EvenGermany’scharacteristicautosectorprofitsfrom

atransatlanticagreement.Hereweexpectgrowthbetween15and20percent.Increasesinauto

productionarerelativelysmallerthaninothersectorsbecausethetraderelationshipsarealready

at a comparativelyhigh level.Wedon’t expect anypositive growth in trade in the textile and

clothingsector,ontheotherhand.Formining,forestry,cokingandpetroleumaswellasglass,

nostatisticallysignificant tradeeffectscanbedemonstrated inexistingagreements.Theother

columnsinTable1indicatetheaveragecustomsdutiesthattheUSAorEUchargeontheirimports

inthespecificsectors.Theyarehighforclothinginbothregions;theEUchargessignificantly

higherduties for foodandautos than theUSA; theUSAchargeshigherduties than theEU in

thecoking/petroleumandleathersectors.Overall, thetariffratesarerelatively low.Tohelp in

understandingtheprocess,wewouldaddherethatthetariffratesindicatedforalltradingpartners

oftheEUortheUSAapplytotheextentthattheyaremembersoftheWorldTradeOrganization

(WTO).ItisassumedthattheTTIPwouldresultinacompleteeliminationoftheseduties.

Table 1: Sector trade effects of TTIP on EU-US trade, underlying decline in tariff and non-tariff barriers

NACE Rev.1.1

Sector designation Trade creation (in percent)*

Trade elasticities according to Broda & Weinstein (QJE 2006)

Customs Importer USA from EU (in percent)**

Customs Importer USA from EU (in percent)**

NTB Importer USA from EU (in percent)**

NTB Importer USA from EU (in percent)**

A & B Agriculture and forestry, fishing and fish farming

47.40 1.33 2.62 3.89 33.02 31.75

C Mining and quarrying . 5.32 0.96 0.77 . .DA Food products and tobacco processing 65.86 3.65 2.31 5.60 15.74 12.45DB Textiles and wearing apparel –19.35 1.89 7.00 8.19 . .DC Leather and leather products 17.35 0.96 7.10 3.91 10.97 14.16DD Wood and wood products . 0.83 0.19 0.96 . .DE Paper, publishing and printing 14.68 1.55 0.02 0.02 9.45 9.45DF Coking, petroleum processing . 3.36 6.63 1.50 0.00 0.00DG Chemical products 21.65 3.75 1.71 1.86 4.06 3.91DH Rubber and Plastics 14.80 1.34 1.71 1.86 9.33 9.18DI Non-metallic mineral products, ceramics . 1.30 2.56 3.11 . .DJ Basic metals and fabricated metals 52.65 2.77 1.67 1.66 17.34 17.35DK Machinery and Equipment 16.42 1.10 1.26 1.25 13.66 13.67DL Manufacture of office machinery, data proces-

sing equipment and installations39.93 2.74 0.58 0.35 13.99 14.22

DM Auto makers 16.88 2.27 1.19 4.67 6.25 2.77DN Furniture, jewelry, musical instruments, sports

equipment, recycling Recycling26.36 0.55 0.84 0.96 47.10 46.98

* „.“ means that econometrically, no effect could be identified that was significantly different from zero. ** Source of customs data: TRAINS Data from WITS. The customs are import-weighted average.Source: Calculations by the ifo Institute on the basis of BACI data.

17


ThetablealsopresentsthetradeelasticitiescalculatedbyBrodaandWeinstein(2006),whichhave

beenaggregatedhereatthesectorlevel.Thehighertheyare,themorestronglytradebetweenthe

countriesreactstochangesintradecosts.Basedonthisinformation,itisnowpossibletoquantify

at the sector level what drop in non-tariff barriers combined with the assumed elimination of

tariffswouldgeneratethecalculatedtradeeffects.Basedonthedifferentaveragedutiesapplied,

thesedifferonlyslightlybetweentheEUandtheUSA.

ThelasttwocolumnsinTable1showthecalculatedNTBindexasadvaloremequivalents.That

means the values should be read as percentage surcharges on the manufacturing price and

thusbeinterpretedsimilartocustomsduties,butwithonebasicdifference:Theyarenotinfact

customsduties.Itturnsoutthatthemeaningofnon-tariffbarriersandthepotentialforlowering

themthroughbilateralagreementslikeaTTIPinindividualindustriesvaries.AdjustableNTBsare

especiallyhighinfurnituremakingoragricultureandforestry.Thelattersectorsespeciallyinclude

manyprotectionistmeasuresthatclaimtobeprotectingconsumersfromharmfulfoodstuffsfrom

abroad. But in machinery, metals and food, the non-tariff barriers are also substantial. Lower

adjustableNTBscanbeidentifiedfortheautoandchemicalsindustries.

TheNTBsreporteddescribecostsavingsinthenon-tariffareathathavealreadybeenachieved

onaveragebyexistingtradeagreements.ItcanbeassumedthataTTIPwouldbeneithermore

nor less successful. Let us again be clear: The values should not be understood as levels, but

as the likelychanges inNTBs. In fact, theNTBpotentialscalculatedcanberegardedas lower

limits,becauseintheexistingtradeagreements,thereistypicallynocompleteuseofthetariff

eliminationpotentialmadebythetradingcompanies.20

4.2 Indirect effects on the service sectors

Theprecedinganalysishasmadeitclearwhichmanufacturingsectorscanexpectanincreasein

tradefromaTTIP.Nowwewillconsiderhowthiseconomicrevitalization,throughinterandintra-

industry links,affectsGermany’swholeeconomybutespecially itsservicesector.Wequantify

theseeffectswiththehelpofinput-outputanalysis.21Thisprovidesuswithinformationonhow

dependenttheproductionofasectorisonintermediateproductsfromitsownandothersectorsof

theeconomy.Inthiswayitispossibletoquantifyindirecteffectsofanincreaseintradeinsectors

thatarenotthemselvesdirectlyaffectedbyaTTIP,forwhich,duetoreasonsofdataavailabilityand

quality,nodirecteffectscanbecalculated.Table2showstheresults.Thethirdcolumnshowsthe

inducedtradevolumesweexpectinthe16manufacturingsectorsfromatransatlanticagreement.

Columnfourshowsthedirectproductioneffects.TheserepresenthowmuchproductioninSector

jischangedbythetradeimpactinSectorj.Itshouldbenotedthattheinducedtradevolumeis

20 Thisisduetothefactthatthebureaucratichurdlestomakingproductsduty-freearehigh(especiallypresentationofacertificateoforigin).

21 ThebasisistheWorldInputOutputDataset(WIOD)from2007.TheWIODisavailableforaperiodfrom1995–2009.Webaseourcalculationson2007inordertoavoiddistortionsfromthefinancialandeconomiccrisisin2009.Toensureadegreeofconsistency,wethereforereferthroughoutthisreportto2007,whenweareusingtheWIOD.

18


onlyslightlydifferentthanthedirectproductioneffects.Thisisduepreciselytothepriorwork

thatSectorjrequiresfromitsownindustryformanufacturingitsfinalproducts.Sincenodirect

tradeeffectcanbecalculatedfortheservicesectors,thereisnodirectproductioneffectinthese

sectors.ThefifthcolumnshowsthetotalproductioneffectthatcanbeexpectedinSectorj.This

totaleffectconsistsofthedirectproductioneffectandtheindirectproductioneffect.Theindirect

effectisthetotalofpreviousworkinSectorjusedtoproducethefinalproductsinallothersectors

(exceptj).Intheservicesector,thisindirecteffectissimultaneouslythetotaleffect.Inordertobe

abletoestimatetheamplitudeoftheeffects,columnsixshowsthetotalproductionofthespecific

sectorfrom2007andinthelastcolumn,theshareofthetotalproductioneffectintotalproduction.

Whatcanbeseen is that the inducedproductioneffectsare in therangeofupto twopercent

oftotalproductionin2007.Therelativelylargestproductionincreasesaretobeexpectedinthe

industriesthatproduceofficeequipment.Themetalproductsandchemicalsindustriesarealso

expectedtorealizesignificantproductionincreasesfromTTIP.

Theservicesectormoststronglyaffectedbyindirecteffectsisleasingmovableswithoutoperators

or thesector thatprovidesservicesmainly tocompanies.Here it isevident that the indirectly

inducedeffectsaloneadduptoashareof0.5percentintotalproductionofthesector.

Table2thusmakes itveryclear thatalthoughthedirect tradeeffectsoccurexclusively inthe

16 manufacturing sectors, the total economy would be noticeably affected by a transatlantic

agreement.Wefindoverallthattheservicesectorexperiencesacomparableimpactfromindirect

effectstothemanufacturingsector.22

22 Itisworthrememberingthatthisisapartialanalyticalmodel,allowingabstractionfromgeneralequilibriumeffectsaswellasfromtradediversioneffects.Sucheffectsareexplainedinthefirstpartofourstudy(Part1:MacroeconomicEffects).

19


Table 2: Quantification of direct and indirect production effects of a TTIP in Germany by sector

NACE Rev.1.1

Sector designation induced trade creation (in millions of Euros)

direct production effect (in millions of Euros)

total produc-tion effect (in millions of Euros)

total pro-duction (in millions of Euros)

total production effect / total production

A & B Agriculture and forestry, fishing 102 106 230 51,950 0.004C Mining and quarrying 0 0 33 13,710 0.002DA Food products and tobacco processing 609 680 745 154,120 0.005DB Textiles and wearing apparel –61 –61 –55 23,730 –0.002DC Leather and leather products 47 47 47 3,480 0.014DD Wood and wood products (without furniture production) 0 0 75 25,540 0.003DE Paper, publishing and printing 416 474 633 88,900 0.007DF Manufacture of coke, refined petroleum products and nuclear fuel 0 0 150 61,050 0.002DG Chemical products 2,863 2,937 3,040 156,970 0.019DH Rubber and Plastics 185 194 423 65,690 0.006DI Non-metallic mineral products, ceramics 0 0 122 41,090 0.003DJ Basic metals and fabricated metals 3,474 4,068 4,887 230,160 0.021DK Machinery and Equipment 2,158 2,401 2,677 224,800 0.012DL Manufacture of office machinery, data processing equipment and installa-

tions, manufacture of electrical and optical equipment4,506 4,883 5,154 209,390 0.025

DM Manufacture of motor vehicles 3,538 4,210 4,337 350,720 0.012DN Manufacture of furniture, jewelry, musical instruments, furniture, sports

equipment, toys and other instruments Recycling288 302 369 39,160 0.009

G-50 Retail trade (not including motor vehicles or service stations); Repairs of personal and household goods

0 0 182 54,360 0.003

G-51 Wholesale trade and commission trade (not including motor vehicles) 0 0 742 170,260 0.004G-52 Sale; Maintenance and repair of motor vehicles and personal and

household goods0 0 659 146,230 0.005

H Hotels and restaurants 0 0 13 66,500 0.000I-60 Land transport services; Pipeline transport services 0 0 312 69,350 0.004I-61 Water transport 0 0 23 25,080 0.001I-62 Air transport 0 0 71 27,370 0.003I-63 Other supporting and auxiliary transport activities; Activities of Travel

Agencies0 0 354 96,580 0.004

I-64 Post and telecommunications 0 0 186 81,100 0.002J Financial intermediation 0 0 468 219,710 0.002K-70 Real estate activities 0 0 573 328,350 0.002K-71-74

Renting of machinery and equipment without operator, data processing, research and development, provision of services predominantly for enterprises

0 0 2321 442,530 0.005

L Public administration, defense, compulsory social security 0 0 94 182,560 0.001M Education 0 0 74 122,380 0.001N Health and social work 0 0 7 221,320 0.000O Other community, social and personal services 0 0 355 167,180 0.002P Households as employers 0 0 0 7,070 0.000Source: Calculations by ifo Institute based on WIOD 2007.

20


4.3 Value creation and employment effects

Wenowshowhowmuchvaluecreationcanbeexpectedfromtheinducedtradeeffectsandhow

manyjobsinthespecificsectorswouldbeaffectedbyit.Hereagain,wedistinguishbetweendirect

andindirecteffects.

Letusfirstexaminetheeffectsonvaluecreationinmanufacturing.Thegreatesttotalvaluecreation

effectisrecordedintheofficeequipmentandelectronicsmanufacturingsector.Ascolumnfivein

Table3clearlyshows,therelativegrowthinvaluecreation(measuredasashareofthetotalvalue

creationeffectofthesectorin2007)forthissector,at2.5percent,isthehighest.Similarlystrong

valuecreationeffectscanbeseeninmetalproductionandchemicals.Intheservicesector,the

valuecreationeffectsagaincomefromindirectlyinducedeffects.Thegreatestincreaseisposted

bymovablesleasing,witharelativeincreaseof0.5percentofvaluecreation.

Asimilarpicturecanbeobtainedfromexaminingtheemploymenteffects.Heretoothesectors

alreadymentionedshowthegreatesteffects.Thetotalforallsectorsresultsinatotalemployment

effectofnearly160,000employees.Thisvalueisonlyslightlydifferentfromthevalueof181,000

jobscreatedreportedbyusinthemacrostudy.Thedifferencecanbeexplainedbythedifferent

modelstructure(partialanalysisversusgeneralequilibrium)andthedifferentdatasetsused.

21


Table 3: Value creation and employment effects in Germany by sector

NACE Rev.1.1

Sector designation Direct value creation effect (in millions of Euros)

Total value creation effect (in millions of Euros)

Total value creation effect /total value creation 2007

Direct effect workers

Total effect workers

Total worker effect/all workers 2007

A & B Agriculture and forestry, fishing 43 93 0.004 911 1,967 0.002C Mining and quarrying 0 12 0.002 0 197 0.002DA Food products and tobacco processing 166 182 0.005 3,785 4,145 0.004DB Textiles and wearing apparel –20 –17 –0.002 –393 –352 –0.002DC Leather and leather products 13 13 0.014 297 300 0.013DD Wood and wood products (without furniture production) 0 22 0.003 0 402 0.003DE Paper, publishing and printing 175 234 0.007 3,039 4,060 0.007DF Manufacture of coke, refined petroleum products and

nuclear fuel0 11 0.002 0 49 0.002

DG Chemical products 986 1,021 0.019 8,494 8,794 0.019DH Rubber and Plastics 69 150 0.006 1,163 2,540 0.006DI Non-metallic mineral products, ceramics 0 46 0.003 0 692 0.003DJ Basic metals and fabricated metals 1,297 1,558 0.021 18,790 22,570 0.020DK Machinery and Equipment 882 983 0.012 11,597 12,932 0.012DL Manufacture of office machinery, data processing

equipment and installations, manufacture of electrical and optical equipment

1,817 1,917 0.025 23,204 24,490 0.024

DM Manufacture of motor vehicles 1,065 1,097 0.012 11,786 12,143 0.012DN Manufacture of furniture, jewelry, musical instruments,

furniture, sports equipment, toys and other instruments, recycling

102 125 0.009 1,998 2,439 0.008

G-50 Retail trade (not including motor vehicles or service stations); repairs of personal and household goods

0 121 0.003 0 2,776 0.003

G-51 Wholesale trade and commission trade (not including motor vehicles)

0 433 0.004 0 6,207 0.004

G-52 Sale; Maintenance and repair of motor vehicles and personal and household goods

0 370 0.005 0 13,163 0.004

H Hotels and restaurants 0 7 0.000 0 282 0.000I-60 Land transport services; Pipeline transport services 0 145 0.004 0 3,932 0.004I-61 Water transport 0 6 0.001 0 20 0.001I-62 Air transport 0 19 0.003 0 165 0.003I-63 Other supporting and auxiliary transport activities;

Activities of Travel Agencies0 142 0.004 0 2,129 0.003

I-64 Post and telecommunications 0 89 0.002 0 1,168 0.002J Financial intermediation 0 184 0.002 0 2,248 0.002K-70 Real estate activities 0 458 0.002 0 679 0.001K-71-74

Renting of machinery and equipment without operator, data processing, research and development, provision of services predominantly for enterprises

0 1,517 0.005 0 23,022 0.004

L Public administration, defense, compulsory social security 0 64 0.001 0 1,363 0.001M Education 0 57 0.001 0 1,335 0.001N Health and social work 0 5 0.000 0 122 0.000O Other community, social and personal services 0 214 0.002 0 3,575 0.002P Households as employers 0 0 0.000 0 0 0.000Source: Calculations by the ifo Institute based on WIOD 2007,

22

5. Impact of education and occupational groups and impact of regions


Nowthatwehavequantified theprojectedtradeaffectsandtheir indirecteffects,wecanturn

to analysis step 2 and transform the trade shocks into shocks for the various education and

occupationalgroupsandregionalshocks.

5.1 Descriptive analysis of the education groups

Within the framework of our analysis we distinguish three groups with different education

levels:first,relativelyunskilledworkers,i.e.,thosewhohavenovocationaltrainingandnothing

equivalent to a high school diploma. Workers with moderate qualifications have either a high

schooldiplomaorcompletedanapprenticeship,whilehighlyqualifiedworkershavecompleteda

technicaltrainingcollegeorauniversitydegree.23

Table4showsthedistributionoftheeducationgroupsinmanufacturingsectors.Thisshowsthe

sharesofthedifferenteducationgroupsintotalemploymentinthesectorsandthetradecreation

potentialcalculatedinthefirstpartofouranalysis.Weexaminethe16manufacturingsectorsfor

whichwecancalculatebothdirectandindirecteffects.Asdiscussedearlier,directeffectscannot

becalculatedfortheservicesector.

Amoreexact impressionofhowimportantthespecificsectorsareforeacheducationgroupis

providedbyTables5through7,whichlisttheindividualgroupsforeachofthefivemostimportant

sectors.ThefirstvalueinTable5shouldbeunderstoodasshowingthat17percentofallunskilled

workersinthemanufacturingsectorareemployedinthemetalproductionandprocessingindustry.

23 ThisclassificationisstandardintheliteratureandfollowssuchexamplesasDustmannetal.(2009)andBaumgarten(2012).ItshouldbenotedherethattheuncleanededucationvariableintheIABdatasetisofrelativelypoorquality.Thatmeansthatmanyentriesaremissingorthatindividualshaveinconsistententries.Beforeouranalysis,wecleantheeducationvariableusingtheimputationproceduresthatarerecognizedintheliterature.SeeFitzenbergeretal.(2006)onthispoint.

23


Table 4: Distribution of education groups across manufacturing sectors

NACE Rev.1.1

Selected sectors Unskilled (in %)

Moderately skilled (in %)

Highly skilled (in %)

Trade creation (in %)

A & B Agriculture and forestry, fishing 19.43 75.52 5.05 47.40C Bergbau und Gewinnung von Steinen 15.81 76.18 8.01 .DA Food products and tobacco processing 18.96 76.98 4.06 65.86DB Textiles and wearing apparel 16.75 75.91 7.34 –19.35DC Leather and leather products 20.14 73.51 6.34 17.35DD Wood and wood products 16.75 75.91 7.34 .DE Paper, publishing and printing 20.14 73.51 6.34 14.68DF Coking, petroleum processing 16.11 78.68 5.21 .DG Chemical products 18.94 70.36 10.70 21.65DH Rubber and plastics 17.87 74.08 8.05 14.80DI Glass, ceramics 13.88 78.10 8.02 .DJ Metal production and processing 16.95 76.10 6.95 52.65DK Machinery and equipment 11.37 74.92 13.71 16.42DL Manufacture of office machinery 10.90 69.06 20.04 39.93DM Manufacture of motor vehicles 10.00 74.13 15.87 16.88DN Manufacture of furniture, recycling 13.88 78.51 7.61 26.36Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

Table 5: Significance of specific sectors for unskilled workers

Ranking Sector designation Relative significance for unskilled workers (in %)

1 Metal production and processing 17.082 Food products and tobacco processing 15.263 Machinery and equipment 10.364 Paper, publishing and printing 9.985 Manufacture of office machinery 9.47Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

Table 6: Significance of specific sectors for moderately skilled workers

Ranking Sector designation Relative significance for moderately skilled workers (in %)

1 Metal production and processing 15.052 Machinery and equipment 13.403 Food products and tobacco processing 12.164 Manufacture of office machinery 11.775 Manufacture of motor vehicles 9.30Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

Table 7: Significance of specific sectors for highly skilled workers

Ranking Sector designation Relative significance for highly skilled workers (in %)

1 Manufacture of office machinery 22.752 Machinery and equipment 16.323 Manufacture of motor vehicles 13.254 Metal production and processing 9.165 Chemical products 8.04Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

24


Figure4showsineachcasethecorrelationbetweentheinducedtradeeffectsandthesectorshare

oftheindividualeducationgroups.Itillustratesthatforalleducationgroups,thereisapositive

linkbetween tradecreation inasectorand therelativemeaningof thissector for thespecific

group.Itisstriking,however,thatthecorrelationforhighlyskilledworkersat0.17isnoticeably

lowerthanfortheothertwogroups.Thisismainlyduetothefactthathighlyskilledworkersplay

asubordinateroleinthesectorsthatshowespeciallystrongtradeeffects(suchasfoodproducts,

metalproductionandagriculture).

Quelle: Berechnungen des ifo Institutes auf Basis des SIAB Datensatzes des IAB Nürnberg.

0.00 0.05 0.10 0.15 0.20 0.25

–0.3

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Trad

e cr

eatio

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Moderately skilled Highly skilledUnskilledTr

ade

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TEX TEX TEX

LFF

LEDCHEM

NAHR

MTL

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RCY

GUM PVD

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LEDLED

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CHEMRCY

CHEMRCY

KFZMB

PVD

LFF

NAHR

MTL

BURO

LFF

NAHR

MTL

BURO

KFZMB

GUM PVD

Figure 4: Correlation of trade creation in specific sectors

LFF = Agriculture and forestry, fishingLED = Leather and leather productsCHEM = ChemicalsMB = Machinery and equipmentBERG = Mining and quarryingHOLZ = Wood and wood products

KFZ = Motor vehiclesTEX = Textiles and wearing apparelOEL = Coking, petroleum processingMTL = Metal production and processingRCY = Furniture production, recycling

GUM = RubberBURO = Office machineryNAHR = Food products and tobacco processingPVD = Paper, publishing and printingGLAS = Glass, ceramics

Sector share Sector share Sector share

25


5.2 Impact of education groups

Thetradeshockfromanalysisstep1wenowtransformintoimpactmeasurements.Whileinthe

firstpartwe identified the impactonspecificsectors fromapotentialTTIP,wearenowtrying

toquantifytheimpactonspecificeducationgroups.Wearefollowingthemethodproposedby

Ebensteinetal.(2012)andcalculatetheindexintwosteps.First,wequantifyaweightingfactor

2010kjα :

(1)2010

20102010

kjkj

k

LL

α = ,

Where 2010kjL isthenumberofemployeesineducationgroupkinsectorjin2010,and 2010kjLthenumberofallemployeesineducationgroupkinallsectorsin2010.Usingtheweightingfactor,

wethencalculatetheimpactmeasurement. 2010kβ :

(2) 2010 20101

J

k kj jj

β α=

= Δ∑ ,

inwhich j∆ representsthetradecreationeffectinsectorjcalculatedinanalysisstep1.

Table 8 shows the susceptibility index for the three education groups. This confirms again

the impression that the correlation analysishas alreadyprovided:Unskilledworkers aremost

significantly affected by the direct trade shock in the manufacturing sectors. Then come the

moderatelyskilledworkersandleastaffectedarethehighlyskilledworkers.

5.3 Descriptive analysis of the occupational groups

TheIABdatasetdistinguishesmorethan300differentoccupations.24Toobtainadegreeofclarity,

wehavecombinedtheindividuallistingsintoatotalof88occupationalgroups.25Table9provides

aninitialimpressionofwhichoccupationalgroupsareinthesectorsthatshowthegreatesttrade

creationpotential.Avalueof34%forsalespersonnelinthefirstpartofthetablethusmeansthat

34%ofallemployeesinthefoodsectorbelongtothisoccupationalgroup.

24 TheoccupationalclassificationintheIABdatasetfollowstheoneusedbytheBundesagenturfürArbeit1988andincludessome330characteristics.

25 Weaggregatetheemploymentcategories(3places),intoemploymentgroups(2places).

Table 8: Education impact measurement

Impact measurement education

Unskilled 0.34Moderately skilled 0.31Highly skilled 0.27Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

26


5.4 Impact on occupational groups

Hereweagaincalculatetheimpact,thistimeonindividualoccupationalgroups.Tables9iand9

iiintheAppendixshowtheimpactmeasurementsofall88occupationalgroupsindetail.What

isclearisthatthoseoccupationsthatworkalmostexclusivelyinthefoodproductionindustryare

mostlikelytobeaffected.Theseinclude,forexample,salespersonnel.Themetalindustryisalso

affectedmorethanaverage.Atthelowerendofthisscaleareoccupationalgroupslikeprinters,

paper makers and construction materials suppliers and occupations in the clothing industry.

However, itbecomesclearthatsomeof theservice industriesareaffectedrathermoreheavily.

Hereforexampleiswhereyoufindhospitalityservicesworkers(indexof0.50)orcleaners(index

of0.33).ThisshowsoncemorethatthetotaleconomyisaffectedbyaTTIP,evenifinthissection,

weonlylookatthedirecttradeeffectsonmanufacturingemployment.

5.5 Descriptive analysis of the regions

In this section, we analyze how much the individual states in Germany would be affected by

possibletransatlantictradeandinvestmentpartnership.Table10showsthetwomostimportant

sectorsperstatewithregardtotheirexportstotheUSAintheyear2012.Wesee,forexample,

thatinBavaria,automanufacturingandtheelectronicssectorgeneratethegreatestpercentageof

exports.InLowerSaxony,itismachineryandautomanufacturing.Anadditionaltable(Table10i)

intheAnnexshowswhichstateshavethegreatestshareoftotalGermanexportstotheUSAper

sector.Herewesee,forexample,thatforagriculture,LowerSaxonyisthestatewiththehighest

exportpercentageintradewiththeUSA,followedbyBavariaandSchleswig-Holstein.

Table 9: The most important occupational groups in certain sectors

Food products and tobacco processing

Metal production and processing

Agriculture and forestry, fishing

Manufacture of office machinery

Manufacture of furniture, recycling

Sales personnel (34 %)

Metalworkers (15 %)

Horticulturalists (36 %)

Office personnel and assistants (14 %)

Carpenters (26 %)

Producers of bakery and pastry products (11 %)

Assembly workers and metal-workers (12 %)

Agricultural workers (26 %)

Electricians (10 %)


Meat and fish processors (9 %)



Assembly workers and metal-workers (9 %)

Laborers (6 %)

Other food occupations (6 %)

Metal workers (6 %)

Farmers (4 %)

Engineers (9 %)

Warehouse, transport workers (5 %)


Laborers (5 %)

Forestry and hunting occupa-tions (3 %)

Technicians (8 %)

Wood workers, makers of wood products (4 %)

Source: Calculations of the ifo Institute based on the IAB SIAB dataset.

27


Table 10: The most important industrial sectors by stateState

Baden-Württemberg43.7 % Automotive manufacturing22.8 % Machinery15.1 % Office machinery manufacturing

Bavaria45.4 % Automotive manufacturing19.0 % Office machinery manufacturing15.9 % Machinery

Berlin31.1 % Automotive manufacturing27.6 % Office machinery manufacturing19.6 % Machinery

Brandenburg58.0 % Chemicals32.1 % Automotive manufacturing5.0 % Office machinery manufacturing

Bremen78.1 % Automotive manufacturing10.3 % Food and tobacco processing4.3 % Metal production and processing

Hamburg53.3 % Automotive manufacturing14.6 % Office machinery manufacturing8.6 % Machinery

Hesse43.8 % Chemicals12.1 % Office machinery manufacturing11.2 % Machinery

Mecklenburg-Vorpommern32.8 % Machinery16. 5% Office machinery manufacturing13.5 % Metal production and processing

Lower Saxony52.0 % Automotive manufacturing9.8 % Machinery9.8 % Chemicals

North Rhine-Westphalia25.5 % Machinery23.1 % Chemicals22.5 % Metal production and processing

Rhineland-Palatinate60.9 % Chemicals12.9 % Machinery6.2 % Metal production and processing

Saarland47.5 % Automotive manufacturing24.2 % Machinery16.4 % Metal production and processing

Saxony60.1 % Automotive manufacturing14.7% Machinery9.9 % Office machinery manufacturing

Saxony-Anhalt40.9% Chemicals20.0 % Metal production and processing14.0 % Machinery

Schleswig-Holstein29.8 % Machinery27.7 % Chemicals13.3 % Office machinery manufacturing

Thuringia36.4 % Office machinery manufacturing26.3 % Machinery10.4 % Automotive manufacturing

Source: Calculations of the ifo Institute for Economic Research based on data from the German Federal Statistical Office

28


5.6 Impact on regions

Basedonthatdata,howwouldthecalculatedtradeeffectsfromaTTIPbedistributedamongthe

states?Ifweassumethatthetradeeffectsonindividualindustriesaredistributedequallyamong

thestates,thefollowingresultsemerge(seeTable11):

Accordingly,weexpectoverallthatimportincreasesinbilateraltradewiththeUSAcouldreach

20to30percentperstate.NorthRhine-WestphaliacouldincreaseitsexportstotheUnitedStates

byabout29percent,dueprimarilytothestrongpositionofmetalproductionandprocessingin

thatstate.

WeexpecttheleastimpactinSaxonyandBrandenburg,tworegionswhoseexportstotheUSA

arelimitedtoafewsectors,suchasthechemicalssectorinBrandenburg.Thesesectorshavea

comparativelylowforecastfortradecreation.

TherelativelystrongvaluesforMecklenburg-Vorpommern,ThuringiaandBremenaremainlydue

tostrongtrade-creatingeffectsinthefoodproductionindustryanditsimportanceinthesestates.

Table 11: Expected export increases by stateStateMecklenburg-Vorpommern 29 %North Rhine-Westphalia 29 %Thuringia 28 %Berlin 27 %Hesse 26 %Saxony-Anhalt 26 %Schleswig-Holstein 25 %Saarland 25 %Bremen 24 %Rhineland-Palatinate 24 %Lower Saxony 23 %Bavaria 23 %Baden-Württemberg 22 %Hamburg 22 %Brandenburg 21 %Saxony 20 %Source: Calculations of the ifo Institute on the basis of the LIAB dataset of the IAB.

29


5.7 Value creation and employment effects in the regions

In addition to the bilateral export effects, we can also draw conclusions about the regional

employmentmarketandvaluecreationeffects.However,thecalculationislimitedtothedirect

tradeeffectsonthemanufacturingindustry.Inlightofthis,weexpectthattheactualextentofthe

effectswillbesubstantiallyhigher,sincearound40percentofthenewlycreatedjobswouldfall

underthenon-exportableservicesector.

Table12demonstratesthatthreestatesinparticularwouldbenefitfromafreetradeagreement:

NorthRhine-Westphalia,Baden-WürttembergandBavaria.Thisisdueprimarilytotheiralready

highexportlevels.Approximately60percentofallmanufacturingexportstotheUSAcomefrom

thesestates.Inaddition,alargernumberofmanufacturingindustriesthatwouldseethegreatest

valuecreationeffectsfromanagreementarelocatedthere,especiallymachinerymanufacturing,

metalproductionandprocessing,electronicsindustry(officemachinerymanufacturing,etc.),and

auto manufacturing. And finally, Bavaria and Baden-Württemberg alone account for almost 20

percentofGermany’sexportstotheUSA.

Table 12: Regional impact on the manufacturing industryState Total employment growth Total value creation effect

(in millions of Euros)North Rhine-Westphalia 21,080 1,433Baden-Württemberg 20,163 1,566Bavaria 19,471 1,597Lower Saxony 7,647 555Hesse 6,796 599Rhineland-Palatinate 4,500 425Saxony 4,014 207Thuringia 2,477 101Schleswig-Holstein 2,116 146Saxony-Anhalt 1,986 72Berlin 1,722 145Saarland 1,460 100Brandenburg 1,452 158Hamburg 1,198 121Mecklenburg-Vorpommern 735 25Bremen 551 199Source: Calculations of the ifo Institute on the basis of the LIAB dataset of the IAB.

30


5.8 Employment and education in the regions

Inthissection,wetakealookathowthecalculatedemploymenteffectsaredistributedamongthe

differenteducationgroupsineachstate.Todoso,wecombinethefindingsfromsection5.1with

thosefromsection5.7andderivethecorrespondingdistributionfortheindividualstates.

Table13illustratesthecorrespondingresultsforallstatesandeducationgroups.ForGermany’s16

states,itshowsessentiallynomajordeviationsfromtheexpectedaveragevalues.

However,afewnoteworthydifferencesemergedbetweenstatesintheareasofrelativelyunskilled

andhighlyqualifiedworkers.

Forexample,employmentgrowthamongrelativelyunskilledworkersinRhineland-Palatinatewas

highestinthenationalcomparison.

Theobviousreasonforthiseffectisthatthechemicalsector,whichemploysacomparativelyhigh

percentageofrelativelyunskilledworkers,isparticularlyimportantinthisstate.

Table 13: Employment effects by state based on education levelState Employment

growth for relatively unskilled workers

Share of relatively unskilled workers in employment growth

Employment growth for workers with moderate qualifications

Share of workers with moderate qualifications in employment growth

Employment growth for highly qualified workers

Share of highly qualified workers in employment growth

Total employ-ment growth

Baden-Württemberg 2,708 13.4 % 14,761 73.2 % 2,694 13.4 % 20,163Bavaria 2,632 13.5 % 14,199 72.9 % 2,640 13.6 % 19,471Berlin 243 14.1 % 1,234 71.6 % 246 14.3 % 1,722Brandenburg 219 15.0 % 1,073 73.9 % 160 11.0 % 1,452Bremen 67 12.2 % 408 74.0 % 76 13.8 % 551Hamburg 153 12.8 % 871 72.7 % 174 14.5 % 1,198Hesse 984 14.5 % 4,943 72.7 % 869 12.8 % 6,796Mecklenburg-Vorpommern 104 14.2 % 542 73.7 % 89 12.1 % 735Lower Saxony 1,087 14.2 % 5,635 73.7 % 925 12.1 % 7,647North Rhine-Westphalia 3,159 15.0 % 15,567 73.8 % 2,354 11.2 % 21,080Rhineland-Palatinate 709 15.8 % 3,305 73.4 % 486 10.8 % 4,500Saarland 212 14.5 % 1,095 75.0 % 153 10.5 % 1,460Saxony 570 14.2 % 2,951 73.5 % 493 12.3 % 4,014Saxony-Anhalt 310 15.6 % 1,465 73.8 % 211 10.6 % 1,986Schleswig-Holstein 309 14.6 % 1,548 73.2 % 258 12.2 % 2,116Thuringia 362 14.6 % 1,813 73.2 % 301 12.2 % 2,477

[ Manufacturing industry [ 16.6 % [ 75.1 % [ 8.8 %Total 13,827 14.2 % 71,411 73.3 % 12,129 12.5 % 97,368Quelle: Berechnungen des ifo Institutes auf Basis der Daten des statistischen Bundesamtes.

31


Interestingly,thedirectcomparisonofthetotalpotentialemploymenteffectsfromanagreement

withthecurrentexistingaveragedistributionofthethreeeducationgroupsinthemanufacturing

industryshowsthattherelativeincreasesforhighlyqualifiedworkersaresignificantlygreater

than for the other two groups (see Table 13). At 12.5 percent, the growth here lies almost 4

percentage points higher than the current sectoral average of 8.8 percent for highly qualified

workers.By contrast, thegrowth formoderately skilled and relativelyunskilledworkers came

inataround2percentbelowthecurrentsectorratio.Thiscanbeexplainedbythefactthatin

sectorssuchasmachinerymanufacturingandelectronicsinparticular,wheremostjobsinthe

manufacturingindustryarecreated,thepercentageofhighlyqualifiedworkersisespeciallyhigh

comparedtotheothersectors.Intheelectronicsindustryalone,itamountstoalmost20percent.

Atthispointwewouldliketoremindreadersagainthattheexpectedemploymentdistribution

calculatedhereonlytakesthemanufacturingindustryintoaccount,andthereforedoesnotinclude

theservicesectorduetoalackofdata.

32

6. Effects of a TTIP on income and the income risk


Wearenowturningtoourthirdanalysisstepandusethecalculatedshockmeasurementtoproject

changesinrealincomeandintheincomeriskthatwewouldexpectastheresultofaTTIP.

6.1 Effects on real income

ForthiswefirstexamineMincerwageequations.Suchequationsmodelthewagesofdependent

employeesasafunctionoftheircharacteristics.Typicallyforthistypeofanalysis,individualwage

data(inlogarithmicform)areregressedonhumancapitalindicators(education,workexperience),

socio-economic variables (age, gender, nationality), and a series of indicator variables (region,

industry). In this way, information is obtained on the role of education in the wages paid, for

example.

Forouranalysis,weexpandtheclassicmodelbyincludingcharacteristicsoftheemployer.What

interestsus inparticular is towhatextent theestablishmentwhere theworker isemployed is

affectedbyinternationaltrade(exportsandimports).

Concretely,weestimatethefollowingMincerregressionsonindividualwagedatafor2010::

(3)ln i i s iw Xβ γ ε= + Ω + ,

whereln iw representsthelogarithmofrealwages, iX isavectorofvariablescontrollingforthe

characteristicsoftheworker,andβ therelatedparametervector.26 sΩ ontheotherhand,measures

howstronglyanestablishmentisinvolvedininternationaltrade;weviewthesevariablesasan

opennessmeasurement.27Accordingly,theestimatedvalueofparameterγ providesinformation

aboutthestrengthofthelinkbetweentheopennessoftheestablishmentandthewagesofthe

employees.

26 SincethewageinformationintheIABdatasetisavailableonlytotheupperearningslimit,wefirstuseTobitestimates,inordertoestablishthecompletewagedistributionfromgradedwagedata.Indoingthis,wefollowDustmannetal.(2009)andCardetal.(2012).Forouranalysisweareusingtheimputedwagedata.Moreover,wetakeintoaccountthedisproportionalityoftheLIABcalculationsampleinthatweadditionallycontrolfortheindustry,stateandsizeofoperationvariables.Formoreinformation,seeFDZMethodReportNo.01/2008.

27 Concretely, theopennessmeasurementmeasures theextentof exports in the total salesof a company.Since theextentofexportsatthecompanylevelcorrelatesstronglywiththeextentofimportsatthecompanylevel(seeonthise.g.Baumgarten,2012),thisvariablecanbeusedasaproxyforthetotalopennessofacompany.Weconsidertheaverageastheopennessofasector.

33


Weconductedseparateregressionsforeachgroupandtherebyobtainedgroup-specificγ values.

Theyofferinsightintohowstronglyopennessinfluencestherealwagesinthespecificsample.

Table14shows the resultsof theMincer regressions for the individualeducationgroups.The

correspondingresultsforalloccupationalgroupscanbefoundintheAppendixasTables14i.

TheestimatesshownintheTableshouldbeinterpretedasfollows:A10%increaseinopennessin

asector(andaccordingly,itisassumed,inanaverageestablishment)leadstoanaverageincrease

inrealwagesof4%forunskilledand3%formoderatelyandhighlyskilledworkers.Thisfinding

issomewhatsurprising.Itmeansthattheexportsuccessofindividualcompaniesalsoresultsin

higherwagesforunskilledworkersandthattheincreaseinwagesinexport-orientedcompanies

isactuallyhigherforlessskilledpersonsthanforworkerswithmoretraining.

Infurtherconductingouranalysis,weusetheshockmeasurementscalculatedpreviouslyinthe

estimatedMincerequations.Insodoing,wetakeintoaccountthattheopennessapproximatedis

onlywithrespecttotradebyGermanfirmswiththeUSA.Thismeansthatopenness,forexample,

in the food products sector rises by 66% * 7%, with 7% representing the ratio of Germany’s

exportstotheUSArelativetoGermany’stotalexports.28Wetherebyobtainaforecastofrealwage

change 'γ ,whichshouldresultfromaTTIPonaveragefortheindividualgroups.Table15shows

theforecastofrealwagechangeforindividualeducationgroups.

28 Weareassumingthis7%valuefor2010asanaveragevalueforallsectors.

Table 14: Regression results for education groupsVariables Unskilled Moderately skilled Highly skilledIndividual characteristics yes yes yesOpenness 0,004*** 0,003*** 0,003***Number of observations 358.768 1.739.263 310.905R² 0,4964 0,2943 0,3377*** indicates statistical significance at 1%.Source: Calculations by the ifo Institute based on the IAB LIAB dataset.

Table 15: Forecasts of real wages

Unskilled Moderately skilled Highly skilled

TTIP effect on real wages 0.0094 0.0065 0.0056Source: Calculations of the ifo Institute on the basis of the IAB LIAB dataset.

34


Different aspects should be noted here. First, it is evident that all three values are positive.

ThatmeansthatincaseofaTTIP,weexpectrealwageincreasesinallthreeeducationgroups.

For unskilled workers, we are forecasting an increase in real wages of about 0.9 percent, for

moderatelyskilledanincreaseof0.7percentandforhighlyskilledworkersweexpectanincrease

inrealwagesof0.6percent.29

Fortheindividualoccupationalgroups,weareforecastingrealwagechangesthatrangebetween

minus5percent (fishery jobs) andplus5percent (agriculturalworkers). For salespersonnel,

inspectorsanddispatchers,buildersofcivilengineeringstructuresandprecisionmetalworkers,

weidentifyrealwageincreasesofmorethanonepercent.Thatalsoappliesformealpreparers,

socialworkersandpersonalcareworkers.Thisagainmakesclearthatthewholeeconomywould

profitsubstantiallyfromatransatlanticagreement.Becauseeventhoughinthissecondsegment

wearelookingonlyatthedirecttradeeffectsonmanufacturing,itcanbeseenthatforexample

cleanersorotherserviceworkersobtainrealwageincreases.

6.2 Effects on income risk

TheeffectsofaTTIPonincomeriskarecalculatedinasimilarway.Asmeasureforthisweusethe

residualwagedisparity,i.e.,theshareofthewageinequalitythatcannotbeascribedtoindividual

characteristicslikeage,education,genderornationality.Itthusrepresentsariskmeasurement,

sinceitcannotbecontrolledforanindividual.30Ifthescatteroftheserandomwagecomponents

growsduetoanincreaseintrade,thentheincomeriskonthelabormarketincreases:Inother

words,thereisanincreaseintheshareofworkerspaidsubstantiallymoreorlessthanthewage

theirformalqualificationswouldentitlethemto.

The procedure takes place in three steps. First, we conduct Mincer wage regressions and

extracttheresidualsfromthem.Thestandarddeviationoftheseresidualsprovidesouranalysis

measurement, the income risk. In the second step, we estimate the extent the income risk is

influencedbyopenness.Todothat,weregressourincomeriskindexonthreeconstructedopenness

measurements,sothatwecanreachsomeconclusionsaboutindividualsubsegmentsofthejob

market.Inthelaststep,weagainusetheimpactmeasurementsthatwehadcalculatedtoseehow

theincomeriskforvariouspartialsegmentschangesastheresultofapossibleagreement.31

Firstwelookattheisolatedresultsforthethreeeducationgroups.Ouranalysisshowsthatforall

29 TheseresultsarecompatiblewiththerealwageeffectsforGermanythatwecalculatedinthefirstpartofthestudy(Part1:MacroEconomicEffects).Theretherealwageeffectswerebetween0.5and2.2percent,wherewedistinguishbetweenapurelycustomsscanerioandadeepliberalizationscenario.Thatdistinctionisnotmadehere.Moreover,ouranalysisdoesnotconsiderthatthepricelevelinthewholeeconomyfallsduetoTTIP(asweknowfromthemacrostudy),sothattherealwageeffectsshownhererepresentlowerlimits.Ifthepriceleveleffectisassumedtobeequalforallpopulationgroups(whichistheusualpractice),thentherealwageeffectcanbereadilycomparedacrossindividualgroups.

30 Ineconometricestimationequations,theunexplainedresidualisusuallydescribedas“shocks”.Thesescatteraroundzeroinalinearestimationmodel.Inourapplication,alowerresidualmeansthattheworkerreceivesahigherwage,whichwouldbeduetohimbasedonhiscalculatedhumancapitalprofile.Apositiveresidualmeanstheopposite.

31 TheconstructionoftheopennessindexisagainbasedontheplantinformationoftheLIABdatabase.Thefirstopennessindexvariesacrosseducationgroups,thesecondvariesacrossoccupationalgroupsandthethirdopennessindexvariesacrossstates.

35


educationgroups,anincreaseintheincomeriskinthecaseofatransatlanticinvestmentandtrade

partnershipwouldbeexpected.Infact,theriskforunskilledworkersrisesmost,withthestandard

deviationrisingby0.011.Workerswithmoderateskillscanexpectanincreaseintheincomerisk

of0.010,whileforhighlyskilledworkers,theincomeriskrisesby0.0089.Theseresultsshould

beinterpretedagainstabackgroundinwhichtheaverageresidualwageinequalityhasavalueof

0.4.32WeshouldthusseetheaveragevalueasthestatusquoandthenexpectthataTTIPwould

increasetheincomeriskofunskilledworkersbynearlythreepercent.Thecorrespondingincome

riskrisefortheothertwoeducationgroupsisjustbelowthat.

Adifferentpictureemergesifwelookattheisolatedeffectsforindividualoccupationalgroups.

Here it turnsout that theoccupationsmore frequently found insectors thataredistinguished

forahighopennessmeasurement,aresubject toa smaller incomerisk. (Thatmeans thatour

opennessfactorinanalysissteptwodescribedaboveisnegativefortheoccupationalgroups).The

result:Foralmosteveryoccupationalgroup,thereisadeclineintheincomeriskfromapossible

TTIP.Theincomeriskwilldeclinemore,themoretheoccupationalgroupisimpactedbytrade

oropenness.Table16iintheAnnexprovidestherelevantoverview.TheeffectsfromaTTIPon

incomeriskonvariousoccupationalgroupsnowliesbetweenplus0.0015(textileprocessors)and

minus0.0058(meatandfishprocessors).Again,themoreopenanoccupationalgroupis(i.e.,the

moreimportantopensectorsareforthisoccupationalgroup),themoretheincomeriskforthis

occupationalgroupfallsfromaTTIP.

Sofarwehaveexaminedtheeffectsontheincomeriskforeducationandoccupationalgroups

inisolation.Toreachsomeconclusionsabouttheoveralleffect,wemustevaluatethetotalofthe

findings.Althoughthereareoppositeeffects, theoveralleffectofaTTIPontheincomeriskis

ambiguous.Whatwecansay,however, is forexample, that foranunskilled (=0.011)concrete

worker(=–0.0014),theincomeriskrises.Foranunskilledsalesperson(=0.0066),theincomerisk

rises,butlessthanfortheconcreteworker.Thusitispossibletocalculateforcombinationsofthe

educationandoccupationofaworkertheexpectedchangeinhisincomerisk.

32 Thisisavaluethatisalsofoundinthisamplitudeintheliterature.SeeDustmannetal.(2009),Cardetal.(2012)andBaumgarten(2012).

36

7. Summary

7. Summary

• Tradecreationpotentialvariesacrossindustrysectors:Thestrongesttradeeffectsaretobe

expectedinthefoodandmetalindustries.

• Therearealsocleardifferencesinthesectorswithrespecttotheimportanceofnon-tariff

barriers. Politically adaptable NTBs are especially high in the recycling and agriculture

sectors.

• Thelargestvaluecreationeffects inGermanyareexpectedintheelectricalsector.Thatis

wherethestrongestemploymenteffectsarefound.

• The transatlantic agreement also has affects in those industrial sectors and on those

workers that are not directly affected by more trade. The reason for that are input-output

interconnections.

• Realwageincreasesforalleducationgroups(unskilled,moderatelyskilledandhighlyskilled)

are to be expected from a TTIP, from 0.6 percent (highly qualified) to nearly one percent

(unskilled).

• Occupationsinthefoodsector,suchassalespersonnel,likeoccupationsinthemetalindustry,

showaboveaveragerealwageincreasesofmorethanonepercent.

• Allstateswouldbenefit fromanagreement,andincreasesintradewiththeUSAof20–30

percentperstateareexpected.Thesizeoftheanticipatedeffectsdependsheavilyonexport

levelsattheoutset.

• TheincomeriskfromaTTIPrisesinalleducationgroupsandregions.However,thetotaleffect

remainsambiguous,sincetheincomeriskfallsalongtheoccupationaldimension.

37

A. Annex

A. Annex

A.1 Gravitation models, estimation methods and results

Theeconomicgravitationequationinitssimplest formstatesthatthetradeflowsbetweentwo

economiesdependproportionallyon theproductof their sizeandnegativelyon theirdistance

fromeachother:

(A1) 1i jij ijW

BIPBIPx d

BIP−= ,

where ijx stands for the trade flows and WBIP for world income, and iBIP and jBIP correspondingly for theGDPofcountries iand j. ijd measures the tradebarriersbetween two

countries.Althoughthesimplegravitationequationintheempiricalliteratureontradeisgenerally

abletoexplain60–80%(dependingonthedataset)ofthevariationinbilateraltradeflows,the

absenceofasolidtheoreticalfoundationforthisconclusionwaslongconsideredamajorcriticism.

Currentresearchhashoweverbeenabletoshowthatthegravitationequationisconsistentwith

manynewertrademodels.33Atheoretically-basedgravitationequationfromAndersonandvan

Wincoop(2003)isverysimilartothesimpleequation(A1):

(A2) (1 ) ( 1) ( 1)i jij ij i jW

BIPBIPx d P

BIPσ σ σ− − −= Π ,

whereσ reflects substitutional elasticity, and iΠ and jP represent themultilateral resistance

terms.Tradepolicyisrepresentedasapartof ijd ,inthatweintegrateanindicatorvariablefor

membershipinapreferentialtradeagreement(PTA).34Weassumethefollowingconnection:

(A3)1 exp( ...)ij ij ijd PHA DISTσ δ β− = + + ,

whereweconsider,besidestheindicatorformembershipinapreferentialtradeagreement,other

geographicandhistoricalvariablesthatinfluencetradefrictionsbetweentwocountries.So,for

example, ijDIST standsforthedistancebetweenthetradingpartners.Bysubstitutionof(A3)in

(A2)andwithaslightmodification,weobtainourestimationequation:

(A4) ( )'ln ln lnij ij ij i j ijx Z PHAβ δ α γ ε= + + + + ,

where'(1, ,...)ijZ DIST= isavectorthatcontainsaconstantaswellasallvariablesthatmake

tradeeasierormoredifficultexceptfor ijPHA .β isavectorofcoefficientsand 1i i iBIP σα −= Π

and1

j j jBIP Pσγ −= apply.

33 Thisappliesonlyundercertainassumptionsinvolvingconsumption,preferences,marketstructureandtransportcosts.DecisivecontributionsweremadebyReddingandVenabeles(2004),BaierandBergstrand(2001),AndersonandvanWincoop(2003)andAndersonandvanWincoop(2004).

34 In this we again distinguish between “deep” and “all other” preferential trade agreements. We include among the “deep”agreementstheNorthAmericanFreeTradeAgreement(NAFTA)andtheEUAgreement.

38

A. Annex

Forouranalysis,weestimatethegravitationequationattheindustrylevelandobtainforeach

sectoracoefficient sδ thatindicatestoustheaveragetrade-creatingeffectofadeepagreement

for that particular sector. Gravitation models can be estimated consistently with the help of

fixed effects (Feenstra, 2004). In specification A) we estimate our industry-specific gravitation

equations,inthatwecontrolfortradepartner/sector-specificfixedeffects,aswellasforcountry/

time-specificfixedeffectsandperformalinearestimate.

Thefixedeffectscontrolforobservedandunobservedheterogeneityinthedata.Inspecification

B) we conduct a non-linear, so-called Poisson Pseudo Maximum Likelihood (PPML) estimate, in

ordertotakeintoaccountsuchfactorsastherelativelyhighnumberofzerosinthetradedata

(SantosSilvaandTenreyro,2006).ThePPMLestimatehasanotherdecisiveadvantage,besidesthe

considerationofthezerosinthetradematrix,comparedtolinearestimationmethods:Itgenerates

consistentestimatorseveninthepresenceofmeasurementerrorsthatcauseheteroscedasticity.

InB)time-consistentcomponentsareincludedthroughtradepartner/sector-specificfixedeffects.

Instead of country/time-specific fixed effects that include the multilateral resistance terms in

specification A), in specification B) we take into account time-specific fixed effects and linear

multilateralresistanceterms.InthiswemakeuseofthefindingsofBaierandBergstrand(2009)

andapproximatethemultilateralresistancetermswithhelpofaTaylorapproximation.

39

A. Annex

Table 1 i shows the results of both specifications. Together the results of both models – both

intermsofthesignandthescaleoftheeffects–arecomparable.Sincethetradeeffectsatthe

sectorlevelarethefoundationforfurthercalculations,Table1ialsopresentshowtheyresultfrom

specificationAandB.

NotetoTable1i:

Theestimatesshownalsoprovetoberobustinalternativespecifications.

Table 1 i: Estimation results

NACE Rev.1.1

Sector designation Specification A Specification B Derived trade creation

A & B Agriculture and forestry, fishing 0,728*** 0,388*** 0,388***C Mining and quarrying –0,0249 0,0774 0,0774DA Food products and tobacco processing 0,714*** 0,506*** 0,506***DB Textiles and wearing apparel –0,0106 –0,215** –0,215**DC Leather and leather products 0,160* –0,140 0,160*DD Wood and wood products 0,0448 –0,0675 –0,0675DE Paper, publishing and printing 0,221*** 0,137* 0,137*DF Coking, petroleum processing 0,00668 0,133 0,133DG Chemical products 0,300*** 0,196** 0,196**DH Rubber and Plastics 0,138** 0,105 0,138**DI Glass, ceramics 0,0337 0,0300 0,0300DJ Metal production and processing 0,423*** 0,0325 0,423***DK Machinery and Equipment 0,0971 0,152** 0,152**DL Manufacture of office machinery 0,0734 0,336*** 0,336***DM Manufacture of motor vehicles 0,159* 0,156* 0,156*DN Manufacture of furniture, recycling –0,00709 0,234** 0,234***, **, *** refer to significance at the 1.5 or 10 percent level. Shortened representation: Since we estimate each sector separately, we will not provide references to additional information here.Source: Calculations of the ifo Institute.

40

A. Annex

Table 9 i: Degree of impact experienced by the occupational groups

Occupational group designation Impact measurement

Farmers 0.43Livestock breeders, fishing occupations 0.43Administrators, farming and livestock consultants 0.39Agricultural workers, animal keepers 0.47Horticulturalists 0.47Forestry, hunting occupations 0.46Miners .Mineral, petroleum, natural gas extractors .Mineral processors .Stone workers 0.01Construction materials producer .Ceramic workers 0.02Glass makers 0.07Chemical workers 0.21Plastic processors 0.18Paper makers, processors 0.14Printers 0.15Wood processors, wood product producers and related occupations 0.08Metal processors, rollers 0.48Form makers, casters 0.45Metal formers (die casters) 0.39Metal formers (under tension) 0.31Metal surface processors, enhancers, coaters 0.42Metal connectors 0.34Blacksmiths 0.42Sheet metal worker, fitters 0.24Metal workers 0.31Mechanics 0.27Tool makers 0.30Precision metal worker and associated occupations 0.37Electrician 0.28Installer and metal working occupations not named elsewhere 0.33Spinning occupations –0.12Textile producers –0.10Textile processors –0.10Textile finishers –0.17Leather producers, leather and pelt processors 0.18Producers of baked goods and pastries 0.65Meat and fish processors 0.65Meal preparers 0.55Food and drink producers 0.62Other food occupations 0.65Masons, concrete installers 0.16Carpenter, roofer, scaffolder 0.13Road and foundation workers 0.29Construction laborer 0.32

41

A. Annex

Table 9 i: Degree of impact experienced by the occupational groups

Occupational group designation Impact measurement

Construction materials supplier 0.14Interior designer, upholsterer 0.20Cabinetmaker, model building 0.18Painters, varnishers related occupations 0.26Goods testers, dispatching packers 0.34Laborers without more detailed designation 0.31Machinists and related occupations 0.26Engineers 0.26Chemists, physicists, mathematicians 0.22Technicians 0.26Technical support personnel 0.27Sales personnel 0.50Bank, insurance sales personnel 0.05Other service sales personnel and related occupations 0.22Land transport occupations 0.37Water and air transport occupations 0.21Telecommunications occupations 0.21Warehouse managers, warehouse, transport workers 0.23Entrepreneurs, organizers, auditors 0.26Elected officials, administrative decision-makers 0.25Accountants, IT specialists 0.28Office staff, clerical workers 0.26Service, security occupations 0.30Security personnel 0.07Journalists, interpreters, librarians 0.15Artists and associated occupations 0.21Doctors, pharmacists 0.18Other health service providers 0.18Social work professions 0.24Teachers 0.16Intellectual and scientific occupations not mentioned elsewhere 0.22Personal care 0.30Hospitality service providers 0.51Domestic services providers 0.33Cleaning occupations 0.36Workers with unidentified occupations 0.27Workers without more detailed designation 0.29Source: Calculations of the ifo Institute based on the IAB LIAB dataset.

42

A. Annex

Tabelle 10 i: The most important states by industrial sector

Sector designation State

Agriculture and forestry, fishing27.9% Lower Saxony24.0% Bavaria17.4% Schleswig-Holstein

Mining and quarrying30.2% Bavaria26.9% Saxony-Anhalt12.9% Lower Saxony

Food products and tobacco processing24.1% Bremen20.9% Lower Saxony11.1% North Rhine-Westphalia

Textiles and wearing apparel26.2% Baden-Württemberg24.5% Bavaria21.1% North Rhine-Westphalia

Leather and leather products28.8% Rhineland-Palatinate25.9% Bavaria20.5% Schleswig-Holstein

Wood and wood products21.1% Rhineland-Palatinate19.4% Bavaria13.8% Lower Saxony

Paper, publishing and printing30.9% Baden-Württemberg29.0% Lower Saxony13.3% North Rhine-Westphalia

Coking, petroleum processing59.9% Hamburg17.0% North Rhine-Westphalia

9.3% Baden-Württemberg

Chemical products20.7% Rhineland-Palatinate19.0% North Rhine-Westphalia18.8% Hesse

Rubber and Plastics23.8% North Rhine-Westphalia17.9% Bavaria16.0% Hesse

Glass, ceramics36.4% Bavaria10.7% Baden-Württemberg10.6% North Rhine-Westphalia

Metal production and processing44.3% North Rhine-Westphalia12.7% Baden-Württemberg10.2% Hesse

Manufacture of office machinery33.8% Bavaria26.9% Baden-Württemberg10.0% North Rhine-Westphalia

Machinery and Equipment30.8% Baden-Württemberg21.5% Bavaria20.2% North Rhine-Westphalia

Manufacture of motor vehicles30.2% Bavaria29.0% Baden-Württemberg11.6% Lower Saxony

Manufacture of furniture, recycling25.1% Baden-Württemberg22.4% Bavaria15.4% Hesse

Source: Calculations of the ifo Institute.

43

A. Annex

Table 14 i: Regression results, occupational groups, TTIP effect

Occupational group designation Openness TTIP effect

Farmers 0.005 0.0000Livestock breeders, fishing occupations –0.016** –0.0479Administrators, farming and livestock consultants 0.005* 0.0136Agricultural workers, animal keepers 0.015*** 0.0495Horticulturalists 0.002 0.0000Forestry, hunting occupations 0.005 0.0000Miners 0.001 .Mineral, petroleum, natural gas extractors –0.007*** .Mineral processors 0.001* .Stone workers 0.003* 0.0002Construction materials producer 0.003*** .Ceramicists 0.001* 0.0001Glass makers –0.002 0.0000Chemical workers 0.002** 0.0030Plastic processors 0.001** 0.0013Paper makers, processors 0.001 0.0000Printers 0.000 0.0000Wood processors, wood product producers and related occupations 0.002 0.0000Metal processors, rollers 0.002*** 0.0068Form makers, casters 0.001 0.0000Metal formers (die casters) 0.001 0.0000Metal formers (under tension) 0.003*** 0.0065Metal surface processors, enhancers, coaters 0.002** 0.0059Metal connectors 0.002*** 0.0048Blacksmiths 0.002 0.0000Sheet metal worker, fitters 0.003*** 0.0050Metal workers 0.003*** 0.0065Mechanics 0.002*** 0.0038Tool makers 0.002*** 0.0043Precision metal worker and associated occupations 0.004*** 0.0104Electrician 0.002*** 0.0040Installer and metal working occupations not named elsewhere 0.002*** 0.0046Spinning occupations 0.001 0.0000Textile producers 0.002 0.0000Textile processors 0.001 0.0000Textile finishers 0.003*** –0.0035Leather producers, leather and pelt processors 0.001 0.0000Producers of baked goods and pastries 0.003 0.0000Meat and fish processors –0.000 0.0000Meal preparers 0.004*** 0.0155Food and drink producers 0.002 0.0000Other food occupations 0.000 0.0000Masons, concrete installers 0.002** 0.0023

44

A. Annex

Table 14 i: Regression results, occupational groups, TTIP effect

Occupational group designation Openness TTIP effect

Carpenters, roofers, scaffolders 0.001 0.0000Road and foundation workers 0.006** 0.0120Construction laborer 0.002 0.0000Construction materials supplier 0.003** 0.0029Interior designer, upholsterer –0.003 0.0000Cabinetmaker, model building 0.004*** 0.0050Painters, varnishers related occupations 0.004*** 0.0072Goods testers, dispatching packers 0.005*** 0.0117Laborers without more detailed designation 0.007*** 0.0154Machinists and related occupations 0.002*** 0.0036Engineers 0.002*** 0.0037Chemists, physicists, mathematicians 0.001** 0.0015Technicians 0.002*** 0.0036Technical support personnel 0.002*** 0.0038Sales personnel 0.009*** 0.0317Bank services, insurance sales personnel 0.003* 0.0011Other service sales personnel and related occupations 0.004*** 0.0061Land transport occupations 0.001 0.0000Water and air transport occupations 0.004** 0.0060Telecommunications occupations 0.003*** 0.0045Warehouse managers, warehouse, transport workers 0.004*** 0.0066Entrepreneurs, organizers, auditors 0.001 0.0000Elected officials, administrative decision-makers 0.003*** 0.0052Accountants, IT specialists 0.004*** 0.0080Office staff, clerical workers 0.003*** 0.0055Service, security occupations 0.004*** 0.0084Security personnel 0.001 0.0000Journalists, interpreters, librarians –0.001** –0.0010Artists and associated occupations 0.005*** 0.0074Doctors, pharmacists 0.005*** 0.0063Other health service providers 0.003* 0.0039Social work professions 0.009*** 0.0152Teachers 0.006*** 0.0066Intellectual and scientific occupations not mentioned elsewhere 0.004*** 0.0063Personal care 0.020* 0.0424Hospitality service providers 0.002* 0.0071Domestic services providers 0.001 0.0000Cleaning occupations 0.004*** 0.0100*, **, *** indicates significance level at 1.5 and 10 percent level.Source: Calculations of the ifo Institute based on the IAB LIAB dataset.

45

A. Annex

Table 16 i: Effect on income risk by occupation

Occupational group designation Impact measurement TTIP effect on income risk

Farmers 0.43 –0.0038Livestock breeders, fishing occupations 0.43 –0.0038Administrators, farming and livestock consultants 0.39 –0.0034Agricultural workers, animal keepers 0.47 –0.0042Horticulturalists 0.47 –0.0041Forestry, hunting occupations 0.46 –0.0041Miners . .Mineral, petroleum, natural gas extractors . .Mineral processors . .Stone workers 0.01 –0.0001Construction materials producer . .Ceramicists 0.02 –0.0002Glass makers 0.07 –0.0006Chemical workers 0.21 –0.0019Plastic processors 0.18 –0.0016Paper makers, processors 0.14 –0.0013Printers 0.15 –0.0013Wood processors, wood product producers and related occupations 0.08 –0.0007Metal processors, rollers 0.48 –0.0043Form makers, casters 0.45 –0.0039Metal formers (die casters) 0.39 –0.0034Metal formers (under tension) 0.31 –0.0027Metal surface processors, enhancers, coaters 0.42 –0.0037Metal connectors 0.34 –0.0030Blacksmiths 0.42 –0.0037Sheet metal worker, fitters 0.24 –0.0021Metal workers 0.31 –0.0027Mechanics 0.27 –0.0024Tool makers 0.30 –0.0027Precision metal worker and associated occupations 0.37 –0.0033Electrician 0.28 –0.0025Installer and metal working occupations not named elsewhere 0.33 –0.0029Spinning occupations –0.12 0.0011Textile producers –0.10 0.0009Textile processors –0.10 0.0009Textile finishers –0.17 0.0015Leather producers, leather and pelt processors 0.18 –0.0016Producers of baked goods and pastries 0.65 –0.0058Meat and fish processors 0.65 –0.0058Meal preparers 0.55 –0.0049Food and drink producers 0.62 –0.0055Other food occupations 0.65 –0.0057Masons, concrete installers 0.16 –0.0014

46

A. Annex

Table 16 i: Effect on income risk by occupation

Occupational group designation Impact measurement TTIP effect on income risk

Carpenters, roofers, scaffolders 0.13 –0.0011Road and foundation workers 0.29 –0.0025Construction laborer 0.32 –0.0029Construction materials supplier 0.14 –0.0012Interior designer, upholsterer 0.20 –0.0018Cabinetmaker, model building 0.18 –0.0016Painters, varnishers, related occupations 0.26 –0.0023Goods testers, dispatching packers 0.34 –0.0030Laborers without more detailed designation 0.31 –0.0028Machinists and related occupations 0.26 –0.0023Engineers 0.26 –0.0023Chemists, physicists, mathematicians 0.22 –0.0019Technicians 0.26 –0.0023Technical support personnel 0.27 –0.0024Sales personnel 0.50 –0.0044Bank, insurance sales personnel 0.05 –0.0005Other service sales personnel and related occupations 0.22 –0.0019Land transport occupations 0.37 –0.0032Water and air transport occupations 0.21 –0.0019Telecommunications occupations 0.21 –0.0019Warehouse managers, warehouse, transport workers 0.23 –0.0021Entrepreneurs, organizers, auditors 0.26 –0.0023Elected officials, administrative decision-makers 0.25 –0.0022Accountants, IT specialists 0.28 –0.0025Office staff, clerical workers 0.26 –0.0023Service, security occupations 0.30 –0.0027Security personnel 0.07 –0.0006Journalists, interpreters, librarians 0.15 –0.0013Artists and associated occupations 0.21 –0.0019Doctors, pharmacists 0.18 –0.0016Other health service providers 0.18 –0.0016Social work professions 0.24 –0.0021Teachers 0.16 –0.0014Intellectual and scientific occupations not mentioned elsewhere 0.22 –0.0020Personal care 0.30 –0.0027Hospitality service providers 0.51 –0.0045Domestic services providers 0.33 –0.0029Cleaning occupations 0.36 –0.0031Workers with unidentified occupations 0.27 –0.0024Workers without more detailed designation 0.29 –0.0026Source: Calculations of the ifo Institute based on the IAB LIAB dataset.

48

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50

Global Economic Dynamics (GED)

About the Authors

Prof.GabrielJ.Felbermayr,Ph.D.,DirectoroftheIfoCenterforInternationalEconomics

ifoInstitut–LeibnizInstituteforEconomicResearchattheUniversityofMunich

SybilleLehwald ifoInstitut–LeibnizInstituteforEconomicResearchattheUniversityofMunich

Dr.UlrichSchoof

ProjectGlobalEconomicDynamics(GED),BertelsmannStiftung,Gütersloh.

MirkoRonge

ProjectGlobalEconomicDynamics(GED),BertelsmannStiftung,Gütersloh.

About the Project “Global Economic Dynamics” (GED)

The Bertelsmann Stiftung established the project “Global Economic Dynamics” (GED) to shed

morelightonthegrowingcomplexityofinternationaleconomicrelationships.Byusingstate-of-

the-art toolsandmethodsformeasuring, forecastinganddisplayingthedynamicsof theworld

economy, theproject aimsatmakingglobalization, its economiceffects aswell as itspolitical

consequencesmoretransparentandtangible.

ContactBertelsmannStiftung

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Global Economic Dynamics (GED)

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ifoInstitut–LeibnizInstituteforEconomicResearchattheUniversityofMunich

Poschingerstraße5

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ContactProf.GabrielJ.Felbermayr,Ph.D.

Phone +498992241428|[email protected]|www.cesifo-group.de/felbermayr-g

SybilleLehwald

Phone +498992241250|[email protected]|www.cesifo-group.de/lehwald-s

54

Imprint

Imprint

©2013BertelsmannStiftung

BertelsmannStiftung

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Germany

www.bertelsmann-stiftung.de

Responsible

Dr.UlrichSchoof

Autors

Prof.GabrielJ.Felbermayr,Ph.D.

SybilleLehwald

Dr.UlrichSchoof

MirkoRonge

Translation

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SanCarlos,California,USA

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