thesis final copy · 1 determinants of seaports container throughput between the hamburg-le-havre...

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Determinants of seaports container throughput between the Hamburg-Le-Havre range and the Mediterranean ports:

Infrastructure investments and their returns on TEU

Master ThesisDepartment Urban, Port & Transport Economics

Antonios FachouridisStudent number 447351Supervisor Mr. Martijn Streng

Rotterdam, March 2018

TableofContents

1.Introduc4on

1.1ProblemDefini-on....................................................................................................................................................................................8

1.2ResearchApproach...................................................................................................................................................................................8

1.3ThesisContribu-on...................................................................................................................................................................................8

2.LiteratureReview

2.1DeterminantsofPortContainerThroughput............................................................................................................................10

2.2EffectofinfrastructureinvestmentsonPorts’Performance............................................................................................10

2.3Theore-calFramework........................................................................................................................................................................12

2.4InfrastructureInvestments.................................................................................................................................................................12

2.4.1Mari%mePortInfrastructureInvestments...................................................................................................................122.4.2RoadTransportInfrastructureInvestments................................................................................................................132.4.3RailTransportInfrastructureInvestments...................................................................................................................132.4.4AirTransportInfrastructureInvestments.....................................................................................................................14

2.5SuperstructureInvestments.............................................................................................................................................................14

2.5.1TransportEquipmentInvestments..................................................................................................................................14

3.Methodology

3.1TimeSeriesData......................................................................................................................................................................................16

3.2CrossSec-onalData..............................................................................................................................................................................17

3.3PanelData...................................................................................................................................................................................................17

3.4PooledData................................................................................................................................................................................................18

3.5ModelChoice.............................................................................................................................................................................................18

3.6Es-ma-onConcerns..............................................................................................................................................................................19

3.6.1LagEffects......................................................................................................................................................................................193.6.2UnitRootTest...............................................................................................................................................................................193.6.3Co-Integra%onandErrorCorrec%onModel................................................................................................................203.6.4FixedEffects..................................................................................................................................................................................203.6.5RandomEffects............................................................................................................................................................................203.6.6Hausmantest...............................................................................................................................................................................21

4.EmpiricalAnalysis

4.1Assump-onsandLimita-ons...........................................................................................................................................................22

4.2DataDefini-ons........................................................................................................................................................................................23

4.2.1DependedVariable.....................................................................................................................................................................234.2.2ExplanatoryVariables..............................................................................................................................................................234.2.2.1Mari%mePortsInfrastructure.........................................................................................................................................234.2.2.2RoadTransportInfrastructure........................................................................................................................................244.2.2.3RailTransportInfrastructure............................................................................................................................................244.2.2.4AirTransportInfrastructure.............................................................................................................................................244.2.2.5TransportEquipment...........................................................................................................................................................24

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4.3DataAnalysis.............................................................................................................................................................................................25

4.3.1PortContainerThroughput(TEU)....................................................................................................................................254.3.2Mari%mePortInfrastructureInvestments...................................................................................................................264.3.3RoadTransportInfrastructureInvestments.................................................................................................................274.3.4RailTransportInfrastructureInvestments...................................................................................................................284.3.5AirTransportInfrastructureInvestments.......................................................................................................................294.3.6TransportEquipmentInvestments....................................................................................................................................29

4.4Sta-onarityTest.......................................................................................................................................................................................30

5.RegressionResults

5.1FixedEffects................................................................................................................................................................................................32

5.1.1Hamburg-LeHavreRange..................................................................................................................................................325.1.2MediterraneanRange............................................................................................................................................................34

5.2RandomEffects.........................................................................................................................................................................................35

5.2.1Hamburg-LeHavreRange..................................................................................................................................................355.2.2MediterraneanRange............................................................................................................................................................37

5.3FixedEffectsorRandomEffects......................................................................................................................................................38

6.DiscussionoftheResults

6.1SynopsisofHypothesisResults.......................................................................................................................................................40

6.2QualityofTransportInfrastructureandSpa-alSynergies................................................................................................41

6.3ComparisontotheLiterature...........................................................................................................................................................42

6.4PolicyImplica-onsandFurtherConsidera-ons.....................................................................................................................42

6.4.1InfrastructureInvestmentGaps.........................................................................................................................................436.4.2ContainerThroughputasanIndexofReturnstoInfrastructureFinancing...............................................436.4.3Contribu%nginaBalancedRegionalPolicy...............................................................................................................43

6.4.4Macro-Construc%ngRequiresMacro-Financing ......................................................................................................45

7.Conclusions

7.1Recommenda-onsforFurtherResearch...................................................................................................................................46

8.Bibliography

.........................................................................................................................................................................................................................................48

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ListofTablesTable1:LiteratureSynopsis...........................................................................................................................................................................11Table2:HypothesisSynopsis........................................................................................................................................................................15Table3:IPStest.....................................................................................................................................................................................................31Table4:RegressionResults:Hamburg-LeHavrerange,FixedEffects..................................................................................32Table5:RegressionResults:Mediterraneanrange,FixedEffects.............................................................................................34Table6:RegressionResults:Hamburg-LeHavrerange,RandomEffects...........................................................................36Table7:RegressionResults:Mediterraneanrange,RandomEffects.....................................................................................37Table8:Hausmantest......................................................................................................................................................................................38Table9:HypothesisSynopsisresults........................................................................................................................................................40Table10:ReturnofinvestmentsonTEU................................................................................................................................................41

ListofGraphsGraph1:Containerthroughputthrough-me:Hamburg-LeHavreandMediterraneancountries....................26Graph2:Mari-mePortInfrastructureInvestments........................................................................................................................27Graph3:RoadTransportInfrastructureInvestments.....................................................................................................................27Graph4:RailTransportInfrastructureInvestments........................................................................................................................28Graph5:AirTransportInfrastructureInvestments...........................................................................................................................29Graph6:TransportEquipmentInvestments........................................................................................................................................30Graph7:TransportInfrastructureInvestmentsandQualityofInfrastructure..................................................................42

Acronyms

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Organiza-onofEconomicCoopera-onandDevelopmentGrossDomes-cProductFreeTradeZoneTrans-EuropeanTransportTwenty-footEquivalentUnitsInterna-onalTransportForumAugmentedDickey-FullerErrorCorrec-onModelIm-Pesaran-Shin

OECDGDPFTZTEN-TTEUITFAFDECMIPS

:::::::::

Acknowledgements

“And if you find her poor, Ithaca will not have fooled you. Wise as you will have become, so full ofexperience,youwillhaveunderstoodbythenwhattheseIthacasmean.”Constan-neP.Cavafy.

Thepresentstudycons-tutesmyfinalefforttowardscomple-ngmymasterstudies attheUrban,PortandTransportEconomicsattheErasmusUniversityRoberdam.As everyjourneycomestoanend,a newchapterin the course of life opens, rich in adventures and challenges. I would like to take advantage of theopportunity of this shortsec-on to express mygra-tudeto the peoplewhosupportedme fulfilling thisobjec-ve.

Firstandforemost,all my teachers attheErasmusUniversityRoberdamandespeciallymy supervisor,mr.Mar-jnStreng,forhisindispensablefeedbackandguidancewhichallowedmetostructuremywri-ngandsharpenmythoughts.Furthermore,Iwouldliketothankthesecondreader,mr.BartKuipersforhiseffortandinterestinreviewingandchallengingmystudy.

Inaddi-on,Iwouldliketothankms.AnkieSwakhovenforhercontribu-onintheimprovingoftheoverallthesislayoutandshowingmetheposi-veside,whenlifegavemehard-me.

Lastbutnot least, I would liketo thankmyparentsGeorgios andSofia,mybrotherPetrosandmysisterEfhymia,fortheirimmensesupportofeverykindandbeingconstantlybymyside.

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Abstract

Thepresentpaperinves-gatestheeffectoftheinfrastructureinvestmentsontheportcontainerthroughputbetweentwoportranges:TheHamburg-LeHavrerangeversus theMediterraneanrange.TheHamburg-LeHavre range includes Germany, Netherlands, Belgium and France. The Mediterranean range includesPortugal, Spain, Italy, Croa-a, SloveniaandGreece. Theinfrastructure investmentsinclude infrastructureinvestments in the four modes of transport, namely Air, Rail, Road and Sea and investments insuperstructures,namelyTransportEquipment.Paneldataanalysishas beenmade,withdependedvariablethe TEU (port container throughput) and independent variables the investments in infrastructures andsuperstructures.Theresultsshowthatboth infrastructureand superstructureinvestmentshavea posi-veandsignificanteffectontheportcontainerthroughput.Therearealsosizabledifferencesinthereturns ofinvestmentsbetweentheHamburg-LeHavreandtheMediterraneanrange.Policyrecommenda-onsundertheprismofthefindingsof thispaperaremade:thecontainerthroughputasaSpecialPurposeVehicleforfinancingtransportinfrastructureprojectsanditspoten-aluseforabalancedregionalpolicyarediscussed.

Key words: TEU, port container throughput, mari%me port infrastructure investments, road transportinfrastructure investments, rail transport infrastructure investments, air transport infrastructureinvestments,transportequipmentinvestments.

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1.Introduc4on

Containerthroughputisthemost importantanddirectfactor for evalua-ngthecompe--vestrengthof aport (Lechao Liu&Gyei-Kark PARK, 2011). Addi-onally,port container throughputfiguresare of utmostimportance for the policy making of the port and regional authori-es. The current port containerthroughputexplanatorymodelsmakelibleornouseoftheinfrastructureinvestments’influenceontheportperformance.

1.1ProblemDefini4on

Theexis-ngmodelsarebasedonfigureswhichgeneratetradedemand,suchaspopula-on, income,GDP,infla-on,tradevolumeandtransportcosts.Theaben-onpaidontheinvestmentsinportinfrastructuresispoorandtheinvestmentsinhinterlandinfrastructureareabsentfromthecurrentliterature.

However,thereareseveralpapers whichunderlinethedecisiveeffectofinfrastructureinvestmentsonportperformance. For example, Grossmann et al. (2006) state that, except for port infrastructure, highlysignificant for the compe--veness of ports are the infrastructure links from theport to the hinterlandmarketbypipelines,rail,waterways,roadandair.

Furthermore,eventhoughinfrastructureinvestmentsintheothermodesoftransport(air,road,rail)playacri-cal role in the compe--veness of a port, they have not been tested for improving the predic-veperformanceofthecontainerthroughputforecas-ngmodelsyet.Thereforethecurrentforecas-ngmodelsare of limited power and the significance of the infrastructure investments, since not en-modeled andmeasured,asaresultareunderes-matedbytheportauthori-esandtherelevantpar-es(liners,shippers,regionalauthori-es,terminaloperatorsetc).

1.2ResearchApproach

ThemissinggapfromthepreviousliteratureistobefilledwithvariablesfoundintheOECDdatabase.Itisassumed,thatthefollowing elementscover thevastmajorityof theinfrastructureinvestmentswhichcanpoten-allyinfluenceportefficiency:

InfrastructureInvestments

• Mari-mePortInfrastructureInvestments

• RoadTransportInfrastructureInvestments

• RailTransportInfrastructureInvestments

• AirTransportInfrastructureInvestments

SuperstructureInvestments

• TransportEquipmentInvestments

1.3ThesisContribu4on

Asaforemen-oned,thereisa gapinthecurrentforecas-ngliterature:researchershavebeenexploringwaystoimprovetheexplanatorypoweroftheportmodelsontheonehand,andontheotherhand,theevidentinfluenceoftheinfrastructureandsuperstructureinvestmentsontheportcontainerthroughput,whichhasnotbeeninves-gatedyet.

***Theobjec%veofthepresentpaper,is toconnecttheinfrastructureandsuperstructure investmentswiththeportscontainerthroughput,quan%fyandhighlightitssignificancefortheperformanceoftheports.***

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Asaresultof thisconnec-on,theaccuracyof thecurrentcontainerforecas-ngmodelsis expectedtobeimproved. Container forecas-ng is of utmost importance for the long term policy making of the portauthori-es, liners, shippers and terminaloperators. Therefore, except for its academic contribu-on, thepresentpapermightaswellassistthestrategicdecisionmakingoftheaforemen-onedagents.

Finally,thepresentedmodelscanbeusedbythegovernmentalorganiza-onsandportauthori-es,inordertopredictthe returnsof infrastructureinvestments in termsof TEU.Withan es-ma-onof theaveragetransportedgoods’valuepercontainer,theamountofrevenuesintaxes,customdu-es,clearanceetceteracanbecalculated.Thiscould bean interes-ng insight for governments,localauthori-esand investmentfundswhich couldmonetarize the returns of infrastructure investments aswell, in order to assess theabrac-venessofinfrastructureinvestments.

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2.LiteratureReview

In this sec-on, the literature review will be presented, for both studies that have not included theinfrastructureinvestmentseffectontheportcontainerthroughput,andarecentstudywhichhasincludedtheinvestmentsinmari-meportinfrastructure.Addi-onally,theeffectoftheinfrastructureinvestmentsontheports’performanceasperthecurrentstudieswillbereviewed.

2.1DeterminantsofPortContainerThroughput

Oneof themostpopularexplanatoryvariablesof theportcontainerthroughput istrade.Seabrookeetal(2003),predictedthecontainerthroughputoftheportof HongKong.Theyusedthevalueofrawmaterials,fastmovingconsumergoodsandchemicals.IntheirstudyfortheimportandexportsforSpanishports1 ,Coto-Millán,Baños-Pino&Castro (2005) foundofsignificantexplanatorypowerthepricesof imports andexports,thepricesofmari-metransportservicesandtheworldandna-onalincome.

Chou,ChubandLiang(2008)usedthevariables ofGDP,worldGDP,exchangerate,popula-on,infla-onrate,interestrateandfuelpriceforpredic-ng theimportandexportsofcontainerthroughputof BangkokPort.LechaoLIU,Gyei-KarkPARK(2011)foundthatporttariffs,terminalstoragecapacity,berthlength,directcallliner, transshipment, hinterland’sGDP,hinterland’simport-exportvolume,FTZ(FreeTradeZone) areaandgovernmentinvestmentinfluencesignificantlythecontainerthroughputoftheKoreanandChineseports.

YasmineRashed(2015)concludedthat,theEU18industrialconfidenceindicatorandtheindex of industrialproduc-onareleadingthecontainerthroughputattheportofAntwerp.AtaHawaM.(2015)study,ForeignDirect Investment,Popula-on andGDPwere chosen as theprinciple components to analyze the port’scontainerthroughput.

Pi-noot Kotcharat (2016) developed a forecas-ng model, predic-ng the container throughput in theChabangPort.Heusedasexplanatoryvariablestheemployment,privateinvestmentindexandthebunkerpriceinSingapore.

Lastbutnot least,asmen-oned in theintroduc-on,onlyonestudywasfoundtoemploymari-meportinfrastructureinvestmentforpredic-ngtheportcontainerthroughput.ArjunMakhecha(2016),usedamongothers Sea Infrastructure Investments for explaining the container throughput of theHamburg-Le Havrerange.However, for someof theportsof theregion,thespecificvariablewasfoundeither insignificantorwith nega-ve coefficient. According to the author, these results indicate that the investments in portinfrastructureintheregionarenotop-mal.

2.2EffectofInfrastructureInvestmentsonPorts’Performance

Except for the aforemen-oned variables, and asmen-oned in the introduc-on, even though there is aplethora of studiesstressingthecri-caleffectof infrastructureinvestmentsontheperformanceofaport,libleresearchhasbeenmadeinordertoverifyandquan-fytheirsignificanceindeterminingportcontainerthroughput.

According toOosterhaven & Knaap (2003), investmentsin thehinterland infrastructurecan improvethecompe--veness of a port.Meersman etal. (2008) emphasizethat successful ports belong to successfulsupply chains. JoseL. Tongzon (2009) inves-gated the forwarders’ port choicecriteriaand found thatadequateinfrastructure (roads and railways) playa crucialrole to forwarders’ decisionschoosing aport.Adequateinfrastructure,decreasesport conges-onandshipwai-ng -me,allows fora quickerandsaferfreight movement and enables the ships to achieve economiesof scale, resul-ng in reduced mari-metransportcosts.

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BartW.Wiegmansetal.(2008),men-onsportphysicalandtechnicalinfrastructureasa portchoicecriteriabyshipping lines.Thisincludesnau-calaccessibility,terminalinfrastructureandequipmentandhinterlandaccessibility(intermodalinterfacefortrucks,rail,bargeandshort-sea).

Lusthaku (2017),men-ons that the determinant of efficiency of port opera-ons is connected with thefactorsofcosts and-me,whicharecorrelatedwiththehinterlandinfrastructuresuchasinlandwaterwayconnec-ons,roadandraillines.Lustakuconcludesthatbothhinterlandandportinfrastructureinfluencetheportperformanceandcontainerthroughput.

Lustaku’sanalysisisqualita-veandtheeffectsofhinterlandinfrastructureonportperformanceareratherblurred. Even thought that generally admibed, the investments in infrastructure posi-vely impact thecompe--venessofports,many-mestheirreturnoninvestmentsisques-oned(TshepoKgareetal,2011).

InTable1,theLiteratureSynopsisispresented,togetherwiththesignoftheEffectoftheVariable(s)ontheTEU.

Table1:LiteratureSynopsisTable1:LiteratureSynopsisTable1:LiteratureSynopsis

Author(s) Variables Effect

Coto-Millán,Baños-Pino&Castro,

2005ImportPrices,CostofMari%meTransportServices -

Coto-Millán,Baños-Pino&Castro,

2005Na%onalIncome,WorldIncome +

Seabrookeetal.,2003 TradeValue,Popula%on +

Gosasangetal.,2010 ExchangeRates,InterestRates,Infla%onRates-

Hui,Seabrooke&Wong,2004 Tradewiththebiggestpartner +

YasmineRashedetal.,2015

BusinessConfidenceIndicator,EconomicSen%mentIndicator,IndustrialConfidenceIndicator,IndexofIndustrialProduc%on,TotalExportVolumeIndex,TotalImportVolumeIndex

+

LechaoLIU,Gyei-KarkPARK,2011 PortTariff-

LechaoLIU,Gyei-KarkPARK,2011TerminalStorageCapacity,BerthLength,DirectCallLiner,Transshipment,Hinterland’sGDP,Hinterland’sImportExportVolume,FTZArea,GovernmentInvestment

+

Pi-nootKotcharat,2016 Employment,PrivateInvestmentIndex +

Pi-nootKotcharat,2016 Bunkerprice-

Chou,ChubandLiang,2008GNP,GNPpercapita,wholesaleGDP,agriculturalGDP,industrialGDPandserviceGDP +

ArjunMakhecha,2016 Quaylength-

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Table1:LiteratureSynopsisTable1:LiteratureSynopsisTable1:LiteratureSynopsis

Author(s) Variables Effect

ArjunMakhecha,2016Terminalarea,Laborproduc%vityindex,GDP,Import,Export,SeaInfrastructureInvestments,ContainerTraffic(calling)

+

HawaMohamedIsmael,2015 FDI,Popula%on,GDP+

Inthefollowingsec-on,thevariousinfrastructureinvestmentswillbeanalyzedandtheirexpectedeffectontheportcontainerthroughput.Finally,relevanthypothesiswillbemade.

2.3Theore4calFramework

Portefficiencyvarieswidelyfromcountry tocountryandspecificallyfromregiontoregion(T.Rajasekar&Malabika Deo, 2014).Therefore,itis crucial to testtheinfrastructureinvestmentsreturnsonports’TEUbetweengeographically andculturallydifferentregions.Asperthecurrentstudy,thechosenport regionsare within the European Union, namely the Hamburg-Le Havre and the Mediterranean region. TheHamburg-LeHavreregionincludesGermany,Netherlands,BelgiumandFrance. TheMediterranean regionIncludesPortugal,Spain,Italy,Slovenia,Croa-aandGreece.Thetworegions,competeeachothernotonlyforthemarketoftheEuropeanhinterland,butalsoforinfrastructureinvestmentfundingfromtheEuropeanUnion(TEN-Tnetwork),whichmakestheanalysisevenmoreinteres-ng.

The variables which will be used are assumed to cover the majority of a country’s infrastructureinvestments.Twogroupsof infrastructureinvestmentshavebeendis-nguished:Infrastructureinvestmentsandsuperstructureinvestments, adis-nc-onthathasbeenmadebyTheWorldBankGroup (2000). Ontheir reportfor theprivatesectorandtheinfrastructurenetwork, docksand storageyards aredefined aport’sinfrastructureandsheds,fueltanks,canesandvancarriersassuperstructure.

2.4InfrastructureInvestments

IntheInfrastructureInvestmentsthefollowingelementshavebeenincluded:

• Mari-mePortsInfrastructure

• RoadTransportInfrastructure

• RailTransportInfrastructure

• AirTransportInfrastructure

2.4.1Mari4mePortInfrastructureInvestments

KazutomoAbeandJohnS.Wilson(2009)stressedtheimportanceoftradecostsandtheirnega-veeffectonthe interna-onal trade flows. Their regression analysis recommends that the expansion of portinfrastructurewouldceterisparibusreducetheimportchargesandtradecostspaidbytheimporters,whichwouldinturnreducethetransportcostsandleadtoatradeexpansionthroughtheports.

According toTshepoKgareetal.(2011), theobsoletetradeinfrastructureofSouthAfricaresultedinportandterminalconges-onwhichinturngainedthereputa-onofaninefficientport.Long portandterminalwai-nghours increasethelead-meandpipelinecosts ofasupplychain,thereforelimitthechancesoftheporttobeusedandfinallyrestrictitspoten-alcontainerthroughput.

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NilGüler(2003),suggested thatthebenefitsof portdevelopmentprojectsaretransportsaving costs andreducedturn-round-me.“A port investmentmay, depending on thesitua-on,ease conges-on,increaseproduc-vity,reduceshipwai-ng-mecost,cargo-handlingcostandfinallyreduceoveralltransportcosts.”Therefore,portinfrastructureinvestmentsincreasethecompe--venessof aportand itsability to abractcontainerflows.

Hypothesis 1:Mari%me PortInfrastructure Investmenthas aposi%veandsignificant effecton the TEU forbothportranges.

2.4.2RoadTransportInfrastructureInvestments

Roadtransport representsthe largestshare,or75%,of thetotal inland freighttransportedwithintheEU(Eurostat, 2017). Road transport in this study refers to the transporta-on of containers through trucks.According toaresearchconductedby CharlesK.etal. (2012), 98%of theques-onnairesresponded thatroadtransportinefficienciesaffectportperformance.Poorroadnetworkisfoundtoresultinaslowuptakeofcargointothehinterland.Asaresult,highertruckturn-round-meandthereforehighcargodwell-meattheportoccurs.Thestudysuggeststhatinvestmentsinroadinfrastructuresshouldbemobilizedforamoreefficientroadnetwork.

Anotherstudy, one conductedbyStephenG. et al.(2012) foundthatroad transport improvementshavedirect effects related to transport cost savings, which are correlated with the accessibility changes.Intui-vely,roadtransportinfrastructure(suchasstreetwidening,longerroadnetwork,tunnels andbridges)canwellresultinshortertransport-mesbetweentheportandthehinterlandandincreasetheflexibilityofthesupplychain.

Concluding,theeffectofroadtransportinfrastructureontheperformanceofasupplychainisposi-veandsizable.Theeffectontheperformanceofportsisexpectedtobesimilar.

Hypothesis2:RoadTransportInfrastructureInvestmentshaveaposi%veandsignificanteffectontheTEUforbothportranges.

2.4.3RailTransportInfrastructureInvestments

Comparingwithroad, theshareof railintranspor-ng freighthas remainedstableataround18.5%since2011 (Eurostat, 2017). Railway transport has advantages of high carrying capacity, lower influence byweather condi-ons, and lower energy consump-on (M. Sreenivas & T. Srinivas, 2008). AmbweneMwakibete’s(2015)study,revealedthatrailtransportplaysasuperbroleintheportperformanceofDaresSalaam. Reduc-on of port conges-on, increase of cargo traffic and lower logis-cs costs are among thecontribu-onsofrailinfrastructureonportperformance.

ErickL.etal.(2012),intheirstudyforthebenefitsofrailandportintegra-on,arguethatportconnec-vitywiththehinterlandisofutmostimportanceforthecompe--venessofa portandrailconnec-onsplaythisrole.Inorderthecompe--vestrengthoftheLa-nAmerica’sporttobeimproved,theysuggestinvestmentsintrack,rollingstockandequipment.

Infrastructureinvestmentsinrailcanbemobilizedforhighspeedrailaswell.AccordingtoOECD/ITF(2014),thebenefits ofhighspeedrailareconges-onreliefandfasterservices.Therefore,inves-nginimprovingthespeedofrail,results indecreasedtransit-me.Eachdayintransitises-matedtocostbetween0.6%and2.6%ofthevalueoftradedgoods(Hummels&Schaur,2012).

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Concluding,Rail TransportInfrastructureInvestments areexpectedtohaveaposi-veandsignificanteffectontheabilityofaporttoabractcontainerflows.

Hypothesis 3:RailTransportInfrastructure Investmentshaveaposi%veandsignificanteffectontheTEUforbothportranges.

2.4.4AirTransportInfrastructureInvestments

Moving cargoviaairlinesis-mesfasterthanbyrail,roadorsea.However,airisusuallyusedforveryhighadded value commodi-es, such as technological advancements, which are-me sensi-ve, andgoods ofstrategicimportance.Addi-onally,containersastheones loadedontheships,arenotcarriedvia airplanes.Finally,airlinesareusedmainlytotransferpassengers,ratherthangoods.

Fromthis perspec-ve,unlikelywiththeMari-mePort,RoadandRailTransportInfrastructure,thereisnotanobviouseffectofapoten-alAirTransportInfrastructureInvestmentontheperformanceoftheseaports.However,Air TransportInfrastructure Investmentshaveadirecteffectona country’sGDP.According toasurvey conducted by Invervistas (2015), a consul-ng company with extensive exper-se in avia-on,transporta-on,and tourism,theEuropeanairportscontributetotheemploymentof 12.3millionpeopleearning€356billioninincomeannually,andgenerate€675billioninGDPeachyear,equalto4.1%ofGDPofEurope.

Thereforeitis expectedAirTransportInfrastructureInvestmentstostrengthentheeconomicac-vityoftheEuropeanUnionandtherefore,indirectlyincreasingtheportscontainerthroughput.

Hypothesis 4:AirTransportInfrastructureInvestmentshave aposi%ve andsignificanteffectontheTEU forbothportranges.

Hypothesis5:TheInfrastructureInvestmentshavejointlyaposi%veandsignificanteffectontheTEUforbothportranges.

2.5SuperstructureInvestments

IntheSuperstructureInvestments,thefollowingelementshavebeenincluded:

• TransportEquipmentInvestments

2.5.1TransportEquipmentInvestments

Transport Equipment is the key elementwhich makesthevarious infrastructures func-on internally andexternally with each other. In thepaper of Grossman et al. (2006), apart from the port infrastructure,superstructures(tractor units, container gantries, cranes, et cetera) arealsoa key factor influencing thecompe--veposi-onofaportandthusthevolumeofcargohandledinthatport.AspertheOECDdefini-onglossary, intheassetsof thetransportequipment,seaport,rail,roadandairporttransportequipmentareincluded.Finally,thedefini-ons oftheinfrastructureandsuperstructureelements willbefurtherelaboratedintheEmpiricalAnalysissec-on.

Hypothesis 6: TransportEquipmentInvestments haveaposi%ve andsignificanteffectontheTEU forbothportranges.

Hypothesis 7: Infrastructure Investments and Superstructure Investments have jointly a posi%ve andsignificanteffectontheTEUforbothportranges.

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Table2providesasynopsisoftheformulatedhypothesis.

Table2.HypothesesSynopsisTable2.HypothesesSynopsis

Hypothesis1 Mari%mePortInfrastructure Investmenthasaposi%veandsignificanteffectonthe TEUforbothportranges.

Hypothesis2 RoadTransport Infrastructure Investments haveaposi%ve andsignificanteffect ontheTEUforbothportranges.

Hypothesis3 RailTransportInfrastructureInvestmentshaveaposi%veandsignificanteffectontheTEUforbothportranges.

Hypothesis4 AirTransportInfrastructureInvestmentshaveaposi%veandsignificanteffectontheTEUforbothportranges.

Hypothesis5 Infrastructure Investments have jointly aposi%ve andsignificanteffect onthe TEU forbothportranges.

Hypothesis6 Transport Equipment Investments have a posi%ve andsignificanteffecton the TEU forbothportranges.

Hypothesis7 Infrastructure Investments and Superstructure Investments have jointly aposi%ve andsignificanteffectontheTEUforbothportranges.

Inthefollowingsec-on,thevariouseconometricmodelsaspertheportcontainerthroughputforecas-ngmodelsavailablewill bediscussedandthechoiceofthemostappropriateonefortheneedsofouranalysiswillbeargued.

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3.Methodology

Whatthepresentpaper inves-gatesis therela-onshipbetween thetransport infrastructureinvestmentsandtheportcontainerthroughput.Thereforewearelookingforcausesandtheircorrespondingeffects.Themostpopularmethodinthescien-ficliteratureinordertofindifandtowhatextenddoesone(ora group)of variables-elements explain aphenomenon is thecause and effectmodels. In ourcase, the causeandeffectmodel is to assist in theunderstanding of the rela-onshipbetween theaforemen-onedtransportinfrastructureinvestmentsontheportcontainerthroughputofthedefinedportranges.

AccordingtoM.Jansen(2014),thismethodis morespecificallyrelatedtoa par-cularportthatcanbeseenasazone.Aninstanceofacauseandeffectmodelis theregressionanalysis.A regressionanalysis helpstodescribedata,es-mateparametersandverifyrela-onsthatarisefromeconomiclogic.

Thereareseveraltypesof regressionanalysis:-meseries,crosssec-onal,paneldataandthepooleddata.Thechoiceof the proper one isbased on the structureof the collected data,which in turn is builtanddependsontheresearchques-ons.Theaforemen-onedtypesof regressionanalysiswillbeelaboratedinthefollowingsec-on,andtheonewhichbestdescribesourownresearchques-onsistobechosen,inordertoperformtheempiricalanalysispart.

3.1TimeSeriesData

Timeseriesdatais theobserva-onsofonevariable,TEU,throughthe-me(months,quarters,years).Timeseries datacanbeunivariateandmul-variate.A-meseriesunivariatemodelwouldabempttoexplaintheTEUvaria-onofasinglecountrythrough-me,usingpreviousobserva-ons ofthegivenvariable(TEU)ofthegivencountry(auto-regressive). The general formula can be described as:

TEUt=β0+βTEUt-j+εt(1)

t=%me,j=lagged%me,β0=constant,β=effectofpreviousyearsTEUonthefollowingyears,εt=errorterm.

Alterna-vely, a mul-variate -me seriesmodel, would abempt to explain theTEU varia-on of mul-plecountriesthrough-me,usingpreviousobserva-onsofthegivenvariable(TEU)ofmul-plecountries.

TEUit=β0+βTEUi,t-j+εit(2)

t=%me,j=lagged%me,i=country,β0=constant,β=effectofpreviousyearsTEUonthefollowingyears,εt=errorterm.

Logically considering, TEUvaria-onexplana-on,employingobserva-onsof thevariable inques-onitself,doesnotprovidecauseandeffectsinsightswhatsoever.Timeseries modelisnotanappropriatemodelforourcase.

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3.2CrossSec4onalData

Ontheotherhand,crosssec-onaldatasets,areobserva-onsatasinglepoint in-me,forseveralen--es(countries).Cross sec-onalmodelsaredividedinunivariateandmul-variatemodels.Anexampleofacrosssec-onalunivariatemodelwouldhavebeenthestudyingoftheTEUvaria-onatasinglepointin-me,formul-plecountries,employing asinglevariableother than TEU (forexampletheinvestmentsinmari-meportinfrastructure).

TEUi=β0+βInvestmentsinmari-meportinfrastructurei+εi(3)

i=country,β0=constant,β=effectofInvestmentsinmari%meportinfrastructureonTEU,εi=errorterm.

Alterna-vely,amul-variatecrosssec-onalmodel,wouldhavebeenthestudyingof theTEUvaria-onatasingle point -me, formul-plecountries, employing mul-ple variables other than TEU (for example theInvestmentsinmari-meportinfrastructure,GDP,etc).

TEUi=β0+βInvestmentsinmari-meportinfrastructurei+cGDPi+...+Variablen,i+εi(4)

i=country,β0=constant,β=effectofInvestmentsinmari%meportinfrastructureonTEU,c=effectofGDPonTEU,Variablen=lastexplanatoryvariable,εi=errorterm.

Even though crosssec-onalmodels are closer to a causeand effect analysis, thedimensionof -me ismissing.Therefore,itmighthappenthatarela-onshipwhichappears tobesignificantforonepointin-me,tobeinsignificantforadifferentpointin-me.

3.3PanelData

Whatdifferen-ates paneldatafromcrosssec-onaldata,isthatthesamecrosssec-onalunitsarefollowedover -me. Panel datasets (or longitudinal data) are structured by observing inmul-ple points in -me(months, quarters, years), mul-ple en--es (countries). Therefore, panel data are characterized by twodimensions,-me(t=1,...,T)anden-ty(i=1,...,N).

An advantage of the panel data comparing to the cross sec-on, is that they allow the researcher toinves-gatetheimportanceofthelageffectsof theexplanatoryvariables onthebehaviorof thedependedvariable(TEU).This informa-oncanbecrucial, sincemanyeconomicpoliciescanbeexpectedtohaveanimpactonlyaferacertainperiodof-mehaspassed(Wooldridge,2012).

Panelmodelsaredivided inunivariate andmul-variatemodels.Anexampleof aunivariatepanelmodelwouldbethestudyingof theTEUvaria-onatmul-plepoints in-me,formul-plecountries,employing asinglevariableotherthanTEU(forexampletheinvestmentsinmari-meportinfrastructure).

17

TEUi,t=β0+βInvestmentsinmari-meportinfrastructurei,t+αi+uit(5)

t=%me,i=country,β0=constant,β=effectofInvestmentsinmari%meportinfrastructureonTEU,αi=unobserved,%meconstantfactorsthataffectTEUi,t,ui,t=%mevaryingerrorwhichrepresentsfactorsthatchangeover%meandaffectTEUi,t.

Alterna-vely,a mul-variatepanelmodel,would havebeen thestudying of theTEU varia-onatmul-plepoints in -me, for mul-ple countries, employing mul-ple variables other than TEU (for example theInvestmentsinmari-meportinfrastructure,GDP,etc).

TEUi,t=β0+βInvestmentsinmari-meportinfrastructurei,t++cGDPi,t+...+Variablen,i,t+αi+ui,t(6)

t=%me,i=country,β0=constant,β=effectofInvestmentsinmari%meportinfrastructureonTEU,Variablen=lastexplanatoryvariable,αi=unobserved,%meconstantfactorsthataffectTEUi,t,ui,t=%mevaryingerrorwhichrepresentsfactorsthatchangeover%meandaffectTEUi,t.

3.4PooledData

Pooleddataaremostlyusedinsurveys,whererandompeopleorspecialists (dependingontheshortofthestudy) areasked(interviewsorques-onnaires)abouttheirintui-on,regardingtheeffectofvariousfactorson the behavior of a certain phenomenon. An example of a pooled model would have been askingspecialists,suchasforwarders,liners,shippinglinesetcetera,regardingthepoten-aleffectsofInvestmentsinmari-meportinfrastructure,GDPetcontheTEUvaria-on.

Pooledmodeling is-me and resourcesdemanding. Addi-onally, thereliability of theanalysisresults arevulnerabletotheextenttheintui-onofthespecialistsiscorrect.Concluding,pooleddataarenota propermethodtoanswerourresearchques-ons.

3.5ModelChoice

Asmen-onedbefore,-meseriesmodelsdonotprovidecauseandeffectsinsights.AccordingtoYasmineR.(2016),-meseriesmethodologydoesnotallowmeasuringthedynamics betweendifferentports andactorsandsuggeststhepaneldatamodeltobeofvalueaddedtohercontainerthroughputmodelingstudyfortheportofAntwerp.

Addi-onally,PeterF.etal(2011),havinganalyzedthedeterminantsofefficiencyofBrazilianports,suggestthesuperiorityofthepaneldataapproachincomparisontotheonesingle-periodorcross-sec-onalmodels.

Finally,VonckIndraetal.(2015),intheirstudyfordevelopingaportforecas-ngtool,concludethat,amongthevariousregressionmodels,paneldataregressionmodelemergesasanavailablesolu-onforforecas-ngcomplexphenomena.Therefore,themostappropriatemodelfortheneedsofthecurrentpaperisthepaneldataanalysis.

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Inthefollowingsec-on,a discussionregardingtheaspectstobeconcernedbeforeexecu-ngtheregressionmodelsfollows.

3.6Es4ma4onConcerns

3.6.1LagEffects

Infrastructureinvestments,beitthesea,air,roadorrail,consumea significantperiodof-meun-ltheyarecomplete. Thedesign, planning, construc-on, comple-onandbeginning of func-on of an infrastructureobjectmight lastfrommonthstoyears.Furthermore, itmightrequire someaddi-onal -meeven to seetheireffectontheeconomyoverall.

Asmen-onedintheliteraturereview,ArjunMakhecha(2016)foundtheinvestmentsinportinfrastructureinsignificant in explaining the port container throughput. However, Arjun Makhecha did not test hisregression models for lag effects, in casethe investments in port infrastructure happens to significantlyaffecttheportcontainerthroughputonlyafersomeyears.

For example, the Channel Tunnel, which connects Folkestone, Kent (UK), with Coquelles, Pas-de-Calais(France)viarail,took20yearsinorderthereturnsof investments tomaketheprojectprofitable(OECD/ITF,2014).Engineering-wise, according toOECD(2011),major infrastructurecantake10-20years toplananddevelop.O. Pokornáand D. Mocková(2001), men-onof a construc-on period between 2-7 years of atransportinfrastructureprojecttocomplete.

An infrastructure development might take some years in order to be officially delivered for func-on.However, real life examples (Egna-a Odos/ Egna-aMotorway,Greece) show that projects are par-allydelivered for use,unofficially, as soonas that part of the project is safeand func-onal. Therefore, it isexpectedthatinfrastructureinvestmentsmightwellbeginimpac-ngtheeconomyandthecompe--venessofaport,notnecessarilytheveryfirstyearandaswellasbefore10years.

3.6.2UnitRootTest

Aunitroottest,testswhethera -meseriesvariableissta-onary(nounitroot) ornot.If a-meseries issta-onary,thatmeansthatits sta-s-calproper-es(mean,variance,covariance)donotvarywith-me.Ifa-meseriesisdescribedasasta-onary(rejec-ngthepresenceofa unitroot),theneconomicshockswouldhavetransitoryeffects.Alterna-vely,ifa-meseriesisdescribedas non-sta-onary,shockshavepermanenteffects(Verbeek,2008).Theimplica-onsofnon-sta-onarityarethefollowing:

• Invalidregressionresults• Existenceofspuriousregression• OLSassump-onofnoserial-autocorrela-onviolated

Chou etal. (2007),presentedtheimportanceof thenon-sta-onary rela-onshipbetween thevolumesofcontainers and themacroeconomicvariables. They conclude that, not taking careof thenon-sta-onaryrela-onship between the TEU (depended variable) and the explanatory variables (investments ininfrastructure)leadstoanoveres-ma-onoftheforecastedcontainerthroughputvolumes.

Various sta-s-cal tests exist in order data sta-onarity to be diagnosed. The most popular one in thecontainer throughput forecas-ng literature is theAugmentedDickey-Fuller (ADF) test. Aferapplying thetest, if any variablesare non-sta-onary on their levelbut sta-onary on their 1stdifference,haveto beturnedinto logarithmsbeforeused in theequa-on (YasmineRashed,2015). This methodology has beenappliedforthemajorityoftheportcontainerthroughputforecas-ng,withthemostrecentexamplesDraganD.etal.(2014)andYasmineRashedetal. (2015)andPi-nootKotcharat(2016).Inthepresentpaper, forprac-calissuestheIm–Pesaran–Shintestwillbeperformed2.

192Bothtestshavebeenperformedyieldingthesameresults.HowevertheAFDtestishardertopresentinasimpleoutputtable.

A major drawback of the first differencing is that, the model only considers the short-run adjustmentsrelatedtohowthedifferencein onevariablecorrelateswiththechangesintheother(M.Jansen, 2014).Hence,itignoresthelong-termrela-onshipbetweenvariables(Huietal.,2004).

3.6.3Co-Integra4onandErrorCorrec4onModel

Co-integra-on isanecessaryproperty inamodelinorder therela-onshipswithinanequilibriumtohavelonglas-ngmeaning.Inageneraldescrip-onofco-integra-onweconsiderthefollowingregressionmodeloftwoI(1)(sta-onaryinthefirstdifference)variables,YtandXt:

Yt=α+bXt+ut(7)

ut=theerrorterm,

Yt=thedependedvariable,Xt=theexplanatoryvariable.

IfYtandXtco-integrate,thenthe:

ut=Yt-α-bXt(8)

isasta-onaryprocesswithmeanzero.

In the co-integra-on the residuals are sta-onary with mean zero and there is a long run equilibriumrela-onship between Yt and Xt. Thenull hypothesis is the existence of no co-integra-on. In theno co-integra-on,therela-onshipbetweenthedependedandtheexplanatoryvariablesisvalidintheshortrunandnotinthelongrun.

To avoid thisissue,theerror correc-onmodel (ECM) canbeperformed.Theerrorcorrec-onmodelisadifferencedmodel that contains an error correc-on term,whichpredicts short-term adjustments of thedependentvariable.Themainideaof ECM isthat,apossibledisequilibriumintheshortruncorrectsitselfover-me,crea-nga paththatfluctuatesaroundthelong-runequilibrium(Huietal.,2004).Therefore,theECM isonlyvalidif thereisatruerela-onshipbetween thevariablesinthelong-run(VanDorsseretal.,2011).Aco-integra-ontestcanbeusedtotestwhethersucharela-onshipexists(Huietal.,2004).

Inthefollowingsec-on,twoapproachesofthepaneldataanalysis,thefixedeffects andtherandomeffectswillbediscussed.

3.6.4FixedEffects

Inthefixedeffectsmodel(orwithines-mator),therela-onshipbetweenthedependedvariable(TEU)andthe explanatory variables (infrastructure investments) within an en-ty (Hamburg - Le Havre andMediterraneanrange) isinves-gated.Eachen-ty(range)has itsownuniquecharacteris-cswhichinfluencethedependedvariables(TEU).Inthefixedeffectsmodelitisassumedthatsomethingwithinanen-tymaybias theresultsoftheregressionanalysis.Inthismodel,the-meinvariantcharacteris-cs(αi)oftheen--es(suchasloca-onforexample),aredifferencedaway,andthereforeitis possibletoes-matetheneteffectoftheexplanatoryvariablesonthedependedvariable.

3.6.5RandomEffects

Alterna-vely,intherandomeffectsmodels,itisassumedthatthevaria-onbetweentheen--esisrandomandnotsystema-callyrelatedwiththeexplanatoryvariableswhichareincludedinthemodel,aretheyfixedornot.Abenefitoftherandomeffectsmodelcompara-velywiththefixedeffectsis thatonecaninclude

20

-me invariant characteris-cs (for example loca-on). Therefore, the αi term is not difference away butes-mated.

3.6.6Hausmantest

TheHausmantestisaspecifica-ontestwhichassists choosingbetweentheFixedandRandomEffects.TheHausmantestassessesif the-me-invarianteffects(αi)arecorrelatedwiththeindependentvariables.Thenullhypothesisstatesthatthedifferencebetweenthecoefficients isnotsystema-candthattheRandomEffectsis consistentandmoreefficientandshouldbepreferred.Ifwerejectthatnull,theRandomEffectsisinappropriateandtheFixedEffectsshouldbeusedinstead.

21

4.EmpiricalAnalysis

Inthissec-on,theassump-onsnecessaryforsimplifyingouranalysis andthelimita-onswhichdonotharmthegeneraliza-onoftheresultsarediscussed.Addi-onally,thewaythecollecteddata havebeenmeasuredaspertheOECDglossary3 willbedescribed,datadescrip-onwillfollowandbasictrendsobservedwillbediscussed.

4.1Assump4onsandLimita4ons

Firstly,theportcontainerthroughputintermsofTEUhasbeenchosenasadependedvariable.Thereasonisthat,themajorityofthetransportedgoodsaretransferredwithincontainers.Asof2009,approximately90%of non- bulkcargoworldwideis movedby containersstacked ontransport shipsandthis trend isbeingconstantly increased(Ebeling,2009).It isassumedthat inthefuture,moreandmorecommodi-es willbecontainerized(Havenga&VanEeden,2011)causingthecontainershippingindustrytoexpandevenmore.Havenga&VanEeden(2011) also predict amaturing inthecontaineriza-ontrend, simplybecauseon agiven point in -me every commodity that can be shipped in containers shall be shipped in containers.Therefore,itis assumedthatcontainersarethemostrepresenta-vemeasurementfortheevalua-onofthebusynessofaport.

Secondly, inlandwaterwaysarenotincludedin theanalysis. Inlandwaterways,especially long rivers,arepresentonly in theHamburg-Le-Have range.Thereforetheanalysisbetweenthetworegionswouldhavebeenunequal.Aferall,inlandwaterwaysrepresentonlya6%ofthetotalinlandtransportfreight(Eurostat,2017).

What is more, we assume port efficiency, compe--veness and performance to have interchangeablemeaning,anditises-matedas theannualnumberofTEUsperport.Throughouttheanalysis,thetermsofcontainerorcargothroughputandTEUareusedinterchangeably.

Addi-onally,TEUfiguresaccountforbothloaded andemptycontainers.Theemptycontainersonaveragerepresentalessthan5%ofthetotalcontainerthroughput(loadedandempty)of thechosencountries,aspertheEurostatdataandarenotexpectedtoinfluencetheanalysisoutcomes.

Furthermore,theavailabledataseriesoftheinfrastructureinvestments refertotheperiod1987-2015.Itisassumed that this period is sufficient in order to apply a reliable panel analysis. Time-wise, this paperincorporatesthe2008economiccrisisin themodelingprocess,whichhasthebenefitof providing insightintotheimpactofthecrisis.

Moreover,thetwomajorportsof FranceareLeHavreinNorthandMarseilleinSouth.Sincethereisnotacleargeographicalperspec-veoftheports ofthiscountry,weassumethatculturally,Franceis closertotheWesternEuropeancountries.Therefore,Franceis classifiedintheHamburg-LeHavrerangeofportsandnotintheMediterraneanrangeofports.

Exceptfortheassump-ons, limita-onsarepresentinthepresentstudyaswell.Twomainlimita-onsareconsidered:Firstly,itisrecognizedthattherearemorevariables thatexplaintheportcontainerthroughput(suchas GDP,Imports-Exports,Income,Popula-onetc)aspertherelevantliterature.Howevertheyhavenotbeenincludedinthepresentpaper,sincethis paperinves-gatessolelytheeffectsoftheinfrastructureandsuperstructureinvestmentsontheportcontainerthroughput.Asaresult,ourmodelisexpectedto sufferfromomibedvariablebias.

22

3AccordingtotheOECDglossaryofsta-s-calterms,theterminologysourcecomesfromtheGlossaryforTransportSta-s-cs,preparedbytheIntersecretariatWorkingGrouponTransportSta-s-cs–Eurostat,EuropeanConferenceofMinistersofTransport(ECMT),UnitedNa-onsEconomicCommissionforEurope(UNECE).

Finally,infrastructuremaintenancehasnotbeenaccountedfor,sincetherehavebeen incompletedataintheOECDdatabase.Therefore,theinfrastructureinvestmentsofthecurrentpaperrefertotheconstruc-onofnewinfrastructures.

4.2DataDefini4ons

Inthis sec-on,thedependedandindependentvariableswillbediscussedinordertodefinewhichphysicalinfrastructureelementsdoeseachvariableinclude.This isanimportantpartoftheanalysis,sinceitprovideswithvisibilityofthecons-tutesoftheemployedvariables.

4.2.1DependedVariable

Thedependedvariable,portcontainerthroughput,ismeasuredinTEU(Twenty-footEquivalentUnits).OneTEUrefers tooneintermodalcontainer,astandard-sizedmetalbox,whichcanbereadilytransportedamongdifferenttransportmodes,suchastrains,trucksandships.Therearedifferentsizesof containers,withthemostpopularalterna-veofthe20feet,beingthe40feetlongcontainer.

4.2.2ExplanatoryVariables

Theexplanatory variablesare thevariableswhich areassumed toexplainthevaria-onof thedependedvariable.Inthepresentpaper,theexplanatoryvariablesconsistofthetransportinfrastructureandtransportsuperstructure. The transport infrastructures include the Mari-me Port Infrastructure, Road TransportInfrastructure, RailTransport InfrastructureandAirTransportInfrastructure.ThetransportsuperstructureincludestheTransportEquipment.According to the OECD glossary, expenditure on new construc-on, extension of exis-ng infrastructure,includingreconstruc-on,renewalandmajorrepairsareincludedinthefollowingdataseries,exceptforthetransportequipment.

4.2.2.1Mari4mePortsInfrastructure

IntheMari-mePortsInfrastructurethefollowingelementsareconsidered:

• Mari-mecoastalarea• Totalportlandarea• Portstorageareas• Containerstackingareas• Roads• Railtracks• Passengerterminals• Quays• Ro-Roberth• Portcranes• Portrepairfacili-es• Lightsandlighthouses• Radars• VTS(VesselTrafficSystem)• Bunkeringfacili-es• Porthinterlandlinks

23

4.2.2.2RoadTransportInfrastructure

IntheRoadTransportInfrastructurethefollowingelementsareconsidered:

• Roads• Pavedroads• Roadnetworks• Carriageways• Lanes

4.2.2.3RailTransportInfrastructure

IntheRailTransportInfrastructurethefollowingelementsareconsidered:

• Tracks• Sidings• Lines• Railwaynetwork• Railwaysta-ons• Halts• Terminals

4.2.2.4AirTransportInfrastructure

IntheAirTransportInfrastructurethefollowingelementsareconsidered:

• Terminals• Runways• Taxiways• Check-infacili-es• Gates• Carparking• Facili-esprovidedwithintheairportforconnec-onwith:Rail,Metro,Bus.

4.2.2.5TransportEquipment

IntheTransportEquipmentthefollowingelementsareconsideredbytransportmode:

Mari-metransportequipment:

• Seagoingvessels• Drycargoseagoingbarges• Ships(Boat)• Merchantships• Drybulkcarriers• Containerships• Specializedcarriers• Passengerships• Cruiseships• Automa-cIden-fica-onSystems• Containers

24

Roadtransportequipment:

• Trucks• Trailers• Lorries• Cars• Buses

Railtransportequipment:

• Railwayvehicles• Highspeedrailwayvehicles• Locomo-ves• Trac-vevehicles• Lightrailmotortractors• Railcars• Passengerrailwayvehicle• Freightwagons• Reefers• Containers• Pallets• Ro-Ros

Airtransportequipment:

• Cargoaircrafs• Passengeraircrafs• Comboaircrafs

4.3DataAnalysis

Inthepresentsec-on,ourvariables willbepresentedaspercountryandanoverallpicturewilldiscussed.Addi-onally,asmen-onedintheEs%ma%onConcerns sec-on,aunitroot testwill takeplace,as theIm–Pesaran–Shintestwillbeapplieduponourvariables.

4.3.1PortContainerThroughput(TEU)

As observed from Graph 1, from 1970 un-l 1995, the country with thehighest number of containerscircula-onwastheNetherlands.Afer thatperiod, Germany becamethe country-champion in abrac-ngcontainers,followedbytheforeverincreasinggrowththeoftheSpanishports.Eversince,theNetherlandscomesinthethirdposi-on,followedbyBelgiumandItalywhichhavebeeninterchangingposi-onsthroughthe-me.

Anotherimportantno-ceisthefactthat,afer1995,thereisawideninggapbetweenthetop5countries(Germany, Spain, Netherlands, Belgium, Italy) versus the bobom 5 countries (France, Greece, Portugal,Slovenia,Croa-a).ExceptforFrance,which istheonlycountryfromtheHamburg -LeHavrerangeinthebobomgroup,therestofthecountriesbelongintheMediterraneanrange.

Interes-ngly, fromageographicalperspec-ve, thegap between the Hamburg - Le Havrerange and theMediterranean rangeintermsof containerthroughput isbeingsteadilydecreasedafer1970.Whereasinthebeginning of 1970theportcontainer throughputra-owasalmost5fortheHamburg-LeHavrerangecountriesto1fortheMediterraneanrangecountriesonaverage,in2015thera-owasalmost1to1(sub-Graph1).

25

0.0

1.0

2.0

3.0

4.0

5.0

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Portcontainerthroughputra9oHamburg-LeHavre/Mediterranean

Graph1.Datasource:OECD.Design:Author

Finally,theeconomiccrisisof2008isalsodepicted.As seenintheredrectangularinsideGraph1,thecrisisinfluenceddeeperthetop5countriescompara-velywiththebobom5countries.Thetop5countries lostapproximately1-2millionTEUsperyear,whereasthebobom5werealmostunaffected,exceptforGreece.Thecountriesofbothrangesneededapproximately2yearstorecoverfromthecrisis,withallofthehavingfullyrecoveredtheircontainerthroughputsafer2011.NetherlandsandBelgiumwerethecountrieswhichfaster recovered, whereasGreecewas thecountrywherethe impactof the crisis lasted for the longestperiodof-me.

4.3.2Mari4mePortInfrastructureInvestments

According toGraph2,from1985un-l2010, themari-meportinfrastructurehasbeenincreasing.A smalldropisobservedaferthe2010,whichmightbeabributedtothe2008economiccrisis andtherestric-vespendingbyeachcountry.

Spainis thecountrywiththehighestspendinginmari-meportinfrastructureinvestments,followedbyItaly.GermanyfollowsandaferthatBelgiumandNetherlands.Therestofthecountries barelyappearinGraph2.It isworth no-cing thattheHamburg- LeHavrecountries, thatis Germany,Belgium,NetherlandsandFrance, seem to follow a more steady spending policy for port infrastructure that in theMediterraneanrangecountries,especiallyItalyandSpain.

Finally,asitcan beseenin thesub-Graph 2 themari-meport infrastructure investmentsra-obetweenHamburg-LeHavreandtheMediterraneanrangehasbeenfluctua-ng.In1995theHamburg-LeHavrerangespent almost twice as much on average than theMediterranean range, whereas un-l 2000 the trendreversedandinvestmentsinportinfrastructureintheMediterraneanrangebecamesignificantlyhigherthanintheHamburg-LeHavrerange.

26

Containerthroughputthrough4meHamburg-LeHavreandMediterraneanrangecountries

4.3.3RoadTransportInfrastructureInvestments

AccordingtoGraph3,from1985un-l2010,theroadtransportinfrastructurehasbeenincreasing.A smalldropisobservedaferthe2010,whichmightbeabributedtothe2008economiccrisis andtherestric-vespendingbyeachcountry.

Contrary to mari-me port infrastructure investments, Spain comes only forth in the road transportinfrastructure. The first place is possessed by Germany, which is nevertheless renowned for the longhighways(autobahn).ThesecondandthirdcountriesareFranceandItalycorrespondingly.Therestofthe

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Belgium

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RoadTransportInfrastructureInvestmentsra<oHamburg-LeHavre/Mediterraneanrange


27

Mari4mePortInfrastructureInvestments


countriesbarelyappear inGraph3. It isworthno-cing that,thebiggest thecon-nentalsizeof acountry(Germany,France,Spain,Italy),thehighertheinfrastructureinvestmentsinroadtransport.

Finally,as itcanbeseeninthesub-Graph3,theroadtransport infrastructure investmentsra-obetweenHamburg-LeHavreandtheMediterraneanrangehasbeenfluctua-ng.In1995theHamburg-LeHavrerangespentalmostthree-mesasmuchonaveragethantheMediterraneanrange,whereasun-l2007thetrendreversed and investments in road transport infrastructure in theMediterranean range became slightlyhigherthanintheHamburg-LeHavrerange.Afer2010, thera-o turnedbackhigheron theHamburg-LeHavrerange.

4.3.4RailTransportInfrastructureInvestments

According toGraph4,from1985un-l2010, therailtransportinfrastructurehasbeenincreasing.A smalldrop isobserved afer 2010,whichmight be abributed to the2008economic crisis and the restric-vespendingbyeachcountry.Thistrendisconsistentwiththemari-meportandroadinfrastructureaswell.

Francewastheonlycountrywhichdidnotdecreasethespending in railinfrastructureduring2011-2015,butalmostdoubledit, compara-vely with thepreviousperiodof 2006-2010. Following France,Germanycomessecond, followedby ItalyandSpain.Itis worthno-cing that,BelgiumandNetherlandshavebeenconstantly inves-ng more in rail transport infrastructure. Thismight be reflec-ng the effortof the twocountries,whichaccommodatetwoof thebiggestEuropeancontainerports(RoberdamandAntwerp),totackleroadtrafficconges-on,beberhinterlandconnec-vityandlessCO2emissions.

Finally, as it can beseen in thesub-Graph4therail transport infrastructureinvestments ra-obetweenHamburg-LeHavreandtheMediterraneanrangehasbeenfluctua-ng.In1995theHamburg-LeHavrerangespentalmostfour-mesasmuchonaveragethantheMediterraneanrange,whereas un-l2007thetrendreversedand investmentsinrail transport infrastructure in theMediterraneanrangebecamesignificantlyhigherthanintheHamburg-LeHavrerange.Afer2010, thera-o turnedbackhigheron theHamburg-LeHavrerange.

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1987 1990 1995 2000 2005 2010 2015

RailTransportInfrastructureInvestmentsra>oHamburg-LeHavre/Mediterraneanrange


28


4.3.5AirTransportInfrastructureInvestments

According toGraph5, from1990un-l2010, theair transport infrastructurehas been increasing.A smalldropisobservedaferthe2010,whichmightbeabributedtothe2008economiccrisis andtherestric-vespending by each country.Thistrendis consistentwith themari-meport, roadand rail infrastructureaswell.

Spainis thecountrywiththehighestfluctua-onswhenitcomestoairtransportinfrastructureinvestments.Betweeneitherdecade2001- 2005,or 2006 - 2010, Spainspentmorethan the rest of theother yearscombined. Germany on the other hand, is the country which spends almost equal amount of eurosthroughoutthe-me.SimilartoGermanyintermsoftheamountinvestedinairtransportpolicies,isFrance.

Finally, as it can beseen in thesub-Graph 5, the air transport infrastructureinvestments ra-obetweenHamburg-LeHavreandtheMediterraneanrangehasahigherfluctua-oncomparingwithmari-meport,roadandrail.Between1990and2000,theHamburg-LeHavrerangespentalmosttwice-mesasmuchonaverage than theMediterranean range, whereas un-l 2004 the trend reversed and investments in airtransportinfrastructureintheMediterraneanrangebecameslightlyhigher than intheHamburg-LeHavrerange.Afer2005,thera-oturnedbackhigherontheHamburg-LeHavrerange.

4.3.6TransportEquipmentInvestments

According to Graph 6, from 1985 un-l 2010, the transport equipment investments have been steadilyincreasing.A smalldropisobservedaferthe2010,whichmightbeabributedtothe2008economiccrisisandtherestric-vespendingbyeachcountry.This trendisconsistentwiththemari-meport,road,railandairinfrastructureaswell.

Germanyisthecountrywiththehighesttransportequipment investments,followedbyFrance,SpainandItaly. Almostallthecountriesareobservedtohavebeenconstantlyincreasingtheirspendingintransportequipmentassetsinasteadyrate.

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3.0

1990 1995 2000 2005 2010 2015

AirTransportInfrastructureInvestmentsra:oHamburg-LeHavre/Mediterraneanrange


29


Finally,asitcanbeseeninthesub-Graph6thetransportequipmentinvestmentsra-obetweenHamburg-LeHavreandtheMediterraneanrangehasahigherfluctua-ngcomparingwithmari-meport,roadandrail.Between1980and1990,theHamburg-LeHavrerangespentalmostfour-mesasmuchonaveragethantheMediterranean range, whereas afer 1993 this trend decreased. Unlike the transport infrastructureinvestments (sea, road, rail, air), superstructure investments (transport equipment investments) in theMediterranean range never surpassed the transport equipment investments in the Hamburg - Le Havrerange.

Overall,themajorityoftheinvestmentsareplacedinthesuperstructureinvestments,namelythetransportequipment. Between thetwo ranges, during 2006 - 2010almost € 200 billion were spent on transportequipment.Thisamountshouldnotbeof asurprise,since,asmen-onedintheDataDefini%onssec-on,itincludestransportequipmentelementsfromallthetransportmodes(sea,road,rail,air).

Regarding the infrastructure investments, road transport infrastructureinvestments reached €50 billion,followedbyrailwith€26billion,€5.5billionmari-meportandairwith€4.5billionforthesameperiod.Addi-onally,thelast decade, theinfrastructureinvestment gap betweenthetworangeshassignificantlydecreased. Concluding the data descrip-on, the port container throughput (TEU) moves in the samedirec-onasthetransportinfrastructureinvestments,showingafirstposi-verela-onship.

4.4Sta4onarityTest

Asmen-oned in theEs%ma%onConcernssec-on,our variableshavetobeexamined forsta-onarity.Onetest which can beperformed in order to test for sta-onarity is the Im-Pesaran-Shin (IPS) test. The zerohypothesisof theIm–Pesaran–Shintestistheexistenceof aunit-rootmeaningthatourvariablesarenon-sta-onary.Thenullhypothesis isrejectedwhenthep-valueislessthanthe5%significancelevel.InTable3,theIPStestresultsispresentedforboththelevelandfirstdifferenceofourvariables.

AsitcanbeobservedfromTable3,allofourvariables arenon-sta-onaryonthelevel,sincealloftheirp-valuesarehigherthan5%significancelevelandthereforethenullhypothesiscannotberejected. Ontheotherhand,all of ourvariables aresta-onaryontheirfirstdifference,sinceallof theirp-valuesarelowerthan5%significancelevelandthereforetheirnullhypothesisisrejected.Themethodologicaldirec-onsoftheportcontainerthroughputliteraturehaveso-farbeenconfirmed.

-

50,000

100,000

150,000

200,000

250,000

1985-1990 1991-1995 1996-2000 2001-2005 2006-2010 2011-2015

Millionsof€

Time

TransportEquipmentInvestments

Slovenia

Croa,a

Italy

Portugal

Greece

Spain

France

Germany

Netherlands

Belgium

-

1.0

2.0

3.0

4.0

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

TransportEquipmentInvestmentsra=oHamburg-LeHavre/Mediterraneanrange


30


Table3:IPStestTable3:IPStestTable3:IPStestTable3:IPStestTable3:IPStestTable3:IPStestTable3:IPStest

Variables

IPS(level)IPS(level)

Result

IPS(firstdifference)IPS(firstdifference)

ResultVariables t-sta4s4c p-value Result t-sta4s4c p-value Result

Portcontainerthroughput(TEU)

0.0130 0.5052 non-sta%onary -9.1035 0.0000 sta%onary

Mari4mePortInfrastructureInvestments 1.2748 0.8988 non-sta%onary -2.3514 0.0094 sta%onary

RoadTransportInfrastructureInvestments 0.7675 0.7786 non-sta%onary -3.5395 0.0002 sta%onary

RailTransportInfrastructureInvestments 1.0352 0.8497 non-sta%onary -4.4803 0.0000 sta%onary

AirTransportInfrastructureInvestments 3.3094 0.9995 non-sta%onary -3.0457 0.0012 sta%onary

TransportEquipmentInvestments -0.9154 0.1800 non-sta%onary -8.1623 0.0000 sta%onary

Therefore,thepanelregressionsaretobebasedonthefirstdifferenceofthevariables.Asmen-onedintheEs%ma%onConcernssec-on,amajordrawbackofthefirstdifferencingis that,themodelonlyconsiderstheshort-runadjustmentsrelatedtohowthedifferenceinonevariablecorrelateswiththechangesintheother(M.Jansen,2014).Therefore,aferrunningtheregressions,itisnecessarytoperformco-integra-ontest,inordertotest if theequilibrium rela-onshipsbetween thedepended(TEU)andtheindependentvariables(infrastructureandsuperstructureinvestments)arevalidonlyintheshortrun,butonthelongrunaswell.Theresultsoftheco-integra-ontestsarereportedattheendoftheregressionresultstables.

31

5.RegressionResults

Inthepresent sec-on theregressionresultswillbepresented. ItbeginswiththeFixedEffectsregressionresultsof theHamburg-LeHavrerangeandcon-nuewiththeMediterraneanrange.Similarly,theRandomEffects follow. Various combina-ons have been tried, regarding different lagged effects. The presentedmodelsaretheoneswherethecombina-onofthelaggedeffectsyieldthehighestR-squared.

Ingeneral,alltheinvestmentsintransportinfrastructureandsuperstructurehaveaposi-veandsignificanteffect on the port container throughput of both ranges. However, in some examples (Road and RailTransport InfrastructureInvestments) theeffect ontheTEU is insignificantor the signs arenega-ve. Yet,theirbehaviorchangesasthemodelisenriched(H5orH7).As men-onedintheLiteratureReviewsec-on,importantexplanatoryvariables(kindlyrefer to Table1) arenotincludedinthemodel. This resultsinanomibedvariablesbias,leadingtounexpectedsignsorsignificantvariablestoappearasinsignificant.

Inregressionanalysis ithappensthat,avariablethatwasnotsignificanttobecomesignificantaferaddingrelevantvariablestothemodel.Theoriginallynotsignificantvariablewassignificantlyassociatedwiththeomibedvariableandreflectstheeffectoftheomibedvariableinaddi-ontoitsowneffect(plussomeotherunobservedvariables).When theomibedvariables(Table1) areaddedintothemodel, theoriginally notsignificantvariablenolongercapturesthepar-aleffectoftheomibedvariablebutnowreflectsthe"true"effect of that variable. It turns out to be sta-s-cally significantly associated with the port containerthroughput.Concluding,ourindependentvariablesdonotpresentunexpectedbehaviorintermsofsignsorsignificance.

5.1FixedEffects

5.1.1Hamburg-LeHavreRange

AsperTable4,ingeneral,all theinvestmentsintransportinfrastructureandsuperstructurehaveaposi-veand significanteffecton theportcontainer throughputof theHamburg - LeHavrerangeof ports. SincemodelH7is themostcompletemodelamongH1-H7,theH7modelwillbeinterpreted.Forprac-calreasonsand in order to avoid repe--on, the regression output of themodels H1-H6 will be not be described.However,themodelsH1-H6areinterpretedinthesameway.

Morespecifically,Mari-mePort InfrastructureInvestments haveaposi-veandsignificanteffectat the1%level on the port container throughput of theHamburg - LeHavre range as per the H7 model. If theMari-mePortInfrastructuresintheHamburg-LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby3,620TEUonaverage,afer5yearsceterisparibus.

Table4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerangeTable4:RegressionResults:Hamburg-LeHavrerange

Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)Dependedvariable:TEU(portcontainerthroughput)

FixedEffectsFixedEffectsFixedEffectsFixedEffectsFixedEffectsFixedEffectsFixedEffectsFixedEffects

ExplanatoryVariables

H1 H2 H3 H4 H5 H6 H7

L5.Mari4mePortInfrastructureInvestments

.00293***(.000659)

.00532***(.000949)

.00362***(.000998)


-.00009(.00009)

32





H1 H2 H3 H4 H5 H6 H7

L3.RailTransportInfrastructureInvestments

.00029***(.00009)

L5.AirTransportInfrastructureInvestments

.00105*(.00061)


.000685***(.000181)

.000382**(.000187)


.00151**(.000649)

.00121**(.000585)

L4.RoadTransportInfrastructureInvestments

.000375*(.000195)

.000222(.00018)


.000096***(.0000143)

.00008***(.0000244)

R-squared 0.215 0.01 0.0942 0.066 0.488 0.286 0.607

Prob>F 0.0000 0.0000 0.0022 0.0953 0.0001 0.0000 0.0000

Cointegra4ontest 0.2773 0.0001 0.0019 0.0777 0.4285 0.0000 0.3176

Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.Standarderrorsinparentheses.

***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1***p<0.01,**p<0.05,*p<0.1

“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.“L”standsforlageffects.ForexampleL5.Mari-mePortInfrastructureInvestmentsaccountsfortheeffectofthevariableafer5years.

H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.H1standsforHypothesis1etc.

RailTransportInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe5%level on theportcontainer throughput of the Hamburg - Le Havre range as per the H7 model. If the Rail TransportInfrastructuresintheHamburg -LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby382TEUonaverage,afer7yearsceterisparibus.

AirTransportInfrastructureInvestmentshavea posi-veand significant effectat the5% levelontheportcontainer throughput of the Hamburg - Le Havre range as per the H7 model. If the Air TransportInfrastructuresintheHamburg -LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby1,210TEUonaverage,afer6yearsceterisparibus.

Road Transport Infrastructure Investments haveaposi-veand insignificant effect on the port containerthroughput of the Hamburg - Le Havre range as per the H7 model. However, the Road TransportInfrastructureInvestments haveaposi-veandsignificanteffectatthe10%levelaspertheH5model.Iftheaforemen-onedinvestmentsintheHamburg-LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby375TEUonaverage,afer4yearsceterisparibus.

33

TransportEquipmentInvestments haveaposi-veandsignificanteffectontheportcontainerthroughputoftheHamburg-LeHavrerangeatthe1% in theH7model.If theTransportEquipmentInvestments intheHamburg-LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby80TEUonaverage,effec-vethesameyear,ceterisparibus.

5.1.2MediterraneanRange

AsperTable5,ingeneral,all theinvestmentsintransportinfrastructureandsuperstructurehaveaposi-veandsignificanteffectontheportcontainerthroughputoftheMediterraneanrangeports.SincemodelH7isthemost completemodelamong H1-H7,theH7modelwill be interpreted.For prac-cal reasons and inordertoavoidrepe--on,theregressionoutputofthemodels H1-H6will benotbedescribed.However,themodelsH1-H6areinterpretedinthesameway.

Table5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:MediterraneanrangeTable5:RegressionResults:Mediterraneanrange




H1 H2 H3 H4 H5 H6 H7


.000707***(.0001518)

.0007595***(.0001707)

.0007246***(.0001827)


-.000089**(.0000315)


-.000146**(.0000685)

-.0003087***(.000082)

-.0002841***(.000088)


.000661**(.0002994)


.0008314***(.0002339)

.000706***(.0002547)


.0001165**(.0000395)

.0001092***(.0000424)


.0000596***(.0000168)

.0000474**(.0000192)

R-squared 0.1271 0.0477 0.0311 0.0590 0.3432 0.0956 0.4001

Prob>F 0.0000 0.0051 0.0338 0.0300 0.0000 0.0006 0.0000






34

Tobeginwith,Mari-mePortInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelontheportcontainerthroughput of theMediterraneanrangeasper theH7model. If theMari-mePortInfrastructures intheMediterraneanregionincreasebyEUR1million, theportcontainerthroughputwillincreaseby725TEUonaverage,afer2yearsceterisparibus.

RailTransportInfrastructureInvestmentshaveanega-veandsignificanteffectatthe1%levelontheportcontainerthroughputof theMediterraneanrangeaspertheH7model.If theRailTransportInfrastructuresInvestments in theMediterranean region increaseby EUR 1million, theport container throughputwilldecreaseby284TEUonaverage,afer3yearsceterisparibus.

AirTransportInfrastructureInvestmentshavea posi-veand significant effectat the1% levelontheportcontainerthroughputoftheMediterraneanrangeaspertheH7model.IftheAirTransportInfrastructuresintheMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwill increaseby706TEUonaverage,afer4yearsceterisparibus.

RoadTransportInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelontheportcontainerthroughputof theMediterraneanrangeaspertheH7model. If theaforemen-onedinvestmentsintheMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby110TEUonaverage,afer3yearsceterisparibus.

TransportEquipmentInvestments haveaposi-veandsignificanteffectontheportcontainerthroughputofthe Mediterranean range at the 5% in the H7 model. If the Transport Equipment Investments in theMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby47TEUonaverage,effec-vethesameyear,ceterisparibus.

5.2RandomEffects

5.2.1Hamburg-LeHavreRange

AsperTable6,ingeneral,all theinvestmentsintransportinfrastructureandsuperstructurehaveaposi-veandsignificanteffectontheportcontainerthroughputoftheHamburg-LeHavrerangeports.SincemodelH7is themostcompletemodelamongH1-H7,theH7modelwillbeinterpreted.Forprac-calreasons andinordertoavoidrepe--on,theregressionoutputofthemodels H1-H6will benotbedescribed.However,themodelsH1-H6areinterpretedinthesameway.

Tobeginwith,Mari-mePortInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelontheportcontainerthroughputoftheHamburg-LeHavrerangeaspertheH7model.IftheMari-mePortInfrastructuresintheHamburg -LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby3,040TEUonaverage,afer5yearsceterisparibus.

Rail Transport Infrastructure Investments have a posi-ve and insignificant effect on the port containerthroughputoftheHamburg-LeHavrerangeas pertheH7model.However,theRail TransportInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelaspertheH5model.Iftheaforemen-onedinvestmentsintheHamburg- LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby565TEUonaverage,afer7yearsceterisparibus.

AirTransportInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe10% levelontheportcontainer throughput of the Hamburg - Le Havre range as per the H7 model. If the Air TransportInfrastructuresintheHamburg -LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby1,000TEUonaverage,afer6yearsceterisparibus.

35



RandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffects


H1 H2 H3 H4 H5 H6 H7


0.00291***(0.000656)

0.00490***(-0.00101)

0.00304***(0.000990)


-0.0000874(0.0000851)


0.000269***(0.0000938)


0.0010628*(0.0006095)


0.000565***(0.000189)

0.0002547(0.0001813)


0.00127*(0.000693)

0.00100*(0.000597)


0.000244(0.000203)

0.000104(0.000178)


0.0000953***(0.0000142)

0.0000913***(0.0000239)

R-squaredwithin 0.0099 0.0099 0.0942 0.0664 0.4811 0.2864 0.5986

R-squaredbetween 0.0335 0.0335 0.9522 0.2384 0.8908 0.0227 0.0117

R-squaredoverall 0.0092 0.0092 0.0775 0.0658 0.3981 0.2779 0.5714

Wald-chi2 19.69 1.05 8.23 3.04 24.47 45.21 47.99






Road Transport Infrastructure Investments haveaposi-ve yet insignificant effect on the port containerthroughputoftheHamburg- LeHavrerange.However,this variablehasyieldedsignificantcorrela-onwiththeTEUintherestoftheregressions.

TransportEquipmentInvestments haveaposi-veandsignificanteffectontheportcontainerthroughputoftheHamburg-LeHavrerangeatthe1% in theH7model.If theTransportEquipmentInvestments intheHamburg-LeHavreregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby91TEUonaverage,effec-vethesameyear,ceterisparibus.

36

5.2.2MediterraneanRange

AsperTable7,ingeneral,all theinvestmentsintransportinfrastructureandsuperstructurehaveaposi-veandsignificanteffectontheportcontainerthroughputoftheMediterraneanrangeports.SincemodelH7isthemost completemodelamong H1-H7,theH7modelwill be interpreted.For prac-cal reasons and inordertoavoidrepe--on,theregressionoutputofthemodels H1-H6will benotbedescribed.However,themodelsH1-H6areinterpretedinthesameway.

Table7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:MediterraneanrangeTable7:RegressionResults:Mediterraneanrange


RandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffectsRandomEffects


H1 H2 H3 H4 H5 H6 H7


.0007669***(.0001607)

.0007936***(.0001797)

.0007483***(.0001883)


-.000089**(.0000314)


-.0000967(.0000712)

-.0002328**(.0000839)

-.0002181**(.0000879)


.000789**(.0002989)


.0008422***(.000247)

.0006975**(.0002636)


.0001065**(.0000417)

.0001007***(.0000438)


.0000693***(.0000171)

.0000549***(.0000196)

R-squaredwithin 0.1271 0.0477 0.0311 0.0590 0.3349 0.0956 0.3932

R-squaredbetween 0.8619 0.0431 0.7946 0.8261 0.2408 0.9678 0.6704

R-squaredoverall 0.1290 0.0389 0.0072 0.0775 0.2931 0.1180 0.3749

Wald-chi2 22.77 8.06 1.84 6.97 36.36 16.46 41.38






37

Tobeginwith,Mari-mePortInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelontheportcontainerthroughput of theMediterraneanrangeasper theH7model. If theMari-mePortInfrastructures intheMediterraneanregionincreasebyEUR1million, theportcontainerthroughputwillincreaseby748TEUonaverage,afer2yearsceterisparibus.

RailTransportInfrastructureInvestmentshaveanega-veandsignificanteffectatthe5%levelontheportcontainerthroughputof theMediterraneanrangeaspertheH7model.If theRailTransportInfrastructuresintheMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwilldecreaseby290TEUonaverage,afer3yearsceterisparibus.

AirTransportInfrastructureInvestmentshavea posi-veand significant effectat the1% levelontheportcontainerthroughputoftheMediterraneanrangeaspertheH7model.IftheAirTransportInfrastructuresintheMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwill increaseby218TEUonaverage,afer4yearsceterisparibus.

RoadTransportInfrastructureInvestmentshaveaposi-veandsignificanteffectatthe1%levelontheportcontainerthroughputof theMediterraneanrangeaspertheH7model. If theaforemen-onedinvestmentsintheMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby101TEUonaverage,afer3yearsceterisparibus.

TransportEquipmentInvestments haveaposi-veandsignificanteffectontheportcontainerthroughputofthe Mediterranean range at the 1% in the H7 model. If the Transport Equipment Investments in theMediterraneanregionincreasebyEUR1million,theportcontainerthroughputwillincreaseby55TEUonaverage,effec-vethesameyear,ceterisparibus.

5.3FixedEffectsorRandomEffects

Asmen-onedinEs%ma%onConcernssec-on,a Hausmantestistobeapplied,inordertodecideiftheFixedEffectsmodelshouldbechoseninsteadoftheRandomEffectsmodelandviceversa.

Table8:HausmantestTable8:HausmantestTable8:HausmantestTable8:HausmantestTable8:HausmantestTable8:HausmantestTable8:Hausmantest

Model

Hamburg-LeHavreHamburg-LeHavre

Result

MediterraneanMediterranean

ResultModel Chi2 p-value Result t-sta4s4c p-value Result

H1 18.54 0.0000 FixedEffect 18.54 0.0000 FixedEffect

H2 0.01 0.9322 RandomEffect 0.02 0.8938 RandomEffect


H4 0.02 0.8888 RandomEffect 54.71 0.0000 FixedEffect




38

Asitis observedinTable8,theHausmantesthasyielded mixedresults.Forthesamemodel(forexample

H4),intheHamburg - LeHavrerangetheRandomEffects aresuggested,whereasfortheMediterranean

rangetheFixedEffects.Furthermore,forthesamerange(forexampletheMediterraneanrange),insome

modelstheFixedEffectses-mators arerecommended,incomparisonwithRandomEffectses-mators for

therestofthemodels.Itisthereforeunclearwhichofthetwomethodsshouldbepreferred.

Likemany tests, theHausman test is performed condi-onally on proper specifica-on of theunderlying

model.Ifwehaveomibedanimportantexplanatoryvariablefrombothformsofthemodel(kindlyreferto

Table1),thenwearecomparingtwoinconsistentes-matorsofthepopula-onmodel(Wooldridge,2006).

Therefore,thechoiceof thees-mators shouldbeguidedbyourobjec-vesanddata characteris-csrather

thanonlybymeansofaHausmantest,sincethetestisnotaccurateinallcases.

Ifthecoefficientsoutputofbothmodelsaresystema-callydifferentfromeachother,aFixedEffects modelwillbemoresuitablethanaRandomEffectsmodel.However,if thecoefficientsoutputof bothmodelsare(nearly) similar(E(αi|xit)=0),aRandomEffectsmodelwillbemoresuitablethanaFixedEffectsmodel.Inthiscase,comparing Table4withTable6andTable5withTable7,weobservethatthecoefficientsaspertheFixedEffectsandtheRandomEffectsareverysimilarwitheachother.Thisis anindicatorthatundertheRandomEffects,thepredictedmodelsaremoreefficient.

In every case, be it Fixed Effects or Random Effects, the differences between the coefficientsand theirsignificancesareveryslight,withoutharminggeneraliza-on.

39

6.DiscussionoftheResults

Exceptfor thecoefficients’ interpreta-on,con-nuingthefindingsdiscussion,furtherpointscanbemade.AccordingtotheR-squaredinthemostcompleteversion,intheH7modelisaround50%.Thismeans thatapproximately50%of theportcontainer throughputvaria-onisexplainedbyourmodel.Considering thattherearemorevariables thaninfrastructureinvestmentsthatpoten-alinfluenceportcontainerthroughput,the current models are significantly improving the predic-ng performanceof the container forecas-ngmodels.

Addi-onally, as per the co-integra-on tests and considering the omibed variable bias weakening therobustnessofthemodels,forthemajorityofthemitseemsthatthereisalongrunequilibriumrela-onshipbetweentheportcontainerthroughputandtheinfrastructureandsuperstructureinvestments.Therefore,theinterac-onamongthemiscausal.

6.1SynopsisofHypothesesResults

Regarding ourhypotheses,they haveallbeen confirmed,exceptfor somecasesforwhichitwasreliablyassumedthattheyareotherwiseduetoomibedvariablebias.InTable6theHypothesesSynopsisresultsarepresented.

Table9:HypothesesSynopsisresultsTable9:HypothesesSynopsisresultsTable9:HypothesesSynopsisresults

Hypothesis1 Mari%mePortInfrastructureInvestmenthasaposi%veandsignificanteffectontheTEUforbothportranges.

Confirmed

Hypothesis2 RoadTransport Infrastructure Investments have aposi%ve andsignificanteffectontheTEUforbothportranges.

Confirmed

Hypothesis3 Rail Transport Infrastructure Investments have a posi%ve and significanteffectontheTEUforbothportranges.

Confirmed

Hypothesis4 Air Transport Infrastructure Investments have a posi%ve and significanteffectontheTEUforbothportranges.

Confirmed

Hypothesis5 Infrastructure Investments have jointlyaposi%ve and significanteffectontheTEUforbothportranges.

Confirmed

Hypothesis6 Transport Equipment Investments haveaposi%veand significanteffectontheTEUforbothportranges.

Confirmed

Hypothesis7 Infrastructure Investments and Superstructure Investments have jointly aposi%veandsignificanteffectontheTEUforbothportranges.

Confirmed

An interes-ng noteisthat, although transport infrastructureinvestmentshaveaposi-ve and significanteffect on the port container throughput for both ranges, significant differences exist between them.InfrastructureandsuperstructureinvestmentsyieldhigherTEU returns in theHamburg - LeHavrerangethan in theMediterranean range, something that is well observed in Table 7, calculated based on theRandomEffectses-ma-ons. Forexample,EUR 1million invested inMari-mePort Infrastructure, yieldsbetween4to6-mesmorecontainersintheHamburg-LeHavrerangecomparingwiththeMediterraneanrange.

40

Table10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEUTable10:ReturnofinvestmentsonTEU

Ra-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrangeRa-ooftheHamburg-LeHavrerangereturnstotheMediterraneanrange

ExplanatoryVariables H1 H2 H3 H4 H5 H6 H7

Mari-mePortInfrastructureInvestments

3.8 6.2 4.1


1 2.31


2.8 2.4 1.2


1.3 0.3 1.4


1.4 1.7

Comparingtheimpactof theinvestmentswitheachother,according to theregressionresults,thelargestimpactof theinfrastructureinvestmentsontheportcontainerthroughputcomesfromtheMari-mePortInfrastructures.Eventhough theMari-mePortInfrastructureInvestments makeupalmosta tenthof theRoadTransport InfrastructureInvestments, their impactontheportcontainerthroughputis muchhigher.Finally,theAirTransportInfrastructureInvestments,yieldmoreorlessthesamereturnsonTEUforthetworanges.

Overall, the transport infrastructure investments appear to systema-cally yield higher returns in theHamburg - LeHavrerangethan intheMediterranean range. This might bedueto variousreasons.Onereasoncanbethehigherlevelsofcorrup-onintheMediterraneancountries.Thismeansthat,foreacheurospend in infrastructureand superstructure investmentsin theMediterranean countries, lesserpartof itfinally reaches an infrastructure project, than in Northern Europe where corrup-on rates are lower(TransparencyInterna-onal,2017).

6.2QualityofTransportInfrastructureandSpa4alSynergies

InGraph7,thescaberplotbetweentheTransportInfrastructureInvestments andtheQualityofTradeandTransportInfrastructureispresented.Twomainareasaredis-nguished:Area1andArea2.InArea1FranceandGermany inthehighersubgroupandSpain andItalyinthelowersubgroup. InArea2therearetwosubgroupsaswell.The lower onewithCroa-a,Greece, SloveniaandPortugaland thehigher one, withBelgium and Netherlands. Inbothareas,theHamburg - LeHavrerange countriesyield higherquality oftransportinfrastructureforsimilaramountofinvestments.

ThedifferenceismoreapparentinArea2.ThesubgroupofBelgiumandNetherlandsspentalmost2billioneurosintransportinfrastructure,which isalmostequaltotheamountspentintheMediterraneanrange.Yet,inscalefrom0to5(5asthetopqualityof infrastructure),BelgiumandNetherlandsyieldmorethan1unit higher quality of infrastructures, which is very significant. Assuming that the technologicaladvancementsaresimilar between the two ranges, theunderlying reasons for this difference could beabributedtothemismanagementofthefundsheadedtoinfrastructureinvestments.

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Another possible reason for which transport infrastructure investments yield more containers in theHamburg-LeHavrerange,isthefactthattheportsinthisregionarecloserwitheachother,incomparisontotheMediterraneanports.Withinadistanceofabout850kilometers,11portsarelocatedwithmorethan1,224,300,000 tons throughput in 2015 (Port of Roberdam Authority, 2016a). Therefore, this spa-alproximity might lead to infrastructure synerge-c effects (Theo Nobeboom, 2012). On top of that, theNortherngovernmentalauthori-es paymoreaben-ontotheeconomicdevelopmentandplanningoftheircountries,resul-nginamorecarefulandaimedspendingoftheinvestments.

6.3ComparisontotheLiterature

It is also interes-ng to make a few notes regarding the relevance of our results with the containerforecas-ng literature. Asmen-oned intheliterature,Makhecka(2016) is theonlyonewhohasusedtheMari-me Port Infrastructure Investments. Makhecka found insignificant effect of the Mari-me PortInfrastructureInvestments ontheportcontainer throughputof theHamburg - LeHavrerangeportsandabributedthistoasubop-mallevelofinvestment.

As per the results of the current paper, Mari-me Port Infrastructure Investments have a posi-ve andsignificant effect on the port container throughput of both the Hamburg - Le Havre range and theMediterraneanrange.However,theeffectoftheinvestmentscanbeseenonlyaferacoupleofyears.

Addi-onally, in the same paper,Makhecka found inland transport in length in kilometers (motorways,railways,waterways) tohaveaninsignificanteffectontheportcontainer throughput.Inorder the inlandtransportnetwork(inlength)effecttobecaptured,therelevantvariableneeds tohaveacertainminimumdegreeofvaria-on.Makheckaimpliesthatforsomeportrangesthesevariablesremainedconstant,makingithard for theeconometric program tocaptureany significance.Makhecka suggests differentmeasuringtechniquestobeused.

Onethoughtsupportedbythefindingsofthepresentstudy,istomeasuretheamountofmoneyinvestedintheextension of theinlandnetwork instead of its length,sincethefirst hasahigher degreeof varia-onthrough-methanthelaber.Inthisway,theeffectof thechangesintheinlandtransportnetworkcanbecapturedbytheeconometricprogram.

BEL

GERESP

FR

GRCCRT

ITL

NLPTR

SLV-

1,000

2,000

3,000

4,000

5,000

6,000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5Tran

sportInfrastructureInvestmen

ts

(millionsof€

)

Qualityoftradeandtransportrelatedinfrastructure

ScaNerplot:TransportInfrastrutureInvestmentsandQualityofInfrastructure

Averageofyears2007-2014

Area1

Area2

Graph7.Datasource:TheWorldBank.Design:Author

Qualityoftradeandtransportinfrastructure

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6.4PolicyImplica4onsandFurtherConsidera4ons

AccordingtoOECD(2013)thecumula-veinvestmentsinlandtransportinfrastructurearepredictedtoreachUS$45trillionby2050.IntheEuropeanUnionalone,thetotalcostofthetransportinfrastructureneedsises-matedoverEUR1,5trillion fortheperiod2010-2030.Only theEuropeanTEN Tnetwork requiresEUR500billionun-l2020for itscomple-on.Themainsourcesof fundingarepublicresourcesfollowedby EUgrantsandloansfromtheEuropeanInvestmentsBank(K.Bodewig&C.Secchy,2014).

However, the anemic economic growth and the long las-ng effects of the 2008 economic crisis, haveconstrained the governmental budgets. Simultaneously, the private sector par-cipa-on in transportinfrastructure projects is modest. Therefore, the pool of the EUR 1,5 trillion transport infrastructureinvestmentneeds,donotseemtobefulfilledupto2030.

6.4.1InfrastructureInvestmentGaps

Evidently,thereisafinancinginfrastructureissue.Under-financingofthetransportinfrastructurescanharmeconomicac-vity andemployment,compe--venessandincreasing externalcosts suchasaccidentsandenvironmental degrada-on. As a solu-on, a plethora of studies supports the idea the infrastructureinvestmentfinancingwillneedtocomeincreasinglyfromtheprivatesector(TorstenEhlers,2014).

Complex legal arrangements inorder to ensurefair distribu-onof the returns to investments and risksbetween the par-es involved, crea-on of cash flows afer many years and unabrac-ve returns toinvestmentsareusuallyfactorsthatdiscourageprivatepar-cipa-onintransport infrastructureprojects(K.Bodewig & C. Secchy, 2014). Therefore, the poten-al financing deficit is rephrasedas to, how tomakeinfrastructure financing returns to investments more abrac-ve to private investors. The current studycontributesinansweringthisproblemasfollows.

6.4.2ContainerThroughputasanIndexofReturnstoInfrastructureFinancing

Inthepresentstudy,therela-onshipbetweenvarioustransportinfrastructureinvestmentsandtheirreturntocontainer throughput hasbeenquan-fied.Undercertainassump-ons andwithin specificboundaries,onecanpredicttheaddi-onalcontainerthroughputaspertheaddi-onalinfrastructureinvestments.Theaddi-onal portrevenues (fromcontainer handling charges, feesand port dues) and governmental taxes(collectedfromimporttariffsandtaxa-on)whichoccurduetotheaddi-onalcontainerthroughputcanbepredictedaswell.

Therefore, it can be agreed among the par-es which are influenced and/or interested in transportinfrastructure investments, a share from the created port and governmental revenues as per addi-onalcontainergenerated.Acontainerunit(TEU),isthereforeapoten-alSpecialPurposeVehicle,whichconnectsthefinancialinvestmentsoftherelatedinfrastructureandtheircorrespondingrevenues,assis-ngintoafairdistribu-onoftheprofitsgeneratedbytheinvestments.

A container unit as a Special Purpose Vehiclehas several poten-al benefits over the current transportinfrastructurefinancing:

• Clearandsimplewayofpredic-ngthereturnstoinvestment• Stablestreamofrevenuesandabrac-vereturns• Reducedrisklevels(asseeninGraph1,portcontainerthroughputisbeingincreasedsince1970,

exceptforthe3yearsofthefinancialcrisis 2008-2011,thereforethereturnstoinvestmentswillbefollowingsimilartrend)

• Reducedbureaucracyandroomforcrowdfunding(relievingtaxpayers)• Improvedtransparency

Exceptforassis-ng inthetransportinfrastructurefinancing,predic-ngthecontainer throughputbasedonthepoten-alvolumeoftheinvestmentscanassistinregionalpolicies.

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6.4.3Contribu4nginaBalancedRegionalPolicy

Asmen-oned in theregression results, transport infrastructure investments in the Hamburg - Le Havrerangeyieldhighercontainer throughputreturnsthanintheMediterraneanrange.Thisimpliesthat inthelongrun,conges-oninthesea(vesseltraffic),rail(traintraffic),road(trucktraffic) andair(avia-ontraffic)transportmodesmightbecomemoreacuteintheNorth,thanintheSouthof Europe.OntheotherhandinfrastructuresintheSouthmightbefunc-oningsub-op-mally.Therefore,thepresentstudyshedslightintothe distribu-on of the (at least on crossborder infrastructureprojects) European funds, in a way thatbalancedgrowthiseasiermonitoredandachievedalongtheEuropeanperiphery.

Apan-EuropeanprojectofsuchaconceptistheTEN-Tnetwork,whichbuildstowardsclosinginfrastructuregaps,removingboblenecksandelimina-ngtechnicalbarriersthatexistbetweenthetransportnetworksofEUMemberStates.Addi-onallyitaims intostrengthening thesocial,economicandterritorialcohesionoftheUnionandcontribu-ngtothecrea-onofasingleEuropeantransportarea.

TheTENTCoreNetworkCorridors.Source:EuropeanCommission

TheNewSilkRoadRoutesonlandandsea.Source:XinhuaNews

44

Another similar infrastructureini-a-veregardingmostly theMediterraneanEurope is theOneBeltOneRoad ini-a-ve from the Chinese. Chinese envisage to “embrace” the European hinterland market byconstruc-ngatranscon-nental railwayfromChinatoLondonandaseamotorwayfromChinatoPiraeusviatheSuezCanal.Assuch,thepresentstudyisappropriateintodeepeningtheunderstandingofthelong-termeffectsonthetransportsystemandtheeconomicgrowthofsimilarmega-projects.

6.4.4Macro-Construc4ngRequiresMacro-Financing

Overall, observing the vision of world’s biggest players, such as Russia, China, Middle East and Europeconcerning thefutureof theworldtransportinfrastructure,oneobservesthatmega-projectsaregoing tobecome the new reality. Mega infrastructural projects implymacro-construc-ng and the laber requiresmacro-financing.Thepresentmodelshowsthatpredic-ngthecontainerthroughputbasedon aggregate-macrofiguresisnotonlypossible,butsufficientlypreciseaswell.

Concluding thePolicyImplica%ons sec-on,itis suggestedthattransportinfrastructureinvestmentplanningtobe implemented in a macro-scale(for example on regional scale), as it isnot onlymore efficient tophysicallycoordinate,butalsomonitoringitseffectsthrough-mesaferforabalancedregionalgrowth.

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7.Conclusions

The present study inves-gated the effect of transport infrastructure investments on port containerthroughputbetweenHamburg-LeHavreandtheMediterraneanrange.Theregressionresultsshowedthatindeed,aspertheobjec-veofthepresentpaper,thereisasizableandquan-fiableconnec-onbetweenthetransportinfrastructuresandsuperstructureinvestmentsandtheportcontainerthroughput.Mari-mePort,Road,Rail,Air transportinvestmentsandTransportEquipmentinvestmentshavea posi-veandsignificanteffectontheportcontainerthroughputonbothHamburg-LeHavreandMediterraneanrange.

Aspertheliterature,theeffectoftheaforemen-onedinfrastructureinvestmentsissignificantonlyaferacoupleof years, since theconstruc-onperiodneedstobeaccountedfor.Onthecontrary, theTransportEquipmentsuperstructureinvestmentshaveaposi-veandsignificanteffecton theportcontainerbythefirstyear,sincetransportequipmentisabuy-to-direct-useasset.

The presented models explainmorethan 50%of theport container throughputvaria-on, even thoughcri-cal explanatory variables such as GDP, popula-on and infla-on have been omibed. Asper the co-integra-ontestsandconsideringtheomibedvariablebiasweakeningtherobustnessofourmodels,forthemajority of our variables, it seems that there is a long run equilibrium rela-onship between the portcontainerthroughputandtheinfrastructureandsuperstructureinvestments.

Compara-velybetweenthetworegions,infrastructureandsuperstructureinvestmentsyieldhighernumberof TEUs in the Hamburg - Le Havre region than in theMediterranean range. Corrup-on and synerge-ceffectsduetospa-alproximitymightexplainthisvaria-on.

Finally,thecausalrela-onshipbetweenportcontainerthroughputandinfrastructurefundingandtheabilityof the laber to predict the volumesof the first, creates room for crea-ve proposals. The idea of thecontainerunitasaSpecialPurposeVehicleservingtheworldwideincreasinginfrastructureneedsshouldbelookedatdeeperbothinacademiaandthebusinessworld.

7.1Recommenda4onsforFurtherResearch

Although the findingsof thepresentpaper areinsigh�ul regarding the forecas-ng of theport containerthroughput,severalideasshouldberesearchedatinthefuture.Firstandforemost,thetwomainlimita-onsasperthesec-onAssump%onsandLimita%onsshouldbetakencareof.

To begin with, theeffectof theinfrastructuremaintenance on theport container throughput should beexamined. On theonehand, properlymaintaining thetransport infrastructureallowsforanefficientandlong term lifeof the infrastructure.However,thereareconcerns whether thecurrentinfrastructures areproperlymaintained.“As thestockof infrastructuregrows,andinmanycasesages,moreeffortis requiredtomaintain the quan-ty and thequality of the infrastructure. In spite of this shif, observers in manycountries haveraised concernsabout underfunding of infrastructuremaintenance. Roadmaintenance isofen postponed on the expecta-on that it will bemade up for in the future and there is no risk ofimmediateassetfailure.”(OECD,2017).

Yet,theshareof infrastructuremaintenanceappearstobegenerallyincreasingattheexpenseof thenewinfrastructureprojectsinthedevelopedcountries.Therefore,thereisa tradeofbetweenthemaintenanceandthenewinfrastructureprojects,dueto limitedfunding.Shouldthecontainertransportrelatedpar-esinvestmorefundsinthemaintenanceofthecurrentinfrastructuresorinnewinfrastructureprojects?

Furthermore,forecas-ngtheportcontainerthroughputinvolvesmoreexplanatoryvariablesotherthantheinfrastructureinvestments.Inthepresentpaper,themodelsincludesolelyinfrastructureinvestments andasexpected suffer fromomibed variablebias. Itwould bethereforeinteres-ng toexaminethebehavior ofenrichedmodels,includingbothtransportinfrastructureinvestmentsandtheomibedvariableswhichhavepreviouslybeenemployedaspertheliterature,suchasGDP,fuelprice,interestrate,popula-onetc.

46

AconcludingroomforfurtherstudycouldbetheuseofthecontainerunitasaSpecialPurposeVehicleinthe financingof transport infrastructure projects.Next to it, itwillbeinteres-ng to inves-gatetowhichextend can monetary revenue streams (port fees, tariffs, taxes, etc) per container be used in order toimprovetheassessmentoftransportinfrastructureprojects(freightrelated)returnsoninvestments.

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