The Future of Migration to Germany - DeZIM€¦ · DeZIM Project Report #DPR 1|20 Berlin, 06 April...

Author: Sulin Sardoschau

DeZIM Project ReportBerlin, 06 April 2020#DPR 1|20

The Future of Migration to Germany

Assessing Methods in Migration Forecasting

Content

I. TheDemandforMigrationForecasts 03

II. UncertaintyinMigrationForecasting 09 2.1 ComplexityofMigrationDeterminants 09 2.2 ImplicitAssumptions 11 2.3 InsufficientData 13 2.4 Future Shocks 15

III.ForecastingMethods 18 3.1 BayesianStatisticalModelling 18 3.2 GravityModelsofMigration 20 3.3 StructuralEquationModels 23 3.4 QualitativeversusQuantitativeModelling 25 3.5 StrengthsandWeaknessesofQuantitativeModels 30

IV.MigrationtoGermany 34 4.1 Selected Forecasts for Germany – Assessment and Plausibility 34 Example from Bayesian Models: Azose, Sevcikova & Raftery (2016) 34 Example from Gravity Models: Hanson and McIntosh (2016) 35 Example from Structural Models: Burzynski, Deuster and Docquier (2019) 36 4.2 Germany-specificUncertainty 38

V. ConclusionandPolicyImplications 42

TablesanDFIGUres

table 1DifferencebetweenEarlyWarningSystemsandMigrationForecasting 04table 2 TerminologyinMigrationForecasting 05Table3SimplifiedOverviewofMigrationTheoriesinSocialSciences 11Table4AdvantagesandDisadvantagesofMainDataSourcesonMigration 13table 5ComparisonbetweenUncertaintiesinQualitativeandQuantitativeModelling 30table 6OverviewQuantitativePapersbyMethod 31table 7StrengthsandWeaknessesofQuantitativeMigrationForecastingModels 33

Figure1StakeholdersinProducingQualitativeandQuantitativeMigrationScenariosandProjections 07Figure2NumberofMigrantstoGermanybySourceCountryovertime 16Figure3NetMigrationFlowtoGermany(inmillion)fromAzoseetal.(2016) 35Figure4NetMigrationFlowtoGermany(inmillion)fromHanson&McIntosh(2016) 36Figure5NetMigrationFlowtoGermany(inmillion)fromBurzynskietal.(2019) 37Figure6ComparisonbetweenMigrationForecaststoGermany(leftWPP2015,rightWPP2019) 38Figure7NetMigrationFlowtoGermany(inmillion)fromBijak(2016) 39

aCknowleDGeMenTs

ThisreportwasproducedwithinthecoreresearchprogrammeoftheGermanCenterforIntegrationandMigrationResearch(DeZIM-Institute)inBerlin.SubstantialcontributionsintheformofdataprovisionandillustrationaswellasvaluablecommentshavebeenprovidedbyJonathanJ.Azose,JakubBijak,ChristophDeuster,FrédéricDocquier,AdrianRaftery,HanaSevcikovaandNathanWelch.

ThisworkwasconductedunderthesupervisionofFranckDüvell.ExcellentresearchassistancewasprovidedbyAndrejSmirnov.

DIsClAIMeR

TheopinionsexpressedinthereportarethoseoftheauthoranddonotnecessarilyreflecttheviewsoftheGermanCenterforIntegrationandMigrationResearch(DeZIM-Institute).Thedatausedinthisreporthavebeenprovidedbythementionedauthors.NeithertheDeZIM-Institutenortheauthorofthisreportguaran-teetheaccuracyofthedataincludedinthisstudy.NopersonactingonDeZIM-Institute’sbehalfmaybeheldresponsiblefortheusewhichmaybemadeoftheinformationcontainedtherein.

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exeCutIve suMMARy

SincetheinfluxofmigrantstoEurope,manypoliticiansandpolicymakershavecalledforimprovedforesightandpreparednesswhenitcomestofuturemigrationflows.Thedemandformigration forecastshas increasedsubstantiallyover the lastyearsandefforts tomeetthisdemandhavebeenmounting.Thisreportassessesthreemajormethodsinquantitativemigrationforecastsinthemediumandlongrun,highlightingtheuncertainties,opportunities,aswellasmethodologicandtheoreticaldissimilaritiesacrossthedifferentapproaches.Ger-manyservesasanillustrativecasestudy.Incooperationwithleadingauthorsinthefieldofmigrationforecasting,thisreportproducesthreedistinctestimatesfornetmigrationflowstoGermanyoverthenext20years.

Inafirststep,thisreportcriticallydiscussesthedemandformigrationforecasts,clarifiestheterminologyandmapsthevariousstakeholders,rangingfromnationalstatisticaloffices,toresearchinstitutesandinternationalorganisations.Alargepartofthisreportisdedicatedtouncertaintiesinmigrationforecasting.Inparticular,itaddressesinhowfarthecomplexityofmigrationdeterminants,insufficientdata,lackofaunifyingtheoryandtheinherentun-predictabilityofpolitical,economicorenvironmentalshockschallengetheaccuracyofsuchforecasts.Withafocusonquantitativemethodsindemographyandeconomics,thisreportshedslightonthreemajorforecastingtools:BayesianStatisticalModelling,GravityModels,andStructuralEquationModels.Thereporthighlightstheirmainfeatures,aswellasadvanta-gesanddisadvantagesofthethreemodelsandexplainstheirpeculiarityvisavisqualitativeandhybridmodelsinmigrationforecasting.

OneofthecoreelementsofthisreportistheextractionofGermany-specificforecasts.Inacomparativeapproach,thereportemphasisesmethodologicdifferencesanddescribeshowthesetranslateintosubstantialdivergenceacrossestimates.Despitethefactthattheseme-thodsarehighlysophisticated,expertlyexecutedandinternallycoherent,theycanproducevastlydifferentoutcomes.Theempiricalanalysisshowsthatthegapacrossthemodelestima-tes(fornetmigrationflowstoGermanyin2040)liesinthemillions.Inaseparateexercise,thereportdemonstratesthatevenwithinacertainmodel,thepredictionscanvarysubstantially,dependingontheunderlyingdata.AcomparisonoftheBayesianModelwithandwithoutpost2015immigrationdataforGermanyrevealsthatforecastsarehighlysensitivetoshort-term shocks.

Overall,thisreportstressestheimportanceofmigrationforecastingasastapleofmigrati-onresearchsimultaneouslycautioningtheusersoftheseforecaststonottakeisolatedesti-matesatfacevalue.Thereportconcludeswithproposalformoretransparencyfrombothproducers(intermsofmethodsanduncertainty)andconsumersofmigrationforecasts(intermsofchoiceandpurposeofforecasts).

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MeThoDoloGy

Thisreportprovidesanoverviewofquantitativeforecastingmodelswithafocusondemo-graphicandeconomicperspectives.Quantitativemodels,inthiscontext,aredefinedasthoseforecastingmethodsthatdonotuseexpert-opinionsorotherqualitativetoolsinestimatingfuturemigration.Theyareconsideredpurelydata-drivenmodelswiththeoreticalunderpin-ningsthatareeitherusedforthestatisticalestimationstrategyorprovideamathematicallymodelledbasisfortheestimation.

Themainandlong-standingapproachtomigrationforecasts indemographyisBayesianStatisticModelling,whichisoneofthethreequantitativemethodshighlightedinthisreport.Morerecently,economistshavefacedthetaskofpredictingfuturemigrationflowswithtwomainmethodologies:GravityModelsofMigrationandStructuralEquationmodels,bothofwhichhaveonlybeenappliedbyveryfewresearchersinthefield.

EstimatesforGermanyhavebeenprovidedbytheleadingresearchersintherespectivefields.JonathanAzose,JakubBijak,andAdrianRafterywithcontributionsfromHanaSevciko-vaandNathanWelchhaveprovidedestimatesforGermanywithintheBayesianFramework.Theeconomistsand(tothebestofmyknowledge)onlyresearchersthathaveappliedthevastlyestablishedGravityModelofMigrationtoMigrationForecasts,GordonHansonandCraigMcIntosh(2016),havemadetheirforecastsfreelyaccessible.Lastly,FrédéricDocquierwithtwosetsofco-authorswere(again,tothebestofmyknowledge)thefirsttouseStructu-ralEquationModelstoestimatefuturemigrationflows.ChristophDeuster,MichalBurzynskiandFrédéricDocquier(2019)havekindlyprovidedtheirestimatesforGermany.

Thereportdoesnotproduceoriginalestimatesbutcollectsandvisualisesestimatesde-velopedbytheleadingexpertsinthefield.Itfollowsacomparativeapproachthatallowshigh-lightingstrengthsandweaknessesofvariousestimationtechniques.Thisreportalsoservesasamapofstakeholdersaswellasaliteratureandmethodsreview.Theassessmentandcom-parisonofmethodsresultsinsuggestionsforimprovedtransparencyinmigrationforecasting.

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Project Report #1|20

Forecastsareararebutpopularcommodityamonggovernments,companiesandindividualsalike.Expertsinvariousfieldsdevoteaconsiderableamountoftheirresearchefforttopredictingeconomic,demographicorclimaticdevelopmentsintheshort-,medium-andlong-termfuture.ThisrangesfromforecastingGDPgrowthinthenextmonths,fertilityandmortalityinthenextdecadesandgoesasfarasmakingpredictionsonclimatechangeoverthenextcentury.Forecastscansupportthedesignofeffectivepoliciesforthefutureandapprop-riatepolicyplanningandthuslaythegroundworktoapathofstability.However,mostforecastsaresubjecttomajoruncertaintiesandcomewithimportantcaveats.Atthesametime,policymakersareparticularlyinteres-tedinpredictionsinareasthatfacethebiggestuncertainties.

SincetheinfluxofmigrantstoEurope,manypoliticiansandpolicymakershavecalledforamorestructuredandunifiedapproachtomigrationpoliciesacrosstheEU.AfullarrayofreformstoEuropeanmigrationpolicieshavebeeninitiatedsince2015,includingtheEuropeanAgendaonMigration,theCommonEuropeanAsylumSystemortheEUBlueCard.Operationalisingsomeofthesereformsinachargedpoliticalenvironmenthasproventobedifficult.Intheaftermathoftheso-called‘refugeecrisis’EUinstitutionsandmemberstateshavebeguntocritically reflectonmigrationmanagementandprocesses (CollettandCamilleLeCoz2018).Onedimensionthathasbeenaddressedfrequentlyoverthepastyearandhasgainedpublictractionisthedevelop-mentofanearlywarningsystemandadequatecrisisresponsemechanismsandearlywarningsystemforfu-turemigrationflows.Forinstance,inFebruaryof2019,AnnegretKramp-Karrenbauer,theheadoftheChristianDemocrats(CDU)andsuccessortoAngelaMerkel,stressedthat‘wehavelearnedourlessonandcalledforan‘earlywarningsystem’tobetterprepareforfuturecrises1.

OntheleveloftheEuropeanUnion,Article33oftheDublinRegulationIIIenvisagesexactlythat:‘amecha-nismforearlywarningpreparednessandmanagementofasylumcrises’.TheEuropeanAsylumSupportOffice(EASO)ispartoftheEarlywarningandPreparednessSystem(EPS),gatheringdataonasylumflows,actingasaclearinghouseforinformationfromorigincountriesaswellasconductingitsownresearchonmigration.TheEPShasalreadybeeninplacesince2013.However,itwasnotabletocentraliseandtransmitthefragmentedinformationontheimpendingrefugeeinflowtotherelevantEUbodiesintime.Asaconsequence,indicationsfromindividualexperts,NGOsornationalgovernmentsdidnotreceivetheattentionnecessarytotriggeracoordinatedpolicyresponse. Instead, in2015theLuxembourgPresidencyactivatedthe IntegratedPoliticalCrisisResponse(IPCR),theEuropeanCouncil’scrisismanagementmechanism2.TheIPCRincludesaweeklyreportingmechanism,theso-calledIntegratedSituationalAwarenessandAnalysisreport,whichgathersinfor-mationfrommajorEUbodies,internationalorganisationsandmemberstates.Sinceitsactivation,ithasbeenusedtolookforsolutionstotherefugeecrisisattheEuropeanCouncillevel.TheIPCRwasinitiallydesignedasacrisisresponsetoolinthecaseofearthquakesorthebirdflu.Itsappropriationasarefugeemanagementtoolistelling.TheEuropeanUnionislookingfortoolsthatrenderfuturerefugeeandmigrationflowsmorepredictable and therefore more manageable.

1 Quotedfromherspeechatthe‘Werkstattgespräch–Migration,SicherheitundIntegration’onthe10thand11thofFebruary2019.2 CounciloftheEU,pressrelease30.10.2015‘Migratorycrisis:EUCouncilPresidencystepsupinformationsharingbetweenmemberstates byactivatingIPCR’.

TheFutureofMigrationtoGermanyAssessing Methods in Migration Forecasting

I. the Demand for MigrationForecasts

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I. The Demand for Migration Forecasts

TherehavebeeneffortsontheEuropeanleveltodevelopearlywarningsystemsfortheshort-run.TheEuropeanAsylumSupportOffice(EASO)haspreparedareportin2018thatanalysesthecurrentuseofearly-warningsystemsforasylum-relatedmigrationacrossEUmemberstatesandneighbouringcountries.Based on theAd-HocQuery on Forecasting andContingency PlanningArrangements for InternationalProtectionApplicantsof theEuropeanMigrationNetwork (2014), the report reviewspotential criteriathatanEU-wideearlywarningsystemwouldhavetofulfil (Bijak,Forster,andHilton2017). Accordingtotheauthors,thebenefitofanEU-wideearlywarningsystemwouldbetochangethedecision-makingfromareactivetoapro-activemanner,whichinturneasescontingencyplanning.Theauthorsstressthatitiskeythatthemodelsuitstheneedsoftheuser,thatlimitationsandunderlyinguncertaintyareclearlycommunicated and that models are updated on a regular basis.

Theauthorsshowthattheapproachesarerathersimpleinstatisticalterms,mostlyproducingforecastsuptoayear.However,theydifferinthedegreeofsophistication–somearebasedonsimpleextrapolationoftrends(e.g.Ireland)whereas,forinstance,SwedenandSwitzerlandputinplacemorecomplexquan-titativemodels.Othersonlyusequalitativeratherthanquantitativeapproaches(e.g.Poland).Themostsophisticatedmodelsincorporatedifferenttypesofinformation,combiningquantitativedataonasylumtrendswithinsightsfromexperts,borderintelligenceandmigrationroutes.Themainstrengthofthesemodels is theconsiderationofqualitative informationbyexpertsbasically in realtime.Thecollection,processinganddisseminationofdatatakestimeandisthereforeusuallynotavailableasquicklyasassess-ments by experts on the ground. The authors stress the quality of a model crucially depends on its regular review,evaluationandadjustment,which is rarelydone inpractice.AnEU-wideearlywarning systemshouldpredictchangesintheasylumflowbasedonEASOdataandbesupplementedbyexpertknowled-ge,formalconflictintensityindicesaswellasstakeholders’subjectiveopiniononsensitivity.Thescopeofthemodelistogeneratewarnings,ifasylum-flowsarepredictedtorisebeyondacertainthreshold,whicharedefinedbystakeholdersupfront.Instatisticalterms,itisdesignedasatwo-stagemodel,followingaBayesianframework.

Ingeneral,earlywarningsystemsborrowconcepts frombothqualitativeandquantitativemigrationforecastingmethods.However, theydonotaimtosayanythingabouthowfundamentalchangesonaglobalscalewillaffectmigrationpatternstoEuropeorGermany.Rather,theyaimtoanticipate‘shocks’toasylum-relatedmigration.ThisincludestheanticipationofcivilwarandbutalsocomprisesthecollectionandanalysisofdataonmigratoryflowsatthegatestoEuropeandbefore.Theseshort-termchangesor‘shocks’tomigrationaretypicallythefactorsthatwillbeexcludedfromlong-termmigrationforecasts.Theremaybeassumptionsabouttheaveragesizeandfrequencyof thoseshocksbut intheend,theyareonlynoiseinthedata(unlessforecastersarewillingtomakeassumptionsabouthowandwherecivilconflictislikelytohappeninthenext50years).Itisimportanttodrawthisdistinctionbecauseithasme-aningfulimplicationsforhowtheseforecastscanandshouldbeinterpreted.

table 1DifferencebetweenearlywarningsystemsandMigrationForecasting

earlywarnungsystems

• short term frame

• asylum-relatedmigration

• focusonshockstomigration

• depend more expert opinions

migrationforecasting

• long term frame

• overallmigration

• averagingoutshockstomigration

• depend more on data

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3 BasedonthePopulationReferenceBureau'sGlossaryofDemographicTerms,theGlossaryoftheInternationalMigrationInstitute,and theStatisticalLanguageTooloftheAustralianBureauofStatistics.

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Theabovementionedearlywarningsystemsareaimedatmakingshort-termpredictions.Thisreport,ins-tead,dealswithmigrationforecastsinthemediumandlongrun,thatis,migrationflowsoverthenext10to80years.Itdoesnotfocusonforcedasylumrelatedmigrationforecasts(althoughtheymaybeonecomponentinoverallmigration).Predictingcivilconflictorotherasylum-relevanteventsovermorethanoneortwoyears,yet20,30oreven50yearsintothefutureandquantifyingitseffectonthenumberofasylumseekerstoaspe-cificdestinationcountryisfairlychallenging,ifnotnearlyimpossible.Migrationforecastscanonlyrelyonlargerandmorefundamentaldynamicsofhumandevelopmentsuchaspopulationchange,economicdevelopment,climatechange,etc.Therefore,policymakersneedtodistinguishbetweensocalled‘earlywarningsystems’ad-dressingshort-termpoliticalrisksandinstabilitiesacrossmajorsourcecountriesandmigrationforecasts,whichtypicallycoveralongertime-frameandfocusonthefundamentaldynamicsofmigration.

Therearemanyoverlappingconceptsandterminologiesinthemigrationforecastingsphere.Policymakerscallformigrationscenarios,projections,forecastsorestimationsoftentimesusingthosetermssynonymously.Table2givesadescriptionofhowthisreportwillusethesetermsbasedontheirtypicalapplicationindemo-graphy.Migrationscenariosaretheoreticalexplorationsbyexpertsoffuturechangesandtheireffectonmigra-tion.Theyaretypicallyofqualitativenature.Section3willdescribeinmoredetailhowqualitativeandquantita-tivemodelsdifferbutmigrationscenariosareusuallythedeparturepointforquantitativemodelsofmigration.Particularlywhenitcomestotheunderlyingassumptionsinquantitativemodelling,scenariosdevelopanswerstothequestion:Whatarereasonableassumptionstomake?Thedifferencebetweenprojectionsandforecastsisnotasclearcut.Therearesignificantoverlapsinthedefinitionsandusewithindemographybutalsootherfields,suchasbusinessfinance.Thisreportwillonlyprovideaworkingdefinitionforthisanalysis,basedonvariousstatisticsboardsandnationalstatisticsoffices.TheAustralianBureauofStatisticsdescribesthediffe-renceasfollows:‘Whilebothinvolveanalysisofdata,thekeydifferencebetweenaforecastandaprojectionisthenatureoftheassertioninrelationtotheassumptionoccurring’.Inotherwords,projectionsaresimplyinferringafuturevalue(formigration)basedonasetofassumptions.Forecastspredictafuturevalueforanexpectedsetoffutureeventsbasedonalikelysetofassumptions.TheformerasksWhatistheoutcomeifcertainassumptionsweretrue?ThelatterasksWhatistherangeofpossibleoutcomesforexpectedfutureevents?Forecastsalsoprovideanestimateandanassociatedconfidenceinterval.

table 2 TerminologyinMigrationForecasting 3

Scenarios

Projection

What are possible/reasonable

assumptionstomake?

What is the outcome if certain

assumptionsweretrue?

Scenariosarenarrativesthatdescribefuturechanges(potential

futurepolitical,economic,social,technologicalandenvironmen-

talchanges)andtheirconsequencesformigration,andhaveno

predictiveobjective.Theyaretypicallyofqualitativenatureand

serveasabasisforassumptionsusedinquantitativemethods.

Description GuidingQuestions type

Computationoffuturechangesinpopulationnumbers,given

certainassumptionsaboutfuturetrendsintheratesoffertility,

mortalityandmigration.Demographersoftenissuelow,mediam

andhighprojectionsofthesamepopulation,basedondifferent

assumptionsofhowtheserateswillchangethefuture.

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Forecast

Estimation

What is the range of possible

outcomes for expected future

events?

Forecastsspeculatefuturevaluesforapopulationofpossible

valueswithalevelofconfidence(usuallyindicatedasconfidence

intervals),basedonthecurrentandpastvaluesasanexpectati-

on(prediction)ofwhatwillhappen.

Forecastsincludeanestimation.AnEstimateisavalue(nota

rangeofvalues)inferredforapopulationofvaluesbasedon

datacollectedfromasampleofdatafromthatpopulation.The

estimatecanalsobeproducesparametricallyorthroughamo-

delsimulation.

Description GuidingQuestions typecontinuationof Table 2

Overall,thedemandformigrationforecastsislargeandmanystakeholdersarecateringtoit.Figure1pre-sentsacrudemappingofresearch institutes(inred),EUbodies(inyellow)and internationalorganisations(ingreen)thatarecurrentlyrunningmigrationforecasts.ThemapincludesstatisticsofficessuchasUNDESAorEurostatthatfactorintheirdemographicforecasting,amigrationcomponent.Foralongtime,populationprojectionsdidnot includemigratoryflows (closedpopulationprojections)orassumednetzeromigration(Bouvier,Poston,andZhai1997).Today,theUnitedNationsPopulationDivisionusessimplifiedassumptionsaboutmigration,forinstance,thatrecentlevelsofmigrationremainstable,orthatrefugeeswillreturntotheircountriesoforiginwithin5to10years(UNDESA2017b).However,theUNisnowworkingonmoresophistica-tedmodelsofmigration(AzoseandRaftery2015).Otherinternationalorganisations,suchastheInternationalOrganizationforMigration(IOM)haverevisedtheirskepticalviewtowardsmigrationforecasts(Bijak2016)andarenowexploringnewwaystomodelfuturemigrationflows.

Moresophisticatedapproacheshavebeenintroducedbydemographersatvariousresearchinstitutes,suchastheWittgensteinCentreorthePewResearchCenter.Earlyonin2009,theInternationalMigrationInstitutebroughttogetherprominentresearchersinmigrationtolaunchthe‘GlobalMigrationFutures’project.Thepro-jectappliesamigrationscenariomethodologythatisexpert-drivenandprimarilyexploratoryandofqualitativenature. In2016, the International Institute forAppliedSystemsAnalysis in cooperationwith theEuropeanCommission’sJointResearchCentrehascreatedCEPAM,theCentreofExpertiseonPopulationandMigration,whichprovidesscience-basedknowledgeonmigrationtosupportEUpolicies,includingmigrationprojections.Similarly,theCentreforPopulationChange(ajointpartnershipbetweenthreeuniversitiesintheUK)ishometosomeofthe leadingdemographersonmigrationforecasting.Additionally,theEuropeanUnionencoura-gesthecreationofevenbroaderresearchconsortiumsonthetopic.In2015,theInternationalOrganizationforMigrationhaslaunchedtheGlobalMigrationDataAnalysisCentre(GMDAC).GMDACtogetherwiththeNetherlandsInterdisciplinaryDemographyInstitutearecurrentlydevelopingexpert-basedmigrationscenariosfortheyear20304.Fundingeconomists,demographers,sociologistsandothersocialscientiststhroughmajorgrants,theEUprioritisesthedevelopmentofmid-andlong-termmigrationscenariosintheacademicsphere5.

Inorderforalloftheseeffortstoresultininformedpolicymaking,twothingsmustholdtrue:migrationforecastsarea)informativeandb)understoodandappliedappropriately.Let’sstartwiththefirstrequirement:migrationforecastsarepredictiveofactualmigrationflows.Allofthestakeholdersthatproducemigrationforecastsprefacetheirworkwithoneimportantcaveat:uncertainty.Thisreportwilldedicatealargeparttotheanalysisofvariousfactorsofuncertaintyinmigrationforecasting.Thisrangesfromambiguitiesinmigra-

4 ThemethodologywillbediscussedinChapter3.FormoredetailsseeAcostamadiedoetal.(forthcoming).5 ArecentHorizon2020Callentitled‘Understandingmigrationmobilitypatterns:elaboratingmidandlong-termmigrationscenarios’ includestheobjectivetodevelop‘projectionsandscenariosthatareessentialforappropriateplanningandeffectivepolicymaking’.

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tiontheory,tothequalityofmigrationdata,totheunpredictabilityof importantfutureevents. InhisDataBrieftotheIOM’sGlobalMigrationDataAnalysisCentre(GMDAC),JakubBijak,oneoftheleadingresearchersinmigration forecasts,warnedthat the ‘belief in thepossibilityofproducingprecisemigration forecasts isnotonlynaïve,butalsocanbackfireifrealitydoesnotconformtotheexpectations’6. Policymakers can only prepareforthefutureifmigrationforecasterspredict itaccurately. Ifexpectationsfallshortofreality,thenmigrationforecastscanevenbecounterproductiveastheymighttriggerinappropriatepolicyresponses.Thesecondrequirement,namelythatmigrationforecastswillserveasabasisforgoodpolicies,isapoliticaloneandthereforeliesoutsideofthedomainoftheforecaster.Apoliticalconsensusonmigrationpolicyisdifficulttoachieve.Inahighlychargedenvironment,itishardtoforeseehowestimatesonfuturemigrationflowstoEurope(whichcanvarysubstantially)willbeinterpretedand/orexploited.Forecastsaresensitivetotheirun-derlyingassumptions,theybringwiththemimportantuncertaintiesandshouldbeinterpretedwithgreatcare.Ifnuancedinterpretationflounders,migrationforecastsmayheatupthepoliticalclimateratherthanprovideabasisforconstructivepolicymaking.

6 IOM,GlobalMigrationDataAnalysisCentre(2016)Migrationforecasting:Beyondthelimitsofuncertainty,DataBriefingSeriesISSN2415- 1653,IssueNo.6,page6

Figure1 stakeholdersinproducingqualitativeandquantitativemigrationscenariosandprojections(EUfocus)

UnitednationsPopulation Division

(unDesA) Center for Population

Change

InternationalorganizationforMigration(IQM,GMDaC)

eurostat PopulationProjections

eu-funded research

Consortium

Migration Forecasting

Efforts

netherlandsInterdisciplinary

Demography Institute

InternationalMigrationIns-titute(GlobalMigration Futures)

vienna Instituteof

Demography

Pew research

Centerwittgenstein

Centre for Demography &Globalhu-man Capital

InternationalInstituteforappliedsys-temsanalysis

european CommissionknowledgeCentre on Migration& Demography

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Whilethisreportwillnotmakeanymajorclaimsonthenormativecomponent,itstillcautionsusersofmigrationforecasts.Section2of this reportwillhighlightthevarioussourcesofuncertainty inmigrationforecasting,discussingthecomplexityofmigrationdeterminants,thestrongunderlying(andofteninvisible)assumptionsinquantitativemodels,aswellasthevariousinaccuraciesintroducedthroughotherforecastsandimperfectdata.InSection3,thereportshedslightonthemajorforecastingmethods,brieflyreviewingqualitativemodelsandmigrationscenariosbeforemovingtoadescriptionandcriticalassessmentofquan-titativemethods.Section4zoomsintotheGermancontext,extractingmigrationforecastsfromrecentrese-archpapers,comparingandassessingthem.ThissectionwillalsolookatGermany-specific,socio-economicuncertainties.Thelastsectionwilldiscusstheusefulnessofmigrationforecastsandproviderecommendedusesandinterpretation.


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Thereisalwaysanelementofuncertaintywhenmakingclaimsaboutthefuture.However,migrationforecastsareparticularlyvulnerabletovariousformsofuncertainty.Thispaperestablishesfivesourcesofuncertainty inmigration forecasting.First, it startswithadiscussionof thecomplexityofmigrationdeterminantsandthelackofaunifiedandgeneralisedmigrationtheoryacrossdisciplines,whichmakesaconsensusonthebestforecastingstrategydifficult(agreeingonactualnumbersisalmostimpossible).Second,itoutlinesthatthiscomplexitycantypicallynotbefullyreflectedinempiricalresearch.There-fore,mostforecastsusestronglysimplifiedassumptionsthatruninthebackgroundofthequantitativemodels.Outcomesarehighlysensitivetosmallchangesinthoseassumptions7.Third,migrationforecastsarethemselvesoftenbasedonforecasts,for instanceaboutfertility,climate,GDPgrowth,etc.Eachoftheseforecastscarriesalevelofuncertaintythatisintroducedintothemigrationforecast.Fourth,itpre-sentssomeofthemajordatasourcesusedinmigration,emphasisingthatmigrationdataisnotoriouslybad (low frequency, lowgeographic resolution, lowaccuracy). Basing forecasts on imprecise datawillintroduceanadditionalsourceofinaccuracyanduncertainty.Lastly,itdiscussesseveraltypesoffutureshocks(technological,political,environmental)thatarenotforeseeablebutultimatelysomeofthemostimportantdriversofmigrationinthefuture.Overall,thissectionservesasacriticalreflectionontheabi-litiesandlimitsofmigrationforecasting.

2.1ComplexityofMigrationDeterminantsMigrationdeterminantsarehighlycomplex.Behavioural,social,cultural,political,economicandmany

otherfactorsareatplay,interactingwithoneanotherinmultifariousways.Becausemigrationtouchessomanyaspectsoflife,differentfieldsexaminethetopicfromdifferentangles.Table2describes,inahighlysimplifiedmanner,howdifferentsocialsciencesapproachmigrationtheory.

Therearemanyoverlappingconceptsandideas,forinstancebetweensociologyanddemography,so-ciologyandeconomics,politicalscienceandlaw,historyandanthropology,andsoon.However,migrationresearchdoesnotyetfeedoffofaunifiedmigrationtheory,thoughtherearetrendstowardsanintegrati-onofthedifferentmicro-,meso-andmacro-leveltheories.Asaresult,migrationforecastsarenotfootedonsuchoverarchingconceptsortheories.Forecastsdepartfromverydifferenttheoreticalfoundationsandcanthereforeproduceverydifferentresults,withinandacrossdisciplines.

Demographers,forinstance,considermigration(togetherwithfertilityandmortality)asonemainde-terminantofpopulationchange(Zelinsky1971;CourgeauandFranck2007).Theso-called‘demographictransition’ isacombinationofthe ‘vital transition’ (birthanddeathrates)andthe ‘mobilitytransition’(spatialmobility, includingmigration). Zelinsky,whowas a trained geographer, coinedmany of theseterms,bringingaspatialcomponenttodemography.Migration– indemography–draws its relevancefromitseffectonpopulationchangeandisanalysedassuch.

Anthropologicalandsociologicalconceptsofmigrationsharemanysimilarities.Theirqualitativescho-larsfocusmoreonspecificcasestudiesandusethosetodevelopbroaderconceptsonmigration.Especial-lytheideathatmigrantsbelongtoasocialspacethatisdynamic,hybrid,ever-changingandspansacrosstheglobeasatransnationalsphereisacoreconceptofmanysociologicalanalyses.Inordertoassessthevolumeandtypeoffuturemigrationflows,theseanalysesdevelopqualitativemigrationscenariosthattry

II. uncertainty in MigrationForecasting

7 Formoredetailsonthemajorassumptionsaswellastheirstrengthsandweaknesses,seeSection3.

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II. Uncertainty in Migration Forecasting

toincorporatethesecomplexanddynamicprocesses.IwilldiscussthedifferencesbetweenqualitativeandquantitativeapproachesinmoredetailinSection3.However,inprinciple,quantitativemodelswouldnotbeabletomeasureandincorporateallofthedimensionsdeemedimportantbyqualitativesociolo-gists and anthropologists.

Politicalscientistsandscholarsofthelaw(aswellasphilosophers)haveaninterest intheeffectsofmigrationonthenationstate,institutionsandthelegalstructure,especiallywhenitcomestostatepow-er,citizenshipandenforcement.

Economiststypicallyconsiderarepresentativeagentwhofacesatrade-offbetweenthecostsandbe-nefitsofmigration.Somefactors,suchasexistingmigrantnetworks,woulddecreasemigrationcostsandthereforeincreasemigration.Otherfactors,suchaslanguageorculturalbarriers,woulddecreasegainsfrommigrationandthereforedecreasemigration.Usually,therepresentativeagent’sbehaviourwillbeaggregatedtorepresentandexplainmigrationpatternsonamacro-level.

Overall,migrationisatopicthatisexaminedbyvariousfieldsfromverydifferentperspectives.Thisisowedtothecomplexityofmigrationprocessesbutitisalsothereasonwhyitisdifficulttobaseforecas-tingonastrongtheoreticalfooting.Thiscomplexitymakesmigrationverydifferentfromotherdimensionsforwhichforecastsaretypically(andmorereliably)created.Forinstance,fertilityisaconceptusedandexaminednotonlyindemographybutalsoineconomics,sociologyorhistory(Leridon2015).Theoreti-calconceptssuchasthethreeorfourstagedemographictransitionmodelshavealsobeenadoptedineconomics(BeckerandBarro1988;Willis1973)andotherfieldsandareusedasabasisforforecastingmodelsinfertility.Fertilityforecastsareratherreliableincomparisontootherforecasts,notonlybecausedataonfertilityisbetter(longertime-frameandhigheraccuracy)andfertilitymovesmoreslowly(whichmakesitmorepredictable)butalsobecauseageneralisedtheoryisveryhelpfulfordevelopingquantita-tivemodels.

The lack of theoretical underpinnings is largely owed to the complexity ofmigrationdeterminants.On amacro-level, themaindeterminants ofmigration include geographic distance, common langua-ge,whethercountrieshaveformercolonial links (whicharenon-time-varyingdeterminants)aswellaseconomicwealthatdestination,unemployment rate, incomeandage structureatorigin, immigrationpolicies,climaticfactorsandconflict(Mayda2010;FlahauxandHaas2016;BeineandParsons2017;KimandCohen2010).Atameso-level,transnationalnetworks(Haug2008;McKenzieandRapoport2010)andmigrationinfrastructurearemajordeterminantsofmigration(XiangandLindquist2014).Andfinally,atamicroorindividuallevel,therearedeterminantssuchasage,familystatus,riskaversion,perceptions,imaginations,localamenities,personalwealthandcreditconstraintsetc.(Jaegeretal.2010).Allofthesecomponentsinteractinvariousways;themechanicsofmigrationpatternsremainopaqueandweonlyobserveaggregatebilateralmigrationflowsandstocks.

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Additionally,nosinglemigrationdecision isalike.Climatemigrantsaredifferent fromeconomicmigrants,whoaredifferentfromthepoliticallypersecuted,andsoon.Researchontheimportanceofdifferentdeterminantsbymigrationcategoryisstillinitsinfancy.Dependingonwhetherwethinkfuture migration may be driven more by climate change or even technological change not only means thatwehavetohaveaclearvisionofhowthesedimensionschangeindividuallybuthaveaconceptofhowthesechanges interactwithotherdeterminantsofmigration.These interactioneffectsareimmenselycomplexandcannotbeallincorporatedintoaquantitativemodel,evenifwehadacleartheoreticalconceptofhowthesedimensionsinteractinreality.

Itmaybepossibletouseartificialintelligence,neuralnetworksanddeeplearningtounderstandmigrationpatternsinthefuture.Thesesystemswouldbeabletoidentifypatternsinhighlycomplexsettings.However, theyneed tobe ‘fed’withenormousdata setswithmillionsofobservations inorder to train their pattern recognition. Section 2.4 discusses the fact that migration data of high accuracy, high frequency and high resolution is not yet available and also not harmonised acrossmost countries. Even sophisticatedAI techniqueswouldnotbeable toovercome the lackofdataavailability.

2.2ImplicitassumptionsThe complexity of migration determinants cannot be fully incorporated into the existing quantita-

tivemodelsandtools.Therefore,forecastsusestrongandsimplifyingassumptionsabouttheworld.Let’staketheso-calledtimeseriesmodelsasanexample.Mainforecastingmodelsandtheirassump-tionswillbediscussedinSection3,buttimeseriesmodelsareagoodexampleofhowstrongsomeunderlying assumptions can be. Time-series models are fully agnostic in terms of the determinants of migration,theydonotincludeanycovariatesthatmayinfluencemigrationinthefuture.Rather,theyaredata-drivenprocessesthatusepastobservationstomodelfutureflows.Theso-calledRandomWalkwithdriftor‘autoregressivemodel’belongstothegroupoftimeseriesmodels(RandomWalkis a special caseofAR(1)models,where theparameter forpastmigrationφ equals1). Itpredictsmigrationinthenextperiodasafunctionofaconstantbaserate,themigrationflowinthepreviousperiodwithacertainparameter,andanormallydistributederrorterm(ittakesthefollowingfunc-

source: basedonBrettellandHollifield(2013),Bijak(2006),Kupiszewski(2002),Zlotnik(1998)

Table3 simplifiedoverviewofMigrationTheoriesinsocialsciences

Demography

Howdoesmigration

affectpopulation

change?

•Demographic

transitionmodel

•Vitaltransition

•Mobility hump

&transition

What explains

incorporationand

exclusion?

•Push&pull

factors

•Transnational

social spaces

•World systems

theory

Howdowe

understand the

immigrant

experience?

•Historyofhu-

man mobility

•Historical-struc-

tural approach

What is the role of

the state in cont-

rollingmigration?

•Statepower,

sovereignty

•Citizenship

•Migrationgover-

nance

What explains

thespatialpatterns

ofmigration?

•Gravity theory

•Entropy

•Catastrophe

theory&

bifurcations

What explains

the propensity to

migrateandwhat

aretheeffectsof

migration?

•Neo-classical

(macro&micro)

•Duallabormar-

ket theory

•Neweconomics

ofmigration

Howdoesmigrati-

onaffectsocietal

change and ethnic

identity?

•Transnationalism

&ethnicity

•Identity&

hybridity

•Social change

Howdoesthelaw

affectmigration?

•Forms,proces-

ses,institutions

ofimmigration

law

•Enforcement

researchQuestion

Concepts

sociologyhistory PoliticalscienceGeography economicsAnthropology law

| 12


tional form: mt+1 = c + φmt + εt).Therearealsomoresophisticatedversionsofthis,addingdifferenterrorterms(autoregressivewithmovingaverage)orintegratelineartrends8.

Onecanarguethatrelyingpurelyonstochasticmodelsinmigrationforecastingistheleastrestrictivesincewedonothavetomakeanyassumptionsabouttheinfluenceofcovariatesonmigrationandhowtheywilldevelopinthefuture.Ontheotherhand,onecouldalsoarguethatthelackofassumptionsandexclusionofimportantco-variates(likedemographicchange,climatechange,worldincomeortechnologicaldisruptions)isinitselfastrongclaimaboutthefutureofmigration.Furthermore,thesimplicityoftheapproachmaskssomeimportantstatisticalassumptionsinthemodel,suchasrequiringnormallydistributederrortermsorlineartrendsinsomecases.

Thereisanimportanttrade-offinthistypeofforecasting.Thefewerassumptionsaboutthestochasticbeha-viourofmigration,thelargertherangeofpossibleoutcomesfurtherintothefuture.Long-termupperandlowerboundpredictionsaboutmigrationcandivergesubstantiallyandmaynotbeofpracticaluse.More‘precise’(notaccurate)forecastscomprisealotofassumptionsandimplicitmodelsaboutmigration.Thiscreatesatensionbetweentheinterestsofproducersandusersofmigrationforecasts.Forecasterswouldliketomakeasfewrestric-tiveassumptionsaboutmigrationaspossible(intheendmanyofthoseassumptionsinvolvevaluejudgements),whichresultsinalargerangeofpossibleoutcomes.Conversely,usersofforecastsexpectareasonablerangeofoutcomestowhichtheycantailorpolicies.Inordertogettothesereasonableranges,forecastersdevelopmoresophisticatedmodelssuchasgravitymodelsofmigrationorstructuralmodels.Thesemodels,whichIwilldiscussindetailinSection3,carryvariousassumptionsaboutthefunctionalformofmigrationanditsrelevantco-de-terminants.Theymaybeabletonarrowdowntherangeofexpectedmigrationflowsbutchangingsomeoftheunderlyingassumptionswouldchangetheoutcomessubstantially.Thisisimportanttonotewheninterpretingtheseforecasts.Cautioususersofforecastsshouldaskthemselves:whataretheunderlyingassumptionsandamIwillingtoacceptthem?

Overall,simpletime-seriesmodelsdonotmakeanyclaimsaboutwhatotherfactorswillimpactmigrationinthefuture.Thisalsomeansthatpotentiallyrelevantdeterminantsofmigrationaresetaside.However,weknowthatmanyfactorsarecrucialforourunderstandingofmigration.Forinstance,theageandeducationalstructureofasocietyisoneofthemostimportantpredictorsofmigration.Economicgrowth,climatechange,migrationpoliciesandpoliticalstabilityarecrucialtothefuturedevelopmentofmigrationaswell.Multivariatemodelsofmigrationtrytoincorporateallofthesedimensionstopredictmigration.

Let’sassumethatwehaveasimplemodelthatexplainsmigrationwithdemographicchange(withafunctionalform similar to: mt = α + β * Dt + εt,wheremtismigrationattimet and Dt is a measure of the demographic structureattimet, βistheelasticityofmigrationtochangesindemographicstructure).Let’salsoassumewehaveuseddataonmigrationandagestructureinthepastandknowthatanincreaseintheagecohortbetween15and35by10%isassociatedwithanincreaseinmigrationby1%.Ifwewanttomakepredictionsaboutmigra-tioninthefuture,wehavetomakepredictionsaboutdemographicchangesinthefuture.Inthiscase,wewouldliketoestimatemt+1 = α + β * Dt+1 + εt+1.ThismeanswehavetohaveaforecastforDt+1 to say something about mt+1.Predictionsonfuturedemographicchangesinthemselvesincorporatevariousassumptionsandfo-recastingerrors.Ifweaddmoreco-variatesoneconomicgrowthorunemploymentforinstance(suchthatmt+1

= α+β * Dt+1+ ∂ * Et+1 + γ * Ut+1 + εt+1),wewillbeaddingmoreimplicitassumptionsandmarginsoferrorthatareultimatelyreflectedinthemigrationforecast.Theseforecastsarebasedonforecaststhemselves,andwillconsequentlyintroducemoreuncertaintyintotheestimation.Theanalysisandassessmentofforecastshouldthereforealsodependontheassessmentontheunderlyingassumptions,bothstochasticallyaswellasintermsoftheunderlyingforecastingmethodsusedtodeterminecovariatesofmigrationinthefuture.

8 Bijak(2015)givesthemostcomprehensiveoverviewonthemajorassumptionsusedindifferenttypesofforecastingmodels.

| 13


2.3InsufficientDataThefirststepinforecastingmigrationistheanalysisofdataonpastmigration.Pastpatternsarethebasisfor

explainingfuturepatterns.Thisistrueforsimpletime-seriesmodelsofmigrationaswellasmorecomplex,mul-tivariatemodels.However,thequalityofmigrationdataisoftentimesinsufficient.Thishasbeenrecognisedbytheinternationalcommunity.TheHigh-LevelDialoguesonInternationalMigrationandDevelopmentof2006and2013havehighlighted‘theneedforreliablestatisticaldataoninternationalmigration’.In2017,theGlobalMigra-tionGroup(withintheWorldBank’sKNOMADinitiativeframework)haspublisheda‘HandbookforImprovingtheProductionandUseofMigrationDataforDevelopment’,outliningthegapsandroomforprogressinthecoverageofallformsofhumanmobility(suchaslabourmigration,asylum-relatedmigration,commuters,expats,students,irregularmigrantsetc.).

Therearethreemaintypesofdatasourcesonmigration,asillustratedinTable4.Onemajortypeofmigrationdataisadministrativedata,whichiscollectedbynational,regionalorlocalauthoritiesinofficialrecords.Theserecordsdonotnecessarilyhavetheprimarygoalofdocumentingmigrationbuttheyareusedforadministrativepurposesandcanincludeinformationonplaceofbirth,citizenshiporresidencystatus.Forinstance,migrationdatacanbeextractedindirectlyfromtaxrecordsorworkpermitsiftheyincludemarkersforcitizenshipormigrationsta-tus.Therearealsomoredirectproxiesformigrationintherecordsonissuedvisasordatacollectionattheborder.Whilethesedatasourceshavemanyadvantagesintermsofcoverage(theoreticallythewholeworkingpopulationofacountryshouldbeincludedinthetaxrecords)andtimeframe(peoplecanbefollowedoveralongperiodoftime),thereareafewmajordrawbacks.Doublecountingorunder-coverageisacommonprobleminadministrativedata,sincerecordsarenotalwayspersons(butcases)andsomeindividualsmayneverberegistered(forinstance,inthecaseofinformallabour).Additionally,thistypeofdatamayonlycoverlittleinformationonsocio-demographics,livingsituation,oreconomicwealth,whichthenmakesitdifficulttouncoverheterogeneityacrossindividuals.

Table4 advantagesandDisadvantagesofMainDataTypesonMigration

•Largedatasets, potentially

•coveringthewhole population

•Trackingovertime•Highfrequencyand geographicresolution

•Realtimemovementof people: velocity

•Available in countries withlowadministrative or survey coverage

•Largecoverage,including „irregular“ migrants

•Wealthofinformation on respondents

•Likelytocapturegroups ofinterest(includingharder toreachpopulations)

•Highlyself-selected•Phones+SMaccounts,

not people•Opacityaboutwhatis

actually measured•Inaccuracy of IP address +phonelocation

•Smallsamplesize,often too smal to make claims abaout sub-groups

•Sufferingfromtypical survey blases

•Lowfrequency

•„Recordsnotpeople“: multipleissuingor registration(within andacrosscountries)

•Rarelycoverexits•No socio-demographic information

advantages Disadvantages

administrative

survey

Border Collection

Visa Permits

Household

Surveys

Census

Google Search

Social Media

Work &TaxRecords

Mobile PhonesBig Data

©De

ZIM

| 14


Moredetailedinformationonindividualscanbeextractedfromsurveydata.Thisincludessocio-demo-graphicandeconomicinformationontherespondentsaswellasmoredetailedquestionsonthedifferenttypesofmigrationstatus.Manysurveyshavedesignatedsectionsthatspecificallytargetthedeterminantsofmigrationandaskaboutreasonsformigration,intendedlengthofstay,ormigrationaspirations9. In some cases,surveysamplingmethodsallowreachingthepopulationofinterest,whichmaynotbecapturedinadministrativedata(forinstance,inthecaseofirregularmigration).Nevertheless,mostsurveysarelimitedinsamplesize.Detailedanalysesofmigrantsfromdifferentorigincountries,withdifferentlengthsofstay,employmentstatusorothersubgroupanalysesaredifficultinsmallsamples.Naturally,thenationalcensusdoesnotsufferfromthisproblem.Itisthesurveywiththelargestcoveragebutitisalsoasurveyoflowfrequency(typicallyeverytenyears),whichcanimpedetimelyanalysesandoftenmasksimportantchangesanddevelopmentsbetweensurveyyears.Incontrasttoadministrativedata,surveysmayalsosufferfromthe usual biases that may lead to inaccurate conclusions.

Inthelastfewyears,alternativedatasourceshaveemergedthatmayhavethepotentialtoaddresstheissueofcoverage,frequencyandbiasedresponse.Thepotentialofbigdataformigrationresearchislarge.TheEuropeanCommissionKnowledgeCentreonMigrationandDemography(KCMD)andtheInternationalOrganizationforMigration(IOM)withtheGlobalMigrationDataAnalysisCentre(GMDAC)havelaunchedaworkshopanddraftedadatapolicybrieftoinformtheGlobalCompactonMigrationabouttheimportanceofBigData10.BigDatareferstoinformationinhighvolume(largeamountsofdata,usuallynotcomputablebystandardstatisticalsoftware),velocity(highfrequency)andvariety(differenttypesofdata,suchasnet-works,preferences,textual,imagery,etc.).Thisdatacancomefrommobilephonecalldetailrecords(CDR),Googlesearches,geo-locationsinsocialmediaorIPaddresses.Inarecenttechnicalreport,theEuropeanCommission’sJointResearchCentrehasusedFacebookNetworkDatatoestimatethenumberof‘expats’in17EUcountries.Onemajordrawbackoftheanalysisistheselectionbiasintosocialnetworks.WhileFace-bookcoversvastpartsoftheworldpopulation(ithasabout2.4billionmonthlyactiveusersworldwide),itsusersarenotarepresentativesampleofthewholepopulation.Inordertomakeclaimsabouthowmigrationcapturedinsocialmediadatareflectsactualmigration,researchershavetomakestrongassumptionsabouthowmigrantsselectintosocialmediaplatformsbyage,gender,origin,etc.

Typically,theanalysisofselectionintosocialmedia(includinggivingmoreweightordiscountingcertainobservations)restsonexistingdataonmigrationfromsurveysandadministrativesources.Inotherwords:checkingwhethermigrationestimates frombigdataareplausiblemeanscomparing themto traditionaldatasources.Thismakesbigdatapronetosimilarproblemsastraditionaldatasources.Whileitisdifficulttoinferoverallmigrationrates,itispossibletodetectchangesinmigrationflowsincertainsub-groups.Afewresearchershavelookedatgeo-locateddatafromTwittertoanalysemovementwithinandacrosscountries(Zaghenietal.2014).Theauthorsuseadifference-in-differencesapproachtoinferout-migrationratesandaccountforselectionintothesocialnetwork.ThismeansthattheycomparechangesinmigrationforTwitteruserstooverallmigrationnumbersandanalysethedifferingpatternstomakeclaimsabouthowtheselectedsamplerelatestothewholepopulation.

Despiteincreasingeffortstoimprovethequalityofmigrationdata,thedatabasisformigrationforecastsremainsunsatisfactory.Animportantimpedimenttocomparableanddetailedmigrationstatisticsisthelackofinformationexchangewithinandacrosscountries.Incomparisontodataoninternationaltrade,whereUNComtradepublishesquarterlydataonthetradeingoodsandservicesworldwidewithdetailedcodesforproductandservicecategories,migrationdataismuchhardertomonitorandcountriesrarelyharmonisetheirimmigrationandemigrationdata.

9 SeeforinstancetheGallupWorldPoll,theEuropeanLabourForceSurvey,theMediterraneanHouseholdInternationalMigrationSurvey (MED-HIMS)andmanyothersthathavemigrationcomponentsintheirquestionnaires.10See‘DataBulletin:InformingaGlobalCompactforMigration’IOMGMDAC,IssueNo.5(2018)

| 15


11Itisworthnotingthatthegraphshowsmigrantstocks(thetotalnumberofmigrantspresentinGermany)andisthusacumulative representationthatincorporatesdeaths,fertilityandexistsinadditiontonewentries.

Inalargeconsolidationeffort,theWorldBankandmigrationresearchershavecombinedmorethanonethousandcensusandpopulationregisterrecordstoconstructdecennialmatrices(bilateralmigrantstocksforabout200countries) spanning1960to2000 (Özdenetal.2011).Thedata-setuses the foreign-borndefinitionofmigrants.Morerecently, theOECDhasdevelopedabilateralmigrationmatrix for theyears2000and2010togetherwiththeWorldBank,whichincludesinformationondemographiccharacteristics(ageandgender),durationofstay,labourmarketoutcomes(labourmarketstatus,occupations,sectorsofactivity),fieldsofstudy,educationalattainmentandplaceofbirth.Despitethelargeeffortsbehindthecon-solidationofvariousdatasourcesacrosscountries,theresultingdatasetsuffersfromimportantbiases,asÖzdenetal.(2011),theresearchersbehindtheWorldBankMigrationDataSet,summarise:

‘In constructing global bilateral migration matrices, several challenges arise. First, destination coun-tries typically classify migrants in different ways—by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight—as when the Soviet Union collapsed—these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries—especially developing countries—have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous.’

Thesedrawbacksinthedataareaseriousimpedimenttoquantitativemigrationresearchwhichoftenti-mesreliesontheseglobalmigrationmatrices(especiallytheso-calledgravitymodels,whichwillbediscus-sedinSection3).Thechallengesareparticularlysevereinthemigrationforecastingsphere.Ananalysisofthemovementofpeopleacrosstheglobeoveralongperiodoftimerequirescomprehensivedata,whichnotonlycoversthefinaldestinationcountrybutalsoallintermediatesteps,includingtransitorymigrationwithintheGlobalSouth(forwhichthereisevenlessreliabledata).However,thelackofalternativesbindsmigrationforecasterstothistypeofdata.Currentdatacollectionandconsolidationeffortswillbearfruitinthefuturebutininterpretingmigrationforecastsfornow,onehastoaccountforpotentialgaps,inconsisten-cies and biases introduced by the data.

2.4Futureshocks

Thestrongestimpedimenttoaccuratemigrationforecastsistheinherentinabilitytoforeseeorpredictimportanteventsormajorshiftsineconomics,politics,technology,climateorothermajordriversofmigrati-on.Figure2showsthenumberofindividualswithaforeigncitizenshiplivinginGermanybetween1989and2019foraselectednumberofsourcecountries(asrecordedintheGermanCentralRegisterforForeigners,the‘Ausländerzentralregister’).Thefigureshowshowbumpsinthenumberofforeignersrelatetoafewrelevantpoliticalevents11.TheaccessionofPoland(2004),RomaniaandBulgaria(2007)totheEuropeanUnionwasassociatedwithanincreaseinthenumberofforeignersfromthosecountries.Additionally,inci-dentsofwarandconflictlikeinSyriaandIraqareequallyassociatedwithsubstantialincreasesinthemigrantpopulation.AlloftheseeventsaremajordriversofmigrationandthesegroupsmakeupasubstantialshareofthemigrantpopulationlivinginGermany.

| 16


_syria

_Bulgaria

_Poland

_Iraq

_Romania

900.000

500.000

700.000

300.000

800.000

400.000

600.000

200.000

850.000

450.000

650.000

250.000

750.000

350.000

550.000

150.000

100.000

50.000

1989 2001 20131995 2007 20191992 2004 20161998 2010

PolandEUaccession

Bulgaria&RomaniaEUaccession

Civil War Syria

ISIS in Iraq

Itisverydifficulttopredictpoliticalchange,especiallyinthelongrun.Consequently,migrationforecastsusuallyignorepotentialincidentsofconflictorchangesinmigrationpolicyinthefuture.However,evensmallinitialdeviationscanleadtosubstantiallong-runchange.Forinstance,existingmigrantnetworksareastrongfactorformigrationfromthesamesourcecountry.Networksdecreaseinformationalbarriers,leadtobetterjoboutcomesatdestinationanddecreasetheoverallcostofmigration(Haug2008;Liu2013;McKenzieandRapoport2010;Munshi2003).Thismeansthatchangesinthesizeofamigrantnetworkduetoanunforeseenevent(forinstance,thewarinSyriaandthesubsequentmigrationtoGermany)mightaffectmigrationfromSyriatoGermanyfordecadestocome.Therearesubstantialrippleeffectsthatcanstemfromeventsthataretypicallynotcapturedinmigrationforecasts.

Anotherexampleofanimportantdriverofmigrationisclimatechange.Weknowthatregionalandinternatio-nalmigrationisdeterminedbyvulnerability,exposuretoriskandadaptivecapacityinthefaceofclimatechange(McLemanandSmit2006;Feng,Krueger,andOppenheimer2010;Blacketal.2011).Evenifitwerepossibletoquantifyhowmigrationpatternsevolvewithclimatechange(volume,regionalversusinternational,favoreddes-tinationcountries)andevenifitwerepossibletoperfectlypredictchangesintemperature,rainfallandweathervolatility,somemajoruncertaintieswouldremain.Evenifclimatechangeisfactoredintomigrationforecasts,itissounderthe‘ceterisparibus’assumption,thatis,undertheassumptionthat‘allelseremainsequal’.Thismeansthatpotentialpolicychangesthatcounteractorreinforceclimatechangewouldbesetaside.Thesameholdsfortechnologicalprogress(somedisruptivetechnologicalchangesmaysignificantlyalterthecourseandpatternsofmigration),economicgrowth,demographicchanges,etc.Sincethesedimensionsinteractwithoneanotherincomplexwaysandmigrationpolicyresponsesmayinturnrespondtothesechanges(forinstance,increasedclimatechangemayincreasemigrationbutthereforetriggerarestrictivemigrationpolicythatmayultimatelyreducemigrationinresponsetoclimatechange),itisverydifficulttopredicttheirconsequencesinacredibleway.

Figure2 numberofMigrantstoGermanybysourceCountryoverTime

source: DeStatis&ownelaborations

| 17


Insum,uncertaintyinmigrationforecastingcoversmultipledimensions:thecomplexityofmigrationdetermi-nants,thelackofdataofhighvelocity,volumeandaccuracy,implicitassumptionsusedtoderiveforecastsofareasonablerange,forecastsbasedonforecaststhatalreadycarryalevelofuncertaintyandasetofassumptionswiththem,andfinally,alloftheeconomic,political,technologicalorclimateuncertaintieswhichpresentsomeofthemostimportantdriversofmigrationbutcannotbeforeseen,especiallynotoveralongperiodoftime.Itisimportanttonotethesecaveatswhenpolicymakersusetheseforecaststoget‘aroughestimate’ofmigrationtoGermany(oranyothercountry)inthefuture.Itismorelikelythannotthateventheseroughestimatesdeviatesubstantiallyfromwhatthefutureofmigrationlookslike.

| 18

Therearevariouswaystoconductmigrationforecastingexercises.Methodsvarysubstantiallywithinandacrossfields.Whilethefocusofthisreportistheassessmentofquantitativeapproaches,itisimportanttoconceptualisehowqualitativeandquantitativemethodsinteractandcaninformoneanother.Themaingoalofthissectionistosketchthelogicbehindthreeofthemostimportantquantitativeforecastingmethods,namelyBayesianStatisticalModelling,GravityModelsofMigrationandStructuralModels,highlightingtheresultsofsomeofthecentralacademicpapers.Attheendofthischapter,therewillbeanoverviewoversomeofthequalitativemigrationforecastingmethodsandhowtheycanbeintegratedwithquantitativeforecastingme-thods.Thischapterwillalsoserveasthemethodologicalbasisforselectedmigrationforecastspresentedinthefollowingchapter.

3.1bayesianstatisticalModellingBayesianmodelscanbethoughtofasanextensionofunivariatetimeseriesmodels,modifiedbyusing

probabilisticmethodsasinput.Theonlyinfluencingfactoroffuturemigrationispastmigration;hence,thismethod is considered as a purely data driven approach12.Thisgivesadditionalflexibilityandtoolstoovercomesomeproblemsofmigrationdata,asputinBijaketal.(2019),uncertaintyinmigrationforecastingcanbedivi-dedintothreecomponents:inherentuncertaintyoffutureevents,uncertaintycomingfrommigrationdata(asdiscussedinChapter2)anduncertaintyinducedbythemodel.AllaforementionedtypesofuncertaintycanbeaccessedbyBayesianmodels.However,thiscomesatthepriceoffurtherstatisticalassumptionsandexclusionofcovariatespotentiallycontainingadditionalinformation.

Differenttime-seriesmodelscanbeusedforBayesianforecasting.Forthesakeofsimplicity,let’sconsideranAR(1)modelwherefuturemigrationdependsonmigrationinthelastperiodandthefutureerrortermyiel-ding,mij,t+1 = c + φ mij,t + εt+1.ThemostintuitivewaytothinkaboutBayesianforecastsistocontrastthemagainstlinearregression.Inalinearframeworkwewouldestimateφ parameter by a linear regression from past data mijt = c + φ mij,t-1 + εt-1additionallyassumingnormaldistributionoftheerrorterm.Oncewehaveestimatedφ є (0,1)forinstance0.7,meaningthatmigrationinperiodtlinearlydependsonmigrationint-1 by factor0.7.Foramigrationflowofthesizeof100inperiodt,ourmodelhencewouldpredictamigrationflowof70int+1,assumingnormaldistributederrorswithzeromean.Migrationdatamostlycomesindecadalfre-quency.Let’sassumewebaseourforecastonthemostrecentDIOC-Edata(whichiswidelyusedinliterature),ourforecastfromtheyear2019onwardswouldbebasedonfivedatapointsfrom1960–2010.Theprecisionofestimatedparametersincreaseswiththenumberofobservationsinlinearregressionmodels.Relyingonlyonafewdatapointsmeansthattheestimateisnoisierandlessprecise.

ThestrengthoftheBayesianframeworkisthatittakesaprobabilisticratherthandeterministicapproachintheestimationofthemodelparameters(φ intheBayesianestimationisafulldistribution,not justoneparameter).WithintheBayesianframework,parametersaretreatedasrandomlydistributedvariables,whicharedrawnfromacertaindistribution.Thetypeofdistributionischosenasaninputvariableadditionaltotheobserveddata.Usingthedistribution,wecansimulatedata,followingarandom,stochasticprocessanddrawi-ng possible values of φfromtheassumeddistributionsbyusingdatagenerativeprocesssuchasMarkovChainMonte Carlo13(MCMC)methods(Barnett,Kohn,andSheather1996).Combiningourobservations(likelihood

III. ForecastingMethods

III. Forecasting Methods

12Inthissection,thereportfocusesonpurelyquantitativeBayesianmodels.Section3.4willshowhowBayesianmodelscanbeextendedto incorporate expert opinion and other qualitative dimensions.13Aprocesswhichrandomlystimulatesdatafromadistribution,withnewdrawsdependingonthecurrentdraw,notinfluencedbypast draws.SeeGilks(1995).

| 19


function)andpriordistributionyieldsaposteriordistribution–whichtosomeextent,increasesthevalidityofobservationsbyoursimulatedprocess.Thereportedresultsaretakenfromtheposteriordistribution,e.g.our parameter φ Bayesisusuallythemedianfromtheposteriordistribution(Bijak2006)incontrasttoφ OLS reflectingtheleastsquaresestimatorofobservations.Moreexplicitly,uncertaintycanbeshownbyreportingcredibleintervals(confidenceintervals)wherethetrueparameterliesinwithaprobabilityofe.g.80%.

Bayesianapplicationscanflexiblyincludedifferenttimeseriesmodels14,asARIMAmodels,dependingonlagged values of the independent variable and the error term. The order of the model usually does not go beyondARIMA(1,1,1)(Keilman2001).Butthereareotherdeterminantsofthefunctionalformresultingfromthedatapropertieswhichinfluencethechoiceofthemodel,inparticularstationarity,whichisacommonlyas-sumedfeatureoftime-seriesdata,statingthatdatahasconstantmeanandvarianceovertime.Withanincrea-singtimehorizon,theassumptionbecomeslesslikelytohold.However,thefunctionalformcanbeadjustedtoincludesuchfeaturesaswell(Abeletal.2013).Moreover,themodelchoicedependsonthecharacteristicsofthemigrationflowandisnotuniversallyapplicableinothercontexts.MigrationofstudentstotheUKare,forinstance,ratherstable(Disneyetal.2015).Long-termBayesianforecastsofparticularmigrationflowsonaworldlevelseldomexist15.

Additionalinformationbettermatchingrealitycanbeincludedaswellinthechoiceofthepriordistributions.Notonlythedistributionofparameters,butalsomaximum/minimumforunivariatedistributionsormean/varianceoftheunderlyingdistribution(forinstancethenormaldistribution)canbeestimated,leadingtoa‘multi-level’orhierarchicalBayesianstructure(Berliner1996).Thechoiceofpriorsandthesemulti-levelpriors(hyperpriors)canbemadefromstatistical,butaswellfromqualitativeperspectives(BijakandBryant2016). Expertknowledgecanimproveforecastingperformance,inparticularwithlowdataavailability(WiśniowskiandBijak2009),orifstructuralbreakscanbeanticipated,withnosimilarexistinginformationfromthepast(Disneyetal.2015).

ThegoalofAzoseandRaftery(2015)istoimproveontheUNPopulationDivision’spopulationprojectionsbyaccountingforuncertaintyininternationalmigration.Internationalmigration(specificallynetmigration),fer-tilityandmortalityarethekeydeterminantsofpopulationchange.WhileUNpopulationprojectionsaccountforuncertaintyinfertilityandmortality,theytakemigrationasdeterministic,e.g.currentmigrationrateswillcontinueintothefuture.Asoutlinedinthepreviouschapter,migrationishardlypredictableanduncertaintyislarge.Therefore,theauthorsdevelopamodelthatcanquantitativelyscopeuncertaintyinmigration16.

AzoseandRaftery (2015)useaBayesianhierarchicalfirst-orderautoregressivemodel tofitmigrationratedataforallcountriesworldwide.Theauthorspredictmigrationfor197countriesfrom2010to2100infive-yearintervals,differentiatedbyageandsex.Theirmodeltakestheform(rc,t - μc = φc (rc,t-1 - μc ) + εc,t, wherethelefthandsidevariableisthedifferencebetweenthemigrationrateincountrycattimet(rc,t) and thecountry’stheoreticallong-termaveragemigrationrate(μc).Therighthandsidevariable(orexplanatoryvariable) isthedifferencebetweenrealisedandaveragemigrationrate inthepreviousperiod,whereφc istheautoregressiveparameter(thatliesbetween0and1toensurestationarity).Itisimportanttonotethat theauthorsuseahierarchicalmodel,whichmeans that themodelparametersarecountry-specificandarenotonlyinformedbytheirownpastmigrationexperiencebutthemigrationexperienceofallothercountries(usingUNWorldPopulationProspectsbetween1960and2010).Thisisnotthecasefornon-hie-rarchicalprobabilisticmodelswhichcalibratethemodelparametersindependently,nottakingintoaccountall countries simultaneously.

14ForanextensiveoverviewconsiderDisneyetal.(2015).15ExceptforAzoseandRaftery(2015)whichwillbediscussedingreaterdetailinthefollowingchapters.16Theauthorsemphasisethatthey‘producebothpointandintervalestimates,providinganaturalquantificationofuncertainty’ (Azose&Raftery,2015)

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InadditiontopurelyquantitativeBayesianmodels,BijakandWiśniowski(2010)includeexpert-basedscena-riosderivedbyatwo-roundDelphi-survey17andconvertedintoprobabilitydistributionsintheirforecast,thuspredictingtotalimmigrationseparatelyforsevenEuropeancountrieswithdatafromnationalstatisticaloffices,aswellas frominternationalorganisations, from2010to2025.BijakandWiśniowski (2010)concludethatforecastingmigrationwhenhorizonsaretooisuseless,inparticularwithnon-stationaritycharacteristicscau-sedbyshocks,suchastheEUenlargement.TheauthorschooseaRandom-Walkmodelandstatethaterrorsbecometoo largetodraw inferenceupon,suggesting limitingthepredictivehorizonto5–10years.Expertknowledge,however,helpsinestimatingmodelparametersandimprovesshort-runpredictions,buthasnoinfluenceonthechoiceoftheunderlyingmodel.

3.2GravityModelsofMigrationThegravitymodelisapopularandcommonlyusedframework,adaptedfromNewton’slawofgravity,

generalisedandappliedacrossdisciplines,forinstance,ininternationaltrade,regionalscienceormigra-tion.Research inthisareastartedwithTinbergen(1962)asanapplicationofsocialphysics,andbeca-memoreinterestingtomigrationrelatedissues,withanincreasingdataavailability(Beine,Bertoli,andFernández-HuertasMoraga2016).Theintuitionisthatmassesattractoneanother,withaforcepropor-tionaltothesumoftheirmasses–andrepeloneanotherwithincreasingdistance.Intrade,thegravityrelationshipwasfoundforGDP,showingthatthehighertheGDPoftwocountries,themoretheytrade.Distance, inturn,decreasestradeflows(HeadandMayer2018).Formigration,thedatarevealsimilarpatterns.Countrieswhicharemoreattractiveformigrants, for instance,measuredbyGDP,experiencehighermigrationflows.Physicaldistance,ontheotherhand,isassociatedwithlowermigrationflows.

PuttingthisintothegravityequationE(mij) = SiDjφij represents the expected number of migrants mo-vingfromcountryitocountryj.Si represents the ability of iforsendingmigrants,φij expresses bilateral accessibility,tothinkofascostofmovingbetweencountryiandj.Lastly,Dj = representstherelativeattractiveness of destination j, depending on potential earnings (wages or GDP) in country j (yj), andrelativecostofmigratingtootherdestinationsthenj(Ωi).Thelattertermisreferredtoasmultilateralresistance, includingattractivenessofalternativedestinations iscrucial tounbiasedestimation(BertoliandFernández-HuertasMoraga2013). Inotherwords, themigrationdecisionmultiplicativelydependsonrelativeearningsatthedestination,costofmoving,countryspecificcharacteristicsandtherelativeattractivenessofotherdestinations.

Ingravitymodels,attractionpointsare typically conceptualisedas theeconomicattractivenessofaspecificdestination country as compared toothers18. Sinceexpected life-timeearnings aredifficult tomeasure, economists use proxies such as the current levels of purchasing-power-parity adjustedGDP(HansonandMcIntosh2016)oran indexconstructedwith10-yearbondyieldsonasecondarymarketcombinedwith consumers expectationof the future (Bertoli, Brücker, and Fernández-HuertasMoraga2016). Whenthinkingaboutcostsofmoving,onecouldthinkofavarietyoffactors interalia:movingcost,costforabsencefromthelabourmarket,psychologicalcost,theeffortoflearninganewlanguage(Sjaastad1962).However,inthe‘baseline’gravitymodel,distanceisencompassingallthefactorsmenti-onedabove,anincreaseindistancebetweentwocountriesthusleadstoanincreaseinmigrationcosts.To that, fixed-effectswhich influence themigrationdecision similarly across countries are includedasdummyvariables.Forinstance,MayerandZignago(2011)includethefollowingvariables:beingaformercolony,commonfirstlanguage,commonsecondlanguage,sharingacommonborder,beinglandlocked,being a small island.

17Surveyanonymouslyaskingexpertstoquantifytheirexpectationsonfuturemigrationscenarios.Formoredetail,seeWiśniowskiandBijak(2009).18 This goes back to the concept of dual labour market theory of migration described in the previous chapter.

yj

Ωi

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19Foranextensiveoverviewoneconometricissues,considerBeine,Bertoli,andFernández-HuertasMoraga(2016).

Empiricallygravitymodelsaretakentodatausingmultivariateregression.Fortheaforementionedscena-rio think of mij,t = βo + β1 ln(GDPi,t) + β2 ln (GDPj,t) + β3 ln(distanceij,t) + β4 dummiesij,t + εij,t describing migrationflowsattimet.Allcoefficientslinearlyinfluencethemigrationflow,sayanincreaseinlogofGDPj by10%isassociatedwithanincreaseofbilateralmigrationby1%.Hence,inthissimplemodelthereisnointeractionbetweenthevariables.

Forforecasting,theestimatedparametersareusedtoextrapolatemigrationwellintothefuture.Inotherwords,pastdatarevealshowtheright-handsidevariablerelatestooutcomevariable(forinstance,howGDPatdestinationrelatestomigrationtothatdestinationcountry)andthisrelationshipisassumedtocontinueinthefuture(e.g.changesinGDPinthefuturecorrelatewithchangesinmigrationflowsinthefuturebythesizeoftheestimatedparameter).Tothat,oneneedsinputoffutureGDPdata,whichisbasedonassump-tionsandforecasts,aswellasassumptionsonthefutureerrorterm.Assumingnormaldistributionwithazeromeanandconstantvariancecomesatthepriceofneglectinginfluenceofshocksorstructuralchanges.Inthismodeloffuturemigration,linearlydependsonparametersfromthepastandassumedgrowthpat-terns of the input variables.

Despitethestraightforwardnatureofthistheoreticalmechanism,severalstatisticalchallengescomewiththeseestimations.Distributionalassumptionshavetomatchindividualcharacteristics,considering,forin-stance,thevaryingpayregardinggender.Also,utilityacrosscountriesmightdifferdependingonindividualcharacteristics(OrtegaandPeri2013).Functionalformandapproximationshavetobewell-specifiedandmultilateralresistancehastobemeasuredappropriately19.

Gravitymodelscanincorporateawidearrayofinformationacrossdisciplines,aslongastheyaremetric.Togetanoverviewoftheliterature,let’stakealookatrecentstudies.Backhaus,Martinez-Zarzoso,andMu-ris(2015)measuretheeffectofclimatechangesonbilateralmigration,byincludingaveragetemperatureandprecipitationincountryoforigin,additionaltothe‘basic’framework.Friebeletal.(2018)examinech-angesinsmugglingroutesandthusmigrationcostonmigrationintentions.NaghshNejadandYoung(2012)examinetheeffectofdiscriminationbygenderinlookingatthemigrationdecisionofhighskilledwomen,bycomputingawomens’rightindex,includingeconomic,socialandpoliticalrightsfromtheCIRIhumanrightsdataset(CingranelliandRichards2010)andlookingatthedifferencesbetweentheoriginanddestinationcountry.Theauthorsfindanon-linearrelationshipbetweenthewomen’srightsgapandmigration.Womenaremorelikelytomigrate(comparedtomen),whenwomen’srightsinthedestinationcountryarehigher,unlessthecurrentlevelofwomen’srightsintheorigincountryisataverylowlevel.

Atamacroeconomiclevel,Bertoli,Brücker,andFernández-HuertasMoraga(2016)examinemonthlyEUmigrationtoGermanyfrom2006to2014includingthesequentialnatureofmigrationdecisions,allowingtheindividualtoassessthediscountedutilityofmigratingin t+1 (Vt+1).SotheindividualutilityisdefinedasUijkt wkt – cjk + bVt+1 (k) + εikt.Here,inadditiontothebasicframework,expectationsonfutureeconomicconditionsinhomeanddestinationcountriesareassumedtobethedrivingfactorsofmigration.Thesearemeasuredby10-yearbondyieldsonthesecondarymarketandconsumers´confidence.Anincreaseof10-yearbondyields(equaltoaworseningofeconomicoutlook)oranincreaseinunemploymentatthehomecountry isassociatedwithmoremigration, themagnitude,however,differswith regard to theempiricalspecification.

Asoutlinedabove,gravitymodelscanincorporateanarrayofdeterminingvariables,dependingontheinterpretationofattractionanddistance.Modelscaninclude,forinstance,environmental,political,sociolo-gical,micro/macro-economic,geographicaldataandtestcorrespondingtheoriesonwhatdrivesmigration.

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20Foranoverviewon‘specialcases’,considerthetechnicalappendixoftheDIOC-Edatabase http://www.oecd.org/els/mig/ DIoC-e-2010-11-methodology.pdf

Empirically,optionsaremuchmorestrongly limitedbydataavailabilityandquality,which isgoingtobefurtherdiscussedinthischapter.Sofar,onlydescriptiveex-postgravitypapershavebeendiscussed.Thedatarequirementsforforecastshoweverareevenhigher.Predictingfuturechangesideallyhastobebasedeitheronvariableswhicharestableinthelong-runor,ifexisting,onmigrationtheoriesobservedinthepast.Economicvariables,forexample,GDPorunemployment,canbesignificantlyaffectedbyshocks,e.g.finan-cialcrises,wars,climatechangeortechnologicalprogress,whichcannotbeforeseen.Whenincludingsuchvariables,assumptionsaboutfuturedevelopmentshavetobemadewhichmightseemsomewhatarbitrary.

Thescopeofthegravitymodelsistobuildaframeworkwhichcanrepresentthemigrationdecisionofa‘representativemigrant’inagravityframework.Thenarrownessofthedefinition,however,islimitedbydataavailabilityandquality.Lookingatthemajorityofthestudieswithabroadgeographicalscope,mostcommonlyusedistheDatabaseonImmigrantsinOECDcountries(DIOC)andDatabaseonImmigrantsinOECDandnon-OECDCountries(DIOC-E),whichmakesitworthlookingintoinmoredetail.Dataisdrawnfromnationalstatisticaloffices,andinfewcases,extrapolatedfromcountry-specificsurveys.Itoffersbilate-ral-stockdata,definedas‘astaticmeasureofthenumberofpersonsthatcanbeidentifiedasinternationalmigrantsatagiventime’(UNDESA2017a),indecadalfrequencyfrom1950–2010,includingvariablessuchas sex,age,education,nationality,andcountryofbirth.However, thecategoriesarenotalwaysdirectlymeasured,butbasedonestimationsaswell.Tothat,usingDIOC-EphasesseveralimpedimentsasoutlinedinChapter2.3including:geopoliticalchanges,differentdefinitionsofnationalstatisticaloffices,andvaryingdata quality across countries20. In spiteof that,decadal stockdataoffersa fairlyunsatisfactorybasis foranalysis.In/out-migrationisnettedandbasinganalysison10-yearsnapshotsmightneglectsignificantmo-vementsinbetween.Yet,theDIOC-EDatabaseremainsthemostcomprehensivemigrationdatasource,andinspiteofalldrawbacks,itisindispensableforgravitymodelanalysis.

Alternatively,gravitymodelscanbebuiltonsurveydata.However,inthemajorityofstudies,thegeogra-phicalcoverageislimited.Toanalyseglobalmigration,theGallupWorldPoll(GWP),offersawidegeographiccoverageandgranularityandisconductedeveryyearforasample-sizeof1,000individualspercountryolderthan15years.GWPcoversmorethan150countries,anddealswithvarioustopics,suchaspersonalhealth,financialwell-being,foodandshelterandseveralquestionsconcerningmigrationintentions(interalia:desi-reddestinationofmigration,migration intentionduringthenext12months,migrationpreparations).Asthedataisnotpubliclyavailable,considerGubertandSenne(2016)fordescriptivestatisticsonmigrationintentionstotheEUorEsipova,Ray,andPugliese(2011)formigrationintentionsontheworldlevel.Inspiteoftherichnessofinformationinthesurveydata,onlymigrationintentionscanbemeasured,whichdiffersubstantiallycomparedtoactualmigration(DustmannandOkatenko2014).Hencehavingmoreinformationcomesatthepriceoflosingpredictivepoweronactualmigration.

HansonandMcIntosh(2016)areamongthefirsttoapplygravitymodelstomigrationforecasting.Com-paredtothepreviousoutlinedstudies,thisonereliesonfundamental,demographicalfactors,suchasthefertilityrate,whichisthoughttobemorestableovertime.HansonandMcIntosh(2016)aimtoanalysehowexposedtheEUistomigrationpressuresstemmingfromdifferentfertilityrates,andcomparingittoUSim-migration.TheyarguethatforUS-Mexicanmigration,differencesinlaboursupply(causedbydifferencesinfertilityrates)werethereasonforsustainedandhighmigrationratesinthepast.TheUSexhibitedrelativelylowbirth-rates,whereasMexicofacedhigherbirth-rates,andthesedemographicfactorsmightbeusedtoinferdifferencesinlabour-supply15to20yearsahead,whennewbornsreachtheworkingage.BasedonLutz,Sanderson,andScherbov(2001),theauthorsargueandassume,thatfertilityratesremainfairlystableandcanbeusedtoforecastpopulationgrowthuptotwoorthreegenerationsahead.LookingattheEU,

http://www.oecd.org/els/mig/DIOC-E-2010-11-methodology.pdf

http://www.oecd.org/els/mig/DIOC-E-2010-11-methodology.pdf

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HansonandMcIntosh(2016)arguethatdecliningfertilityratesintheEUandincreasingratesinSub-SaharaAfricaandtheMiddle-East-Asiacouldleadtoanincreaseinmigrationpressure,similartotheUSinthepast.Intheirempiricalanalysis,builtonastandardgravityframeworkincludingGDP,distanceandasetofdummyvariables,thefocusliesontwoadditionalexplanatoryvariables.First,migrationnetworks,whichmeasurethepresentnumberofmigrantsfromthesamecountryoforigininthedestinationcountry,decreasemigra-tioncost.Second,differencesinage-cohortbirthsizeareconsideredtoinferdifferencesinfuturelaboursupply.Tworegressionsareconducted:thefirstwithoutnetworks,thesecondincludingnetworks.

Takingbilateralmigrant stockdata from1960–2010, theauthors computemigrant stocks in receivingcountriesandcalibratetheparametersfor175sendingand25receivingcountries.TheyusetheprojectionsonpopulationgrowthfromtheUNWorldPopulationProjectionsfrom2017andGDPgrowthfromtheIMFforecastforasinputs.Basedontheirempiricalanalysisandforecast,theauthorsconcludethatimmigrationfromSub-SaharanAfricawillrisefrom2010to2050from4.6to13.4million,whilstatthesametime,thenumberofworking-ageadultsintheregionwillrisefrom500millionto1.3billion.Overall,theauthorsfocusonthedemographiccomponentofmigration,illustratinghowchangesinpopulationgrowth(particularlyintheNorthAfricanregionandinSub-SaharanAfrica)willresultinchangesinmigrationpressures.TheyconcludethattheUnitedStateswilllargelybeinsulatedfrompopulation-growthdrivenmigrationsinceitisfarawayfromthemotorsofpopulationgrowth.Ontheotherhand,intheeyesoftheauthors,Europewill‘facestrongpopulationpressuresforimmigrationfordecadestocome’.Nonetheless,aswillbedetailedinthenextchapter,theauthorspredictdecreasingmigrationflowstoGermanyoverthenextdecades.

3.3structuralequationModelsThebestwaytounderstandstructuralequationmodelsisinacomparativeexercisetostandardregression

models(orOrdinaryLeastSquaresEstimation,OLS).Asexplainedintheprevioussection,gravitymodelsinmigrationaretakentothedataintheformofamultivariateregressionanalysis.Theseregressionanalysesesti-matetheeffectofafew(presumablyexogenousorindependent)explanatoryvariablesonthemainvariableofinterest,inthiscasemigration.Thesizeoftheeffectiscapturedinthecoefficientoftheexplanatoryvariable(denotedasβbelow).Imagineasimpleregressionwithmigrationnetworksatdestinationasanexplanatoryvariableformigrationtothatcountry,whichtakesthefollowingform:migrationijt = α+ β * networksijt + εijt. Imaginewefindthata10%increaseinmigrationnetworksfromsourcecountryj,livingatdestinationcountryi,attimetincreasesmigrationby1%.Inthissimpleregression,wehaveassumedthatthereisalinearrelations-hipbetweennetworksandmigration,thatnoother(omitted)variableinfluencesmigration.Wealsoassumethatmigrationinitselfhasnoeffectonmigrantnetworks(whichisclearlynotthecase)andotherassumptionsthatultimatelyrelatetothedistributionoftheerrorterm.

Regressionanalysisisadatadrivenapproach,structuralequationmodels(SEMs),ontheotherhand,aretheory driven empirics21.Atthebeginningofastructuralestimation,thereisatheoreticalmodelthatdetermi-neshowandwhethercertainvariablesarerelatedtooneanother(so-called‘weakassumptions’).Forinstance,migrantnetworksdeterminemigrationbutwecanalsostipulateinaSEMthatmigrationinitself,aswellasavectorofotherexplanatoryvariables(describedbelowasZ_ijt)affectmigrationnetworks.Inthiscase,wewouldadditionallywritethatnetworksijt = α+ β * migrationijt + γ * Z‘ijt + εijtThismeansthatwedonothavetoassumeaunidirectionalorcausalrelationshipbetweentheexogenous(networks)andtheendogenous(migration)variable,butwecanincorporatethepossibilityofareverserelationshipbetweenthem.However,thetheoreticalmodelhastodeterminehowthesevariablesrelatetooneanother.Additionally,SEMshavetomakeso-called‘strongassumptions’onwhichvariablesareindependentfromoneanother.Inourexample,thismeansthatthetheoreticalmodelshouldidentifyavariablewithinthevectorZthathasaneffectonnet-

21ThisexplanationlargelyfollowsHoyle(2015);HeckmanandVytlacil(2005)andAlanC.Acock(2013).

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worksbutnotonmigrationitself.Byrearrangingmultipleequations,anSEMshouldbeabletoexpresseachdependentvariablewithat leastoneexogenousvariable. Inthisway,SEMspartiallyreflectthe logicofanInstrumentalVariableEstimation.

ThecoefficientofanOLSestimationrepresentstheslopeofafittedlinethatminimisesthedifferencebet-weenpredictedandactualdatapoints.TheparametersofanSEMsminimisethedifferencebetweenthepre-dictedandactualvariance-covariancematrix(typicallywithaMaximumLikelihoodMethod).Whilethecoef-ficientofanOLS(denotedasβ)wouldsuggesthow–allelseremainingequal–onevariableinfluencestheother,theparameterofastructuralequationreflectstheeffectofmultiple,interactingvariablesonavariableof interest.

SinceSEMs incorporatemultiple relationships (asopposed to regressionmodels thatarebasedononerelationshipbetweenadependentvariableandasetofpredictors),theyareusefulinanalysingcomplexsys-temswithmanyinterdependencies.SEMsarealsoparticularlysuitedtoaddressconstructsthataremeasuredwitherror(sinceSEMsmakeexplicitassumptionsonhowerrorsrelatetooneanother)andtheyareusefulinanalysingindirectormediatedeffectsbetweenvariables(sinceSEMsconceptualiseindirectrelationships).Overall,SEMstrytogetatthecausalmechanismbetweenvariables.Whilesimpleregressionanalysisdependsontheresearchdesigntomakecausalclaims,SEMsdependontheunderlyingmodelanditsassumptions.Thecredibilityofthecausalclaimthereforedependsonthestructureofthemodel(asitwouldrelyontheresearchdesignforasimpleregressionanalysis).

TheabilitytoaccommodatecomplexsystemsmakesSEMsausefultoolinmigrationforecasting.However,themorecomplexthephenomenon,themoredifficultitistoconstructanappropriatetheoreticalframeworkaroundit.Thisalsorelatestotheprevioussectiononuncertaintiesinmigrationforecasting.Oneissueisthelackofaunifiedtheoreticalframeworkforinternationalmigration(thatdoesnotexistwithintheeconomicsdisciplines,letaloneacrossdisciplines)thatcouldinformsuchSEMs.Instead,somerecentpapers,notablyDao,Docquier,MaurelandSchaus(2018)andBurzynski,DeusterandDocquier(2019)presenttheirowntheoreticalframeworkstoestimateinternationalmigrationthroughanSEM.

Intheirpaper‘GlobalMigrationinthe20thand21stCenturies:TheUnstoppableForceofDemography’Daoetal.developamodelofmigrationthatisdeterminedbywagedisparitiesbetweencountries,differencesinamenitiesandmigrationcosts.Theauthorsalsoassumethattherearetwoskillslevelsamongindividuals,whichhavedifferentreturnstotheirlabouracrosscountries.Atthesametime,theauthorsmodeltheeco-nomyintheformoffirmswithacertainproductiontechnologythatproducesthesewagedisparities.Wagedisparitiesinthemselvesaredependentontheallocationoflabouracrosscountries,whichisaffectedbyin-ternationalmigration.Allthesefactorsjointlydeterminetheworlddistributionofincomeandtheallocationoftheworldpopulation.

Inasimpleregressionmodel,migrationwouldonlydependondifferencesinwagesandamenities,aswellasmigrationcosts.WewouldestimatetheeffectofallofthesefactorsonmigrationinanOLS.TheSEMnowallowsustoincorporatethefactthatwagedifferencesareaffectedbymigrationaswell.Ifmorepeoplemovetoaplacewherewagesarehigher,thosewageswillinturndecreaseaslaboursupplyincreases.Thiswillinturnaffecttheincentivestomigrateduetochangesinwages.ThisloopofcausationcanbeincorporatedinanSEM,whichwouldn’tbethecaseinasimpleOLS.However,onemayarguethatthetheoreticalmodelisstilltoosimpleanddoesnotincludeotherimportantfactors,suchassocio-culturaldeterminants.Ifweweretomakethemodelmorecomplex,wewouldhavetospecifyhowexactlyothervariablesaffectoneanother.

Additionally,allthevariablesinthemodelhavetobecapturedindatasets.Evenifwethinkthatsomedi-mensionsareimportanttoamodelintheory,wehavetobeabletomeasuretheminpractice(andideallywithalargesamplesizetoincreasetheaccuracyoftheestimatedparameters)(Bollen1990;Bearden,Sharma,and

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Teel1982).Thisisparticularlytruewhenitcomestousingstructuralmodelsinforecasting.Asmentionedinthesectiononimprecisedata,thechallengeisnottoonlymeasurevariablesinthepastbuttoreasonablypredicthowtheywilldevelopinthefuture.Togobacktothepreviousexample,ifwebelievemigrationisdeterminedbymigrationnetworks,wewillhavetobeabletopredictfuturemigrationnetworkstosayanythingaboutfuturemigration.ThiscaveatalsoholdsforSEMs.

Intheirpaper,Daoetal.(2018)calibratetheirmodeltomatchtheeconomicandsocio-demographiccharac-teristicsof180countriesandthebilateralmigrationstocksof180times180countrypairs(byskilllevel)fortheyear2010.TheauthorsusedataonthesizeandthestructureofthelabourforcefromtheWittgensteinCentreforDemographyandGlobalHumanCapital,theyusethewagerationbetweenskilledandlessskilledworkersfromHendricks(2004),GDPdatafromtheMaddison’sproject22,anddataonmigrationfromtheOECD23. In a second step,theauthorschecktheplausibilityoftheircalibratedmodelinabackcastingexercise,wheretheyretrospecti-velypredictbilateralmigrationgivenpastdataandcomparethemwiththeactualmigrationfiguresfromthepast.Theauthorsfindaverygoodmatchbetweenmodelledandactualmigrationfigures.

Inordertopredictbilateralmigrationstockswellintothefuture(inthiscaseuntiltheyear2100),theauthorshavetoturntopredictionsonthevariablestheyusedforthecalibrationandbackcastingexercise.Theyuseso-cio-demographicscenariosfromLutz,Butz,andSamir(2014),whoprovideprojectionsbyage,sex,andeducati-onlevelsforallcountriesoftheworld.Therefore,theyareabletousethesepredictionsforallrelevantvariablesandmakeestimatesaboutfuturemigrationstocks(oftheworkingagepopulation,age25to64)betweenallcountrypairsworldwideuntiltheyear2100.Theauthorsusedifferentscenariosinthepopulationpredictionsandshowpredictionsfordifferentassumptionsaboutthesubstitutabilityoflowandhighskilledlabour,aswellasdifferentmigrationpolicies(reflectedinmigrationcosts).Theypredictthattheshareofmigrantsovertheworldpopulationincreasesfrom3.6%in2010to4.5%in2050andto6.0%in2100,whichequalsanabsoluteincreaseofabout111millionpeoplebetweentodayand2100.InOECDcountriestheproportionofworkingageimmigrantswillincreasefrom11.9%to27.5%inthenext80years.

Inasimilarattempt,Burzynski,Deuster,andDocquier(2019)developanSEMthathasasimilarbasicmodelstructurebutextendsitsubstantially.Thetheoreticalframeworkadditionallyincorporatesdifferentsectors,ac-countsforin-countrymigration,technologicalchangeandindividualdecisionsabouteducationandfertility.Thegoalofthepaperisto‘quantitativelyanalysetherootdriversunderlyingthelong-termtrendintheworldwidedistributionofskills(i.e.,domesticaccesstoeducation,sectorallocationofworkers,andinternationalmigration)andhighlighttheimplicationsoftheserootdriversforeconomicconvergenceandglobalinequality’.Oneofthewaysinwhichskillisdistributedacrosscountriesismigrationandthereforemigrationcanfosterordampeneco-nomicinequalityintheirframework.Sincetheauthorsmodelhowmigrationreactstochangestodemographicandtechnologicalchange,theyareabletopredictfuturemigrationstocks(again,fortheworkingagepopulation).

TheauthorsofthelatterpaperhavekindlyprovideduswiththeirmigrationsimulationsforGermanyoverthenext80years.Wewillpresentandcomparetheirresultswithotherpredictionsinthenextchapter.

3.4QualitativeversusQuantitativeModellingManyofthecaveatstoquantitativeforecastsoutlinedinthepreviouschapteralsoapplytoqualitative

forecasts.Thelackofaguidingandunifiedtheoryofmigrationandthecomplexityof itsdeterminantsare independentofanymethodologicapproach; theanticipationof futureshocks remainsdifficult forqualitativeandquantitative researchers alike. In contrast toquantitativemodels, qualitative scenarios

22DescribedinBoltandvanZanden(2014).23DescribedinArslanetal.(2016).

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24Özdenetal.(2011).25SeePaoletti,Hein,andCarlos(2010)andHaas,Carlos,andSimona(2010)foraconceptualandmethodologicalreview

areexpert-basedratherthandata-driven.Plausibilitychecksalongthewayhelptoavoidtypicaldata-dri-veninaccuraciesthatstemfromerroneousextrapolationofpast(andoftenimperfectlymeasured)dataor over-interpreting statistical artefacts (considering, for instance, that the increase in the number ofrecordedmigrantsafter the collapseof theSovietUnion is a statistical artefact, rather than themassmovementofpeopleafter199024).Whilequantitativemodelsrelyonmethodologicassumptions,rootedinstatisticalanalysis(thenextsectionswillexplainafewofthoseassumptions),qualitativescenariosde-mandthatexpertspostulatecertainassumptionsfromwhichfuturescenariosarethenderived.Theseareindividualorconsensusassessmentsaboutthedeterminantsofmigration,potentialshockstomigration,includingpoliticalorsocialchangeinthefuture.Usually,expertsdevelopavarietyofscenarios,wheretheyexploredifferentset-upsandtheirpotentialconsequences.Often,qualitativescenariosareinformedbyexistingdataonmigrationandquantitativeassessmentsofsocio-economicanddemographicdevelop-ments in the future.

TheInternationalMigrationInstitute(IMI)incooperationwiththeAmsterdamInstituteforSocialScien-ceResearch(AISSR)hasdevelopedaMigrationScenarioMethodologywhichisanexploratory,qualitativemigrationprojectionorforecastingtoolthatseekstoidentifypossiblefuturesourcesofstructuralchangeatthegloballevelandtheirconsequencesformigration.Insteadofcomingupwithforecastandprojec-tions in the formofnumbersandgivingconcretetime-frames fordifferentscenarios, thetoolaimstodevelopnarrativesaboutthefutureofmigrationdrivenanddevelopedbymigrationexperts.Interactionsbetweenmigrationexpertsintheformofworkshopsaimtofosteravividdebateamongresearchersandpolicymakers alike.

Theprojectwascomprisedoffourmainphasesrolledoutbetween2009and2013.Inthefirstphase,theresearchersreviewedtheliteratureonthemaindriversofmigrationandadaptedscenariometho-dologiesfrombusinessandmilitarytothemigrationcontext25.Theauthorselaboratedatheoreticalfra-meworkof thesocial,political, cultural,economic,demographicandenvironmental factors in sendingand receiving countries thatdrivemigrationand set the framework throughwhichexpertswouldde-velopdifferentscenarios.Inthesecondphase,25expertsandstakeholdersfromdifferentbackgrounds(geographically, academically, etc.)were invited toaworkshopwith theaimofdeveloping ‘first-gene-ration’scenariosformigrationtoEurope.Expertsdeveloped16scenariosandidentifiedfuturerelativecertaintiesanduncertainties.Asubsetof8scenarioswereselectedinthethirdphaseoftheprojecttobereiteratedanddeepened.Anonlinesurveyamong50migrationexpertswasconductedinordertocri-tiqueunderlyingassumptionsandcheckplausibility.Respondentsassessedtheeffectsoftechnologyandinternationalnetworksonmobility;theeffectofsocialnormsandvaluesonthecompositionofmigrantpopulations; the interactionbetweenxenophobiaandmigrationpolicies, aswell as the consequencesofclimatechange,allwithinthecontextofmigrationfromNorthAfricatoEurope.Inthefourthandlaststageoftheproject,moreexpertsgatheredinvariousworkshopstoidentifyotheremergingtrendsanduncertaintiesandapplythescenariomethodologytoconcretecontextsandcasestudies.

Oneofthemanyoutputsoftheexerciseistheidentificationofglobal ‘megatrends’forfutureinter-nationalmigration.Theexpertshaveidentifiedninemainfactors:climatechange,increasingnetworks&globalisation,ageingpopulationsandshiftingdemographics,changingtechnology,decliningpopulati-onfertility,diversifyingsocieties,increasingeducation,increasinglongevityandurbanisingdevelopingcountries.Otheroutputs includedcasestudiesonmigration in thePacificor theHornofAfricaandYemenaswellaspolicybriefsthatcontextualisedandexplainedthescenariomethodologytovariousstakeholders and policymakers.

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Inamorerecentqualitativeexercisein2017,theInternationalOrganizationforMigrationtogetherwithFriedrich-Ebert Foundation and Global Future developed four scenarios for the future of internationalmigrationandmobilitywiththehelpofagroupof50individuals,comprisedofmigrants,policymakers,academics,opinion-makersandindividualsfromtheprivatesector,thinktanks,andinternationalorgani-sations.SimilartotheMigrationScenarioMethodologyoftheIMIandAISSR,theseexperts(althoughnotonlyacademicexperts)gatheredandexchangedtheirknowledgeduringseveralworkshops.First,ascopingworkshopservedasatooltoidentifytheoverarchingprinciplesofthemigrationscenariobuilding;thenasurveyamongtheexpertswasconductedtonarrowdownthemostimportantfactorsshapingthefutureofmigration;lastly,twoscenariobuildingworkshopsandonewebinarwereheldtodesignandfleshouttheconsequencesofpotentialoutcomes.Aswithmostqualitativemigrationscenarios,theprojectwasnotdesignedtodevelopandproposeconcretenumbersonfuturemigrationflowsbuttoillustratehowpoliticaldecisionstodaymayleadtodifferentoutcomesinthefuture(visualisingtheyear2030asthecut-offpoint).Participantsoutlinedtheconsequencesofvariousscenariosforpoverty,demography,inequalitybetweenandwithincountries,aswellasthenexusbetweenconflicts,failedstates,andbadgovernance.

Bothoftheseprojectsareillustrativeoftheexploratoryapproachbehindqualitativemigrationscena-rios.Expertsfromvariousbackgroundsengageandexchangewithoneanothertodevelopplausiblescena-riosforthefuture,criticallyassessingvariousdimensionsthatdetermineandaredeterminedbymigration.Mostoftheseeffortsdonotaimtoquantify,projectorforecastmigrationbuttocontextualisethedebateandpointtopotentialconsequencesofpolicydecisionsandchangesonthemacro-level.

There are someefforts to combinequalitativeandquantitativeapproaches inmigration forecasting.Sander,Abel, andRiosmena (2013)usea so-calledmultiregionalflowmodel26 and combine itwithex-pert-basedwhat-ifscenariostodevelopasetofprojectionsuntiltheyear2060.Theauthorsfirstestablishthemainforcesofmigrationthroughareviewoftheliterature:I)geographyandtimingofinternationalmigrationflows,II)thecontinuationofmigrationflows(statedependenceandnetworkeffectsofmigra-tion),III)economicforces,developmentandemigrationIV)climateandenvironmentalchangeV)shocks,violence,politicalupheaval,displacement,VI)migrationpoliciesandVII)socio-demographicfactors.

Inasecondstep,theyuseglobalestimatesofinternationalmigrationflowdatabetween1990and2010for 196 countries (estimated from sequential stockdata) to create apictureof current emigrationandimmigration rates.Departing fromthemaindeterminantsofmigration fromthe literatureanddataoncurrentmigration,expertviewsonthefutureofmigrationwerecollectedintheformofanonlinesurvey.Thesurveywassenttoallmembersofinternationalpopulationassociationtoobtainexpertopinionsontheimpactofvariousfactorsonfuturemigrationtoandfromacountryoftheexpert’schoice.Respondentsweregivenvariousarguments.Theargumentsweredividedacrossfivebroadthematiccategories,alongthelinesofthedeterminantsestablishedintheliteraturereview(suchaseconomicdevelopment,climatechange,demographicfactors,costofmigration,migrationregimesandpolicy).Withineachofthesecate-gories,theresearchersidentifiedfivetosevenargumentsorstatements.

Overallexpertshadtomakeanassessmentonascalefrom-1to+1aboutwhetheracertainargumentwouldhaveanegativeorpositiveeffectonnetmigrationandgiveavalidityscoretothatargument.Forinstance, one argumentwas ‘Remittanceswill becomemore important for the economic developmentofmigrant-sendingcountries’andrespondentshadtoratehowvalidthisargumentwasandhowstrongofaneffect itwouldhaveonemigration/immigration.Basedonthesescores, theauthorswereabletonumericallyweightdifferentargumentsforthedevelopmentofmigrationscenarios.Theresultsfromtheonlinesurveywerecombinedwithanexpertgroupmeeting.Similartopurelyqualitativescenario-building

26SeeAbelandSander(2014)foranoverviewontheestimationofmigrationflowdata.

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workshops,expertsfromdifferentgeographicregions,scientificdisciplinesandareasofexpertise(socalled‘meta-experts’intotal)exchangedtheirexpertiseandexpressedtheirviewsontheimportanceofsomemigrationdeterminantsinthefuture.Incontrasttoconventionalscenario-workshops,theseexpertshadtoquantifytheirassessments (onascale, thesameas in theonlinesurvey). In theend,combiningtheonlinesurveywiththemeta-expertassessment,theresearcherscoulddevelop‘netimpactfactors’forallarguments.Theseactassomeformof‘weights’thatnotonlydeterminethelikelihoodoftheargumentoccurring(plausibility)butalsohowimportantitisforfuturemigrationflows(impact).Basedonalloftheseassessments,theauthorsthendevelopedthreedifferentscenarios.

Thefirstscenario(the‘mediumscenario’) followedthemeta-experts’suggestiontoassumeconstantmigrationrates(notabsolutenumbers,inordertoaccountforpopulationchange)throughouttheprojec-tionhorizonin2060.Inotherwords,existingemigrationandimmigrationratesbetween2005and2010wereassumedtocontinuelinearlyuntil2060.For25countries,theexpertgroupmadeadjustmentstothebaselinerateof2005to2010sincethesecountrieswereconfrontedwithan‘unusual’migrationpatternduringthattime.Inthisverybasicmodel,theauthorsestimatetheworldmigrantpopulationin2060tobeat350millionandthenetmigration(immigrantsminusemigrants)toEuropeandNorthAmericatobeatabout6millioneachin2060.EmigrationfromSouthAsiaandAfricaisprojectedtoincreaseoverthenext40years.

Inadditiontothemediumscenario,theauthorsandexpertsdevelopedtwootherscenarios,onenamed‘RiseoftheEast’ (RE)andtheother ‘IntensifyingGlobalCompetition’(IGC).Thesealternativescenarioswerebuiltbasedontheassumptionsandargumentsdevelopedbythemeta-experts.Themetaexpertsidentified sevenarguments asbeing themost relevant to shaping future trajectoriesofmigration. TheREscenarioisbasedontheargument‘Majoreconomicrecessions/stagnationinindustrializedcountrieswillleadtolessdemandformigrants’withintheeconomicdevelopmentcategory.ItassumesstagnatingeconomiesintheWest,resultinginrestrictivemigrationpolicies,andtheriseofSouth-EastAsiaasamaindestinationregion.IGCassumesincreasedeconomicgrowthworldwidewithanincreaseincompetitionforlabourandotherresources,resultinginliberalimmigrationpoliciesandincreasedmobility.AssumptionsintheIGCscenarioarebasedonthenetimpactfactorforfivedifferentarguments,namelylabourandskillshortages,waterconflicts,youthbulge,establishednetworksandpoliticalinstability.

Theauthorsestimatethatin2060,IGCproducesover500millionmigrants,RESlessthan300million.Dependingonthescenario,thegeographicdistributionofmigrantsvariessubstantially.ThefundamentalfeatureofREisthatWesterncountriesbecomelessattractivetomigrantsduetotheirstagnanteconomies;atthesametime,Westerngovernmentsbecomemorerestrictiveintermsofmigrationpolicy,whichleadstoadeclineinmigrationtoWesterncountries(cutbytwothirdsinEuropebetween2010and2060).IGC,ontheotherhand,presentsanalternativescenariowithaflourishingeconomy intheWest.Combinedwithdemographicchangesinthedevelopingworldandclimaticandpoliticalshocks,migrationtoEuropeisprojectedtoincreasebyover50%until2060,itmayevenalmostdoubleforNorthAmerica.

Overall, Sander, Abel, andRiosmena (2013) combine expert opinionswith quantitativemodelling todevelopdifferentscenariosforthefuture.However,theiranalysisshowsthatexpertopiniononwhatisacrucialfactorforthefutureofmigrationcanvarysubstantially.Incontrasttopurelyqualitativestudies,theauthorsaimtoattributeprobabilitiesandweightstoexpertopinionsbylettingthemgradetherespectiveimportance,whichisimportantifwewanttosystematicallyintegratequalitativeassessmentsintoquan-titativestudies.Nevertheless,theselectionofexpertsandtheirpersonalbiasesmakeithardtoconsiderthoseassessmentsasuniversalorcommonwisdominthefield.

Buildingonasimilarstrategy,Acostamadiedoetal.(forthcoming)studiedfutureimmigrationtoEuropein2030usingatwo-stepapproach.Inthefirststep,theauthorsreviewedmigrationscenariosandforecas-

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tingstudiesfromacademicandgreyliterature, includingtheEuropeanAsylumSupportOffice,theJointResearchCentrefromtheEU,OECD,IOM,amongothers.Basedonthereview,theysynthesisedthemostimpactfulanduncertainmigrationdriverstoEuropein2030,andsummarisedfourmigrationscenarios.Inthesecondstep,usingaDelphisurveytoshowthedegreeofexpertagreement,theresearchteamaskedmigrationexpertstoratetheprobabilityofeachofthefourscenariosbecomingreal,andtheimplicationsfortotallabour,irregular,andhumanitarianinflowstoEuropeaccordingtoeachscenario.Followingthis,theycanprovideaquantitativeestimateoffutureinflowstoEuropein2030withinarangeofplausiblefuturescenarios.IncontrasttoSanderetal.(2013),theresearchersprovidethepossibilityforexpertstoincorporatechangesinthesizeanddirectionofmigrationdriversinthefuture.However,theirquantitativeassessmentspurelyrelyontheexperts’predictionsanddonotfollowaquantitativemodellingmethod.Thatis,expertssuggestaspecificnumberformigrationflowsfortheyear2030andareabletoincorporateuncertaintyandchanges inmigrationdeterminantsdynamically.However, theseexpertsuggestionsarenotframedwithinoranchoredinaquantitativemodel(likeinSanderetal.,2013).

Asmentioned in the introductory paragraph, bothquantitative andqualitativemigration forecastingmethodshave importantcaveats. Theuncertaintiesdescribed inChapter2almostuniversallyapply toattemptstopredictthefutureofmigrationaroundtheworld.Thequestionishowdifferentapproachesareabletomitigatetheseuncertainties.Table5compareshowqualitativeandquantitativemodelscanaddressthedifferentdimensionsofuncertainty.Ingeneral,qualitativeandquantitativemodelsarequitecomplementaryinthewaytheydealwithdifferentsourcesofuncertainty.Thecomplexityofdeterminantsandthelackofaunifyingtheoryonmigrationcanbeaccountedfor,inpart,throughanexchangeofexper-tiseacrossfieldsandcanincludeexperiencesofnon-academicexpertsandotherstakeholders.Quantitati-vemodelsareusuallyhighlysimplified(formanyreasons,asdescribedinChapter2)andfocusononeme-thodologythatisthenfedwithdata.Hybridmodelsacrossdisciplinesarerareinmigrationforecasting.Ontheotherhand,themethodologicrigorofquantitativemodelsallows(toanextent)transparencyabouttheassumptionsrequiredtorunthequantitativemodel(bothstatisticassumptionsandmodelassumptions,asingravityorstructuralmodels).Theseassumptionsoftenoperateinthebackgroundbutareuniversallyagreedoninthefield,differentassumptionscreatedifferentquantitativemethods.Therefore,thechoiceofmodel clearly reflectsand signals the choiceof implicit assumptions (for theexpert,notnecessarilyforthelayman).Qualitativemodelsaremoreopaqueinthesetofassumptionsthatfeedtheexperts’as-sessments.AsinSanderetal.’s(2013)model,expertsratetheaccuracyandimportanceoftheargumentswithoutmakingexplicitwhichassumptionsledthemtoaspecificassessment.Additionally,thescopeofqualitativeworkshopsislimited.Thechoiceofexpertsandworkshopformatsmaycruciallyinfluencetheoutcomesofqualitativescenarios.Whichassumptionsledtoaspecificworkshopdesign?Whyweretheseexpertsselectedandnotothers?Whatistheoptimalsizeofworkshops?Whataretheparticipants’biasesandhowaretheyaccountedfor?Ifdivergingopinionsexist,howisthisresolved?Allofthesequestionsareinstrumentalinunderstandinghowaspecificforecastwascreated.Unfortunately,manyoftheseassump-tionsremaininthebackgroundofmanyqualitativeanalyses.

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Canbepartiallyaccountedforthrough

expertopinions&exchange

Dependingonmodel,complexitycanonly

bereflectedinalimitedway.

Assumptionsarestructurallyclear(statistic

assumptionsarepartofthemodel,theo-

reticalassumptionsareexplicitlystated).

Assumptiontendtobestrong.Modelsare

verysensitivetochangesinassumptions.

Data-drivenapproachmakespredictions

vulnerable to imprecise data.

„One-Off“shocksinshort-andlong-run

difficulttoincorporateinmodels.

Complexity of

Determinants

Implicit

assumptions

Imprecise

Data

Future

shocks

Difficulttomaketransparenthowassump-

tionsareformedandhowtheyareweigh-

ted. „Trust“ in experts and compromise

betweendivergignopinionsrequired.

If data used: plausibility checks for obser-

veddataasabasisforfutureprojections.

Assessmentonwhetherthisis„outofthe

ordinary“orexpectedtocontinue.

Short-runexpertpredictionsmaybe

possible.Long-runduncertaintyremains.

QualitativeModels QuantitativeModels

Forcasesinwhichqualitativeapproachesusesomedatatoinformtheirscenarios(eveniftheoutputisnotnumerical,qualitativemethodsmayalsoconsultexistingdatasetsonmainmigrationcorridors,migrationde-terminantsorsurveys),theytendtobelesssensitivetoimprecisedata.A‘glib’inthedata,migrationcausedbyuniquecontexts,statisticalormeasurementartefactscanbereviewedandreappraisedmoreeffectively(espe-ciallyifthemodeldoesnotrequiredataasinputbutonlyascontext).Quantitativemodels,bynature,extrapo-lateindifferentwaysfromexistingdataandarethereforemorevulnerabletotheirinaccuracies.However,itisimaginablethatquantitativemigrationforecasterscouldconstructmigrationdatasetsfrompastmigrationthattreattheseinaccuraciesmorecarefullyandmayevenremovethatfractionofmigrationthatis‘contextual’andseparateabasictrendfrom‘noise’.Still,thiscanonlybedonethroughtheintroductionofmanymoreassump-tionsthatmaybesomewhatarbitrary(andintroduceanadditionalsourceofuncertainty).Lastly,futureshockstotheeconomy,technology,climatechangeorpoliticalstabilitycansubstantiallyalterthecourseofmigrationinthefuture.Bothqualitativeandquantitativemodelsarepronetothissourceofuncertainty,especiallyinthelong-run.Quantitativemodelsfollowapre-determinedmetricwhichmakesitdifficulttoincorporate‘one-off’shocks,eveniftheycouldbeforeseenbyexperts.Qualitativemodelswould,intheory,beabletoaccountforpotentialshocksthatannouncethemselveswellinadvance.Unfortunately,politicalescalation,economicrecessionorothershocksaredifficulttopredict,evenintheshort-run.

3.5strengthsandweaknessesofQuantitativeModels

Thereisnotonlycomplementaritybetweenqualitativeandquantitativemodels.Differentapproacheswi-thinquantitativemodellingshouldbeconsideredjointlytogetamorenuancedpictureoflikelyscenariosinthefuture.Table6providesanoverviewofafewofthemainpapersforeachofthemethodspresentedintheprevioussections,includinghybridmodels(e.g.mixbetweenquantitativeandqualitativeapproach).Thetablepresentsthemainmechanismsbehindthetheory,whichisparticularlystrongforstructuralmodelsastheydependonexplicitrelationshipsbetweenvariables.Inordertoestimatefuturemigrationflows,structuralmo-delshavetoprovideaguidingtheorythatdetermineswhichvariablesinfluenceoneanotherandwhichdonot.Inthepapersonstructuralmodelsdescribedabove,migrationisdrivenbyafewmainfactors,includingwage

table 5 ComparisonbetweenUncertaintiesinQualitativeandQuantitativeModelling

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disparities,differencesinamenities,migrationcosts,education.Gravitymodelsfollowthebasicmechanismthatdescribesgravityasatrade-offbetweensizeanddistance,whichisre-interpretedinmigrationeconomicsaspullfactors(size,forinstance,GDP)andmigrationcosts(distance,forinstance,geographicdistanceorlangu-agebarriers).Howthemodelisspecifiedinanestimatingequationandwhichvariablesareincludeddependsontheunderlyingtheoryoftheresearcher.Bayesianmodelsarenottiedtoaspecificmechanism.Pastmigra-tiondetermines(withvariousdeviations)futuremigrationwithoutmakingstrongassumptionsaboutchannelsand mechanisms.

table 6overviewQuantitativePapersbyMethod

Model

type

structual

Models

Gravity

Models

bayesian

Models

Hybrid

Models

Daoetal.

(2018)

GlobalMigrationinthe

20thand21stCenturies:

theUnstoppableForceof

Demography

Migrationduetodif-

ferencesinwageand

amenities

Socio-Demo-

graphicProjec-

tionsbyLutz

etal.(2014)

2020-2100

(10year

rhythm)

180

countries

Burzynski

et al.

(2019)

Hanson&

McIntosh

(2016)

Azose&

Raftery

(2015)

Sander

et al.

(2013)

Thefutureofinternational

migration:Developing

expert-basedassumptions

forglobalpopulationpro-

jections

CasualForecasting

Lutz(2012)

Experts opinions on

likelihood of certain

scenarios

UNWPP2010

Experts opinion

(Membersof

2011WPcouncil)

2010-2060

(5year

rhythm)

10

regions

BayesianProbabilistic

ProjectionofInternational

Migration

Futuremigrationde-

pends on past migra-

tionandlong-term

migration

UNWPP

2010

2010-2100

(5year

rhythm)

197

countries

Bijak&

Wiśniowski

(2010)

Bayesianforecasting

ofimmigrationbyusing

expertknowledge

No theorethical

implications

NationalData

Experts opinion

onmigration

flows

2010-2025

(yearly

rhythm)

7EU

countries

Is the Mediterranean the

NewRioGrande?USand

EUImmigrationPressures

intheLongRun

Migrationdueto

differencesinlabor

supply,resultingfrom

changesinfertility

UNWPP

2017IMF

GDPforecast

2010-2050

(10year

rhythm)

175

sending/

25 receiving-

countries

Geography of Skills and

global Inequality

Migrationdueto

differencesinwages,

consumptionand

schooling cost

UNWPP&WDI

EducationalAtti-

anmentData

Theil Index

2010-2100

(30year

rhythm)

145

developing

34OECD-

countries

Inputsstudy Mechanism time Horizon Coverage

Mostforecastingeffortsaredirectedtowardsverylong-runpredictionsthatmayreachuntil2100.Quantitativemethodsarenotnecessarilylimitedinthespantheycancover.Theoretically,underagivensetofassumptions,pastdatacouldbeextrapolatedindefinitely.However,overlongertimehorizons,confidenceintervalsoftheseforecastsincreasesubstantially,suchthattheuncertaintyandrangeofpossibleoutcomesbecomessolargethatitisdifficult

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tomakeanydependableclaimsonthefutureofmigration.Mostquantitativeforecasterswarnabouttheexponen-tiallyincreasinguncertaintyandcautiontheusersofforecaststodiscountclaimsmadefarintothefuture.

Geographiccoveragemostlydependsontheavailabilityofdataandisquitelargeformostquantitativeforecasts.Mostly,migrationforecastsaremadeonthereceivingcountrylevel,e.g.howmanymigrantscanacertaincountryexpectoverthecourseofafewdecades.Breakingestimatesdownbysourcecountrybe-comesincreasinglychallenging.Inparticular,structuralmodelsrelyonalargenumberofobservationstobeabletocalibratetheparametersofvariousvariablesandpredictfuturemigration.Onthebilaterallevel(e.g.betweensendingandreceivingcountries),thereareonlyveryfewobservationsavailable(forinstance,theWorldBankbilateralmigrationmatrixwouldonlyincludesixobservationsforeachcountrypair).Consequent-ly,estimateswillbelessaccurateonthebilaterallevelandbecomemorereliableastheaggregationlevelincreases(country,region,continent).Ahigheraggregationlevel,however,limitstheabilitytomakeclaimsaboutthestructureoffuturemigrationflows(mainfuturecorridorsetc.)andconsequentlyweakenspolicy-makers’abilitytodesigntargetedmigrationpolicies.

Thisreportassessesthestrengthsandweaknessesofdifferentquantitativeforecastingmodelsaswellaspotentialcomplementaritiesbetweenthem.Table7providesanassessmentofStructuralModels,GravityModelsandBayesianModelsalongfourdimensions:theoreticalfoundation,transparencyofassumptions,datarequirementsandpredictivenessofthemodel.Strengthsarehighlightedingreen,weaknessesinred,mediocreperformancealongthedimensionismarkedinyellow.

• TheoreticalFoundation:thisdimensionassessesinhowfarthemodelmakesexplicitthroughwhichchannelsfuturemigrationwillbeaffectedandhowdifferentfactorsinteractwithoneano-ther.Astrongtheoreticalfoundationrequiresaguidingtheoryaboutmigrationanditsfunctioning.

• Transparencyofassumptions:thisdimensionassesseshowtheguidingtheoryistranslatedintoaquantitativeestimationoffuturemigrationflows.Ahighleveloftransparencypresentstheunder-lyingassumptionsofthemodelinanopenandcomprehensivemanner.

• Datarequirements: this dimension assesses the scope and level of granularity required for the estimationstrategy.Highdatarequirementscanposeahurdletoapreciseforecastoffuturemigrationflows,asonlyhighvolumesofdataallowfordecreasingerrorsandconfidenceintervals.

• Predictiveness:thisdimensionassesseswhetherthemodelispredictive,explanatoryordescrip-tiveinthedesign.Highpredictivenessmodelsincludetimeseriesmodelswhicharedesignedtoextrapolateintothefutureratherthandescribeorexplaincurrentorpastmigration.

AsalreadyoutlinedinTable6forconcreteresearchpapers,thevariousquantitativemethodsapproachmigrationforecastingfromdifferentangles.WhileStructuralModelsaretheory-intensiveandmakeexplicithowdifferentvariablesinteractwithoneanother,GravityModelsareguidedbytheprincipleofsizeanddis-tance(asdescribedintheprevioussections).BayesianModelsdonotmakeanyclaimsaboutthedeterminingfactorsofmigration.ThisisalsowhymanyofthepapersusingBayesianorGravityModelsdonotmakethekeyunderlyingassumptionsoftheirmodelsveryexplicit.Thismaybeexplainedbythefactthat,inmanyca-ses,oncethequantitativeapproachischosen,theunderpinningstatisticsareassumedtobeunderstood.Butrarelydothesemodelscarefullydevelopandexplainthechoiceofmodelsorvariablesusedintheestimationandthesensitivityoftheestimationtochangesinthechoiceofmodelorvariables.

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strong

medium

weak

high

medium

low

high

medium

medium

medium

low

high

Model type

structuralModels

bayesianModels

GravityModels

Theoretical

Foundation

Data

requirements

Transparencyof

assumptionsPredictiveness

Thereisanimportanttrade-offbetweenastrongtheoreticalfoundationandlowdatarequirements.Whilecomplexityofthetheory(andthenumberofrelevantfactorsandvariablesassociatedwithit)isnotnecessa-rilyagoodproxyforthequalityorstrengthofthetheoreticalmodel,itisobviousthatamultitudeoffactorsinfluencemigrationtodayandinthefuture.Incorporatingonlyasub-sampleofthemostimportantvariablesisdataintensive.WhileBayesianmodelscaninferfuturetrendsfrompastdataonly,explanatoryordescriptivemodelsneedanarrayofexplanatoryvariablestomakepredictionsaboutthefuture.Additionally,structuralmodelsrequiremanyobservationstoincreaseprecisionoftheparametercalibration.Consequently,boththeestimationmethodandtheunderlyingtheoryofstructuralmodelsrequireasubstantialamountofdata.Thesedatarequirementscanintroducevariousbiasesandinaccuraciesandmaysometimesnotevenbeavailableasforecasts(asdescribedinSection2.3).

Each forecastingmethod has its advantages and pitfalls. Overall, themethods are complementary andshouldbeconsideredjointlybyusersofquantitativeforecasts.Dependingonthepreferencesregardingtheo-reticalfoundations,thetransparencyofassumptions,datarequirementsorthepredictivestructureofthemo-del,differentmethodsmaybemoresuitableincertaincontexts.However,allmethodscomewithsubstantialuncertaintyandshouldbeinterpretedwiththiscaveat.

Iv.table 7 strengthsandweaknessesofQuantitativeMigrationForecastingModels

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ThischapterpresentsGermany-specificforecastsfromdifferentmethodsandcomparesthem.Thegoalistomaketransparenthowuncertaintiesandmethodologicdifferencesmanifestthemselvesinquantita-tivemigrationforecasts.Withthesupportoftheauthorsofthemainstudiesintherespectiveforecastingfields,thisreportextractsforecastsforGermany,visualisesandcomparesthem.TheselectedforecastswillbeassessedalongthefourdimensionspresentedinTable7anddifferenceswillbehighlighted.Inlightof theseforecasts, the lastsectionofthischapteroutlinestheparticularitiesof theGermanmigrationcontextanddescribestheirpotentialconsequencesfortheaccuracyoftheseforecasts.

4.1selectedForecastsforGermany–assessmentandPlausibilityThedifferentforecastsforGermanywereprovidedbytheleadingresearchersinthefieldofmigration

forecasting.Onetotwoforecastswereselectedforeachquantitativemigrationforecastingmodelpresen-tedinthepreviouschapterasawayofillustratingthewidemethodologicaloptionspace.ThenumbersarepresentedasnetmigrationflowstoGermany,e.g.thedifferencebetweenthenumberofpeoplewhowill immigratetoGermanyminusthenumberofpeoplewhowillemigratefromGermany(inmillions).Dependingonthedataprovidedbytheauthors,weareable toalsopresentconfidence intervalsasameasuresofuncertaintyfortherespectivepredictions(wedosofortheBayesianmodels).Thisdoesnotmeanthatotherquantitativemodelsdonotproducetheseconfidenceintervals;theyarejustnotrepre-sentedinthegraphsforthegravityandstructuralapproach.Ingeneral,itholdstruethatforallmodels,theuncertaintywillincreasesubstantiallyovertime.

Additionally,time-horizonsoftheforecastsdiffer.Inprinciple,allmodelscouldyieldpredictionsforanytime-horizonandforanytime-intervals(yearly,5-year,10-yearor30-yearintervals).Forgravitymodelsandstructuralmodels,whichneedforecastsforthedeterminantsofmigration,thetimehorizoncover-edformigrationwilldependonthetime-horizonscoveredinforecastsfortheirinputvariables(suchasdemographicchangeorproductivity).Choosingthetimeintervalsbetweenreportedestimateslieatthediscretionoftheresearchers.ProducingandcomparingestimatesforfuturemigrationflowstoGermanybearstheriskofconcealingimportantdifferencesinmethodology,theory,anddataused.Evenifresear-chersproducesimilarestimates,thatinitselfwouldnotsufficetovalidateacertainnumber.

ThefollowingchapterwillpresentoutcomesofdifferentquantitativeforecastingmethodsforGermany.ForecastsforeachmodelshouldbeinterpretedwithcaveatsanduncertaintiespresentedintheChapters2and3.Allforecastshavebeendevelopedusingmethodsatthefrontieroftheirrespectivefieldswithhighinternalvalidity.Withregardtocomparisonsbetweenmethods,thisreportdoesnottakeastanceonwhichmodelispreferablebutratherhighlightsthedifferencesintheapproachesandthusresults.Thereportwillbrieflycompareandcontextualizetheoutcsmesoftheseforecasts,highlightingthesourcesofdivergenceintherespectiveestimates.

examplefrombayesianModels:azose,sevcikova&raftery(2016)BasedonaBayesianhierarchicalmodelonnetmigrationratesusedinAzoseandRaftery(2015)de-

scribedinChapter3,Azose,Sevcikova&Raftery(2016)providepopulationprojectionsforallcountries,developingprobabilisticprojectionsformortality,fertility,andmigration.TheyarguethatUNpopulationprojectionsvastlyunderstateuncertaintybecausetheydonottakeintoaccounttheuncertaintyinmigra-tionprojections, onemajor factor in population change. In order to adequately reflect uncertainty inpopulationforecast,Azoseetal.(2016)forecastsmigrationandincludetheuncertaintyarisingfromittotheoveralluncertaintyinpopulationprojections.

Iv. Migration toGermany

Iv. Migration to Germany

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Forthisreport,Azose,SevcikovaandRafteryhavesharedupdatedmigrationforecastsforGermanyuntil2100,usingmigrationdatafromtheUnitedNationsWorldPopulationProspects2019.Aswithalltimeseriesmodels,therearenoassumptionsaboutthedeterminingfactorsofmigration.Futuremigrationissimplyin-ferredfrompastmigrationpatterns(againinhierarchicalform,e.g.takingintoaccounttheparticularcountryandtheworld),asdescribedinmoredetailinChapter3.TheredlineinFigure3depictsnetmigrationflowstoGermany,theshadedlinesshowthe80%probabilityinterval.Migrationflowsareexpectedtodecreaseshar-plyoverthenext15yearsandthenstabiliseataround1millionstartingin2040.Confidenceintervalsremainroughlyconstantandverylargeafter2040,rangingfromapproximately-1toapprox.3million.

Inferringfrompastdata,thepost-2015influxisconsideredasaone-timeshocktonetmigrationflowsandtheforecastshowsthatmigrationwillrevertbacktoalowerlevel.However,theoveralllevelofnetmigrationcomparedtothe1950to2015periodisestimatedtobehigheronaverage.Inotherwords,thepost-2015influxisfactoredinasaonetimeshockbutonethati)adjustsexpectednetmigrationflowsupwardsandii)increasestherangeofuncertaintyinprojectionsforGermany.

examplefromGravityModels:hansonandMcIntosh(2016)IntheGravityframework,migrationisdrivenbydifferencesinlaboursupplyresultingfromdifferencesin

fertilityrates,whichisademographicfactorandisoughttobemorestableovertime.Theauthorsarguethatonecaninferdifferencesinlabour-supplyfromfertilityratesatleast15–20yearsahead.Furthermore,theyin-cludemigrationnetworksintheiranalysisandascribeadrivingroleinpredictingmigrationtoit.Onlywiththeinteractionwithexistingmigrationnetworksinthedestinationcountrydochangesinfertilityratestranslateintomoremigration.

Using theUNworld population prospects from 2017, the DIOC data base and an extrapolation of theIMFGDPforecastof2018,Hanson&McIntoshpredictmigrationtoGermany,measuredbyfirst-generationmigrants,intheagegroupof15to64.Relyingon2010data,themigrantnetworkinGermanyseemstobenotsufficientlyhightofostermigrationintothecountry,converselypredictinganetoutflowstartingafter2020.Infact,theauthorsevenpredict‘negativemigrationstocks’forGermany,whichinrealityarenotpossible.Thegraphbelowconvertsnegativestocksintonetmigrationflowsbysubtractingestimatedstocksovertime.

Figure3netMigrationFlowtoGermany(inmillion)fromazoseetal.(2016)

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Figure4netMigrationFlowtoGermany(inmillion)fromhanson&McIntosh(2016)

GravitymodelsbytheirconstructionascribealargeshareofeffecttoGDPanddistance,combinedwithpredicteddecliningfertilityratesofcountries incloseproximity,whichleadstoadeclineinmigrationfromclose-bycountries.Hanson&McIntoshtakeincreasingfertilityrates,forSub-SaharanAfricaasgiven.However,themovingcostsaresignificantlyhigher,withnofirstcommonlanguage,andfewpastcolonialrelationships.Migrationnetworksarenotbigenoughtoreducethecostofmovingandleadtoincreasingmigration.Thegravitymodellinearlydepictstherelationbetweenthedependentandindependentvariables.Inspiteofin-cludinginteractiontermsanddummyvariables,someinteractionamongstthevariablesmightbeneglected.Usingastructuralmodelcouldgiveamorenuancedprediction.

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examplefromstructuralModels:burzynski,DeusterandDocquier(2019)Intheclassofstructuralmodels,Burzynski,DeusterandDocquier(2019)developacomplexeconomet-

ricalmodelwithhighdatarequirements,wheremigrationdependsondifferencesinwages,consumptionand schooling cost. The factors are modelled endogenously and depend on individual decisions about educationandfertility.Thesechoicesfurtherdependonthesector(high-vs.low-skilled)andtheregion(urbanvs.rural)wherethepotentialmigrantslive.

UsingtheUNWorldPopulationProjectionsandWDIdataoneducationalattainment(theTheilIndexforinequalityisendogenoustotheirmodel),theauthorspredictthetotalmigrationstockforGermany,which is converted toflowvariables forbettercomparison.Theydifferentiatebetweenhomecountry,skill-levelandregionoforigin(urbanvs.rural).ThegraphbelowshowsthenetmigrationtoGermany.Calculatedforevery30-years,themodelpredictsnetmigrationof2.4milliontoGermanyin2040.Thisnumberreflectsthenetmigrationinthepast30yearsfrom2010–2040.Theauthorspredictadropofnetmigrationto lessthan500,000 intheyear2100.Whilethestockofmigrantsslowly increases, thenumberofnetmigrantsdecreases.TheauthorsestimatemigrantstocksfortheworkingagepopulationinGermany,whichareconvertedintoflowsforthebelowgraph.Thedecreaseinforecastedmigrationflowsizecomesdirectlyfromtheunderlyingassumptionswithinthemodeloftheinputdata.Inparticular,theauthorsassumeastagnationoftheshareofcollegeeducatedworkersandamarkedslowdowninpopu-lationgrowthfortheOECDcountries,whilstaccesstoeducationandmobilityrestrictionsindevelopingremain at a similar level.

Althoughthemodelgivesdetailedpredictionsoncountryandskilllevel,thesehavetobetreatedwithcaution.FortheparticularcaseofGermany,forinstance,thestockofMexicanmigrantsishighlyover-pre-


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dictedwith approximately 950,000migrants in 2010,whichdoesnot reflect reality. Furthermore, theauthorsusetheDIOCdatabaseforcalibrationwhichdoesnotincludemigrationdataforGermanyfrom2015onwards.Consequently,thesubstantialincreaseintheimmigrantstockovertherecentyearhasnotbeen taken into account.

Allofthemethodspresentedarehighlysophisticatedandexecutedbytheleadingresearchersintherespectivefields.Nevertheless,theymakedifferentpredictionsonthevolumeanddirectionofnetmigra-tionflowstoGermany.ItisdifficulttofindanadequatecomparisonpointforthethreemainreferencepapersAzoseetal. (2016),HansonandMcIntosh (2016)andBurzynskietal. (2019).Allof thepapersprovideadistinctpredictionfortheyear2040.However,theinterpretationofthosepointestimatesaredifferent. A first glance, the numbers reveal that the estimates vary substantially across themodels,rangingfromanegativeinflow(morepeopleleaveGermanythanmovetoGermany)of-0.75millioninthegravitymodelto+2.34millioninthestructuralmodel(and+1.23millionintheBayesianmodel).Therangeofpredictedoutcomesacrossmodels liesat3million.Thisfirstglanceevenunderestimatesthedifferencesacrossmodels.

Infact,thedifferencesintime-intervalshaveaneffectontheinterpretationonthepointestimate.Thedatapoints illustrated forAzoseetal. (2016), for instance,predict thenetmigrationflowtoGermanyovertheprevious5years,whilethedatapointinBurzynskietal.(2019)showsnetmigrationflowsoverthepast30years,thatis,from2010to2040.Ifwecomparedthe20-yearspanbetween2020and2040forallthreepapers,Azoseetal.predicta5.8million27netmigrationflow,HansonandMcIntoshpredictapproximately-1.5million(anetdecreaseinmigrationflows)andBurzynskietal.predict1.5million28. All oftheestimationsreveallargelydivergingpatternsforthenexttwodecades.

Thestarkdifferencesacrosstheseforecastsdemonstratestheuncertaintyinvolvedinmakingpredic-tionsaboutthefuture.Asoutlinedinthepreviouschapters,thedifferencesinestimatesarisefromdif-ferences inestimationmethods,datauseand theoreticalunderpinnings.Therefore, it is crucial toun-derstandtheunderlyingconceptsoftheseestimatesbeforetakinganynumberatfacevalue.InChapter5,thereportwilloutlinehowforecastsshouldbecontextualisedinordertomakethemausefultoolforpolicymakers,highlightingtheroleofbothconsumersandproducersinthemigrationforecastecosystem.

Figure5netMigrationFlowtoGermany(inmillion)fromburzynskietal.(2019)

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27Thisnumberiscumulativelyaddedfromthe5-yearmedianestimatesbetween2025and2040;forfullcomparabilityonewouldhavetoadd themigrationflowsof2010to2020,sinceBurzynskietal.(2019)aswellasHansonandMcIntosh(2016)usetheWorldBankmigrationdata andthus2010asreferencepoint.Azoseetal.use2014asareferencepoint.28Burzynskietal.(2019)donotprovideapointestimatefor2020.The20-yearspannetflowislinearlyextrapolatedfromthenetflowpredicti onbetween2010and2040.

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Figure6ComparisonbetweenMigrationForecaststoGermany(leftwPP2015,rightwPP2019)

4.2Germany-specificUncertaintyThefirstpartofthischapterillustratesthatforecastsforGermanycanvarysubstantially.Oneofthemain

sourcesofvariation(evenwithinmethods)canbetracedbacktowhethertherefugeeinfluxof2015isalreadyincludedinthedatausedfortheforecasts.Particularlywhenitcomestonetworkeffects(oneofthemaindeterminantsoffuturemigrationistheexistingmigrantstockfromaspecificsourcecountry),smallshockscanalterfuturetrajectoriessignificantly.Forinstance,Burzynski,Deuster,andDocquier(2019)onlyincludebila-teralmigrationdatafrom2010fortheircalibration,whichreflectsaverydifferentmigrantcompositionthanonlyfiveyearslater.DecomposingtheaggregatemigrationforecastforGermanybysourcecountriesrevealsthatthemodelproducesverylowimmigrationratesfromSyria.Intheirmodel,outofanestimated9.3millionimmigrantsinGermanyby2040,onlyabout25,000comefromSyria.Infact,thisisonlyafractionofthecurrentSyrian migrant stock in Germany.

GravityandBayesianmodelssufferfromthesameissue.AzoseandRaftery(2015)providedacomparisonbetweenmigrationforecaststoGermany,usingtheUnitedNationsWorldPopulationProspects(WPP)of2015,whichdidnotincludetherecentinfluxtoGermany,withaforecastusingtheWPPof2019(seeFigure3).TheredlinepresentsthemedianforecastsundertheAzose,Ševčíková,andRaftery(2016)model,thebluelineistheUnitedNationsprojection(withdeterministicmigration).NotonlydotheUNandAzoseetal.diverge(dif-ferenceliesinhowmigrationismodelled)butthesamemodelproducessubstantiallydifferentmigrationpre-dictions,dependingonwhetherthe2019dataisusedornot.MediannetmigrationtoGermanyin2100goesfrom400,000toalmost900,00029andtheprobabilityboundsof80%(highlightedinred)expandsubstantially.

29 The values should be interpreted as the net median number of migrants per five-year period30TheGlobalMigrationDataAnalysisCentreDataBriefingSeriesisavailablehere: https://publications.iom.int/system/files/gmdac_data_ briefing_series_issue_6.pdf

source:MemoSevcikova&Raftery(2019)

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netMigration

AsimilarexamplecomesfromtheGlobalMigrationDataAnalysisCentre(GMDAC),forwhichBijakhasauthoredapolicybriefundertheIOMDataBriefingSeries30emphasisinguncertaintyinmigrationforecas-ting.Forthisforecast,BijakfollowsafullyBayesianapproachsimilartotheexampleinChapter3.1,choosinganAR(1)model,intheformmij,t+1 = c + φ mij,t + εt +1.Hence,migrationinperiodt+1solelydependsonmigrationintandthechoiceofthemodelparameters.Asaninputvariable,noexpertopinionsareincluded(Bijak&Wisniowski2010extendsimpleBayesianforecastswithexpertknowledge),parametershenceare


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determinedbythechoiceofpriors,theirlimitsanddistributions.Usingtheseparameters,dataissimulated,possiblevaluesfromthedistributionaredrawnandcombinedandjointlydeterminetheposteriordistribu-tion.Theparametervaluesfromtheposteriordistributionarethenusedforforecasting.

Themiddlelinedepictsthemedianprediction(upperandlowerconfidenceboundsmarkedinorange).TheestimationprocessfollowsasimpleAR(1),theauthorspredictaslowdecayofnetmigrationtoGerma-ny31.Withnormallydistributederrors,theauthors’mostlikelyscenario(meanforecast)expectedshockstomigrationequalzero.Migrationispredictedtodeclinefrom580,000peryearin2015toapproximately320,000in2020,withadeclining influenceofpastmigration.Thisdevelopmentfollowsstrictlyfromthecalibratedparametervalue.Theconfidenceintervalshowstheuncertaintycomingwiththeforecast;ifanyshockstomigrationoccur(forwhateverreason),themigrationrateislikelytoliewithintheconfidencein-tervalfromminimum-110,000to900,000peryear.Thedegreeofuncertaintyincreasesquickly:withinfiveyearstheDeltaisalreadyonemillionmigrantsperyear.Incontrasttothestructuralequationmodel,wecan-notascribethechangestounderlyingvariablesordevelopmentoffundamentalvariableswhichdeterminemigration,butrathertomodelcalibrations.

ThemodelwasestimatedusingdataonpastmigrationflowsforGermanybetween1990and2014.Again,the2015influxhasnotbeenincludedintheestimation.Eventhoughtheuncertaintyboundsarelargeandrangefromapproximately475,000in2015toalmostonemillionin2020,theactualmigrantinflowof2015lieswelloutsideoftheestimateduncertaintybounds(withintheshadedlines).Itfollowsthatevenifforecastsgivespacetouncertainty,unforeseenshockscanbesolargethateventheindicatedrangeofuncertaintyisnotenoughtocoverallpotentialoutcomesinthefuture.

Whileitispossibletocontinuouslyfeedthemodelswiththemostrecentdata,shockstothestructureofmigrationarealmostimpossibletoforeseewellinadvance.Thesensitivityofquantitativemodelstotheseshocksisimmenseandmostmethodscannotdistinguishbetweenauniqueeventorachangeintrendsofmigrationpatterns.Ifandtowhatextenttheseshocksshouldbeincludedintheforecastingmo-delsisatthediscretionoftheforecasters.Typically,theytakeanagnosticapproachandincludethedataatfacevaluewithoutmakinganyassumptionsonthenatureoftheshockanditsprobabilitytocontinueinacertainpattern.Thisiswherehybridmodels,e.g.acombinedapproachofexpert-basedjudgementandquantitativemethods,canbecomeavalidalternative.Understandingthesensitivityofquantitative

31Bijakdoesnotusethestationarityassumptioninthismodel.Theauthorpointstothefactthatthenon-stationarityofthisAR(1)processis quitehigh,namelyat10%forimmigrationandalmost25%foremigration.

Figure7netMigrationFlowtoGermany(inmillion)frombijak(2016)

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modelstofluctuationsinthedataandadaptingtheinputaccordingly,basedonplausibilityandqualitativeassessmentofthedatastructure,mayhelpattenuatethesesourcesofuncertaintyinquantitativemodels.However,theusualcaveatstoexpert-basedassumptionsoutlinedinthepreviouschapterstillapplyandshouldbetakenintoaccountwheninterpretinghybridmodels.

InadditiontoparticularitiesinGermany’srecentchangeinmigrationstructure,therearealsoothereconomic, political and social dimensions that quantitativemodels (especially if they are anchored intheory)cannotintegratesufficiently.Structuralmodels,forinstance,canincorporatemeta-leveltrendsthataffectmigration,suchaspopulationgrowth,technologicalchangeorassumptionsabouttherestric-tivenessofmigrationpolicies.Thesemodelsaredesignedtoreflecttransformationsatthegloballevel,usuallybecausetheyarefedwithglobalmigrationdata.ThesetrendsmaynotapplytotheGermancon-textandcandistortforecastsatthenationallevel.Forinstance,thestructuralmodelinDaoetal.(2018)incorporates theeffectsof globalwage inequalitiesonmigrationand theeffectofmigrationonwageinequality.Incomparisontopurelydescriptivemodels,thisisasophisticatedapproachofapproximatingthetwo-wayinteractionbetweenwagedifferencesandmigrationinaforecastingmodel.However,thisinteractioneffectisassumedtobehomogenousacrosscountries(dependingontheexistinginequalitiesandpopulationgrowth).Inotherwords,themechanismsthroughwhichincomeinequalityaffectsmigra-tionandhowmigrationaffectsinequalityisassumedtobeidenticalforallcountries,controllingforbase-linecharacteristics.Thisisquestionablesincerigiditiesinthelabourmarket(forinstance,theexistenceofaminimumwageorthebargainingpowerofunions)canvarysubstantiallyevenwithinhighincomecountries. Themodelparameterswillbe calibratedbasedonanaverageeffect, combining theoverallinteractioneffectbetween inequalityandmigrationworldwide.AnapplicationtotheGermancontext,usingthecalibratedparametersfromaglobalanalysis,isthereforeimprecisebyconstruction.Thisisalsowhystructuralmodelsareperformingwell inback-castingexercisesattheglobal levelbutdecrease inaccuracyatthedisaggregatelevel(whenretrofittingthemodelattheregionalornationallevel).

When assessing uncertainty of forecast at the country level, economic and societal challenges ofthe future need to be accounted for. The attractiveness of a certain destination country is constantlyre-evaluated.Oneoverarchingdevelopment inGermany is thechanging incomestructure,particularlythedecreasingmiddleclass.RecentdatafromtheSOEPconfirmsthatincomeinequalityisincreasinginGermany.Whilethehighestincomesincreasedoverthelastyears,middlenethouseholdincomestag-nated.The lowestdecilesevenfacedecliningreal income,stagnatingrealwages,and increasingshareofpart-timeemployment(GrabkaandGoebel2018).WhileGermanytodayisperceivedasanattractivedestinationcountry,recentstudiespaintamorenuancedpicture.Germanyappealsmainlytostudentsandentrepreneurs,butisofonlyaverageattractivenessforhigh-qualifiedworkersascomparedtootherOECDcountries(OECD/BertelsmannStiftung2019)32.Hence,incomeopportunities,economicinequalityandthestructureofthejob-marketaremajordeterminantsofmigrationandhavebeeninfluxoverthelastdecade.Aneconomyinfluxchangestheattractivenessofacertaindestinationcountry.Atthesametime,changesintheeconomicmake-upofacountryinfluencesmigrationpolicies,whichinturnaffectsubsequentmigration.For instance,GermanyhasrecentlypassedtheSkilledImmigrationActwiththeaimtofacilitatehigh-skilledmigrationfromcountriesoutsideoftheEuropeanUnion.Thisisareactiontoasteadilyincreasingdemandforhigh-skilledlabourthatisnotmetwithdomesticorEUworkers.

AnotherparticularityofGermanyisitsmembershipintheSchengenarea.Forecastingmodelsconsidereachcountryseparatelyandgeneralequilibriumeffectsareusuallynottakenintoaccount(exceptforhie-rarchicalBayesianmodels–butquitecrudely–orforassumptionsofzerototalmigration,e.g.emigrationhastobethesameasimmigrationatthegloballevel).Forinstance,migrationfromUkrainetoPoland(aSchengenmember)underaspecialvisaagreementbetweenthetwocountrieswillalsoaffectallotherSchengencountries,includingGermany,becauseoffreemobilityofpersonsintheSchengenspace.Therearesubstantialspill-oversinmigrationflowsacrosscountriesthatbelongtothesamemobilityspace.The-

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seinteractioneffectsaretypicallynotconsideredinforecastingmodels,whichconceptualisemigrationfromandtoanothercountryasabilateralratherthanamultilateralprocess.Germany’spositionintheSchengenareapullstogetheralloftheuncertaintiesdescribedinChapter2.

Thecomplexityofmigrationdeterminantsgoesbeyondnationalorglobaltrendsto includeregionalinteractionsandpolicyspaces,whichareevenlesstheorisedthan‘traditional’migrationprocesses.Impli-citassumptionsinthesemodelsincludethefactthatGermanyandallothercountriesareconsideredtobeindependentorinsulatedintheirmigrationpolicies.Inthestructuralmodelpresentedinthepreviouschapter,forinstance,Germany’smigrationpolicyresponseisindependentofotherEUorSchengencoun-tries,whichisnotthecase.WhileEUcountriesarefreetolegislatemigrationlaws,policiessuchastheEuropeanBlueCard,aEUworkingpermitsimilartotheGreenCardintheUnitedStates,aretheresultofnegotiationsamongmanycountries.Evenifthesecomplexitieswereincorporatedinthemodels,thedataonmobilitywithintheSchengenzoneisevenweakerthandataonmigrationfromthird-countriesintotheEU.Intheabsenceofbordercontrolsandlaxenforcementofmandatoryregistrationatlocalpopulationregisters,dataofhighvelocityandvolumeareevenhardertofindinthissetting.

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v. Conclusion and Policy Implications

The goal of this report is to critically assess the increasing demand and the supply of quantitative(andqualitative)migrationforecastsinmigrationresearchandpolicymakinginrecentyears.EspeciallyinGermany,theinfluxofrefugeesin2015was–amongmanythings–alsoacrisisofpreparednessandforesight,andthelackthereof.AsoutlinedinChapter1,manylegislatorsandpolicymakersconsiderthepoliticalandfinancialinvestmentinforecastingeffortsasanecessarysteptoremedypastmistakesandprepareforthefuture.However,over-confidenceinmigrationforecastingtools,particularlyintheverylongrun,canleadtoadverseoutcomes.Thegreatestriskisthatinsteadofpreparingforuncertainty,de-cision-makers prepare for a false certainty.

Itisimportanttonotethedistinctionbetweenearlywarningsystems,orshort-runforecasts,andfo-recastingmethodsthatspanseveraldecades.Earlywarningsystemscanuseexpertopinionsand ‘realtime’datatomakeajudgementonhowmanypeopleareexpectedtomigrateinresponsetoaneventoroverall(political,technological,climatic)developmentincertainsourceregions.Monitoringmigrationfromsourcecountriestotheirneighbouringcountries,informationprovidedbyembassies,developmentagencies,NGOs,orad-hocinterviewswithmigrantsinthefieldcangiveinsightsintothemagnitudeandlikelihood of onwardmigration. This is a decentralised information gathering effort that results in anassessmentofmigrationflowsinthecomingyears.Aforecastdrawsitspredictionsfromapre-definedmethod.Theapproachescanoverlapinpartbutfollowdifferentmethodsandhavedifferentgoals.Whileshort-termforecastshavethecapacitytoinformandguidepolicydecisions,migrationforecastsserveasanoverarchingframeworkthathelpstounderstandbasicmechanismsofmigrationandmarriestheorywithdatainanattempttobuildscenariosforthefutureofmigration.

Forecastingisanindispensablepartofbasicresearchondemographyandmigration.Developingscena-riosforthefuturemeansunderstandingmigrationtoday,theunderlyingtheory,thestrengthsandfall-backsofdata,andtheirvulnerabilitytoshocks.Paradoxically,researchinmigrationforecastingservesasaremedytotheproblemsfromwhichitsuffers.Morethananydeterministicorexplanatoryquantitativemodel,forecastingmodelsconfrontuswiththelimitsofwhatweknow,orcanknow,aboutmigrationto-day.Migrationforecastingis(especiallywhenitcomestogravityorstructuralforecastingmodels)stillinitsinfancy.However,effortstoimprovethemhavebeenfruitful,evenoverashorttimespan.Continuedresearchcanandshouldbeencouraged,asmanyofthelimitsofforecastingcanbeovercomeinthenextdecades.Improveddatagatheringandsharing(ascalledforintheGlobalCompactforMigration33)mayhelptodrawfrommoreaccuratedataofhighervolumeandvelocityandmoresophisticatedmachinele-arningtoolshelptofacilitatetheprocessingofcomplexmigrationdeterminants.Theseareimportantde-velopmentsthatwillcontinuetoimprovemigrationforecasting.However,theresultsoftheseforecasts,atleastforthemoment,shouldbeinterpretedwithgreatcareiftheyareusedtoinformmigrationpolicy.

Aspresentedthroughoutthereport,therearesubstantialuncertaintiesinmigrationforecasts.Predic-tionsvarysubstantiallydependingontheunderlyingmodelsandthedataused.Evensmallchangesinas-sumptionsoradditionaldatapointscanresultinlargedeviationswithinandacrossforecastingmethods.Consequently,itisimportanttointerpretforecastswithcautionandbecomefamiliarwiththefundamen-tal structureof themodels inorder tocontextualiseanyspecificnumber that the forecastingmethodproduces.Ratherthanfocusingontheabsolutepredictednumberofmigrationinflowsinthefuture,the

v. Conclusionand PolicyImplications

33TheGlobalCompactforSafe,OrderlyandRegularMigration,aresolutionadoptedbytheUnitedNationsGeneralAssemblyon December19th2018,agreedon23objectives.Thefirstobjectiveisto‘Collectandutiliseaccurateanddisaggregateddataasabasisfor evidence-basedpolicies’.

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carefuluserofmigrationforecastswilldirecthisorherattentiontohowchangesinassumptionsresultinchangesinpredictions.Inotherwords,howwillachangeintheincomestructure(intheformofhigherwage inequality in industrialisedcountries, for instance)affectmigrationfromtheGlobalSouthtotheGlobalNorth?Whatdodifferentmodelsconclude?Isitpositive,negative?Istheexpectedeffectlargeinmagnitude?Focusingsolelyonapredicted,absoluteoutcome,orevenarangeofoutcomes,willobscurethegroundonwhichtheforecastisrestingandmakeithardtoassessitsplausibility.

Asthedemandandsupplyofmigrationforecastshasincreasedoverthelastyears,policymakershavetonavigateaneverincreasingmazeofmethodsandpredictions.Acomparativeanalysisofdifferentme-thodsbecomesmoredifficultand forecasters canhelp to increase transparency.Divergingpredictionsmay cause confusion. In order to contextualisemigration forecasts for policymakers, researchers andexpertscanprefacetheiranalyseswithasheetthatmakestheanalysismoretransparentandcanserveasuser’sguide.Ifforecastersuseashortandconcisesummaryalongthesimilarguidingquestions(ex-plainedbelow),policymakerscanmakemoreinformedinferencesfromtherespectiveforecastsandareenabledtousethemasanimpulsefordiscussionsratherthantakinganyspecificnumberatfacevalue.

Theguidecouldcoversevendimensions(seeboxbelow),whicharenecessarytounderstand,interpretandcomparetherespectivemodel:modeltype,theoryandassumptions,determinantsandmechanis-ms,data,timehorizonandfrequency,predictionanduncertainty,scenariosandsensitivity.Theguidingquestionsaddressthemainsourcesofuncertainty(assumptions,dataetc.)andmakeexplicitwhattheoryandmechanismsareatplay.Notethatonlyoneofthesevendimensions,‘predictionsanduncertainty’,isconcernedwithproducingaspecificnumberandthenumberisnotdivorcedfromtheuncertaintyasso-ciatedwithit.Additionally,differentscenariosshouldbeprovidedandthequantitativepredictionshouldbereexaminedusingdifferentassumptions,differentdatatimeframesordefinitions,etc.Thismayhelptounderstandhowsensitive(inmagnitudeandsign)modelsaretothesechanges.

Atthesametime,usersofmigrationforecastsshouldformulateclearexpectationsofsuchforecasts.First,isthereaninterestinlong-runforecastsorareprimarilyearlywarningsystemsthesourceofatten-tionforthistopic?Encouraginganddevelopingforecastingmodelsmaybeverydifferentfromshort-runforecastsandthatshouldbemadetransparentfromthebeginning.Inasecondstep,theuseroftheseforecastsshouldconsiderwhethertheorybasedmodelsorpurelydatadriven(oftentime-series)modelsaremoreappropriate.Iftheorybasedmodelsaremoreattractive,thenitistimetoinvestigatewhetherthemodelassumptions,thedeterminingvariablesusedandthemechanismsareconvincing.Additional-ly,uncertaintyanddifferentscenariosshouldbeconsidered,knowingthattheforecastismorelikelytoprovideinaccurateratherthanaccurateresults.Lastly,usersshouldinterprettheseforecastswithaneyeonthepoliciesorstrategiesthatwillbeinformedbytheseforecasts;aretheycompatiblewiththelargeuncertaintiesinvolvedinmakingthesepredictions?

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vI. Migration to Germany

TransparencyinMigrationForecasting

• Model type: towhichfamilyofforecastingmodelsdoesyourapproachbelongto?

• Theoryandassumptions: whatarethetheoreticalfoundationsofthemodelthataffectfuturemigration?Whicharetheassumptionsunderlyingthemethod(statisticalassumptions)andwhicharetheassumptionsintroducedbytheresearchers(theoryassumptions)?

• DeterminantsandMechanisms: whatarethemaindeterminingvariablesusedinthemodelandwhatarethemecha-nismsthroughwhichthedeterminantsaffectfuturemigration?

• Data: whichdatasourcesarebeingconsultedforallvariablesincludedinthemodel?

• TimehorizonandFrequency: whatisthetimehorizonoftheforecastandwhywasaspecifictimespanandtimeintervalschosenfortheforecast?

• PredictionsandUncertainty: whatistheestimatedstockorflowofmigrantsandhowlargearetheuncertainties?Ifforecastsarebasedonotherforecasts,howaretherespectiveuncertaintiesincor-porated?

• scenariosandsensitivity: canyouprovideforecastsfordifferentscenarios?Howdoesthemodelreacttotwe-akingassumptionsandtheory?Howdoesthemodelreacttodifferentuseofdata?Howdoesthemodelcomparetootherforecastsandwhydotheydiffer?

Migrationforecastsareanimportantpolicytool.However,researcheffortsandpolicyperspectiveshaveyet tocometogether inacomprehensivemanner.This reportgivesanoverviewonthemost importantforecastingmethodsinmigrationandusesGermanyasanillustrationofhowthesemethods–whilebeinghighlysophisticatedandinternallycoherent–canproducedifferentoutcomes.Thisreportisalsoacallformoretransparencyfrombothproducers(intermsofmethodsanduncertainty)andconsumersofmigrationforecasts(intermsofchoiceandpurposeofforecasts).Asmentionedbefore,migrationforecastshavebeco-meandwillremainamainstapleofbasicmigrationresearchandnewdataandstatisticaltoolspromisegreatimprovementsinforecastinginthefuture.Forthemoment,however,theyshouldberegardedasawindowtounderstandingtheoverarchingconceptsandtrends,dynamicsandmechanismsofmigration,ratherthanawindowtothefuture.

lIteRAtuRe

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The DeZIM-Institute is a research institution funded by theGerman Federal Ministry for Family Affairs, Senior Citizens,WomenandYouth. Itscentralmission iscontinuous,metho-dically sound research that transfers intopolitics, thepublicandcivilsociety.AlongsidetheDeZIMresearchcommunity,itisoneoftwopillarsoftheGermanCenterforIntegrationandMigrationResearch(DeZIM).

sulinsardoschau(2020): TheFutureofMigrationtoGermany.AssessingMethodsinMigrationForecasting DeZIMProjectreport–DPr#1/20.Berlin:DeZIM-Institut

Publishedby

DeutschesZentrumfürIntegrations-undMigrationsforschung(DeZIM-Institut)

GermanCenterforIntegrationandMigrationResearch(DeZIM-Institute)

Mauerstraße7610117Berlin +49(0)3080492893 [email protected] www.dezim-institut.de

DirectorsProf.Dr.NaikaForoutan,Prof.Dr.FrankKalter

AuthorDr.SulinSardoschau

Gefördert vom:funded by

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