the future of migration to germany - dezim€¦ · dezim project report #dpr 1|20 berlin, 06 april...
TRANSCRIPT
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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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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I. The Demand for Migration Forecasts
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.
I. The Demand for Migration Forecasts
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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
(macroµ)
•Duallabormar-
ket theory
•Neweconomics
ofmigration
Howdoesmigrati-
onaffectsocietal
change and ethnic
identity?
•Transnationalism
ðnicity
•Identity&
hybridity
•Social change
Howdoesthelaw
affectmigration?
•Forms,proces-
ses,institutions
ofimmigration
law
•Enforcement
researchQuestion
Concepts
sociologyhistory PoliticalscienceGeography economicsAnthropology law
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II. Uncertainty in Migration Forecasting
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.
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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
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II. Uncertainty in Migration Forecasting
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)
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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.
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II. Uncertainty in Migration Forecasting
_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
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Insum,uncertaintyinmigrationforecastingcoversmultipledimensions:thecomplexityofmigrationdetermi-nants,thelackofdataofhighvelocity,volumeandaccuracy,implicitassumptionsusedtoderiveforecastsofareasonablerange,forecastsbasedonforecaststhatalreadycarryalevelofuncertaintyandasetofassumptionswiththem,andfinally,alloftheeconomic,political,technologicalorclimateuncertaintieswhichpresentsomeofthemostimportantdriversofmigrationbutcannotbeforeseen,especiallynotoveralongperiodoftime.Itisimportanttonotethesecaveatswhenpolicymakersusetheseforecaststoget‘aroughestimate’ofmigrationtoGermany(oranyothercountry)inthefuture.Itismorelikelythannotthateventheseroughestimatesdeviatesubstantiallyfromwhatthefutureofmigrationlookslike.
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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).
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Project Report #1|20
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,
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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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III. Forecasting Methods
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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III. Forecasting Methods
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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III. Forecasting Methods
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.
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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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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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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.
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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