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DonaldTrump'sFalseNarrativeonMexicanMigrationandTrade:

AGeopoliticalEconomicAnalysis

RaulHinojosaOjedaExecutiveDirectorUCLANAIDCenter

With

MaksimWynnandZhenxiangChenUCLAInstituteforResearchonLaborandEmployment

October25,2016

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Abstract:1

DonaldTrump'spoliticalriseutilizedthenarrativethat(1)Americaceasedbeinggreatbecauseof(2)illegalimmigrantsand(3)tradeagreementsthattakeU.S.jobs.Trump’spopularityhasbeenconflatedbymanyobserverswiththeactualexistenceofmeasurablenegativeimpactsfromtradeandmigrationonthelivesofTrumpsupporters,aswellasevidencefortheneedformorerestrictiveimmigrationandtradepolicyresponses.SomeobservershavealsopostulatedapositiverelationshipbetweenTrump’ssupportamongvotersand"ChinaShock"tradeexposure,whileothershavesuggestedthatthereisapositiverelationshipbetweensupportforTrumpandthelevelofMexicanimmigrants.However,anexaminationofthegeographicalconcentrationofsupportforDonaldTrumpinthepresidentialprimariesindicatesanegativecorrelationbetweenthenumberofTrumpsupportersandthepopulationsizeofMexicanimmigrants,aswellasanegativecorrelationbetweenTrumpsupportandimportcompetitionfromMexicoorChina.AreaswithhighconcentrationofMexicanimmigrantsandimportexposuretoMexicoandChinaareactuallymorelikelytofavorHillaryanotherRepublicancandidateorHillaryClinton.Infact,only2%ofU.S.countiesintheU.S.actuallyfittheTrumpnarrativeofveryhighTrumpsupportcombinedwithveryhighlevelsofimmigrationortrade,whilenearly60%ofcountiesarepolarizedaseitherhighTrump/lowMexicanorlowTrump/highMexican.ThesecorrelationsholdatboththeCountyandCommutingZonelevels,aswellasaftercontrollingfortheshareofTrumpsupportamongRepublicanvotersandtotalvotersinbothcontestedandnon-contestedprimaryelections.WhiletheseresultsdirectlyrefuteandinverttheTrumpnarrative,theyalsoconfirmthatDonaldTrumpenjoyshighlevelsofsupportinparticularregionswhicharestrugglingeconomicallyandcontainhighconcentrationsofWhitepoverty,highunemploymentratesandalowmedianincome.ThelessonsfromthefalseTrumpnarrativeforfutureU.S.andMexicopolicymakers,however,isthatneitherthecausesnorthesolutionsfortheselaggingregionaldynamicsarerelatedtoU.S.migrationpolicies,ortotraderelationshipswithChinaandMexico.

1AspecialthankyoutoPatrickPastorandMarceloPleitezfortheirresearchandhardwork.

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1. ExecutiveSummary FromtheinitiallaunchofhiscampaigninJune2015,DonaldTrumpadeptlycreatedasimpleyetdangerousmedianarrativethat(1)Americaceasedbeinggreatbecauseof(2)borderraidingillegalimmigrants(“murderersandrapists”)and(3)tradeagreementslikeNAFTAandTPPwhichproducetradedeficitsthattakeU.S.jobs.RealAmericanworkingpeoplearehurtbecauseAmerica'sborderisbeingoverrunbecauseof"Mexicosendingtheirworstpeople"and"unfair"tradedealsmadeby"ourbadleaders."Inhishands,thisdiagnosisleadstothemagicalsolutionthatcan“makeAmericagreatagain”:buildaGreatWall,deportmillions,andimposehugetariffs."Wehavenochoice,orelsewewillceasetobeacountry." ThenowhistoriccollectivefailurebymediaandpoliticalleaderstoimmediatelycounterthemanifesteconomicabsurdityandblatantbigotryofhisinitialpositionallowedTrumptoelaboratehisnativistnarrativethroughouthispoliticalrisewithspecificcallsfor:

1. DeportingallundocumentedimmigrantsandtheirUSbornchildren.2. MakingMexicopayforthewallbyseizingfamilyremittancessenttoMexico.3. VoidingNAFTAandothertradedeals,whileaggressivelyimposingnewtradetermson

Chinaandothertradingpartners. Thepowerofthissimplyconstructedyetfictitiouscross-bordernarrativeshouldnothavebeenunderestimated,especiallygivenTrump’sdangerouslyracistdemonizing,whichhasnoprecedentinmodernpresidentialcampaigns.SeeminglydesignedtosetofftheLimbicbrainwhichwascreditedwiththesuccessfulBrexitLeavecampaign,2Trump'snarrativeconsistsofanappealtowhiteethnicidentitypoliticsandnostalgia,bothofwhicharebeingfueledbytheUS’shistoricallegacyofwhitesupremacy,andthechallengetothatsupremacypresentedbythecurrentdemographictransitiontoanon-whitedominant,multiracialAmerica.Trump'sclaimthat"thisisourlastchance"remainedadesperateattempttoridethewhitebacklashtothepresidencyandshouldhavemadeclearthehighstakesforAmericandemocracyinthe21stCentury. Analystshavebeendescribingthe“huge”negativeeconomicconsequencesofimplementingTrump’spolicyprescriptionssincesoonafterheannouncedhiscandidacy,includingtheUCLANAIDCenter'sreviewofmigrationproposalsin"SixHUGEnumberswhichshouldDisqualifyDonaldTrump",3andthePetersonInstituteforInternationalEconomicsPIIE’smorerecentstudyofthepotentialimpactofhistradepolicy.4

2DennisSandole.Immigrationissuemayallowthelimbicbraintoprevail(FinancialTimes,2016)3RaulHinojosa.SixHUGENumbersWhichShouldAutomaticallyDisqualifyTrumpFromBeingPresident.(UCLANAIDCenter,2016).ThecostsofTrump’simmigrationproposalinclude:2.6trillionGDPlossduetomassdeportationsandrestrictiveimmigration;5trillionlossofendingbirthrightcitizenship;and1.6trilliononwallsandenforcement.4GaryHufbauer,TylerMoran,MarcusNoland,andShermanRobinson.AssessingTradeAgendasintheUSPresidentialCampaign.(PetersonInstituteforInternationalEconomics,2016)

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Trump'ssimplisticandincreasinglydivisivediatribeswerenormalizedoverthecourseofhundredsofTrumpralliesandthroughtheirextensivecoverageinthemedia.Thishashadtheeffectofsaturatingthepressandsocialmediainapost-data,post-trutherahaze.ManynewsorganizationcontributedtothenormalizationprocessbynotquestioningTrump’slogicorthedetailsofhispolicyproposals.

ByandlargemediaorganizationsandthepunditclasscontinuedtorepeatTrump'santi-

Mexicannarrative,whileconflatingtheriseofTrump’spopularitywiththeexistenceofmeasurablenegativeimpactsfromtradeandmigrationonthelivesofTrumpsupporters.Suchreportingimplicitlyjustifiesdeplorableattitudesagainstimmigrantsandforeigners,asHillaryClintonhasdescribedthem,bysuggestingthattheseattitudesaretheproductoflegitimatematerialgrievances.Some,liketheWallStreetJournal(WSJ),havegonefurther.ThatpaperexplicitlyvalidatedTrump'snarrativewithweakcorrelations.Extrapolatingonawellknowresearchpaper’s5narrowanalysisofthe"tradeexposure"thathasbeencausedbytheimpactsofChineseimportsonsomeeconomicsectorsinsomepartsofthecountry,theWSJattributedTrump’ssupportinthesepartsofthecountry,andawidearrayoftheUSeconomy’sshortcomingsgenerally,totradewithChina.TheWSJ’sBobDavisreportsthat"[i]nthisyear’sRepublicanpresidentialprimaryraces,Mr.Trumpwon89ofthe100countiesmostaffectedbycompetitionfromChina,accordingtoananalysisbyTheWallStreetJournal."6Yetthisanalysiscanbeverymisleadingduetoalimitedsamplesizeofvotingcountiesaswellasarelianceonlargemulticounty"commutingzones"anda"tradeexposure"calculationbasedonnationallevelimportsfromChina,whileexcludingananalysisofexportsormigrationatthecountylevel. Theneedtoprovidesoliddataandcriticalanalysisisnowmoreimportantthanever,particularlywithrespecttoanunderstandingoftherealforcesdrivingtheTrumpphenomenon.WeaklyinformedquestioningbythemedialegitimizesDonaldTrump’sfalseclaimsabouttherealproblemsfacingtheeconomyhasimplicitlyendorsedadangerouslywrong-headedsetofsolutions.ItisthuscriticallyimportantthatHillaryClintonandtherestofAmericahavethetoolstodemonstratethefalseassumptionsanddangerousimplicationsofTrump'snarrative.Wemustnotlearnthewronglessonfromthe2016electionandbeswayedbytheassumed"politicalnecessities"ofimplementinganti-immigrantandanti-tradepoliciesinorderto"addressthelegitimateconcernsofTrumpvoters."Similarly,therestoftheworld,andespeciallytheMexicangovernment,shouldalsonotreadilyacceptthisnarrativeasabasisforrenegotiatingimmigrationandtraderelationswiththeU.S.7

5DavidAutor,DavidDorn,andGordonHanson.TheChinaShock:LearningfromLaborMarketAdjustmenttoLargeChanges.(2016) 6BobDavis,andJonHilsenrath.HowtheChinaShock,DeepandSwift,SpurredtheRiseofTrump.(TheWallStreetJournal,2016)7ThisappearstohavebeenthemotivationbehindPresident’sPenaNieto’sinvitationtoDonaldTrumptoMexico

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NewData,NewFindings Theendoftheprimaryseasonprovideddetailedvotingresultsformostofthenation's3,007counties,andthisdatacanbeintegratedwithlargescaledatabasescontainingsocio-economicdata,Mexicanimmigrationdata,anddataontwo-waytradewithMexicoandChina.Theresultingdatabaseallowsustheabilitytoanalyzeavarietyofstatisticalregressionsontherelationshipbetween:1)votingpatterns,2)migrationpatterns,3)exportsaswellasimports,and4)patternsofeconomicwellbeingatthecountyandcommutingzonelevel. OurresearchshowsthatthereareclearlymanypeopleintheUSwhoarestrugglingfinancially,manyoftheminTrumpvotingcounties,butthatdoesnotmeanthattradeandmigrationaretoblameforthosestruggles.Onthecontrary,ourresearchshowsthatvirtuallynoaspectsofTrumpssimplenarrativetohisvotershasanyfactualbasis,andthatthedataactuallyshowstheoppositeofTrump'snarrative.WefoundthatTrump'sprimaryvotersarelesslikelytoliveinareasthathaveasignificantnumberofnon-citizenMexicanimmigrantsorincountiesthatareexperiencingnegativeimpactsfromtrade.Infact,wefindthathigherexposuretoimportcompetitioninacounty(ie.tradethatcoulddepresslow-skillhigh-payemployment)actuallypredictslesssupportforTrump.WealsofoundthatahigherlevelofexportsfromacountyactuallypredictsgreatersupportforTrump. Becauseweexaminedprimaryvotingpatterns,thesefindingsprovideaclearerpictureofthefactorsmotivatingTrumpvotersthanwouldasimilaranalysisfocusingonthegeneralelection.PrimaryvotingdataallowsustodelineatetheforcesthataremotivatingsupportforTrumpspecifically,ratherthanfortheRepublicanPartygenerally.Inadditiontothedetailedcountylevel,wehavealsoconducedouranalysisatthelevelofmoreaggregatemulti-countycommutingzones(CZs).WefoundthatvirtuallyallthecountylevelresultsreportedbelowarecloselyreproducedattheCZlevel.Specifically,ourresearchshowsthat:• Trump’ssupportisconcentratedincounties(andCZs)thatarelesslikelytohavesignificant

numbersofMexicanimmigrants.o Thelessnon-citizenMexicanimmigrantsliveinacounty,themorelikelyitisthat

primaryvotersinthatcountysupportedTrumpbylargermargins.o Themorenon-citizenMexicanimmigrantsliveinacounty,themorelikelyitisthat

primaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.

o ThemorenaturalizedMexicanimmigrantsliveinacounty,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.

o Theimpactofimmigrationlegalization(eitherviaDACA/DAPAorComprehensiveImmigrationReform)ismuchhigherincountieswherevotersweremorelikelytosupportaDemocraticcandidateoranotherRepublican.andismorestronglypositivethemoreimmigrantsareresidinginacounty.

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• Countertothedominantnarrative,Trumpsupportersarelesslikelytoliveincounties(andCZs)thatreceiveasignificantlevelofimportsfromChinaandMexicoandismorelikelytoliveinanareathatsendsasignificantlevelofexportstoChinaandMexico.

o ThemoreimportcompetitionacountylikelyfacesfromChina,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.

o ThemoreexportsacountylikelysendstoChina,themorelikelyitisthatprimaryvotersinthatcountysupportedTrumpoveraDemocraticcandidateordifferentRepublicancandidate.

o ThemoreimportsacountylikelyreceivesfromMexico,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.

o ThemoreexportsacountylikelysendstoMexico,themorelikelyitisthatprimaryvotersinthatcountysupportedTrumpoveraDemocraticcandidateordifferentRepublicancandidate.

• Trumpsupportisnonethelessconcentratedincountiesand(CZs)inwhichtheeconomicconditionsareworsethanthoseofthecountryasawhole.

o IncountiesinwhichprimaryvotersweremorelikelytosupportTrumpoveraDemocraticcandidateordifferentRepublicancandidate,theaveragepovertyrateis15.19%.In2015,thenationalpovertyratewas13.5%.

o TheunemploymentrateintheseTrumpcountiesis9%.AsofAugust2015,thenationalunemploymentratewas4.9%

o TrumpvotersareincountieswherethewhiteswithloweducationarestrugglingeconomicallyandwherelargemajoritiesofpeoplelivinginpovertyareWhite.Countiesstrugglingeconomicallywithmorenon-whitepopulationsarenotTrumpvotingcounties.

• Fromaregionalperspective,thereisasharppolarizationbetweenthoseareas(counties

andCZs)expressinghighsupportforTrumpandareasthathaveahighconcentrationofMexicanimmigrantsand/ortradeexposure.

o Lessthan2%ofU.S.countiesexhibitedbothveryhighsupportforTrumpaswellasa

veryhighnumberofFBMexicansoraveryhighexposuretoMexicanimports(veryhighdefinedasthetop25%quadrantforeachvariable).

o AmongcountieswithveryhighconcentrationsofMexicanimmigrants,only17%alsoexhibitedveryhighsupportforTrump.

o AmongcountiesandCZswithveryhighsupportforTrump,over60%hadloworverylowconcentrationsofMexicanimmigrantsortradeexposure.

o AmongCZs,morethan80%arecharacterizedaseitherhigh/low,low/highorlow/lowofTrumpsupportcomparedtoMexicanimmigrationortradeexposure.

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OurresearchcontradictsthecoreTrumpnarrativeandopenstheneedtodevelopacounter-narrative.WhilemanypeopleintheUSarestrugglingfinanciallyinTrumpvotingcounties,tradeandmigrationaretonottoblameforthosestruggles.Thedifferencebetweenthetwoshouldnotbeunderstated.Trump’ssupportersmayfeelliketradeandmigrationhavedamagedtheireconomicprospects,buttheempiricalevidencesaysotherwise.InthewakeofTrump’spoliticalascension,theworstthingthatAmerica’spolicymakerscoulddoistotreatTrumpsupporters’misdirectedangerasasetoflegitimategrievancesinneedofredressthroughanti-immigrantandanti-tradepolicies. Inthatsense,ourworkreinforcesandgoesbeyondrecentresearchbyGallup’sJonathanRothwellwhosimilarlyfoundthatTrumpvotershadlesscontactwithMexicanimmigrants(andarelesslikelytobenegativelyaffectedbytrade)thanhavevoterswhosupporteitherDemocratsorotherRepublicans.Similarly,ourworkreinforcesaBrookingsreportwhichfoundthat“attitudesaboutimmigrantsarenotsignificantlycorrelatedwiththeperceivedeffecttheyarehavingonlocalcommunities,buttheyarehighlycorrelatedwiththeperceivedeffecttheyarehavingonAmericansociety.”8Butunlikethesetwoopinionbasedresearcharticles,ouranalysisisbasedonactualrecordedvoting.AlsotheRothwellreportreliesontheAuthor,etal.paperthatlooksat"importexposure"fromChinaatthemulti-countylevelcommutingzones(CZs),whilewelookatexportsandnettradewithChinaandMexicoatboththecountylevelandcommutingzonelevel.

TherelationshipsbetweenTrump’selectoralsupportandeithermigrationortradethatweobservedatthecountylevelwereextremelysimilartothosethatweobservedatthecommutingzonelevel.WeconductedourfirstroundofanalysisattheCountylevel,andthenconductedanadditionalroundofanalysisatthecommutingzonelevel,inordertoconfirmthatthetrendsweobservedwerenottheproductofthegeographiclevelthatweexamined.Aswouldbeexpected,therewassomevariationbetweentherelationshipsweobservedatthecountylevelandthoseweobservedatthecommutingzonelevel,butthesevariationswereslight.Atbothgeographiclevels,Trumpsupportersweremorelikelytoliveinplaceswithfewernon-citizenforeignbornMexican,lessimportexposure,andgreaterlevelsofexportsandnetexports.9 ThisreportisthefirstinaseriesthatseekstoanalyzeandcorrecttheUnitedStates’counterproductiveapproachtotheintertwinedissuesofimmigration,trade,andeconomicdevelopment.Fordecadesthepoliciesandpoliticalrhetoricsurroundingtheseissueshaverespectivelydeepenedandmisrepresentedtheproblem.Theseproblemspre-dateDonaldTrump,buthiscampaignhaspushedtheseissuesawayfromaspaceinwhicharationalpolicydiscussionsarepossible,andintoatoxicfogofdemagoguery.Thearcofthisseriesisasfollows:

8ElizabethMcElvein.Borderbattle:newsurveyrevealsAmericans’viewsonimmigration,culturalchange.(Brookings,2016)9Formoreinformationontheresultsofourcommutingzoneanalysisseesection4.3.

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1. Thisfirstreportseekstostripawaythefalsenarrativesthathaveblankedthiselectioncycle’spolicydebate.WedothisbyshowingthatthemostconcentratedoppositiontotradeandmigrationcomesfromplacesthathavebenefitedfromtradeanddonothaveasignificantpopulationofMexicanimmigrants.

2. Havingclearedtheairoffalsenarratives,thesecondreportintheserieswillbegindiagnosingtheactualproblemsfacingAmerica’simmigrationandtradepolicylandscape,whilealsoprovidingconcretepolicyrecommendationssupportedbyempiricalevidence.Thefirstissuewewilladdressisoutofcontrolimmigrationenforcementspendingandthenegativeimpactthisspendinghashadontheeconomy,thebudget,aswellasthelivesofmigrantsandcitizensalike.

3. Thethirdreportwillexaminethesignificanteconomicbenefitsoflegalizingundocumented

immigrantsandshowthattheimpactsfromtradepaleincomparisontothepositiveimpactsoflegalization.

4. Thefourthreportwilladdresstheotherhalfofmigrationpolicyandhighlighthowremittancescanbeleveragedtoaddresstherootcausesofmigration.Ratherthanseizingremittancestobuildaborderwallthatwouldbeneithercosteffectivenoraneffectivedeterrent,harnessingremittancesproductivepotentialwouldencourageeconomicdevelopmentinmigrants’countriesoforigin.Thiswoulddisincentivemigrationinawaythatbenefitsthepopulationsonbothsidesoftheborder.

2. DataMostrecentattemptstodescribetheforcesthataredrivingsupportforDonaldTrumphaveeitherlackedempiricalsupportorfocusedtoonarrowlyonasinglefactor.Thisreportaspirestoofferadatadrivenandmultifacetedcorrective.Inaddition,webelievethedepthofourdataanalysesallowustoexpandonRothwell’sGallupresearchbyprovidingdatathatoffersstrongersupportforhishypothesis.Ourrevisionofearlierarguments,andsupportforRothwell’s,ismadepossiblebyourfocusontheintersectionof2016RepublicanandDemocraticprimarydata,10tradedata,11anddemographicdata.12Thesedatasources,andtheirrelationship,revealstheforcesdrivingpoliticalchoicebothwithintheRepublicanpartyandwithout,whilealsohighlightinghowfocusingononlyonefactor,andignoringothers,hascompromisedpreviousresearchefforts.WeconductedaseriesofOrdinaryLeastSquared(OLS)regressionsinordertoquantifytherelationshipbetweenTrumpsupportandbothtradeandmigration.Electiondatavariablesare10CNN.Elections2016:Primaries+Caucuses.(CNN,2016)11CalculationsbasedondatafromWISERTradeandthe2012EconomicCensus,SurveyofBusinessOwners(SBO).Seecitationsforthesedatabasesinbibliography.12Census’ACS20105-yearand20145-year.

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ourdependentvariableswhiletradeandmigrationvariablesareourcentralexplanatoryfactors.132.1ElectionDataWeexaminedprimaryoutcomesatthecountylevelandtheseoutcomesaredescribedinourmodelasElectOutcomescounty.Weusedthreevariablestodescribetheseprimarymodels:

• “TrumpWinning,”whichisadichotomousvariablethatindicateswhetherTrumpreceivedthemostvotesinagivencounty.

• Trump’sshareofRepublicanprimaryvotesinagivencounty• Trump’sshareoftotalvotesinboththeDemocratandRepublicanprimaries.

WemadethethreeElectOutcomescountyvariablesourdependentvariablebecausetheyarerobustanddirectmeasuresofwhereTrump’ssupportisthemostprevalent.WeusedCNN’sprimaryelectiondataasthesourceforallthreeofthesevariables.14Inourregressionswealsocontrolledforanumberofpoliticalfactors.Specifically,thenumberofcandidatesinagivenprimary,and,whenusingTrump’sshareofRepublicanprimaryvotesasourdependentvariable,whetherornotagivencountywaswonbyRomneyin2012.Therearethreemajorbenefitstousingprimarydatatomeasuretheimpactofourcentralexplanatoryvariablesonpoliticaloutcomes.First,ithighlightsintra-partdistinctionswhichwouldhavebeenobfuscatedbytheuseofeitherpresidentialorcongressionalgeneralelectiondata.Second,itallowsforanalyzingintra-partydistinctionswhilealsoaccountingforageography’sgeneralpoliticalcomposition.Thatis,itallowsforacknowledgingtherelativeRepublicansupportforDonaldTrumpinacounty,whilealsoaccountingthefactthathissupportmaybelessrelevantinanoverwhelminglydemocraticcounty.ThefinalmajorbenefitofprimarydataistheabilitytocrossreferencetherelationshipsrevealedbytheRepublicanprimarydataandtheprimarydatafrombothpartiescombined.ThiswasusefulwhenanalyzingtherelationshipbetweenTrumpsupportanddemographicdata.Unsurprisingly,wefoundthatthemoreMexicanimmigrantslivedinacounty,thesmallerTrump’sshareoftotalprimaryvotersinthatcountywaslikelytobe.ButwhenlookingatjustRepublicanprimarydatawefoundthatthetrendheld,andthisfindingcontradictsamajortenetofthenarrativedescribingTrump’ssupport.Thereareanumberofreasonsthatcountiesmightbedescribedasdatadeficientinourfindings.Fortheelectiondata,theexplanationsareallrootedintheunevennessofthepresidentialprimaryprocess.Anumberofstateshaveaprimaryinonepartyandacaucusin

13FormoreinformationonourregressionsseeSection5:MethodologyandRegressions14CNN.Elections2016:Primaries+Caucuses.(CNN,2016)

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theother,whileotherstatescountvotesateitherthecongressionaldistrictorprecinctlevel.Stillmorestatescanceledeithertheirprimariesortheircaucuses,andWashingtonStatetheyhaveboth.Inordertohaveuniformdataacrossallthestatesbeinganalyzed,weusedonlydatafromthosestatesinwhichbothpartiesheldeithercaucusesorprimaries.Thefollowingarethespecificstatebystateissuesthatresultedinthedatabeingomitted:

• InColorado15andNorthDakota16thestateGOPexecutivecommitteevotedtocancelitspresidentialvoteatitsstatecaucus.

• BothpartiesinWashingtonstatehavebothacaucusandaprimarybutonlyoneofthemcountstowardstheselectiondelegates.ThestateRepublicanpartychosetoselectdelegatesbasedontheprimary,whilethestateDemocraticpartychosetoselectdelegatesbasedonthecaucus.17

• VotesaretalliedinKansas18andMinnesota’s19primariesandcaucusesbycongressionaldistrictsratherthanbycounties

• VotesaretalliedinNewHampshire20andMaine’s21primaryandcaucus(respectively)byvotingprecinctsratherthanbycounties.

2.2TradeDataToquantifytherelationshipbetweensupportforDonaldTrumpandtrade,wecollecteddataonimports,exportsandnetexportsbysector.22ThisdatawascollectedfromtheWorldInstituteforStrategicEconomicResearch(WISER)Tradedatabase.23ThisdatawasavailableforExportsatthestatelevelandimportsatthenationallevelbysector.Todistributethistradedataatthecountylevelwecreatedaratiobasedoncountysalesbysectorandthendistributedthehigherleveldataaccordingtothisratio.ThissectorsalesdatacamewascollectedfromtheUSCensusBureau’s2012SurveyofBusinessOwnersandSelf-Employed(SBO).24OuranalysissoughtreplicatecoreaspectsoftheAutor,DornandHansonmethodologyformeasuringregionaltradeexposureswhilealsoextendingand,webelieve,improvingthespecificityofthismeasurementbyincludingimportsandexportsforChinaandMexicoformoredetaillevelsi.e.countiesversustheiruseofmulticountycommutingzones.Theiranalysisofbasedon“theshareofeachindustryinregioni’stotalsalesontheU.S.marketsummarizedifferencesinindustryspecializationpatternsacrossU.S.regionsandthuscapturevariationin15JohnFrank.ColoradoRepublicanscancelpresidentialvoteat2016caucus.(TheDenverPost,2015)16EmilySchultheis.WhyNorthDakotaGOPvotersdon’tvoteinthepresidentialnominationprocess.(CBSNews,2016)17SeattlePi.WhydoesWashingtonstateholdbothacaucusandaprimary?(SeattlePi,2007)18DaveHelling.RulesvarybetweenpartiesasKansaspreparetocaucusforpresident.(TheKansasCityStar,2016)19LilyMihalik,AnthonyPesce,andBenWelsh.ResultsformtheMinnesotacaucuses.(LosAngelesTimes,2016)20TheNewYorkTimes.NewHampshirePrimaryResults.(TheNewYorkTimes,2016)21TheNewYorkTimes.MaineResults.(TheNewYorkTimes,2016)22Formoreinformationonthecontoursofourregression,andspecificallywhattradevariableswerecontrolledforandwhy,seeSection5:MethodologyandRegressions.23FullcitationofWISERtradedata24FullEconomicCensusCitation

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regionalexposuretoChina’ssupply-drivenexportgrowth.”25However,ouranalysisexaminesbothexportsandnetexports---valueofexportsminusvalueofimports---inadditiontoimportsallowedustoprovideamorecompletecharacterizationoftherelationshipbetweentradeTrumpsupport.EarlierresearchandpublicdiscussionofthisrelationshiphasfocusedonlyontherelationshipbetweenimportexposureandTrumpsupport.However,importsareonlyonesideofthetradepicture.Acountycouldbereceivingsignificantimports,butifthatcountyisalsoproducingasignificantamountofgoodsforexport,itmayberunningasurplusinnetexports.AnycountiesthataredescribedasdatadeficientinourfindingsaredescribedassuchbecausetheirsalesintradedsectorsaretoolowtobeincludedintheEconomicCensus.2.3MigrationandDemographicandSocioeconomicDataToquantifyfortherelationshipbetweenTrumpsupportandimmigrationwecollecteddataonMexicannaturalizedandMexicannon-citizenpopulationcountsbycountyfromtheUSCensusBureau’s20105-yearAmericanCommunitySurvey(ACS).26Wedidnotincludetotalnaturalizedforeignbornandtotalnon-citizenforeignbornbecausewefoundastrongcorrelationbetweenMexicanforeignbornandtotalforeignborn.WealsochosetouseMexicanforeignborn(naturalizedandnon-citizen),ratherthantotalforeignborn(naturalizedandnon-citizen),becausetotalforeignbornincludesimmigrantsfrommanydifferentcountrieswhichmayintroducetoomuchnoiseintotheanalysis.WealsohaveattemptedtodescribewhatdifferentiatescountiesthatsupportDonaldTrumpfromthosethatdon’t.TodothiswecollecteddemographicandsocioeconomicdatafromtheCensus’20145-yearACS.Wealsocontrolledforthesefactorsinourregressions.Specifically,wecollecteddataonandcontrolledfortotalpopulation,medianhouseholdincome,percentageofpopulationwiththathasatleastahighschooldiploma,povertyrate,unemploymentrate,andtherace/ethniccompositionofagivencounty.AnycountiesthataredescribedasdatadeficientinourfindingsaredescribedassuchbecausetheMexicanimmigrantpopulation---bothnaturalizedandnon-citizen---istoolowtomeettheACS’sminimumthresholdforinclusion.

3. CharacteristicsofTrumpCountiesandCommutimgZonesDonaldTrump’ssuccessintheRepublicanprimaryandbeyondmaynotbedrivenbytradeandmigration,buttherearesubpareconomicconditionsinthecountieswherehissupportisthegreatest.Beforedeterminingtheforcesthataredrivinghissupport,itisimportanttofirst

25DavidAutor,DavidDorn,andGordonHanson.TheChinaShock:LearningfromLaborMarketAdjustmenttoLargeChangesinTrade.(2016)26FullcitationofACS5-year

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establishwherehissupportismostconcentrated,andwhatthedemographicandsocioeconomiccharacteristicsofthoseplacesare.Insummary,Trump’ssupportisconcentratedincountiesthatarewhiterthanthenationasawhole,significantlylesspopulated,slightlypoorerandslightlylesseducated.ForouranalysisofthecharacteristicsofTrumpcountieswelookedatthreegeographiccategories:CountieswonbyTrumpinthe2016primaryandbyRomneyinthe2012generalelection(Trumpcounties),countieslostbyTrumpinthe2016primaries;andallcountiesintheUnitedStates.WeusedcountieswonbybothRomneyandTrumpbecauseitallowsustoaccountforTrump’ssuccessinlateuncontestedprimariesonthewestcoastwithouthavingtoignoreinlandcountiesthatTrumpislikelytowininthegeneralelection.ForouranalysisofthecountiesTrumplostwedidnotmakeadjustmentsbasedon2012becauseTrumplostnocountiesinuncontestedprimaries,andbecausewethinkitisimportanttoincludethecharacteristicsofdensecoastalcitieswhencomparingwherehissupportersareandarenotconcentrated.TheendresultisthatwearecomparingcountiesTrumpislikelytowinin2016withcountiesthatsupportedmoderaterepublicansintheprimaryand/orwillsupportTrump’sopponentinthegeneral.

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3.1DemographicCharacterisiticsTrumpcountiesaremuchsmalleronaveragethanthosehedidn’twinandthoseinthecountryasawhole:

• Trumpcountieshadanaveragepopulationof68,449.• Countieshelostinthe2016primaryhadanaveragepopulationof128,604.• Thisishigherthan,butmuchcloserto,thenationalaverageof123,980.

Trumpcountiesarealsomorewhitethanboththecountieshelostandthecountryasawhole.ThistrendholdsevenwhenadjustingforTrumpcounties’smallerpopulationsize:

• OnaverageTrumpcountieshave55,620whiteresidents.• Theyare81.3%white,comparedtothecountieshelostwhichare75.1%white,andthe

nationasawhole,whichis72.6%white.• Trumpcountieshaveanaverageofonly9,400hispanicresidents.Thatmakesthese

counties13.7%hispanic,comparedtothecountieshelostwhichare22.8%hispanic,andthenationasawholewhichis19.4%hispanic.

• Trumpcountieshaveanaverageofonly5,792blackresidents.Thatmakesthesecounties8.5%black,comparedtothecountieshelostwhichare13.7%black,andthenationasawholewhichis12.6%black

Inadditiontobeingmorelikelytobewhite,residentsinTrumpcountiesarealsomorelikelytobenative-born.ThistrendholdsevenwhenadjustingforTrumpcounties’smallerpopulationsize:

• Thereareonly,onaverage,5,854foreignbornresidentsinTrumpcounties.

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• Theforeignbornmakeuponly8.7%percentofthepopulationinTrumpcounties,buttheymakeup,onaverage,13.6%ofthepopulationincountiesthatTrumplost,and14.6%ofthepopulationinanaverageUScounty.

• Trumpcountieshaveanaverageofonly2,548naturalizedforeignbornresidents.Thenaturalizedforeignbornmakeuponly3.7%percentofthepopulationinTrumpcounties,buttheymakeup,onaverage,5.4%ofthepopulationincountiesthatTrumplost,and6.7%ofthepopulationinanaverageUScounty.

• Trumpcountieshaveanaverageofonly3,406non-citizenforeignbornresidents.Thenon-citizenforeignbornmakeuponly5%percentofthepopulationinTrumpcounties,buttheymakeup,onaverage,8.2%ofthepopulationincountiesthatTrumplost,and7.9%ofthepopulationinanaverageUScounty.

Mean Demographic Characteristics of Counties

Variables Won by Trump in 2016 and Romney in 2012

Lost by Trump in 2016

All Counties Nationwide

TotalCountyPopulation 68,449 128,604 123,980

TotalForeignBorn 5,954 17,530 18,100

NaturalizedForeignBorn 2,548 6,958 8,282

Non-CitizenForeignBorn 3,406 10,570 9,815

MexicanForeignBorn 6,981 8,148 10,970

MexicanForeignBornNaturalized 1,460 1,691 2,526

MexicanForeignBornNon-Citizen 5,522 6,458 8,441

Race/Ethnicity:Hispanics 9,400 29,264 24,103Race/Ethnicity:White 55,620 96,530 90,050

Race/Ethnicity:Black 5,792 17,680 15,660

Race/Ethnicity:AmericanIndian 613 927 991

Race/Ethnicity:Asian 2,079 4,669 6,777

Race/Ethnicity:NativeHawaiian 66 122 210

3.2SocioeconomicCharacteristicsTrumpcountiesarepooreronaveragethanthosehedidn’twin,andthoseinthecountryasawhole:

• TheaverageTrumpcountyhasamedianhouseholdincomeof$44,020.• Theaveragecountyhelosthasamedianhouseholdincomeof$48,846.• TheaverageUScountyhasamedianhouseholdincomeis$46,845.

WhitesarepoorerintheaverageTrumpcountiesthantheyareintheaveragecountyhelost,aswellasintheaverageUScounty.

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• WhitesintheaverageTrumpcountyhaveamedianhouseholdincomeof$45,953.• Whitesintheaveragecountyhelosthaveamedianhouseholdincomeof$51,179.• WhitesintheaverageUScountyhaveamedianhouseholdincomeis$49,495.

IntheaverageTrumpcounty,therearemorewhiteswhoareunemployedthantherearepeopleofallotherracesandethnicitiescombined.Inthesecounties,therearealsomorewhiteswhoareinpovertythanthereareofallotherracesandethnicitiescombined.Thatsaid,povertyratesandunemploymentratesarehigherforblacksandhispanics.

• IntheaverageTrumpcounty,thereare2,232unemployedwhites,and7,566whitesarelivinginpoverty.

• IntheaverageTrumpcounty,thereare506unemployedhispanics,and2,463hispanicsarelivinginpoverty.

• IntheaverageTrumpcounty,thereare395unemployedblacks,and1,581blacksarelivinginpoverty.

• IntheaverageTrumpcounty,thereare80unemployedasians,and271asiansarelivinginpoverty.

Mean Socioeconomic Characteristics of Counties

Variables Won by Trump in 2016 and Romney in 2012

Lost by Trump in 2016

All Counties Nationwide

PovertyRate(%) 17.4% 15.9% 16.8%UnemploymentRate(%) 9.034 7.388 8.545ReceivedatLeastAHighschoolDiploma(%) 84.2% 84.6% 85%HouseholdMedianIncome($) 44,020 48,846 46,845HouseholdMeanIncome($) 57,224 63,857 61,046NumberofWhitesinPoverty 7,566 12,923 11,465NumberofUnemployedWhites 2,232 3,304 3,550WhiteMedianHouseholdIncome($) 45,953 51,179 49,495NumberofHispanicsinPoverty 2,463 7,232 5,797

NumberofUnemployedHispanics 506 1,206 1,236HispanicMedianHouseholdIncome($) 39,669 39,601 40,367NumberofAsiansinPoverty 271 608.5 861.1NumberofUnemployedAsians 79.69 136.6 251.6AsianMedianHouseholdIncome($) 60,606 61,953 60,879NumberofBlacksinPoverty 1,581 4,634 4,090NumberofUnemployedBlacks 395 1,233 1,178

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BlackMedianHouseholdIncome($) 33,117 34,797 34,768

4. Forces Driving Trump Voting InordertodeterminewhethertheforcesthatDonaldTrumphasidentifiedasdamagingtheUSeconomy---migrationandtrade---aredrivinghissupport,weanalyzedtherelationshipbetweenthoseforcesandhissupportintheprimaries.Ifhissupportersweremotivatedbytheeconomicimpactsoftradeandmigration,onewouldexpecttofindmoreMexicanimmigrantsandmoreexposuretoimportsinthecountiesandCZsinwhichheenjoysgreaterlevelsofsupport.Wefoundtheoppositetobetrue.Allresultsinthissectionarebaseduponourfullmodelinwhichwecontrolledfortheeconomiccharacteristicsofthecounty,thenumberofforeignbornresidents(totalforeignborn,non-naturalizedforeignborn,andMexicanforeignborn),politicalfactors(numberofcandidatesintheprimary,whetherthecountyvotedforRomneyin2012,andwhethertheprimarywascontested),andindustrialcharacteristics(employmentandsalarybyindustry).274.1MigrationTrump’snarrativeblamesMexicanmigrantsformanyoftheUS’seconomicandsocialills.IfmigrantswerehavingsuchanegativeimpactontheUS,itstandstoreasonthatvoterswholiveinthesamecountiesasthesemigrantswouldbeattractedtoananti-immigrantcandidate.However,ourresearchshowsthatTrump’ssupportisnegativelycorrelatedwiththepresenceofbothcitizenandnon-citizenMexicanForeignborn.ThistrendholdsregardlessofwhetherweexaminedTrumpsupportintermsofhisshareofallprimaryvotersorhisshareofvotesinonlytheRepublicanprimaries.ThelattermetricisusefulbecauseitshowsthatTrumpisalsomorelikelytobeunpopularwithactiveRepublicansiftheyliveinareaswithsignificantnumbersofMexicanmigrants.ItsuggeststhatRepublicanswhoknowMexicanimmigrantsarelesslikelytoapproveofDonaldTrumpthanthosewhodonot.ItisimportanttonotethatthescatterplotfiguresdepictedinthissectionhavenegativevaluesalongtheirXaxesbecausethoseaxesnotdescribingtherawvalueofthevariablesbutratherthevaluesofvariablesaftertakingintoaccountalltheothervariablesthatarecontrolledforinourfullregressionmodel.

27Formoredetailseemethodologysection

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(Fromlefttoright:scatterplotatcountylevel,scatterplotatCZlevel.)Ourresearchshowsanegativecorrelationbetweennon-naturalizedMexicanimmigrantsandsupportforDonaldTrumpintheprimaries.Inotherwords,themorenon-citizenMexicanimmigrantsliveinacounty,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.Interestingly,thiseffectbecomemorepronouncedaswemovefromthesmallercountyleveltointhelargerCZaggregation.TheCZsare,bydefinition,morepopulatedsincetheyeachrepresentaclusterofcounties,andarerelativelymorelikelytoincludealargecity.

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(Toprow:mapandscatterplotatcountylevel,bottomrow:mapandscatterplotatCZlevel)ThecorrelationtrendalsoholdswhenweexaminetherelationshipbetweennaturalizedMexicanimmigrantsandTrumpsupportatthecountylevel.ThemorenaturalizedMexicanimmigrantsliveinacounty,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.Interestingly,thisrelationshipisreversedattheCZlevel.ParsingwhythereisaslightlypositiverelationshipfornaturalizedMexicanforeignbornattheCZlevelbutnotatthecountylevel,norfornon-naturalizedMexicanforeignbornateitherthecountyorCZlevel,isapotentialavenueforfutureresearch.

(Fromlefttoright:scatterplotatcountylevel,scatterplotatCZlevel.)

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(Toprow:mapandscatterplotatcountylevel,bottomrow:mapandscatterplotatCZlevel)ThetrendalsoholdswhenweexaminetherelationshipbetweenTrump’sshareofRepublicanprimaryvotesandthenumberofbothnaturalizedandnon-naturalizedMexicanimmigrantsinanygivencounty.ThemorenaturalizedMexicanimmigrantsliveinthatcounty,themorelikelyitisthataRepublicanprimaryvoterinthatcountysupportedacandidateotherthanTrump.

(Bothscatterplotsdescribedataatthecountylevel)

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4.2TradeTrumphasarguedthattwooftheUS’smajortradingpartnersaretakingadvantageofweakDemocraticleadership.28Accordingtothislineofthinking,unfetteredimportsfromChinaandMexicohavehollowedouttheUSeconomy,destroyedgoodpayingjobsthatrequirelittleeducation,andreducedtheAmericanheartlandtoapost-industrialeconomicwasteland.Meanwhile,TrumpclaimsthatourcurrenttraderelationswithChinaandMexicohavenotonlylimiteddomesticconsumptionofUSmadegoods,theyhavehamstrungtheabilityofUSfirmstoproducegoodsforexport.29Ifthiswerethecase,onewouldassumethatTrump’ssupportwouldbeconcentratedinthecountiesthatimportthemostandexporttheleast.Again,ourresearchshowsthatthisisnotthecase,andinfact,theoppositeistrue.ThemoreimportsacountyreceivesthelesslikelyitisthatprimaryvotersinthatcountysupportedTrump.Ontheotherhand,themoregoodsacountyproducesforexporttoChinaandMexico,themorelikelyitisthatthatcounty’sprimaryvoterssupportedTrump.Thesameistrueforcountiesthathavegreaterlevelsofnetexports---thatisthevalueofgoodsproducedforexportminusthevalueofimports.ThesetrendsholdattheCZlevel.ItisimportanttonotethatthescatterplotfiguresdepictedinthissectionhavenegativevaluesalongtheirXaxesbecausethoseaxesnotdescribingtherawvalueofthevariablesbutratherthevaluesofvariablesaftertakingintoaccountalltheothervariablesthatarecontrolledforinourfullregressionmodel.

(Fromlefttoright:scatterplotatcountylevel,scatterplotatCZlevel.)ThegreaterthevalueofimportsacountyreceivesfromChina,themorelikelyitisthatprimaryvotersinthatcountysupportedaDemocraticcandidateoraRepublicanotherthanTrump.

28DonaldTrump.ChinaTradeReform.(TrumpPencewebsite,2016)29Ibid.

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ThesefindingsdisprovethecoreunderlyingassumptionofDonaldTrump’stradenarrative.WhilemanypeoplesupportingTrumpliveincountiesthatarestrugglingeconomically,Chineseimportexposureisnotthecauseofthosestruggles.ThecontinuedlegitimizationofTrump’snarrativeunderminesthepossibilityofidentifyingandaddressingthetruecausesoftheseeconomicwoes.ThistrendholdsatCZlevel.

(Toprow:mapandscatterplotatcountylevel,bottomrow:mapandscatterplotatCZlevel)ThegreaterthevalueofimportsaCZreceivesfromMexico,themorelikelyitisthatprimaryvotersinthatCZsupportedaDemocraticcandidateoraRepublicanotherthanTrump.ThesefindingsalsodisprovethecoreunderlyingassumptionofDonaldTrump’stradenarrative.WhilemanypeoplesupportingTrumpliveinCZsthatarestrugglingeconomically,Mexicanimportexposureisnotthecauseofthosestruggles.ThecontinuedlegitimizationofTrump’snarrativeunderminesthepossibilityofidentifyingandaddressingthetruecausesoftheseeconomicwoes.

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(ScatterplotdepictsregressionanalysisatCZlevel)

(Toprow:mapandscatterplotatcountylevel,bottomrow:mapandscatterplotatCZlevel)

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Surprisingly,TrumpsupportersaremorelikelytoliveincountiesthataretherelativebeneficiariesoftradewithChinaandMexico.Thegreaterthevalueofacounty’sexportstobothChinaandMexico,themorelikelyTrumpsupportersaretolivethere.ThefactthatTrumpsupportersaremorelikelytobetherelativebeneficiariesoftradeisreinforcedbytherelationshipbetweenthatsupportandnetexports.Netexportsarethevalueofexportsminusthevalueofimports.Ourfindingsshowthatthegreateracounty’snetexportstoChinaandMexicothemorelikelyitisthataprimaryvoterwillsupportTrump.ThisagaincontradictsacoretenetofthenarrativesurroundingTrump’stradepolicies.Trumpisnotbeingsupportedbyagroundswellofwhiteworkerswhohavebeenmarginalizedbytrade.Trumpsupportmaybedrivenbymarginalizedwhiteworkers,butifso,tradeisnotgenerallythecauseofthatmarginalization.TherelationshipbetweenTrumpsupportandbothexportsandnetexportsissimilarwhenlookingateitherChinaorMexico:

(Fromlefttoright:scatterplotatcountylevel,scatterplotatCZlevel.)ThescatterplotsaboveshowthatthemoreexportsacountyorCZsendstoChina,themorelikelyitisthatprimaryvotersinthatcountyorCZsupportedTrumpoveraDemocraticcandidateoradifferentRepublicancandidate.

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ThemoreexportsacountyorCZsendstoMexico,themorelikelyitisthatprimaryvotersinthatcountyorCZsupportedTrumpoveraDemocraticcandidateordifferentRepublicancandidate.

(Toprow:mapandscatterplotatcountylevel,bottomrow:mapandscatterplotatCZlevel)Again,ourresearchrevealedthatthegreaterthelevelofnetexportsbetweenanygivencountyandChina,andbetweenanygivencountyandMexico,themorelikelyitisthatprimaryvoterssupportedTrumpasopposedtoaDemocratoranotherRepublican.ThegreatIronyoftheseresultsisthatthemoreacountybenefitsfromnetexportstoMexico,themoreinclineditistosupportTrumpwhosetradepolicieswouldpotentiallyhurtthemthemost.

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Thefollowingfiguresillustratethispoint:

(Allfourfiguresdescribenettradeatthecountylevel)

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(AllfourfiguresdescribenettradeattheCZlevel)

4.3 ContinuityattheCommutingZoneLevel

Intermsofgeography,thebulkofourresearchwasoriginallyfocusedontheCountylevelwhichallowedforaclearerdepictionoftherelationshipbetweenTrumpsupportandeithertradeormigration.Becauseweexaminedprimaryvotingpatternsatthecountylevel,thesefindingsprovideaclearerpictureofthefactorsmotivatingTrumpvotersthanwouldasimilaranalysisfocusingonthegeneralelection.PrimaryvotingdataallowsustodelineatetheforcesthataremotivatingsupportforTrumpspecifically,ratherthanfortheRepublicanPartygenerally.

AfterweconductedourfirstroundofanalysisattheCountylevel,wethenconductedanadditionalroundofanalysisatthecommutingzonelevel,inordertoconfirmthatthetrendsweobservedwerenottheproductofthegeographiclevelthatweexamined.WealsoconductedouranalysesatthecommutingzoneleveltoallowourresearchtobemoreeasilycomparedtoanumberofearlierstudiesthathadexaminedtherelationshipbetweenTrump’spoliticalsupportandeithertradeormigrationatthatgeographiclevel.

TherelationshipsbetweenTrump’selectoralsupportandeithermigrationortradethat

weobservedatthecountylevelwereextremelysimilartothosethatweobservedatthecommutingzonelevel.WefoundthatalmostallthecountylevelresultsarecloselyreproducedattheCZlevel.

Aswouldbeexpected,therewassomevariationbetweentherelationshipsweobserved

atthecountylevelandthoseweobservedatthecommutingzonelevel,butthesevariationswereslight.Atbothgeographiclevels,Trumpsupportersweremorelikelytoliveinplaceswithfewernon-citizenforeignbornMexicans,lessimportexposure,andgreaterlevelsofexportsandnetexports.

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Thegeneraltrendweobservedatthecountylevelheldatthecommutingzonelevel,butthereweresomevariations.Themostimportantoftheseare:

• Thepopulationsizeofnon-citizenforeignbornMexicansstillhasanegativerelationshipwithTrumpsupport,andthatrelationshipisstrongeratthecommutingzonelevel.

• ThevalueofimportsfromChinaarestillnegativelyrelatedtoTrumpsupport.However,thisrelationshipislessstrongatthecommutingzonelevelthanitisatthecountylevel.

• ThevalueofexportstoChinaarestillpositivelyrelatedtoTrumpsupport.However,thisrelationshipislessstrongatthecommutingzonelevelthanitisatthecountylevel.

• ThevalueofexportstoMexicoarestillpositivelyrelatedtoTrumpsupport.However,thisrelationshipisnolongerstatisticallysignificant.

Comparingresultsfromaregionalperspective,thereisasharppolarizationbetweenthoseareas(countiesandCZs)expressinghighsupportforTrump,andthoseareasthathaveahighconcentrationofMexicanimmigrantsand/ortradeexposure.

o Ina4x4quadrantlevelanalysis,lessthan2%(1.56%)ofU.S.countiesexhibitedboth

"veryhigh"supportforTrumpaswellasaveryhighnumberofnon-naturalizedforeign-bornMexicansoraveryhighexposuretoMexicanimports(“veryhigh”definedasthetop25%quadrantforeachvariable).

o ForCommutingZones,thecomparableshareis3.60%o Ina2x2levelanalysis,nearly60%(59.31%)ofCZsarecharacterizedaseither

high/low,low/highorlow/lowofTrumpsupportcomparedtonon-naturalizedforeign-bornMexicansortradeexposurefromMexicanimports.

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o Amongcounties,thecomparablenumberis57.16%

o Ofthecountiesthathad"veryhigh"levelsofsupportforTrump,two-thirdshadloworverylowconcentrationsofnon-naturalizedforeign-bornMexicans(85.8%)ortradeexposurefromMexicanImports(63.5%).

o AmongCZswith"veryhigh"supportforTrump,twothirdshadloworverylowconcentrationsofnon-naturalizedforeign-bornMexicans(66.96%)ortradeexposurefromMexicanimports(65.76%).

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o Amongcountieswith"high"concentrationsofMexicanimmigrants,only8.7%alsoexhibited"high"supportforTrump.ThecomparablenumberamongCZis18.62%ina2x2levelanalysis.

WhilethecomparativeregionallevelanalysisbetweencountiesandCZsbothshowverysimilarrefutationsoftheTrumpnarrative,bothalsodisplayasharppolarizationbetweenthoseareas(countiesandCZs)thatareexpressinghighsupportforTrumpandthoseareaswithhighconcentrationofMexicanimmigrantsand/ortradeexposure.Thecountyanalysisatthe4x4level(veryhigh,mediumhigh,mediumlowandverylow),however,allowsustospecifythattheTrumpnarrativeisonlyappliestoaverysmallnumberofcountieswithinparticularcommutingzones.FutureresearchshouldfurtheranalyzethesecountiesandCZsinordertobetterspecifywhythey,unlikethevastmajorityofcountiesandCZs,conformtotheTrumpnarrative.

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MexicanForeignBornNonNaturalized

Trump Voters

VeryHigh MediumHigh MediumLow VeryLow TotalVeryHigh 1.56% 1.96% 1.92% 19.55% 24.99%

MediumHigh 2.29% 2.39% 3.26% 17.05% 24.99%MediumLow 3.23% 4.03% 3.81% 13.96% 25.03%VeryLow 4.72% 3.41% 2.79% 14.07% 24.99%Total 11.79% 11.79% 11.79% 64.64% 100.00%

USChinaImportsVeryHigh 1.74% 3.45% 3.95% 15.85% 24.99%

MediumHigh 3.37% 4.64% 4.64% 12.33% 24.99%MediumLow 4.86% 4.17% 4.03% 11.97% 25.03%VeryLow 6.06% 3.81% 3.41% 11.72% 24.99%Total 16.03% 16.07% 16.03% 51.87% 100.00%

USMexicoImportsVeryHigh 1.89% 3.19% 3.84% 16.07% 24.99%

MediumHigh 3.19% 4.39% 4.75% 12.66% 24.99%MediumLow 5.01% 4.39% 4.21% 11.43% 25.03%VeryLow 5.95% 4.10% 3.26% 11.68% 24.99%Total 16.03% 16.07% 16.07% 51.83% 100.00%

MexicanForeignBornNonNaturalized

Trump Voters

VeryHigh MediumHigh MediumLow VeryLow TotalVeryHigh 43 54 53 539 689

MediumHigh 63 66 90 470 689MediumLow 89 111 105 385 690VeryLow 130 94 77 388 689Total 325 325 325 1782 2757

USChinaImportsVeryHigh 48 95 109 437 689

MediumHigh 93 128 128 340 689MediumLow 134 115 111 330 690VeryLow 167 105 94 323 689Total 442 443 442 1430 2757

USMexicoImportsVery High 52 88 106 443 689

MediumHigh 88 121 131 349 689MediumLow 138 121 116 315 690VeryLow 164 113 90 322 689Total 442 443 443 1429 2757

MexicanForeignBornNonNaturalized

TrumpVoters

High Low TotalHigh 226 1152 1378Low 424 955 1379Total 650 2107 2757

USChinaImportsHigh 364 1014 1378Low 521 858 1379Total 885 1872 2757

USMexicoImportsHigh 349 1029 1378Low 536 843 1379Total 885 1872 2757

MexicanForeignBornNonNaturalized

TrumpVoters

High Low TotalHigh 8.20% 41.78% 49.98%Low 15.38% 34.64% 50.02%Total 23.58% 76.42% 100.00%

USChinaImportsHigh 13.20% 36.78% 49.98%Low 18.90% 31.12% 50.02%Total 32.10% 67.90% 100.00%

USMexicoImportsHigh 12.66% 37.32% 49.98%Low 19.44% 30.58% 50.02%Total 32.10% 67.90% 100.00%

Counties

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32

33

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5. Methodology and Data Regressions 5.1EconometricMethodologyInordertodeterminetherelationshipbetweenbothinternationaltradeandmigrationandsupportforDonaldTrumpinthe2016republicanprimaries,weappliedthefollowingOrdinaryLeastSquared(OLS)regressions:

ElectOutcomes+,-./0 = US_MX_Export+,-./0,;<+/,= + US_CN_Export+,-./0,;<+/,=+US_MX_Import+,-./0,;<+/,= + US_CN_Import+,-./0,;<+/,=+MxForeignBornNat+,-./0 + MxForeignBornNonNat+,-./0

+PoliFactors+,-./0 + SocEconFactors+,-./0 + SalesSectors+,-./0+AveTradeGrowth+,-./0 + Uncontested+,-./0 + ε+,-./0

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ElectOutcomescountyincludes:“TrumpWinning,”whichisadichotomousvariablethatindicateswhetherTrumpreceivedthemostvotesinagivencounty;Trump’sshareofRepublicanprimaryvotesinagivencounty;andTrump’sshareoftotalvotesinbothprimaries.US_MX_ExportcountyreferstothevalueofU.S.exportstoMexicobysector,US_CN_ExportcountyreferstothevalueofU.S.exportstoChinabysector,US_MX_ImportcountyreferstovalueofU.S.importsfromMexicobysector,andfinallyUS_CN_ImportcountyreferstothevalueofU.S.importsfromChinabysector.MxForeignBornNatcountyreferstothenumberofnaturalizedMexicanforeignborninanobservedcounty,whileMxForeignBornNonNatcountyreferstothenumberofnon-citizenMexicanforeignborninanobservedcounty.PoliFactorscountyreferstothepoliticalcharacteristicsofanobservedcounty,whileSocEconFactorscountyreferstothesocioeconomiccharacteristicsofanobservedcounty.Specifically,politicalcharacteristicsincludesthenumberofcandidatesontheprimaryballotforagivenpartyinanobservedcounty,andadichotomousvariablethatindicateswhetherthecountyisa"Republicanoriented"county.ThisvariableisdefinedaspositiveifMittRomneywonagivencountyinthe2012generalelection.Socioeconomiccharacteristicsincludetotalpopulationnumber,medianhouseholdincome,percentageofthepopulationthathasatleastahighschooldiploma,povertyrate,unemploymentrate,andrace/ethniccomposition(i.e.numberofWhite,Black,Asian,andsoon)inaspecificcounty.SalesSectorscountyincludetheannualemploymentrateandannualpayrateinaspecificsectorandinaspecificcounty.AveTradeGrowthcountyincludestheannualaverageexportandimportgrowthfortradebetweentheUSandMexicoandbetweentheUSandChina.Finally,Uncontestedcountyisadichotomousvariablethatwillequalto1ifthegivencountyisinastatewhichhadanuncontestedprimaryin2016. WemadethethreeElectOutcomescountyvariablesourdependentvariablebecausetheyarerobustanddirectmeasuresofwhereTrump’ssupportisthemostprevalent.WeexaminedbothTrump’sshareoftotalprimaryvotesandhisshareofRepublicanprimaryvotesbecausethesetwometricsallowustodrawimportantdistinctions.Forexample,theformerallowsustomeasuretheforcesthatourinfluencingthegeneralelectorate,whilethelatterallowsusthemeasuredifferentforcesinfluencetherepublicanelectorate.ThisisespeciallyrelevanttoouranalysisoftherelationshipbetweenTrumpsupportandthesizeoftheMexicanmigrantpopulation.Tradeandimmigrationaregenerallythecentralexplanatoryfactorsthatwewishtoexplore.Tomeasureimpactsoftradeonourdependentvariables,wehavefocusedontradewithChinaandMexico.ThesetwocountrieswereselectedbecausetheyaretwooftheUS’slargesttradingpartners,andbecausetheyhavebothbeenspecificallytargetedbyDonaldTrump’s

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traderhetoric.WemeasuredexportsandimportsbetweentheUSandMexicoandbetweentheUSandChina.Inordertomeasurethecollectiveimpactsofbothexportsandimportsonagivencounty,wealsoconductedthefollowingregressionwhichusesnettrade,insteadofseparatelyusingeitherexportsorimports,astheexplanatorytradevariable:

ElectOutcomes+,-./0 = US_MX_NetTrade+,-./0,;<+/,=+US_CN_NetTrade+,-./0,;<+/,=

+MxForeignBornNat+,-./0 + MxForeignBornNonNat+,-./0+PoliFactors+,-./0 + SocEconFactors+,-./0 + SalesSectors+,-./0

+AveTradeGrowth+,-./0 + Uncontested+,-./0 + ε+,-./0Besidesofusingnettrade,wealsouseTotalimportandTotalexportasexplanatoryvariablesfortrade.WedidthisbecausetherearesignificantdifferencesbetweenthevolumeofimportsandexportsfromMexicoandthevolumeofimportsandexportsfromChina.Theregressionmodelisshownasfollow:

ElectOutcomes+,-./0 = Total_Export+,-./0,;<+/,= + Total_Import+,-./0,;<+/,=+MxForeignBornNat+,-./0 + MxForeignBornNonNat+,-./0

+PoliFactors+,-./0 + SocEconFactors+,-./0 + SalesSectors+,-./0+AveTradeGrowth+,-./0 + Uncontested+,-./0 + ε+,-./0

Regardingtheexplanatoryvariablesforimmigration,wecontrolledforbothMexicannaturalizedforeignbornandnon-citizenMexicanforeignborn.Wedidnotincludetotalnaturalizedforeignbornandtotalnon-citizenforeignbornbecausewefoundastrongcorrelationbetweenMexicanforeignbornandtotalforeignborn.WealsochosetouseMexicanforeignborn(naturalizedandnon-citizen),ratherthantotalforeignborn(naturalizedandnon-citizen),becausetotalforeignbornincludesimmigrantsfrommanydifferentcountrieswhichmayintroducetoomuchnoiseintotheanalysis.Wecontrolledforpoliticalandeconomicfactorsbecauseoftheirpotentialimpactonelectoraloutcomes.Itisclearthatboththenumberofelectoralcandidatesand,whenusingtheshareofRepublicanprimaryvotesasthedependentvariable,whetherornotacountyis"Republicanoriented,"canhaveanoutsizedimpactonanelectoraloutcome.Socioeconomicfactorsmaynothavedirectimpactonvotingpatterns,buttheywillhaveadirectimpactonvoters,whichwillindirectlyaffectelectoraloutcomes.Thus,controllingforthesefactorsiscritical.Salessectorsarecontrolledforbecausetheymayreflectthechannelinwhichtradeaffectselectoraloutcomes.Thatis,differenttradesectorswillaffectthesalesofdifferentsectorsindifferentways.Averagegrowthintradeiscontrolledduetothetemporallimitationsofouranalysis.Ouranalysisusedcrosssectionaldatainsteadofapaneldata.Therefore,controllingforaveragegrowthiscrucialbecauseittakesintoaccountthedynamicoftradeovertime.

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Finally,controllingforuncontestedprimariesisofobviousimportance,giventhatacandidateinalateuncontestedprimarywillhavenearunanimoussupport,whilethatsamecandidateinthatsamestatewouldhavehadmuchlowerlevelsofsupporthadtheybeencompetingagainstafullslateofcandidatesearlierintheprimaryseason.5.2RegressionsatCountyLevel

FullModelatCountyLevel

Trump'sShareofVotesinDandRPrimaries

Trump'sShareofVotesinRPrimaries

dummy_uncontested 20.81530*** 25.01678*** (0.36003) (0.30233)net_trade_us_cn net_trade_us_mx nofcandidates -0.13425 -2.97907*** (0.08346) (0.07408)republican_oriented 10.88495*** -1.44234*** (0.34020) (0.29639)mx_fb_nat 0.00328 0.00342 (0.02306) (0.01922)mx_fb_non_nat -0.03300*** -0.04601*** (0.00920) (0.00770)ave_growth_us_mx_export -0.00219*** -0.00044 (0.00036) (0.00030)ave_growth_us_cn_export 0.00017** 0.00080*** (0.00008) (0.00006)o.ave_growth_mx_cn_import - - ave_growth_us_cn_import 1.42428*** 1.91470*** (0.15694) (0.13234)county_tot_pop 0.00000* 0.00002*** (0.00000) (0.00000)county_hh_median -0.00049*** -0.00032*** (0.00002) (0.00002)county_higher_hs -41.39497*** -26.15968*** (2.83353) (2.37644)county_poverty_rate -103.61370*** -78.00082***

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(4.73863) (3.97566)county_unemployment_rate 61.49366*** 151.36502*** (5.53475) (4.59380)annualaverageemployment -0.00000 0.00001*** (0.00000) (0.00000)annualaveragepay -0.00002*** 0.00002*** (0.00001) (0.00001)white -0.00189 -0.01515*** (0.00283) (0.00231)black -0.00095 -0.02193*** (0.00320) (0.00262)ame_indian 0.01618 -0.04540*** (0.01252) (0.01032)asian -0.02060*** -0.00789** (0.00492) (0.00401)native_hawaiian 0.02315 -0.14914*** (0.02279) (0.01853)county_us_cn_export 0.01021*** 0.00309*** (0.00053) (0.00043)county_us_cn_import -0.00069** -0.00127*** (0.00028) (0.00024)county_us_mx_export 0.00012 -0.00005 (0.00011) (0.00009)county_us_mx_import -0.00166*** 0.00103*** (0.00043) (0.00038)Constant 65.60082*** 63.92579*** (3.66528) (3.08713) Observations 9,339 8,836R-squared 0.45455 0.70324Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1 5.3RegressionsatCommutingZone(CZ)Level

FullModelatCommutingZoneLevel

Trump'sShareofVotesinDandRPrimaries

Trump'sShareofVotesinRPrimaries

39

dummy_uncontested 20.81530*** 25.01678*** (0.36003) (0.30233)net_trade_us_cn net_trade_us_mx nofcandidates -0.13425 -2.97907*** (0.08346) (0.07408)republican_oriented 10.88495*** -1.44234*** (0.34020) (0.29639)mx_fb_nat 0.00328 0.00342 (0.02306) (0.01922)mx_fb_non_nat -0.03300*** -0.04601*** (0.00920) (0.00770)ave_growth_us_mx_export -0.00219*** -0.00044 (0.00036) (0.00030)ave_growth_us_cn_export 0.00017** 0.00080*** (0.00008) (0.00006)o.ave_growth_mx_cn_import - - ave_growth_us_cn_import 1.42428*** 1.91470*** (0.15694) (0.13234)cz_tot_pop 0.00000* 0.00002*** (0.00000) (0.00000)cz_hh_median -0.00049*** -0.00032*** (0.00002) (0.00002)cz_higher_hs -41.39497*** -26.15968*** (2.83353) (2.37644)cz_poverty_rate -103.61370*** -78.00082*** (4.73863) (3.97566)cz_unemployment_rate 61.49366*** 151.36502*** (5.53475) (4.59380)annualaverageemployment -0.00000 0.00001*** (0.00000) (0.00000)annualaveragepay -0.00002*** 0.00002*** (0.00001) (0.00001)white -0.00189 -0.01515*** (0.00283) (0.00231)black -0.00095 -0.02193*** (0.00320) (0.00262)

40

ame_indian 0.01618 -0.04540*** (0.01252) (0.01032)asian -0.02060*** -0.00789** (0.00492) (0.00401)native_hawaiian 0.02315 -0.14914*** (0.02279) (0.01853)cz_us_cn_export 0.01021*** 0.00309*** (0.00053) (0.00043)cz_us_cn_import -0.00069** -0.00127*** (0.00028) (0.00024)cz_us_mx_export 0.00012 -0.00005 (0.00011) (0.00009)cz_us_mx_import -0.00166*** 0.00103*** (0.00043) (0.00038)Constant 65.60082*** 63.92579*** (3.66528) (3.08713) Observations 9,339 8,836R-squared 0.45455 0.70324Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1 BIBLIOGRAPHY

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