Intheswingofthings

What does Donald Trump’s victory tell us about America?

StephenClarke&DanTomlinson

November2016

@Stephenlclarke/@Dan_Tomlinson_/@resfoundation

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“IT’SNOTTHEECONOMY,STUPID"REALLY?

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Incomewasnotastrongpredictorofanindividual'svote......leadingsometosayeconomicsdidn’tmatter

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Source: 2016 Presidential election exit poll, The Daily Record, LSE Blog

Butwhileotherfactorswereclearlyveryimportant...

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Source: 2016 Presidential election exit poll

…differentialswingtowardstheRepublicansamonglowandmiddleincomevoterssuggeststhatwritingofftheeconomyispremature

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Source: 2016 Presidential election exit poll

ADEEPERDIVEINTOTHEIMPORTANCEOFPLACE

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InanalysingtheswingtowardstheRepublicans,geographymatters

•  Post-electionanalysishashighlightedtheimportanceofdemographic,economicandculturalfactors

•  Inthisnote,weconsiderwhydifferentpartsofAmericavotedastheydid.Welookacross93percent(2,932of3,143)ofUScountiesspreadacross46statesincludingthe11battlegroundstates

•  WetestthestrengthoftherelationshipbetweentherelativechangeintheRepublicanmarginofvictory(ordefeat)andvariouseconomic,demographicandculturalfactors,whileholdingallelseconstant(usingaseriesofregressionmodels)

•  WeexploreeconomicfactorsinSection1;adddemographicfactorsintotheanalysisinSection2;andbringinculturalissuesinSection3

•  Section4looksatthedifferencesbetweenDonaldTrump’svictoryandtheLeavevoteinBritain’sEUreferendum

•  Section5includesafulldescriptionoftheregressionresults7

Afewimportantreminders

•  Don'tforgetthebaggage–thiswasnotaone-offBrexitstylereferendum,butratherthelatestinalonglineoftwo-partycontestsaftereightyearsofaDemocratpresidency

•  Thiswasclose–HilaryClintonwonthepopularvoteandDonaldTrumpwonPennsylvania,Wisconsin,andMichigan(thestatesthatgothimoverthewinningline)byaround100,000votes(outofatotalofover120millionvotescast)

•  ThiswasasmuchaboutHilaryClintonasaboutDonaldTrump–that’swhywe'remeasuringrelativeimprovementinRepublicanvoteshare

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1.THEECONOMYDIDPLAYAROLE

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AreaswithlowerlevelsofhouseholdincomeswungmoretowardsTrumpthanricherareas

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Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Notes: Each dot represents 300 counties. Scatter plot adjusts for a range of economic indicators (labour force participation, employment in manufacturing, share of rural area in county).

Areaswithhighersharesofworkersinthe

manufacturingsectoralsorecordedbiggerswingsto

Trump

Asdidthosewithlowerlabourforceparticipation

rates

Butshort-runeconomicchangeshadlittleeffectontheswingtowardstheRepublicans:thisisnotaboutrecenteconomicperformance

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Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Notes: Each dot represents a county. .

Therewasnorelationshipbetweenchangeinlabour

forceparticipationrateandswingtowardsTrump.For

example,SussexCountyandBuckinghamCounty,near-neighbours,recordedthe

sameswingtotheRepublicansdespitewildlydifferentrecenteconomic

experiences

Similarly,therewasnorelationshipbetweenchangeinshareofpeopleemployed

inmanufacturingandRepublicanswing

2.BUTIT’SALSODEMOGRAPHICS

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TherewasaswingtoTrumpinareaswithlargerwhitepopulations(althoughwithlotsofexceptions)

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Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Notes: Each dot represents a county.

BiggestswingawayfromTrumpwasinUtahCounty–84percentwhite(and88percent

Mormon)

ManyplaceswithasmallshareofwhitesswungtowardsTrump,

butstillvotedDemocrat.Forexample,RoletteCounty(77percentNativeAmerican)sawa26

percentagepointincreaseinRepublicanvoteshare.

Infact,allcountieswith

populationlessthan20percentwhitevotedDemocrat;halfof

thosecountiesareinRepublicanvotingTexas

ButbiggerswingsawayfromtheRepublicansinareaswithhighersharesofpeoplebornoutsideoftheUS

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CountieslikeSantaClaraswungtotheDemocrats–butdidn’thelpClintonin

theelectoralcollege

Miami-DadeCountyalsoswungtotheDemocrats,

buckingthetrendinFloridawhere80percentof

counties(e.g.HernandoCounty)swungtothe

Republicans

Areaswithhighersharesofpeople60yearsandolderwerealsomorelikelytovoteforDonaldTrumpSource: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau

Notes: Each dot represents a county. This includes naturalised and non-naturalised US citizens

Thedriversofdemographicdifferenceswillbecomplexandvaried,butworthnotingdifferingviewsonthehealthoftheAmericaneconomybyrace

15 Source: Pew Research Centre. Polling carried out June 15-26 2016

3.EDUCATION,EDUCATION,EDUCATION

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Areaswithhighersharesofpeoplewithonlyahighschooleducation–capturingbotheconomicandculturaltrends–swungtowardstheRepublicans

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Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Each dot represents 300 counties. Scatter plot adjusts for a range of indicators (labour force participation, employment in manufacturing, share of rural area in county, race and share of population foreign-born).

ThestrongestpredictorofhowmuchacountyswungtowardstheRepublicansis

theshareofpeoplewithonlyahighschool

education

Itissuchapowerfulpredictorthatitsinclusionintheanalysismeansthatlabourforceparticipation

becomestheonlyeconomicindicatorwhichstillhasaseparateeffect

onswinginthebattlegroundstates

Educationappearstoexplainmorethantheeconomicvariables,butitiscloselylinkedtothestrengthofthelocaleconomy

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Countieswithhigherannualhouseholdincome

tendtohaveahighershareofthepopulation

withabachelor’sdegreeorhigher

Assuch,includinganeducationvariableinthe

analysisofthevotereducestheexplanatory

poweroftheincomemeasure

Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Notes: Each dot represents a county.

Theexplanatorypoweroftheeducationvariableisstrongestinareaswithlargerwhitepopulations

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Inthebattlegroundstates,theswingtowardsTrumpwasgreaterincountieswithbothahighshareofwhiteresidentsandahighshareofpeoplewithonlyahighschooleducation

TheswingtowardsTrumpwasover20pptsincountieswhere70-75percentofthepopulationiswhiteandhasonlyahighschooleducation

Bycontrasteducationallevelshadnoeffectincountieswhereonly25percentofresidentsarewhite.ThoughtheseareasstillswungtowardsTrump

Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau

Evenaftercontrollingforallthesefactors,statelevelfactorsplayedabigpartinhowcountiesvoted

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HoldingallelseconstantHawaiirecordedthebiggestswing

towardsTrump(Obamalikelyhadbirthstateadvantagethere)

NorthDakotaalsoswungstronglytoTrump(possibly

relatedtoitsoilindustry)

UtahrecordedthebiggestswingawayfromtheRepublicans

(Romney’sMormonadvantagefading)

36of46states(78%)testedswungawayfromthe

Republicansoncewecontrolforeconomicanddemographicdifferencesbetweenstates

Source: Leip, David. Dave Leip's Atlas of U.S. Presidential Elections. http://uselectionatlas.org (15/11/16); ACS, US Census Bureau Notes: These are the percentage point swings after controlling for the economic and demographic factors we tested in our regression model. Swings are relative to the state where the swing was smallest which was Colorado

4.PARTOFAWIDERSTORY?Assessingthesimilaritiesanddifferencesbetweenthe

USPresidentialelectionandtheEUReferendumintheUK

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DonaldTrumpEconomyPoorerareasswungtowardsTrumpAreaswithlowerlabourforceparticipationratesmorelikelytoswingtoTrumpButrecentchangesineconomydidn’taffectthevoteEducationCountieswithahighershareofpeoplewithonlyahighschooleducationtendedtoswingtowardsTrump–thesinglemostimportantvariableDemographicsCountieswithahighershareofthose60andovertendedtoswingtowardstheRepublicans

Brexit*EconomyPoorerareasmorelikelytovoteforBrexitAreaswithloweremploymentratesmorelikelytovoteforBrexitRecentchangeinincomedidn’taffectlikelihoodofvoteforBrexitEducationAreaswithahighershareofpeoplewithdegreeslesslikelytovoteforBrexit–thesinglemostimportantvariableDemographicsAreaswithahighershareofolderresidentsmorelikelytovoteforBrexit

ü ü ü

TherearesimilaritiesbetweentheswingtowardsDonaldTrumpandthevotetoleavetheEuropeanUnion…

*For detail on the drivers of the vote to leave the European Union see S. Clarke and M. Whittaker, The importance of place, Resolution Foundation

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DonaldTrumpEconomyPoorerareasswungtowardsTrumpAreaswithlowerlabourforceparticipationratesmorelikelytoswingtoTrumpButrecentchangesineconomydidn’taffectthevoteEducationCountieswithahighershareofpeoplewithonlyahighschooleducationtendedtoswingtowardsTrump–thesinglemostimportantvariableDemographicsCountieswithahighershareofthose60andovertendedtoswingtowardstheRepublicansRaceAreaswithahighershareofblackresidentsweremuchmorelikelytoswingawayfromTrump,evenaftercontrollingforotherdifferencesTurnoutTurnoutinthepresidentialelectionisestimatedat58.1percent,downfrom58.6percentin2012and61.6percentin2008–thiswasnotanenthusiasticvoteNationalityandmigrationAreaswithahighershareofnon-USborncitizenstendedtoswingawayfromtheRepublicans

Brexit*EconomyPoorerareasmorelikelytovoteforBrexitAreaswithloweremploymentratesmorelikelytovoteforBrexitRecentchangeinincomedidn’taffectlikelihoodofvoteforBrexitEducationAreaswithahighershareofpeoplewithdegreeslesslikelytovoteforBrexit–thesinglemostimportantvariableDemographicsAreaswithahighershareofolderresidentsmorelikelytovoteforBrexit

RaceEthnicminoritieswerenomorelikelytovotetoRemainonceyoucontrolledforotherpersonalcharacteristicsTurnoutTurnoutintheEUreferendumwas72.2percent,comparedto66.4percentinthe2015GeneralElection–thiswasanenthusiasticvote

NationalityandmigrationTherewasnorelationshipbetweentheshareofnon-UKbornpeopleinanareaandthevoteforBrexit,althoughareasthatsawalargerecentincreaseinimmigrationtendedtovoteforBrexit

ü

û

ü ü

û û

…butthereareimportantdifferencestoo

*For detail on the drivers of the vote to leave the European Union see S. Clarke and M. Whittaker, The importance of place, Resolution Foundation

5.FULLREGRESSIONRESULTS

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Ourregressionmodels

•  Weconstructsixregressionmodels.IneachourdependentvariableistherelativeimprovementintheRepublicanmarginofvictory(ordefeat)comparedtothe2012presidentialelectionmeasuredatthecountylevel.

•  WetesttherelationshipbetweentheswingtowardstheRepublicansandvariouseconomic,demographicandeducationalvariables.

•  Thefirstmodelincludeseconomicvariables(inbothlevel*andchange**),thesecondintroducesdemographicvariablesandthethirdintroducesoureducationalvariable.Allthreemodelsarerunwithacontrolfortheshareofthecountythatisclassifiedasruralandwithstatedummies.Wealsoclusterstandarderrorsbycounty.Statedummiescontrolforunobservabledifferencesbetweenstatesandtheclusteredstandarderrorsaddressthecollinearityofcountyresultswithinastate.

•  Thefourthmodelanalysesthebattlegroundstates.

•  Modelsfiveandsixintroduceaninteractivetermbetweentheshareofwhiteresidentsandtheshareofresidentswithonlyahighschooleducation,firstacrossallstates(modelfive)andthenacrossthebattlegroundstates(modelsix)

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*Variablesinlevelsareaveragestakenbetween2010and2014

producedbytheACS.

**Changevariablesmeasurethechangebetween2006-2010and

2010-2014.Theythereforereflectshort-runchanges.

Regressionresults:models1&2(economics&demographics)

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Themajorityofchangevariableshavenoeffect.Theoneexceptionisthatinmodels1and2,changesinhouseholdincomeinanareaareassociatedwithaswingtowardsTrump.ThisisprimarilydrivenbytraditionallyRepublicanareas(forexample,22ofthetop50countiesrankedintermsofrecentincome

growthareinNorthDakota)

Alleconomiclevelsvariableshaveaneffect,asdoestheshareofthepopulationthatisforeignborn

1 2 3 4 5 6

Economics Demographics Education Battleground InteractionBattlegroundinteraction

Rural 0.0805*** 0.0599***

Medianannualhousholdincome -0.0727*** -0.0800**

Changeinmedianincome 0.0505** 0.0554***

Shareofemploymentinmanufacturing 0.267*** 0.261***

Changeinshareofemploymentinmanufacturing -0.00432 -0.000189

Labourforceparticipationrate -0.303*** -0.233***

Changeinlabourforceparticipationrate 0.0252 0.0296

Shareofpopulationthatisforeignborn -0.327***

Changeinshareofforeignbornpopulation 0.000455

Shareofpopulationthatis60andover 0.0181

Shareofpopulationthatiswhite 0.0185

Shareofpopulationwithahighschooleducationorless

Interactionbetweenhighschooleducatedandwhitepopulation

Constant 0.992*** 1.041***

Observations 2,932 2,918R-squared 0.628 0.648

Economicvariables *significantatthe10%levelDemographicvariables **signficantatthe5%levelEducationvariable ***sigificantatthe1%level

Models

Regressionresults:models3&4(addingeducationandbattlegroundfocus)

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ThevariablewiththegreatestimpactontheRepublicanswingwasthe

shareofresidentsinacountywithahighschooleducationorless.A1per

centincreaseinthisshareisrelatedtoa0.44percentagepointincreasein

theswingtowardsTrump

Whenthisvariableisincluded,almostalleconomicvariables(asidefrom

labourforceparticipation,inbattlegroundstates)losetheir

significance

Demographicvariables(thoughnottheshareofpopulationthatisforeignborn)becomemoresignificantwhen

educationvariableincluded

1 2 3 4 5 6

Economics Demographics Education Battleground InteractionBattlegroundinteraction

Rural 0.0805*** 0.0599*** 0.00959 -0.00143

Medianannualhousholdincome -0.0727*** -0.0800** 0.00180 0.0219

Changeinmedianincome 0.0505** 0.0554*** 0.0277 0.0529

Shareofemploymentinmanufacturing 0.267*** 0.261*** 0.0688 0.0142

Changeinshareofemploymentinmanufacturing -0.00432 -0.000189 0.000126 0.00348

Labourforceparticipationrate -0.303*** -0.233*** -0.105 -0.210**

Changeinlabourforceparticipationrate 0.0252 0.0296 0.00958 0.0289

Shareofpopulationthatisforeignborn -0.327*** -0.412*** -0.355*

Changeinshareofforeignbornpopulation 0.000455 0.00004 0.00178

Shareofpopulationthatis60andover 0.0181 0.116* 0.176**

Shareofpopulationthatiswhite 0.0185 0.0431 0.0702*

Shareofpopulationwithahighschooleducationorless 0.440*** 0.478***

Interactionbetweenhighschooleducatedandwhitepopulation

Constant 0.992*** 1.041*** -0.0872 -0.282

Observations 2,932 2,918 2,918 775R-squared 0.628 0.648 0.699 0.680

Economicvariables *significantatthe10%levelDemographicvariables **signficantatthe5%levelEducationvariable ***sigificantatthe1%level

Models

Regressionresults:models5&6(addinganinteractionterm)

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Thepositivecoefficientsontheinteractionterm(0.402and0.912)

indicatethatastheshareofthewhitepopulationinacounty

increasessodoestheeffectoftheshareofpeoplewithonlyahigh

schooleducation

Inshort,countieswithahighshareofwhiteresidentswithonlyahighschooleducationswungheavily

towardstheRepublicans

1 2 3 4 5 6

Economics Demographics Education Battleground InteractionBattlegroundinteraction

Rural 0.0805*** 0.0599*** 0.00959 -0.00143 0.00964 0.00652

Medianannualhousholdincome -0.0727*** -0.0800** 0.00180 0.0219 0.00113 0.0226

Changeinmedianincome 0.0505** 0.0554*** 0.0277 0.0529 0.0289 0.0312

Shareofemploymentinmanufacturing 0.267*** 0.261*** 0.0688 0.0142 0.0654 0.0142

Changeinshareofemploymentinmanufacturing -0.00432 -0.000189 0.000126 0.00348 0.00171 0.00643

Labourforceparticipationrate -0.303*** -0.233*** -0.105 -0.210** -0.108 -0.205**

Changeinlabourforceparticipationrate 0.0252 0.0296 0.00958 0.0289 0.00904 0.0233

Shareofpopulationthatisforeignborn -0.327*** -0.412*** -0.355* -0.437*** -0.410**

Changeinshareofforeignbornpopulation 0.000455 0.00004 0.00178 0.000308 0.00113

Shareofpopulationthatis60andover 0.0181 0.116* 0.176** 0.124* 0.200**

Shareofpopulationthatiswhite 0.0185 0.0431 0.0702* -0.179* -0.416***

Shareofpopulationwithahighschooleducationorless 0.440*** 0.478*** 0.135 -0.255

Interactionbetweenhighschooleducatedandwhitepopulation 0.402** 0.912***

Constant 0.992*** 1.041*** -0.0872 -0.282 0.0931 0.0931

Observations 2,932 2,918 2,918 775 2,918 775R-squared 0.628 0.648 0.699 0.680 0.703 0.695

Economicvariables *significantatthe10%levelDemographicvariables **signficantatthe5%levelEducationvariable ***sigificantatthe1%level

Models