LessonsfromCoca‐Cola’sMarketingStrategiesforNon‐Profits

MichelleKim

NorthwesternUniversity

MMSSSeniorThesis2013

Advisor:CynthiaKinnan

Abstract

Improvingtheefficiencyandreachofnon‐profitstranslatesintomorepeoplein

needreceivingaid.Sincemanynon‐profitsfocusonworkinreachingdevelopingcountries,

theyfacechallengesinattemptingtoincreasethescopeoftheirreach.TheCoca‐Cola

Companyhasbeensuccessfulinpenetratingthemarketsofalmosteverycountryinthe

world.Ifnon‐profitsweretoemulatethistypeofmarketpenetration,theirpotential

impactwouldbegreatlyincreased.Thispaperexaminesthemarketingstrategiesthathave

allowedCoca‐Colatosuccessfullyandprofitablyserveruralandunder‐developedareas

withtheintentofaidingnon‐profitswhoseaimsaretopenetratemarketssustainablyand

efficiently.ItanalyzesanumberofmagazinesfromtheCoca‐Colaarchivesdetailing

employees’articlesonthecompany’spresenceandeventsincountriesaroundtheworld.

Combiningthisdatawithcorruption,infrastructure,andeducationalvariables,correlations

arisebetweenthesecircumstancespresentinacountryandthetacticsCoca‐Colauses.

Thesefactorswillthenbeusefulfornon‐profitsattemptingtoincreaseawarenessabout

theirorganizationsinthesecountries,astheywillbeabletoidentifykeyfactorsandhowto

addressthemintheirownmarketingcampaigns.

I.Introduction

Non‐profitsseekingtoenterdevelopingcountriesfaceanumberofchallenges.

Amongthem,theissuesofraisingpublicityineachcountryaswellasnavigatingoftentimes

difficultinfrastructureissuesarise.Whenenteringanewcountry,itisimportanttoanalyze

howtorespondtotheeconomic,political,andsocialfactorspresentinthecountrytoknow

howbesttoapproachthemarket.Duetolimitedresources,beingabletoidentifywhich

factorsaremostimportantaswellashowtogoaboutaddressingthemisinvaluableto

manynon‐profits.Inregardstothisissue,Coca‐Colahashadarichhistoryofenteringand

beingprofitableinalmostallcountriesaroundtheworld.Althoughthebottomlinesofnon‐

profitsmaydifferfromthatofCoca‐Cola,theabilitythatthecompanyhashadtopenetrate

almostanycountryshowsthattherearevaluablethingstolearnfromwhatitdoes.Also,

manynon‐profitsseektodistributegoodssuchasvaccinations,supplies,andbednetsto

peoplewhoneedthem.ThisissimilartoCoca‐Cola’sproductsinthattheyareexperience

goods–peoplecannotfullyknowthevalueoftheproductuntiltheyhaveusedor

consumedit.Thus,itishelpfulfornon‐profitstoexplorethemethodsthatCoca‐Cola

employsinthewaystheybuildupreputationaswellasacustomerbasefortheirgoods.

LearningfrommethodsandtacticsthatCoca‐Colahassuccessfullyimplementedwould

helpnon‐profitslearnhowtoefficientlyandeffectivelyprovidegoodsandservicestothose

whoneeditmost.

Furthermore,whenanon‐profitseekstoenteradevelopingcountry,itisoftentimes

across‐culturalventure.Thus,theabilitytoidentifywhichfactorsofacountryare

importanttotailortowhilestillmaintainingaclearorganizationalmissionandvisionis

criticaltothesuccessoftheorganization.Itisevidentthroughthehistoryandpresenceof

Coca‐Colathatithasbeensuccessfulindoingjustthat.Thisabilitywouldallownon‐profits

tobetterdelegatetheirresourcestohoninginontheissuesofacountrythatarethemost

effectiveandessential.

Thispaperisstructuredinthefollowingway:section2looksatpreviousresearch

andhowthispaperfitsintoit;section3explainsthecodingprocessintranslating

magazinearticlesintodataaswellasthebackgroundtothecorruption,infrastructure,and

educationalvariables;section4laysoutthehypothesesIamseekingtovalidate;section5

detailsmyfindingsonCoca‐Cola’sgeneralpracticesaswellasdataanalysisonwhich

variablesaffectmarketingdecisions;section6looksspecificallyatfourcountriesascase

studiesofthedata;section7discussesimplicationsfornon‐profits,andsection8concludes

thepaperwithfurtherresearchopportunities.

II.LiteratureReview

Therearetwolinesofresearchwithinwiththispaperrests.Thefirstishowthe

presenceofmulti‐nationalenterprisesproduceseconomical,social,political,andcultural

spillovereffects.Non‐profitsshouldlearnfromtheseeffectstounderstandandcontrolthe

second‐handeffectsoftheirpresence,andnotonlyfocusonthemaingoalofthe

organization,asthesespillovereffectscontributetothereputationaswellassustainability

oftheorganization.Thesecondistheresearchthathasbeendoneontheadvertisingthat

companiesinvestinandhowtheyestablishtheirreputation.

Whenamulti‐nationalenterprise(MNE)entersdevelopingmarkets,itisalwaysa

cross‐culturalendeavor.Inlightofthis,AlanRugmanandJonathanDohfoundthat

successfulemergingMNEsfromsmall,openeconomiesarethosethatnotonlyfocuson

economicfactors,butareapartofthesocial,political,andculturalaspectsoftheregions

theyarein(54).Thus,non‐profitswhosegoalsaretohelpstimulatetheeconomiesand

societiesthattheyservemustbemindfuloftheaspectsofsocietythatgobeyondthe

explicitgoalsoftheorganization.ForMNEsfromwhattheycallthetriadmarkets

(EuropeanUnion,UnitedStates,Japan),thesecompanies“cancontributetotheeconomic

developmentbyindirectlytransferringtechnologythroughthesaleofproductsand

servicesindevelopingcountries”(33).Also,mostcompanieshaveincentivetodemand

transparencyandaneradicationofcorruption.Especiallyrelevantforthispaper,“In

August2006,Coca‐ColaHBCBulgariaADjoinedtheBulgarianGlobalCompactNetwork

andwasrecognizedforitscommitmentto“promotingthetenuniversalprinciplesinthe

areaofhumanrightsprotection,laborstandards,environmentandfightagainstcorruption

initseverydaybusiness”(GlobalCompact2006)(117‐118).Itisimportanttokeepinmind

thatcompanieswhoarepromotingtheirbrandandimagealsohaveincentivetoaddress

thepolitical,social,andculturalissuesinacountry.

Ilookatthreespecificcontrolsincountriesthatmayaffectadvertising,whichare

corruption,infrastructure,andeducationlevelsinacountry.ResearchdonebyHabiband

Zurawickishowsthatcorruptionaswellasthedifferencebetweencorruptionlevelsofthe

countryfromwhichthecompanyiscomingfromandthecountryinwhichitisthinkingof

expandingtohavenegativeeffectsonforeigndirectinvestment.Shapiro’sworkon

corruptionalsoshowsthatitisanimportantfactorinbothforeigndirectinvestment

inflowsandoutflows.OneconsequenceofcorruptionisevidentinSmarzynskaandWei’

work,showingthatmorecorruptiontendstoleadtowardmorejointventures.Intermsof

infrastructure,Munnellshowsthatpublicinvestmenthasalarge,statisticallysignificant

effectongrowth,investment,andemployment.

Thereisalotofresearchinthefieldofadvertisements,especiallyasitappliestothe

reputationbuildingthatcompaniesdo.Ingeneral,researchshowsthat“…afirm’s

reputationmaybehardtorebuildonceshattered,andthatanincreaseinproductmarket

competitionmaymakeitdifficultforfirmstosustaintheirreputation”(Tirole3).Thiscan

presentaproblemtomanynon‐profitorganizationswhencountriesbecomesaturated

withcharityorganizations.Oneorganizationthatobtainsabadreputationcouldthen

destroythereputationsthatotherorganizationshaveworkedtobuildup.

Inthecaseofexperiencegoods,howheavilyanorganizationadvertisessignalsto

themarketabouttheproductitself.Thus,advertisementsarecrucialingettingpeopleto

trytheproductforthefirsttime.Duetothis,“theconsumerbelievesthatthemoreabrand

advertises,themorelikelyitistobeabetterbuy”,andindeed,“heavilyadvertisedbrands

arelikelytoprovidealowerP*(priceperunitofutilityofthebrand)totheaverage

consumerthanlessheavilyadvertisedbrandsofthesameproduct”(Nelson732).Fornon‐

profitstryingtopenetratemarketsthen,especiallythosewhoarepushingforexperience

goodssuchasvaccinesorfertilizer,itmaybeimportanttoinvestinfrequentadvertisingto

signalthequalityoftheproductthattheyareoffering.Duetothefactthatreputation,once

itisshattered,ismuchmoredifficulttorebuild,thegoodsmustalsoliveuptoclaimsmade

inadvertising.Nelsonfindsthisinplayaswell,andnotesthatadvertisementscontain

informationbecauseofthepowerofconsumersinthemarket(730).Thus,organizations

handlingexperiencegoodsmustbewillingtoinvestinadvertisingtogetconsumersto

trustthebrand,andthepowerofthemarketwillholdthoseadvertisementstoastandard

ofintegrity.

Inthispaper,Iexaminehownon‐profitsshouldproceedintargetingtheir

advertisementsbyspecificallylookingathowCoca‐Colahasdoneit.Coca‐Colaidentifies

themselveswithcertaincausesandeventsaswellasusingdifferingtacticsintheir

advertisingdependingonthecountry.Thus,Iidentifykeyfactorsthatnon‐profitsshould

considerandactuponwhenenteringnewmarketsandattemptingtoestablishaconsumer

baseforthegoodsandservicesthattheyareoffering.

III.Methodology

ThedataforthispapercomeslargelyfromacollectionofissuesofCoca‐Cola

Overseas,amagazinefromthearchivesofthecompany,rangingfrom1948to1966.The

magazinesarecompilationsofarticlesfromCoca‐Colaemployeesaroundtheworldwho

documentedwhatthecompanyisdoingintheirrespectivecountries.Itincludes

informationsuchassignificantevents,thehistoriesofthecompany’sactivities,and

marketingstrategiesthatCoca‐Colausesinthecountry.Asamplearticlecanbefoundin

AppendixA.Fromthesemagazines,Icreateindicatorvariablestocreatedata,recording

whetherornotcertainelementswerepresentinthemarketingstrategiesinthatcountry.

Thebasicfactorsincludedarethecountry,date,andyear.Theindicatorvariablesmark

whetherornotitwasaspecialevent,marketingcampaign,wasanentrancetoanew

market,andwhetherornottheyusedcelebrities,athletics,government,businesses,

tourism,ordinarypeople,charities,partnership/sponsor.Otherfactorsareifitwasan

internalevent,atraining,ortheystressedtheserviceoftheircompany,modernity,orthe

refreshingnatureoftheproduct.Icalltheindicatorvariables“marketingvariables”

throughoutthepaper.

Thereweretwowaysthesefactorswerechosen.Thefirstwastohypothesizewhat

factorsmightbeimportantwhenadvertising,andthentolookoutfortheminthemagazine

articles.Thesecondwastonoticewhatfactorsseemedtobesignificantandfrequent

acrossthearticles,andtotakenoteofthem.Themaindrawbackforthisdatasetisthatitis

subjecttowhichevercountriesandeventsthattheemployeesdecidedtowriteaboutand

thatwereincludedinthemagazines.Thus,selectionbiasisaconcern.However,thestories

andcampaignsthatthecompanydecidedtohighlightalsoshowswhattheyfindtobe

importanttotheircompanyandworthsharing.Thus,theinformationtheychoosetoshare

actsasinformationonthetacticsthecompanywantstoperpetuateandthatareeffective.

ThisdataonCoca‐Cola’sstrategieswasthencombinedwithdatafromthree

sources.First,economicdatawasusedfromthePennWorldTables,suchasGDPand

exchangerates,usingmeasurementsinUSDorlocalcurrency.Second,corruptiondatafor

countriescamefromdatapublishedbytheInternationalCountryRiskGuideandthe

overallsystemisbasedonthreemaingroupsofpolitical,financial,andeconomicrisk.

Corruptionismeasuredonascaleof0to100,with100beingfreefromcorruption,and0

beingentirelycorrupt.Thereare22componentsoverall,andeachcomponenthasa

maximumnumberofpoints.Thehigherpointsineachcomponentcorrespondtoless

potentialrisk.Sincethecorruptiondataareonlyavailablefrom1995onward,Iusethe

datafrom1995asarepresentationofthecorruptionlevelsofthecountriesduringthetime

ofthemagazines.Thisisbecausetheimportantconsiderationisthedifferencesin

corruptionlevelsacrosscountries,andIassumethatthecountriesthatarelesscorrupt

nowwerealsolesscorruptinthepast.Third,politicalandculturaldatasuchaseducation

levels,infrastructure,andthetypeoflegislatureweretakenfromthecross‐nationaltime

seriesdatabase.

Thiscombineddatasetisthenusedfortheanalysesofathowdifferentfactors

impactthestrategiesthatCoca‐Colausesindifferentcountriespresentinthispaper.

IV.Hypothesis

Asasuccessfulmulti‐nationalcompany,Coca‐Colahasperfectedmanyaspectsof

marketingtobeabletoreachsuchremoteareas.Knowingthatthecompanyhasdifferent

productsindifferentcountries,andyetalsothatitsbrandnameisrecognizable

everywhere,ithastheabilitytomaintainaunifiedbrandimagewhilealsotailoringtothe

needsandculturesofeachcountry.Myhypothesisisthatthoughtherearesome

generalizablemarketingstrategiesthatCoca‐Colauses,therearecertaincountryspecific

factorsthattheyconsiderastheytrytotailortheirstrategiesforagivencountry.

Anotherhypothesisisthatfromthesefactors,Coca‐Colathenemphasizesdifferent

aspectsoftheircompanyandproductdependingonthecountriesthattheyarein.My

researchistotrytoidentifywhatthesefactorsareandhowCoca‐Colathenbrandsitself

basedonthefactors.

V.DataAnalysis

a.Overview

LookingatCoca‐Cola’sgeneralmarketingpractices,thereafewthemesthatrecur

throughoutthemagazines.OneoftheseisthatwheneverCoca‐Colaentersanewmarket,

theyuseotherlocalbusinesses.Thebottlersareoperatedbycitizensofthatcountry,and

theyfocusondevelopinglocaleconomies.Thus,theytiethemselvesintothefabricofthe

community.Bydoingthis,theyareabletogainafavorableimpressiononthecommunity,

andlocalsaresupportiveofthebusinessventuresthatCoca‐Colacarriesout.

Anotherfeatureisthatthereisalotoffocusonexpandingavailabilityoftheir

products.Theystressthat“availabilityequalssales”,andmakesurethatpeoplehave

accesstoCoca‐Colawherevertheygo.Tothisend,theymentionfivetargetsofthemarket:

route,home,atwork,specialevents,andyouthmarkets.Coca‐Colaseekstoadvertise

heavilyineverycountrythattheyoperatein,tomaximizetheexposuretotheirbrandin

eachofthesefivemarketsegments.ThisfindingisinlinewithNelson’sresearchinwhich

hefindsthat“producersofexperiencegoodsadvertisemorethanproducersofsearch

goods”(740).Also,lookingatthenatureoftheadvertisementsalsosupportshisfindings

that“forexperiencegoodstheinformationconveyedisdominantlyindirect—simplythat

thebrandadvertised”(752).ThisisevidentinCoca‐Cola’sstrategyinthatthecompany

oftentimessimplydisplaysitslogoinasmanyplacesaspossiblesothatpeopleareexposed

totheirbrand.Forexample,intheNetherlands,thecompanyenteredintoanexclusive

contractwiththegovernmenttohaveitslogoonbusstopsandbusessothatpeoplewould

seeiteverywhere.Coca‐Colatakesgreatlengthstoinsurethattheirbrandisseenacross

manydifferentmarketsegments.

Coca‐Colaalsostressesitsbrandnameoverallotherfactors.Thisfitsinwiththe

literaturewhichfindsthat“brandnameshavebeenfoundtobemoreimportantthanprice,

whichisinturnmoreimportantthanphysicalappearance;retailreputationorstorename

hasbeenfoundtobeleastconsequentialinsignalingproductquality”(Jacoby,Szybillo,and

Busato‐Schach1977;RaoandMonroe1989)(DawarandParker84).Thismaybeone

reasonwhyCoca‐Colahasbeensosuccessful,asitfocusesonpromotingitsbrandrather

thanthepriceorphysicalaspectsofitsproducts.Itsprimarymotivethroughoutthe

articlesistofurtherthenameandreputationofthecompany.Anotheraspectthatexplains

Coca‐Cola’saggressivemarketingstrategiesisthatTirole’sresearch“showsthatafirm’s

reputationmaybehardtorebuildonceshattered,andthatanincreaseinproductmarket

competitionmaymakeitdifficultforfirmstosustaintheirreputation”(3).Thus,inorder

forCoca‐Colatomaintainitsstandinginthemarket,itmustalwaysbecarefultoguard

theirnameandbeproactiveaboutpromotingtheirnameconstantly.Coca‐Colafiercely

pushesitsbrandnameandreputationacrosseverycountryitoperatesin.

b.DataAnalysis

TotestthehypothesesthatCoca‐Colaconsidersdifferentfactorsinthecountries

theyoperatein,andthenemploysdifferentstrategiesbasedonthesefactors,Iruna

numberoflogisticregressions.ItestwhetherornotCoca‐Colausesthemarketing

variablesmentionedinsection3,controllingfordifferentsetsofvariables.First,Ieliminate

themarketingvariablesthatdonothaveenoughvariationtobeinformative.These

variablesarecelebrities,businesses,training,andcharities.Next,Irunthelogistic

regressionswithdifferingcontrols.Thecoefficientsarethemarginaleffects,tocapturethe

affectofachangeofoneunitofthecontrolvariablesontheuseofthemarketingvariables.

Theregionsarecodedasfollows:

Number Region

1 Africa

2 Asia

3 Europe

4 NorthAmerica

5 Oceania

6 SouthAmerica

Akeytosomeofthevariablesaregivenbelow:

Variable Description

Railroad1 RailroadMileage

Media2 RadiosPerCapita{.0001}**

Media4 TelevisionsPerCapita{.00001}**

Media5 DailyNewspaperCirculationPerCapita

School12 PercentLiterate{.1}**

School06 Primary+SecondarySchoolEnrollmentPerCapita

School09 UniversityEnrollmentPerCapita

1.RegressiononGDP

IfirstrunlogisticregressionsofthedifferentvariablesononlyCGDP,definedasthe

“PPPConvertedGDPpercapita,G‐Kmethod,atcurrentprices(inI$)”(Asdefinedbythe

PennWorldTables).ThesevariableshavecoefficientsonCGDPthatarestatistically

significant:

Variable  Coefficient Standard Error P‐value 

Athletics  0.000606  0.00002  0.005 

Partner/Sponsor 0.0000673  0.00003  0.018 

Thisfindingisinformative,astheathleticsvariableoftentimesentailsCoca‐Cola

partneringorsponsoringasportingevent.TheresearchdonebySpeedandThompson

statesthatwhensponsoringanevent,attitudetowardthesponsor,perceivedsincerity,and

sponsor‐eventfitaresignificantpredictorsonfavorability,interest,anduse(233).Oneof

thefactorsthatmaybedifferentforrichandpoorcountriesisthesponsor‐eventfit

variable.Thisvariabledetermineshowwellthepairingbetweenaneventandasponsoris

agoodmatch.Perhapsinrichercountries,Coca‐Colaismoreassociatedwithbigscale

eventsandspecialoccasions.Thismaybeduetotherelativeeasewithwhichtheseevents

happeninrichcomparedtopoorcountries.Coca‐Colamaypurposefullytrytoalign

themselveswithathleticevents,astheyaremorelikelytogetmorepublicitythroughsuch

asponsorshipwheninfrastructurefactorssuchasmediaoutletsaremorereadilyavailable.

Also,inpoorercountries,theremayexistabiggerdisconnectbetweenlarge‐scaleevents

andpeople’severydaylives.Thus,thesponsor‐eventfitmayworkbetterforricher

countries.

2.Regressionswithcontrols

Now,Irerunregressionswhileaccountingforvaryingcontrolvariables.Iinclude

CGDPaswellasbasicvariablesYearandRegion.Theinfrastructureandcommunications

controlsareRailroad1,Media2,Media4,andMedia5.ThecorruptioncontrolsareOverall

CorruptionScore,FreedomfromCorruption,BusinessFreedom,andInvestmentFreedom.

Finally,theeducationcontrolsareSchool06,School09,andSchool12.Belowisatablewith

resultsofthemosttellingregressions.Thefullregressiontableswitheachsetofcontrols

canbefoundinAppendixB.

*Statisticallysignificantatthe10%level**Statisticallysignificantatthe5%level***Statisticallysignificantatthe1%level

Variable

Ord.People

SpecialEvent

Athletics Government MarketingCampaign

Tourism Partner/Sponsor

Internal Modern Refreshing

CGDP ‐0.00012*0.00005

‐0.00017**0.00007

‐0.0000530.00005

‐0.000106*0.00006

0.000192**0.00008

‐0.00021**0.00009

‐0.00033*0.00019

0.00027**0.00011

0.00009*0.00005

‐0.00028*0.00015

Region2(AS)

0.13470.11334

0.02954740.13244

‐0.0572800.06496

0.03818280.12365

0.078700.193

‐0.0303830.1802

‐0.27563*0.15802

0.1740410.18808

0.1044810.14753

‐0.2202***0.0724

Region3(EU)

0.2324*0.13496

‐0.22637580.26629

‐0.125005*0.06871

0.41518410.25791

‐0.21776**0.09277

‐0.0107160.13293

‐0.1392780.20226

‐0.0495160.14583

0.0493590.12208

‐0.1456**0.07379

Region4(NA)

0.11880.13714

‐0.15705350.18541

‐0.10031**0.04125

0.22713360.20809

‐0.0896310.15143

0.2374050.2398

‐0.0647360.18755

‐0.1342990.12378

0.1210620.16213

0.1874690.2127

Region5(OC)

0.422**0.1981

0.09711130.18933

0.01085160.13636

0.45523270.31322

‐0.1876970.155

0.57056**0.24233

0.38035*0.21039

0.0711610.22759

‐0.21680.17887

Region6(SA)

0.065560.10346

‐0.10675670.15479

‐0.1479***0.04601

0.2518240.16692

‐0.1364840.10399

0.1745640.1465

‐0.28805**0.11826

0.1712160.14568

0.0432740.10089

0.007800.06691

Year ‐0.002130.00532

0.03033***0.00808

0.02779***0.00628

0.01999***0.00651

‐0.0251***0.00735

0.01488**0.00295

0.03712**0.01621

0.0030470.0109

‐0.008030.00606

‐0.0077090.00598

School12 0.00101***0.00037

0.00043**0.0002

‐0.00001840.00028

School06 ‐0.00031**0.00016

‐0.0000410.00008

‐0.000187(*)0.00011

School09 ‐0.00009380.0016

‐0.0004860.00098

‐0.00025770.00141

Overall ‐00079960.01074

‐0.0099360.001102

Freedom ‐0.0020260.00285

0.004912*0.00295

Business ‐0.0013900.00467

0.008907*0.00514

Investment 0.0053660.00429

0.008406*0.00484

Railroad1 0.00000020.00001

0.0000010.00001

0.0000030.00000

‐0.0000010.00000

Media2 0.000015*0.00008

‐0.0000810.00006

0.0000450.00004

0.000107*0.00006

Media4 0.00001120.00002

‐0.00003*0.00001

‐0.00003**0.00001

0.0000040.00001

Media5 ‐0.0000330.0008

0.0000660.00006

‐0.0000370.00004

0.000010**0.00004

Ofthese,IdiscusstheresultsforAthleticsandRefreshing.

Athletics:Withnocontrols,CGDPwasastatisticallysignificantvariable.Areasonthat

now,controllingforbasicaswellaseducationalvariables,itisnot,maybebecausethereis

collinearitybetweenCGDPandtheregionvariable.Indeed,runninganalyses,weseethat

thisisthecase.Thus,itmaybethatinthecaseofthisvariable,itwouldbemore

informativenottoincludebothCGDPandtheregionvariables.However,Ichosetoinclude

bothofthesevariables,asinothercases,bothcameouttobesignificant.Interesting

findingsarethestrongsignificanceoftheyearvariableaswellasthepositivecorrelation

betweenathleticsandliteracy.Astheyearsprogressed,Coca‐Colaassociateditselfwith

athleticsmoreandmore.AccordingtoGwinnerandEaton,whenacompanysponsorsan

event,howpeoplefeelabouttheeventisthentransferredtohowtheyfeelaboutthe

company.Thus,thisresultmaysuggestthatCoca‐Colaisestablishingitsreputationasone

thattheywanttoalignwiththatofathletics.Theremaybesomethingaboutthenatureof

athletesorbigathleticeventsthatCoca‐Colawishestopromote.Someoftheseimagesmay

beoneofhealth—bywayofaligningwithphysicalactivity,ortheexcitementthatcomes

fromsports.Also,withamoreliteratepopulationCoca‐Colausesathleticsmore.Thismay

beindicativeoftherebeingacertaintypeofathleticeventandathletethatCoca‐Colaseeks

toalignwith,onethatappealstomoreeducatedaudiences.Thisissupportedbythe

exampleofCoca‐Colapartneringwithaflower‐arrangingclubinSouthAfrica,explicitly

statingthattheywishtobeassociatedwithgentleliving.Thus,Coca‐Colausespartnerships

andsponsorshipsofbigeventssuchasathleticcompetitionstoaligntheirbrandwitha

certaintypeofimage.

Refreshing:ThisvariableisnegativelycorrelatedwithCGDP,Regions2and3,and

positivelycorrelatedwithRadiospercapitaandNewspaperCirculationpercapita.The

correlationwithCGDPmaybeexplainedbynotingthatgenerally,countrieswithlowerGDP

tendtohavelesstechnologytobeabletokeepdrinkscold.Thus,promotingCoca‐Colaas

beingrefreshingmaybealargerdifferentiatingfactorforpoorercountriesthanforricher

ones.Followingthis,thenegativecoefficientsontheregionsofAsiaandEuropemeansthat

comparedtoRegion1,Africa,theaspectofCoca‐Colabeingrefreshingislessused.This

couldalsobeexplainedbysimilarthreadasintheCGDPvariable.SincebothCGDPanda

coupleregionvariablesaresignificant,itsuggeststhatmulticollinearitybetweenthetwo

variablescannotfullyexplaintheoutcome.Thereisapositivecorrelationwithradiosper

capitaanddailynewspapercirculationpercapita.DrawingfromRöllerandWaverman’s

research,theyfindthatimprovingtelecommunicationinfrastructurefuelsgrowth.The

statisticallysignificantresultonthetwotelecommunicationsvariablescouldsuggestthat

Coca‐Colamayalwayspromotethefactthattheirproductsarerefreshing,buttheysimply

advertisemoreincountrieswithbetterinfrastructure,astheyoperatemoreheavilyin

countrieswithhigherCGDP.However,thecoefficientonCGDPisalsostatistically

significant,suggestingthiscannotbethefullanswer.OnepossibilityisthatwhenCoca‐Cola

hastheoptionofradioandnewspaperadsmoreavailabletothem,theypromotethe

refreshingnatureoftheproduct,asthismaybesomethingthatismoreeasily

communicatedthroughtheradioornewspaperthanotherintangibleaspectsofthebrand.

VI.CaseStudies

Tomorecloselyexaminetheresultsgiveninthepreviouspart,Ilookspecificallyat

afewcountriestoseehowtheyplayoutinparticularcircumstances.IchoseBrazil,Italy,

Mexico,andSouthAfricatogetabroadspectrumofcountries.Ilookattheresultsofthe

marketingvariablesalongwiththepredictionsgivenbythedifferentsetsofcontrolsgiven

intheprevioussection.Then,Iconsidertwofactorstodecidewhichcontrolsbestpredict

outcome.First,Ilookatwhichsetofcontrolsresultsintheclosestmeasurementofthe

actualoutcomesinthegivencountries.Inordertopickwhichcontrolsbestrepresentthe

data,foreachvariable,Ihighlightthepredictionsthatmostcloselymatchtheactual

outcomes.Whentherearemultipledatapointsforagivenyear,Itaketheaveragevalue.

Second,inordertomaximizepredictivepower,Ilookattheregressionsthathave

statisticallysignificantcoefficients.Combiningthesetwoconsiderations,Ilookfirstat

whichregressionshavestatisticallysignificantvariables,andthenofthose,choosetheset

ofcontrolswiththemostnumberofclosematchingpredictions.Thisresultsindifferent

controlsbeingthebestpredictorsdependingonthetypeofanalysis.

a.Overview

Beforeanalyzingthedata,IwentbacktotheCoca‐ColaOverseasarticlesforthese

fourcountriestoseeifthefactors(education,infrastructure,corruption,economy)are

mentionedinthem.Atleastsomeofthefactorsarementionedineachcountry,supporting

thehypothesisthatCoca‐Coladoestakethemintoaccountwhendecidinghowtooperate

inagivencountry.

InBrazil,mostlyeconomicfactorsarementionedwithalittlebitofinfrastructure,as

theytalkaboutthegrowthofparticularlySaoPauloasthe“greatestindustrialcenterin

LatinAmerica”(June53)andaboutthesuppliersthatarelocatedinpartsofBrazil.

InItaly,thecompanyalsomentionseconomicfactorssuchasSicilybeingoneof

Italy’smostrapidlydevelopingregions,andhowanewplantopeningisamarkof

confidenceinItaly’seconomy.Italsomentionsthewarandtheconcernswiththe

reconstructionduring1948‐1949,andhintsatissuesofcorruption,whenitmentions

TriesteandthepeacetreatyalongwiththeYugoslavironcurtain.

InMexico,Coca‐Colamentionsinfrastructureinthatallsuppliesforitsmarketing

campaignweremanufacturedlocally.Italsomentionsthattherearefrequentplane

servicesandgoodhighwaysaswellasarailroadandinternationalhighwaythatconnectto

Texas.Furthermore,itputsadsontheradiothatreachesalmostallofMexicoandCentral

AmericaandReader’sDigest,suggestingthatitusesthegoodlocaltelecommunications

infrastructureformarketingpurposes.Thecompanyalsotouchesontheeconomy,

mentioningtheagriculturaleconomyandtakingpartinsponsoringacouplefairson

commerceandindustry.WhenitopenedtheirfirstbottlingplantinMonterrey,itdescribed

itas“oneofthemostimportantindustrialandmanufacturingcentersinMexico…”.Thus,

Coca‐ColaoperatesineconomicallystrategiccitiesinMexico.

InSouthAfrica,Coca‐ColamentionsmanyofthefactorsIanalyze.Itsponsorsa

flowerarrangementclubandmagazinepublicationtoassociateCoca‐Colawiththesocially

andeconomicallyprominentwomenofJohannesburg.Itmentionsalsothegrowthinthe

citrusindustryafterWorldWarIIandhowitsjuicingplantwillaidthegrowingindustry.

Coca‐Colaalwaysseekstopartnerwithlocalbusinesses,ascanbeseenthrough

infrastructurefactors.TheadministratorofSouthAfrica,similartoagovernorintheU.S.,

speaksataplantopening,mentioningthat“approximately500SouthAfricansaredirectly

employedbythecorporation,whoseactivities,ofcourse,contributeinnosmalldegree

towardstheemploymentofmanyhundredsmoreofourpopulationintherailways,local

industriesandpublicutilities…”.Also,Coca‐Colamakessuretousethelocalinfrastructure

andusesuppliesoflocalorigininitsmanufacture.Itfurthermentionsinfrastructureinits

operationsintheProvincialHospitalatPortElizabeth,asit“wasintheonlylocationwhich

couldbeassignedtous,butwasnotreadilyaccessibleandconsequentlysaleswerepoor”.

Itthuscreatesamobilecoolertoaddressthisproblem.Thecountryisdescribedasone“of

immensedistancesandonenotusuallygiventothecollectionofvastcars”,andthe

companyalsousestherailroadtotransfer480,000bottlesofCoca‐ColafromJohannesburg

tothesiteoftheMonumnetcelebrations.Thegovernmentcomesintoplay,asbefore

mentioned,whenanadministrator,speaksattheplantopening.Heevenexplicitlystates

that“thegrowthofindustryisamatterthatvitallyaffectslocalgovernment”.Heoffersfull

support,admitting“…Thedangerofthisone‐sidedeconomy…hasbeenapparenttomostof

usforaverylongtime…itisimperativethatsecondaryindustryshouldreceiveevery

possibleencouragement,consistentwithnationalpolicy,todevelopasrapidlyaspossible”.

ThusinSouthAfrica,Coca‐Colaexplicitlymentionsitsconsiderationsofmanyofthefactors

Ianalyze.

b.DataAnalysis

Iconsidertwotypesofanalysesforthecasestudies.Thefirstisatimeseries

analysisofhowvariableschangeovertimeinagivencountry,andthesecondisacross‐

countryanalysisofhowtheyvaryinthesametimeperiodacrossthedifferentcountries.

Formeasurementmethodology,Itakeintoconsiderationthenumberoftimesa

factorapplieswithinagivenyear.Forexample,ifinoneyear,avariableapplies2outof3

times,theentryfortheyearwillbe2/3.Thus,Ilookatboththeintensiveandextensive

margins,andthedatareflecthowoftenavariablehappenswithinagivenyear.Also,dueto

alackofinfrastructurevariablesintheearlieryears,Ionlyconsiderthatparticular

regressionforlateryears’cross‐countryanalysis.

1.TimeSeriesAnalysis

ThefirstanalysisIdoiswithineachcountryacrosstime.Becausethereare

variablesthatarethesameforagivencountry,theycannotbeconsideredintheresults.

Thesevariablesaretheregionvariableaswellasthecorruptionvariables.Withthat

consideration,thevariablesthatcomeintoplayare:CGDP,Year,School12,andSchool06.

Doingthisanalysisresultsinthefollowingcontrolsbestexplainingeachvariable:

MarketingVariable

Controls StatisticallySignificantCoefficients

SpecialEvents Education CGDP,Year,School12,School06

MarketingCampaign

Corruption CGDP,Region3,Year

Athletics Education Region3,Region4,Region6,Year,School12

Government Education CGDP,Year,School6(*)

Tourism Corruption CGDP,Region5,Year,FreedomfromCorruption,BusinessFreedom,InvestmentFreedom

Partner/Sponsor Corruption Year,BusinessFreedom

Internal Corruption Year,OverallCorruptionScore

Service Corruption CGDP,Region3,Region5,Year

Modern Corruption Year,OverallCorruptionScore

Refreshing Education Region3,Year(*),School12

Entrance Basic Region4,Year

Ilookatthepredictionsforeachmarketvariable.FurthertablesarefoundinAppendixC.

Oneexample,forspecialevents,isprovidedbelow:

SpecialEvents:

countryisocode  year  cgdp  school06 school12 Prediction*  Actual 

BRA  50  262.89818 1054 494 ‐0.2308  1 BRA  51  284.52129 1002 499 ‐0.1740  0 BRA  53  305.52547 903 511 ‐0.05639  1 BRA  55  348.14119 906 523 0.01430  1 BRA  63  562.54767 1347 570 0.12306  0.6667 BRA  66  645.16023 1534 587 0.15056  1 *Predictionisnormalizedbytheaveragevalueforagivencountry.Avalue<0predictsanoutcomeof0andavalue>0predictsavalueof1.countryisocode  year  cgdp school06 school12 Prediction*  Actual

ITA  50  780.00382 1216 875 ‐0.17089  1ITA  51  880.84147 1218 877 ‐0.15391  1ITA  58  1401.7459 1236 898 ‐0.01142  0.5ITA  61  1776.8338 1315 907 ‐0.00075  0.6667ITA  63  2010.2104 1318 914 0.02155  1ITA  66  2362.5771 1330 925 0.04993  1*Predictionisnormalizedbytheaveragevalueforagivencountry.Avalue<0predictsanoutcomeof0andavalue>0predictsavalueof1.

countryisocode  year  cgdp school06 school12 Prediction*  Actual

MEX  50  540.43919 1100 575 ‐0.05505  0MEX  51  601.30299 1028 582 0.00275  0MEX  53  592.87469 1142 598 0.05448  1MEX  55  698.12973 1171 614 0.11450  0.5MEX  61  905.68259 1565 662 0.19583  0.3333MEX  63  995.69433 1724 680 0.21109  1MEX  66  1236.4116 1849 715 0.25890  0.5*Predictionisnormalizedbytheaveragevalueforagivencountry.Avalue<0predictsanoutcomeof0andavalue>0predictsavalueof1.

countryisocode  year  cgdp school06 school12 Prediction*  Actual

ZAF  50  485.49526 1215 425 ‐0.31595  1ZAF  51  506.55508 1311 426 ‐0.31816  0ZAF  58  634.20836 1573 440 ‐0.17476  0.6667ZAF  61  2197.1384 1697 446 ‐0.12132  0ZAF  63  762.37911 1730 450 ‐0.06831  0.5ZAF  66  864.26406 1867 494 0.01310  0.7692*Predictionisnormalizedbytheaveragevalueforagivencountry.Avalue<0predictsanoutcomeof0andavalue>0predictsavalueof1.

Aspecialeventisdefinedasanythingoutsideday‐to‐dayoperationsforthe

company.Oftentimes,specialeventsinvolveCoca‐Colapartneringorsponsoringanoutside

event.WeseeinthedatathatCoca‐Colausesspecialeventsquitefrequently.Then,from

GwinnerandEaton’sresearch,whichfindsthatsponsoringaneventtransferspeople’s

feelingabouttheeventtohowtheyfeelaboutthecompany,wecanconcludethatthisis

onemarketingtacticthatCoca‐Colaheavilyfocuseson.

Lookingspecificallyatthesefourcountries,thepredictionsarerightalittleoverhalf

ofthetimes.Whilenotasaccurateasdesired,thisismostlikelyduetoidiosyncratic

conditionswithinthesespecificcountriesthataren’tcapturedinthedata.Furthermore,the

samplesizeisnotverylarge,makingitdifficulttodrawmeaningfulconclusionsfromthis

setofdata.

2.Cross‐CountryAnalysis

Thesecondanalysisisacrossthefourcountries.Similarlytotheabovesection,there

arevariablesthatarethesameacrosscountries,andthuscannotbeincludedinthe

analysis.Excludingthese,thestatisticallysignificantvariablesare:Regions,Year,

Railroad1,Media2,Media5,CGDP,FreedomfromCorruption,BusinessFreedom,

InvestmentFreedom,andSchool12.Doingthisresultsinthefollowingcontrolsthatbest

describethedifferentmarketingvariables:

Thefourcountriesrepresentregions1,3,4,and6.Thus,alongwiththeyear

variable,region2and5variablesarealsodisregarded.Takingthisintoaccount,Ilookat

thepredictionsthattheaboveregressionsgiveforthedifferentvariables.

Twoexamples,theAthleticsandRefreshingvariables,aregivenhere:

MarketingVariable

Controls StatisticallySignificantCoefficients

SpecialEvents Basic Region5,Year

MarketingCampaign

Basic Region3,Year

Athletics Education Region3,Region4,Region6,Year,School12

Government Infrastructure Region3,Region6

Tourism Corruption CGDP,Region5,Year,FreedomfromCorruption,BusinessFreedom,InvestmentFreedom

Partner/Sponsor Corruption Year,BusinessFreedom

Internal Infrastructure CGDP,Media4

Service Corruption CGDP,Region3,Region5,Year

Modern Basic Year

Refreshing Infrastructure CGDP,Region2,Region3,Media2,Media5

Entrance Basic Region4,Year

Athletics:

countryisocode  year  school12  Region  Prediction  Actual 

BRA  63  570 6 0.03457  0BRA  66  587 6 0.06147  0ITA  61  907 3 ‐0.21890  0.16667ITA  63  914 3 ‐0.16952  0.5ITA  66  925 3 ‐0.07371  1MEX  61  662 4 0.09526  0MEX  63  680 4 0.14195  0MEX  66  715 4 0.25162  0ZAF  61  446 1 ‐0.29642  0ZAF  63  450 1 ‐0.18445  0.5ZAF  66  494 1 0.03239  0.61538

*Predictionvariableisthepredictiongivenbythemodelminustheaveragevalueofthevariableinthegivencountry.Thus,avalue>0predictsa1,andavalue<0predictsa0.

Refreshing:

countryisocode  year  cgdp  media2 media5  Prediction  Actual 

BRA  63  562.54767 862 435 0.35298  0BRA  66  645.16023 784 330 0.22448  0

ITA  61  1776.8338 2033 1010 0.00400  0ITA  63  2010.2104 2569 1070 0.00366  0ITA  66  2362.5771 3616 1120 0.00454  0MEX  61  905.68259 975 830 ‐0.01312  0.66667

MEX  63  995.69433 1568 995 0.12226  1MEX  66  1236.4116 2241 1242 0.16511  0

ZAF  61  2197.1384 656 570 ‐0.07153  0ZAF  63  762.37911 1165 554 ‐0.03710  0.5ZAF  66  864.26406 1394 530 ‐0.07910  0.15385

*Predictionvariableisthepredictiongivenbythemodelminustheaveragevalueofthevariableinthegivencountry.Thus,avalue>0predictsa1,andavalue<0predictsa0.

Thesetwoexamplesshowthatthepredictionsareoftenfarofffromtheactualvalues.This

couldagainbeduetothesmallsamplesize.InthecaseoftheRefreshingvariable,thefact

thattheinfrastructurecontrolsareunavailablefortheearlieryearsmakesthesamplesize

evensmaller.

VII.Discussion

DrawingfromCoca‐Cola’sstrategies,non‐profitscanapplysomeofthesetotheir

ownoperations.Lookingattheoveralltacticsthecompanyusesineverycountry,non‐

profitsthathavespecificexperiencegoodstooffershouldfocusonexpandingtheirbrand

nameratherthanemphasizingthefactualinformationabouttheirproducts.Thiswill

encouragepeopletotryouttheproductsinthefirstplace,andassociatethebrandname

withabetterreputation.FromCoca‐Cola’sstressonwidespreadavailabilityandserviceof

itsproducts,non‐profitsshouldlearntousetheirresourcestospreadoutacrossasmany

differenttargetsegmentsaspossible.Forexample,theyshouldconsider the five target

markets that Coca-Cola identifies: route,home,atwork,specialevents,andyouth

markets,andtrytoadvertiseacrossthemasmuchastheyareableto.Non‐profitsshould

alsoconsideraligningtheirbrandnametobigscaleeventsthatfitinwiththeircompany

name.Thiscouldnotonlyhelppromotethebrand,butalsobeashortcuttogivethem

credibilityandresultinpositiveassociationsfromtheeventbeingtransferredtotheirown

name.

Fromthedataanalyses,therearemanyvariablesthatcameouttobestatistically

significantthatnon‐profitsshouldlooktoindecidinghowtooperateinagivencountry.

Becausethereareoftensomanyfactorstopotentiallyconsiderineverycountry’smarket,

non‐profitscanusetheseanalysestogetasenseofwhichofthesefactorsaremore

importanttoconsiderthanothers.However,fromtheresultsinthecasestudiesanalyses,

theremustbesomecautioninapplyingtheregressionresultsinspecificscenarios,as

countrieshaveidiosyncraticfactorsthatmaybehardtoidentify.Non‐profitsshouldthus

learnfromthestrategiesCoca‐Colaappliesuniversallyandalsolookatkeyfactorsin

specificcountries,butwithconsiderationthattheymightnotalwaysbefullyapplicable.

VIII.Conclusion

Theresultsinthispapercanhelpinformnon‐profits’decisionsinhowtobest

allocatetheirresources.Forexample,oneresultisthatoftentimestheCGDPvariableas

wellastheregionalvariablesaresignificanteventhoughthereismulticollinearitybetween

thetwo.Thissuggeststhatorganizationsshouldtakebothintoaccountwhenmaking

decisionsonmarketingstrategy.Myresearchfoundthatcorruption,infrastructure,and

educationalvariablesareoftenstatisticallysignificant.Ignoringthesefactorsandsimply

enteringeachmarketwiththesamestrategiescouldbedetrimentalfortheorganization’s

brandname.Drawingfrompreviousresearch,afirm’sreputation,oncetarnished,ismuch

hardertobuildbackup.Thus,non‐profitscannotaffordtodisregardthesevariablesin

theiroperatingandmarketingstrategies.Althoughthecasestudies’outcomeswere

inconclusive,onabroaderscale,organizationsshouldconsiderthemessagetheywishto

portrayindifferentmarkets.Expandingchannelsofmarketingmaybeusefultoexplore,to

createmoreexposureoftheorganizationaswellasadditionalcredibility.Astheliterature

showed,theseadditionalmethodsofmarketingcanprovidesignalsthattheproducthas

higherqualitythancompetitors.

Therearesomelimitationstothisresearchthatshouldbetakenintoaccount.Allof

thedataanalyzedherewascollectedbetweenthe‘40sandthe‘60s.Muchhaschanged

sincethen,andthefindingsmaynotbeasaccurateintoday’seconomicandcultural

climate.Duetothenatureofthedata,therealsoisalargeprobabilityofselectionbias.The

articlesinCoca‐ColaOverseasonlyrepresentasmallsampleofwhatisgoingoninthe

companyaroundtheworldandarealsopredisposedtopresentthecompanyinapositive

light.Thereisnowaytoseewhatwasnotincluded,whichwouldalsoprovidevaluable

insight.Also,themagazinesonlymentioncountriesinwhichthecompanyhasoperations,

andthereisnowayoflookingatwhyCoca‐Colaisnotoperatingincertaincountries.The

smallsamplesizealsorequirescautionininterpretingtheresultsofthispaper.Despite

theseshortcomings,thereareoverarchingthemesandlessonsthatnon‐profitscandraw

fromCoca‐Cola’soperations.Theresearchalsoclearlyshowstheimportanceofconsidering

corruption,infrastructure,andeducationalfactorsindecidingthemessageanorganization

shouldportray.

Therearemanywaystofurtherthisresearch.Itwouldbevaluabletoexploreother

companiesaswellaslargeinternationalnon‐profitstoseewhichfactorsareuniqueto

Coca‐Colaandthefor‐profitsector,andwhicharemoreeasilytransferrabletothenon‐

profitsector.Iftherearemorerecentdataavailable,comparingthatwiththeresultsinthis

paperwouldprovideinsightintowhichconsiderationsarealwaysapplicable,andwhich

havechangedovertime.Incontinuingwiththisresearch,obtainingdirectinformationon

thefactorsthatCoca‐Colausesindeterminingitsmarketingstrategywouldeliminatethe

potentialbiasinsimplyobservingwhattheymentioninmagazinearticles.

Fromthisresearch,itisclearthatCoca‐Colastrategicallypositionsitsbrandname

acrossdifferentcountries,andnon‐profitscanbenefitfromemulatingitsstrategies.Non‐

profitsshouldconsiderwaystoeffectivelypromotetheirbrandnameacrossdifferent

channelsofadvertisingandfocusonestablishingtheirreputationamongstconsumers.

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Appendix

AppendixA:SampleCoca‐ColaOverseasArticle

AppendixB:VariablesTables

AppendixC:FurtherCaseStudyRegressionTables

AppendixA:SampleCoca‐ColaOverseasarticle

AppendixB:Variablestables

BasicVariables

*Statisticallysignificantatthe10%level**Statisticallysignificantatthe5%level***Statisticallysignificantatthe1%level

Variable

SpecialEvent

MarketingCampaign

Athletics Government Ord.People

Partner/Sponsor

Internal Service Modern Refreshing Entrance

Year 0.017*0.00569

‐0.021***0.00592

0.029***0.00508

0.0093*0.00535

‐0.002130.00532

0.0377***0.00735

0.018***0.00568

‐0.0140***0.00419

‐0.019***0.00451

‐0.007420.00524

‐0.0123***0.00429

CGDP ‐0.000060.0005

0.000050.00005

‐0.000020.00004

‐0.000091**0.00004

‐0.00012*0.00005

‐0.000050.00005

0.000050.00005

0.00009***0.00003

0.0000140.00004

‐0.0000070.00004

0.0000450.00004

Region2 0.04350.0936

‐0.12280.08737

‐0.05260.06391

‐0.099060.07687

0.13470.11334

‐0.08520.10842

0.077210.10683

‐0.09490.00003

0.081670.10762

0.157450.13412

0.0366290.08389

Region3 0.13320.08944

‐0.164*0.09011

0.007430.08762

0.199720.12728

0.2324*0.13496

0.024340.12907

‐0.102290.09511

‐0.087350.06118

0.145460.11751

0.158760.13775

‐0.029540.07518

Region4 0.08430.10121

‐0.02520.11548

‐0.03600.08119

0.084940.1267

0.11880.13714

0.016140.14052

‐0.105040.09842

‐0.015630.08339

0.099140.1253

0.3418**0.15425

‐0.1228**0.05536

Region5 0.175***0.9256

‐0.04630.14279

0.024600.18517

0.422**0.1981

0.347**0.16364

‐0.158790.09992

‐0.1273***0.04529

0.026560.13865

0.298740.20427

‐0.1016670.06254

Region6 0.05900.08629

‐0.08480.08728

‐0.095*0.04857

0.09040.09658

0.065560.10346

‐0.124330.09818

0.001590.09493

0.036670.07968

0.024120.09545

0.3006**0.13212

‐0.023390.06879

WithInfrastructureandCommunicationsControls

*Statisticallysignificantatthe10%level**Statisticallysignificantatthe5%level***Statisticallysignificantatthe1%level

Variable

SpecialEvent

MarketingCampaign

Athletics Gov’t Tourism Partner/Sponsor

Internal Modern Refreshing

CGDP ‐0.0002**0.0001

‐0.000080.00011

‐0.000120.0001

‐0.0000860.00011

‐0.0000930.00013

‐0.00033*0.00019

0.00027**0.00011

0.00009*0.00005

‐0.00028*0.00015

Region2 0.2409***0.09025

‐0.286***0.07625

0.0594360.16302

0.177680.17885

‐0.22371*0.13333

‐0.27563*0.15802

0.1740410.18808

0.1044810.14753

‐0.2202***0.0724

Region3 0.2731***0.09443

‐0.19424*0.10585

‐0.065410.14892

0.5223***0.1805

‐0.180360.14751

‐0.1392780.20226

‐0.0495160.14583

0.0493590.12208

‐0.1456**0.07379

Region4 0.059150.12497

‐0.028960.12598

‐0.048780.12328

0.139230.20748

0.0074670.16791

‐0.0647360.18755

‐0.1342990.12378

0.1210620.16213

0.1874690.2127

Region5 0.2272**0.09413

‐0.0572170.18315

0.188970.28049

0.4200910.37541

‐0.1247190.19979

0.38035*0.21039

0.0711610.22759

‐0.21680.17887

Region6 0.009630.09778

‐0.13886*0.07971

‐0.179***0.06834

0.37395**0.15216

‐0.102652‐0.11302

‐0.28805**0.11826

0.1712160.14568

0.0432740.10089

0.007800.06691

Year 0.0211**0.01006

‐0.012000.0097

0.0428***0.01396

0.0108250.01066

‐0.003050.01175

0.037115**0.01621

0.0030470.0109

‐0.008030.00606

‐0.0077090.00598

Railroad1 0.0000060.00001

‐0.0000070.00000

0.0000020.00000

0.0000010.00000

0.0000080.00001

0.00000020.00001

0.0000010.00001

0.0000030.00000

‐0.0000010.00000

Media2 0.0000250.00006

0.0000540.00006

0.0000550.00006

‐0.0000080.00006

0.0000800.00007

0.000015*0.00008

‐0.0000810.00006

0.0000450.00004

0.000107*0.00006

Media4 0.0000010.00001

0.0000080.00001

‐0.0000080.00001

‐0.0000030.00002

‐0.0000120.00002

0.00001120.00002

‐0.00003*0.00001

‐0.00003**0.00001

0.0000040.00001

Media5 0.000040.00006

0.0000060.00006

0.0000450.00006

‐0.0000610.00006

0.0000350.00007

‐0.00003320.0008

0.0000660.00006

‐0.0000370.00004

0.000010**0.00004

WithCorruptionControls

*Statisticallysignificantatthe10%level**Statisticallysignificantatthe5%level***Statisticallysignificantatthe1%level

Variable

MarketingCampaign

Tourism Partner/Sponsor

Internal Service Modern Refreshing

CGDP 0.000192**0.00008

‐0.00021**0.00009

‐0.0000600.00009

‐0.0000280.00007

0.00014***0.00005

0.00001470.00005

‐0.000187**0.00008

Region2 0.078700.193

‐0.0303830.1802

0.0511640.194889

0.26556640.19867

0.10247130.19454

‐0.00745130.15777

‐0.00960690.17511

Region3 ‐0.21776**0.09277

‐0.0107160.13293

‐0.3197840.15475

‐0.0199940.12676

‐0.10171**0.05063

0.12698960.12789

0.1291240.15916

Region4 ‐0.0896310.15143

0.2374050.2398

0.09192480.22072

0.08096530.17849

0.03323360.12396

0.25029270.20333

0.469645**0.23845

Region5 ‐0.1876970.155

0.570561**0.24233

0.38242040.24256

0.16733990.29694

‐0.1353***0.04252

0.32194480.33114

0.66464***0.24361

Region6 ‐0.1364840.10399

0.1745640.1465

‐0.14366660.1363

0.12792450.12897

0.04370640.09767

0.01484940.0‐434

0.374004**0.16321

Year ‐0.0251***0.00735

0.014880**0.00295

0.03636***0.00907

‐0.01459**0.00643

‐0.0163***0.0044

‐0.0129***0.00471

0.00323220.00661

Overall ‐00079960.01074

‐0.0099360.001102

‐0.00558850.01205

‐0.01613*0.00959

‐0.0007720.00682

‐0.011646*0.00685

0.00614910.01039

Freedom ‐0.0020260.00285

0.004912*0.00295

‐0.00002470.00333

0.00358960.00253

0.00002070.00182

‐0.00135140.00185

0.00177170.00249

Business ‐0.0013900.00467

0.008907*0.00514

0.0097957*0.00556

0.00064850.00429

0.00409620.00293

0.004110.00309

0.00596320.00459

Investment 0.0053660.00429

0.008406*0.00484

0.00137480.00476

0.00546520.00396

0.00238930.00295

0.00495310.00309

‐0.00133660.00416

Witheducationcontrols:

*Statisticallysignificantatthe10%level**Statisticallysignificantatthe5%level***Statisticallysignificantatthe1%level

Variable

SpecialEvent

Athletics Government Tourism Partner/Sponsor

Internal Service Refreshing Entrance

CGDP ‐0.00017**0.00007

‐0.00005340.00005

‐0.000106*0.00006

‐0.0000850.00008

‐0.00021**0.0001

0.00011*0.00007

0.00005840.00005

0.0000090.00006

0.00009*0.00005

Region2 0.02954740.13244

‐0.05727960.06496

0.03818280.12365

‐0.2779***0.07832

‐0.3087***0.09663

0.2289780.15012

‐0.10509*0.05451

‐0.092300.10966

0.21807860.16127

Region3 ‐0.22637580.26629

‐0.125005*0.06871

0.41518410.25791

‐0.1712200.15918

‐0.2490420.16752

0.1344580.2410

‐0.1759***0.06748

‐0.2822***0.09312

0.07977650.20949

Region4 ‐0.15705350.18541

‐0.10031**0.04125

0.22713360.20809

‐0.0817030.13043

‐0.1400300.13993

0.048950.17274

‐0.0982**0.04594

‐0.01428440.1386

0.02788850.15283

Region5 0.09711130.18933

0.01085160.13636

0.45523270.31322

‐0.1017510.017568

0.1910900.26442

‐0.0763110.18298

‐0.1550***0.04056

‐0.14824570.11233

‐0.0254600.15406

Region6 ‐0.10675670.15479

‐0.1479***0.04601

0.2518240.16692

‐0.17440*0.10166

‐0.3248***0.09533

0.1923870.15987

‐0.0666500.06475

‐0.05640420.12008

0.1239140.15822

Year 0.03033***0.00808

0.02779***0.00628

0.01999***0.00651

‐0.0015530.00802

0.0363***0.00943

‐0.0195***0.00752

‐0.0127**0.00533

‐0.01151(*)0.00701

‐0.0058620.00589

School12 0.00101***0.00037

0.000425**0.0002

‐0.00001840.00028

0.00007550.00033

0.0005740.00037

‐0.0002360.00032

0.00031510.00021

0.000676**0.00029

0.00002690.00026

School06 ‐0.00031**0.00016

‐0.00004060.00008

‐0.000187(*)0.00011

‐0/0000510.00014

‐0.0000610.00016

0.0001530.00015

‐0.0000050.00009

‐0.00016720.00013

‐0.00002630.00012

School09 ‐0.00009380.0016

‐0.00048610.00098

‐0.00025770.00141

0.00359**0.00156

0.0021450.00171

‐0.0018970.00151

‐0.0004490.00095

0.00013230.00132

‐0.00184690.00124

AppendixC:FurtherCaseStudyRegressionTables

TimeSeriesAnalysis

Athletics:

countryisocode  year  school12 Prediction Actual

BRA  50  494 0.00163 0BRA  51  499 0.00213 0BRA  53  511 0.00374 0BRA  55  523 0.00618 0BRA  63  570 0.03457 0BRA  66  587 0.06147 0

 

  

countryisocode  year  school12 Prediction Actual

ITA  50  875 ‐0.22058 0ITA  51  877 ‐0.21800 0ITA  58  898 ‐0.16392 0ITA  61  907 ‐0.11634 0.16667ITA  63  914 ‐0.06695 0.5ITA  66  925 0.02886 1

   

countryisocode  year  school12 Prediction Actual

MEX  50  575 0.00650 0MEX  51  582 0.00854 0MEX  53  598 0.01515 0MEX  55  614 0.02583 0MEX  61  662 0.09526 0MEX  63  680 0.14195 0MEX  66  715 0.25162 0

 

  

countryisocode  year  school12 Prediction Actual

ZAF  50  425 ‐0.38560 0ZAF  51  426 ‐0.37977 0ZAF  58  440 ‐0.25832 0ZAF  61  446 ‐0.14301 0ZAF  63  450 ‐0.03104 0.5

ZAF  66  494 0.18580 0.61538 

  Tourism:  

countryisocode  cgdp  freedom business investment Prediction  Actual

BRA  262.89818  50 55 50 ‐0.00553  1BRA  284.52129  50 55 50 0.00437  0.5BRA  305.52547  50 55 50 0.02966  0BRA  348.14119  50 55 50 0.05229  0BRA  562.54767  50 55 50 0.14504  0BRA  645.16023  50 55 50 0.18329  0

 

   

countryisocode  cgdp  freedom business investment Prediction  Actual 

ITA  780.00382  70 85 70 0.22161  1 ITA  880.84147  70 85 70 0.21427  0 ITA  1401.7459  70 85 70 0.20912  0 ITA  1776.8338  70 85 70 0.17047  0.16667 ITA  2010.2104  70 85 70 0.14968  0 ITA  2362.5771  70 85 70 0.11954  0 

  

countryisocode  cgdp  freedom business investment Prediction   Actual 

MEX  540.43919  50 55 70 ‐0.14264  0.5 MEX  601.30299  50 55 70 ‐0.14025  0 MEX  592.87469  50 55 70 ‐0.10838  0.5 MEX  698.12973  50 55 70 ‐0.09940  0 MEX  905.68259  50 55 70 ‐0.04511  0.333333MEX  995.69433  50 55 70 ‐0.03083  1 MEX  1236.4116  50 55 70 ‐0.03491  0.5 

 

  

countryisocode  cgdp  freedom business investment Prediction  Actual 

ZAF  485.49526  50 85 70 ‐0.21803  0.5ZAF  506.55508  50 85 70 ‐0.20602  0ZAF  634.20836  50 85 70 ‐0.11139  0.33333ZAF  2197.1384  50 85 70 ‐0.07459  0ZAF  762.37911  50 85 70 ‐0.04981  0.5ZAF  864.26406  50 85 70 ‐0.01917  0.69231

 

 

CrossCountryAnalysis

Tourism:

country  year  cgdp  Freedom from Corruption 

Business Freedom 

Investment Freedom 

Prediction  Actual 

BRA  63  562.5477  50 55 50 0.3672641  0BRA  66  645.1602  50 55 50 0.4055122  0ITA  61  1776.834  70 85 70 0.3243131  0.16667ITA  63  2010.210  70 85 70 0.3035219  0ITA  66  2362.577  70 85 70 0.2733876  0MEX  61  905.6824  50 55 70 0.3395039  0.33333MEX  63  995.6943  50 55 70 0.3537852  1MEX  66  1236.412  50 55 70 0.3497086  0.5ZAF  61  2197.138  50 85 70 0.4708629  0ZAF  63  762.3791  50 85 70 0.495646  0.5ZAF  66  864.2641  50 85 70 0.5262802  0.69231

Internal:

countryisocode  year  cgdp  media4  Prediction  Actual 

BRA  63 562.5477 2363 0.2447965 0.66667 BRA  66 645.1602 3005 0.2524972 0 

ITA  61 1776.834 5005 0.2304559 0.5 ITA  63 2010.210 7018 0.2079535 0 ITA  66 2362.577 11550 0.1329484 0 MEX  61 905.6826 2508 0.0873372 0 

MEX  63 995.6943 2709 0.0815978 0 

MEX  66 1236.412 3434 0.0879391 0 ZAF  61 2197.138 0 0.2257793 1 ZAF  63 762.3791 0 0.2050658 0 ZAF  66 864.2641 0 0.2221015 0.15385