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Edited by Subhasis Bera, Yixin Yao, Amitendu Palit,

and Dil B. Rahut

ASIAN DEVELOPMENT BANK INSTITUTE

DIGITAL TRANSFORMATION

FOR INCLUSIVE AND

SUSTAINABLE DEVELOPMENT

IN ASIA

ASIAN DEVELOPMENT BANK INSTITUTE

Digital Transformation for

Inclusive and Sustainable

Development in Asia

Edited by

Subhasis Bera, Yixin Yao,

Amitendu Palit, and Dil B. Rahut

Institute of South Asian Studies

ISBN 978-4-89974-299-9 (Print)

ISBN 978-4-89974-300-2 (PDF)

DOI: https://doi.org/10.56506/HSDC4319

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DIGITALTRANSFORMATIONFORINCLUSIVEANDSUSTAINABLEDEVELOPMENTINASIAEditedbySubhasisBera,YixinYao,AmitenduPalit,andDilB.RahutASIANDEVELOPMENTBANKINSTITUTEInstituteofSouthAsianStudiesDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaEditedbySubhasisBera,YixinYao,AmitenduPalit,andDilB.RahutASIANDEVELOPMENTBANKINSTITUTE©2023AsianDevelopmentBankInstituteAllrightsreserved.ISBN978-4-89974-299-9(Print)ISBN978-4-89974-300-2(PDF)DOI:https://doi.org/10.56506/HSDC4319TheviewsinthispublicationdonotnecessarilyreflecttheviewsandpoliciesoftheAsianDevelopmentBankInstitute(ADBI),itsAdvisoryCouncil,ADB’sBoardorGovernors,orthegovernmentsofADBmembers.ADBIdoesnotguaranteetheaccuracyofthedataincludedinthispublicationandacceptsnoresponsibilityforanyconsequenceoftheiruse.ADBIusesproperADBmembernamesandabbreviationsthroughoutandanyvariationorinaccuracy,includingincitationsandreferences,shouldbereadasreferringtothecorrectname.Bymakinganydesignationoforreferencetoaparticularterritoryorgeographicarea,orbyusingtheterm“recognize,”“country,”orothergeographicalnamesinthispublication,ADBIdoesnotintendtomakeanyjudgmentsastothelegalorotherstatusofanyterritoryorarea.Usersarerestrictedfromreselling,redistributing,orcreatingderivativeworkswithouttheexpress,writtenconsentofADBI.ADBrecognizes“China”asthePeople’sRepublicofChina;and“Korea”astheRepublicofKorea.Note:Inthispublication,“$”referstoUnitedStatesdollars.AsianDevelopmentBankInstituteKasumigasekiBuilding8F3-2-5,Kasumigaseki,Chiyoda-kuTokyo100-6008,Japanwww.adbi.orgContentsTablesandFiguresvAbbreviationsviiiContributorsxAcknowledgmentsxiiiIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxivSubhasisBera,DilRahut,AmitenduPalit,andYixinYaoPARTI:BridgingtheDigitalDivide1.InequalityandAccesstoMobileData1JonathanBrewerandYooneeJeong2.DigitalDivideAmongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?29EishaMaghfiruhaRachbini,AriyoDharmaPahlaIrhamna,andSyifaRifaRosyadah3.IncentivisingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowDigitalDivide54DioHerdiawanTobingPARTII:DigitalTransformationforSustainability4.TwinningDigitalTransformation(Dx)andGreenTransformation(Gx)towardsSustainableDevelopmentinAsiaandthePacific77JoniJupesta,KeigoAkimoto,KirstenHalsnaes,FatimaDenton,FeiTeng,FelixCreutzig,andAntonetheCastaneda5.DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-pandemicScenarioinIndianStates99KrishnaNairJ.andPulakMishraiiiivContentsPARTIII:DigitalFinanceforResilienceandProsperity6.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh121MasanoriMatsuura,AbuHayatMd.SaifulIslam,andSalauddinTauseef7.TheRiseofDigitalFinanceandtheDevelopmentofExpressDeliveryinthePeople’sRepublicofChina145PinghanLiangandWeiZouPARTIV:GlobalTradeandConnectivity8.DigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment167QasimRazaSyedandDilB.Rahut9.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific188UpalatKorwatanasakulConclusion:DigitalConnectivityandDigitalTrade—UnderstandingtheLinkagesandPolicyChallenges211AmitenduPalit,DilRahut,SubhasisBera,andYixinYaoTablesandFiguresTables1.1GDIP’sMeaningfulConnectivityFramework41.2SummaryStatisticsofCountriesintheStudy92.1ICTDevelopmentIndex,byCategoryandProvince362.2KnowledgeandTechnologyOutputsRankinginASEAN-5382.3Thailand’sAsianIndexofDigitalEntrepreneurship412.4ListofVariables442.5DescriptiveStatistics452.6DigitalAdoptionofHouseholdEnterprises,bySector462.7EstimationResultsofDifferentMeanEquations472.8ProbitEstimation473.1InclusivenessGoalunderITUConnect2030—Targetsby2023573.2GovernmentIncentivestoBoostCompanies’AccountabilityinDigitalInclusion694.1EnergyConsumptionandGreenhouseGasEmissionsofDigitalCompanies855.1DetailsonMeasurementoftheVariables1055.2SummaryStatisticsofVariables1085.3RegressionResultsfortheEstimatedFixedEffectsModel1095.4RegressionResultsfortheEstimatedFixedEffectsModel1106.1SummaryStatistics1276.2CorrelatesofSelf-ReportedShock1306.3ImpactofRainfallShocksonConsumptionforMobileMoneyUsersandNonusers1346.4HeterogeneousEffectsoftheImpactofRainfallShocksonConsumptionforMobileMoneyUsersandNonusers1366.5MechanismforMobileMoneyRemittances1387.1DescriptiveStatistics1517.2BaselineResults1537.3TheImpactofSubindexesofDigitalFinance1547.4InstrumentalVariablesEstimation1557.5HeterogeneityAnalysis1567.6RobustnessChecks1577.7ImproveServiceEfficiency1597.8ConsumptionPromotion1607.9FinancialConstraint1618.1ComparisonofEnergyPovertybyRegion172vviTablesandFigures8.2SummaryofData1768.3DescriptiveStatistics1778.4UnitRootAnalysis1788.5FindingsfromRandomandFixedEffectsModels1798.6SensitivityAnalysis1819.1PatternsofEngagementinForeignTradebyFirmType1939.2SummaryStatistics1989.3EffectsofEmailAdoptiononGVCParticipation2039.4EffectsofWebsiteAdoptiononGVCParticipation204Figures1.1Data-onlyMobileBroadbandBasketfromITUandThisStudy51.2MobileDataTrafficperDevicefromEricssonMobilityVisualizer71.3MobileAffordabilityinIndonesia111.4MobileAffordabilityinKyrgyzRepublic121.5MobileAffordabilityinMongolia131.6MobileAffordabilityinthePhilippines141.7MobileAffordabilityinSriLanka161.8MobileAffordabilitybyDecile,DataLevel,andCountry171.9MonthlyCostperGigabyteforAllProvidersandPlans181.10MobilePricingbyCountryandPlanType191.11ProvidersOfferingContent-SpecificBundles212.1GDPperCapitaofSelectedASEANMemberStates342.2Indonesia’sIncomeDistribution352.3InternetAccess,byAreas363.1PercentageofIndividualsUsingtheInternet,byRegionandGender,2022583.2Data-OnlyMobileBroadbandServiceBasketPrices,2021–2022593.3ThePrivateSector’sRoleinBridgingtheDigitalDivideBasedonIndonesianRespondents613.4DigitalSocialInclusionAspects—Children’sSafetyandAccessforWomenandGirls634.1SequenceoftheFourthIndustrialRevolutionaccordingtotheWorldEconomicForum794.2HighWell-BeingwithLowResources814.3GreenNudgeinOnlineFoodDeliveryApps904.4FrameworkfortheDigitalInnovationtoGenerateFoodSystematScale915.1ConceptualFramework103TablesandFiguresvii6.1MobilePhoneSubscriptionandInternetUsersinBangladesh1236.2PovertyRatebyDivision1257.1NationwideTrendofComplaintRatesofExpressDeliveryService1598.1DigitalTradeinTermsofICTGoodsExportsbyRegion1688.2DigitalTradeinTermsofICTServicesExportsbyRegion1698.3UnemploymentRatebyRegion1738.4ConceptualFramework1759.1ShareofSMEs,GVCfirms,andSMEsengaginginGVCsbyRegion1949.2SectoralDistributionofGVCFirmsintheAsiaandPacificRegion1949.3AdoptionRateofEmailbyRegionandFirmSize1959.4AdoptionRateofWebsitebyRegionandFirmSize1969.5DigitalReadinessIndexofSMEsinAsiaandthePacific197AbbreviationsA4AIAllianceforAffordableInternetADBAsianDevelopmentBankAFOLUagriculture,forestry,andotherlanduseAIartificialintelligenceASEANAssociationofSoutheastAsianNationsBIHSBangladeshIntegratedHouseholdSurveyCOVID-19novelcoronavirusdiseaseCSRcorporatesocialresponsibilityDTRdigitaltechnologytradeEPOVenergypovertyFEfixedeffectsGBgigabyteGDIPGlobalDigitalInclusionPartnershipGDPgrossdomesticproductGDPgrossdomesticproductGNIgrossnationalincomeGSDPgrossstatedomesticproductGSMAGSMAssociationGVCglobalvaluechainHHEhouseholdenterpriseICTinformationandcommunicationstechnologyICTinformationandcommunicationtechnologyIoTInternetofThingsIPCCIntergovernmentalPanelonClimateChangeIPSIm,Pesaran,andShinITUInternationalTelecommunicationUnionIVinstrumentalvariableLDCleastdevelopedcountryLLDClandlockeddevelopingcountryMSMEsmicro,small,andmedium-sizedenterprisesNSDPnetstatedomesticproductPIPPovertyandInequalityPlatformPOIpointofinformationPRCPeople’sRepublicofChinaREERrealeffectiveexchangerateSDGSustainableDevelopmentGoalSIDSsmallislanddevelopingstatesSMEssmallandmedium-sizedenterprisesviiiAbbreviationsixSTEMscience,technology,engineering,andmathematicsUDISE+UnifiedDistrictInformationSystemforEducationPlus(India)UNUnitedNationsUNEunemploymentWDIWorldDataInstituteWGDPworldglobalgrossdomesticproductContributorsKeigoAkimotoisagroupleaderandchiefresearcherattheResearchInstituteofInnovativeTechnologyfortheEarth(RITE),Japan.SubhasisBeraisanassociateprofessorineconomicsandquantitativetechniquesandchairpersonoftheResearchandProfessionalDevelopmentProgrammeattheInternationalSchoolofBusiness&Media,Kolkata,India.JonathanBrewerisaconsultantatTelco2Limited,NewZealand.AntonetheCastanedaisasocialsciencesscientistattheChairfortheConservationandEcotourismofRiparianandDeltaicEcosystem,UNESCO,Guatemala.FelixCreutzigisheadoftheWorkingGrouponLandUse,Infrastructures,andTransportattheMercatorResearchInstituteonGlobalCommonsandClimateChangeandinSustainabilityEconomicsofHumanSettlementsattheTechnicalUniversity,Germany.FatimaDentonisdirectoroftheInstituteforNaturalResourcesinAfrica,UnitedNationsUniversity,Ghana.KirstenHalsnaesisaprofessorattheTechnicalUniversityofDenmark,Denmark.AriyoDharmaPahlaIrhamnaisaresearcherattheInstituteforDevelopmentofEconomicsandFinance,Jakarta,Indonesia,alecturerattheFacultyofEconomicsandManagement,ParamadinaUniversity,Jakarta,Indonesia,andapostgraduateresearcherattheSchoolofGlobalDevelopment,UniversityofEastAnglia,UnitedKingdom.AbuHayatMd.SaifulIslamisaprofessorattheDepartmentofAgriculturalEconomics,BangladeshAgriculturalUniversity,Bangladesh.YooneeJeongisaseniordigitaltechnologyspecialist(digitalinfrastructureandeconomy)attheClimateChangeandSustainableDevelopmentDepartment,AsianDevelopmentBank,Philiipines.xContributorsxiJoniJupestaisaresearchfellowandacademicassociateattheInstitutefortheAdvancedStudyofSustainability,UnitedNationsUniversity,Japan.UpalatKorwatanasakulisanassociateprofessorattheFacultyofSocialSciences,WasedaUniversity,Japan.PinghanLiangisaprofessorattheSchoolofGovernment,SunYat-senUniversity,People’sRepublicofChina.MasanoriMatsuuraisaresearchfellowattheSouthAsianStudiesGroup,AreaStudiesCenter,theInstituteofDevelopingEconomies,JapanExternalTradeOrganization,Chiba,Japan.PulakMishraisaprofessorattheDepartmentofHumanitiesandSocialSciencesIndianInstituteofTechnology,Kharagpur,India.KrishnaNairJ.isaresearchscholarintheDepartmentofHumanitiesandSocialSciencesIndianInstituteofTechnology,Kharagpur,India.AmitenduPalitisaseniorresearchfellowandresearchlead(tradeandeconomics)attheInstituteofSouthAsianStudies,NationalUniversityofSingapore,Singapore.EishaMaghfiruhaRachbiniisalecturerattheFacultyofEconomicsandManagement,IPBUniversity,Bogor,Indonesia,andaresearcherattheInstituteforDevelopmentofEconomicsandFinance,Jakarta,Indonesia.DilB.Rahutisvice-chairofresearchandaseniorresearchfellowattheAsianDevelopmentBankInstitute,Japan.SyifaRifaRosyadahisaresearchassociateattheInstituteforDevelopmentofEconomicsandFinance,Jakarta,Indonesia.QasimRazaSyedisanassistantdirectorattheNationalTariffCommission,Pakistan.SalauddinTauseefisanassociateresearchfellowattheDevelopmentStrategiesandGovernanceUnit,theInternationalFoodPolicyResearchInstitute,Vientiane,LaoPeople’sDemocraticRepublic.xiiContributorsFeiTengisaprofessoranddeputydirectorattheInstituteofEnergy,Environment,andEconomy,TsinghuaUniversity,People’sRepublicofChina.DioHerdiawanTobingistheheadofpublicpolicy(Asia)attheWorldBenchmarkingAlliance.YixinYaoisaseniorresearchfellowattheAsianDevelopmentBankInstitute,Japan.WeiZouisanassociateprofessoratZhongnanUniversityofEconomicsandLaw,People’sRepublicofChina.AcknowledgmentsThisbookisanoutputofthevirtualconference“DigitalConnectivityPathways:ExploringtheIssues,Constraints,andCollaborationProspectsforAsiaandthePacific,”co-hostedbytheAsianDevelopmentBankInstitute(ADBI)andtheInstituteofSouthAsianStudies,NationalUniversityofSingapore(ISAS–NUS),heldon5–6June2023.TheeditorswouldliketoexpresstheirgratitudetoTetsushiSonobe,deanofADBI,andIqbalSinghSevea,directorofISAS–NUS,fortheirguidanceandsupport.ThanksarealsoduetoAdamMajoeforhispivotalroleincoordinatingtheeditingandproductionprocess,IanYoungsforthedesignofthebookcover,AinslieSmithandKaeSugawaraforcopyediting,andAileenMagparangalanfortypesettingthebook.TheeditorsalsothankCathyNg,JieMi,andDavaatserenNarmandakhfortheirvaluablesupportinpreparingthisvolumeandensuringitstimelycompletion,aswellasorganizingtheconferenceandpaperreviewandrevision.WewouldalsoliketothankJordanAngoftheISAS–NUSeventteam.Finally,weacknowledgethecontributorstothispublicationfortheircooperation,effort,andprecioustime.xiiiIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificSubhasisBera,DilRahut,AmitenduPalit,andYixinYaoTheincreasinginterdependenceamongnationsnecessitatestheestablishmentofadvancedcommunicationmechanisms.Thesemechanisms,builtondigitaltechnologies,aremarkedbyrapidinnovationandwidespreadadoption.Digitalconnectivityisanindispensablecatalystforglobalprogress,collaboration,anddevelopment,transcendinggeographicalboundaries.Itfostersinclusivegrowth,facilitatestheexchangeofknowledgeandideas,andisacornerstoneofthemoderninterconnectedworld.Nevertheless,discrepanciesinitsadoptionrateshavegivenrisetowhatiscommonlyknownasthedigitaldivide.Thisdivideobstructstherealizationofthebenefitsofdigitalization,evenforthosewhopossessaccessalongsidethosewhodonot.Recognizingthesignificanceofdigitalconnectivity,variouscountriesandinternationaldevelopmentorganizationsemphasizeitsimportance.Consequently,therapidproliferationofdigitaltechnologiesacrossnationsexhibitsaK-shapedgrowthpattern.Somehigh-incomecountriesexperiencefavorablegrowthrates,whilelow-incomecounterpartsfacenegativetrajectories.Evenwithinindividualcountries,disparitiesingrowthamongincomegroupspersist.Inbothcases,asignificantportionofthepopulationremainsunabletoreapthebenefitscomparedtotheircounterparts,perpetuatingthedigitaldivide.Affordabilityemergesasacrucialissue,promptinggovernmentstoprovidesubsidiesfordigitalconnectivity.However,recentstudiesrevealthatfederalsubsidiesforuniversalbroadbandaccessintheUnitedStateshavenotyieldedthedesiredoutcomes(Kane2023).Consequently,subsidiesalonecannotbridgethedigitaldivideentirely,necessitatingtargetedeffortstoidentifyandaddressnonfinancialbarriers.Inthiscontext,disruptivetechnologiescanbepivotalintargeteddigitalinclusioninitiatives.Inpursuingeconomicgrowth,Asiancountrieshavewitnessedtherapidadoptionofdigitaltechnologies.However,thisadoptionhasnotxivIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxvresultedinconvergenceduetotheregion’sheterogeneouseconomic,social,andpoliticallandscapeandregulatoryframeworks.Thislackofconvergenceposeschallengestobuildingeffectivedigitalregionalintegration.Therefore,comprehendingthecurrentconvergencestatusandanalyzingthedeterminantsofdigitalconnectivityacrosscountriesisimperative.Thisstudydelvesintotheconvergencestatusofvariouscommunicationtechnologiestoformulateacomprehensivepolicyframework.WhilemanystudiesdelveintotheSchumpeterianconvergenceoftechnology,therapidevolutionofdigitaltechnologieshasledtothecoexistenceofvarioustechnologiesandplatforms.Thiscoexistencenecessitatesacomparisonbasedonacompositeindex.However,constructingameaningfulstrategyorpolicyframeworkrequiresrevisitingtheoriginaldatasetduetoaggregationbias,oversimplification,andalackoftransparency(Grecoetal.2019;Freudenberg2003).Digitalconnectivitycanbeachievedthroughvariouscoexistingcommunicationtechnologies,suchasmobilenetworks,theinternet,orbroadbandconnections.Hence,separateanalysesforeachtechnologytypemayprovideadeeperunderstandingofdigitalconnectivityacrosscountries.Additionally,theapplicationofspecifictechnologiesdependsonthefieldofoperation,suchaseducation,trade,health,andtheenvironment.Therefore,digitalconnectivitypathwaysmustconsiderusingdigitaltechnologiesinvariousoperationalfieldswiththeexistingframework.ImpedimentstoCross-borderDigitalConnectivityChapter1ofthisbookdealswiththestatusofthedigitaldivideandimpedimentstocross-borderdigitalconnectivity.Asiancountriesintermsofdigitalconnectivityandeconomicgrowth,continuetobethefastest-growingintheworld.Theadoptionofdisruptedtechnologiesremainscolossalwhiletheregionhostsleadersin5Gandfiberopticrollouts.Consequently,thedataflowduetodigitalconnectivityismorevoluminousinEastAsiathantheglobalaverage,contributingtoeconomicgrowth.InEastAsia,smallbusinessescomprise60%to99%ofallbusinesses,areresponsiblefor50%to98%ofallemployment,andcontribute35%to70%ofgrossdomesticproduct(GDP).Mostofthesebusinessesspendasignificantamountoninformationandcommunicationtechnology(ICT).Again,dailytimespentusingtheinternetisalsoveryhighinAsiancountries,especiallyinthePhilippines,Malaysia,Thailand,andIndonesia.Indiaisexhibitingthefastest-growingnumberofinternetsubscriptions.xviDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaDespitetherapidinnovationandsurgeinthediffusionofinternetpenetration,thedigitaldividestillexistsacrossandwithincountries.Therefore,regardlessofgovernmentinitiatives,thereisacallforfurtherstrategiestoprovidetheaffordable,accessible,resilient,andreliantdigitalconnectivityneededforthefoundationandoperationofaninclusivedigitalsociety.Securinganinclusivedigitalfutureforall,includingthemostvulnerable,isanurgentpolicypriority.Althoughstudiesattempttodistinguishthefactorsresponsibleforthisdigitaldivideindevelopedanddevelopingcountries,researchersconsiderubiquitousaffordabilityasacommonreasonforthedigitaldivideindevelopedanddevelopingcountries(Reddicketal.2020;Fisteretal.2022;WorldBank2021;Weissetal.2015).Notwithstanding,variousstudiessuggestsubsidiestotacklelowaffordability(Oughtonetal.2022;Oughton2023),whiletheInformationTechnology&InnovativeFoundationarguesthatfederalbroadbandsubsidyprogramsareamessofredundanciesintheUnitedStatesandhavefailedtoclosethegeographicdigitaldivide.Themajoraffordabilitycomponentsaretheuser’sincomeandthedataprice.Sincethedatapriceisconditionalonthevolumeandquality(i.e.,uninterruptedandspeed)oftheservices,theInternationalTelecommunicationUnion(ITU)collectsdataasapricebasket.AWorldBankstudydeemsdatapriceslessthan2%ofPCIaffordable.BrewerandJeong(2023)inChapter1considertheincomedeciletoanalyzetheaffordableconnectivityoffiveAsiancountries.Thestudyshowsthatalthoughmultipledataplansandplansfortargetedgroups(suchasstudents,remoteworkers,etc.)prevailinthesecountries,pricevariationremainsconditionalonserviceprovider,volumeofdata,urbanization,andgovernmentinterventions.Therefore,intheAsiaandPacificregionwhereincomeinequalityprevails,enhancingdigitalconnectivityacrossbordersrequiresaconsensusanduniversalaccessplan.TheeconomicgrowthofcountriesinAsiaandthePacificismainlybackedbythemicro,small,andmedium-sizedenterprises(MSMEs),especiallysincethenovelcoronavirusdisease(COVID-19)pandemic.MSMEs’relianceondigitaltechnologyhelpedthemsurviveindifficulttimesoflockdownandprosperduringthepost-COVIDera.AdoptingdigitaltechnologyallowsMSMEstoaccessmoreinformation,resources,inputsandfinance,whichinturnhelpsreducethedistributiontimeandcostsandenhanceaccesstothelargermarket.Moreover,digitaltechnologytransformationalsostimulatedmoreinnovationinbusinessmodelsand,thus,moreefficientproductionforMSMEs.Therefore,improveddigitalconnectivitycanhelpMSMEstoprosper,driveeconomicgrowth,andreduceincomeinequality(MustaffaandBeaumont2002).Ontheotherhand,lackofaffordability,awareness,andaccesstoavailabledigitaltechnologymayhinderdigitaltransformationIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxviiandpotentialeconomicgrowth.ThislackofaccessmayexacerbatethegrowthofMSMEsinadisadvantageouspositionintheglobalmarketandmayleadtoextinction.Consequently,thedigitaldivideintheMSMEsectormaybedetrimentaltothesmoothmarketmechanismandstalleconomicgrowth.Therefore,successfuldigitalconnectivityalsorequiresunderstandingthedigitaldivideinMSMEs.Inthisbook,thestudybyRachbini,Irhamna,andRosyadah(Chapter2)showsthatthedigitaldivideexistsinIndonesianMSMEs.ThisdivideisprominentbetweenruralandurbanMSMEsandlegalandunauthorizedones.UsingWorldBanksurveydataontheIndonesiandigitaleconomy,thestudyanalyzesthedeterminantsofdigitaltechnologyusebyIndonesianMSMEs.ItshowsthatfinancialliteracyandfinancialskillsinoperatingbusinessessignificantlyaffectMSMEs’digitalizationprocess.Therefore,enhancingfinancialliteracywiththehelpofdigitalfinancecanfosterthedigitalizationprocess.Reapingthebenefitsfromdigitalconnectivityisconditionalontheparticipationofallstakeholders.Participationintheprocessisagainconditionalontheincentivesthatstakeholdersreceive.Recentinitiativestousecitizen-generateddatatorespondquicklytoqueriesorservicerequestsencouragecitizenstoparticipateinthedigitalconnectivityenhancementprocess.Similarly,througheffectivemarketregulationandcollaboration,technologycompaniesareencouragedtoparticipatetoenhancedigitalconnectivity.Theroleoftechnologycompaniesisessentialastheycreateasupplybydevelopingvarioushardwareandsoftwareandenhancingtheskillsetsoftheworkers.Ontheotherhand,employeesarealsorequiredtousethetechnologytoimprovetheirstandardofliving.Privatecompaniescancreateandexpandthemarketbyempoweringcitizensandprovidingfacilitiestoenhancedigitalconnectivity.However,therapidpaceofinnovationindigitaltechnologyandshiftsinconsumptionpatternsdrivetechnologicalcompaniestoinnovatecontinuously.Nonetheless,technologicalinnovationseldomoccursinisolation,ashighlightedbytheUnitedNationsConferenceonTradeandDevelopment(UNCTAD)in2021.Consequently,thereisapressingneedtointegratetechnologycompaniesintoeffortstobolsterdigitalinclusivity.AsurveyconductedbytheWorldBenchmarkingAlliance1in2023revealsthatwhiletheglobalaveragedigitalinclusionscorehas1Thecompanieshavebeenassessedonfourmeasurementareas:enhancinguniversalaccesstodigitaltechnologies;Improvingalllevelsofdigitalskills;fosteringtrustworthyusebymitigatingrisksandharms;andinnovatingopenly,inclusively,andethically(WBIn.d.).xviiiDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaincreasedby6.8%,asubstantial174outof200companiesfallshortofcriticalscores,necessitatingsubstantialprogressintheirdigitaltransformationefforts.Thissurveyunderscoresthatlessthan14%oftheworld’sleadingtechnologycompaniesactivelycontributetodigitalinclusion.NoteworthyinitiativesinthisrealmincludeMeta’ssubseacablenetwork,Google’sAffordableConnectivityProgram,Cisco’sNetworkingAcademy,Amazon’sAWSEducateprogram,IBM’sSkillsBuildprogram,andMicrosoft’sDigiSkillsprogram.However,amongthetoptechnologyfirms,merely14%areengagedindigitalinclusionefforts.Additionally,itisworthnotingthatseveraltechnologycompanieshavediscontinuedtheirdigitalinclusioninitiativesaspartoftheircorporatesocialresponsibilitypractices.Furthermore,thesecompanies’initiativesoftenoperateinisolationandmaynotalignwithnationaldigitalinclusionplans.Therefore,fosteringcollaborationbetweenthegovernmentandtechnologycompaniesisimperativetofullyharnessthebenefitsofdigitalization.Thiscollaborativeendeavor’ssuccesshingesonacomprehensivedevelopmentframework,robustregulatorymeasures,andtransparentpractices.InChapter3,Tobingadvocatesforacollaborativeapproachbetweengovernmententitiesandprivatetechnologyfirms.Tobingsuggestsprovidingincentivestoencouragetechnologycompaniestoparticipateinsuchcollaboration,whethertheseincentivestakeadirectorindirectform.Clearindustrystrategies,sounddatagovernance,andtransparentpracticesindirectlymotivatetechnologycompaniestoengageininitiativesthatpromotedigitalinclusion.Therefore,stakeholdersneedtoconstructacomprehensivedigitaldevelopmentframework,delineatingspecificrolesandresponsibilitiesforeachstakeholder.Asaregulatoryauthority,thegovernmentmustmonitorthefunctioningofthestrategyand/orpolicyframework.DigitalTransformationforSustainabilityRapidinnovationanddiffusionofdigitaltechnologiestransformthesocialandenvironmentalstructureandfunctioning.Therefore,sustainabledevelopmentrequiresconsideringdigitaltransformationduetoitsimpactonsocietyandtheenvironment.Digitaltransformationexpectstoreducetherelianceonfossilfuels,reducepollution,andconserveresources.Ontheotherhand,digitaltechnology’sresource-hungrynatureisexpectedtoincreaseenergyconsumptionandelectronicwaste.Accordingtoexperts(ShiftProject2019),theshareofglobalcarbondioxide(CO2)emissionscausedbydigitaltechnologyincreasedfrom2.5%to3.7%between2013and2018.AnotherstudybytheBorderstepInstituteshowsthatthegreenhousegasemissionsIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxixcausedbytheproduction,operation,anddisposalofdigitalenddevicesandinfrastructurearebetween1.8%and3.2%ofglobalemissions(asof2020).Consideringboththepositiveandnegativeimpactsofdigitaltechnology,apolicyframeworkrequiresachievingaconvergenceofthecirculareconomyandIndustry4.0toenhanceresourceuseefficiencyandsustainability(Sarcetal.2019).Environmentalsustainability,asoneofthecrucialprinciplesofsustainability,concernsthepursuitofmeetingneedswithoutcompromisingthequalityoftheenvironment.ThestudybyJupestaetal(2023)(Chapter4)depictsthetwinningrelationshipbetweendigitalizationandclimatechangemitigationacrosssectors,focusingontheexistingtechnology,market,andpolicyontheinteractionbetweendigitalizationandclimatechangemitigation.Inagriculture,usingartificialintelligence(AI)forprecisionfarmingcanreducetherequiredresourcesaswellasutilizeweatherpredictions.Therefore,theuseofAIcanreducewasteandhence,CO2emissions.Moreover,bycombininghigh-resolutionsatelliteimagesandcloudcomputingtohandlebigdata,acountrycanhelppreventtheconversionofforests.Alongthesamelines,usingsmarthomesystemscanhelpincreaseresidentialenergyefficiency,and3Dprintingtechnologycanhelpinsustainableconstructionandreducegreenhousegasemissions.Energyefficiencyisalsoconditionalontheaccuracyofdemandandsupplyofenergy,anddigitaltechnology-basedenergymanagementstrategiescanenhancetheaccuracyofdemandandsupplyofenergy.Inthisregard,strategicuseofsolarenergycanincreasethesupplywithoutcausingenvironmentalstressifsolarpanelsareinstalledwithoutobstructingagriculturalland.Theuseofsolarpanelscanalsobeextendedtoresidentialareasandindustrialareas.However,energyrequirementsoutpacedtheenergy-efficiencyimprovement,albeitIndustry4.0technologiesuseresourcesmoreefficiently(Freitag,Berners-Lee,andWiddicks2021).Therefore,thereisaneedtoenhanceawarenessregardingenergy-efficientmachines,tools,andothertechnologies.Sincetheenergyrequirementishighinthetransportindustry,astudyonEuropeancountriesarguesforincreaseduseofdigitaltechnologyinthetransportsector.Accordingtothestudy,digitaltechnologyonpassengervehiclescanreduceenergyconsumptionandgreenhousegasemissionsby34%and43%,respectively.Thestudyalsoarguesthatthereisatrade-offbetweendigitalizationandclimatechangemitigation.Therefore,thereisacallforformulatingastrategytoreapthebenefitsofdigitalconnectivity.Researchersarguethattheenergyefficiencyusingdigitaltechnologyisconditionalontheawarenessandskilloftheuser.Furthermore,digitalizationandgreentransitioncenteraroundtheanthropogenicemissionscausedbyhumanactivities.Inevitably,strategyformulationxxDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaremainsinadequatewithoutconsideringeducationandskillsinunderstandingthepatternofhumanactivities.Hence,providingbettereducationandpublicawarenessondigitaltechnologyandclimatechangemitigationwillbeahighprioritytoachieveUnitedNations2030sustainabledevelopmentagenda.Lackofawareness,education,andskillimpactssocialinequity.Educationcanincreaseawarenessandpreparestudentsforthefutureofworkandsocietyasdigitaltechnologytransformsvarioussectors,creatingdemandsfortechnicalskills,softskills,digitalcitizenshipskills(i.e.,ethics,safety,andresponsibilities),andcompetencies(IEEEn.d.).However,onlyprovisionfordigitalconnectivitytoimproveawareness,education,andskillsisunlikelysufficienttoachievedigitalinclusion(DijkandHacker2003).Thereisaneedtoimparttrainingandequipindividualswithdigitalskills.Inthisregard,schoolshaveanimportantroletoplay.ThestudybyNairandMishra(Chapter5)revealsthatinIndia,enrollmentinaschoolisconditionalontheexistingdigitalinfrastructure,especiallyaftertheCOVID-19pandemic.Thestudyalsoshowsthatfunctioningcomputersstimulateenrollmentalongwiththeteacher-studentratio.Therefore,thereisaneedfordigitalinfrastructuredevelopmentinschools.Thisdigitalinfrastructure,ontheonehand,familiarizesstudentswiththetechnology,andontheotherhand,demandsprerequisitedigitalskillsoftheteachertoopenawidearrayoflearningopportunities.2EducationcanhelpthesegroupsleverageICTtoovercomediscrimination,exclusion,orisolation,improvetheirqualityoflife,andcontributetosocialchange.Educationcanalsohelpraiseawarenessoftheissuesandchallengesfacedbythesegroupsandfosteracultureofrespect,diversity,andsolidarityamongallmembersofsociety.Usingpaneldata,thestudyshowstheroleofschoolsinclosingthetechnologicaldividebyprovidingtechnologyaccessanddigitalresourcestostudentswholacksimilaropportunitiesathomeorwithintheircommunities.Moreover,digitalliteracytrainingprovidedbyschoolscanalsoenhancetherequireddigitalskillstonavigatethedigitalrealm.Theroleofschoolsinprovidingdigitalliteracyismoresubstantialinmarginalizedareas.Thestudyalsopointedoutthepossiblevariationacrossregions.2Thestudyfocusesonidentifyngthefactorsthatinfluencethechoiceofschoolforenrollment.Introduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxxiDigitalFinanceforResilienceandProsperityAmongthemulti-dimensionalaspectsofdigitalconnectivity,two-waycommunicationsforrapidandeffectiveresponseandsecureandsafefinancialtransactionsareofimmenseimportancetoreapthebenefits.Oneofthefundamentalcomponentsofthedigitaldividerelatestoaccesstobasicfinancialservices.Millionsofpeople,especiallyinunderservedandremoteareas,lackaccesstotraditionalbrick-and-mortarbanks.Digitalfinance,whichencompassesmobilebanking,digitalwallets,andonlinepaymentplatforms,hasthepotentialtoreachtheseunbankedandunderbankedpopulations.Thedrivetousedigitaltechnologyforfinancialtransactionsstimulatesinnovationsandgrantsdigitalfinance,alsoknownasfintech,toofferproductsandservicesthroughmultiplechannels.Digitalfinancialtransactionsthroughvariouschannelsprovidegreateropportunitiesandbenefitsforthedigitallyexcludedorunderservedandconsequentlyhelpconnectpeopleandreduceinequality(CarraroandAnand2019).Therefore,theimpactofdigitalconnectivityisalsoconditionalonthewideandefficientuseofdigitalfinance.Digitalfinancecanfosterfinancialtransactions,savings,andinsuranceclaims.Therefore,analyzingthevariouswaystousedigitalfinancialtransactionstoexpeditedigitalconnectivityisalsoessential(ITU2021).PartIIIofthisbookdealswiththetwospecificusesofdigitalfinance—onedealswithmobilemoneytomitigateweathershocks,whiletheotherpertainstotheuseofdigitalfinanceinanexpressdeliverysystemconnectingthedigitalworldwiththephysicalworld.ThestudybyLiangandZou(Chapter7)showsthatinthePeople’sRepublicofChina,atthecitylevel,a10percentagepointincreaseindigitalfinanceissignificantlyassociatedwitha3.16percentagepointincreaseinthenumberofexpressdeliverypointsanda3.81percentagepointincreaseinthenumberofnewexpressdeliverypoints.Thestudy,usingtheinstrumentalvariablemethod,showsthatdigitalfinanceimprovestheserviceefficiencyoftheexpressdeliverysystemandprovidesmoresignificantbenefitsofdigitalconnectivity.Therefore,thereisaneedforimprovingdigitalfinancetoreapthebenefitsofdigitalconnectivity.However,thepromotioneffectofdigitalfinanceonexpressdeliveryismoresalientinareaswithhigheducationlevels,largerinternetusersizes,andbetterroadinfrastructure.Therefore,countriesshouldimprovebasicinfrastructureandeducationtoenhancedigitalconnectivity.Developingcountriesmainlysufferfromthelackofdigitalpublicinfrastructuretoreapthebenefits,especiallywhenthereisanexternalshock.Weathershocks,includingdroughts,floods,andextremexxiiDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaweatherevents,posesignificantchallengestocommunitiesworldwide,particularlyinregionsdependentonagricultureandvulnerabletoclimatechange.Inrecentyears,theadoptionofmobilemoneyserviceshasemergedasapowerfultoolinenhancingresilienceandmitigatingtheadverseeffectsofweathershocks.Kenya’sM-Pesa,Pakistan’scropinsurance,andUganda’srefugeecommunityareexamplesofusingmobilemoneyindealingwithweathershocks.ThestudybyMatsuura,Islam,andTauseef(chapter6)focusesonusingmobilemoneyindealingwithweathershocksandpovertyreductioninBangladesh.Thestudyusesanationallyrepresentativehouseholdsurveyandhistoricalgranularmonthlyprecipitationdata.Byemployingfixedeffectandinstrumentalvariableapproaches,thestudyfoundthatmobilemoneycompensatesforthenegativeeffectofrainfallshocksinBangladesh.Thestudyalsorevealsthatmobilemoneyenablesgeographicallydisadvantagedandpoorhouseholdstosmoothouttheirfoodconsumptionduringdroughtsandreceiveincreasedoverseasremittances,whichenhanceshouseholdwelfarecomparedtothenonusersofmobilemoney.Therefore,digitalfinanceusingmobilemoneyhelpsabsorbweathershocksandscopestoovercomedisasterandpoverty.GlobalTradeandConnectivityDigitaltechnologyplaysacrucialroleinfosteringinclusivegrowthasameanstoreduceincomeinequality.Countriesandinternationalorganizationsconsidercross-bordertradeasignificanteconomicgrowthchanneltoreduceincomeinequality(KrugmanandObstfeld2018;Segerstrom2013).Cross-bordertradecanincreaseforeignincome,createemployment,andpropelgrowth.Adoptingnewtechnologiesmakesthetraditionaltradeprocessfasterandmorecost-effective,creatingmoreopportunities.However,competitionandheterogeneousregulationsacrosscountriescanlimitthebenefitsofcross-bordertrade.Furthermore,continuousandrapidtechnologicaldevelopment,especiallydigitaltechnology,disruptsthetraditionaltradeprocessandforcescountriestobemoreefficientindigitaltradetosustainthemselvesinthecompetitiveworld.Despitethetheoreticalframeworkofinternationaltrade,recentstatisticsinthepast3decadesshowatrendofrisinginequalitywithinandacrosscountries.Oneplausibleexplanationforthisincreasinginequalityistheriseinglobalizationregardingtradeflows,tariffs,capitalflows,oroffshoringindevelopedanddevelopingcountries(Harrison,McLaren,andMcMillan2011).Increasedtradebetweendevelopedanddevelopingcountriesalsodrivesefficientuseofresources.Introduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxxiiiOneoftheprimaryresourcesforproductorservicetradeisenergy.Therefore,energyefficiencyinproductionandconsumptioncanbenefiteconomies.Ontheonehand,usingdigitaltechnologyinproductionandtradeactivitiesrequiresenergyefficiency,whereasontheotherhand,openness,theelementofglobalization,boostsinvestmentinrenewableenergyresearchanddevelopment,andreducesenergypoverty(Zhangetal.2022).Again,digitaltradehasawiderangeofprofoundpositivesocioeconomicimpacts.Ontheoneside,itupsurgescompetitiveness(Soyluetal.2023).IntheAsianregion,itisworthnotingthatAsianeconomiesconfrontvariousissuesandchallengesthatmayaffectdigitaltrade.OneoftheissuesinAsianeconomiesisenergypoverty,whichcouldbeinterlinkedwithdigitaltrade.ThestudybySyedandRahut(Chapter8)dealswiththeimpactofdigitaltradeonenergypovertyandunemployment.Usingapaneldatamodel,thestudyshowsthatdigitaltradeisimmunetoglobaladverseshockssuchastheCOVID-19outbreak.Therefore,itisimperativetopromoteand/orfacilitatedigitaltrade.Furthermore,thestudyshowsthatenergypovertydecreasesdigitaltrade,whereasunemploymenttriggersandpromotesdigitaltrade.Thestudysuggestsimprovingtheenergyinfrastructureanduseofrenewableenergyinpromotingdigitaltradetoreduceinequalityandfostereconomicgrowth.Digitalizationusestechnologiestotransformbusinessprocesses,products,andservices.Digitalizationcanpromoteglobalvaluechain(GVC)participationbyenablingfirmstoaccessinformation,markets,andresourcesmoreefficientlyandeffectively.Digitaltechnologiescanreducetransactioncosts,communicationcosts,coordination,andtransportationacrossborders,loweringtradebarriers.Byprovidingonlineservicesandinformation,digitalplatformscanalsohelpfirmsovercomenon-tariffbarrierssuchascustomsprocedures,standards,andregulations.Forexample,theAsia-PacificTradeFacilitationForumisaregionalinitiativethataimstoenhancetradefacilitationthroughdigitalsolutions(ReddyandSasidharan2023).Digitaltechnologiescanimprovetheefficiencyandqualityofproductionprocessesandenablefirmstocreatenewproductsandservicesthatmeetthedemandsofglobalmarkets,henceincreasingGVCparticipation.DigitalplatformscanalsofostercollaborationandcoordinationamongdifferentactorsintheGVC,suchasleadfirms,intermediaries,andendusers.Forexample,theAsianDevelopmentBankhaslaunchedtheproject“EnhancingSMEParticipationinGlobalValueChains”,whichaimstosupportsmallandmedium-sizedenterprises(SMEs)inAsiaandthePacifictoleveragedigitaltechnologiestointegrateintoGVCs(Urata2022).Therefore,itisclearthattounderstandthebenefitsofdigitalconnectivity,itisalsoimportanttounderstandGVCs.xxivDigitalTransformationforInclusiveandSustainableDevelopmentinAsiaThestudybyKorwatanasakul(Chapter9)elucidatesthelinkbetweendigitalizationandGVCparticipationatthefirmlevel,focusingonSMEsinAsiaandthePacific.Thelogitandprobitmodelstudyshowsthatdigitalizationinfirms,especiallySMEs,exhibitshigherGVCparticipation.Therefore,ithighlightstheroleofdigitaltechnologiesinfacilitatinginternationalmarketaccessandenhancingsupplychains.However,theimpactofdigitalizationonGVCparticipationdiffersbetweenSMEsandlargefirms.Inthecaseoflargefirms,theeffectsofdigitalizationonGVCparticipationdiminishduetotheirwidespreadadoptionofbasicdigitaltechnology.Therefore,toimprovetradeactivities,digitalconnectivityisessentialforallowingsmallerfirmstoparticipateinGVCsandfostereconomicgrowth.ConclusionThisbookhighlightsthemultifacetedrelationsbetweendigitalconnectivity,economicgrowth,andsustainabilityinthemoderninterconnectedworld.Therapidinnovationandadoptionofdigitaltechnologieshavetransformedvariousaspectsofsocietyandtheeconomy,offeringbothopportunitiesandchallenges.Undoubtedly,thelackofuniversalaccesstodigitaltechnologiesanddiscrepanciesinaccesstodigitalconnectivityremainsasignificantbarriertoreapingthebenefitsofdigitalization.Despitegovernmentinitiativesandsubsidies,affordabilityremainsacommonhurdle,particularlyinregionswithincomeinequality.Tobridgethisdivide,targeteddigitalinclusioneffortsthataddressnonfinancialbarriersareimperative.Inexpandingdigitalconnectivity,thereisaneedtopromotedigitalfinance,includingmobilemoney,asacrucialinitiativeinmitigatingtheimpactofweathershocksandfosteringfinancialinclusion.Mobilemoneyserviceshaveproveneffectiveinhelpingcommunitieswithstandthechallengesposedbyweather-relateddisasters,contributingtoresilienceandpovertyreduction.Awarenessandempowermentareessentialtoreapthebenefitsofdisruptivechangecausedbytheadoptionofdigitaltechnologies.Toenhanceawarenessandempowerment,educationandskillsdevelopmentarekeycomponents.Schoolsarepivotalinincreasingawarenessanddevelopingskillsbyprovidingdigitalinfrastructureandskillstraining,especiallyinunderservedareas.Schoolshelpenhancefinancialliteracyanddigitalskillstoprepareindividualsforthedigitalfuture.Increasingawareness,inturn,helpstoachievesustainabilitywithoutstressingtheenvironment.However,therelationshipbetweendigitalizationandsustainabilityiscomplex.WhiledigitaltechnologyIntroduction:NavigatingtheDigitalDivide—Connectivity,Inclusion,andProgressinAsiaandthePacificxxvhelpsreduceresourceconsumptionandpromotesustainability,italsocontributestoenergyconsumptionandelectronicwaste.AcomprehensivepolicyframeworkthatalignscirculareconomyprincipleswithIndustry4.0isnecessarytoenhanceresourceuseefficiencyandenvironmentalsustainability.Increaseddigitalconnectivityopenswindowsofopportunitiesfortradeandglobalization.Digitaltradeandglobalizationpresentbothopportunitiesandchallenges.Digitaltechnologiescanmakecross-bordertrademoreefficientandinclusive,reducingincomeinequality.However,competitionandheterogeneousregulationsacrosscountriescanlimitthebenefitsofglobalization.Tofostertrade,countriesrequireefficientuseofresources.Energyefficiencyandrenewableenergyadoptionarecrucialforsustainableeconomicgrowth,anddigitaltradecanplayasignificantroleinthiscontext.Therefore,countriesmustdevelopenergyinfrastructureandpromoterenewableenergytosupplyenergysources.Acomplexandmultifacetedinterplayexistsbetweendigitalconnectivity,economicgrowth,sustainability,andinclusiveness.Toharnessthefullpotentialofdigitalizationandensureequitablebenefits,basicinfrastructurefordigitaltechnologies,energyresources,andeducationisrequired.Therefore,aholisticapproachthatconsiderseducation,skillsdevelopment,policyframeworks,andinternationalcollaborationisessential.xxviDigitalTransformati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portunities.Povertyisafactorthatlimitsaccess.ManyintheAsianDevelopmentBank’s(ADB)developingmembercountriesfinditdifficulttoaffordaccesstolevelsofmobiledatathatenableMeaningfulConnectivity.WithmostwebtrafficinAsiaoriginatingfrommobiledevices(Williams2021),affordabilityofmobiledataislikelyasignificantcontributortothisconnectivitygap.Thedefactostandardfordeterminingaffordableconnectivityisameasurementofthecostofanallocationofmobiledatarelativetoapercentageofgrossnationalincome(GNI)percapita(BroadbandCommissionforSustainableDevelopment2018).Thebestknowntargetsare“1for2”(A4AI2018)and“5for2”(A4AI2021).TheseweredevelopedbytheBroadbandCommissionforSustainableDevelopmentledbytheInternationalTelecommunicationUnion(ITU)andtheAllianceforAffordableInternet(A4AI).Theydefineaffordablebroadbandasa1or5gigabyte(GB)allocationofdataavailablefor2%ofGNIpercapita.Assumingacountryhasaffordableconnectivitybecauseitmeetsasimpleaffordabilitytargetisproblematicforafewreasons.These12DigitalTransformationforInclusiveandSustainableDevelopmentinAsiameasurementscantelluswhethermobiledataisgenerallyaffordableinacountry,butnotifitisnotaffordableforpeopleatallincomelevels.Theydonotaccountforpricingthatvariesbylocationacrossacountry.TheA4AI’s5GBthresholdsettobemetby2026isjustone-thirdofcurrentglobalaveragemobiledataconsumption(Ericsson2023),andhasnotbeenadjustedtotakeintoaccountmoderncommunicationsdemands.Thesetargetsalsoconsiderameasurementofgeneralinternetaccess,whenprovidersincreasinglyofferdifferentialpricingforapplicationsandserviceswithhightrafficrequirements.ThischapterbuildsontheresearchdoneforADB’sworkingpaper“LastMileConnectivity:AddressingtheAffordabilityFrontier”(Brewer,Jeong,andHusar2022).Itaimstoprovideacomprehensiveviewofaffordabilityacrossincomedecilesbyevaluatingthecostofbroadbandagainstanincome-adjustedGNIpercapitafigurebasedontheWorldBank’sPovertyandInequalityPlatform(PIP)data.UsingincomedecilesallowsustoestimatethepercentageofapopulationthatcanaffordthedatarequiredforMeaningfulConnectivitymoreaccuratelythanexistingmethods.Itaddressestheissueofhigherdatarequirementsbycalculatingtheaffordabilityofthreeutilizationthresholds:5GBpermonth,15GBpermonth,and40GBpermonth.Thesethresholdstakeintoaccount2026targets,existingglobalaverageuse,andlevelsofusethatwillbenormal5yearsfromnow.Thischapteralsoidentifiesandexploressituationswhereaffordableproductshavelimitedgeographiccoverage.Finally,itexaminesanumberofwayssomecarriersmakemobiledatamoreaffordableforspecificapplicationsandsegmentsoftheirpopulations.1.2BackgroundFormuchoftheworld’spopulation,theinternetisaninextricablepartofdailylife.Itsreachextendsintovirtuallyeveryaspectofhumanactivity,facilitatingeverythingfromcommunicationtoentertainment,andfromworktostudy.GlobalconsumerdemandforbroadbandgrewduringtheCOVID-19pandemicevenasitbecamelessaffordable(ITU2022).Thismeanspeoplechosetomaintaininternetaccessoverothergoodsandservices.Thispervasivetechnologyismostoftenaccessedviasmartphonesindevelopingeconomies.Asignificantnumberofpeopleareunconsciousconsumersofinternetaccess;theyknowonlytheapplicationsontheirsmartphones,butnotthattheappsaredependentontheinternet(Silveretal.2019).Thepervasivenessoftheinternethasfar-reachingeffects,assuggestedbyanextensivebodyofacademicliterature.Onenotableareaofimpactiseducation.A2020literaturereviewoninternetaccessandeducationunderscoredasignificantcorrelation:InequalityandAccesstoMobileData3studentswithhome-basedinternetaccessexhibitedimprovededucationalachievementsandskills(Daoudetal.2020).Thisfindinghighlightsthecriticalroleoftheinternetinfosteringknowledgeacquisitionandeducationalattainment.Beyonditseducationaleffects,theinternetandmobiletechnologieshavethepotentialtoalleviatesocietaldisparities.Theycansignificantlycontributetoreducingincomeinequality,aphenomenonobservedinbothlowandmiddle-incomeeconomiesandhigh-incomeones(Canhetal.2022).Socialmediause,primarilyonmobiledevices,hasproventohaveapositiveeffectontheperformanceofsmalltomediumenterprisesindevelopingeconomies(Qalatietal.2021).Theseplatformsprovideapotenttoolforbusinessgrowth,enablingenterprisestoreachbroaderaudiences,betterinteractwiththeircustomers,andexpandtheirmarkets.Theinternet’simpactextendsintotherealmofhealthandwell-being,especiallyforolderadults(Tavares2020).Researchsuggestsapositiveassociationbetweeninternetuseandtheoverallhealthandwellnessofthisdemographic.Byprovidingaccesstohealthinformation,socialconnections,andmentalstimulation,theinternethasbecomeavaluableresourceforpromotingthewell-beingofolderadults.Theculturalimplicationsoftheinternetarealsosignificant.Smartphoneusersaremorelikelytointeractwithindividualsfromdiversebackgrounds,enhancingtheglobalinterconnectednessandmutualunderstanding(SilverandHuang2019).Theyalsotendtostaymoreconnectedwithfriendsandaremorelikelytoaccessnewinformationabouthealthandgovernmentservices,therebypromotingsocialcohesionandpublicengagement.MeaningfulConnectivitytotheinternetenablesallthesepositivebenefits.Weintroducethisframeworkforevaluatinginternetconnectivityinthenextsectionofthechapter.Laterweexplorelevelsofmobiledatause,itsgrowth,andhowCOVID-19increaseddatause.Finally,weintroducetheconceptofincomeinequalityandhowitaffectsaffordability.1.2.1MeaningfulConnectivityAstheworldhasmovedonfromthetelephoneasitsprimarymeansofcommunication,existingframeworksformeasuringaccesstoconnectivityhavefailedtokeeppace.Universalservicepromotedtheconceptofallhomeshavingatelephoneandtheabilitytomakeacall.Universalaccessrecognizedthatindevelopingcountriesthiswasnotalwaysachievable,andpromotedtheideathateveryoneinapopulationshouldbeabletoaccesstelecommunicationsservices—whetherin-homeorviapubliclocations.4DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaMeaningfulConnectivityisaconceptdevelopedinparallelbytheA4AIandtheBroadbandCommissionforSustainableDevelopment(BroadbandCommission)ledbytheITUandUNESCO.Eachreleasedsimilarframeworksin2019.A4AI’sframework,nowpromotedbytheGlobalDigitalInclusionPartnership(GDIP),hasclearandaggressivegoals(Table1.1).Table1.1:GDIP’sMeaningfulConnectivityFrameworkAFastConnection4G-likespeedAnAppropriateDeviceSmartphoneownershipEnoughDataAnunlimitedbroadbandconnectionRegularAccessDailyuseGDIP=GlobalDigitalInclusionPartnership.Source:GlobalDigitalInclusionPartnership(2023).https://globaldigitalinclusion.org/our-work/meaningful-connectivity/(accessed6March2023).Minimumspeeds,personaldeviceownership,anddailyaccessareallreasonablegoalstomeetonaglobalbasis.MeaningfulConnectivity’scallforanunlimitedconnectionismoredifficulttoachievegiventhelimiteddatacapacityofmobilenetworks.AnotherofA4AI’sadvocacyprograms,alsodevelopedinparallelbytheBroadbandCommissionforSustainableDevelopmentledbytheITU,istheideathataffordabledatashouldbeavailableforlessthan2%ofGNIpercapita.Howmuchdataisconsideredenoughhasevolvedovertime.A“1for2”goaltargeting1GBpermonthofdatawassetadecadeagotobemetby2016.A“5for2”goaltargeting5GBoftrafficpermonthisacurrentgoaltobemetby2026(A4AI)or2030(BroadbandCommission2018).1.2.2TrendsinMobileBroadbandPricingTheITUhasconductedanannualinformationandcommunicationstechnology(ICT)pricebasketsurveysince2012(ITU2012).Oneofthedatapointscollectedisthecostoflowamountsofmobilecellulardata.Whilethequantityofdatausedinthesurveyhasneverpreciselyalignedwith“1for2”targets,somestudieshavederived“1for2”scoresfromthecollectedpricing(UNESCAP2021).OvertimetheITUhaschangedthelevelofdataassociatedwiththeirlow-usagebasket(previouslycalledData-onlymobile-broadbandInequalityandAccesstoMobileData5basket)from0.5GBbetween2012and2017,to1.5GBbetween2018–2020,andto2GBin2021(ITU2023).Figure1.1showspricingoverthepast10yearsinseveralmarkets.Figure1.1:Data-onlyMobileBroadbandBasketfromITUandThisStudyPriceinUSDollarsIndonesiaSurveyYearMongoliaPhilippinesKyrgyzRepublicSriLankaITU=InternationalTelecommunicationUnion.Sources:ITUICTPriceBaskets,historicaldataseries,April2023release,author.TheITU’schoiceinbasketsizeovertimehasbeenwidelysupportedbyacademicliterature.TheWorldDataInstitute(WDI),viaa2021Brookingsblogposttitled“Measuringinternetpoverty”(CrespoCuaresmaetal.2021)positedthat1.5GBofdatapermonthwasenoughtoparticipateintheinternetrevolution.Theysaidthislevelofdatawasenoughtosatisfythebasicneedsofinternetaccesstocheckemail,doshopping,andbrowsewebpagesforupto40minutesaday.TheWDIconsideredpeoplewithaccesstolessthan1.5GBofdatapermonthtobeininternetpoverty.Figure1.1showsusthatoverthepast7yearspricinghasremainedrelativelystatic,whiledataallocationsmeasuredhavetripled.BoththeWDI’sanalysisandtheITU’schoiceofbasketsizeareproblematic;6DigitalTransformationforInclusiveandSustainableDevelopmentinAsiawhiledataallocationshaverisen,theyhavenotriseninlinewithactualconsumption.1.2.3COVID-19andMobileDataConsumptionTheCOVID-19pandemicinstigatedasurgeinmobiledatauseduetotheonsetofworldwidemovementrestrictions.Theselockdownsdrasticallychangedthedynamicsofeducationandwork,withmanyindividualscompelledtoadapttovirtualmodesofengagement.Studentsnewtoe-learningfoundthatparticipatinginonlineclassescouldrequireupto1GBofdataperday(WorldBank2021b).Theshifttoremoteworksawteleworkersusingbetween0.15and0.6GBofmobiledataperhourwhileperformingtheirdutiesfromhome(Bai2020).VideoconferencingapplicationslikeGoogleMeet,WebEx,andZoombecameasubstituteformanyformerlyin-personinteractions.Theseplatformscanconsumebetween0.38and1.1GBoftrafficperhour,ademandthatescalatedasthenumberofparticipantsineachmeetingrose(Changetal.2021).Thepandemicunderscoredtheessentialroleofmobiledatainenablingconnectivityinasocially-distancedworld.1.2.4DataTargetsforTodayandtheFutureIna2021paper,theWorldBankGroup’sdigitaldevelopmentteamestimatedthatfoundationalinternetaccessindevelopingeconomiesrequiredaround0.66GBpermonth(WorldBank2021a).Thiscoveredactivitieslikeusingwebsitesforpublicservices,healthinformation,shopping,learning,andnews.Theyfoundthataveragerecreationaluseforsocialmediaandonlineentertainmentaddedanother5.2GBpermonthtothatbaseline,foratotalrequirementofaround6GBpermonth.Thisfigure,afullfourtimesgreaterthantheWDI’smeasurementofinternetpoverty,stillfallsfarbelowthethresholdofaveragemobiledatausetoday.Ericssonfoundin2022thataveragemobiledatawas15GBperdevice,andnearly25GBperdeviceinIndiaasshowninFigure1.2(Ericsson2023).Theirdatashowsasteadyincreaseindataconsumptionperuserovertime,andtheypredicttherateofgrowthwillremainsteadyforthenext5years.Overthelongterm,trafficontheinternetincreasesatarateofaround30%perannum.Thistrendhasbeenobservedandpredictedformorethanadecade,withCisco’sVisualNetworkingIndexrecognizingthephenomenonearlyon(Cisco2012).Mobiledatatrafficconsumptionfollowsasimilartrend.Ericsson’sMobilityVisualizerpredictsglobalaveragemobilegrowingfromjustunder2GBperdevicein2016toaround46GBperdeviceby2028.InequalityandAccesstoMobileData7Figure1.2:MobileDataTrafficperDevicefromEricssonMobilityVisualizerGigabytesoftracpermonthYearNortheastAsiaIndia,Nepal,BhutanWorldTotalSoutheastAsiaandOceaniaSources:Ericsson(2023).A2022ADBpaperpositsthataveragemobilebroadbandshouldbeaffordableforall,meaninganever-increasingbucketofdatashouldbeavailablefor2%ofGNIpercapita(Brewer,Jeong,andHusar2022).Thispositionisthebasisforourinclusionof15GBand40GBtargetsinthischapter’sanalysis.Lastly,thereareexploratoryinitiativesbytheUnitedNationsOfficeofSecretaryGeneral’sEnvoytoTechnologytosetanewglobaltargetarounduniversalandmeaningfulconnectivitybyaddingaffordabilityindicatorsforthebottom40%ofincomeearnersamongothers.Thischapterwillcontributetowardjustifyingtheneedformoregranularassessmentofmobiledataprice.1.2.5InequalityandMobileAffordabilityIncomeinequalityisaworldwidephenomenonespeciallyprevalentindevelopingeconomies(Moffatt2019).Incomedistributionsareoftenpositivelyskewed,withmeanincomesgreaterthanthemedian.Thisleavesmostmembersofapopulationwithnormalorlowincomes,nothighincomes(SulistyaningrumandTjahjadi2022).8DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaMeasuringthecostofanamountofdataagainst2%ofGNIpercapitacantelluswhetherornotmobiledataisgenerallyaffordableinacountry,butnotifitisaffordableforpeopleatallincomelevels.Inparticularitcannottelluswhatpercentageofapopulationwouldfindbroadbandaffordableiftheywereinabroadbandservicearea.Measuringthecostofdatausingrealisticincomedistributionscanhelpsolvethisproblem.AdjustingGNIpercapitaforexaminingbroadbandaffordabilityisanestablishedtechnique.ThemostrecentexampleisapaperbytheWorldBankwhichcomparedthecostofbroadbandasapercentageofaverageGNIpercapitatothatofthebottom40%forasetofsixcountries(WorldBank2021a).Theyfoundasignificantdisparityinaffordabilityinallmarketsbetweenaveragemembersofapopulationandmembersbelongingtothelowestfourdecilesofincomeearners.1.2.6TheWorldBank’sPovertyandInequalityPlatformTheWorldBankisthesourceoftheincomedistributionsusedinthischapter,viatheirPovertyandInequalityPlatform(PIP).Atabasiclevel,thePIPincomedistributionsarederivedfromhouseholdsurveysonbudgetsorspending,generallyconductedinconjunctionwithnationalstatisticsofficesineachcountryconsidered.Thoughexactmethodsvaryfromcountrytocountry,statisticsareoftenbasedonthemonetaryvalueofhouseholdconsumption.ThePIPstatisticsareonlygeneratedwhenrecentsurveydataareavailableandmanydevelopingeconomieslackrecentsurveys.ThisproblemisparticularlyacuteamongADBdevelopingmembercountriesinthePacificislands,Uzbekistan,Azerbaijan,Nepal,andIndia.1.3TargetCountriesThisanalysisdependsontheavailabilityoftransparentmobiledatapricingfrommultipleprovidersandontheavailabilityofrecentpovertyandinequalitydata.Fordevelopmentrelevance,lowermiddleincomedevelopingmembercountrieswereconsidered.Indonesia,theKyrgyzRepublic,Mongolia,thePhilippines,andSriLankametthesecriteriaandwerechosenasexamplesforanalysis.Theyrepresentarangeofcountrysize,populationdensity,levelofurbanization,andlevelofinternetuse,asdescribedinTable1.2.InequalityandAccesstoMobileData9Table1.2:SummaryStatisticsofCountriesintheStudyCountryPopulationPopDensityUrbanizationGNIperGiniInternet(2021)(persquare57%Capita,CoefficientUserskilometer)37%Atlas(2021)Indonesia273,753,19137.9%(2021)145$4,18062%Kyrgyz6,691,8003429.078%Republic$1,180284%Mongolia3,347,78237669%$3,73032.753%35467%Philippines113,880,32848%$3,55040.7SriLanka22,156,00019%$4,03037.7Source:WorldBank(2023).1.4MethodologyUsingthelatestTelegeographyGlobalCommsdatabase,18carrierswereidentifiedinthefivetargetcountries.Detailsof111differentplanswerefoundoncarrierwebsites.Dataforeachplanwererecordedinatableincludingtheplan’sname,whetheritispreorpost-paid,thetermoftheplan,itscostinlocalcurrency,andtheamountofdatatrafficsupplied.Planpricingwasalsosavedintheinternetarchiveatsurveytime.Planswereselectedforofferinggeneralinternetaccessatlowprices;hundredsofspecialpurposeplansbundlingentertainmentorsocialmediaforanextrafeewereexcluded.Incaseswherethelowestcostplansalsohaddatabonusesavailabletoparticularapplications,orvalidonlyinparticulargeographicareas,databonuseswerenotrecorded.Planswithvaliditiesof1dayto1monthwererecorded,whileplanswithlongertermswerenot.Thelowestcostplanfromeachcarrierthatincludedatleast40GBoftrafficpermonthwasrecorded,whileplanswithevengreatermonthlytrafficallocationsathighercostswerenot.CostsforallplanswerenormalizedtoUSdollarsusingaveragemarketconversionratesfortheweekbeginning8May2023.1Varyingplanlengthswerenormalizedtoexactly1month(365/12)bymultiplyingtheirdurationandcost.Whereusingasetofsmallertime-limitedtop-upscouldresultintheonlyavailableproductfor5,15,or40GBpermonth,thetimevalidityofplanswasadjusteddowntoemulatefasterdataconsumptionbyaconsumer.1Rp1=$0.000068,Som1=$0.011425701,MNT1=$0.00028606559,₱1=$0.018,SLR1=$0.0031.10DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaPopulationdataandGNIforeachcountrywereretrievedfromtheWorldBank’sAPIusingthewbstatsRmodule(Piburn2020).Atlasmethod,acalculationofthesizeoftheeconomybasedonGNIconvertedtoUSdollars,smoothedforcurrencyfluctuationsusinga3-yearmovingaverage,waschosenfortheGNImeasurement(WorldBank2014).IncomedeciledatawereretrievedfromtheWorldBank’sPIPusingthepiprRmodule(Fujsetal.2022).Themostrecentyearofincomedecilesforeachcountrywasused.2Adecile-adjustedmonthlyGNIpercapitawascalculatedforeachcountrybydividingtheGNIbythepopulation,thendividingby12,thenbymultiplyingtheresultbyeachoftheincomedeciles.Mobileaffordabilitywasfoundbycalculatingthepercentageofthedecile-adjustedmonthlyGNIpercapitarequiredtopurchasemobiledataplansat5GB,15GB,and40GBpermonth.Inthechartsgeneratedforeachcountryintheresultssection,thecoloredbarsrepresentthemedianplancost,anderrorbarsshowtheminimumandmaximumcostsfoundforrelevantplans.1.5Results1.5.1IndonesiaIndonesia’spopulationof274millionisspreadacrossaround6,000islandsspanningroughly5,000kilometersfromeasttowest.Around57%ofitspopulationlivedinurbanareasin2021,afigureincreasingfasteroverthepast40yearsthananyoftheothercountriesinthissurvey(WorldBank2023).PopulationdensityacrosswideareasofIndonesia,especiallyinBorneoandWestPapua,isfewerthan10peoplepersquarekilometer.Indonesia’sfourmajormobileoperatorsreport4Gnetworkcoverageofbetween80%and98%ofthepopulation(Telegeography).Operatorsreporttheyhave294million4Gsubscribers,whichisgreaterthan100%penetration.TheITU(2021)estimatesthat67%ofthepopulationhasinternetaccess(EconomistImpact2022).Where4Gcoverageisavailable,5GBpermonthofmobiledataisalmostuniversallyaffordable.2Indonesia=2022,KyrgyzRepublic=2020,Mongolia=2018,Philippines=2021,andSriLanka=2019.InequalityandAccesstoMobileData11Figure1.3:MobileAffordabilityinIndonesiaPercentageofGNIpercapitaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileDataLevelGBGBGBGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023),WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https://im3.id/(accessed27April2023),https://www.smartfren.com/(accessed16May2023),https://www.telkomsel.com/(accessed12March2023),https://www.xl.co.id/(accessed16May2023).Themediancostfor5GBofdatainamonthasshowninFigure1.3islessthan2%ofGNIpercapitaacrossalmostallsegmentsofthepopulation.Thelowestcost5GBpermonthpackageisofferedbyim3,theproviderclaimingthelargest4Gcoverage.Atthe15GBpermonthlevel,medianbroadbandpricesareaffordablefor50%ofthepopulation.Onlythewealthiest20%ofIndonesianswouldfind40GBpermonthpricedatthemedianaffordable,but60%ofthepopulationmightbeabletofindanaffordableservicebasedonthelowestavailableprices.Indonesia’scarriersareuniqueamongstthoseinthisstudyinofferingextraurbanusebroadbandquotas.These“area”,“local”,or“zone”offeringsaredataallocationsthatcanbeconsumedincertaincitiesaroundthecountryasabonustomanyprepaiddatapackages.im3,Smartfren,TelkomselandXLallofferlocalizedbonusquotaswiththeirsizesometimesvaryingfromlocationtolocation.ThesizeandpriceofbonusofferingsmeansthatforsomeofIndonesia’surbanpoor,15GBoreven40GBpermonthplanscanbeuniversallyaffordable.12DigitalTransformationforInclusiveandSustainableDevelopmentinAsia1.5.2KyrgyzRepublicAround6.6millionpeopleliveintheKyrgyzRepublic,withlargeconcentrationsaroundBishkekandOsh.Overallurbanizationin2021was37%,anincreaseofonly4%overthepast60years(Telegeography).Alargelymountainouscountry,manyregionshavefewerthan10peoplepersquarekilometer.The4GcoverageofKyrgyzRepublic’sthreemobileoperatorsisbetween97%–99%ofthepopulation(EconomistImpact2022).Theyreport5,170,0004Gmobilesubscriptions.TheITU(2021)estimatesthat78%ofpeopleintheKyrgyzRepublicusetheinternet.Figure1.4:MobileAffordabilityinKyrgyzRepublicPercentageofGNIpercapitaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileDataLevelGBGBGBGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023),WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https://beeline.kg/(accessed),https://mega24.kg/(accessed12May2023),https://o.kg/(accessed7March2023).MobiledataintheKyrgyzRepublichasuntilrecentlybeenaffordableatlowallocations—forexample1or2GBpermonthquotas,asshowninFigure1.1.In2023carrierschangedtheirplans,eliminatinglowendplansandincreasingdataallocationsforallotherofferings.Figure1.4showsthatbasedonavailablepricingtoday,no5GBplansareavailableatall.15GBofdataisaffordableforthoseinthetoptwodeciles,andclosetoaffordableforthoseinthe7thand8thdeciles.InequalityandAccesstoMobileData13Onlyoneoperatornowsellsalow-useplan;itincludes100MBofdataperday.AtSOM225per28days,Beeline’sZhanyBirge3.1GBpermonthplanexceeds2%ofGNIpercapita,meaningtherearenoaffordableentry-levelmobiledataplansavailableintheKyrgyzRepublic.1.5.3MongoliaMorethanhalfofMongolia’srapidlyurbanizingpopulationofaround3.2millioniscenteredaroundthecapital,Ulaanbaatar.Inall,69%ofitspopulationisurban,thehighestrateofurbanizationinthisstudy(Telegeography).Outsideahandfuloftownsseparatedbylongdistances,mostofMongolia’slandhasfewerthanonepersonper10squarekilometers.AnextensiveterrestrialfiberbackboneconnectsallofMongolia’surbanareas(ITU2023).Mongolia’smobileoperatorsclaimbetween40%and85%populationcoverofthecountry(EconomistImpact2022).Theyreport2,985,0004Gsubscribers.TheITU(2021)estimatesthat84%ofMongoliansusetheinternet.Figure1.5:MobileAffordabilityinMongoliaPercentageofGNIpercapitaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileDataLevelGBGBGBGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023),WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https:///gmobile.mn/(accessed1December2022),https://www.mobicom.mn/(accessed1May2023),https://www.skytel.mn/(accessed6February2023),https://www.unitel.mn/(accessed5December2022).14DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaWherethelowestcostplansareavailable,Figure1.5showsthatdatainMongoliaareaffordableforroughly90%ofthepopulation.Consideringaveragemobiledataprices,5GBofdatapermonthisonlyaffordabletothoseinDecile4orabove.FormostofMongolia’spopulationlivinginremoteareas,mobiledataishighlyunaffordable.GMobileisMongolia’sonlyproviderwithextensiveremoteareacoveragewiththeirCDMA450network.IneverycaseinFigure1.5thehighestcostplansonoffer,shownbythetopsoftheerrorbars,arefromGMobile.Dataforremoteuserscanalsobeveryslow;CDMA450doesnotoffer4G-likespeeds.1.5.4ThePhilippinesMuchlikeIndonesia,thePhilippinesisanarchipelagowithmorethan7,000islands.Itspopulationof114millionliveonaround2,000ofthem.WhileurbanizationinthePhilippinesislowerthaninIndonesia,itsoverallpopulationdensityisaroundtwoandahalftimesgreater(Telegeography).Itsmaincitiesarewellconnectedwithfiberopticnetworks(EconomistImpact2022)andhaveextensivemobilecoverage,butruralandremoteareasoftenhavefewornocommunicationsoptions.Figure1.6:MobileAffordabilityinthePhilippinesPercentageofGNIpercapitaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileDataLevelGBGBGBGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023),WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https://dito.ph/(accessed3May2023),https://www.globe.com.ph/(accessed6March2023),https://smart.com.ph/(accessed8April2023).InequalityandAccesstoMobileData15ThePhilippines,threemobileoperatorsclaimbetween78%–96%populationcoverageofthecountrywiththeirlong-termevolutionnetworks(EconomistImpact2022).Theyreport114,097,0004Gand5Gmobilesubscribers,forgreaterthan100%penetration.TheITU(2021)estimatesthat53%ofpeopleinthePhilippinesusetheinternet.AsshowninFigure1.1,until2016thePhilippineshadthemostexpensivemobiledataofferingsofthecountriesinthisstudy.DualthreatsofregulationbyformerpresidentRodrigoDuterte(CNNPhilippines2016)andpotentialcompetitionfromnewoperators(Ramli2015)mayhavehelpedreduceratesbymorethantwo-thirdsbetween2015and2016.DataratestodayfromdominantoperatorsGlobeandPLDTarestillexceptionallyhighcomparedtootheroperatorsinthissurvey,andFigure1.6showsthemedianpricefordataisstillunaffordableforallbutthewealthiestthreedeciles.NewentrantDitoTelecommunityhasthesmallestcoverageareaofPhilippineoperatorsat78%ofpopulation,buthasthelowestdatapricesbyfar.IneverycaseinFigure1.6thelowestendoftheerrorbarsisaDitomobiledataplan.Whereitisavailable,their8GBLevelUpplanat₱99permonthisuniversallyaffordable.WhilecompetitorGlobeoffersasimilar8GBplanavailablefor₱99,theGlobeplan’svalidityisonly7dayswhileDito’splanisvalidfor30days.ShortvalidityofdataplansinthePhilippinesisalargepartofthereasonMeaningfulConnectivityremainselusiveformostofthepopulation.BothGlobeandSmartoffer5GBprepaiddataplansfor₱50astheirleastexpensiveofferings.Thispricewouldbeuniversallyaffordableifthedatapurchasedwasavailablefor1monthandnot3or5days.Figure1.6showsthat15GBpermonthplansaremoreaffordablethan5GBplans.Whilemoreaffordabletheymaybelessconsumed.15GBpermonthplansoftenrequirea1-monthadvancepurchasethatdoesnotfitwellwithacultureaccustomedtopurchasingsmallquantitiesofmostgoodsasneeded(Soriano2019).1.5.6SriLankaSriLanka’spopulationin2021wasaround22millionpeople.Withanoverallurbanizationrateof19%,SriLankahasthemostruralpopulationofthecountriesinthisstudy(Telegeography),anditsmajorcitieshavethesmallestpopulations.Urbanizationhasnotchangedsignificantlyin60years(Telegeography);SriLankaislikelytoremainaruralcountryinthefuture.Despiteitshighlydispersedpopulation,densityishighat354peoplepersquarekilometer,onparwiththePhilippines.16DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaSriLanka’sfourlargestmobilenetworkoperatorsclaimbetween70%–95%populationcoveragewiththeir4Gnetworks(EconomistImpact2022).2Gand3Ghoweverremaindominanttechnologies.Withonly12,660,000claimed4Gsubscriptions,SriLankahasthelowestlevelof4Gsubscriptionsinthisstudy.TheITU(2021)estimatesthatasof202167%ofSriLankansusetheinternet.Figure1.7:MobileAffordabilityinSriLankaPercentageofGNIpercapitaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileDataLevelGBGBGBGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023);WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https://www.airtel.lk/(accessed24January2023),https://www.dialog.lk/(accessed12April2023),https://hutch.lk/(accessed19May2023),https://www.mobitel.lk/(accessed9May2023).AsshowninFigure1.7,mobiledataaccessinSriLankaisuniversallyaffordable.Whereavailable,some15GBpermonthplansalsofallbelow2%ofGNIpercapitathresholdforallincomedeciles.AswithDitointhePhilippines,SriLanka’slowestpricedataisnotavailableeverywhere.ItsbestpriceplansareofferedbyAirtel,whocoveronly70%ofthepopulationwiththeir4Gnetwork.InequalityandAccesstoMobileData17PercentofGNIpercapitaadjustedbydecile1.6DiscussionLookingattheaggregateofcountrydatageneratedbythestudyinFigure1.8,itisclearthatoutsideIndonesiamostdataplansinmostcountriesareunaffordableforpeopleinthelowerthree-orfour-incomedeciles.Observationsmadeonindividualcarriercoverage,pricing,andspecialoffersleaveanumberofareastobediscussedbeyondtherawstatistics.Figure1.8:MobileAffordabilitybyDecile,DataLevel,andCountryGBGBGBIndonesiaKyrgyzRepublicMongoliaPhilippinessSriLankaDecileDecileDecileDecileDecileDecileDecileDecileDecileDecileGB=gigabyte,GNI=grossnationalincome.Sources:WorldBank(2023),WorldBankPovertyandInequalityPlatform(version20230328_2017_01_02_PROD),pip.worldbank.org(accessed23July2023),https://im3.id/(accessed27April2023),https://www.smartfren.com/(accessed16May2023),https://www.telkomsel.com/(accessed13March2023),https://www.xl.co.id/(accessed16May2023),https://beeline.kg/(accessed7March2023),https://mega24.kg/(accessed12May2023),https://o.kg/(accessed7March2023),https:///gmobile.mn/(accessed1December2022),https://www.mobicom.mn/(accessed1May2023),https://www.skytel.mn/(accessed6February2023),https://www.unitel.mn/(accessed5December2022),https://dito.ph/(accessed3May2023),https://www.globe.com.ph/(accessed6March2023),https://smart.com.ph/(accessed8April2023),https://www.airtel.lk/(accessed24January2023),https://www.dialog.lk/(accessed12April2023),https://hutch.lk/(accessed19May2023),https://www.mobitel.lk/(accessed9May2023).18DigitalTransformationforInclusiveandSustainableDevelopmentinAsia1.6.1MobileDataisaCommodityGiventhewiderangeofpopulation,urbanization,geography,andGNIpercapitaacrossthefivecountriesofthestudy,onemightexpecttofindasignificantvariationinthecostofdatafromonemarkettoanother.AnalysisofdataplansshowninFigure1.9doesnotprovethis.Everycountryinthestudyhadsomeplanswithdataavailableforlessthan$0.25pergigabytepermonth.Consideringthispricing,itisnotsurprisingthattheKyrgyzRepublichasaffordabilityissuessinceGNIpercapitathereisone-thirdoftheothercountriesinthissurvey(WorldBank2023).Figure1.9:MonthlyCostperGigabyteforAllProvidersandPlans.CostperGBpermonth...imBeelineegacomOhobicomSkytelUniteglMobileDITOGlobeSmartAirtelDialogHutcMhobitelMMrtfrenomselxiataSmaTelkXLAIndonesiaKyrgyzRepublicMongoliaPhilippinesSriLankaGB=gigabyte.Sources:https://im3.id/(accessed27April2023),https://www.smartfren.com/(accessed16May2023),https://www.telkomsel.com/(accessed13March2023),https://www.xl.co.id/(accessed16May2023),https://beeline.kg/(accessed7March2023),https://mega24.kg/(accessed12May2023),https://o.kg/(accessed7March2023),https:///gmobile.mn/(accessed1December2022),https://www.mobicom.mn/(accessed1May2023),https://www.skytel.mn/(accessed6February2023),https://www.unitel.mn/(accessed5December2022),https://dito.ph/(accessed3May2023),https://www.globe.com.ph/(accessed6March2023),https://smart.com.ph/(accessed8April2023),https://www.airtel.lk/(accessed24January2023),https://www.dialog.lk/(accessed12April2023),https://hutch.lk/(accessed19May2023),https://www.mobitel.lk/(accessed9May2023).InequalityandAccesstoMobileData19MonthlyCostinUSDollarsOutsideMongolia,highpergigabytepricesdependmoreonoperatorthantheydooncountry.Withincountries,Figure1.10showsthatwhetherornotaplanwasprepaidorpostpaidhasagreaterimpactonitscostthananyotherfactor.Mostpostpaidplansaremoreexpensivethanprepaid.Figure1.10:MobilePricingbyCountryandPlanTypeUSDperGB...IndonesiaKyrgyzRepublicMongoliaPhilippinesSriLankaPostpaidPrepaidGBperMonthGB=gigabyte.Sources:https://im3.id/(accessed27April2023),https://www.smartfren.com/(accessed16May2023),https://www.telkomsel.com/(accessed13March2023),https://www.xl.co.id/(accessed16May2023),https://beeline.kg/(accessed7March2023),https://mega24.kg/(accessed12May2023)https://o.kg/(accessed7March2023),https:///gmobile.mn/(accessed1December2022),https://www.mobicom.mn/(accessed1May2023),https://www.skytel.mn/(accessed6February2023),https://www.unitel.mn/(accessed5December2022),https://dito.ph/(accessed3May2023),https://www.globe.com.ph/(accessed6March2023),https://smart.com.ph/(accessed8April2023),https://www.airtel.lk/(accessed24January2023),https://www.dialog.lk/(accessed12April2023),https://hutch.lk/(accessed19May2023),https://www.mobitel.lk/(accessed9May2023).1.6.2GeographicAvailabilityandAffordabilityIndonesia,Mongolia,thePhilippines,andSriLankaallhavesituationswhereruraluserslackaccessorchoicewhenitcomestothelowestcostplans.Notallcarriershaveextensivecoverage,andsomecarrierswithbettercoveragechargeapremiumfortheirservices.InMongoliaSkytelhasthelowestcostbroadband,butitslong-termevolutioncoverageislimitedtomaincities.Indonesia’sSmartfrenhaslowercostdatathanTelkomsellorXL,buthasminimal4Gnetworkcoverageoutside20DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaJava.InthePhilippinesthelowestavailablepricesarefromDITOTelecommunity,whichhasfarlesscoveragethanincumbentsGlobeandSmart.InSriLanka,thebestpricingisfromAirtel,whocoveronly60%–70%ofthepopulationwiththeir4Gnetwork.Wheremobilebroadbandserviceisavailabletoruralusers,lowerincomesmeanitislessaffordable.Inanalyzingtheissue,theGSMAfindsthat“ruralandremoteareasacrossdevelopingworldmarketsaretypicallyinhabitedbythepoorestsegmentofthepopulationlivingsignificantlybelowthecountry’saverageGDPpercapita”(GSMAIntelligence2016).Quantifyingtheimpactofruralpovertyoninternetaffordabilityispossibleinsomemarkets;intheAsiaandPacificregion,thePIPprovidesseparateruralandurbanincomedistributionsforthePeople’sRepublicofChina,India,andIndonesia(WorldBankGroup2020).Amoresophisticatedmodelmightanalyzetheaffordabilityofproductswiththebestgeographiccoveragethroughthelensofruralincomedistributions.1.6.3TermsandValidityThecomplexityofmobileplansissignificantacrossallfivemarkets.CarriersinIndonesia,Mongolia,andthePhilippinesalloffersomeplanswithlessthan7daysofvalidity.IntheKyrgyzRepublicandMongolia,plansarecommonlyvalidfor28days.Inothermarketsthelongestdurationforprepaidplanstendstobe30days.Fewerthanone-quarterofplanssurveyedhaveadurationtiedtoacalendarmonth,andthemajorityoftheseplansarepostpaid.Inexpensiveprepaiddataplanswithshorttermscanseemattractivetothosewithaneedandenoughmoneytotopup.Overtimetheyarealmostalwaysmoreexpensivethanplanswithlongervalidity.Unuseddataontheseplansexpireswiththeirtermlimits,whichcanbeasshortasoneday.Userswhohavenotyetloadedanewprepaidplanintotheiraccountcanfindtheiraccountbalancesexhaustedbypunitivedefaultdatacharges.1.6.4PrepaidvsPostpaidPlansWhenitcomestodataaccess,prepaidplansgenerallyofferbettervaluefromacostpergigabyteperspectivethanpostpaidplans,asshowninFigure1.9.Thisbettervaluedoesnotalwaystranslateintobetteraffordability.AuserinMongoliaonMobicommightpayaround$0.72perGBonapostpaidplanfor5GBofdataamonth.Onaprepayplantheycouldachievearateof$0.29perGB—butonlyonplansthatlastlessthan1month.ExtendingtheseshortdurationplanstogetabetterperGBrateInequalityandAccesstoMobileData21wouldmorethandoubleauser’smonthlyspendoverapostpaidplan.PerhapsforthisreasontherateofpostpaidsubscriptionsinMongoliahasincreasedoverthepast5yearsfromaround12%to19%(GovernmentofMongoliaStatistics2022).SimilarsituationscanbefoundinthePhilippineswithallthreecarriers.OneexampleisDito’s“LevelUpP99”8GBpostpaiddataplan.WhileitisfarmoreexpensivepergigabytethanDito’sP50“Data50”plan,itlasts1month,while“Data50”lastsonly7days.Dito’s“LevelUpP99”planistheleastexpensivepostpaidplanofallsurveyedfromanoverallcostperspectiveat$1.78permonthandisatthelowerendofthescalefromadatacostperspectiveat$0.23pergigabyte.1.6.5AffordableMobileInternetvsAffordableMobileDataAcrossallmarkets,mobiledataplansbundleaccesstoentertainmentorsocialmedia,oftenwithseparatedataquotasdedicatedtostreamingsocialmedia.Figure1.11showstheavailabilityofspecialbundlesbycarriershadedingreen.Allbuttwocarrierssurveyedhavespecialsocialmediaplans,andallbutthreehavespecialeducation-focusedplans.Figure1.11:ProvidersOfferingContent-SpecificBundlesIndonesiaimSmartfrenKyrgyzTelkomselRepublicXLAxiataSriLankaPhilippinesMongoliaBeelineMegacomOhgMobileMobicomSkytelUnitelDITOGlobeSmartAirtelDialogHutchMobitelEducationEntertainmentSocialMediaWorkSources:https://im3.id/(accessed27April2023),https://www.smartfren.com/(accessed16May2023),https://www.telkomsel.com/(accessed13March2023),https://www.xl.co.id/(accessed16May2023),https://beeline.kg/(accessed7March2023),https://mega24.kg/(accessed12May2023),https://o.kg/(accessed7March2023),https:///gmobile.mn/(accessed1December2022),https://www.mobicom.mn/(accessed1May2023),https://www.skytel.mn/(accessed6February2023),https://www.unitel.mn/(accessed5December2022),https://dito.ph/(accessed3May2023),https://www.globe.com.ph/(accessed6March2023),https://smart.com.ph/(accessed8April2023),https://www.airtel.lk/(accessed24January2023),https://www.dialog.lk/(accessed12April2023),https://hutch.lk/(accessed19May2023),https://www.mobitel.lk/(accessed9May2023).22DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaInthePhilippines,Smart’sGigaplansoffer1GBperdayofaccesstoTVstreamingsiteslikeNetflixplusadditionaldatafortherestoftheinternet.DoubleGigaprovides2GBofdatatoTVstreamingsitesperdayplusmoredataandcallsandtexts.SmartPowerallplanscanincludeunlimitedtexts,unlimitedtextsandaccesstoTiktok,orthoseplusunlimitedcalls.Dito’sAppBoostersoffer1GBofgeneralinternetaccessaweekwithanadditional7GBofapplication-specificdataforentertainment,socialmedia,orwork.SriLanka’sAirteloffersinexpensiveworkfromhomeplanswithverylow-costaccesstoMicrosoftapplications,Zoom,andGoogleClassroom.TheyhavesocialmediapackswithunlimitedaccesstoWhatsApp,Facebook,YouTube,andFacebookMessenger.InIndonesia,XLAxiataoffersspecialYouTubequotasonmanyoftheirplans,andaccesstoarangeofproductivityapplicationsontheirXtraComboPlusVIPpackages.ManyofTelkomsel’splansincludespecialdatabucketsforaccesstoappslikeTiktok,PrimeVideo.1GBofTiktokaccessfor1dayonTelkomselischeaperthan500megabytesofgeneralinternetaccess.Beeline,Mega,andOhintheKyrgyzRepublicallbundleaccesstopopularsocialmediaapplicationswiththeirdefaultmobiledataplans,eitherwithadditionaldataquotasorwithunlimitedaccess.InMongoliabothSkytelandUnitelhaveawiderangeofapplicationspecificbundles,whileMobicomandGMobilehaveamorelimitedselection.ConsideringtheWorldBank’sfindingsattributing87%ofdatausetosocialmediaandstreaming,specialpurposedataplanscouldhelppromoteMeaningfulConnectivity(WorldBank2021a).Theyundeniablyprovidepeoplewiththehigherdataquotastheywant,oftenatpricesfarbelowthatofgeneralinternetaccess.Whatisproblematicabouttheseplansistheyviolatethebasicprinciplesofnetworkneutrality.Mobileoperatorshaveessentiallycuratedtheinternetfortheirsubscriberswhoeithercannotaffordgeneralinternetaccessormightnotseeitasaprioritygiventheclosed,app-basedenvironmentsonoffer.1.7TargetedAffordabilityInterventionsDuringtheCOVID-19pandemicmanygovernmentsandmobileoperatorsworkedtogethertohelpeasethefinancialburdenonworkersandstudentsstuckathomeduringlockdowns.SomemorenotableinterventionsincludedIndonesia’sXLgivingaway350,000mobilepackagestostudentsfordistancelearning(XLAxiata2020)andMongolia’spartnershipwithitsoperatorstomakealltraffictoitsgovernmenteducationportalfreetoaccess(GovernmentofMongolia2020).InequalityandAccesstoMobileData23Withmostworldgovernmentsconsideringtheircountriestobepost-COVID,fewprogramsestablishedtohelpstudentsandremoteworkersduringthepandemicremaininplace.Someoperators,however,stillofferplansthathelpstudentsorallcitizenswithaccesstoeducation.TocounterthelackofaffordableaccessinMongolia,GMobileandSkytelofferMeta’sFreeBasicsserviceacrosstheirnetworks(gMobile2021;SKYtel2023).Throughthisprogram,operatorsallowunlimitedfreeaccesstoFacebook,Twitter,Wikipedia,andmorethan20otherwebsitesprovidingcommunications,general,business,education,andhealthresources.Thesesitesareprovidedastextonly;photosandvideosarenotdisplayed.TheKyrgyzRepublic’sMegacomoffersfreeaccesstoWikipediaandCodecademywiththeireducationtariff,afreeoptionforsubscribersthatcanbeactivatedbyaUSSDcode(MEGA2023).Theoptionisprovidedtoanyuserswithapositivebalanceaslongastheyarenotroaming.Low-costdataplansorfreeaccesstoeLearningplatformsforstudentsandteachersarealsopresentinIndonesia(PaketBelajar),theKyrgyzRepublic(MugalimorBilimplans),Mongolia(UniteleMeeting),andSriLanka(e-Thaksalawa).1.8ConclusionAccesstoMeaningfulConnectivity,includingenoughdata,isanessentialfoundationforsocialinclusionandeconomicprosperity.Asschools,governments,healthproviders,andcompaniescontinuetorelymoreheavilyondigitalinteractions,itisvitaltoensurethateveryonecanparticipateintheseinteractions.Basedona5GBpermonthdatatargetestablishedandacceptedbyGDIPandtheBroadbandCommission,thisstudyfindsmobiledatatobeuniversallyaffordable,whereavailable,inIndonesiaandSriLanka.Itfindsmobiledataaffordablefor80%ofMongolians,whereavailable.CarriersintheKyrgyzRepublichavepricedoutallbutthewealthiestinsociety.InthePhilippinesoneoperatoroffersuniversallyaffordabledataservicesinlimitedareas,andtwooperatorsofferingdatathatismorebroadlyavailablebutunaffordableforallbutthenation’swealthiestpeople.Inequalityisamajorfactorinthesemarkets.Thedemandsofe-learningandworkfromhome,andEricsson’smeasurementsofaveragemobiledatausemakeitclearthat5GBofdatapermonthisnotenough.Highertargetslike15GBshouldbeevaluatedtoday,and40GBpermonthshouldbethegoal5yearsfromnow.Whiletargetsneedtoincrease,apragmaticevaluationoftheproblemmightconsiderthedemandsofsocialandstreamingmedia,andhowthosedemandsarebeingfulfilledinsomemarketsatalower24DigitalTransformationforInclusiveandSustainableDevelopmentinAsiacostthangeneralinternetaccess.Adjustingtargetstomatchthisrealitycouldbeexpedientbutwouldcomeatthecostofnetworkneutrality.Therestrictedgeographicavailabilityofthelowestcostdataplansshouldbeaconcernforpolicymakersandcampaignersforequality.Infourofthefivecountriesreviewed,whereinequalityandgeographyintersectthereisacrisisofaffordability.AsshownFigure1.8,themostbroadlyavailableplansinthesemarketsaremorethantwicetheaffordabilitythreshold.Whileincomeinequalitycanbeafactorpreventinginternetaccess,MercedesGarcía-EscribanooftheInternationalMonetaryFundalsofindsthat“thelackofuniversalandaffordableaccesstotheinternetmaywidenincomeinequalitywithinandbetweencountries”(Garcia-Escribano2020).Workingtowardequalityinaccesstotelecommunicationscouldbeanimportantsteptowardreducinginequalityoverall.InequalityandAccesstoMobileData25ReferencesAllianceforAffordableInternet(A4AI).2018.UNBroadbandCommissionAdoptsA4AI“1for2”AffordabilityTarget.23January.https://a4ai.org/news/un-broadband-commission-adopts-a4ai-1-for-2-affordability-target/(accessed7March2023).____.2021.AffordableInternet–Journeyfrom1to5.22July.https://a4ai.org/wp-content/uploads/2021/07/A4AIs-journey-1-to-5/(accessed28February2023).Bai,J.2020.HowMuchDataDoesVoIPUse?TipstoSaveBandwidth.NextivaBlog.16January.Scottsdale,Arizona:Nextiva.Brewer,J.,Jeong,Y.andHusar,A.2022.LastMileConnectivity:AddressingtheAffordabilityFrontier.ADBSustainableDevelopmentWorkingPaperSeries.Manila:AsianDevelopmentBank.BroadbandCommissionforSustainableDevelopment.2018.TheStateofBroadband:BroadbandCatalyzingSustainableDevelopment.Geneva:InternationalTelecommunicationUnion.Canh,N.P.,C.Schinckus,S.D.Thanh,andF.ChongHuiLing.2020.EffectsoftheInternet,Mobile,andLandPhonesonIncomeInequalityandTheKuznetsCurve:CrossCountryAnalysis.TelecommunicationsPolicy44(10):102041.Chang,H.,M.Varvello,F.Hao,andS.Mukherjee.2021.CanYouSeeMeNow?AMeasurementStudyofZoom,Webex,andMeet.21stACMInternetMeasurementConference.NewYork,US:AssociationforComputingMachinery.Cisco.2012.VisualNetworkIndex.SanJose,CA,US:Cisco.https://web.archive.org/web/20120202012434/http://www.cisco.com/web/solutions/sp/vni/vni_forecast_highlights/index.html(accessedMay2023).CNNPhilippines.2016.DuterteWarnsTelcos:ShapeuporFaceForeignCompetition.24May.https://www.cnnphilippines.com/business/2016/05/23/Duterte-to-telcos-improve-Internet.html(accessedMay2023).CrespoCuaresma,J.,M.Fengler,K.Fenz,H.Kharas,andL.Saenger.2021.MeasuringInternetPoverty.26July.Washington,DC:TheBrookingsInstitution.https://www.brookings.edu/blog/future-development/2021/07/26/measuring-internet-poverty/(accessedMay2023).Daoud,R.,L.Starkey,E.Eppel,T.D.VoandA.Sylvester.2020.TheEducationalValueofInternetUseintheHomeforSchoolChildren:ASystematicReviewofLiterature.JournalofResearchonTechnologyinEducation53(4).26DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaEconomistImpact.2022.InclusiveInternetIndex(E3i)SixthEdition.London:TheEconomist.Ericsson.2023.MobilityReportMobilityVisualizer.https://www.ericsson.com/en/reports-and-papers/mobility-report/mobility-visualizer?f=11&ft=1&r=1,7,8,9&t=8&s=1&u=5&y=2017,2028&c=2(accessedMay2023)Fujs,T.,A.Eilertsen,R.Shah,andR.A.Castañeda.2022.pipr:ClientforthePIPAPI.Washington,DC:WorldBank.García-Escribano,M.2020.LowInternetAccessDrivingInequality.29June.Washington,DC:InternationalMonetaryFund.https://www.imf.org/en/Blogs/Articles/2020/06/29/low-internet-access-driving-inequality(accessed24March2023).gMobile.2021.FreeBasicsService.GMobileCorporation.https://gmobile.mn/en/cs/11.html(accessed22May2023).GovernmentofMongolia,CommunicationsandInformationTechnologyAuthority.2020.COVID-19CrisisResponseinICTSectorofMongolia.Ulaanbaatar.GovernmentofMongoliaStatistics.2022.ҮүрэнхолбооныдатаSeptember.Ulaanbaatar:GovernmentofMongolia.https://statistic.crc.gov.mn/uploaded/documents/2022/Sep/Үүрэнхолбооныдата.xlsx(accessed2May2023).(inMongolian)GSMAssociation.2022.TheMobileEconomyAsiaPacific2022.London:GSMA.GSMAssociation.2023.NetworkCoverageMaps.https://www.gsma.com/coverage/(accessedMay2023.)GSMAIntelligence.2016.UnlockingRuralCoverage:EnablersforCommerciallySustainableMobileNetworkExpansion.London:GSMA.InternationalTelecommunicationUnion(ITU).n.d.ICTPriceBasketMethodology.Geneva:ITU.https://www.itu.int/en/ITU-D/Statistics/Pages/definitions/pricemethodology.aspx(accessedMay2023).____.2012.MeasuringtheInformationSociety.Geneva:ITU.____.2022.PolicyBrief:TheAffordabilityofICTServices2021.March.Geneva:ITU.____.2023.InfrastructureConnectivityMap.https://bbmaps.itu.int/bbmaps/(accessedMay2023).MEGA(AlfaTelecomCJSC).2023.Education.https://mega24.kg/ru/services/education(accessed22May2023).Moffatt,M.2019.EssentialEconomicsTerms:KuznetsCurve.10April.https://www.thoughtco.com/kuznets-curve-in-economics-1146122(accessedMay2023).InequalityandAccesstoMobileData27PiburnJ.2020.wbstats:ProgrammaticAccesstotheWorldBankAPI.OakRidge,TN,US:OakRidgeNationalLaboratory.Qalati,S.A.,L.W.Yuan,M.A.S.Khan,andF.Anwar.2021.AMediatedModelontheAdoptionofSocialMediaandSMEs’PerformanceinDevelopingCountries.TechnologyinSociety64:101513.Ramli,D.2015.TelstraPartnerSanMiguelReadytoSpendtheTelco’sCashinthePhilippines.TheSydneyMorningHerald,6October.https://www.smh.com.au/business/the-economy/telstra-partner-san-miguel-ready-to-spend-the-telcos-cash-in-the-phillipines-20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ltechnologydevelopment,particularlytothedigitaldivideamongMSMEsinIndonesia.Inaddition,thechapteralsoexaminesdeterminingfactorsexplainingMSMEs’digitaltechnologyusageforbusiness.ThestudylooksatseveralvariablesofinterestasdeterminantsofMSMEs’digitaltechnologyutilization,suchasfinancialfactors,location,firmsize,andformallegalization.Byexaminingthesevariablesempirically,thischapteraimstounderstandthechallengesMSMEsfaceinusingdigitaltechnologyandthusdeterminethemosteffectivestrategiesforsupportingMSMEsintheirdigitaltransformation.TheroleofgovernmentinsupportingMSMEsindigitaltransformationhasalsobeenasubjectofdebate.SomescholarsarguethatgovernmentsshouldprovideincentivesandsupportsothatMSMEsareabletoadoptandeffectivelyutilizedigitaltechnology(MustaffaandBeaumont2002).OthersarguethatthegovernmentshouldfocusoncreatinganenvironmentthatsupportsthedevelopmentofdigitalsolutionsandthegrowthofMSMEs,ratherthandirectlyfundingthem(Rupeika-Apoga,Bule,andPetrovska2022).DigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?31InthecaseofIndonesia,morestudiesonMSMEs’usageofdigitaltechnologyspecificallyfortheirbusinessactivityarerequired.Duetolimiteddata,manypreviousstudiesexploringMSMEs’digitaltechnologydevelopmentusestheiractivitiesonaccessinginternet,communication,andtechnology,withoutspecificallyfocusingonwhethertheseactivitiesrelatedtotheirbusinessorproduction.Therefore,thischapterhelpsfillthatgapbyprovidinganempiricalquantitativestudybasedoninternetuseofhouseholdenterprisesforproductiveactivitiesanditsdeterminingfactors.2.2LiteratureReviewThereareseveralliteraturereviewsaboutMSMEs’digitaladoption.Somecategorizetheiradoptionofdigitaltechnologyintothreephasesofdigitalintegration:digitalinfrastructure,digitalplatform,andfrontiertechnology.First,MSMEsadoptbasictechnologyinfrastructure,suchassmartphonesandtheinternet,tointegrateintothedigitaleconomy(Middleton2021).Inthisstage,MSMEsareabletointroducetheapplicationofinformationandcommunicationtechnology(ICT)intotheirbusiness.Thesecondstageisdigitalplatformadoption.Atthislevel,MSMEsutilizedigitalplatformstoimprovebusinessprocesses(Cenamor,Parida,andWincent2019).Inamoreadvancedstage,MSMEsadoptfrontiertechnologiessuchasartificialintelligence,bigdata,andblockchainintheirproductionandbusinessprocesses(UNCTAD2022).Digitaltransformationisdefinedastheuseoftechnologytoincreaseacompany’sperformance(Westermanetal.2011).Barland(2013)revealedthatdigitaladoptioncouldformnewrevenuestreamsthroughatwo-sidedmarketbusinessmodel.Furthermore,theuseofdigitaltechnologycouldimproveuserexperience(Abel-Hamidetal.2022;Sahu,Deng,andMollah2018),operationalefficiency,andinnovationfornewbusinessmodels(Fitzgeraldetal.2013).Digitaltransformationisessentialforentrepreneursandmanagersinconductingtheirbusiness(ChonsawatandSopadang2020).However,theuseofdigitaltechnologiessuchassocialmediaandcloudcomputingmightnotnecessarilyindicatethatfirmsorbusinessenterprisesunderwentadigitaltransformation(Everett2021).Further,Everett(2021)highlightedthatdigitaltransformationrequirescontinuouslypursuinginnovation,capacityinrespondingchangepromptly,andabilitytocapitalizeonchallengesandprospects.However,notallMSMEscanbenefitfromdigitaltechnology.SomeMSMEsfacechallengesaccessingandusingdigitaltechnology.Thisisknownasthedigitalgap.Intheirrecentwork,Lythreatis,Singh,and32DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaEl-Kassar(2022)describethedigitaldivideasthegapinICTaccess,usage,andoutcome,whichisinfluencedbythreedistinctsegments(education,age,andbelief)aswellasbyninemajorcategories(sociodemographic,socioeconomic,personalelements,socialsupport,typeoftechnology,digitaltraining,rights,infrastructure,andlarge-scaleevents).Theyalsosuggestthatknowledgeandskillsareasignificantfactorinthedigitaldivide.Tosuccessfullytakeadvantageofdigitaladoption,MSMEsneedtosecurethreemaintypesofinfrastructure:(i)ICT,(ii)financial,and(iii)legalandregulatory.Inaddition,sufficientdigitalskillsarealsoimportant(Muller2020).However,manyMSMEshavelimitedaccesstotheinfrastructureanddigitalskills.VanDijk(2006)distinguishesfourkindsofbarrierstodigitaltechnology.First,motivationalaccessgapduetothelackofbasicdigitalexperience,fearoftechnology,andperceivedthreatsfromnewtechnologies.Second,materialaccessgap,whichreferstobarriersthatsetphysicalboundariesforaccesstoacomputerormobilephoneandnetworkconnection.Thisalsoincludesthecostofinternetsubscriptionsandmobilephoneaccounts.Third,skillaccessgap,whichrelatestousers’abilitytomaximizethebenefitsofICT.Andlast,usageaccessgap,whichmeanshowICTuseisinfluencedbyusers’demographiccharacteristicsandthedevelopmentofICTinfrastructure.Forexample,peoplewithhighbroadbandconnectivitymayuseICTforalongertime.InthecaseofMSMEs,theyoftenencounterthesefourdigitaltechnologybarriers.ConsistentwithVanDijk(2006),astudybyDyerson,Harindranath,andBarnes(2009)foundthatMSMEsareconcernedaboutthecostofICTinvestmentandthebenefitstobusiness.WhenitcomestoICTinvestment,itisimportanttoconsiderthatthebenefitsshouldoutweighthecostsofinvestmentandmaintenance.Therefore,commercialaspectsandpotentialbenefitsarethedriversofadoption.NotallMSMEswillnecessarilycatchupwithlargecompaniesoncethepenetrationanddiffusionofICTexceedacertainlevel.ThisissimplybecauseICTmaynotbringsignificantbenefitsandMSMEssticktotraditionalbusinessprocesses(Barba-Sánchez,Martínez-Ruiz,andJiménez-Zarco2007).Inaddition,theICTgapwithlargecompaniesmaybeinfluencedbyMSMEs’limitedknowledgeofdigitaltechnology.ThelackofexperienceinthisfieldandthesmallsizeofthebusinessmakeithardtohireadedicatedICTexpert(Solaymani,Sohaili,andYazdinejad2012;Arendt2008).Regardingfinancialinfrastructure,itisharderforMSMEstoobtaincreditfromlegitimatefinancialinstitutionsthanforlargecompanies(ILO2019).FinancialinstitutionsaremorereluctanttogivecredittoMSMEsbecauseoftheirlowercreditworthiness.Moreover,MSMEsDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?33alsolackinformationaboutfinancialproducts.AdequatefinancialliteracyamongMSMEsmeansnotonlytheirincreasedawarenessofvariousfinancialinstrumentsbutalsothebusinessskillsandtechniquesnecessarytorunbusinessprudentlyandeffectively.Therefore,MSMEsneedsupporttoimprovetheirfinancialliteracytomaketheirbusinessesmoreprofitable(AFI2020).AsMSMEsdonothavesufficientaccesstoinformationtoimprovedigitalliteracyandfinancialinfrastructure,regulatorysupportbecomesimportantforthem.AstudybyNghi,Trinh,andThuan(2020)revealedthatregulatorysupporthasapositiveimpactonMSMEs’digitaladoption.Thegovernmentcanimplementpoliciesthatarepro-MSMEssuchasdigitaltechnologytrainingorprovidingcreditassistancetoencouragedigitaladoption.2.3DigitalAdoption:OpportunitiesandChallengesforMSMEsinIndonesiaIndonesia’sdigitaladoptionhasbeenoneofthefastestgrowingamongSoutheastAsiancountriesinrecentyears.Itwastriggeredduringthepandemicwhenbusinessandeconomicactivitieswereconstrainedduetolockdown.SeveralaspectsareconsideredasopportunitiesandchallengesforMSMEs’digitalaccelerationinIndonesia.2.3.1DemographicAspectIndonesia’spopulationwas275.77millionin2022,dominatedbypeopleintheirproductiveagewithcloseto70%ofthetotalpopulation.Inaddition,Indonesiaalsohasalargepopulationundertheageof15,around24%ofthetotalpopulation(StatisticsIndonesia2022a).ThehighnumberofyoungpeopleisastrengthfortheIndonesianeconomy,especiallyintermsofdigitaltransformationbecausetheytendtobemoreadaptivetodigitalparticipation.2.3.2EconomicGrowthIndonesiaisasanarchipelagiccountrywitharound17,499islandsspanningoveranareaof7.81millionsquarekilometers.Indonesia’svastterritoryseparatedbyseaposeschallengestoequitabledevelopment.Forthepastdecade,Indonesia’seconomicgrowthwas4.27%onaverageanditseconomicvaluereachedRp19,588.4trillionin2022,makingIndonesia’sgrossdomesticproduct(GDP)thelargestinSoutheastAsia.ThegovernmentpredictedIndonesiawillbetheworld’sfourth-largesteconomyin2045.34DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaInlinewiththisincrease,GDPpercapitainIndonesiaalsoimproved,increasingmorethan50%from2010to$4,327in2021.However,theGDPpercapitaisstilllaggingrelativetootherSoutheastAsiancountries(Figure2.1):$72,794inSingapore,$31,449inBruneiDarussalam,$11,109inMalaysia,and$7,066inThailand.Figure2.1:GDPperCapitaofSelectedASEANMemberStates($),,,,,,,,IndonesiaSingaporeBruneiDarussalamThailandMalaysiaASEAN=AssociationofSoutheastAsianNations,GDP=grossdomesticproduct.Source:WorldBank(2022).Indonesia’ssituationwithahigheconomicgrowthbutlowGDPpercapitaindicatesthatthecountry’sincomedistributionamonghouseholdsisunlikelytoimproveovertheyears.Figure2.2displaysIndonesia’sincomedistributionfromthetop1%andbottom50%ofearners.Thegraphshowsthatthedistributionofincomefrom2004to2018hasnotimproved;infact,thegaphasslightlyincreased.In2004,thetop1%contributed41.2%oftotalincomeandthebottom50%contributed16.5%.In2018,thecontributionofthetop1%hadincreasedto46.8%oftotalincomebutthebottom50%onlycontributed12.4%.DigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?35Figure2.2:Indonesia’sIncomeDistribution(%)Pre-taxnationalincomePre-taxnationalincomeBottomTopNote:Percentageincomedistributionfromtop10%andbottom50%.Source:WorldInequalityLab(2018).2.3.3InformationandCommunicationTechnologyDevelopmentInthecontextofICTdevelopment,internetandtechnology-relatedproductshavebeenrisingdramaticallyovertheyears.In2023,about78.19%oftheIndonesianpopulation,or215.26millionpeople,haveinternetconnection(AssociationofIndonesianInternetServiceProviders2023).Inaddition,mobilephoneownershiphasincreasedby37.25%since2012(StatisticsIndonesia2022b).TheWorldBank(2021)reportedthat80%oftheaveragetimespentonlinewasforleisure,communication,andsocialmedia.AccordingtoStatisticsIndonesia(2021a),theICTDevelopmentIndexinIndonesiawas5.76ona10-pointscalein2021.However,thevalueoftheindexwasnotequalbetweenprovinces.Whencategorizedintohigh(7.51–10.00),medium(5.01–7.50),low(2.51–5.00),andverylow(0.250),mostprovincesinIndonesiafallintothemoderatecategory(Table2.1).Onlyoneprovince(Jakarta)isinthehighcategory,andtwoprovinces(EastNusaTenggaraandPapua)arestillinthelowcategory.36DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable2.1:ICTDevelopmentIndex,byCategoryandProvinceHighModerateLowJakartaYogyakarta,RiauIslands,Bali,EastKalimantan,Banten,NorthEastNusaKalimantan,WestJava,NorthSulawesi,WestSumatra,Riau,Tenggara,SouthKalimantan,EastJava,Bengkulu,CentralJava,SouthPapuaSulawesi,NorthSumatra,SoutheastSulawesi,Jambi,BangkaBelitung,CentralKalimantan,Maluku,SouthSumatra,Gorontalo,Lampung,Aceh,CentralSulawesi,WestPapua,WestKalimantan,andWestNusaTenggaraICT=informationandcommunicationtechnology.Source:StatisticsIndonesia(2021a).ThedevelopmentofICTitselftendstowidenthegap,ascanbeseenbetweenJakarta,whichhasthehighestICTDevelopmentIndex,andPapua,whichhasthelowest.In2020,thedifferencebetweenthesetwoprovinceswas4.11andwidenedto4.31in2021.Nonetheless,someprovinces,suchasWestSulawesiandNorthMaluku,haveraisedtheirindexfromthelowtothemoderatecategory.TheICTgaphashappenednotonlyattheprovincelevelbutalsoattherural–urbanlevel.In2021,88.53%ofpeopleinurbanareasaccessedtheinternetcomparedwithonly73.57%inruralareas(Figure2.3).Figure2.3:InternetAccess,byAreas(%)UrbanNationalRuralSource:StatisticsIndonesia(2021b).DigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?37ThegapinICTdevelopmentencouragesthegovernmenttoimproveICTinfrastructureineveryregion,especiallyintheruralareas.Moreover,thisefforthasbeenincludedintheNationalMedium-TermDevelopmentPlanfor2020–2024asstrategicpriorityprojects.GovernmentprogramsthathavebeencarriedouttoimproveICTinfrastructureincludethePalapaRingProject,theMultifunctionalSatelliteIndonesiaRaya,andtheProgramfortheProvisionofMultifunctionalSatellitesandBaseTransceiverStations.2.3.4MSMEDigitalAdoptionAsurveybytheAssociationofIndonesianInternetServiceProviders(APJII)in2022showsthatasmuchas87.43%ofMSMEshaveusedtheinternetinbusinessprocesses.Thatis,only12.57%ofMSMEsdonotusetheinternet.Breakingdownfurthertheinternetusagebysizeofthebusinesstothetotalnumberofcompanies,thefiguresare63.59%microenterprises,65%smallbusinesses,and72.04%medium-sizedbusinesses.AccordingtothesurveybyKatadataInsightCenter(2020),themainobjectiveofMSMEsusingtheinternetisforpromotingproductsthroughsocialmedia,whichwasmentionedby60.2%ofrespondents.MSMEsalsousedtheinternettoseekinformationonbusinessdevelopment(mentionedby44.7%ofrespondents)andfindandorderrawmaterials(35.9%).However,Java’sMSMEsdigitaladoptionisstillhigherthanonanyotherislandinIndonesia.BasedonEastVenturesDigitalCompetitivenessIndex2022,Javascored49outof100pointsintermsofdigitaladoption,followedbyBaliandNusaTenggarawithascorewas22andSumatrainthirdplacewithascoreof21.Meanwhile,Kalimantanscored19,Sulawesi14,andMaluku–Papua10.ThisindicatesthatdigitaladoptionamongMSMEsisnotequalbetweenareas.2.3.5KnowledgeandSkillsAsmentioned,althoughmanyMSMEshaveadoptedtheinternet,theyalsofacechallengestofullymaximizingdigitaladoption.Amajorchallengeisdigitalliteracyanddigitalcompetence.AccordingtoIndonesia’sMinistryofCommunicationandInformationTechnology,theDigitalLiteracyIndexofIndonesiansis3.54oratamoderatelevel.Still,IndonesialagstheothermajorAssociationofSoutheastAsianNations(ASEAN)countries,particularlyMalaysia,Philippines,Singapore,andThailand.ThiscanbeseenbytheknowledgeandtechnologyoutputsrankingontheGlobalInnovationIndexoftheseASEAN-5countries(Table2.2).Overthepast5years.Since2018,IndonesiahasalwayshadthelowestrankingamongtheASEAN-5countries,with38DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaSingaporeconstantlyinfirstposition.ThelimiteddigitalskillsamongMSMEsinIndonesiadiscouragesinternetadoption.EveniftheMSMEsusetheinternet,manyofthemfacedifficultyoptimizinginternetusage.Table2.2:KnowledgeandTechnologyOutputsRankinginASEAN-5Country2018201920202021202211141313Singapore113538313982717478Malaysia343844404331262441Indonesia86Thailand40Philippines49Source:GlobalInnovationIndex(2022).2.4MSMEDigitalAdoptioninSelectedCountriesoftheAssociationofSoutheastAsianNationsMSMEsinASEAN,likeinIndonesia,playanimportantroleintheeconomiesastheycompriseasignificantshare(95%–99%)oftotalenterprisesintheregion(Muller2020).Forinstance,theMSMEshareoftotalenterprisesin2019was99.9%inIndonesia,99.5%inThailand,and99.5%inthePhilippines(ADB2020).Meanwhile,inMalaysiaandVietNam,MSMEsalsohavethelargestshareintotalenterpriseunits,atabout97.2%.Intermsofemployment,MSMEsinASEANeconomieshavealsocontributednotablybyprovidingjobs.MSMEsinIndonesia,forexample,employabout97%ofworkersnationally.Meanwhile,MSMEsaccountforabout69.5%oftotalnationalemploymentinThailand,48.4%inMalaysia,and38.0%inVietNam.ThesedataindicatethatMSMEsinASEANhavebecomevitaldriversoftheregionaleconomyaswellasthebackbonetoeconomicdevelopmentintheregion.Inthisdigitalera5.0,MSMEs’digitaladoptionhasbeenacknowledgedtopositivelyimpacttheirbusiness.Tooptimizepotentialbenefits,digitaltransformationisinevitableforMSMEs.WhatMSMEsinASEANhaveincommon,particularlywiththoseinemergingDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?39economies,isthechallengesindigitaltechnologyadoption.Therefore,thissectiondetailsabenchmarkingstudyofMSMEs’developmentofdigitaladoptionanditschallenges,aswellaspolicysupportforMSMEs’digitaltransformationinselectedASEANcountries,specificallyMalaysia,thePhilippines,Thailand,andVietNam.2.4.1MalaysiaDuringtheCOVID-19lockdown,businessesinMalaysiawereforcedtoadoptdigitaltechnology.Thus,thevalueofe-commerceimprovedin2020,soaringtoRM896billionfromRM677billionin2019.Despitetheincreasingtechnologyadoptionamongbusinesses,thecountry’sMSMEsfacedifficultymakingoptimaluseofit.Around77%ofMSMEsinMalaysiastillusebasicdigitaltechnology.AsurveybySMECorporationandHuaweiTechnologies(2018)showsthatMSMEsrarelyusedadvancedtechnologysuchastheInternetofThings(IoT),cloudcomputing,anddataanalytics.Of2,033MSMEs,only35%utilizedIoTwithintheirorganizationsprimarilyforsecurity,surveillance,andfleetmanagement.Mohamad(2021)highlightedthatthelackofdigitalskillsamongtheMSMEworkforcewasthemainreasonforthedigitallagofMSMEs.Anotherobstaclewasthefinancinganddigitalizationcostsuchasforinternet,hardware,andsoftwaresubscriptions.Approximately34%ofbusinessownersintheSMECorp.andHuawei(2018)surveyfeltthatcloudcomputingisexpensive.Moreover,around60%ofparticipantswereunawareofavailablefinancingoptions.GiventhesignificanceoftechnologyandthedigitaldivideamongMSMEs,theGovernmentofMalaysiahasintroducedtheNationalPolicyonIndustry4.0(Industry4WRD).Itsmainobjectiveistocreatearobustecosystemthatsupportscompaniesintheindustry4.0era.AkeyaspectofthispolicyinvolvesraisingawarenessaboutthebenefitsoftechnologyforMSMEs.Tobridgethegapindigitalcompetencyamongworkers,thegovernmenthasalsoestablishedtrainingcentersfocusingontechnology.In2023,thenationalbudgetallocatedsubstantialfundstopromoteMSMEs.Specifically,thegovernmentisgrantingRM100milliontosupportthedigitaltransformationofMSMEs.Additionally,MSMEswillenjoyareducedtaxrateof15%in2023.TheseinitiativesareaimedatfosteringthegrowthandcompetitivenessofMSMEsinMalaysia.40DigitalTransformationforInclusiveandSustainableDevelopmentinAsia2.4.2PhilippinesGoogle,Temasek,andBain&Co.(2022)reportedthatthedigitaleconomyinthePhilippinesamountedto$20billionin2022,oranincreaseof22%fromthepreviousyear.Thereportalsohighlightedthatthecountry’sdigitaleconomywillcontinueitsupwardtrend.Overall,e-commercecontributed$14billiontothedigitaleconomy,makingitthelargestdigitalsectorinthePhilippines,followedbytheonlinemediasector,whichcontributed$3.1billion.However,theWorldBank(2020)reportedthatthecountry’sdigitaladoptionfallsbehinditsmiddle-incomeregionalpeers.Theprimaryobstacleshinderingtheadoptionofdigitaltechnologyinthebusinesssectorincludelimiteddigitalinfrastructure,expensiveinternetservices,andinsufficientmarketcompetition(WorldBank2020).AccordingtoOokla(2023),theaveragemobilebroadbandspeedinthePhilippinesstandsat26.98megabitspersecond,whichlagsbehindcountriessuchasSingapore,Malaysia,Thailand,andVietNam.Additionally,Filipinosfacehigherinternetcoststhantheirregionalcounterparts.Toaddressthesechallenges,theGovernmentofthePhilippines,throughtheDepartmentofInformationandCommunicationsTechnology,hasinitiatedtheNationalBroadbandPlan.Thisstrategicplanaimstoexpeditethedeploymentoffiber-opticcablesandwirelesstechnologiesacrossthecountry.Insupportofthisinitiative,the2023NationalBudgethasallocated₱1.5billionfortheNationalBroadbandPlanproject.Thehopeisthatthisinvestmentwillimprovedigitalinfrastructure,reduceinternetcosts,andenhancetheoverallcompetitivenessofthecountry’sbusinesslandscapebyencouraginggreateradoptionofdigitaltechnology.2.4.3ThailandTheGovernmentofThailandhasbeenactivelyinvolvedinthecountry’sdigitaltechnologyreadinessasindicatedintheNationalStrategy(2018–2037).In2020,theEuropeanCenterforDigitalCompetitivenessrankedThailandasthesecondmostdigitallycompetitivecountrybasedontheprogressmadeindevelopingitsecosystemandtheshiftingmindsettowarddigitizationintheASEANregion.Table2.3providesinformationabouttheIndexofDigitalEntrepreneurshipSystems.ThailandhasarelativelyhighindexscorecomparedtotheworldandASEANaverage.Theindexevaluatesthecountry’sinfrastructure,finance,andnetwork,aswellassupportrelatedtodigitizationofentrepreneurship.Thailandpossessesareasonablywell-developeddigitalinfrastructure,althoughitsavailabilityandaccessibilityarenotDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?41evenlyspreadthroughoutthecountry.Accordingtodatafrom2021,about85.27%ofthecountry’spopulationhasaccesstotheinternet.ThailandisalsooneofthepioneersinAsiaandthePacifictorollout5Ginthecommercialmarket.ThehighscoreontheIndexofDigitalEntrepreneurshipSystemsindicatesthatentrepreneurshiphassufficientaccesstoarelativelywidevarietyoffinancingoptions.IfThailandcanfullyoptimizedigitaltechnology,thedigitaleconomyisexpectedby2030tocontributearound$79.5billiontotheeconomy(AlphaBeta2021;UNCTAD2021).Table2.3:Thailand’sAsianIndexofDigitalEntrepreneurshipDigitalDigitalDigitalEntrepreneurshipEntrepreneurshipEntrepreneurshipStand-upStart-upScale-upASEAN35.4534.2036.44Global31.9631.9131.96Thailand43.1441.6944.34ASEAN=AssociationofSoutheastAsianNations.Source:Prasarnphanich(2022).TourismservesasasignificantdriveroftheThaieconomy,contributing18.21%toGDPbeforethepandemic.MostbusinessesinthecountryareMSMEs.Takentogether,ThailandhasmanyMSMEsinthetourismsector.Digitalizationinthissectorcanenhancemarketaccess,butdigitaltechnologyinfrastructureandfinancingarestillaproblemforMSMEsinthesector,especiallythoseinruralareas.ManyruraltourismMSMEsfinditdifficulttoaffordinternetconnectivityandaccesscredit,especiallyshort-termloans.Despitethelargeinvestmentindigitalinfrastructure,theavailabilityofandaccesstotheinternetinruralareasremaininferiortourbanareas.Thestakeholdersalsomentionedthatlocalvendorsmayhavetopayforinternetaccess,creatingbarrierstointernetadoption.Thecountryhasseasonaltimesfortourism:InternationaltourismpeaksfromOctobertoFebruaryanddomestictourisminApril,whiletheothermonthsarecategorizedaslowseason.Creditintheoffseasonisrequiredtokeepbusinessesgoing(GSMA2022).42DigitalTransformationforInclusiveandSustainableDevelopmentinAsia2.4.4VietNamTheGovernmentofVietNamactivelypromotesentrepreneurshipandsupportsthegrowthofitsentrepreneurialecosystem.Thegovernmenthaseasedbusinessregulationsthroughseveralpolicyreformsthatfacilitatetheestablishmentofbusinesses.Thesereformshavesignificantlyimprovedthecountry’srankingintheWorldBankDoingBusinesssurvey2020to70thposition,whichis29placeshigherthanitsrankingin2014(OECD2021).Toenhancedigitaltransformation,thegovernmentattractstechnologycompaniesbyofferinga10%taxincentiveforaperiodof15years.Additionally,VietNamhasimplementedvariouspoliciesthataimtobolsterthedevelopmentofMSMEs.The2011–2015SMEDevelopmentPlanisacomprehensivepolicytosupportMSMEsthroughinitiativessuchasimprovingthelegalframework,enhancingaccesstocredit,fosteringtechnologicalinnovation,andstrengtheninghumanresources(HoaandKhoi2017).TheproblemsMSMEsfaceinadoptingdigitaltechnologyareaccesstofinancinganddigitalliteracy.CommercialbankscontinuetoencouragetraditionalcommercialcreditprocessesthatarenotalignedwiththecharacteristicsofMSMEs.Toovercomethesechallenges,thegovernmentprovidesspecialcreditforMSMEsthroughtheSMEsDevelopmentFundandtheCreditGuaranteeFund.Unfortunately,thefullpotentialofthesefundshasnotbeenrealizedduetothecomplexityoftheapplicationprocessandalackofawarenessamongMSMEs(OECD2021).ToenhancedigitalawarenessamongMSMEs,theMinistryofPlanningandInvestment’sBusinessDevelopmentDepartment,incollaborationwiththeUnitedStatesAgencyforInternationalDevelopment,haslaunchedaplatformthatservesasacomprehensiveresourceofferinginformationonbusinessdigitaltransformation,supportactivities,consultations,andtrainingprograms.2.5MethodologyandDataThisstudyappliedaprobitmodeltoexaminethedeterminingfactorsexplainingMSMEs’digitaladoptioninIndonesia.Bydefinition,inaccordancewiththeGovernmentRegulationoftheRepublicofIndonesiaNo.7/2021,MSMEsarecategorizedbysizeofcapitalandannualsales.Inaddition,StatisticsIndonesiaalsocategorizesfirmsbasedontotalnumberofemployees:microenterprise(1–4),smallenterprise(5–19),medium-sizedenterprise(20–99),andlargeenterprise(100andabove).ThisstudyutilizedtheWorldBankDigitalEconomyHouseholdSurvey(DEHS)2020,whichprovidesinformationonhouseholdDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?43enterprises,aswellasindividualdigitaladoptionandeconomicactivity.Byusingthisdataset,thischapterfocusesonobservinghouseholdenterprises(HHEs),whichthesurveydefinesashouseholdsconductingbusinessactivities.ThisstudyintendstoprovideempiricalevidenceonHHEsrepresentingMSMEsinIndonesia.TheDEHS2020consistedof3,063households,ofwhichabout1,542areHHEs.IntheDEHS2020,suchenterprisesemploybetween1and28workers.Therefore,accordingtotheclassificationfromStatisticsIndonesia,HHEsinthesurveybydefinitionrepresentmicro,small,andmedium-sizedenterprises.TheHHEsinourstudycomprise1,515microenterprises(98.2%),23smallenterprises(1.5%),and4medium-sizedenterprises(0.3%).Also,thisstudyprovidesinformationonwhetherHHEsusetheinternetfortheirbusinessoperationsandeconomicactivity.WeonlyfocusonHHEs’domesticeconomicactivity,sincetheDEHS2020doesnotprovideinformationabouttheirengagementininternationaltrade.ThestudyforthischaptercoveredvariedinformationaboutHHEs’characteristics,includinglocation,yearofestablishment,legalstatus,financialaccess,financialskills,anddigitalliteracy.TheobservationsampleoftheDEHS2020wascollectedfromabout27provincesinIndonesia,includingurbanandruralareas.Theequationsfortheprobitmodelareasfollows:𝑦𝑦𝑖∗𝑖=𝛼𝛼𝛼𝛼𝛼𝑖𝑖𝛽𝛽𝛽𝛽𝛽𝑖𝑖𝛾𝛾𝛾𝛾𝛾𝛾𝑖𝑖𝑦𝑦𝑖∗𝑖=𝛼𝛼𝛼𝛼𝛼𝑖𝑖𝛽𝛽(𝛽1)𝛽𝛽𝑖𝑖𝛾𝛾𝛾𝛾𝛾𝛾𝑖𝑖𝑦𝑦𝑖𝑖=1[𝑦𝑦𝑖∗𝑖>0]𝑦𝑦𝑖𝑖=1[𝑦𝑦𝑖∗𝑖>0(2])𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃(𝑦𝑦𝑖𝑖=1𝑥𝑥𝑖𝑖)=𝐺𝐺(𝛼𝛼𝛼𝛼𝛼𝑖𝑖𝛽𝛽𝛽𝛽𝛽𝑖𝑖𝛾𝛾𝛾𝑃𝑃𝑃𝑃𝛾𝑃𝛾𝑃𝑖𝑖𝑃)𝑃(𝑦𝑦𝑖𝑖=1(3𝑥𝑥)𝑖𝑖)=𝐺𝐺(𝛼𝛼𝛼𝛼𝛼𝑖𝑖𝛽𝛽𝛽Equatio𝑦𝑦n𝑖∗𝑖(1)expressesthedependentvariable𝑦𝑦𝑖∗𝑖,whichrepresentsadummyvariableof1ifhouseholdenterpriseiusesinternetforitsbusinessandeconomicactivity.Theparameterxiisasetofmainindependentvariables,includingfinancialaccess,financialskill,location,andfirmlegalization.Inaddition,theparameterziisasetofcontrolvariables,suchasfirmage,totalemployment,andfirmsize.Theparameteruirepresentstheerrorterminthemodel.Thismodelfollowsastandardnormalcumulativedistributionfunctionandisestimatedusingmaximumlikelihoodestimation,giveninequation(3).44DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable2.4:ListofVariablesVariablesVariableDescriptionUnitHypothesisSourceCodeWhetherHHEusesDependentVariablesInternetforbusinessandeconomicactivity.InternetInternetBinaryWorldTheageoftheHHEsince(1=Yes;Bankitsfounding0=No)HHEhasaformalIndependentVariablesbusinesspermit,legalizedbytheGovernmentFirmageAgeWhetherHHEisinurbanYears–WorldareasBankFirmformalLegalBinary+Worldlegalization(1=Yes;0=No)BankLocationLocationBinary+World(1=Urban;Bank0=Rural)TotalTot_empTotalofemployeesPerson+WorldemployeesBankFirmsizeSizeFirmhasatleastoneBinary+World(1=Yes;employee0=No)BankFinancialBankHHEhasabankaccountBinary+Worldaccess(1=Yes;0=No)BankFinancialFinanceOwnerofHHEhasBinary+Worldskillseparatedtheirfinancial(1=Yes;managementbetween0=No)BankpersonalandbusinessaccountsHHE=householdenterprise.Source:Authors’compilation.Table2.4listsvariablesincludedinthemodel.Themainindependentvariableincludesfirm’slocation,formallegalization,financialaccess,andfinancialskill.Locationisadummyvariableof1iftheHHEislocatedinanurbanarea.ThisvariableisexpectedtobepositivelyrelatedtoHHEinternetuse.ItrepresentsoccurrenceofadigitaldivideforHHEadoptionofinternetuse,specificallythoseinurbanareaswithmoreaccesstointernetconnectioncomparedtothoseinruralareas.FormallegalizationisalsoamongthemainindependentvariablesandexpectedtobepositivelyrelatedtoHHEinternetuse.ThisdummyvariableDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?45of1representswhethertheHHEsatisfiesthelegalrequirementstobeconsideredaformalbusiness.Firm’sfinancialaccessisadummyvariableof1iftheHHEownsabankaccount.Thisvariableisexpectedtobepositivelyrelatedtoafirm’sdigitaltechnologyadoption,i.e.,internetuseforbusiness.Furthermore,thefinancialskillvariabledenotesfinancialcapacityoftheHHEinmanagingitsfinances.ThisdummyvariableshowswhethertheHHEseparatesitsfinancialaccountsforpersonalandbusinessmatters.Otherindependentvariables,suchasfirmage,totalemployment,andfirmsize,arealsoincludedascontrolvariables.FirmageisexpectedtobenegativelyrelatedtoHHEinternetuse.Theyoungerfirmsareexpectedtobemoreadaptablewithdigitaltechnology.Othervariables,suchastotalemploymentandfirmsize,areexpectedtobepositivelyrelatedtoHHEdigitaladoption,i.e.,internetuseforbusiness.Table2.5summarizesthedescriptivestatisticsofallHHEsandthoseusingtheinternetforbusinessandeconomicactivity.Table2.5:DescriptiveStatisticsAllHHEsUsingInternetStd.VariableUnitObsStd.InternetBinary1,542MeanDevObsMeansDev1,5420.4800.5001,54210.79510.980290101,5420.1730.378AgeYears1,5420.6620.4732907.5727.8071,5420.5691.641LegalBinary1,5420.3380.4732900.3310.4711,5420.4990.500LocBinary0.3290.4702900.8170.387Tot_empAges2902.8983.739SizeBinary2900.3930.489FinancialaccessBinary2900.6860.465FinanceskillBinary2900.4550.499HHE=householdenterprise.Source:Authors’compilation.TheDEHS2020alsoprovidescomprehensiveinformationrelatedtoHHEsandtheirdigitaleconomyactivities,particularlytheclassificationofbusinesssectors.Table2.6summarizesHHEsbasedoneconomicsectors.ThebiggestshareofHHEsoperatedinwholesaleandretailtrade,orrepairofmotorvehiclesandmotorcycles(592MSMEs),followedbymanufacturing(238MSMEs)andaccommodationandfood46DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaserviceactivities(200MSMEs).Intermsofdigitaladoption,thesectorwiththemostHHEsthathaveadoptedtheinternetwaswholesaleandretailtrade,orrepairofmotorvehiclesandmotorcycles.Table2.6:DigitalAdoptionofHouseholdEnterprises,bySectorEconomicSectorNo.ofHHEsNo.ofHHEsAgriculture,forestry,andfishing256UsingInternetMiningandquarrying3Manufacturing23815Electricity,gas,steam,andair-conditioningsupply1–Watersupply;sewerage;wastemanagementand261remediationactivities–Construction–Wholesaleandretailtrade;repairofmotorvehiclesandmotorcycles116TransportationandstorageAccommodationandfoodserviceactivities592112InformationandCommunicationRealestateactivities9524Professional,scientificandtechnicalActivitiesAdministrativeandsupportserviceactivities20032Publicadministrationanddefense;compulsorysocialsecurity146EducationArts,entertainment,andrecreation31OtherserviceactivitiesActivitiesofhouseholdasemployers;122undifferentiatedgoodsandservicesproducingactivitiesofhouseholdsforownuse183Others1–HHE=householdenterprise.11Source:Authors’compilation.716018125163Table2.7showstheresultofdifferentmeanequationsgroupedbyinternetusage.Basedonthetable,thegroupthatusestheinternetisyoungerthanthegroupnotusingtheinternet.Intermsoffinancialaspects,MSMEsthatusetheinternethavehigherfinancialaccessandskills.ThismeanstheymostlikelyhavebankaccountsandhaveDigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?47separatedtheirfinancialaccountsforpersonalandbusinesspurposes.Therewasameandifferenceinthelocationvariablebetweenthesetwogroups.Theaverageforthegroupthatusestheinternetishigher,whichmeansmanyinternetusersarelocatedincities.Last,onaverage,theinternetgrouphasalargerbusinesssizeincomparisontothenon-internetgroup.Table2.7:EstimationResultsofDifferentMeanEquations(1)(2)(3)(4)(5)(6)(7)VariableNotUsingUsingDifferenceProbProbProbInternetInternet(diff<0)(diff=/0)(diff>0)7.572414(2)–(3)1.00000.00000.0000Age11.541530.33103453.969120.00000.00001.00000.8172414–0.1944530.00000.00001.0000Legal0.13658151.07931–0.19044710.00000.00001.00000.3931034–0.62875210.01460.02930.9854Location0.62679430.6862069–0.06722490.00000.00001.0000–0.230864Tot_emp0.45055820.00000.00001.0000Size0.3258786Financial0.4553429accessFinanceskill0.29952080.4551724–0.1556516Source:Authors’estimation.2.6EmpiricalResultsandAnalysisTable2.8:ProbitEstimationVariableofInterest(1)(2)Age–0.0300894–0.0295959Legal0.25664270.2910457Loc0.69157240.698107Tot_emp0.0467289–Bank0.27789890.2766375Finance0.19771880.2098315Size–0.1421825R20.10040.0967Prob>chi20.0000.000Note:,,andrepresentsignificancelevelatp<0.01,p<0.05,p<0.1,respectively.Source:Authors’estimation.48DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaInTable2.8,columns1and2providemodels1and2probitestimationresults,respectively.Model1usesthenumberoftotalemployeesasacontrolvariable.Meanwhile,model2usesthefirmsizedummyvariable(1iftheHHEhasemployees;0otherwise)asacontrolvariable.Asobserved,MSMEs’tendencytousetheinternetdependsonthefirm’slocation,formallegalization,ageandsize,accesstoformalfinanceinstitution,andfinancialskill.Theprobitresultssuggestthattheprobabilityofinternetadoptionisassociatedwiththefirm’slocation(ruralorurbanarea).Ifafirmisinanurbanarea,theprobabilityofinternetadoptionincreasesby69%inmodels1and2.ThisfindingisconsistentwithLaiandWidmar(2021)whoemphasizethatinternetadoptioninremoteareashasbeenproblematicforyears.TheresultsindicatethatadigitaldivideremainsamongMSMEsintheregion.MSMEsinurbanareasaremorelikelytousedigitaltechnologyfortheirbusiness.Thisisnotsurprising,asmentionedearlier,sinceICTdevelopmentinurbanareasismoreadvancedthaninruralareas.Ourresultsconfirmthehypothesisofapositiverelationshipbetweenurbanareasandinternetadoption.Columns1and2showapositiverelationshipbetweenformallegalizationandinternetadoption.Specifically,firmformallegalizationincreasestheprobabilityofinternetadoptionby25.66%inmodel1and29.10%inmodel2.Inlinewiththeseresults,Muller(2020)alsohighlightstheimportanceofthelegalandregulatoryinfrastructurerequiredtooptimizeMSMEs’digitalization.Accordingly,thisstudyconfirmsthepositiveimpactofformallegalizationoninternetadoption.Thebankvariableshowsapositiveandsignificantinfluenceonthefirm’sinternetadoption.Theprobabilityofinternetadoptionincreaseswhenafirmhasabankaccount.Bothmodelsshowa27%increaseinprobability.Thefinanceexplanatoryvariablealsohasasignificantpositiveimpactoninternetadoption.Inmodel1,whenafirmhasadequatefinancialskills,theprobabilityofinternetadoptionincreasesby19.77%.Meanwhile,model2estimatesanincreasedprobabilityof20.98%.Ourfindingssuggestthatfinancialaccessandskillsareimportantforafirm’sinternetadoptionandconfirmourhypothesisofapositiverelationship.Theresultsofmodels1and2indicatethatyoungerfirmshaveahigherprobabilityofadoptinginternetuse.Inmodel1,youngerfirmageincreasesthelikelihoodofinternetadoptionby3.0%.Theresultsofmodel2showthattheprobabilityofinternetadoptionincreasesby2.9%theyoungerthecompanyis.ThisresultisalsoinlinewithLythreatis,Singh,andEl-Kassar(2022)thatyoungerfirmshavemoreaccesstointernetthanolderfirms.DigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?49Thecontrolvariableinmodel1,tot_emp,showsapositiveandsignificantinfluenceonfirminternetadoption.Anincreaseinthenumberofemployeesincreasestheprobabilityofinternetadoptionby4.6%.However,thecontrolvariablesinmodel2showapositiveeffect,thoughnotsignificant.Themodel2resultsindicatethatthepresenceofmoreemployeesincreasesthelikelihoodofinternetadoptionby14.21%.2.7ConclusionThefast-growinginternetdevelopmentinIndonesiaisseentobeanopportunityforMSMEstoimprovetheirbusiness.However,MSMEsthemselvesfacechallengesindigitaladoptionbecauseofseveralfactors,includinglimitedfinancialandICTaccess.ThischapterlookedatMSMEsanddigitaltechnologydevelopment,particularlythedigitaldivideamongMSMEsinIndonesia.Inaddition,thischapteralsoexaminedthedeterminingfactorsexplainingMSMEs’digitaltechnologyusefortheirbusiness.Anon-linearprobabilitymodelisappliedtoanalyzemicrodataforIndonesiafromtheWorldBankDEHS2020.OurstudyalsotestedthemeandifferencestoconfirmthattherearesignificantdifferencesintheMSMEcharacteristicsbetweentwogroups,suchasthoseusingtheinternetandthosenot.Ourfindingsshowthattheprobitmodelindicatesasignificantresultinestimatingtheprobabilityofdigitaltechnologyadoption,i.e.,internetusageforMSMEsinurbanandruralareas.ThishighlightsthatadigitaldivideremainsamongMSMEsintheregion.Ruralareaswheremoreinternetconnectivityandservicesareneededfacechallengessuchastalentshortage,skills,training,andjobs.AnotherimportantfindingofourstudyisthatMSMEs’formallegalizationhasapositiveandsignificanteffectontheirparticipationindigitalization.Therefore,thegovernmentshouldactivelypromoteMSMEstoregisterlegallyandtosimplifyproceduresandadministrationforMSMEstoregisterasformalenterprises.ThischapteralsofindsthatMSMEs’financialliteracyisimportanttoexplaintheiruseofdigitaltechnology.TwosignificantvariablesdeterminewhetherMSMEsusedigitaltechnology:accesstoformalfinancialinstitutionsandfinancialskillsinoperatingtheirbusiness.MSMEswithadequatefinancialliteracyaremorelikelytousedigitaltechnologyfortheiroperations.Basedonourfindings,MSMEsmustbeabletoeasilyaccessformalfinancialinstitutions.Moreover,thegovernmentshouldencouragebanksandotherformalfinancialinstitutionstoexpandfinancialaccesstoMSMEsandtoprovidedigitaltrainingfortheirbusiness.50DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaReferencesAbdel‐Hamid,T.A.,M.A.E.A.S.Ahmed,M.A.F.Zohry,G.A.Elshabrawy,andE.M.Elgohary.2022.TheRoleofDigitalTransformationinImprovingCustomerSatisfaction:AnEmpiricalStudyonEgyptianHotels.TheElectronicJournalofInformationSystemsinDevelopingCountries.ADB(AsianDevelopmentBank).2020.AsiaSmallandMedium-SizedEnterpriseMonitor2020:Volume1—CountryandRegionalReviews.https://data.adb.org/dataset/2020-adb-asia-sme-monitor-vol1-country-regional-reviews(accessed18May2023).AFI(AllianceforFinancialInclusion).2020.FinancialEducationfortheMSMEs:IdentifyingMSMEEducationalNeeds.https://www.afi-global.org/publications/financial-education-for-the-msmes-identifying-msme-educational-needs/(accessed20May2023).AlphaBeta.2021.UnlockingThailand’sDigitalPotential:TheEconomicOpportunitiesofDigitalTransformationandGoogle’sContribution.https://accesspartnership.com/promoting-thailands-digital-transformation/(accessed24July2023).AssociationofIndonesianInternetServiceProviders.2022.PenggunaanInternetOlehUMKM.https://dataindonesia.id/digital/detail/survei-mayoritas-umkm-pakai-internet-untuk-berjualan(accessed20May2023).AssociationofIndonesianInternetServiceProviders.2023.SurveyAPJII.https://survei.apjii.or.id/(accessed19May2023).Autio,E.,andK.Fu.2022.DigitalFrameworkConditionsandtheProductivityPotentialofaCountry’sEntrepreneurialDynamic:AStudyofSelectedADBMemberEconomies.Arendt,L.2008.BarrierstoICTAdoptioninSMEs:HowtoBridgetheDigitalDivide?JournalofSystemsandInformationTechnology10(2):93–108.Barland,J.2013.InnovationofNewRevenueStreamsinDigitalMedia:JournalismasCustomerRelationship.NordicomReview34(s1):99–111.Barba-Sánchez,V.,M.D.P.Martínez-Ruiz,andA.I.Jiménez-Zarco.2007.Drivers,BenefitsandChallengesofICTAdoptionbySmallandMediumSizedEnterprises(SMEs):ALiteratureReview.ProblemsandPerspectivesinManagement5(1):103–14.Cenamor,J.,V.Parida,andJ.Wincent.2019.HowEntrepreneurialSMEsCompetethroughDigitalPlatforms:TheRolesofDigitalPlatformCapability,NetworkCapabilityandAmbidexterity.JournalofBusinessResearch(100):196–206.DigitalDivideamongMicro,Small,andMedium-SizedEnterprises:WhatCanWeLearnfromHouseholdEnterprises?51Chonsawat,N.,andA.Sopadang.2020.DefiningSMEs’4.0ReadinessIndicators.AppliedSciences10(24):8998.Dyerson,R.,G.Harindranath,andD.Barnes.2009.NationalSurveyofSMEs’UseofITinFourSectors.ElectronicJournalofInformationSystemsEvaluation12(1):39–50.EastVentures.2022.EastVentures–DigitalCompetitivenessIndex2022.https://east.vc/reports/east-ventures-digital-competitiveness-index-2022/(accessed19May2023).Everett,C.2021.HowSMEsCanCureTheirFearofDigitalTransformation.Raconteur(blog),16June.https://www.raconteur.net/business-strategy/small-business-digital-transformation/(accessed29May2023).Fitzgerald,M.,N.Kruschwitz,D.Bonnet,andM.Welch.2014.EmbracingDigitalTechnology:ANewStrategicImperative.MITSloanManagementReview55(2).Google,Temasek,andBain&Co.2022.E-conomySEA.https://economysea.withgoogle.com/intl/id_id/home/(accessed20July2023).GSMA.2022.DigitalisingRuralMSMEs:Thailand’sAgricultureandTourismSector.https://www.gsma.com/mobilefordevelopment/resources/digitalising-rural-msmes-in-thailand/(accessed19July2023).Hoa,D.T.P.,andN.V.Khoi.2017.Vietnamesesmallandmedium-sizedenterprises:legalandeconomicissuesofdevelopmentatmodernstage.Economicannals-XXI(165):128–32.ILO(InternationalLabourOrganization).2019.FinancingSmallBusinessesinIndonesia:ChallengesandOpportunities.https://www.ilo.org/jakarta/whatwedo/publications/WCMS_695134/lang--en/index.htm(accessed10May2023).KatadataInsightCenter.2020.TujuanPelakuUMKMMengaksesInternethttps://databoks.katadata.co.id/datapublish/2020/06/27/internet-mendukung-umkm-jangkau-pelanggan(accessed19May2023).Lai,J.,andN.O.Widmar.2021.RevisitingtheDigitalDivideintheCOVID–19Era.AppliedEconomicPerspectivesandPolicy43(1):458–64.Lythreatis,S.,S.K.Singh,andA.N.El-Kassar.2022.TheDigitalDivide:AreviewandFutureResearchAgenda.TechnologicalForecastingandSocialChange175(121359).Mohamad,J.2021.Streng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cesstotheinternet.Ofthese,mostliveinleastdevelopedcountries(LDCs),landlockeddevelopingcountries(LLDCs),andsmallislanddevelopingstates(SIDS).Thegapsinaccessalsopresentbetweencountriesandwithincountries,with87%ofpeoplelivingindevelopedcountrieshavingaccesstotheinternetcomparedtolessthanhalfofthepopulationlivingindevelopingcountries.Inaddition,only40%ofpeopleinruralareasareconnectedonline,thesameashalfofthosewholiveinthecitiesthathaveinternetaccess.Thishascenteredthepublicdiscourseonhowtoaddresstheintricacyofallocatingtheperksofdigitalization,knownasdigitalinclusion,tothoseatthemargin.Insomecases,digitalinclusionmeansprovidingtherightaccessandopportunitiestotherightpeople(UnitedNations2022a);inothercases,inclusionalsoextendstohowmuchsupportactorsowningmoreresourcesprovidetodifferentgroupsinsocietytoobtainthegainsoutofavailableopportunitiesonlineNDIAn.d.).Forthisstudy,digitalinclusionreferstothedegreeofequalopportunityforeveryone,withoutanyexceptions,toaccess,utilize,54IncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide55andinnovateinthevariousaspectsoftechnology,encompassingdigitalaccess,skills,andusage.1Reflectingsuchdiscrepanciesindefiningdigitalinclusion,theapproachestotackleissuesalsovary.Governmentsemploydifferentpolicies,andprivatesectoractorshavelaunchedawiderangeofinitiativesdependingonthemostpressingissuesrelevanttotheirdigitalusers.Forinstance,programsinAfricasuchasKenyaAjira’sdigitalprojectfocusesonpromotingdigitalliteracytohelpyoungpeopleimprovejobopportunities,thusreducingunemployment.Similarly,apublic–privatepartnershipsuchasSmartAfricaDigitalAcademywasthedirectresultofaninitialcommitmentfromAfricangovernmentstoutilizeAfricans’digital-basedknowledgeeconomythroughaffordableaccesstobroadbandandtechnologydevicesbyinvitingprivatesectorssuchasMTN,aprominentmarketoperatorcompany,tocontributetoAfricans’digitalskillsacademy(SmartAfrica2022).Ontheotherhand,anapproachemployedbytheGovernmentoftheUnitedKingdom(2017)slightlydiffersthroughitsPlanforBritain,whichaccentuatedtheplanto“buildastronger,fairercountrythatworksforeveryone,notjusttheprivilegedfew”andemphasizethattheprioritieswillbedirectedtoresearchanddevelopmenttoupliftitsdigitalspherebasedonenhancedartificialintelligence(AI),aswellasexpandingitsfinancingfordigitalspaces.Meanwhile,theAfricaDigitalFinancialInclusionFacility,whichissupportedbytheAfricanDevelopmentBankandbackedbytheBill&MelindaGatesFoundation,theAgenceFrançaisedeDéveloppement,theMinistryofFinanceoftheGovernmentofLuxembourg,andtheMinistryoftheEconomyandFinanceoftheGovernmentofFrance,prioritizes60%ofinitiativestoensureappropriatedigitalinfrastructureisinplacesuchasoninteroperability,marketinfrastructure,digitalidentity,anddigitalregistriesinkeysectors.Another20%ofinitiativeshavebeenlaunchedtoinstilldigitalproductsandinnovationthroughsupportdirectedthroughfinancialtechnologies(fintech),governmentpayments,credits,microinsurance,andvaluechains,whiletheremaining20%aresplitbetweenimprovingpolicyandregulationanddistributingneededcapacitybuilding(ADFI2023).Whileprioritizationdiffersamongstakeholders,whole-of-societypartnershipisattheheartofthesedigitalinclusion-driveninitiativesand/orpolicies,andcontextualizingtheinitiativesand/orpoliciesitselfmatterstoaddressthecoreissueofwhydigitalexclusionstillpresentsinparticulargroupsand/orregions.Toassessthestarkproblemaffectingdigitalinclusion,variousinstitutes,thinktanks,governments,and1Giventheintentionofthestudyistoexploretheuniversalityofdigitalization,itdoesnotexplorethemeasurementofthequalityofdigitalinclusion.56DigitalTransformationforInclusiveandSustainableDevelopmentinAsiapartnerships,suchasEconomistIntelligenceUnit(EIU),WorldBank,ITU,InstituteforBusinessintheGlobalContext(IBGC)oftheFletcherSchool,RolandBerger,andWorldBenchmarkingAlliance(WBA),issuetheirownclassificationsandmeasurements.Theserecurringmeasurementsareexpectedtoprovidetherightmeasurementfortheexistingissuesondigitalexclusionandthusofferwell-fittingsolutionstotheproblem.Mostofthesemeasurementsrankhowdifferentcountriesperforminensuringtheirpopulationsreapthebenefitsofdigitalization(WorldBank2021;EIU2022;ITU2023;RolandBerger2021),withmeasurementslikeWBA(2023a)providinganicheapproachtoassessmajorcompaniesinparticipatingtoenhancedigitalinclusionfortheirusers,whileIBGC(2023)examinestheissueofdigitalinclusionapplyingthesocioeconomicparitylenstodifferentcountries.ThischapterseekstoexplorehowtherelationshipbetweengovernmentsandprivateactorsshouldlookintheeffortstobridgingdigitalgapsinAsiaandthePacificthroughregulatoryandpolicyprogramapproaches.Indoingso,thischapterfirstdiscussestheoverallperformanceofdigitaltechnologycompaniesondigitalinclusionglobally,whiledrawinginternationalandregionalpolicycommitmentstoaddresstheexistingdigitaldisparity.Thechapterwillthenillustratethatsuchcommitmentsarebetteraddressedthroughacloserpartnershipwiththeprivatesector.IttakesacloserlookatexistinggovernmentinitiativesinAsiaandthePacificthatenabledigitalinclusion.Finally,thechapterdrawsoutpossibleentrypointsasregulatoryproposalsforgovernmentsandtheprivatesectortocollaborateinnarrowingthedigitaldivideincountriesintheregion.3.2CoursingDigitalInclusion:WhereIstheGlobeAt?Theworldremainsonitsgradualcoursetoclosethedigitaldivideandreduceexclusionofpopulations.Awiderangeofmultilateralcommitments,public–privatepartnerships,andwhole-of-communityapproachesseektoalleviatethenotionthatdigitalizationonlybenefitsafewgroups.Amongotherinstances,ITUplansitsagendacalledConnect2030tobridgethedivideandbuildabetterworldusingtechnologicalandrapidadvancement.Thisagendaincludesfivegoalstoachieveby2023,oneofwhichisadvancinginclusivenesstobridgethedigitaldivideandprovidebroadbandaccessforall.Thesearemappedthrough10targetsthatencompassinternetaccessbasedonhouseholds,countries,affordabilitybetweencountries,gender,andage(Table3.1).Yet,thecoursetoachievethesetargetsmightbesteeperthanexpected,lookingatthestateofglobaldigitalaccessin2022.IncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide57Table3.1:InclusivenessGoalunderITUConnect2030—Targetsby2023Goal2.Inclusiveness:BridgethedigitaldivideandprovidebroadbandaccessforallBy2023,inthedevelopingworld,60%ofhouseholdsshouldhaveaccesstotheInternetBy2023,intheleastdevelopedcountries,30%ofhouseholdsshouldhaveaccesstotheInternetBy2023,inthedevelopingworld,60%ofindividualswillbeusingtheInternetBy2023,intheleastdevelopedcountries,30%ofindividualswillbeusingtheInternetBy2023,theaffordabilitygapbetweendevelopedanddevelopingcountriesshouldbereducedby25%(baselineyear2017)By2023,broadbandservicesshouldcostnomorethan3%ofaveragemonthlyincomeindevelopingcountriesBy2023,96%oftheworldpopulationcoveredbybroadbandservicesBy2023,genderequalityinInternetusageandmobilephoneownershipshouldbeachievedBy2023,enablingenvironmentsensuringaccessibletelecommunications/ICTsforpersonswithdisabilitiesshouldbeestablishedinallcountriesBy2023,improveby40%oftheproportionofyouth/adultswithtelecommunication/ICTskillsICT=informationandcommunicationtechnology,ITU=InternationalTelecommunicationUnion.Source:ITU(2023).Comparedtoadecadeago,thetotalnumberofinternetusershasdoubledfrom2.4billionto5.3billionusers(ITU2022c).Whilethisiscommendableasastartingpointinproliferatingequalaccesstotheinternet,ITU’s(2022d)recentpublication,MeasuringDigitalDevelopment,revealedexistinggapsbetweengender,countryincome,andcontinentsin2022.Comparedtowomen’saccess,259millionmoremenhadinternetaccess;thisisequivalenttoa0.6%differencebetweenmenandwomenconnectedonline(ITU2022e).Onapositivenote,genderparityhasgenerallybeenachievedinhigh-incomeanduppermiddle-incomecountrieswherethedifferenceis1.0%.However,disparitypersistsinlowermiddle-incomeandlow-incomecountries,respectivelyindicatinga10.0%and11.0%differencebetweenmenandwomenwithinternetaccess(ITU2022e).The2023targettohave30%ofthepopulationinLDCsusingtheinternetwasachieved,with36%ofthepopulationinthesecountriesconnected,butdiscrepanciesinLDCsandLLDCsremain,withminimalprogressmadeongenderparityinthepastthreeyears(Figure3.1).Thedisparityisalsoclearacrosslargerregionsandcontinents:Whilemorethan80%ofindividualslivingintheAmericas,theCommonwealthofIndependentStates(CIS),and58DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaSIDSLLDCsEuropehaveinternetaccess,only40%ofpeopleinAfricaareconnectedLDCs(ITU2022b).The2023targetofbroadbandsubscriptionshasalsobeenachieved,with108mobilephonesubscriptionsper100inhabitantsHigh-income(ITU2022b).However,itisimportanttonotethatthehighpenetrationUpper-middleincomedoesnotclearlyexplainwherethesubscriptionsconcentrateandLower-middleincomewhichhouseholdspayforthesubscriptions.RegionaldisparitiesacrossLow-incomecontinentsalsoremain,with86per100inhabitantsinAfricahavingaccesstomobilephonesubscriptions,comparedwith109intheAmericas,111inEuropeAsiaandthePacific,121inEurope,and147intheCIS.Mostimportantly,CISwhile75%ofyoungpeopleworldwideusetheinternet,only39%inlow-Asia-Paciﬁcincomeeconomiesactivelyusingit(ITU2022d).ArabStatesAmericasFigure3.1:PercentageofIndividualsUsingtheInternet,AfricabyRegionandGender,2022Worlda.Percentageofindividualsusingtheinternetbyregion,b.PercentageoffemaleandmalepopulationusingtheInternet,FemaleMaleCIS=CommonwealthofIndependentStates,LDC=leastdevelopedcountry,LLDC=landlockeddevelopingcountry,SIDS=smallislanddevelopingstates.Source:ITU(2022d).SIDSLLDCsLDCsHigh-incomeUpper-middleincomeLower-middleincomeLow-incomeEuropeCISAsia-PaciﬁcArabStatesAmericasAfricaWorldIncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide59Thepersistentgapsinindividuals’accesstotheinternetinLDCsandlower-incomecountriesacrossdifferentsocioeconomicindicatorspointtowhatextenttheservicesareaffordablerelativetotheirincome,whichaffectsthepopulation’sunderstandingandskillsinutilizingtechnologiesandtheinternet.AlthoughthepriceforpurchasingmobilebroadbandserviceshasdroppedworldwideaftertheCOVID-19pandemic,themedianpricesindividualspayforinternetaccessinlowermiddle-incomeandlow-incomeeconomiesarerespectively10and30timeshighercomparedwithhigh-incomeeconomies,uponadjustingforthedifferenceoftheirgrossnationalincomepercapita.Onapositivenote,Africasawa1.5%reductionofsharepricesinmobilebroadbandin2022comparedtothepreviousyear.Low-incomecountriesnotedasimilartrend,witha2.1%declineinshareprices,from11.4%sharein2021to9.3%sharein2022(ITU2022e;Figure3.2).Further,thecomplexityinconnectingthelackofownershipandaffordabilitywithmeaningfulconnectivitycanbetracedtolimiteddataavailabilitytomeasurethelevelsofdigitalskillsamongthepopulation.Todate,acrossthefiveclusterstomeasuredigitalskills—communicationandcollaboration,problemsolving,safety,contentcreation,andinformationanddataliteracy—only78countriessubmitteddata,withvarianceofFigure3.2:Data-OnlyMobileBroadbandServiceBasketPrices,2021–2022(%ofgrossnationalincomepercapita)............................SIDSLLDCsLDCsHigh-incomeUpper-middleincomeLower-middleincomeLow-incomeEuropeCISAsiaPaciﬁcArabStatesAmericasAfricaWorldCIS=CommonwealthofIndependentStates,LDC=leastdevelopedcountry,LLDC=landlockeddevelopingcountry,SIDS=smallislanddevelopingstates.Source:ITU(2022d).60DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaavailabilitydataineachofcategory.Outofthesefivecategories,amedianof50%andaveragebetween31%and65%ofindividualsperformedbestincommunicationandcollaborationskills.Meanwhile,fromtheselimiteddata,thelowestmedianofpopulationpointedtolackofskillsininformationanddataliteracy(ITU2022).Tomovetheneedleandadvancedigitalinclusionamidthepersistentgapsoutlinedearlier,internationalcommunitieshavevowedforgreatercollaborationandpartnership.Forinstance,OurCommonAgendareleasedbytheUnitedNationsSecretary-General’sEnvoyonTechnologyin2021encompassesawiderangeofstakeholdersfromgovernments,multilateralsystems,theprivatesector,civilsociety,grassrootsorganizations,andacademiatoindividuals,includingyoungpeople,whowillberequiredtoensurethataGlobalDigitalCompacttobeagreedattheSummitoftheFutureinSeptember2024foreseesanopen,free,andsecuredigitalfutureforall.Atleastsevenactionsareproposedtoimprovedigitalcooperation:(i)connectallthepeopletotheinternet,includingallschools;(ii)avoidinternetfragmentation;(iii)protectdata;(iv)applyhumanrightsonline;(v)introduceaccountabilitycriteriafordiscriminationandmisleadingcontent;(vi)promoteregulationofAI;and(vii)fosterdigitalcommonsasapublicgood(UnitedNations2021).TheG20DigitalEconomyMinisters’MeetinginSeptember2022highlightedthepublicsector’sroleinensuringtherightincentivesexisttoboostprivateinvestmentandinnovation,thussupportingcommunity-ledprogramsfordigitalskillsandtrainingasneeded(IndonesiaMinistryofCommunicationsandInformatics2022).Thenextsectionwillshedlightontheextenttowhichtheprivatesectorshouldbeprovidedspacetopartakeintheroleofadvancingdigitalaccessfortheglobalpopulation.3.3WhyShouldthePrivateSectorBeGivenaSeattoHelpAdvanceDigitalInclusion?Theprivatesectorisarguablyanimportantactorinadvancingdigitalinclusionasitincorporatesthenecessaryresourcestospurinnovation,thoughsomemayarguethattheirprofit-seekingtendencystraysfromtheideaofcontributingtoprovidingaccessforthoseatthemargin.Yet,evidencesuggeststhatinsomedevelopingcountries,theprivatesectorsserveasaplatformtoacceleratedigitalfinanceadoption,particularlyattheonsetoftheCOVID-19pandemic.Forinstance,e-commerceplatformshavemushroomedacrossdevelopingcountries,withLatinAmericatoppingthegrowthat37%(GPFIandWorldBank2021).IncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide61Thisisarationalchoiceforprivatesectors:themorepeopleareconnectedonline,themorepeoplewoulduseprivateservicesofferedbycompanies.Inaddition,shouldtheinclusionpoliciesthatextendtoadvancingliteracyskillssucceed,moredigitaluserswouldbeawareoftheavailableservicesandcouldgeneratemeaningfulfeedbackforthecompaniesandthusgeneratebetterinsightsonneededimprovements.Ultimately,digitalinclusionwouldprecipitatemeaningfulaccessthatwouldenableprivatesectorstoofferadvancedservicesandproducts,aswellasexpandedmarketsabroad(EIU2022).AsurveylaunchedbyEIU(2022)inIndonesia(Figure3.3)revealedthattheprivatesectorisbelievedtohavethegreatestroleindevelopinginfrastructure(45.5%),catalyzeinnovation(39.5%),andprovideICTtraining(33.0%)andeducation(31.5%).Figure3.3:ThePrivateSector’sRoleinBridgingtheDigitalDivideBasedonIndonesianRespondentsInfrastructuredevelopment(e.g.,buildingphysical.networksorenhancingexistingones).Innovation(e.g.,introducingnewtechnologies.andbusinessmodels)..ICTtraining(e.g.,helptoelevateworkerskills)..ICTeducation(e.g.,helptoelevate.schoolcurriculums).Knowledge(e.g.,supportingschoolsandacademicinstitutions)Funding(e.g.,creatingorimplementingprogrammesfocusedonICTinclusion)Bycreatingcontent(e.g.,creatinglocalresourcesinlocallanguages)Byprovidingleadership(e.g.,businessassociationsorPPPs)Throughmarketing(e.g.,enhancingawarenessofICTsgenerally)ICT=informationandcommunicationtechnology,PPP=public–privatepartnership.Source:EIU(2022).Withtheprominenceoftheprivatesector’sroleinbridgingthedivide,theassessmentofferedbytheWorldBenchmarkingAlliance(WBA),whichaimstogenerateamovementtoincentivizeprivatesectorstakingpartinthesustainabilityagenda,canserveasabasistogenerateinsightonhowtheseactors’commitmenttranslateintopractice.62DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaAmongdifferentindexes,WBA(2023a)justlauncheditsthirdDigitalInclusionBenchmark,whichscores200oftheworld’smostinfluentialtechnologycompaniesinadvancinganinclusivedigitalsociety.WBA(2023)hasdefineddigitalinclusionfromtheperspectiveofequitableaccess,qualityskills,meaningfulutilizationthatcouldmitigateriskandharm,andinclusiveandethicalinnovation.Of200assessedcompanies,64areintheUnitedStates,46inAsia(excludingthePeople’sRepublicofChina),41inEurope,25inthePeople’sRepublicofChina,andtheremaining24inotherareas.Bycompanytype,mostassessedcompaniesweretelecomservices(40%),followedbyITsoftwareandservices(31%)andhardwarecompanies(29%)(WBA2023b).SimilartotheITUfindingsattheglobalandregionallevel,mosttechnologycompanieswerestillbehindinfulfillingtheirparttoensuretheirusersbenefitedfromthedigitalservicestheyoffer(WBA2023a).Of200assessedcompanies,only27scored50orabovethebenchmark.Although38of200assessedcompaniesprovideddevicesattheonsetoftheCOVID-19pandemictoensureequitableaccess,24ofthese38discontinuedtheirassistancedespitevulnerablegroupscontinuingtoneedaccesstodevices.Onaverage,bytypeofindustry,telecommunicationcompaniesperformedthebestcomparedtohardwarecompaniesandITsoftwareandservicesfirms.Inaddition,basedongeographicallocationoftheassessedcompanies,thoseinEuropehadthehighestaverageprogressindigitalinclusion,followedbycompaniesinAsia,theUnitedStates,othercontinents,andthePeople’sRepublicofChina(WBA2023b).Inaddition,thetop10%ofthe200assessedcompaniesaregeographicallyvaried:eightwithheadquartersintheUnitedStates(Apple,Microsoft,Cisco,Dell,Verizon,HP,IBM,andQualcomm),followedbysevenfirmsinEurope(Telefonica,Orange,DeutscheTelekom,Telia,Telenor,Vodafone,andEricsson)andfivecompaniesinAsiaandthePacific(Samsung,Telstra,Singtel,SKTelecom,andAIS)(WBA2023a).The200majorcompaniesassessedbyWBAalsofareddifferentlyinaspectslinkedtodigitalusethatarecloselyassociatedwiththeircorebusinesses.ThecommitmenttocybersecurityoftechnologycompaniesgloballyandfocusedonAsiaandthePacificwasratherlowcommitment,asonlyaround32%ofcompaniesdisclosedahigh-levelcommitmenttocybersecurityinitsbusinesscodes,governancestatements,orotherrelatedpolicydocuments.Asregardsprotectingtherightsofchildrenonlineandprovidingaccessforwomenintechnology,amongotherissues(Figure3.4),onlythree(Singtel,Telefonica,andVodafone)ofthetop20companieswithbestperformanceindigitalinclusiondemonstratedacommitmentintheircompanypoliciestosafeguardingchildrenonline.Meanwhile,ofthe200assessedcompanies,28haveIncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide63committedtokeepingchildrensafeonline(WBA2023a).InAsiaandthePacific,withatotalnumberof74companiesassessedinthebenchmark,only9companieshaveshownacommitmenttoonlinechildprotection.ThesefiguressuggestthatthepercentageofcompaniescommittedtoonlinechildsafetyintheAsiaandPacificat12.16%islowerby1.84%comparedtotheglobalscore(14.00%).Byindustry,telecommunicationcompaniessuchasTelia,Vodafone,PLDT,Millicom,andAT&Thavethehighestscoresinkeepingchildrensafeonline.Meanwhile,assessedcompaniesinEuropeperformedthebestbyfaronchildonlinesafety(WBA2023a).Inaddition,thefindingspointglaringlytotheneedforinternetcompaniessuchasMeta,ByteDance,andNetflixtoadoptonlineFigure3.4:DigitalSocialInclusionAspects—Children’sSafetyandAccessforWomenandGirlsa.AverageScoresonChildOnlineSafetyIndicatorPRC.UnitedStates..AsiaOther.Europe.Allcompanies.Hardware.ITservices.Telecom.b.CompanieswithInitiativeforDigitalInclusionofWomenandGirlsAllPRCAsiaOtherEuropeUnitedStatesAllcompaniesITsoftware&servicesTelecomservicesHardwareIT=informationtechnology,PRC=People’sRepublicofChina.Source:WBA(2023a).64DigitalTransformationforInclusiveandSustainableDevelopmentinAsiasafetyanddataprivacymeasuresforchildrenasadolescentsaccesstheirplatformandthusmayalsosharetheirpersonaldataonit.Intermsofproportion,moremajorcompanieshaveshowntheircommitmenttoalleviatinggenderdisparity,particularlywomeninscience,technology,engineering,andmathematics(STEM)comparedagainsttheproportionofcompaniesthathavecommittedtokeepingchildrensafeonline.Slightlymorethanhalfofthe200assessedcompanieshaveatleastoneinitiativetoprovideaccessand/ordigitalskillstowomenandgirls.However,companiesheadquarteredinAsiaandthePacificscoremuchlower,withonly21of74supportingdigitalinclusivityforwomenandgirls.Thisscoreis22.1%lowerthanglobalcompanies(WBA2023a).Suchinitiativesmostlyareledbythehardwareindustry,whereasITsoftwareandserviceshavethefewestinitiativesinthisspace.Thepositiveprogressinadvancingwomen’sdigitalaccesshasalsobeendrivenbyeffectivepartnershipwithnonprofitorganizationsand/orothercompanies(WBA2023b).Nevertheless,thefindingsillustratethatsustainabilityofthesupportbeyondone-offprograms,aswellasincorporatingbalancedwomenrepresentationwithinthecompaniesaftertheyfinishtheireducation,willbemoresignificanttobringimpactintermsofdigitalinclusion.Fromtheassessmentof200majordigitalcompaniesbyWBA(2023b),itisimportanttonotethatalthoughmajorcompaniesmayhavecommitmentsinsomeaspectsofinclusion,suchasensuringchildrenaresafeonlineorequitableaccesstoSTEMamongwomenandmen,theimplementationmayvary.Thiswoulddependonthepartnersthesecompaniesjointlycollaboratewithand/oravailableresourcesdirectedtospecificcorporatesocialresponsibility(CSR)areaswithinagivenperiod.Thenextsectionillustratesgovernmentpoliciesinseveralmiddle-incomecountriesinAsiaandthePacifictocross-matchwithpossiblepublic–privatesectormodelsthatcanbegearedtoensuredigitalizationbenefitsall.3.4DigitalInclusioninAsiaandthePacific:NavigatingtowardParityAcrossAsiaandthePacific,despitebeinghometoamushroomingnumberofdigitaluserscomingonlineduringthepandemic,moreneedstobedonetonotleaveanyonebehind.Asof2022,only64%ofpopulationhadaccesstotheinternet,slightlylowerthantheworld’saverageat66%ofindividualsusingtheinternetduringthesameperiod(ITU2022c;Figure3.1).Thisisequivalenttomorethan1.46billionpeoplelivingintheregionwithoutinternetaccess,contributingtohalfoftheworld’spopulationwhodonotusetheinternet.ThegapsalsopersistsacrossIncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide65differentsocioeconomicindexes.Atleast804millionmeninAsiaandthePacifichaveaccesstotheinternetcomparedwith683millionwomen.Thestarkdiscrepancyinaccessbetweenruralandurbanareasacrosstheregionalsomimicstheglobalgap,withonly47%ofindividualsinruralareashavingaccesstotheinternetcomparedwithashighas82%ofthepopulationinurbanareas(ITU2022d).thelevelofinclusionvariesalsobetweencountriesofdifferentincomelevels.InEastAsiaandthePacific,forexample,88%ofSingapore’spopulationhasaccesstotheinternet,butonlyathirdofthepopulationinthePhilippinesandtheLaoPeople’sDemocraticRepublicisusingtheinternet(Euromonitor,inTufts2021).AsimilartrendheldintheMiddleEast,with88%ofthepopulationoftheUnitedArabEmiratesbeingconnectedonlinecomparedwithonly57%inJordan.TheevidencestarklyrevealsthatinallcountriesinSouthAsia(Bangladesh,Pakistan,India,andSriLanka)lessthanhalfofthepopulationisconnectedonline(Euromonitor,inTufts2021).Mostimportantly,tohavemeaningfulinternetaccess,peopleneedtobeequippedwithproperICTskills.However,limiteddataareavailablefromcountriesinAsiaandthePacifictoassessthelevelofdigitalskillsincommunicationandcollaboration,problemsolving,safety,contentcreation,anddataliteracy.Mindfulthattheunderlyinggapsforthoseatthemargintogaindigitalaccessmayaffectoverallnationaldevelopment,governmentsacrossAsiaandthePacifichaveallocatedtheirresourcestolaunchawiderangeofpoliciesondigitalinclusion.InSoutheastAsia,digitalinclusionisoftenlinkedwiththeambitiontoelevatetheboomingdigitaleconomy.Forinstance,Indonesiahasleverageditsmissiontoelevatethecountry’sdigitalandfinancialinclusioninonego.Of210milliondigitalusersinIndonesia,79%usetheinternetforonlinetransactionsand72%alsoadmitthattheymostlyaccessfinancialservices(BI2022).Indonesia’sprioritiestousedigitalizationinexpeditingfinancialinclusionalignwithBankIndonesia’stargettohaveanintegrateddigitalpaymentsystemby2025(BI2022).Thistargetcorrespondswithevidencethatsuggeststhatdigitalfinancialinclusion,supportedbyinfrastructureaccess,digitalandfinancialliteracy,andgoodgovernance,wouldimprovestates’economicgrowth(Kheraetal.2021;OzturkandUllah2022).Inthisregard,IndonesiahaslaunchedtheQRCodeIndonesianStandard(QRIS)inaformofatwo-dimensionalbarcodetosupportBankIndonesia’s2025ambition.Itcomesintheformofasimplifiedcode,whichmerchantscanusetoeasilycollectpaymentthroughmoneystoredinadigitalwallet(BI2022).ThisprogramwaslaunchedtoactasabridgebetweenmushroomingMSMEsandboomingdigitalusersinIndonesia.The2022Business20(B20)ForuminIndonesiahighlightedthatdigitalfinancialinclusionwaspromotedtoincludeyoungpeople66DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaandMSMEstofosterasustainableeconomy(MinistryofFinance2022).Onapositivenote,morethan19millionmerchantsinIndonesiaarecurrentlyusingQRIStocollectpayment.Inanotherinstance,MalaysiahasalsoinstalledMyDIGITALasthegovernment’sinitiativetoadvancedigitaleconomyaspartofTwelfthMalaysiaPlanandSharedProsperityVision2030.Inthisvein,theMalaysianDigitalEconomyBlueprintwasissuedtoenhanceinfrastructure,buildaskilledworkforce,striveforaninclusivedigitalsociety,andensureasecureandethicaldigitalization(GovernmentofMalaysia2021).By2025,theMalaysiangovernmentforeseesthat22.6%ofthecountry’sgrossdomesticproductwouldbecontributedbythedigitaleconomy.InSouthAsia,inadditiontofocusingonboostingthedigitaleconomy,thepriorityisalsoonensuringequitableinfrastructureinruralareasgiventhelowpenetrationofinternetaccess,aswellasonupskillingandreskillingthepopulation.Forexample,IndialaunchedtheDigitalIndiaprogramin2015thatintendstotransformIndia“intoadigitallyempoweredsocietyandknowledgeeconomy”(GovernmentofIndia2015a,2015b).Itservesasanumbrellaprogramthatspansdifferentdepartmenttoachievetargetsacrossthreemaincomponents:digitalinfrastructure,digitalservices,anddigitalliteracy.Thesefurtherencompassninepillars:broadbandhighways,universalaccesstophones,publicinternetaccessprogram,e-governance,electronicdeliveryofservicesthrougheKranti,universalinformationforall,electronicsmanufacturing,informationtechnology(IT)forjobs,andanearlyharvestprogramtoupskillandreskillIndia’spopulationwithdigitalskills.AnotherinstanceinSriLankarevealedacaseinpointofagovernmentpartnershipwithmultilateralorganizations,inwhichtheInformationandCommunicationTechnologyAgency(ICTA)andtheUnitedNationsDevelopmentProgramme(UNDP)launchedtheNationalDigitalStrategybuildingonthelessonslearnedduringtheCOVID-19pandemictonotleaveanyonebehindunderthedigitalizationmomentum.Inthissense,thestrategywouldutilizeCitraLabasjointinitiativestoenhancethepublicsectorcapacitythroughitsNextGenGovFellowshipProgramme(UNDP2020).Thedigitaltransformationstrategyisrevolvesaroundhuman-centereddesigntoensurethatitiscitizencentric.Evidenceindeedsuggeststhatpartnershipswiththeprivatesectorhaveplayedasignificantroleinfacilitatinginclusionfordigitaltransformation.Theprivatesectorwithitsabundantresourcescouldenhancedigitalskillsandthuscatalyzefurtherinnovation(Șerbanetal.2022;Hammerschmidetal.2023;Horan2021;StottandMurphy2020).Inaddition,basedoncasestudiesin100countriesaftertheCOVID-19pandemic,digitalinclusionalsorequiresinclusivityinIncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide67engagingawiderangeofrelevantstakeholders,suchastheprivatesector,totakepracticalstepsinmaintainingtheagilitytoadaptandelevatecapacities(OECD2021).Businessescanalsoprovideanenablingenvironmentfornewstart-upsthatenshrinesimpactcommitments,providesneededfinancing,andacceleratestechnologicaladoption(OECD2021).Forinstance,oneprominentissueinadvancingdigitalinclusionistobewellinformedaboutthedegreeofprogressmadeonavailabilityandaffordabilityofdigitalaccess.FacebookhassupportedtheEIUsince2017,launchingtheInclusiveInternetIndextomeasuredigitalinclusionacrossfouraspects:availability,affordability,relevance,andreadiness.Thisindexhashelpedinleveragingawarenessontheimportanceofdigitalinclusionacrossdifferenteconomies(EIU2022).GooglehasalsoinitiateditsNextBillionUsers(NBU)initiativesince2015withprogramstoimproveaccess,buildconfidence,providevoice,ensuregenderequality,andgeneratemoreopportunitiesforthoseatthemargin.IthascreatedtheDigitalConfidenceToolkittohelppeoplewithlowdigitalliteracyindevelopingdigitalappsandfeatures.Inaddition,theinitiativealsoincludeslocallanguageservices,privatesearchesforwomen,andjob-matchingappstosupportyoungpeopleandvulnerablegroups(EIU2021).DespitetheinitiativesbymajorcompaniessuchasGoogleandFacebook,catalyzingsustainableprogramsfromprivatecompanieswillrequiresolidoffersfromgovernments.AsunderlinedbyUNCTAD(2021),privatesectorsrarelyinnovateinisolation.Thismeansthatfirmsand/orcompaniesoperatewithinnetworksthatarelinkedwithotherbusinesses,financialinstitutions,consumers,andregulatorsthatadhereand/orfollowasetoflawsappliedinparticularcountries.Basedonananalysisofthesurveyof14,125firms,thoughdigitalizationhasalwaysbeenofbenefit,thisdoesnotalwayscorrespondwithsustainabilitypracticesandinnovation(Ardito2023).Thus,toeffectivelyengagetheprivatesectortoadvancedigitalinclusioninasustainablemanner,governments,particularlyinlower-incomeandlowermiddle-incomecountries,needtoauthorizeappropriateincentivesfortheprivatesectortoattracttherightinvestmentandpartnership.Therefore,theincentivesthatgovernmentscanofferwillneedtotakeintoaccountthecontextsofthosewhoneedthemthemost,coupledwithopportunitiesforvulnerablegroups,whileatthesametimebenefitingtheprivatesector.Thesecanbecategorizedintoindirectenablersanddirectincentives.Indirectpolicies,launchedbygovernmentsnationally,willbenefitprivatecompaniesthathelpadvancedigitalinclusionundertheirownindividualCSRprograms.Meanwhile,directincentiveswouldbetargetedatthefirmsand/orcompaniesthemselves(Table3.2).68DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaAtleastfour(inter)governmentalpoliciesthatrelyontransparentdata,aclearindustrialstrategy,andabetterdatagovernancesystemwouldserveasindirectincentivesfortheprivatesectortoadvancetheiragendaofinclusionforthepeople.First,withnationsgloballykick-startingnegotiationprocessesontheUnitedNationsGlobalDigitalCompact,aimedtobeadoptedduringtheSummitfortheFuture2024(UnitedNations2023),governmentsmustacknowledgethesubstantialcontributionoftechcompaniesinacceleratingdigitaltransformationglobally.ThisacknowledgmententailsrealizingthatwithoutdigitaltechnologycompaniesgivenrolesprescribedundertheGlobalDigitalCompactthataimstouniversalizedigitalinclusion,thegoaltonarrowthedigitaldividewouldunlikelybeachieved.Thus,itisimportantthattheGlobalDigitalCompactserveasatoolthatincentivizestechcompaniestofulfilltheircommitmentsondigitalinclusion.TheGlobalDigitalCompactmomentumisrighttokick-starttheseinitiativestoconnectthe“unconnected”andincludingthe“unincludedorexcluded”inthedigitaltransformationpathways,suchasbytaxincentives,technologysubsidies,andotherreliefoptions(Tobing2023).Second,governmentsmayconsiderhavingaproperandcomprehensiveassessmentthroughdatatransparency(UnitedNations2023),lookingattheutmostneedsintermsofdigitalinclusionbasedondifferentgroupsinthecountries.Havingdisaggregateddatabasedondifferentsocioeconomicgroupsiskeytoprovidetransparencyfortheprivatesectoringeneratingnationalandregionalinsightsofdifferentgroups’access,thusbetterdesigningtheirCSRinadvancinginclusion.Thisweaknessofgovernmentcapacitytoprovidedatatransparencymayopenupopportunitiesfortheprivatesectortocollaborate,suchasonhowtooptimizepublicplatformstopresenttheavailabledata.Forinstance,ifyoungpeoplehavelimitedaccessbecauseoflackofdeviceownership,assistanceshallbeprovidedforthemtoaccessthedigitalinfrastructure(i.e.equipment,devices).Also,low-incomegroupsmayalsolackmeaningfulaccesstothedigitalplatformsduetolimitedskills.Interventionssuchasphasedupskillingandreskillingand/orvocationaltrainingwouldberequired.Hence,governments’commitmenttoensuredatatransparencycouldserveasanentrypointfortheprivatesectortocollaborateinenhancinggovernmentsystems.Inthemediumterm,thistransparencycanbeutilizedfurtherbytheprivatesectortoshapetheircollaborationwithdifferentactorsinadvancingdigitalinclusionforawiderangeofpopulationgroups.Third,governmentsmaywanttoprepareanindustrialstrategythatreassuresthatthetrainingand/orreskillingprogramsprovidedbytheprivatesectorwouldbeabsorbedinthelongterm(UNCTAD2021),presentingoptionsfortheprivatesectortoexpandtheirmarketsIncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide69and/oroperations.Thisincludeshavinginplacearobustandclearnationalinnovationsystem,typesofsupportprovidedforSTEMstudentsand/orearlydigitalincubators,andplatformstoconnectthescientistsandcivilsocietywiththeprivatesector.Thecomprehensiveindustrialpolicystrategiesmayalsoconsideramechanismtoshareintellectualpropertyrightsbetweentheprivateandpublicsectorstopushcompaniesinvestingindigitalinclusion-linkedinitiatives(UNCTAD2021).Finally,asregardsdatatransparency,improvingdatagovernancewillclearlydelineatewhothedatastewardsareandhowtohandledataondigitalplatforms.Further,betterdatagovernancewouldclearlydefineindividuals’dataprivacy,distinguishingdatathatcannotbesharedandaggregatedatathatshouldbeaccessibleforalltoimproveinnovation.Clearmanagementonthismayaddressthereluctanceofinternetcompaniestobuildhubsincountriesandrethinkprogramstoensureinclusiveaccessforitsusers.Table3.2:GovernmentIncentivestoBoostCompanies’AccountabilityinDigitalInclusionIndirectEnablersDirectIncentives•Ensureddatatransparency•Comprehensive,updated,andoDisaggregateddatatargeteddigitalinclusionpolicyoContextualassessmentoDataassessmentofruraland/oroTargetedcorporatesocialoutskirtareasresponsibilityoIncentivesforcompaniesthatcan•Robustindustrialstrategyprovideinfrastructureand/ortrainingassistanceoClearphasedinnovationsystemoSupportforearlyincubators•BundledpackageforprivatesectoroPlatformstoconnectscientistsandandstate-ownedenterprises(SOEs)privatesectoroAttractivebundledcontractpackageoSharingintellectualpropertyrightsbetweenprivatesectorandSOEs•BetterdatagovernanceoClearlong-termdigitalinclusionoCleardatastewardsstrategybytheprivatesectoroDistinctionbetweenpublicandprivatedataoReassuranceforinternetcompaniestoinvestSource:Author’sanalysis.Whileindirectenablerswouldprovideareassurancefortheprivatesectortoinitiatealong-termbusinessplanandstrategyincountriesacrossAsiaandthePacific,targetedincentivesforfirmsandcompaniesmaystrengthentheircommitmenttorolloutcollaborationwithpublicandotherstakeholdersinadvancingdigitalinclusion.70DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaFirst,governmentsmaywanttohaveatargeted,updated,andcomprehensivepoliciestoadvancedigitalinclusioninspecificoutskirtand/orruralareas.Thisincludeshavingevidence-baseddataontheextenttowhichthedisparityisglaringintheseareasandthereasonstheseareasrequirepublic–privateinterventiontoofferaccessandaffordabilityofdigitalservices.Withthesedata,governmentsmaylaunchpoliciesannouncingincentivessuchastaxreliefand/oradiscountforbusinesspermitlicensingtoattractprivatesectorassistanceinthesespecificareas.Second,itisundisputablethatstate-ownedenterpriseslargelyoperateanddriveinnovationpolicyacrosscountriesinAsiaandthePacificwithlargedigitaluserssuchasthePeople’sRepublicofChina,Indonesia,andVietNam,amongothers.Takingthisintoaccount,governmentsmayofferbundledcontractpackagesbetweenstate-ownedenterprisesandtheprivatesectorforthosecompaniesthathaveaclearlong-termplaninadvancinginclusioninthecountries,includingdigitalskillstrainingfortheworkforce,assistanceforwomeninSTEM,and/orprovisionofinfrastructureinneededareas.Thesebundledpackagesmaycomeintheformofapartnershipcontractand/orlong-termconcessionprogramthatwillbenefitboththepublicandprivatesectorinthelongrun.3.5ConclusionTheworkinadvancingdigitalinclusionclearlycannotbeconcludedinashortspanoftime.Itsometimesiscomplicatedbyhavingtojugglebetweenprioritizinggrowthgenerationtoboostthedigitaleconomyandattractingquickinvestmentthatmightonlybenefitafew,ontheonehand,andensuringthatthedigitalizationisstrategicallydesignedtoincludeallofsocietywithoutleavinganyonebehind,ontheother.Suchagrandmissioncannotbesimplifiedasataskforgovernments.Instead,itwillrequirearobustpartnershipbetweenpublicandprivatesectors,andevenextendtostakeholderssuchasacademia,thinktanks,scientists,andcivilsocietyorganizations.Whilepublicandprivatesectorsmayfillthevoidofpoliciesandresourcesneededtoensureequitableaccessandaffordability,theaspectsofinclusionmayonlybetoucheduponbythegrassrootscivilsocietyorganizationsand/orcommunitiesthatarefamiliarwiththeday-to-daystruggleofvulnerablegroups.Inaddition,collaborationwithscientistswhohavethetechnicalknowledgeneedstobesharpenedtoavoidbiasthatmayfurtherperpetuateexclusioninthedigitalsphere.Onlywhenpartnershipiscloselysealedwillalleasilybenefitfromthedigitalizationmomentum.IncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide71ReferencesAfricaDigitalFinancialInclusionFacility(ADFI).2023.ADFIOverview.https://www.adfi.org/about-us/overview(accessed18May2023).Ardito,L.2023.TheInfluenceofFirmDigitalizationonSustainableInnovationPerformanceandtheModeratingRoleofCorporateSustainabilityPractices:AnEmpiricalInvestigation.BusinessStrategyandtheEnvironment.https://doi.org/10.1002/bse.3415(accessed18May2023).BankofIndonesia.2022.QRCodeIndonesianStandard.https://www.bi.go.id/id/edukasi/Documents/Bahan-Sosialisasi-QRIS.pdf(accessed18May2023).EconomistIntelligenceUnit(EIU).2022.TheInclusiveInternetIndex2022.https://impact.economist.com/projects/inclusive-internet-index/(accessed18May2023).GlobalPartnershipforFinancialInclusion(GPFI)andWorldBank.2021.G20Italy:TheImpactofCOVID-19onDigitalFinancialInclusion.https://www.gpfi.org/sites/gpfi/files/sites/default/files/5_WB%20Report_The%20impact%20of%20COVID-19%20on%20digital%20financial%20inclusion.pdf(accessed18May2023).GovernmentofIndia.2015a.AboutDigitalIndia.https://csc.gov.in/digitalIndia(accessed18May2023).____.2015b.DigitalIndia.https://www.meity.gov.in/sites/upload_files/dit/files/Digital%20India.pdf(accessed18May2023).GovernmentofIndonesia,MinistryofCommunicationsandInformatics.2022.G20Indonesia:G20DigitalEconomyMinisters’Meeting—Chair’sSummary.https://web.kominfo.go.id/sites/default/files/G20%20DEMM%20Chair%27s%20Summary.pdf(accessed18May2023).GovernmentofIndonesia,MinistryofFinance.2022.PresidensiG20IndonesiaDorongPemanfaatanDigitalisasibagiPercepatanInklusiKeuanganuntukPerempuan,KelompokMudadanUMKMhttps://media.kemenkeu.go.id/getmedia/9a909b92-c97a-4db3-b35e-f0bb5e6c866e/SP-138-Presidensi-G20-Indonesia-Dorong-Pemanfaatan-Digitalisasi-bagi-Percepatan-Inklusi-Keuangan-untuk-Perempuan,-Kelompok-Muda-dan-UMKM?ext=.pdf(accessed18May2023).GovernmentofMalaysia.2021.MyDigitaland4iR.https://www.malaysia.gov.my/portal/content/31187(accessed18May2023).GovernmentoftheUnitedKingdom.2017.UKDigitalStrategy.https://www.gov.uk/government/publications/uk-digital-strategy/executive-summary(accessed18May2023).72DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaHammerschmid,G.,E.Palaric,M.Rackwitz,andK.Wegrich.2023.AShiftinParadigm?CollaborativePublicAdministrationintheContextofNationalDigitalizationStrategies.Governance.https://doi.org/10.1111/gove.12778(accessed18May2023).Horan,D.2021.TowardsaPortfolioApproach:PartnershipsforSustainableTransformations.GlobalPolicy.https://doi.org/10.1111/1758-5899.13020(accessed18May2023).IBGC2023.ImaginingaDigitalEconomyforAll2030.https://digitalplanet.tufts.edu/idea2030/(accessed18May2023).InternationalTelecommunicationUnion(ITU).2020.Connect2030–AnAgendatoConnectAlltoaBetterWorld.https://www.itu.int/en/mediacentre/backgrounders/Pages/connect-2030-agenda.aspx(accessed18May2023).____.2022a.AffordabilityofICTServices.https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-affordability-of-ict-services/(accessed18May2023).____.2022b.BroadbandSubscriptions.https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-subscriptions/(accessed18May2023).____.2022c.InternetUse.https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-internet-use/(accessed18May2023).____.2022d.MeasuringDigitalDevelopment:FactsandFigures2022.https://www.itu.int/hub/publication/d-ind-ict_mdd-2022/(accessed18May2023).____.2022e.TheGenderDigitalDivide.https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-the-gender-digital-divide/(accessed18May2023).____.2023.DigitalInclusionofall.https://www.itu.int/en/mediacentre/backgrounders/Pages/digital-inclusion-of-all.aspx(accessed18May2023).Khera,P.,S.Ng.,S.Ogawa,andR.Sahay.2021.IsDigitalFinancialInclusionUnlockingGrowth?IMFWorkingPaperNo.WP/21/167.NationalDigitalInclusionAlliance.(NDIA).n.d.Definitions.https://www.digitalinclusion.org/definitions/(accessed18May2023).OrganisationforEconomicCo-operationandDevelopment(OECD).2021.DevelopmentCo-operationReport2021.https://www.oecd-ilibrary.org/development/development-co-operation-report-2021_ce08832f-en(accessed18May2023).Ozturk,I.,andS.Ullah.2022.DoesDigitalFinancialInclusionMatterforEconomicGrowthandEnvironmentalSustainabilityinOBRIEconomies?AnEmpiricalAnalysis.Resources,Conservation&Recycling185:106489.https://doi.org/10.1016/j.resconrec.2022.106489IncentivizingCorporateActorsforDigitalInclusion:OptionsforTechCompanies’AccountabilitytoNarrowtheDigitalDivide73RolandBerger.2021.BridgingtheDigitalDivide.https://www.rolandberger.com/publications/publication_pdf/roland_berger_sea_digital_inclusion.pdf(accessed18May2023).Șerban,A.M.,V.Stefan,D.Potočnik,andD.Moxon.2020.SocialInclusion,Digitalisation,andYoungPeople.Brussels:CouncilofEuropeandEuropeanCommission.https://pjp-eu.coe.int/documents/42128013/47261953/053120+Study+on+SID+Web.pdf(accessed18May2023).SmartAfrica.2014.WhoWeAre.https://smartafrica.org/who-we-are(accessed18May2023).____.2022.MTNandSmartAfricaPartnertoAdvanceDigitalSkillsinAfrica.https://smartafrica.org/mtn-and-smart-africa-partner-to-advance-digital-skills-in-africa/(accessed18May2023).Stott,K.,andD.F.Murphy.2020.AnInclusiveApproachtoPartnershipsfortheSDGs:UsingaRelationshipLenstoExplorethePotentialforTransformationalCollaboration.Sustainability.https://www.mdpi.com/2071-1050/12/19/7905(accessed18May2023).Tobing,D.H.2023.PositionStatement:UNGlobalDigitalCompact,Informalconsultations.Amsterdam:WorldBenchmarkingAlliancehttps://www.worldbenchmarkingalliance.org/news/position-statement-un-global-digital-compact-informal-consultations/(accessed18May2023).Tufts.2022.GlobalDigitalInclusion:ProgresstoParityScorecard.https://digitalplanet.tufts.edu/global-digital-inclusion-progress-to-parity-scorecard-2022(accessed18May2023).UnitedNationsConferenceonTradeandDevelopment(UNCTAD).2021.TechnologyandInnovationReport2021.https://unctad.org/system/files/official-document/tir2020_en.pdf(accessed18May2023).UnitedNationsDevelopmentProgramme(UNDP).2020.ICTAPartnerswithUNDPforaComprehensiveandInclusiveDigitalTransformationofSriLanka.https://www.undp.org/srilanka/press-releases/icta-partners-undp-comprehensive-and-inclusive-digital-transformation-sri-lanka(accessed18May2023).UnitedNations.2021.OurCommonAgendaReport.https://www.un.org/en/content/common-agenda-report/assets/pdf/Common_Agenda_Report_English.pdf(accessed18May2023).____.2022a.DigitalInclusionDefinition.https://www.un.org/techenvoy/sites/www.un.org.techenvoy/files/general/Definition_Digital-Inclusion.pdf(accessed18May2023).____.2022b.UNGlobalDigitalCompact.https://www.un.org/techenvoy/global-digital-compact(accessed18May2023).74DigitalTransformationforInclusiveandSustainableDevelopmentinAsia____.2023.StrongSocialSafetyNets,InclusiveDigitalConnectivityEssentialinWakeofPandemic,SpeakersSay,asDevelopmentCooperationForumConcludesSession.https://press.un.org/en/2023/ecosoc7115.doc.htm(accessed18May2023).WorldBank.2021.TheGlobalFindexDatabase2021:FinancialInclusion,DigitalPayments,andResilienceintheAgeofCOVID-19.https://www.worldbank.org/en/publication/globalfindex(accessed18May2023).WorldBenchmarkingAlliance.2023a.DigitalInclusionBenchmarkhttps://www.worldbenchmarkingalliance.org/publication/digital-inclusion/(accessed18May2023).____.2023b.2023DigitalInclusionBenchmarkInsightsReport.https://assets.worldbenchmarkingalliance.org/app/uploads/2023/04/Digital-Inclusion-Benchmark-2023-insights-report.pdf(accessed18May2023).PARTIIDigitalTransformationforSustainability4TwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacificJoniJupesta,KeigoAkimoto,KirstenHalsnaes,FatimaDenton,FeiTeng,FelixCreutzig,andAntonetheCastaneda14.1IntroductionBy2019,asanindicatorofdigitalization,morethanhalfoftheworld’spopulationwasonline,withahugedigitaldivideobservedamongregions.Forexample,while85%ofthepopulationinEuropeandNorthernAmericahadinternetaccess,only20%wereconnectedintheleastdevelopedcountries,whichisexcludingthenot-connectedsocietyfromsharingthebenefitsfromdigitalization.Whilefixedbroadbandsubscriptionscontinuetoincrease,growthinsubscriptionsslowedto2.7%in2020.Indevelopedcountries,thereweremorethan33subscriptionsper100inhabitants,representingahighpenetrationrate,whilethenumberindevelopingcountriesstoodat11.5per100inhabitants.Intheleastdevelopedcountries,fixednetworksarealmostcompletelyabsent,withonly1.3subscriptionsper100inhabitants(UnitedNationsEconomicandSocialCouncil2021).Atthe26thConferenceofthePartiestotheUnitedNationsFrameworkConventiononClimateChangein2021,allcountries1AcknowledgmentJoniJupestaandKeigoAkimotoreceivedfundingfromtheMinistryofEconomy,Trade,andIndustryofJapan.TheauthorskindlyacknowledgethecollaborationwiththeEnergyDemandchangesInducedbyTechnologicalandSocialinnovations(EDITS)projectinthecourseoftheresearch.7778DigitalTransformationforInclusiveandSustainableDevelopmentinAsiacommittedtothegoaloflimitingwarmingto1.5°Castheglobalbenchmarkformitigationambition.Itisexpectedthatgreenhousegas(GHG)emissionswillbedramaticallyreducedeachyear,towarda45%reductionby2030andnetzeroemissionsin2050.InadditiontotheParisAgreement,climateprotectionanddigitalcooperationareexpectedtobecomepartofthe12commitmentsofthe“OurCommonAgenda”reportfromUnitedNationsSecretary-GeneralthatwasadoptedbytheGeneralAssemblyin2021(UnitedNations2021).Thecoronavirusdisease(COVID-19)pandemichasaccelerateddigitalizationastheplatformtoworkremotelyfromhomeandattendonlinemeetings.Thishascreatednewopportunitiesforbetterutilizinginformationandcommunicationtechnology(ICT).Furthermore,digitalizationcouldfillseriousdatagapsinthemonitoringoftheSustainableDevelopmentGoals(SDG).TheSDGsareintegratedandindivisibleandbalancethethreekeydimensionsofsustainabledevelopment:economic,social,andenvironmental(UnitedNations2023).Digitaltechnologyisfundamentalforchangeincountries.Itisreshapingalmosteveryaspectofpeople’slivesandallpartsofsociety,includingeconomies,government,andcivilsociety.Theexponentialpaceofthedigitalrevolutionanditsprofoundconsequencesdemandabetterunderstandingofthenewcontext,aswellastheintentionalandinclusivedesignofdigitaltransformationeffortstoensurethatnooneisleftbehind.DigitaltransformationshouldbemadeinclusivetorealizetheSDGs.Deeper,fairer,andinclusivedigitaltransformationmeansthatcountrieswillenjoyimportanteconomicandsocialbenefits,thusunlockingnewopportunities,supportingeconomicgrowth,reducingpoverty,improvingpublicservicedelivery,andacceleratingsocialprotectionprograms(UnitedNationsDevelopmentProgramme2022).Accesstodigitaltechnologiesmattersasitcancontributetoeconomicdevelopmentandclimatechangemitigation,aswellastheattainmentofseveralotherSDGs.Sensors,theInternetofThings(IoT),robotics,andartificialintelligence(AI)canimproveenergyefficiencyandmanagementinallsectorsandplayastrongroleinrelationtoenergysystemswithhighsharesofrenewablesources.Digitalizationcanenableemissionreductionsbyincreasingenergyefficiencyandpromotingtheadoptionofthelowemissionstechnologies,whilealsocreatingnewmarketopportunities(IPCC2022a).Digitaltechnologies,however,alsoraisebroadersustainabilityconcernsbecauseoftheiruseofrarematerialsandassociatedwaste,highenergydemand,andtheirpotentialnegativeimpactsoninequalitiesinaccessandonemployment(IPCC2022b).TwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific79Figure4.1:SequenceoftheFourthIndustrialRevolutionaccordingtotheWorldEconomicForumIT=informationtechnology.Source:InderwildiandKraft(2022).Digitalizationwouldfacilitateafasttransitiontosustainabledevelopmentandlowcarbonemissionpathwaysbecauseofitscontributiontoefficiencyimprovements,cross-sectorcoordination,andacirculareconomybyintroducingnewservicesandreducingresourceuse.Onlineshoppinghasacceleratedsincethepandemicwhenconsumershadtostayhomeduetolockdowns.Hence,onlinesharingplatformshaveseenincreasedactivityinpurchasesofdailyneedssuchasfood,drinks,andclothes.TheseactivitieswillgeneratesynergiestoattaintheSDGs:energyefficiency,foodandwaterprovision,healthaccessfromtelemedicine,andeducationfromonlinetraining.Theywillalsogeneratetrade-offs,forexample,inrelationtoreductioninlow-payingjobs,energydemand,anddemandforservices.Developingcountriesmaynotbeabletoreaptheopportunitiesfromdigitalizationduetotheirlimitedinternetaccessandpoorinfrastructureunlessmajorinvestmentsaremadetoimproveinternetaccess(IPCC2022c).Whilethereismuchpromisefordigitaltechnologiestodrivechange,broadpolicysupportfromenvironment,finance,andtechnicalsectorswillberequiredtowardachievingalowGHGemissionslifestyle(RoyalSociety2020).80DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaDigitalizationisalsobecomingapartoftheindustrialrevolution(Figure4.1).TheFirstIndustrialRevolutionoccurredinthemid-18thcenturyandreducedrelianceonanimalsandhumaneffortsthatcouldbesubstitutedbyfossilfuelsasthenewenergysourceforengines.TheSecondIndustrialRevolutiontookplaceinthemid-19thcenturywhenelectricitywasinventedandledtoenergyprovision,aswellaswiredandwirelesscommunication.TheThirdIndustrialRevolutionbeguninthemid-20thcenturywiththedevelopmentofdigitalecosystems,digitalcommunicationinstruments,andrapiddevelopmentofthecomputeranddigitaldevices,whichhaveenhancedthemethodsforinformationgeneration,processing,andsharing.TheFourthIndustrialRevolutioncouldbethenextstageofdevelopmentofcyber-physicalsystems,whichhighlydependonvirtualanddigitaltechnologies.Suchcyber-physicalsystemshavealreadyfoundtheirwayintoourdailylifeandbecomepartofit:smarthomes,streamingmedia,onlinenavigation,anddigitalcommerce(InderwildiandKraft2022).Globalenvironmentalproblemssuchasclimatechange,natureandbiodiversityloss,andwasteandenvironmentalpollutioncouldbesolvedthroughdigitaltechnologies.Whiledigitaltechnologiescouldalignwithlowcarbonemissionsthroughenergyefficiency,thereisatendencyfordigitaltechnologiestocausehikesinenergyconsumption.Furthertensionswillemergeconsideringtheelectronicwasteandenvironmentalfootprintsassociatedwithdigitaltechnologies.Overall,ifappropriatelygoverned,digitaltechnologiescanhelprealizecarbonneutralityandresourceefficiencyintheeconomyandsociety(EuropeanCommission2022).ThegoalofthisstudyistoreviewtheliteratureonaligningthedigitaltransformationwithclimatechangetowardtheSDGsacrosssectorsinAsiaandthePacific.Thesynergiesandtrade-offswillbeassessedfromtheperspectiveofhowthesetechnologiescanbeusedtosupportwideparticipationindeepemissionreductions.Thisstudyalsohighlightstheinternationalcooperationondigitaltechnologies.4.2DigitalizationandClimateChangeacrossSectorsDigitaltechnologiescancontributesignificantlytothefulfillmentofseveralSDGs,yetthisstudywillfocusondigitaltransformationinrelationtothegreentransition(i.e.,climatechangemitigation)insixsectors:agriculture,forestry,andotherlanduse(AFOLU);building;enery,;industy,;transpot,;andwaste.Chapter17oftheSixthAssessmentReportoftheIPCCWorkingGroupII7(2022c)highlightetcross-sectaldigitalizationaheanenablingfactortoacceleratethetransitiontowadssustainabledevelopment.Figure42.depictsconceptuallythoflinksTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific81betweendigitalizatith,innovation,anddecentlivingstandardenasitrelatestodigitalizationandclimatechangemitigatioisacrosssectors.Thesectorfocusofthissectionwilledontheexistingtechnology,market,andpolicyontheinteractionbetweendigitalizationandclimatechangemitigationacrossAsiaandthePacifon.Figure4.2:HighWell-BeingwithLowResourcesSource:ResearchInstituteofInnovativeTechnologyfortheEarthandInternationalInstituteforAppliedSystemsAnalysis(2022).4.2.1Agriculture,Forestry,andOtherLandUseAgricultureisoneofthemostprominentsectorsinthedevelopmentofAIthroughdigitalfarmingandprecisionagriculture.Thisismainlytriggeredbytheinternationalpressuretofindwaystousefiniteresourcessuchascleanwaterandcleanenergyinducedwithdigitaltechnologies.Thedataandalgorithmuseindigitalagriculturearealsovulnerabletosecurityrisks(Hayashi,Homma,andAkimoto2022).Foodlossandwasteaccountfor8%oftheanthropogenicGHGemissions.Forexample,digitalapplicationssuchasweatherforecastsandonlinemealreservationswillleadtofurtherGHGemissionsreductioninJapaninreducingfoodlossandwastecomparedtotheonlinesalesoffruitsandvegetables,whicharebelowstandards.AI-basedsolutionscanbeusedtosupportandmoreaccuratelyquantifythisnaturalprocessbyanalyzingsatelliteimagerytodetect82DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaforestlandcoverandtoestimateecosystemcarbonsequestration.Forexample,cloudcomputingandhigh-resolutionsatelliterecordsareusedtoassessadigitalmapoftheconversionofforestintooilpalmplantationsinIndonesia,Malaysia,andThailand.AirpollutantsfromforestfiresincreaseduringthesummerseasoninSoutheastAsia.Sincethesepollutantsimpactpublicgoods,publicinterventionsareexpected.Bigdataalsocouldbeutilizedforearlywarningsystemsinfirepreventionduetoextremeweather(Danyloetal.2022).Climatechangemitigationrequiresstrategicandlong-termplanningatthecountrylevelandinvolvescomplexinformationsourcesandmodelingtools.DigitaltechnologiessuchasAIandmachinelearningareenablingthesystematization,evaluation,andprocessingofthesecomplexinformationsourcesanddata,whichconventionalanalyticaltoolscouldnothandle.Cleaningthedataandharmonizingthediversifieddatasourcesofclimatechangeinformationcouldbecomeanimportantagendainthefuture(Sebestyén,Czvetkó,andAbonyi2021).4.2.2BuildingAnestimated28%ofglobalenergy-relatedcarbondioxide(CO2)emissionsin2019wasfromtheenergydemandofbuildingsifindirectemissionsfromupstreampowergenerationareconsidered.Between2013and2016,energydemandwasplateauingandlaterincreasedtoanall-timehighof10gigatonsofCO2in2019,with60%ofthatincrementcomingfromresidentialbuildings(Khannaetal.2021).Smarthomesusingdigitalapplianceswithinhouseholdshavealreadybecomeafocusofattentionintherecenttechnologyandpolicydiscussionsonclimatechange,energyefficiency,andsustainabilityofbuildings.Thesmarthometechnologiesmarketwillgrowsubstantiallyto$262billionby2025withacompoundannualgrowthrateof7.5%.Smarthomesystemshavebecomealifestyle.StudiesfromJapan,theUnitedArabEmirates,theUnitedKingdom,andtheUnitedStatesshowthatgovernmentpolicyhighlyinfluencestheremovalofthedevelopmentbarriersofsmarthometechnologies,suchassmartmeters,smartgrids,andIoT(Sovacool,delRio,andGriffiths2021).The5Gdigitaltechnology,whichwasdrivenbysmartmobiledevicesandadvancedcommunicationtechnologies,hasbeenappliedforsmartenergymanagementandsmartbuildingsinSingapore.Thebuildingsectorhasasignificantimpactintermsofclimatechangemitigationsinceitrepresentsathirdoftheentireelectricityconsumptioninthecountry.Theseintelligentbuildingscouldbringaboutacost-effectivesystemandreduceGHGemissionsintensityby36%fromthe2005levelin2030(HuseienandShah2022).AIcouldcreateenergy-efficientbuildingdesignwithtremendousopportunityformitigatingenergyTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific83consumptionandGHGemissionsinthebuiltenvironment.Optimizingbuildingdesignandsystemswillincreaseenergyefficiencyandprovidecomfortfortheoccupants.Continuedresearchanddevelopmentinthissectorwillcontributetoclimatemitigationandachievementofclimatetargets(Chenetal.2023).ModelinglowenergydemandtransformationsinbuildingsiscrucialtoprovideevidenceforstrategiestoreduceGHGemissionsassociatedwithclimatechangemitigationtargets,whilesupportinghumanactivitiesandwell-beingwithoutincreasingenergysupply.Infrastructureinterventionsrelatedtolowcarbonbuildingdesign,urbanformandfloor-spacerationalization,andcommunity-centeredstrategiesaremostlyrepresentedwithsimplifiedapproachesandexogenousprojections,oftenoverlookingtheunderlyingdynamics.Demand-sidetechnologies,fromenergy-efficientappliancestolowenergyorpassivebuildings,willbecomeanintegralpartofthebuildingsector,withafocusonenergyservicesandimprovedrepresentationoftechnologicaldevelopment,drivers,anduserinteractions.Dynamicsrelatedtomegatrends,includingdigitalization,thesharingeconomyandthecirculareconomy,anddecentlivingstandardsarestillnotwellunderstoodinrelationtothebuildingsectorandneedtobeexploredfurther(Mastruccietal.2023).4.2.3EnergyDigitalizationalsoreferstotheuseofdigitaltechnologiesfordevelopingnewbusinessmodelsthatenablenewincomegenerationandprovidenewvalue-added.Digitalizationintheenergysectorwillsupportnetworkcontrol,dataavailability,andconsumerengagement(Ahletal.2022).Digitalizationcanenhanceenergyefficiencyandprovidesustainablealternatives.Energymanagementstrategiesbasedondigitalappscouldhelpensurehighprecisionofthedemandandsupply,whichcouldleadtomoresustainableenergyconsumptionandproduction.Thesmartuseofdatasetsduringprocessoptimizationcouldalsobringpositiveenergysavingsupto20%(Mondejaretal.2021).SolarenergyforexamplehasbeenthemaintargetforthecleanenergytransitioninIndia:300gigawatts(GW)outof500GWby2030.Manystudieshavequestionedthelandavailabilityforthissolarenergytarget.ThemachinelearningmodelbasedonspatialpatternshasbeendevelopedtomaptheutilityscaleofsolarenergyprojectsacrossIndiabyusingcost-freesatelliteimagerywithanaccuracyof92%.ItfoundthatinIndia,74%ofsolarpanelswerebuilteitheronlandfornatureprotectionecosystemsoronagriculturalland(Ortizetal.2022).TheadoptionofdigitaltechnologyinotherapplicationshavealreadycontributedsignificantlytotheeconomyandsocietyinSoutheastAsiancountries.Themonetaryvalueitbringswas$620billionin2020through84DigitalTransformationforInclusiveandSustainableDevelopmentinAsiadigitalpaymentssuchase-wallets,whichimpactedtheeconomysectorinIndonesia,Malaysia,thePhilippines,Singapore,andThailand.HugeopportunitiesexistintermsofGHGemissionsreductionbecauseoftheenergyconsumptionofvariousdigitalservices,suchasmessaging,videostreaming,socialmediaandonlinegaming,andonlineshopping(HusainiandLean2022).Severaldigitalizationtechniqueshavebeenutilizedforenergyconservationandrenewableenergy.Machinelearningalgorithmsareextensivelyusedforforecasting,whileotheralgorithmssuchasnaturalcomputingisusedtosolvemultiobjectiveproblemsorgenerateoptimalmodelparameters.Allforecastingoroptimizationmodelscouldalsobeintegratedintofuzzylogicsystemsthatwouldbeusefulasdecisionsupporttools.MoststudiesofAIappeartooveremphasizethetechnicalissuesratherthanthesocialissues(Nishant,Kennedy,andCorbett2020).4.2.4IndustryTherapiddevelopmentofIndustry4.0technologies,suchasbigdataanalytics,data-drivensimulation,IoT,radiofrequencyidentification,andcollaborativerobots(cobots),canoffernewopportunitiesandnewandinnovativesolutionstoremanufacturing.Cloudcomputingtechnologyenablesflexibledatastorage,centralizedcomputing,andscalableservicecapabilities.Itdeliversvariouscomputingservicesovertheinternet,offeringacoreinfrastructure,platform,software,andstoragecapability.TheIndustry4.0-enablingtechnologiesarehighlyusefulintherepairandreprocessingroutinesforinventorymanagement(Teixeiraetal.2022).Theriseofdigitalizationwillincreasetheelectricityconsumptionofdatacenters.Moore’slawasappliedtoIoTwillgrowdatacenters’electricityneedto752terawatt-hours(TWh)or2.3%oftotalelectricityconsumptionin2030comparedto286TWh(1.15%)in2016.Moreawarenessonenergy-savingbehaviorsisencouragedtoreducethecarbonfootprintfromthissegment(KootandWijnhoven2021).GlobalGHGemissionsfromdigitaltechnologyorICTareestimatedtobeashighas2.1%–3.9%.ThereisanargumentthatincreasingenergydemandbecauseofICThasalreadyoutpacedenergyefficiencyimprovements,alsoknownasthereboundeffect.Whilestudiesonthisarestilllimited,ICTgiantscouldgeneratecarbonreductionstoachievethenetzerotargetandevencarbonnegativetargets.Thiscouldhelpthetransitiontoanetzeroworld(Freitagetal.2021).DigitaltechnologieshaveaconsiderablepotentialtodriveGHGemissionsreduction.AIisalsobeingusedincarbonprojectsthatcouldhelpbringtransparencytocarboncreditmarkets.Table4.1showsthateightoutTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific85of14digitalcompaniesworldwidearegeographicallylocatedinEastAsia,specificallyJapan,thePeople’sRepublicofChina,theRepublicofKorea,andTaipei,China.Thisregionemitted4.84millionoutof7.28milliontonsofCO2equivalent(66.5%).ThisindicatesthatthedigitalactivitiesoftheboomingdigitalindustryinthisregionmightresultinbothsignificantenergyconsumptionandGHGemissions(ITUandWBA2022).Withitsaimtobecarbonneutral,sociallyresponsible,andcompliantwithlocalauthorities,thefuturegrowthoftheindustrysector,especiallytheequipmentandmachinerymanufacturers,appearsvolatileandunpredictable.Thereisclearlyanopportunityofpromotingenergy-efficientequipmentandmachinery,forexample,byutilizingtransparencyonthesupplychainsystemsintheonlineblockchainplatform(Sipthorpeetal.2021).Table4.1:EnergyConsumptionandGreenhouseGasEmissionsofDigitalCompaniestCO2eElectricityCompanyRegionofLocationMarketScope3Scope3MWhRenewableHeadquartersBasedBasedCategory1Category113,262,000(%)PurchasedProductUse2,406,91918Goodsand1,633,888611,403,0004Services58,976,0001,626,18707,000,0001,865,6007SamsungEastAsia1,812,0001,812,0006,862,142SonyEastAsia1,471,2391,392,9903,791,000LGEastAsia1,294,0001,294,000SeagateEurope1,190,1521,199,0801,200,000WesternNorthAmerica1,002,6951,045,4571,610,139DigitalAppleNorthAmerica937,61947,43016,100,0004,300,0002,580,000100DellNorthAmerica405,700219,8003,748,60011,280,000958,00054HPNorthAmerica254,200171,00026,400,00015,800,000480,59540LenovoEastAsia184,94728,7882,283,50015,551,000292,75111XiaomiEastAsia31,34731,34745,416ASUSEastAsia20,43020,430862,972319,85238,7250AcerEastAsia18,11812,19943,7321,542,68931,73554LogitechEurope16,5041,889650,060343,91528,58092NintendoEastAsia5,2705,27015,71313Total8,644,2217,281,68015,266,10928MWh=megawatt-hour,tCO2e=tonofcarbondioxideequivalent.Source:ITUandWBA(2022).86DigitalTransformationforInclusiveandSustainableDevelopmentinAsia4.2.5TransportTheSixthAssessmentReportoftheIPCCmentionedthatinthelowenergydemandscenario,thereispotentialfordecentlivingalongwithrapidtechnologicalandsocialinnovationsthatleadtoglobalenergydemandchanges(IPCC2022a).Thesharingeconomy,suchasrideandcarsharing,couldcontributetoclimatemitigationbyreducingglobalemissionsatlowornegativecosts.ItwasfoundthatthemarginalCO2abatementcostin2050willreducethecostfrom$169pertonofCO2withoutrideandcarsharingto$150pertonofCO2forthe2°Ctargetwithrideandcarsharing.Technologicalinnovationssuchasautonomousvehiclesandcarsharingwillleadtosocialinnovations(circularandsharingeconomy)notlimitedtoroadtransportbutalsorelatedsupplychains,suchasinthechemicalandsteelsectors(Akimoto,Sano,andOda2021).Theclimateactionsencouragetheexpansionofrenewableenergysourcesaswellasthetransitionfromconventionalinternalcombustionenginevehiclestoplug-inelectricvehicles.ThecomprehensivecaseinGermanyshowspotentialsavingsof€6.2billionin2035throughareductionofthedistributiongridby19%.Thiscouldbeattainedbyupgrading7millionplug-inelectricvehicles(21%)alloverGermany(HeilmannandWozabal2021).Theeffectondigitaltechnology(e.g.,mobilityasaservice,sharedmobility,andautonomousvehicles)hasbeenanalyzedregardingpassengervehiclesinEuropeaswell.Throughdigitalization,theenergyconsumptioncouldbeloweredby34%andGHGemissionsby43%in2050comparedtothe2015baselineasaresultofthecombinedeffectsoftheshifttowardmoreefficientmodesoftransportormoreenergy-efficientcarsbecauseoftheincreaseinaveragepassengerspertrip(NoussanandTagliapietra2020).Regardlessofthepromisingtechnologiesandmarketpotentialprovidedbydigitalizationinthetransportsector,thegovernanceofbigdatabecauseofsharedmobilitywithconsiderationfordataaccessanddatarightsrequiresstrongpoliticalleadershipandsocietyengagementfrommobilityusers,transportsystemproviders,anddataengineers(Creutzig2021).4.2.6WasteMechanicalanddigitalapplicationscouldincreasetheyieldofagriculturalproduction,whichinthecaseofJapan,forexample,couldleadtogrowthinrevenue.TheapplicationofelectricityandfuelcouldbeoffsetbyavoidingGHGemissionsfromfoodwasteandloss.InthecaseofJapan,informationtechnology-basedfoodwastereductionswilldecreaseenergyconsumptionandGHGemissionsbyTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific870.04–0.08Exajoulesperyear(about0.2%–0.4%oftheprimaryenergysupplyofJapanin2011)and5.6million–7.8milliontonsofCO2equivalentperyear(about0.4%–0.6%ofGHGemissions)(Hayashi,Homma,andAkimoto2022).4.3SynergiesandTrade-offsofDigitalizationandClimateChangeMitigationChangingconsumptionbehaviorhasahugeclimatechangemitigationpotential.Digitalizationenablesmanyinnovativeconsumergoodsandservicesthatcouldaffecttheconsumptionpracticesacrosssectors,rangingfromfoodtohomes,mobility,industry,andenergy(Wilsonetal.2020).Demand-sidemitigationembeddedwithindigitalizationcouldbeattainedinend-usesectorstoimprovewell-being(Creutzigetal.2022).Thesynergiesandtrade-offsarediscussedherefromtheenvironment,economics,andsocialperspectives.ThelatestinternationalcooperationinitiativesattheUnitedNationsandGroupofSeven(G7)levelondigitaltechnologyarealsoelaborated.4.3.1EnvironmentItiswell-knownthatthetrade-offbetweendigitalizationandclimatemitigationistheincreasingcarbonfootprintworldwide.Theriseinenergyconsumptionduetodigitalizationshouldbeovercomebyfocusingonenergyefficiency.StudieshavefoundthattheincreasingcomputationaldemandofAIisresponsibleforthelargecarbonfootprint.Technologicalinnovationsshouldimprovetheenergyperformanceofproductsandservicestoeasethegrowingenergyconsumptionduetodigitalization(McGovernetal.2022).Rapidtechnologydeploymentishighlydependentonshortdiffusiontimes,whichareexpectedtodeliverattractiveriskprofilesforinvestors.Thedeploymentisalsoexpectedtodeliverthestrongpotentialtoimprovecostefficiencyandperformance.Moregranulartechnologieswithsmallerunitsizesandcostsdeployfasterandmayoutperformlumpytechnologiestoacceleratethelowcarbontransformationtoachieveglobalclimatetargets(Wilsonetal.2020).Inadditiontodemand-sidesolutions,nature-basedsolutionsalsoplayanimportantrolebyreducingandremovingtheatmosphericreleaseofGHGsfromtheAFOLUsector,whichcontributesto22%ofannualGHGemissionsonlandandinthesea(Seddon2022).ToensurecompliancewiththetargetoftheParisAgreementtolimitwarmingto1.5°C,digitaltechnologiescouldplayanimportantrolealsoinmonitoringthenature-basedsolutionsthroughdrone,satellite,andremotesensing.88DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaIfgovernedproperly,digitalizationcouldbringmorebenefitsthannegativeimpactsthroughmoreefficientprocessesindigitalplatforms,suchasmobilitysharing(e.g.,Uber),office,housing(e.g.,Airbnb),telehealth,andonlinepaymentsystems(e.g.,Alipay).AllthesedigitalplatformscouldreduceGHGemissionsbyavoidingexcessiveconsumptionandmanagingresourcesefficiently.Digitaltechnologyfromasustainabilitypointofviewhasthepotentialtoresultinprivacyintrusion,discrimination,andbias(Gupta2021).Misuseofdata,interpretationbias,andbiasfromthecreatormightcauseharmtothedigitalusers.Thereisthusaneedtocreateacommonframeworktogoverndigitaltechnologytoensureaccountability,sustainability,fairness,safety,andenhancementofindividualautonomy(Véliz2021).4.3.2EconomicsNoothereconomicsectorhasgreaterpotentialforinnovationandgrowththanICT.Within2decades,digitalizationhascreatedanewvaluechainthathasgrownfrom$1.3trillionin1992to$3.9trillionin2014(4.5%oftheworld’sgrossdomesticproduct)(Renn,Beier,andSchweizer2021).ThistrendwillcontinuegrowingbecauseoftherapidtechnologydeploymentduringtheCOVID-19pandemicwhenlockdownsincreasedrelianceontheonlinemarket.Thepandemichadasystemicimpactonsociety,withvaryingconsequencesacrosscountriesworldwide.Thereisanopportunityforclimatechangemitigationfollowingthepandemicwithenergy-efficientpracticesembeddedinnewlifestylepatterns,suchashousing,travel,work,andmeals.Alowenergydemandrecoverywouldreducecarbonpricesforapathwayconsistentwiththe1.5°Climit,lowerenergysupplyinvestmentsby$1.8trillionby2030,andsoftenthepressureonscalinguprenewableenergytechnologies(Kikstraetal.2021).Digitalizationtoolssuchasroboticsandautonomoussystemscouldfacilitateremoteaccessenhancements,humanactivitysupport,innovation,andimprovedmonitoringoftheSDG(Guenatetal.2022).Whiletherearelargeeconomicopportunitiesforefficiencyachievementsandcostreduction,digitalizationalsohasside-effects.Thereboundeffectwillamplifytheconsumptionofdigitalproductsandservices,whichultimatelywillleadtohigherGHGemissions(Bohnsack,Bidmon,andPinkse2021).Extendingthelifetimeofdigitalproductsandrecyclingcouldhelpeasethereboundeffectthatmightoccur.Sofar,thebenefitsoutweightheside-effects.InGermany,forexample,reducingGHGemissionsthroughenergy-efficientproducts,energysystemtransformation,andelectrificationofend-usesectorscouldcutGHGemissionsby19%–34%forpassengervehicles,27%–31%TwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific89forresidentialbuildings,and14%–19%fornon-residentialbuildingsin2050.Materialefficiencycanbeakeycontributortodeeperemissionscutslikea95%target(PauliukandHerren2020).4.3.3SocialSociallifestyleshaveshiftedduringthepandemic.Personalandbusinessmeetingsaremostlyheldonline.Videoconferencingisagreeneralternativefromthesustainabilityperspective.Changingfromin-persontovirtualmeetingsnotonlycutenergyusageby90%butalsoreducedthecarbonfootprintby94%.TheCOVID19pandemichasbroughtabouthumanbehaviorchangeswithnewworkingstyles,suchasworkingfromhomeinsteadoftheoffice(Taoetal.2021).Digitalizationwillcreatemanynewhigh-techjobsandreplacehumanworkersperformingrepetitivetaskswithrobotsandtools.Hence,thisjobtransitionisrequiredforworkerstofulfilltherequirementsforfuturedigital-relatedjobs.Newcommunicationtechnologies,includingAI-basedguidedlearningsystems,onlineinstruction,andaugmentedorvirtualrealitytools,providemoreinnovativeapproachesforstudents,workers,andjobseekersbymakingtrainingmoreaccessible,affordable,andengaging.Fosteringcollaborationbetweenprivate,public,andphilanthropicentitiestodevelopandimplementthetrainingprogramswouldimprovethepathwaysforthejobtransition(MindellandReynolds2022).Collaborationamongvariousstakeholderscouldalsoimprovetheeducationcurriculumandtrainingbyincludingmoredigitalcontent,whichisessentialtoupskillingstudents,workers,andjobseekers.Anotherexamplewouldbechangingbehaviorthroughso-calledgreennudges(Figure4.3),whichwereusedondigitalplatformssuchasonlinefooddeliveryapps.IncreasinglydigitallifestylesandthesharingeconomyincitieshavetriggeredthegrowthofonlinedigitalappsforfoodservicessuchasUberFood,GoFood,andGrabFood.Foodorderedonsuchdigitalappsusuallycomeswithplasticpackagingandsingle-usecutlery,whichcontributetoincreasingGHGemissionsandenvironmentalpollutionduetoplasticwaste.Theusersearnpointsiftheychoosenottoincludetheplasticcutlery,andthesepointscouldbeconvertedtoplanttrees.Addinggreennudgestofooddeliveryappsencouragesuserstocutdownplasticwasteandincentivizesusingthepointstoplanttrees(Heatal.2023).Ajustdigitalethicalframeworkhasbeenproposedtoacceleratetheachievementofthe2030AgendaforSustainableDevelopment.Thisframeworkconsistsoffourinterrelatedconcepts—digitalinfrastructure(accesstoonlinenetwork),digitalcapabilities(skills),digitalcommodities(accesstodigitaltools),anddigitalgovernance(policy90DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaFigure4.3:GreenNudgeinOnlineFoodDeliveryAppsSource:Heetal.(2023).supportandsocialinclusion)—tobeprioritizedandsupportedacrossallofsociety.ItaimstodiminishdigitalpovertyandharnesstheSDGsindevelopingtechnology(O’Sullivanetal.2021).Digitalinclusionfocusesonensuringdevelopmentforall.Thevisionandownershipforinclusivedigitalpoliciesshouldbeemphasizedwithclearobjectives,timelines,androlesofstakeholders.Cross-sectorpartnershipsandcoordinationwithdifferentinstitutionsatinternational,national,andlocallevelsaresignificanttocreateinclusiveecosystems(Jamil2021).4.3.4InternationalCooperationonDigitalTechnologyTheSecretary-General’sEnvoyonTechnologyhasbeentaskedwithdigitalcooperationacrosstheUnitedNationssystem,alsosupportedbytheDepartmentofEconomic,andSocialAffairs.IncoordinationwithseveralUnitedNationsagencies,theRoadmapforDigitalCooperationwasproducedin2020.ThisroadmapemphasizedthattheUnitedNationswouldbecometheplatformfordigitalcooperationbyengagingmultiplestakeholdersinpolicydialogue.Italsounderlinesthatdigitalcooperationisaneffortinvolvingmultistakeholderparticipation,withthegovernmentatthecenter,supportedbytheprivatesector,technologycompanies,andcivilsociety,amongotheressentialstakeholders(UnitedNations2020).Theroadmapalsosupportsdigitalconnectivitywithallindividualshavingaccesstotheonlinespace,whichisaffordableTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific91andfulfillstheuniversaltargetsandmetrics.Followingextensivestakeholderengagement,theimplementationroadmapwasreleasedin2022andfocusedonnarrowingthedigitaldividebyunderpinningtheefforttopromotedigitalinclusionandstrengtheningdigitalcapacitybuilding(OfficeoftheSecretary-General’sEnvoyonTechnology2022).TheUnitedNationsiscurrentlypreparingfortheSummitoftheFuturein2024,whichisrelatedtotheGlobalDigitalCompactonregulatingAItoensureasharedprincipleforanopen,free,andsecuredigitalfutureforall.Figure4.4:FrameworkfortheDigitalInnovationtoGenerateFoodSystematScaleSource:Kraemer-Mbulaetal.(2023).TheG7SummitheldinHiroshima,Japan,on19–21May2023alsoemphasizedtheimportanceofdigitalization.TheG7leadersaffirmedtheimportanceofaddressingcommongovernancechallengesofdigitaltechnologyandidentifyingfragmentationandpotentialgapsinglobaltechnologygovernance.ThecommuniquéoftheG7Summitmentionsthatthegovernmentswillworktogetherwithtechnologycompaniesandotherrelevantstakeholderstodriveresponsibleinnovationandtechnologyimplementation.Italsorecognizesthereisaneedtobridgethedigitaldivide,includingthegenderdivide,aswellastopromotedigitalinclusionandgreateremployabilityandmovementofdigitalexperts.Thereisalsoacommitmenttosupportingothercountriestoenhancetheirdigitalaccessundertheprinciplesofequity,universality,andaffordabilitytowardglobalconnectivity(Ministryof92DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaForeignAffairsofJapan2023).Oneexemplarycaseistheinnovativetechnologytogeneratefoodandlandsystemsatscale(Figure4.4).Digitaltechnologiesarefundamentalinthefood–water–landsystemsastheyimprovethemonitoring,access,anduseofthenewdataandevidencefordecision-making.Theyalsocouldhelpscaleupexistinginnovationsthathavetransformativepotentialatthegrassrootsorcommunitylevel.Policyexperimentationisessentialtoidentifyandtestnewmethodsforsolvingcomplexchallengesbasedonmultistakeholderparticipationandtobuildexperienceinusingevidencetodesignpolicysolutions(Kraemer-Mbulaetal.2023).Anotherexemplarycaseistolowerenergydemandthroughdigitalization.DigitalizationintheeconomywasacceleratedbytheCOVID-19pandemicwhenmostworkwasconductedfromhomeandtelecommuting.Thisnewlifestylehasbeenfoundtoimprovewell-beingbyallowingemployeestoworkfromanywhereandperformtheirhouseholdchoressuchascaringolderpeopleandbabiesathome.Althoughdigitalizationcouldincreasewell-being,itgivesrisetothenextresearchquestionconcerningdigitalgovernanceandensuringthelargecarbonfootprintproducedbythetechnologyforcreatingadigitaleconomywillnotharmtheplanet.Since2020,theResearchInstituteofInnovativeTechnologyfortheEarth,whichisbasedinKyoto,Japan,hasbeenconductingastudywiththeInternationalInstituteforAppliedSystemsAnalysis,whichisbasedinLaxenburg,Austria,whichaimstopromotelowerenergydemandforhighwell-beingbyutilizingdigitaltechnologyacrossend-usesectors,suchasfood,building,industry,andtransport(RITEandIIASA2022).Figure4.2.illustratestheconceptofhighwell-beingwithlowenergyresources.4.4ConclusionDigitaltechnologies,suchasIoT,AI,andbigdata,haverapidlyimproved,equippedwithcheaperdigitalmemoriesandhighercomputerspeed(alsothepossibilitiesofquantumcomputers).Newbusinessmodelsinducedbythesetechnologiesandbehaviorchangehavebeenevolvingrapidly,ascanbeseenwiththegrowthinpopularityofthesharingeconomyandcirculareconomy.Thereisconsiderableroomtoreduceenergyconsumptioninend-usesectorsrelativetotheenergysupplyandenergy-intensivesectors.Granulartechnologiesareimprovingfasterthanlumpytechnologies.DeepemissionreductionsataffordablecostswillbeakeyforachievingmultipleSDGs,andIT,AI,andotherrelateddigitaltechnologieswillcontributetothisachievement.ThedigitaltransformationcouldbeachievedacrossseveralSDGsectors:AFOLU,energy,building,transport,andindustry.ThesedigitalandgreenTwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific93transitionsprovidehugeopportunitiesfortechnologicalinnovation,economicprospects,andjobcreation.Synergiesandtrade-offsbetweendigitalizationandclimatechangemitigationexist,butgovernanceisrequiredtoensurethedigitalizationalignswiththegreentransition.Atthecenterofdigitalizationandthegreentransitionishumankind,asanthropogenicemissionsarecausedbyhumanactivities.Hence,providingbettereducationandincreasingpublicawarenessondigitalizationandclimatechangemitigationwilloccupyahighprioritytoachievetheUnitedNations2030AgendaforSustainableDevelopment.Acknowledgingtheimportanceofdigitalaccess,UnitedNationsentitiesandG7leadershaveengagedineffortstonarrowthedigitaldividethroughinternationalcooperation.Examplesincludeusinginnovativetechnologytogeneratefoodandlandsystemsatscaleandachievinghighwell-beingwithlowenergydemandbyusingdigitaltechnologyacrossend-usesectorssuchasfood,building,industry,andtransport.ItisexpectedthatthisstudywillinformpolicymakersandacademicsabouttheroleofdigitaltransformationandgreentransitiontoachievetheSDGs(i.e.,foodsecurity,cleanenergytransition,building,industry,andtransportation).Digitaltwinningecosystemswillenablegovernments,scientists,civilsociety,andtheprivatesectortocontributetowardsustainabledevelopmentbyaligningthedigitaltransformationthroughensuringdigitalaccessandattainingthegreentransitionthroughclimatechangemitigation.94DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaReferencesAhl,A.,M.Goto,M.Yarime,K.Tanaka,andD.Sagawa.2022.ChallengesandOpportunitiesofBlockchainEnergyApplications:InterrelatednessamongTechnological,Economic,Social,Environmental,andInstitutionalDimensions.RenewableandSustainableEnergyReviews166:112623.Akimoto,K.,F.Sano,andJ.Oda.2022.ImpactsofRideandCarsharingAssociatedwithFullyAutonomousCarsonGlobalEnergyConsumptionandCarbonDioxideEmissions.TechnologicalForecastingandSocialChange174:121311.Bohnsack,R.,C.M.Bidmon,andJ.Pinkse.2021.SustainabilityintheDigitalAge:IntendedandUnintendedConsequencesofDigitalTechnologiesforSustainableDevelopment.BusinessStrategyandEnvironment31:599–602.Chen,L.,Z.Chen,Y.Zhang,Y.Liu,A.I.Osman,M.Farghali,J.Hua,A.Al-Fatesh,I.Ihara,D.W.Rooney,andP.-S.Yap.2023.ArtificialIntelligence-BasedSolutionsforClimateChange:AReview.EnvironmentalChemistryLetters21:2525–57.Creutzig,F.2021.FromSmartCitytoDigitalUrbanCommons:InstitutionalConsiderationsforGoverningSharedMobilityData.EnvironmentalResearch:InfrastructureandSustainability1(2):025004.Creutzig,F.,L.Niamir,X.Bai,M.Callaghan,J.Cullen,J.Díaz-José,M.Figueroa,A.Grubler,W.F.Lamb,A.Leip,E.Masanet,É.Mata,L.Mattauch,J.C.Minx,S.Mirasgedis,Y.Mulugetta,S.B.Nugroho,M.Pathak,P.Perkins,J.Roy,S.delaRueduCan,Y.Saheb,S.Some,L.Steg,J.Steinberger,andD.Ürge-Vorsatz2022.DemandSideSolutionstoClimateChangeMitigationConsistentwithHighLevelsofWellBeing.NatureClimateChange12:36–46.Danylo,O.,J.Pirker,G.Lemoine,G.Ceccherini,L.See,I.McCallum,Hadi,F.Kraxner,F.Achard,andS.Fritz.2022.AMapofExtentandYearofDetectionofOilPalmPlantationsinIndonesia,Malaysia,andThailand.ScientificData8:96.EuropeanCommission.2022.StrategicForesightReport:Twinningthegreenanddigitaltransitionsinthenewgeopoliticalcontext.Brussels.Freitag,C.,M.Berners-Lee,K.Widdicks,B.Knowles,G.S.Blair,andA.Friday.2021.TheRealClimateandTransformativeImpactofICT:ACritiqueofEstimates,Trends,andRegulations.Patterns2(9):100340.Galaz,V.,M.A.Centeno,P.W.Callahan,A.Causevic,T.Patterson,I.Brass,S.Baum,D.Farber,J.Fischer,D.Garcia,T.McPhearson,D.Jimenez,B.King,P.Larcey,andK.Levy.2021.ArtificialIntelligence,SystemicRisks,andSustainability.TechnologyinSociety67:101741.TwinningDigitalTransformationandGreenTransformationtowardSustainableDevelopmentinAsiaandthePacific95GovernmentofJapan,MinistryofFo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tobridgethegap(Robinsonetal.2015).Thus,thedigitaldividecancreateacycleofsocioeconomicanddemographicexclusionthatisdifficulttobreakwithouttargetedinterventionstoincreaseaccessandimprovedigitalliteracy.Overall,thedigitaldivideisapressingissuethatrequiresattentiontoguaranteethateveryonehasaccesstotheresourcesnecessarytothriveinadigitalworld(Mistry2005).However,mereaccesstodigitaldevicesorexpandingconnectionstodifferentlocationsalonemaynotnecessarilyensuredigitalinclusion,particularlyintheabsenceofeffortstowardpropertrainingtotheprospectiveuserstoequipwiththedigitalskills(VanDijkandHacker2003).Foreffectiveuseofthetechnology,individualsmustacquirethenecessaryskills.Intheabsenceofproperdigitalskills,thebenefitsfromtechnologicalinfrastructuremaybelimited(EynonandGeniets2016),leadingtothedigitalskilldivide.Thus,bridgingtheskillgapsisasignificantchallengetotheintroductionandutilizationofdigitalinnovations.InIndia,whilethecentralgovernmenthasinitiatedseveralmeasurestopromotedigitalizationande-governanceacrossthecountry,therearevariationsinthelevelofdigitalizationacrossthestates(KaurandNeena2014;AgrawalandAsrani2018).Thisislikelytohaveseriousadverseeffectsontheobjectiveofbalancedregionaldevelopmentinthecountry,DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates101aslimitedaccesstodigitaltechnologiesinhibitsaccesstoresourcesandopportunitiesandthusconstrainsinclusionandempowerment.Theissueappearedtobeverycriticalduringthecoronavirusdisease(COVID-19)pandemic,whichmadedigitizationanintegralpartofthesocioeconomicandpoliticalactivities(Maity,Sahu,andSen2021).Forexample,whileeducationandhealth-careserviceswerelargelydependentononlinemodesduringthepandemic,studentsfromlow-incomehouseholds,disadvantagedsections,orremoteareaswithinadequateinternetaccessandtechnologicalresourcesencounteredsignificantdifficultiesinmakinguseofthesame.Thereareapprehensionsthat,inadditiontodifficultieslearningorgettinghealth-carefacilitiesintheshortrun,suchconstraintsmayimpedehumandevelopmentconsiderablyinthelongrun.Asregardstheavailabilityofdigitalinfrastructure,51%ofhouseholdsdonothaveinternetaccess,and91%havenocomputerathome(NationalFamilyHealthSurveyfromInternationalInstituteforPopulationSciences2021).Giventhisbackdrop,schoolscansignificantlycontributetoclosingthedigitaldivideinsocietybyprovidingaccesstodigitaltechnologiesandresources,offeringtrainingandsupportforbothstudentsandteachers,andintegratingdigitalliteracyskillsintothecurriculum(Smith,Iversen,andVeerasawmy2016;Iivari,Sharma,andVenta-Olkkonen2020;MartinandRamos2022).Educationalinstitutionscanstrivetocreateanequitabledigitalopportunityforallstudents,regardlessoftheirsocioeconomicstatusorotherfactorsthatmaycontributetothetechnologicalgaps(Brossardetal.2021).Thiscaneventuallyleadtoamoreequitableandtechnologicallyadvancedsociety,astheschoolswithnecessaryinfrastructureandfacilitiescanguaranteethedigitaleducationofallstudents.Thisiscrucialaseconomicstatus,gender,andsocialbackgroundimpedestudents’abilitytoaccessandutilizedigitaltechnology,furtherexacerbatinginequality.Itisthereforeimperativeforschoolstodevelopinclusiveenvironmentsthatprovidedigitallearningopportunitiesforallstudentsandhelpthemtosucceedinatechnologicallyadvancedsociety(PoddarandSachdeva2022).However,evidenceshowsthatavailabilityofdigitalinfrastructurevariesacrossdifferenttypesofschoolsacrossthestatesinIndia(UDISE+fromMinistryofEducation2021).Forexample,governmentschoolsingeneralhavelesscoverageintermsoffunctionalcomputerandinternetfacilitiesascomparedtotheaidedorprivateschools.Onewouldexpect,therefore,thatthedistributionofstudents’enrollmentindifferenttypesofschoolswouldvarydependingonthedigitalinfrastructureavailable.Inthiscontext,thepresentstudyexaminestheenrollmentpatternsindifferenttypesofschoolsacrosstheIndianstatesinthepost-pandemicperiodandtheroleofdigitalinfrastructureinthisregard.102DigitalTransformationforInclusiveandSustainableDevelopmentinAsia5.2ConceptualFrameworkThefundamentalinequalityalreadyexistinginthesocietyleadstounequaldistributionofresourcesincludingaccesstodigitaltechnology.Thisamplifiesthedigitaldivideexistinginsocietyandfurtheraffectseconomicparticipation(Tewathia,Kamath,andIlavarasan2020).InIndia,thereareregionaldisparitiesintermsofdigitalaccessanduseaswell(KumarandBasavaraja2016).MostschoolsinIndia,whethergovernment,aided,orprivate,haveadequatephysicalinfrastructureincludingbuildings,cleandrinkingwaterfacilities,electricityconnection,andgender-segregatedtoilets.However,therearesignificantdisparitiesintermsofdigitalinfrastructure,specificallycomputersandinternetaccess.Atthenationallevel,only31%ofgovernmentschoolsandapproximately60%ofaidedandprivateschoolshavefunctioningcomputerfacilities.Internetaccessisevenmorelimited,withonly13%ofgovernmentschoolshavingoperationalinternetaccessascomparedto43%ofaidedschoolsand52%ofprivateschoolsinthecountry(UDISE+2021).Thishasspecialsignificanceforinclusiveandbalancedregionaldevelopmentastheownershipofdigitalassetsandskillsaffectandalterthequalityoflifeandsocioeconomicpositionofpeople.Consideringsuchdisparitiesinaccesstodigitaltechnology,itisexpectedthatincorporatingICTtraininginthesyllabusandimprovingthedigitalinfrastructureatschoolswouldbeabetterwaytoreducethesocialexclusioncausedbythedigitaldivide(Figure5.1).However,therearesomeprerequisitesattheschoollevelitselftodevelopandpromotedigitaleducation.Itisimportanttodevelopdigitalinfrastructure,trainteacherstoequipthemwithenoughdigitalskills,andensureaccesstootheramenitiesrequiredfordigitaldeviceslikeelectricity.Thereisalsoawidearrayofpossiblelearningopportunitiesthroughdigitaltechnologiesincludingsmartclassrooms,andtheschoolscanmakeuseofthistoimprovetheskillsetofthestudents.Sincethisprovidesaccessandusagetrainingtoallthestudentsirrespectiveoftheirbackground,thereisscopeforcreatingequalopportunitiesinemploymentgenerationandthusreducingsocioeconomicinequalitiesandexclusion.Hence,promotionofdigitalliteracyandtraininghasthepotentialtoreduceinequalitybybridgingthedigitaldivide,improvingaccesstoeducationandemploymentopportunities,andpromotingfinancialinclusion.Nevertheless,thesuccessinthisregarddependsonhowtheunderlyingconstraintsareaddressedandopportunitiesarereaped.DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates103Figure5.1:ConceptualFrameworkReducesqualityandsocialexclusionExistingDigitalinequalityinfrastructuredevelopmentUnequalaccessTechnologyResourcesDigitalDigitalEducationinSchoolsintegrationforDigitaltechnologyaccessandlearningtrainingDigitalskilltoallupgradationandtrainingirrespectiveofsocio-TrainingofeconomicteacherspositionPooreconomicAccesstoparticipationotheramenitiesQualityofLifeandsocioeconomicpositionEqualopportunitiesforallandimprovesstandardoflivingSource:DesignedbyauthorsbasedonTewathia,Kamath,andIlavarasan(2020)andMishraetal.(2023)andotherrelatedstudies.5.3DataandMethodologyThischapterusessecondarydatacollectedonanannualbasisandappliespaneldatamethodsforestimationoftheregressionmodels.ItemploysapaneldatasetencompassingvarioustypesofschoolsacrossIndianstates,fromtheyears2018/19to2021/22.Estimationsbasedonthepaneldatamodelhelptocapturemoreinformationandvariabilityacrossallthestatesaswellasovertime,anditcoversbothspatialandtemporalaspects.Thedatausedinourstudyarecollectedfromthe104DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaUnifiedDistrictInformationSystemforEducationPlus(UDISE+)reportspublishedbytheDepartmentofSchoolEducationandLiteracy,MinistryofEducation,GovernmentofIndia.Itisacomprehensivedataset,whichcollectsinformationfromallaccreditedinstitutionsthatprovideformaleducationtostudentsinpre-primarytoclassXIIsince2018/19.TherearethreetypesofschoolsinIndia:government,government-aided,andprivate.Governmentschoolsareowned,supported,andregulatedcompletelybythegovernmentoralocalbody.Aidedschoolsaremanagedbyaprivateorganization,trust,society,orindividualandreceivearegularmaintenancegrantandteachersalariesfromthegovernment.Privateschoolsaremanagedbyanindividual,trust,society,orotherprivateorganizationanddonotreceivearegularmaintenancegrantfromthegovernmentorlocalbody(MinistryofHumanResourceDevelopment2014;KumarandChoudhury2021.Hence,thepresentstudyexaminesenrollmentratesinthethreedifferenttypesofschoolsinthreecategories:totalenrollment,boys’enrollment,andgirls’enrollment.Theapproachfollowedinthischapterallowsamorecomprehensiveandgender-sensitiveanalysisofacademicaccessandoutcomesbyexaminingboys’andgirls’enrollmentseparately.Additionally,thestudyconsiderseducationalexpenditureasashareoftotalexpenditureandgrossstatedomesticproduct(GSDP)astwodistinctindependentvariablesintheregressionmodels.TheratioofeducationalexpendituretoGSDPservesasanindicatoroftheproportionofastate’seconomicoutputallocatedtoeducation,andthusitsprioritiesforeducation.Similarly,educationalexpenditureasashareoftotalexpenditurereflectstheshareofeducationintotalbudgetorexpenditureofthestategovernment.Thus,thevariablecapturesthegovernment’semphasisoneducationinrelationtootherbudgetarypriorities.Byconsideringbothvariables,amorecomprehensiveunderstandingofinvestmentineducationanditsrelationshiptobudgetaryprioritiescanbeobtained.Basedontheabovediscussedconceptualframeworkandinsightsfromtheexistingstudies,thefollowingfunctionalrelationshipsarespecifiedforeconometricmodeling:•Model1:TotalEnrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,Per-capitaNSDP,EDU/TE)•Model2:Boys’Enrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,Per-capitaNSDP,EDU/TE)•Model3:Girls’Enrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,Per-capitaNSDP,EDU/TE)DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates105•Model4:TotalEnrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,EDU/GSDP)•Model5:Boys’Enrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,EDU/GSDP)•Model6:Girls’Enrollment=f(Pupil–teacherratio,Electricity,Functionalcomputer,Internet,Pandemic,PandemicAidedschool,PandemicPrivateschool,EDU/GSDP)ThevariablesaredefinedindetailinTable5.1.Table5.1:DetailsonMeasurementoftheVariablesVariablesDefinition/MeasurementDataSourceDependentVariablesTotalenrollmentRatioofstudentsenrolledinpre-primarytohighersecondaryUDISE+classesindifferenttypesofschools(government/aided/private)tototalenrollmentintheseclassesinanacademicyearBoys’enrollmentRatioofboys’studentsenrolledinpre-primarytohigherUDISE+secondaryclassesindifferenttypesofschools(government/aided/private)tototalenrollmentintheseclassesinanacademicyearGirls’enrollmentRatioofgirls’studentsenrolledinpre-primarytohigherUDISE+secondaryclassesindifferenttypesofschools(government/aided/private)tototalenrollmentintheseclassesinanacademicyearIndependentVariablesPupil–teacherratioRatiooftotalnumberofstudentstototalnumberofteachersUDISE+ElectricityPercentageofschoolswithelectricityfacilitiesavailableUDISE+FunctionalcomputerPercentageofschoolswithfunctionalcomputerUDISE+facilitiesavailableInternetPercentageofschoolswithinternetfacilitiesavailableUDISE+Per-capitaNSDPLogofNetStateDomesticProducttototalpopulationRBIEDU/TEShareofgovernmentexpenditureoneducationinRBItotalexpenditureEDU/GSDPShareofgovernmentexpenditureoneducationinGrossStateRBIDomesticProductPandemicBinaryVariabletakingvalue1forpost-pandemicyears,Dummyand0otherwisePandemicAidedBinaryVariabletakingvalue1forAidedschools,Dummyschooland0otherwisePandemicPrivateBinaryVariabletakingvalue1forPrivateschools,Dummyschooland0otherwiseRBI=ReserveBankofIndia,UDISE+=UnifiedDistrictInformationSystemforEducationPlus.Source:Authors’owncompilation106DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTheeconometricmodelsspecifiedhereincludepercapitanetstatedomesticproduct(NSDP),pupil–teacherratio,variousfacilitiesavailableinschoolsincludingelectricityconnection,availabilityoffunctionalcomputer,internetfacility,andshareofeducationalexpenditureintotalexpenditureorGSDPastheindependentvariablestoexplainthevariationsinstudents’enrollmentinthethreedifferenttypesofschools(government,aided,andprivate).Further,necessarydummyvariablesarealsoaddedtoexaminethedifferencesinstudentenrollmentinapost-pandemicsituationinrelationtothatinthepre-pandemicperiodaswellthatacrossdifferenttypesofschools.Thedependentvariableismeasuredhereasschoolenrollmentofbothgirlsandboysseparatelyalongwithtotalenrollment.Thepupil–teacherratioisanimportantaspectwhenevaluatingthequalityofeducationinaschoolanditspotentialimpactonstudentenrollment(Duraisamyetal.1998;Tiwari,Bhattacharjee,andChakrabarti2020).Similarly,reliableelectricityconnectionisnecessaryfortheproperfunctioningofvariousdigitalamenities.Hence,electricityconnectionisconsideredoneofthevariablesthatinfluencesdigitaleducationandstudentenrollment.Digitalamenitiessuchasanavailablefunctionalcomputerandinternetaccessareotherkeyvariablesthatdeterminetheenrollmentofstudentsinaschool.Amenities,bothphysicalanddigital,arenecessaryandoftenadecidingfactorforparentstochoosetheschoolfortheirchildren(Hill,Samson,andDasgupta2011;Kim,Yi,andHong2021;NarwanaandGill2022).TheCOVID-19pandemicalsohadanenormousinfluenceontheenrollmentofstudentsinIndianschools.TheclosureofschoolsandcollegesduetolockdownsandotherrestrictionsimposedtocontainthespreadofthevirusdisruptedtheregulareducationsystemandinducedashifttoonlineeducationinIndia,severelyaffectingschoolenrollment.Theimpactonenrollmenthasbeensevereparticularlyinruralareas,whereaccesstotechnologyandinternetinfrastructureislimited(AgrawalandAsrani2018;AlviandGupta2020).Manystudentsintheseareashavebeenunabletoattendonlineclassesoraccesseducationalmaterials,leadingtoawideninggapineducationbetweenurbanandruralareas(Selvarajetal.2021).Hence,adummyvariableforthepandemicisincludedtocapturetheeffectsofthepandemiconenrollment.Further,manystudentswereforcedtodropoutofschoolduetofinancialhardshipscausedbythepandemic(AlviandGupta2020).Thus,whiledigitizationcanplayacrucialroleininfluencingenrollmentofstudentsinfavorofgovernmentschools,suchchangesintheenrollmentpatternsduringtheCOVID-19pandemicperiodseemtobeDigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates107largelyduetotheincomeconstraintscreatedbythepandemicaswell.Thismaybesobecausetherehavebeensignificantjoblossesduringthepost-pandemicperiodresultinginatrendofstudentenrollmenttowardthegovernmentschoolstoreduceexpensesrelatedtoschoolfeesandotherrelatedexpenditures(AlviandGupta2020;ASERCentre2022).Accordingly,inadditiontothepandemicdummyvariable,variablessuchaspercapitaincomeandeducationalexpenditurebythegovernmentarealsoincludedintheeconometricmodelstocontroltheimpactoftheeconomicfactorsonthechangesintheenrollmentpatterns(Bhakta2014;SinghandShastri2020).Thepandemic’seconomicrepercussionshaveleftmanyhouseholdsstrugglingtomakeendsmeet.Asaresult,theyhavebeenunabletopayschoolfeesoraffordthenecessarytechnologyandinternetaccessrequiredforonlineclasses.Hence,theimpactonthepandemictodifferentschoolsmightbedifferent.Aninteractiontermofthetypeoftheschoolstothepandemicisincludedtocapturethis.Threealternativemodels—pooledregression,fixedeffects,andrandomeffects—areestimated,andstatisticaltestsliketheLagrangeMultiplier(LM)test,Hausmantest,andtherestrictedF-testareappliedtochoosetheappropriatemodelforfurtheranalysis.TherejectionofthenullhypothesisoftherestrictedF-testsuggestsselectionofthefixedeffectsmodeloverthepooledregressionmodel,whereastheBreuschandPaganLMTesthelpstodeterminetheappropriatemodelbetweenpooledregressionandrandomeffects.ArejectionofthenullhypothesisintheLMTestindicatesselectionoftherandomeffectsmodeloverthepooledregressionmodel.Finally,rejectionofthenullhypothesisincaseoftheHausmantestindicateschoiceofthefixedeffectsmodelovertherandomeffectsmodel.Followingthisprocedureinthepresentstudy,thefixedeffectsmodelisfinallyselectedforfurtheranalysisontheindividualregressioncoefficients.Thischapterisbasedonsecondarydataatthestatelevelandthuscoversdifferentgeographicallocationsthathavesystematicdifferencesinsocioeconomicanddemographicconditions.Further,inthefederalstructureofIndia,educationisincludedintheconcurrentlistwhereboththecentralgovernmentandthestategovernmentshavepoweroverthesubjects.Accordingly,alongwithdifferentcentral-levelprograms,thereareseveralinitiativesofthestategovernmentstowardimprovementofeducationaloutcomes.Suchcoexistenceofinitiativesofthecentralandthestategovernmentscanpotentiallyresultinsystemicinterstatevariationsineducationaloutcomes.Itisexpectedthatthefixedeffectsmodelwillcovertheheterogeneityineducationaloutcomesatthestatelevel.108DigitalTransformationforInclusiveandSustainableDevelopmentinAsia5.4ResultsandDiscussionThesummarystatisticsoftheselectedvariablesaregiveninTable5.2.TotalenrollmentinschoolsistakenasthedependentvariableinModel1,whereasboys’enrollmentandgirls’enrollmentaretakenasindependentvariablesinmodels2and3,respectively.Tables5.3and5.4showtheregressionresultsoftheestimatedfixedeffectsmodels.Theresultsshowthatalltheestimatedmodelsarestatisticallysignificant.Further,thestandarderrorsoftheteststatisticsfortheindividualcoefficientsarecorrectedforheteroscedasticitybyusingtherobuststandarderrors.Table5.2:SummaryStatisticsofVariablesVariableNumberofStandardMinimumMaximumObservationMeanDeviationIndependentVariablesPandemic3480.50.501PTR34824.010.9062.39Electricity3480.250.2300.85Computer3480.140.1500.84Internet3480.080.1000.67PCNSDP34811.40.5310.212.6Edu/TE3480.150.020.070.23Edu/GSDP34823.531.00.44157.8Aidedschool3480.160.3701Privateschool3480.160.3701DependentVariablesEnrollment3480.320.2300.88Boys’enrollment3480.320.2200.87Girls’enrollment3480.320.2300.89GSDP=grossstatedomesticproduct,PCNSDP=percapitanetstatedomesticproduct,PTR=pupil–teacherratio,TE=totalexpenditure.Source:Authors’ownestimationbasedonUnifiedDistrictInformationSystemforEducationPlus(UDISE+)andReserveBankofIndiadata.DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates109Table5.3:RegressionResultsfortheEstimatedFixedEffectsModelModel1Model2Model3TotalEnrollmentBoys’EnrollmentGirls’EnrollmentVariableCoefficientt-StatisticCoefficientt-StatisticCoefficientt-StatisticPandemic0.0092.370.0122.800.0061.78PTR0.0023.450.0023.470.0023.41Electricity0.0170.620.0160.550.0170.68Computer0.0481.940.0501.870.0442.02Internet0.0581.390.0661.420.0491.36PCNSDP–0.03–1.16–0.03–1.21–0.02–1.08Edu/TE0.0040.060.0020.030.0030.05Aidedschool–0.009–2.28–0.01–2.68–0.006–1.73Privateschool–0.023–4.50–0.02–5.16–0.017–3.59Constant0.5962.050.6401.980.5442.11R2-Between0.0770.0590.098R2-Within0.3450.3740.299R2-Overall0.0770.0600.097F-stat7.468.436.31Restricted376.46332.16438.34F-testLMtest409.45387.42431.06Hausmantest201.87469.27116.43Numberof348348348observationsGSDP=grossstatedomesticproduct,PCNSDP=percapitanetstatedomesticproduct,PTR=pupil–teacherratio,TE=totalexpenditure.Notes:significantat10%;significantat5%Figuresinparenthesesindicatethecorrespondingsignificancelevel.Theteststatisticsfortheindividualcoefficientsarebasedonheteroscedasticitycorrectedrobuststandarderrorstocorrectheteroscedasticity.Source:Authors’ownestimationbasedonbasedonUnifiedDistrictInformationSystemforEducationPlus(UDISE+)andReserveBankofIndiadata.110DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable5.4:RegressionResultsfortheEstimatedFixedEffectsModelModel4Model5Model6TotalEnrollmentBoys’EnrollmentGirls’EnrollmentVariablesCoefficientt-StatisticCoefficientt-StatisticCoefficientt-StatisticPandemic0.0102.920.0133.420.0072.23PTR0.0023.330.0023.330.0023.31Electricity0.0160.620.0150.540.0170.69Computer0.0461.900.0481.830.0431.97Internet0.0511.350.0581.380.0441.33Edu/GSDP–0.058–0.91–0.061–0.89–0.053–0.90Aidedschool–0.101–2.57–0.012–3.03–0.007–1.94Privateschool–0.024–4.59–0.030–5.24–0.017–3.67Constant0.25713.480.24911.810.26515.71R2-Between0.1030.0810.126R2-Within0.3410.3700.296R2-Overall0.1010.0810.122F-stat7.818.866.58RestrictedF-test368.44324.66430.13LMtest388.15367.72408.32Hausmantest234.20638.78134.39Numberof348348348observationsGSDP=grossstatedomesticproduct,PTR=pupil–teacherratio.Notes:significantat10%;significantat5%Figuresinparenthesesindicatethecorrespondingsignificancelevel.Theteststatisticsfortheindividualcoefficientsarebasedonheteroscedasticitycorrectedrobuststandarderrors.Source:Authors’ownestimationbasedonUnifiedDistrictInformationSystemforEducationPlus(UDISE+)andReserveBankofIndiadata.Asregardstheindividualcoefficients,theregressionresultsareconsistentacrossthemodels.Itisfoundthatthevariablespandemic,pupil–teacherratio,andfunctionalcomputerhavestatisticallysignificantandpositiveimpactsontotalenrollment,includingthatofbothboysandgirls.Incomparisonwithgovernmentschools,bothgovernment-aidedschoolsandprivateschoolshaveexperiencedsignificantnegativeimpactsofthepandemiconenrollment.However,variablessuchaselectricityconnection,accesstointernet,percapitanetstatedomesticproduct,andshareofeducationalexpenditureintotalDigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates111expenditureorGSDPdonothaveanysignificantimpactonenrollmentastherespectivecoefficientsarenotsignificant.Thepositivepupil–teacherratiocoefficientisnotsurprising.Asenrollmentgrows,thepupil–teacherratiowillalsoincrease,giventhenumberofteachersdoesnotincreaseproportionately.IntheIndiancontext,theUDISE+(2021)reportshowsthatthenumberofteachersisnotincreasingproportionatelytostudentenrollment;hence,suchapositiveassociationisobvious.However,thisaspectrequiresdeeperscrutinythroughqualitativeanalysisasahigherpupil–teacherratioisnotbeneficialforthestudentsforbettereducationaloutcomes.Thisissobecausealimitednumberofteachersinrelationtothenumberofstudentscannotgiveadequateattentiontoallthestudents(Tiwari,Bhattacharjee,andChakrabarti2020),whereasasmallerstudent–teacherratiocancontributetoabetterstudentexperience,betterengagement,andimprovedacademicperformance(SinghandSarkar2015).Hence,ahigherstudent–teacherratiomaybereflectedinhigherenrollment,butmaynotnecessarilyensurequalityeducation.Itisalsofoundthatenrollmentratesaresignificantlyimpactedbyavailabilityoffunctionalcomputerfacilities.However,thecoefficientofinternetfacilitiesatschoolisnotstatisticallysignificant.Thisissopossiblybecause51%ofIndianhouseholdsdonothaveregularoradequateaccesstotheinternet(NationalFamilyHealthSurveyfromInternationalInstituteforPopulationSciences2021).Sincecomputersaretangiblypresentinschools,parentsaremorefamiliarwithsuchfacilitiesthantheinternetconnections.Thiscouldbethereasonwhyinternetconnectionhasnoeffectonenrollment.Theregressionresultsindicatethatschoolenrollmenthasimproved,especiallyinthegovernmentschoolsduringthepost-pandemicperiod.ThisfindingisinlinewiththelatestASERreport(2022).Thepandemicseemstohaveaffectedtheschoolchoicedecisionsoftheparentsandforcedthemtochoosethelessexpensivegovernmentschoolsthatprovideallthenecessitiesandrequirementsalmostfreeofcost(UDISE+2021).Whileavailabilityofbasicinfrastructurefacilitiesanddigitaltechnologyhadledtothehighenrollmentrateinprivateschoolsbeforethepandemic(Nambissan2012),thepandemicseemstohavealteredtheenrollmentpatternsinfavorofthegovernmentschools,whereinfrastructurefacilitiesarenotsodevelopedastheprivateortheaidedschools.Thisispossiblybecauseofthelowereducationalexpensesthatthehouseholdsneedtobearatthegovernmentschools.Thehouseholds,particularlythosefromeconomicallydisadvantagedbackgrounds,facedsignificantchallengestotheirlivelihoodsasaresultofthepandemic-inducedeconomicslowdownmakingprivateschoolsunaffordable(AlviandGupta2020).112DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaThefindingshavespecialsignificanceasthemarginalizedsegmentsofIndiansocietyexhibitlimitedornoownershipofICTassetsandlackthenecessaryskillstoutilizethem.Asaresult,thebenefitsofICTaccrueovertimetoalreadyresourcefulsegmentsofsociety,whereasmarginalizedgroupsarenotincludedenoughinthetechnologicalintegrationpractices.InIndia,around21%ofstudentsinurbanareashadaccesstoacomputerwithinternetascomparedtotheircounterpartsinruralareas(4%).Asregardsthesocialgroups,only4%oftheScheduledTribeand4%oftheScheduledCastestudentshaveaccesstoacomputerwithinternet.Incontrast,7%ofstudentsfromtheOtherBackwardClassesand21%fromthe“Others”castegrouphaveaccesstoacomputerwithinternetfacility(OxfamIndia2022).Thisposesasignificantconcern,particularlyinlightoftheIndiangovernment’semphasisonICT-drivendevelopmentdiscourseoverthepastdecade.Itisrecognizedthatschoolshelptoreducethedigitalgapbyprovidingaccesstotechnologyanddigitalresourcestothestudentswhomaynothavethesameavailableathomeorintheircommunitiesotherwise(Kim,Yi,andHong2021).Furthermore,schoolscanalsoprovidedigitalliteracytrainingtoassiststudentsindevelopingtheabilitiesnecessaryforeffectivetechnologyusageandnavigatethedigitalworld(Roy2012).Thefindingsofthestudy,therefore,suggestanemphasisondevelopmentofdigitalinfrastructurefacilitiesatthegovernmentschools.Thisiscrucialgiventhecommonnotionthatthesociallyandeconomicallybackwardhouseholds,whoarealreadyunderprivileged,choosethegovernmentschoolsbecauseoftheirgreateraccessandlowerexpenses(Härmä2011).Itmaynotbepossibleforsuchsocialandeconomicgroupstohavedigitalresourcesandconnectivityavailableatthehouseholdlevel.Theexistingdigitalinequalityisanindicationofthesame(Tewathia,Kamath,andIlavarasan2020).Hence,itisnecessarytoequipthegovernmentschoolswithnecessarydigitalinfrastructure,sincemorethanhalfoftheenrolledstudentsrelyonsuchschoolsfortheireducation.Lackofdigitalfacilitiescreatesadigitaldivide,whichfurtherwidensthesocioeconomicinequalityexistinginthesociety.Alongwiththis,thelackofdigitalskillsamongteachersalsoposesacriticalchallengetothequalityofdigitalization.Thepoliciesandotherinitiativesshouldthereforeaimatbridgingthedigitaldivideandensurethatthestudentshavegreateraccesstothesetechnologiesandtheassociatedresourcesattheirrespectiveschools.Itispossibletofacilitateaccesstoeducationaltechnologiesandresourcesbycollaboratingwiththegovernment(s),educationalinstitutions,theprivatesector,andvariousdevelopmentagencies.Inparticular,thecorporatesectorinitiatesvariousyouthtrainingprogramsasapartofcompanies’corporatesocialresponsibility(CSR),andtheeducationDigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates113sectoraccountsforthehighestshareofsuchCSRfunds,followedbythehealthsector.Around25%oftheCSRfundsin2020/21and30%ofthefundsinthepast6yearswerespentoneducation(MinistryofCorporateAffairs2022).Itcanbefurtherextended,andnewopportunitiescanbecreatedifapropermechanismisinplacethatallowsformorepublic–privatepartnershipsinthearea.5.5SummaryandConclusionsThedigitaldivideisrecognizedasasignificantfactorcontributingtosocioeconomicinequality,particularlyintermsofdisparitiesinaccessingandutilizingdigitaltechnologyamongvariousdemographicandsocialgroups.Thesedisparitiesextendbeyondincome,education,age,andgeographicallocation.Thedevelopmentofdigitalinfrastructureinschoolsandequippingstudentswiththerequiredskillscanplayacrucialroleinresolvingthisissue.Studentswhohaveaccesstodigitaldevicesandhigh-speedinternetatschoolexperiencepositiveimpactsontheiracademicachievements.Inthisconnection,schoolsplayavitalpartofclosingthetechnologicaldividebyprovidingtechnologyaccessanddigitalresourcestostudentswhomaynothavethesameopportunitiesathomeorwithintheircommunities.Additionally,schoolscanalsoofferdigitalliteracytrainingtohelpstudentsdeveloptheskillsrequiredtoeffectivelyutilizetechnologyandnavigatethedigitalrealm.Inthiscontext,thepresentstudyfocusesonexaminingenrollmentpatternsindifferenttypesofschoolsacrossIndianstatesinthepost-pandemicperiodandtheroleofdigitalinfrastructureinfacilitatingaccesstoeducation.Intermsoftheindividualcoefficients,theregressionresultsremainconsistentacrossallthemodels.Thestudyrevealsthatseveralfactors,includingthepandemic,pupil–teacherratio,andthepresenceoffunctionalcomputers,havestatisticallysignificantandpositiveimpactsontotalenrollment,aswellasenrollmentforbothboysandgirls.However,theinteractiontermofthepandemicwiththeaidedandprivateschoolsdemonstratesasignificantnegativeimpactonenrollment.Ontheotherhand,variablessuchaselectricityandinternetconnection,educationalexpenditureasashareoftotalexpenditure,andGSDPandpercapitanetstatedomesticproductdonotshowanysignificantassociationswithenrollmentlevels.Thefindingsofthestudyhighlightthesignificanceofgivingprioritytothedevelopmentofdigitalinfrastructureingovernmentschools.Thisisparticularlycrucialconsideringthatsociallyandeconomicallydisadvantagedhouseholds,whoarealreadymarginalized,oftenchoosegovernmentschoolsbecauseoftheirrelativelyhigheraccessibility114DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaandlowerexpenses.Suchhouseholdsmaynothaveaccesstodigitalresourcesandconnectivityatthehouseholdlevel,leadingtoexistingdigitalinequalities.Giventhatmorethanhalfoftheenrolledstudentsrelyongovernmentschoolsfortheireducation,itbecomesnecessarytoequiptheseschoolswiththeessentialdigitalinfrastructure.Thelackofdigitalfacilitiescontributestothedigitaldivide,exacerbatingexistingsocioeconomicinequalitieswithinsociety.Policiesandinitiativesshouldthereforefocusonbridgingthisdigitaldivide,ensuringthatstudentshavegreateraccesstotechnologyandtheassociatedresourcesintheirrespectiveschools.Facilitatingaccesstoeducationaltechnologiesandresourcescanbeachievedthroughcollaborativeeffortsinvolvinggovernments,educationalinstitutions,andprivatesectorcompanies.Theestablishmentofarobustmechanismenablingpublic–privatepartnershipscanfacilitatethiscoordinatedcollaboration.However,theresearchconductedinthischapteroffersinsightsatabroaderlevelbyanalyzingdataatthestatelevel.Itisimportanttonotethatwithineachstate,therearesignificantvariationsinsocioeconomic,cultural,andgeographicalcharacteristics.Therefore,amoredetailedanalysisatadisaggregatedlevelisrequiredtoacquireadeepercomprehensionofthedynamicsandrobustinterventionstrategies.Inordertoaccomplishthis,futureresearchshouldfocusonbuildingacomprehensiveandsystematicprimarydatabasethatcapturesbothquantitativeandqualitativeinformation.Inaddition,thedigitalskillstrainingoftheteachersandthecostsofdigitalconnectivityareimportantaspectsthataffectthequalityoutcomeofdigitalization.Nevertheless,thestudyreliesonsecondarydataatabroaderandaggregatelevel(i.e.,atthestatelevel),whereascapturingthequality-relatedaspectswouldrequiredetaileddisaggregatelevelanalysisusingsystematicdataontrainingandskilldevelopment.Nonavailabilityofsuchsystematicdatapreventsthepresentchapterfromexaminingtheseaspects,andthusleavesacrucialareaforfutureresearch.DigitalInfrastructureandStudentEnrollment:ExperiencesofthePost-PandemicScenarioinIndianStates115ReferencesAgrawal,A.,andC.Asrani.2018.DigitalDivideamongtheIndianHouseholds:ExtentandCorrelates.EconomicsBulletin38(4):2444–66.Ahmed,E.M.2017.ICTandHumanCapitalSpilloverEffectsinAchievingSustainableEastAsianKnowledge-BasedEconomies.JournaloftheKnowledgeEconomy8(3):1086–112.Alvi,M.,andM.Gupta.2020.LearninginTimesofLockdown:HowCovid-19IsAffectingEducationandFoodSecurityinIndia.FoodSecurity12(4):793–6.ASERCentre.2022.AnnualStatusofEducationReport(Rural).https://asercentre.org/aser-2022/Asrani,C.2022.SpanningtheDigitalDivideinIndia:BarrierstoICTAdoptionandUsage.JournalofPublicAffairs22(4):e2598.Asrani,C.,andA.K.Kar.2022.DiffusionandAdoptionofDigitalCommunicationsServicesinIndia.InformationTechnologyforDevelopment28(3):488–510.Bhakta,R.2014.ImpactofPublicSpendingonHealthandEducationofChildreninIndia:APanelDataSimultaneousEquationModel.IGIDRWorkingPaperWP-2014-049.Mumbai:IndiraGandhiInstituteofDevelopmentResearch.Brossard,M.,M.Carnelli,S.Chaudron,R.Di-Gioia,T.Dreesen,D.Kardefelt-Winther,C.Little,andJ.L.Yameogo.2021.DigitalLearningforEveryChild:ClosingtheGapsforanInclusiveandProsperousFuture.G20InsightsPolicyBrief,UNICEF.https://www.unicef.org/media/113896/file/Digital%20Learning%20for%20Every%20Child.pdfDuraisamy,P.,E.James,J.Lane,andJ.P.Tan.1998.IsThereaQuantity–QualityTrade-offasPupil–TeacherRatiosIncrease?EvidencefromTamilNadu,India.InternationalJournalofEducationalDevelopment18(5):367–83.Eynon,R.,andA.Geniets.2016.TheDigitalSkillsParadox:HowDoDigitallyExcludedYouthDevelopSkillstoUsetheInternet.Learning,MediaandTechnology41(3):463–79.GovernmentofIndia,MinistryofCorporateAffairs.2022.AnnualReport2022–23.https://www.mca.gov.in/content/mca/global/en/data-and-reports/reports/annual-reports/annual-report.htmlGovernmentofIndia,MinistryofEducation.2021.UnifiedDistrictInformationSystemforEducationPlus(UDISE+).https://udiseplus.gov.in/#/PublicationGovernmentofIndia,MinistryofHumanResourceDevelopment.2014.StatisticsofSchoolEducation2011–12.https://www.education.gov.in/sites/upload_files/mhrd/files/statistics/SSE1112.pdf116DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaGreen,L.C.2000.BridgingtheDigitalDivideinOurSchools–AchievingTechnologyEquityforallStudents.InterculturalDevelopmentResearchAssociationNewsletter27:1–20.Härmä,J.2011.Low-CostPrivateSchoolinginIndia:IsItProPoorandEquitable:InternationalJournalofEducationalDevelopment31(4):350–6.Hill,E.,M.Samson,andS.Dasgupta.2011.ExpandingtheSchoolMarketinIndia:ParentalChoiceandtheReproductionofSocialInequality.EconomicandPoliticalWeekly46(35):98–105.Iivari,N.,S.Sharma,andL.Venta-Olkkonen.2020.DigitalTransformationofEverydayLife–HowCOVID-19PandemicTransformedtheBasicEducationoftheYoungGenerationandWhyInformationManagementResearchShouldCare.InternationalJournalofInformationManagement55:102183.InternationalInstituteforPopulationSciences.2021.NationalFamilyHealthSurveyNFHS–5,2019–2021.Mumbai.Kaur,K.,andNeena.2014.PatternofInter-stateDigitalDivideinIndia.EconomicAffairs59(3):379–88.Kim,H.J.,P.Yi,andJ.I.Hong.2021.AreSchoolsDigitallyInclusiveforAll?ProfilesofSchoolDigitalInclusionUsingPISA2018.Computers&Education170:104226.Koutsikouri,D.,R.Lindgren,O.Henfridsson,andD.Rudmark.2018.ExtendingDigitalInfrastructures:ATypologyofGrowthTactics.JournaloftheAssociationforInformationSystems19(10):1001–19.Kumar,B.,T.Sampath,andM.T.Basavaraja.2016.ComputerAccessandUse:UnderstandingtheExpectationsofIndianRuralStudents.QualityAssuranceinEducation24(1):56–69.Kumar,D.,andP.K.Choudhury.2021.DeterminantsofPrivateSchoolChoiceinIndia:AllAbouttheFamilyBackgrounds?JournalofSchoolChoice15(4):576–602.Maity,S.,T.N.Sahu,andN.Sen.2021.PanoramicViewofDigitalEducationinCOVID‐19:ANewExploredAvenue.ReviewofEducation9(2):405–23.Martin,A.V.D.,andJ.M.L.Ramos.2022.DEIFDCFramework:EvaluationofDigitalEducationDeploymentinIndiaintheMidstoftheCOVID-19Pandemic.SocialSciences&HumanitiesOpen6(1):100281.Mishra,P.,B.Behera,N.SekharBagchi,B.Paria,V.RatnaReddy,C.Tallapragada,S.Majumdar,andD.B.Rahut.2023.DevelopmentofCapitalsandCapabilitiesofSmallholderFarmersforPromotingInclusiveIntensificationinAgriculture:ExperiencesfromNorthernWestBengal,India.ADBIWorkingPaperNo.1356.Tokyo:AsianDevelopmentBankInstitute.Digit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owth.Bangladeshhasalsoexperiencedanexpansionofmobilephonesubscriptions(Matsuura,Islam,andTauseef2023).Thisinturnhasledtothedevelopmentofmobilemoneyservicesenablingpeopletotransfer,deposit,andwithdrawmoneyfromanonlineaccountwithouthavingabankaccount(Surietal.2023).Mobilemoneygreatlyreducestransactioncosts,whileenhancingtheconvenience,security,andtimetakenfortransactions.(Surietal.2023).Sincemobilemoneyallowspeopletotransferanddepositmoneyusingshortmessageserviceswithoutaccesstotheinternetandovercomesthechallengesofformalinsurance,itisimportantthatweexaminehowmobilemoneycanhelphouseholdssmooththeirconsumptioninBangladeshwherethenumberofmobilecellularsubscriptionsper100peoplewasover100in2020,buttheratioofindividualinternetusersremainedatonly25%in2020(Figure6.1).Inthischapter,welookatthreeprimaryresearchquestions:First,whatistherelationshipbetweenmobilemoneyadoptionandahousehold’sabilitytosmoothconsumptioninresponsetoweathershocks?Second,arethereheterogeneouseffectsbetweenmobilemoneyandconsumptionsmoothinginresponsetoweathershocksfromtheviewpointofspatialinequalityandpovertystatus?BangladeshhasoneofthehighestpovertyratesamongSouthAsiancountriesandisparticularlypronetoflooding(Islam,Newhouse,andYanez-Pagans2021).Giventhatpoorhouseholdsareespeciallyvulnerabletoweathershocks,thisquestionisofparticularinterest.Third,whatisthemechanismbywhichhouseholdscanmitigatethenegativeimpactoftheweathershocksthroughtheadoptionofmobilemoney?Totheseends,weutilizearecentlycollectedlongitudinaldatasetonBangladeshihouseholdsandcombinewithgranularmonthlyprecipitationdata.Thereisarichbodyofliteraturelookingattherelationshipbetweenmobilemoneyandconsumptionsmoothinginresponsetoeconomicshocks.JackandSuri(2014)andRiley(2018)findthatmobilemoneyhasvariedrisksharingbyallowinguserstosendandreceiveremittancesincasesofnegativeeconomiceventstothehousehold.Moreover,TabetandoandMatsumoto(2020),AhmedandCowan(2021),andAbionaandKoppensteiner(2022)findthatmobilemoneyusersareabletosustaintheirinvestmentsinhumancapitalbeyondhouseholdconsumption.Themechanismbehindtheuseofmobilemoneyforinformalandformalinsurancehasalsobeenwelldocumented.JackandSuri(2014),Riley(2018),andTabetandoandMatsumoto(2020)showthatthefundamentalmechanismisanincreaseinremittancereceipts.Inaddition,theadoptionofmobilemoneyfacilitatesthereceiptofsocialprotectiontransfersthatlikelyimprovestheresilienceofthesehouseholds(Akeretal.2016;AbionaandKoppensteiner2022).MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh123Thecontributionofthischapteristhreefold.First,weprovidenovelevidencethatmobilemoneyservicesenablegeographicallydisadvantagedhouseholdstosmooththeirfoodconsumptioninresponsetodroughtsaswellastheirnonfoodconsumptioninresponsetofloods,combininganationallyrepresentativehouseholdpanelsurveyandhistoricalprecipitationgriddata.Italsoindicatesthattheresultshavegreaterinternalandexternalvalidityintheliterature,especiallyforSouthAsiansettings.Second,weshowthatrelativelypoorerhouseholdscansmooththeirconsumption,thesameasgeographicallydisadvantagedhouseholds.Finally,wefindthatthelikelymechanismthatimproveshouseholdresilienceagainsteconomicshocksisthroughanincreasedlikelihoodofreceivingremittancesduetotheadoptionofmobilemoney.Therestofthechapterisstructuredinthefollowingmanner.Section6.2describesthedatasourceandkeyvariablesofinterest.Section6.3presentstheidentificationstrategyandempiricalspecificationusedintheanalysis.Section6.4discussestheempiricalresults,whileSection6.5providesconcludingremarksandpolicyimplications.Figure6.1:MobilePhoneSubscriptionandInternetUsersinBangladeshShareIndicatorYearMobilecellularsubscriptionsIndividualsusingtheinternet(perpeople)(ofpopulation)Source:CalculatedbyauthorsfromtheWorldBank(2022).6.2Methodology6.2.1DataWeusedatafromtheBangladeshIntegratedHouseholdSurvey(BIHS),whichisanationallyrepresentativeruralhouseholdpanelsurveycarriedoutbytheInternationalFoodPolicyResearchInstitutein2015and2018–2019(fromhereon2019)insevendivisions.ThesamplingstrategyoftheBIHSfollowedatwo-stagedstratifiedsamplingmethod.Followingthe124DigitalTransformationforInclusiveandSustainableDevelopmentinAsiasamplingframeworkfromthecommunityseriesofthe2001PopulationandHousingCensusofBangladesh,thefirststageconstitutedaselectionofprimarysamplingunitsorvillagesafterwhichhouseholdswererandomlyselectedforsurveyinterviewfromeachselectedprimarysamplingunitinthesecondstage(AhmedandTauseef2022).AlthoughtheBIHShasthreerounds,wefocusouranalysisonthistwo-periodpanel.Forourstudy,weusethebalancedpanelincludedinbothsurveyrounds,resultingin9,860observationsfrom4,930householdsasshowninTable6.1.Figure6.2showsthepovertyrateofsevendivisionsofBangladesh.ThepovertyrateisfoundtobemostsevereinRangpurDivisioncomparedtotherestofBangladesh,whichisconsistentwithstudiesbyKhandker(2012)andMatsuura,Islam,andTauseef(2023).RajshahiandSylhetdivisionsaresituatedinthenorthernpartofBangladeshandarehistoricallymoreneglectedandpoor(Hossainetal.2019;AgriculturalExtensioninSouthAsia2018).Rurallivelihoodsinthisregionareheavilydependentonagriculture.Inthefollowingsections,wediscusshowmuchmobilemoneymitigatesthenegativeeffectofweathershocksinpoorsectionsofthecountrylikeRangpurDivisioncomparedtotherestofBangladesh.Ontheotherhand,togenerateindicatorsofweathershocks,weusedatacollectedbytheBangladeshMeteorologyDepartment,whichincludemonthlyrainfallandtemperaturefromMarch1996toFebruary2019onaglobalgridusingunitsof0.5-degreelatitudeby0.5-degreelongitude.Duetotheissuesofdataavailability,wetransformedthegridweatherdatainto64district-level.FollowingHossainetal.(2018),weatherinformationisdividedintotwoseasons:(i)rabi,fromMarchtoNovember;and(ii)kharif,fromDecembertoFebruary.Inthischapter,weuseonlydatafromthekharifseasonsincetherainfallshockvariableintherabiseasonisnotsignificantlyassociatedwithhouseholdwelfare.11Theresultsareavailableonrequest.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh125Figure6.2:PovertyRatebyDivision..PovertyratebyDivision..........SylhetBarisalChattogramDhakaKhulnaRajshahiRangpurSource:CalculatedbyauthorsfromBIHS2015and2019.6.2.2DescriptionofKeyVariablesInthischapter,wedefine“shock”intwoways:(i)arainfallshockand(ii)aself-reportedshock.First,wedefinearainfallshockasusedinMakateetal.(2022):𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑑𝑑𝑑𝑑=𝑟𝑟𝑟𝑟𝑟𝑟𝑛𝑛𝑑𝑑𝑑𝑑−𝑟̅𝑟̅𝑟̅𝑟̅𝑟̅𝑟𝑛̅𝑛̅𝑑̅𝑑(1)𝜎𝜎𝑟𝑟𝑟𝑟𝑟𝑟𝑛𝑛𝑑𝑑whereRainfallsh𝑟̅𝑟̅o𝑟̅𝑟c̅𝑟̅𝑟k𝑟̅𝑟d̅𝑑t̅𝑑isa𝑟r𝑟a𝑟𝑟𝑟i𝑟n𝑛𝑛f𝑑a𝑑𝑑𝑑ll−sh𝑟̅𝑟̅𝑟̅o𝑟̅𝑟c̅𝑟𝑛̅k𝑛̅𝑑̅𝑑measureforacluster(district)(fodr),tihnet𝑅ht𝑅w𝑅e𝑅𝑅𝑅ok𝑅𝑅h𝑅m𝑅a𝑅𝑅ar𝑅𝑅i𝑅i𝑅fn𝑠𝑠sℎrei𝑜a𝑜c𝑜s𝑜e𝑘o𝑘s𝑑n𝑑e𝑑𝑑ia=nsothnes,yAe𝜎𝜎ua𝑟𝑟𝑟rs𝑟𝑟𝑟(a𝑛𝑛t𝑑n)𝑑,dwAhmicahnis(MfroatmsuMuraar,cLhutho,NaonvdeImslbaemr2se0a2s3o)n.,M𝑟̅𝑟̅𝑟̅o𝑟̅𝑟̅𝑟r𝑟̅𝑟e̅̅oivsetrh,eraaivnedrtaigsetsheeasoobnsae𝑌l𝑌r𝑖𝑖𝑖rv𝑖ae=idnf𝛽pa𝛽1rlle+fcoi𝛽pr𝛽2itth𝑆a𝑆e𝑆ti𝑜do𝑜𝑜ni𝑜s𝑘t𝑘fr𝑖𝑖o𝑖i𝑖cr+tt(𝛽hd𝛽e3)𝑀od𝑀v𝑀ee𝑀fri𝑖𝑖n𝑖t𝑖he+de𝛽𝛽4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑑𝑑isthestandarddeviationofra+in𝛽𝛽f5a𝑋l𝑋l𝑖𝑖d𝑖𝑖u+ri𝜂n𝜂𝑖𝑖g+th𝜔e𝜔𝑡𝑡sa×m𝛾e𝛾𝑑𝑑+ϵitraind20years,andσperiod.Ontheotherhand,𝑌w𝑌𝑖𝑖𝑖𝑖e=d𝛽e𝛽1fin+e𝛽𝛽s2e𝑆𝑆l𝑆f-𝑜𝑜r𝑜e𝑜𝑘p𝑘𝑖𝑖o𝑖𝑖r+te𝛽d𝛽3𝑀s𝑀h𝑀o𝑀𝑖c𝑖𝑖𝑖k+a𝛽s𝛽4𝑀e𝑀q𝑀u𝑀a𝑖𝑖𝑖𝑖l×to𝑆𝑆𝑆1𝑜𝑜i𝑜f𝑜𝑘𝑘𝑖𝑖𝑖𝑖householdslosecrops,livestock,p𝑟𝑟+𝑖r𝑖𝑖o𝑖𝛽𝛽d=5u𝑋𝑋c𝛾𝑖𝑖𝛾𝑖t𝑖1i+o+n𝜂𝜂𝛾𝑖𝑖𝛾a2+s𝑆s𝑆𝜔𝑆e𝜔t𝑡𝑜𝑡s𝑜×𝑜,𝑜𝑘o𝑘𝛾𝑖𝛾𝑖r𝑑𝑖𝑑𝑖++coϵ𝛾n𝛾i3ts𝑀u𝑀m𝑀𝑀p𝑖𝑖t𝑖𝑖io+n𝛾𝛾4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆assetsduetofloodorcyclone,and0otherwise.+𝛾𝛾5𝑋𝑋𝑖𝑖𝑖𝑖+𝜂𝜂𝑖𝑖+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+eit𝑟𝑟𝑖𝑖𝑖𝑖=𝛾𝛾1+𝛾𝛾2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛾𝛾3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛾𝛾4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖𝑌𝑌𝑖+𝑖𝑖𝑖𝛾=𝛾5𝑋𝑋𝛼𝑖𝛼𝑖𝑖1𝑖++𝜂𝜂𝛼𝑖𝑖𝛼+2𝑆𝑆𝜔𝑆𝜔𝑡𝑜𝑡𝑜×𝑜𝑜𝑘𝛾𝑘𝛾𝑖𝑖𝑑𝑖𝑑𝑖++e𝛼𝛼it3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛼𝛼4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆+𝛼𝛼5𝑋𝑋𝑖𝑖𝑖𝑖+𝛼𝛼6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖𝑌𝑌𝑖𝑖𝑖𝑖=𝛼𝛼1+𝛼𝛼2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼3𝑀𝑀𝑀𝑀+𝑖𝑖𝑖𝑖𝜂+𝜂𝑖𝑖𝛼+𝛼4𝑀𝜔𝑀𝜔𝑀𝑡𝑡𝑀×𝑖𝑖𝑖𝑖×𝛾𝛾𝑑𝑆𝑑𝑆𝑆+𝑜𝑜𝑜μ𝑜𝑘i𝑘t𝑖𝑖𝑖𝑖126DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaOurmainindependentvariableofinterestismobilemoneyuse.Weconsiderahouseholdtobeamobilemoneyuserifatleastonememberusedamobilemoneyagencyduringaparticularsurveyyear.Mobilemoneyusersarecapturedthroughadummyvariableatthehouseholdlevel.Tomeasurehouseholdwelfare,thepercapitavalueofmonthlyfoodconsumptionandnonfoodconsumptionareused.Bydecomposingthehouseholdconsumption,wecandistinguishhowhouseholdssmooththeirfoodandnonfoodconsumptioninresponsetoshockswithmobilemoneyadoption.6.2.3DescriptiveStatisticsTable6.1showsthesummarystatisticsfortheanalysissample.Thenumberofmobilemoneyusersincreasefrom506in2015to2,254in2019,whichisnearlyhalfofthesamplein2019.Thepercapitamonthlyfoodexpendituredecreasesoverthesurveyperiodinbothmobilemoneyusersandnonusers.Rainfallshockisnegativeoverallindicatingdroughts,regardlessofmobilemoneyuse.In2015,about2.2%ofmobilemoneyusersand2.8%ofnonuserhouseholdsself-reporttolosingcrops,livestock,productionassets,orconsumptionassetsduetofloodorcycloneinthesurveyyear.However,theprobabilityofself-reportedweathershocksdecreasesin2019.Asforsocioeconomicvariablestobecontrolledintheanalysis,householdsheadedbyfemalesarelesslikelytousemobilemoneyservices.Theaverageyearsofschoolingofhouseholdheadsusingmobilemoneywas5.23yearsin2015butfellto4.52in2019.Onelikelyreasonforthisistherapidexpansionofmobilemoneyservicesacrossthecountrythatenabledevenpoorerandlesseducatedsegmentsofthepopulationtoavailofsuchservices.WegenerateawealthindexofassetsusingprincipalcomponentanalysissincethevalueofassetsownedwasnotcollectedinalltheroundsoftheBIHS.Variouscomponentsofwealth,suchasownershipofradios,televisions,telephones,computers,animalcarts,bikes,motorbikesorfridges,andcarsortrucksareusedforthecalculation.22Thedatasetsareavailableathttps://www.ifpri.org/blog/ifpris-bangladesh-integrated-household-survey-bihs-second-round-dataset-now-available.Table6.1:SummaryStatistics20152019ObsMeanSDObsMeanSDObsMeanSDObsMeanSDNonuser1,036.622,254MMuserNonuser907.09VariablesMMUser1,713.741,735.951,691.35Percapitamonthlyfoodexpenditure(Tk)5061,871.94927.344,424890.952,676Percapitamonthlynonfoodexpenditure(Tk)5061,784.941326.004,4241,269.841,086.232,2541,651.381,178.992,6761,447.581,167.32RainfallshockSelf-reportedshock506–1.711.034,424–1.601.062,254–1.661.362,676–1.471.30MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:Temperatureshock0.030.162,2540.020.132,6760.020.13ImplicationsforRiskSharingandPovertyReductioninBangladesh127inkharifseason5060.020.154,4240.010.002,254-0.000.002,676-0.000.00Temperatureshockinrabiseason5060.010.004,424Domesticremittances(Tk)Foreignremittances5060.010.014,4240.010.012,2540.000.022,6760.000.01Totalhouseholdincome(Tk)5069,676.6331,879.294,4243,477.7415,848.322,2549,855.8043,777.772,6766,101.9622,944.88Femalehousehold946.8411,724.942,2546,204.2133,721.532,6764,660.3272,604.28Ageofhousehold5062,981.5927,037.804,424218,739.502,254296,410.3536,127.12,676192,815199,381.4Householdsize181,344.60Yearsofeducationof5062,766,445.9281,891.74,424householdAccesstoirrigation(%)5060.150.364,4240.180.392,2540.190.392,6760.220.41Wealthindex45.9913.552,25446.3312.432,67648.9713.8150646.3013.504,4242,2542.242,6762.034.891.882,2545.884.252,6765.213.655065.242.084,4243.253.834.522.835065.234.464,4245060.480.504,4240.450.502,2540.470.502,6760.450.505060.782.014,424–0.561.862,2540.711.882,676–0.411.83MM=mobilemoney,Obs=observation,SD=standarddeviation.Notes:Percapitalmonthlyfoodandnonfoodexpenditure,thevalueofdomesticandforeignremittances,andtotalhouseholdincomearedeflatedtotherealvalueofthetakain2011.Wealthindexiscalculatedbytheprincipalcomponentanalysisofassetvariables,followingVyasandKumaranayake(2006).Theassetvariablesincludetrunks,stoves,beds,electricfans,televisions,motorcycles,horses,cows,ducks,computers,printers,etc.Thefulllistofassetvariablesisavailablefromhttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BXSYELSource:CalculationbyauthorsfromBIHS(2015,2019).128DigitalTransformationforInclusiveandSustainableDevelopmentinAsia6.3EmpiricalMethodologyInthissection,wedescribeourempiricalstrategytoelicit(i)theimpactsofmobilemoneyonconsumptionsmoothingbycomparingtheresponseofmobilemoneyadoptersandnon-adopterstorainfallandself-reportedshocks,(ii)theresponseofremittancereceiptsofmobilemoneyadopterhouseholdstoweathershocks,and(iii)theheterogeneouseffectofmobilemoneyadoptiononconsumptionsmoothing.Mobilemoneyisbelievedtoenablehouseholdstosendandreceivemoneymoreeasilyduetothereductionoftransactioncosts.Therefore,wehypothesizethatmobilemoneyservicesallowhouseholdstodorisksharingeveniftheyexperiencesignificantweathershocksadverselyaffectingtheirlivelihood.Themechanismproposedinthisstudyisremittances.Remittancesallowfamilymemberstobettersmooththeirconsumptioninthefaceofadverseshocks.Intheempiricalanalysis,welookatwhethertheamountofremittanceincreasesinresponsetoweathershocksthankstomobilemoneyadoption.Inthefollowingsubsections,weshowhowourhypothesisistestedbyeconometricsmethods.𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠ℎ𝑜𝑜𝑜6𝑜𝑘.𝑘3𝑑𝑑𝑑.𝑑1=E𝑟m𝑟𝑟𝑟𝑟p𝑟𝑛𝑛i𝑑r𝑑𝑑i𝑑c−a𝑟̅l𝑟̅𝑟S̅𝑟̅𝑟̅𝑟p𝑛̅𝑛̅e𝑑̅𝑑cification(1)𝜎𝜎𝑟𝑟𝑟𝑟𝑟𝑟𝑛𝑛𝑑𝑑Wefollowtheliteraturetosetourregressionspecificationtoelicit𝑟̅𝑟̅𝑟̅𝑟̅𝑟̅𝑟𝑟̅𝑟̅𝑑̅𝑑theeffectofshocksonconsumptionforhouseholdsusingornotusingmobilemoneyservices(JackandSuri2014;Riley2018).Theeconometricspecificationisasfollows:𝑌𝑌𝑖𝑖𝑖𝑖=𝛽𝛽1+𝛽𝛽2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛽𝛽3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛽𝛽4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛽𝛽5𝑋𝑋𝑖𝑖𝑖𝑖+𝜂𝜂𝑖𝑖+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+ϵit(2)(2)𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠ℎ𝑜𝑜𝑜𝑜w𝑘𝑘𝑑h𝑑𝑑e𝑑r=e𝑟Y𝑟𝑟𝑟𝑟𝑟𝑛is𝑛𝑑𝑑𝑑t𝑑h−e𝑟̅o𝑟̅𝑟̅u𝑟̅𝑟t̅𝑟𝑛̅c𝑛̅o𝑑̅𝑑mevariablewhichispercapitafood/nonfood(1)itexpen𝑟d𝑟𝑖𝑖i𝑖𝑖tu=re𝛾𝜎,𝛾𝜎1M𝑟𝑟𝑟+𝑟𝑟M𝑟𝑛𝑛𝛾𝑑i𝑑𝛾t2i𝑆s𝑆𝑆m𝑜𝑜o𝑜𝑜b𝑘i𝑘l𝑖𝑖e𝑖𝑖+mo𝛾𝛾n3e𝑀y𝑀𝑀a𝑀d𝑖𝑖𝑖o𝑖p+ti𝛾o𝛾4n𝑀a𝑀t𝑀𝑀t𝑖h𝑖𝑖𝑖e×ho𝑆𝑆u𝑆s𝑜e𝑜𝑜h𝑜𝑘o𝑘𝑖l𝑖𝑖d𝑖levelandMMit×Shockitisth+e𝛾𝛾i5n𝑋t𝑋𝑖e𝑖𝑖𝑖ra+ct𝜂i𝜂o𝑖𝑖n+t𝜔e𝜔r𝑡m𝑡×b𝛾e𝛾𝑑t𝑑w+eeenitmobilemoneyandthe(3)𝑟̅𝑟̅𝑟̅𝑟̅𝑟̅𝑟𝑟̅𝑟̅𝑑̅𝑑shockvariables,Xitisthevectorofhouseholdcharacteristics,ηi,ωt,andditγarehousehold,year,anddivisionfixedeffect,respectively,andϵisanerr𝑌o𝑌𝑖𝑖r𝑖𝑖t=er𝛼m𝛼1.+In𝛼t𝛼h2e𝑆𝑆𝑆sp𝑜𝑜e𝑜𝑜c𝑘𝑘i𝑖f𝑖𝑖i𝑖c+ati𝛼o𝛼3n𝑀,𝑀β𝑀4𝑀i𝑖𝑖s𝑖𝑖t+he𝛼𝛼c4o𝑀e𝑀f𝑀f𝑀ic𝑖𝑖𝑖i𝑖e×nt𝑆𝑆o𝑆f𝑜𝑜i𝑜n𝑜𝑘t𝑘e𝑖𝑖𝑖r𝑖estinourmodel.t𝛽h𝛽1e+str𝛽𝛽a2t𝑆e𝑆+g𝑆y𝑜𝛼𝑜,𝛼𝑜5w𝑜𝑘𝑋𝑘𝑋e𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖c++an𝛽𝛼𝛽𝛼a36l𝑀𝑋s𝑀𝑋o𝑖𝑀𝑖𝑖𝑖t𝑀×e𝑖𝑖𝑖s𝑖t+𝑆𝑆t𝑆h𝛽𝑜𝛽e𝑜4𝑜𝑜𝑀m𝑘𝑀𝑘𝑖𝑀𝑖e𝑖𝑖𝑀c𝑖h𝑖𝑖𝑖a×ni𝑆s𝑆m𝑆𝑜s𝑜𝑜𝑜w𝑘𝑘h𝑖𝑖𝑖𝑖ereU𝑌𝑌s𝑖𝑖i𝑖𝑖n=gmobile((24))moneysmoothsri+sk𝛽𝛽s5h𝑋+𝑋a𝑖𝑖r𝑖𝜂𝑖i𝜂n+𝑖𝑖g+—𝜂𝜂𝑖𝜔𝑖i𝜔n+𝑡𝑡p×𝜔a𝜔𝑡r𝛾𝑡𝛾t𝑑×i𝑑c+u𝛾𝛾l𝑑aμ𝑑r+i,ttϵhietroleofremittances,byestimatingthefollowingmodel:𝑟𝑟𝑟𝑖𝑟𝑖𝑖𝑖𝑖𝑖𝑖𝑖==𝛾𝜃𝛾𝜃11++𝛾𝜃𝛾𝜃22𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖++𝛾𝜃𝛾3𝜃3𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖++𝛾𝛾𝜃4𝜃4𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖××𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑘𝑜𝑘𝑘𝑖𝑘𝑖𝑖𝑖𝑖𝑖𝑖𝑖++𝛾𝛾5𝜃𝑋𝜃5𝑋𝑖𝑋𝑖𝑖𝑖𝑋𝑖+𝑖𝑖𝑖+𝜂𝜂𝑖𝑖𝜃𝜃+6𝑋𝑋𝜔𝑖𝜔𝑖𝑖𝑖𝑡𝑡××𝑆𝛾𝑆𝛾𝑆𝑑𝑑𝑜𝑜+𝑜𝑜𝑘𝑘e𝑖𝑖i𝑖𝑖t(3)(3)+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+ψit(5)𝑌𝑌𝑖𝑖𝑖𝑖=𝛼𝛼1+𝛼𝛼2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛼𝛼4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼5𝑋𝑋𝑖𝑖𝑖𝑖+𝛼𝛼6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh129whereritisthetotalvalueofremittancebymobilemoneyatthehouseholdlevelandMMit×Shockitistheinteractiontermbetweenmobilemoneyandtheshockvariables,eitisanerrorterm,andγ4isthecoefficientofinterestinourmodel.6.3.2IdentificationStrategyInthissubsection,wediscusstheidentifyingassumptionsbehindEquations(2)and(3).Thereareself-selectionproblemsassociatedwiththeadoptionofmobilemoneythatcouldbiasourestimates.Ourestimateswouldbebiasedifahousehold’sselectionoftheuseofmobilemoneyiscorrelatedwithunobservablefactorsthatalsoimpacttheircapacitytodealwithshocks,creatingapseudopositiveassociationbetweenmobilemoneyadoptionandshocksmoothing.Giventheconditions,thefixedeffects(FE)estimatorisamoresuitablechoicebecauseitcontrolstime-invariantunobservedcharacteristics(CameronandTrivedi2005).However,time-variantunobservedcharacteristicsarenotaddressedbytheFEestimator.Toovercomethisissue,weuseaninstrumentalvariable(IV)approach.Forourresearchquestion,theIVapproachissupposedtoaffectthedecisionsonmobilemoneyadoptionbutnottheoutcomevariables(percapitafoodandnonfoodconsumptionexpenditure).𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠ℎ𝑜𝑜a𝑜𝑜n𝑘𝑘dRM=aohtu𝑟i𝑟v𝑟t𝑟a𝑟2𝑟t𝑛0e𝑛2𝑑d𝑑2𝑑𝑑b;−yMs𝑟̅ao𝑟̅𝑟̅ac𝑟̅𝑟n̅i𝑟𝑛a̅𝑛d̅l𝑑̅𝑑lAebaurndiunlgaia2m0o2n0g),rwueraclahlcouulsaetheothldess(hZahreenogfm,Zohboiule,(1)𝑟̅𝑟̅𝑟̅𝑟̅𝑟̅𝑟𝑟̅𝑟̅𝑑̅𝑑𝑑𝑑𝑑𝑑users𝜎𝜎t𝑟o𝑟𝑟𝑟𝑟𝑟t𝑛𝑛h𝑑𝑑enumberofrespondentsinaunion(thesmallest(2)money(3)(4)administrativeunitinBangladesh)(exceptforsampledhouseholds)astheIVapproach.Empirically,weconductedafalsificationtesttoverifytheappropriatenessofthecreatedIV(DiFalco,Veronesi,andYesuf2011;Zheng,Zhou,andRahut2022).Theresults,presentedinTableA6.1,s𝑌u𝑌𝑖𝑖g𝑖𝑖g=es𝛽t𝛽s1t+ha𝛽t𝛽2t𝑆h𝑆𝑆e𝑜I𝑜𝑜V𝑜𝑘𝑘d𝑖𝑖𝑖o𝑖+es𝛽n𝛽3o𝑀t𝑀h𝑀𝑀a𝑖v𝑖𝑖𝑖e+a𝛽s𝛽i4g𝑀n𝑀i𝑀f𝑀ic𝑖𝑖𝑖a𝑖n×t𝑆r𝑆e𝑆l𝑜a𝑜𝑜t𝑜i𝑘o𝑘𝑖n𝑖𝑖𝑖shipwithhousMehooreldovweer,lfatoreao+cfc𝛽to𝛽h5u𝑋en𝑋𝑖n𝑖t𝑖𝑖o+fnour𝜂𝜂s𝑖𝑖oe+rbss.𝜔e𝜔r𝑡v𝑡e×d𝛾𝛾c𝑑𝑑h+araϵcitteristicsthatcouldbeassociatedwithbothmobilemoneyuseaswellasfacilitateahouseholdtosmoothconsumptioninresponsetoanaggregateshock(Riley2018),wepro𝑟𝑟p𝑖𝑖𝑖𝑖o=se𝛾a𝛾1n+ad𝛾𝛾d2i𝑆t𝑆i𝑆o𝑜n𝑜𝑜a𝑜𝑘l𝑘𝑖𝑖e𝑖𝑖m+p𝛾i𝛾r3i𝑀c𝑀a𝑀l𝑀s𝑖𝑖t𝑖𝑖r+ate𝛾𝛾g4y𝑀.𝑀I𝑀t𝑀𝑖𝑖e𝑖𝑖x×te𝑆n𝑆𝑆d𝑜s𝑜𝑜e𝑜𝑘q𝑘𝑖u𝑖𝑖𝑖ations(2)and(3)toincludeth+e𝛾𝛾i5n𝑋t𝑋e𝑖𝑖𝑖r𝑖a+ct𝜂i𝜂o𝑖𝑖n+te𝜔r𝜔m𝑡𝑡×so𝛾f𝛾𝑑𝑑th+eeshitockwithallobservableexplanatoryvariables(Xit×Shockit)usingthefollowingmodel:𝑌𝑌𝑖𝑖𝑖𝑖=𝛼𝛼1+𝛼𝛼2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛼𝛼4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼5𝑋𝑋𝑖𝑖𝑖𝑖+𝛼𝛼6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜂𝜂𝑖𝑖+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+μit(3)𝑟𝑟𝑖𝑖𝑖𝑖=𝜃𝜃1+𝜃𝜃2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜃𝜃3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝜃𝜃4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜃𝜃5𝑋𝑋𝑖𝑖𝑖𝑖+𝜃𝜃6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+ψit(5)𝑌𝑌𝑖𝑖𝑖𝑖=𝛼𝛼1+𝛼𝛼2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝛼𝛼4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝛼𝛼5𝑋𝑋𝑖𝑖𝑖𝑖+𝛼𝛼6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖130DigitalTransformationf+or𝜂I𝜂n𝑖𝑖c+lusi𝜔v𝜔e𝑡𝑡a×nd𝛾S𝛾𝑑u𝑑st+ainμaibtleDevelopmentinAsia(4)𝑟𝑟𝑖𝑖𝑖𝑖=𝜃𝜃1+𝜃𝜃2𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜃𝜃3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖+𝜃𝜃4𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜃𝜃5𝑋𝑋𝑖𝑖𝑖𝑖+𝜃𝜃6𝑋𝑋𝑖𝑖𝑖𝑖×𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑘𝑘𝑖𝑖𝑖𝑖+𝜔𝜔𝑡𝑡×𝛾𝛾𝑑𝑑+ψit(4)(5)whereXitarethesamesetofcontrolsdescribedabove.μitandψitareerrorterms,respectively.Byaccountingfortheinteractiontermsbetweentheshocksandhouseholdcharacteristics,wereducesomeoftheconcernsaroundtheinterpretationofα4andθ4,asproposedbyJackandSuri(2014).Equations(4)and(5)representourpreferredspecificationthroughoutthearticle.Becausetheremittancevariableistruncatedinzero,Equation(5)isanIV-tobitregressionmodel,whileEquation(4)isestimatedbytwo-stageleastsquares(2SLS).Hence,weareinterestedinα4inEquation(4)andθ4inEquation(5),andinterpretanddiscussthemintheresultsection.Table6.2:CorrelatesofSelf-ReportedShock(1)Self-reportedshockMobilemoneyuser–0.006(0.005)Shareofhouseholdsadoptingmobilemoneyintheunion–0.001(0.016)Femalehouseholdhead–0.007(0.008)Ageofhousehold–0.000(0.000)Householdsize0.003(0.003)Schoolingyearofhousehold–0.003(0.002)Marketaccess(minute)–0.000(0.000)Assetindex0.000(0.002)DivisionYearFEYesObservations9,860FE-fixedeffect.Note:Robuststandarderrorsinparentheses.Significantatthe1%level.Significantatthe5%level.Significantatthe10%level.Estimatedbyordinaryleastsquares.Source:Calculatedbyauthors.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh131ForEquations(4)and(5)toidentifythecausaleffectofmobilemoneyonriskssharing,wehavetoassumethattheinteractionofMMit×Shockitisexogenous,oruncorrelatedwiththeerrorϵit,conditionalonthehouseholdFEandtheothercontrolvariables.Especiallyforaself-reportedshockas“shock”,itmaybesystematicallycorrelatedwithanumberofhousehold-levelvariables.WetestthisbyrunninganFEregressionfordifferenthouseholdcharacteristicsfortheself-reportedshockandpresenttheresultsinTable6.2.Wefindthatself-reportedshockisnotcorrelatedwithotherhouseholdcharacteristics,norwiththeinstrumentalvariableormobilemoneyadoption(Table6.2).6.4ResultsandDiscussions6.4.1EmpiricalResultsTable6.3showstheresultofourbasespecificationfromEquation(2).AllregressionsincludethefullsetsofhouseholdcharacteristicsfromTable6.1.PanelAinTable6.3presentstheregressionresultsoftwo-wayfixedeffectmodelwithoutIVasrobustnesscheckoftheresults,whereasPanelBinTable6.3showstheregressionresultsoftheIV–FEmodel.FromColumns(1)to(8)inPanelB,coefficientsofinteractiontermsarewhatwearemostinterestedin,whichareinequation(3)andinequation(4).InPanelA,theshockvariablesarenegativelyassociatedwithpercapitanonfoodexpenditureinColumn(4).Itindicatesthataonestandarddeviationpositiverainfallshock(indicatingaflood)raisesthelikelihoodofafallinpercapitamonthlynonfoodconsumptionexpenditureby7.2percentagepoints.Moreover,theinteractiontermsbetweenthemobilemoneyuserandshocksarepositivelyandstatisticallysignificantlyassociatedwithpercapitamonthlynonfoodexpenditure,whiletheyarepositivelybutstatisticallyinsignificantlyassociatedwithpercapitamonthlyfoodexpenditure,regardlessofthetypeofshock.However,thepositiveandstatisticallysignificantsignontheinteractiontermsindicatethatmobilemoneyuseservesasinformalinsurancetomobilemoneyadopterhouseholdsagainstrainfallshocksasseeninColumns(4)and(8).InPanelB,thetrendsofsignsonthecoefficientsaresimilartotheresultsofPanelA.ResultsfromthefullspecificationarereportedinColumns(3),(4),(7),and(8).InColumn(3),thecoefficientoftheinteractiontermisnegativeandsignificant.Itindicatesthatmobilemoneyusersseemtobeabletosmoothalargepartofpositiverainfallshocks,indicatingdroughtsinthekharifseason,onpercapitafoodconsumption.Moreover,therainfallshockinthekharifseasondecreasesthepercapitanonfoodexpenditure,whiletheself-reportedshockisnot132DigitalTransformationforInclusiveandSustainableDevelopmentinAsiastatisticallysignificantlycorrelatedwithhouseholdwelfare.Thus,onlyrainfallshocksareusedinfollowingheterogeneityanalysis.Inaddition,theinteractiontermbetweenthemobilemoneyuserandtherainfallshockispositivelyandstatisticallysignificantlyassociatedwithpercapitamonthlynonfoodexpenditureinColumn(4).Themagnitudeofthecoefficientoftheinteractiontermis0.120whilethemagnitudeofthecoefficientoftherainfallshockis–0.098.Itthusseemsthatmobilemoneyadoptionovercompensatesforthenegativeeffectofrainfallshocks.TheseresultsareconsistentwithpaststudiesbyJackandSuri(2014),Riley(2018),TabetandoandMatsumoto(2020),andAbionaandKoppensteiner(2022).Furthermore,inTable6.4weexploretheeffectusingdifferentsubsamples.InPanelA,thesampleisdividedintotwogroupswhicharehouseholdsinSylhet,Rangpur,andRajshahidivisionsandhouseholdsinotherdivisions.ThethreedivisionsarerelativelyfarfromDhaka,thecapitalofBangladesh.Moreover,RangpurDivisionhasthehighestpovertyrateamongallthedivisionsinBangladeshasseeninFigure6.2.SylhetandRajshahidivisionshavethelowestvaluesintheHumanDevelopmentIndex(GlobalDataLab2019).Therefore,wetestwhethermobilemoneyadoptionhasanyheterogeneouseffectbetweenlessdevelopedareasandmoreurbanizedareas,intermsofeconomicactivity.InColumns(1)and(2),weshowtheresults.Thecoefficientofrainfallshockispositivelyassociatedwithpercapitamonthlyfoodconsumption.Itindicatesthatrainfallbelowthehistoricalaveragerainfall(indicatingadrought)decreasespercapitamonthlyfoodexpenditure.Theplausibleexplanationisthenatureofincomesourcesinthoseareas.InthethreedivisionsincludingRangpur,theagrariansectorisdominantcomparedtotherestofBangladeshincludingDhaka(Khandker2012).Theirlivelihoodsrelyheavilyoncropproduction.Therefore,droughtwouldsignificantlyaffectcropyields,leadingtoafallinfarmincomeand,therefore,reducesfoodconsumptionexpenditure.Thus,thecoefficientoftheinteractionterminColumn(1)isnegativeandstatisticallysignificant.ItindicatesthatmobilemoneyadoptionovercompensatesforthenegativeeffectofdroughtinSylhet,Rangpur,andRajshahidivisions.AnotherplausibleexplanationisthatgeographicallydisadvantagedareasusuallyhavepoorerlogisticsandmarketaccesscomparedtoareasnearDhaka.Therefore,nonfoodgoodsmaynotbeconveyedfromurbanareastothedisadvantagedareas.Asaresult,thecoefficientoftheinteractionterminColumn(2)isstatisticallyinsignificant,indicatingthathouseholdsusingmobilemoneyarenotabletomaketheirnonfoodconsumptionstableinresponsetotherainfallshocks.InlinewithTable6.3,Column(4)showsthepositivecoefficientoftheinteractiontermandnegativecoefficientofrainfallshock.TheMobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh133plausiblereasonisthatthosethatlivenearDhakahavebetterlogistics,thenmobilemoneycanbeusedfortransactionsofpurchasingnonfoodproducts.Itimpliesthatmobilemoneyworksdifferentlyasinsuranceforconsumptionsmoothinginsurancedependingonthegeographicalfactors.Thisisanencouragingresultthatmobilemoneyhasapro-pooreffectandcanbepromotedinthegeographicallydisadvantagedareastoreducedisparityamongstregions.InPanelB,thesampleisdividedintofoursubsamplesbyconsumptionquota.Theyarebeloworabovepercapitaexpenditurein2015.TheresultinColumn(1)showsthatthecoefficientoftherainfallshockisstatisticallysignificantand0.67,andthecoefficientoftheinteractiontermisstatisticallysignificantand–0.158.Theresultsuggeststhatmobilemoneyadoptionovercompensatesthenegativeimpactofdeficientrainfallindicatingdroughtsonfoodconsumptionforthepoorerhouseholds.Moreover,wefindsignificantmitigatingeffectsofmobilemoneyonnonfoodconsumptionforthepoorerhouseholdsinColumn(2).Thelogicbehindtheeffectsofmobilemoneyisthatpoorhouseholds’livelihoodismorelikelytorelypredominantlyonagriculture,whichishighlysusceptibletorainfallshocks,especiallydroughts.Thus,droughtsdevastatecropyieldsmorethanfloodsdo,inducingamoresevereeffectonfoodinsecurity.ThisisconsistentwithJackandSuri(2014);TabetandoandMatsumoto(2020)andawelcomefindingfromruraldevelopmentperspective.FromColumn(3)and(4),theresultsforhouseholdsabovemedianpercapitaexpenditurearepresented.WhilethecoefficientsinColumn(3)arestatisticallyinsignificant,thecoefficientofrainfallshockissignificantand–0.098andthecoefficientoftheinteractiontermissignificantand0.093inColumn(4).Itindicatesthatmobilemoneyadoptionmitigatesthenegativeeffectsofexcessiverainfallonnonfoodexpenditure.Thericherhouseholdsaremorelikelytohavealternativeinstrumentsratherthanmobilemoneytosmooththeirfoodconsumptioninresponsetotherainfallshocks.Thus,mobilemoneyisfoundtohaveoppositeeffectsonhouseholdwelfareforpoorerandricherhouseholds.InPanelC,wealsoseparatethesampleintotwosubsamples,withrespecttohouseholdincome.WefindthattheresultsareconsistentwiththeresultsofPanelB.Theresultimpliesthatdigitalconnectivityofpoorhouseholdsbuildstheirresiliencetoweathershocksunderclimatechange.Table6.3:ImpactofRainfallShocksonConsumptionforMobileMoneyUsersandNonusers134DigitalTransformationforInclusiveandSustainableDevelopmentinAsia(1)(2)(3)(4)(5)(6)(7)(8)OLSPercapitaPercapitanonfoodfoodRainfallexpenditureSelf-reported0.040expenditurePanelAPercapitafoodPercapitaPercapita(0.020)–0.020PercapitaPercapitaPercapitaMMuserexpenditurenonfoodfood(0.013)nonfoodfoodnonfood–0.015expenditure0.0170.088expenditureexpenditureInteraction(0.019)expenditure(0.010)(0.078)expenditure0.001–0.072–0.0420.005Shock(0.009)0.042–0.022(0.027)(0.043)0.005–0.019(0.014)–0.019YesHouseholdFE(0.007)(0.020)(0.020)YesYes(0.014)(0.013)0.152DivisionYearFEYesYesYes(0.087)CovariatesYes0.018–0.004YesNo0.1930.110–0.102InteractionYesYeswithshockNo(0.009)(0.009)9,860(0.077)(0.084)(0.187)Observation9,860Yes9,860–0.0640.003–0.068–0.073YesYes(0.007)(0.026)(0.041)(0.176)YesYesYesYesYesYesYesYesYesYesYesYesYesNoYesNoYes9,8609,8609,8609,8609,860continuedonnextpageTable6.3continued(1)(2)(3)(4)(5)(6)(7)(8)2SLSRainfallSelf-reportedPanelBPercapitaPercapitaPercapitaPercapitaPercapitaPercapitaPercapitaPercapitaMobileMoneyMitigatestheNegativeEffectsofWeatherShocks:MMuserfoodnonfoodfoodnonfoodfoodnonfoodfoodnonfoodImplicationsforRiskSharingandPovertyReductioninBangladesh135expenditureexpenditureexpenditureexpenditureInteractionexpenditure0.290expenditure0.302expenditureexpenditure–0.073(0.109)–0.090(0.112)–0.0000.0060.0010.009Shock(0.103)0.113(0.105)0.120(0.084)(0.084)(0.084)(0.084)–0.050(0.030)–0.061(0.033)0.0180.3220.0600.279HouseholdFE(0.029)–0.095(0.032)–0.098(0.184)(0.178)(0.212)(0.218)DivisionYearFE–0.003(0.012)0.014(0.029)–0.026–0.096–0.057–0.120Covariates(0.011)(0.027)(0.052)(0.056)(0.179)(0.197)InteractionYesYesYesYesYesYeswithshockYesYesYesYesYesYesYesYesObservationsYesYesYesYesYesYesYesYesNoNoYesYesNoYesYesYesNoYes9,8609,8609,8609,8609,8609,8609,8609,860FE=fixedeffect,MM=mobilemoney,OLS=ordinaryleastsquares,2SLS=two-stagedleastsquares.Notes:Robuststandarderrorsclusteredbyhouseholdsinparentheses.Outcomevariablesareconvertedintologarithms.Significantatthe1%level.Significantatthe5%level.Significantatthe10%level.Cragg-DonaldWaldFStatisticfortheinstrumentedvariablesis69.846.Thus,itrejectsthenullhypothesisofweakinstruments.Theinteractiontermbetweenthemobilemoneyuserandshocksisinstrumentedbytheinteractiontermbetweenshareofmobilemoneyusersandtheshocks,whichisexogenousinourmodel.Thefirststagefor2SLSandthefulltableoftheoutcomeequationsareavailableonrequest.Source:Calculatedbyauthors.136DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable6.4:HeterogeneousEffectsoftheImpactofRainfallShocksonConsumptionforMobileMoneyUsersandNonusers(1)(2)(3)(4)Sylhet,Rangpur,RestofBangladeshandRajshahiPanelAPercapitaPercapitaPercapitaPercapitaMobilemoneyuserfoodnonfoodfoodnonfoodexpenditureexpenditureInteractionexpenditureexpenditureRainfallshock–0.6790.0440.0170.313HouseholdFE(0.279)(0.249)(0.118)(0.129)DivisionYearCovariates–0.303-0.001–0.0140.165InteractionwithshockObservations(0.065)(0.056)(0.044)(0.045)PanelB0.169–0.053–0.038–0.127Mobilemoneyuser(0.061)(0.050)(0.034)(0.038)InteractionYesYesYesYesRainfallshockYesYesYesYesHouseholdFEDivisionYearYesYesYesYesCovariatesInteractionwithshockYesYesYesYesObservations3,3823,3826,4766,476(1)(2)(3)(4)BelowmedianpercapitaAbovemedianpercapitaexpenditurein2015expenditurein2015PercapitaPercapitaPercapitaPercapitafoodnonfoodfoodnonfoodexpenditureexpenditureexpenditureexpenditure-0.1940.4210.1550.332(0.135)(0.146)(0.157)(0.165)–0.1580.083–0.0390.093(0.045)(0.044)(0.049)(0.050)0.067–0.055–0.025–0.098(0.038)(0.038)(0.040)(0.045)YesYesYesYesYesYesYesYesYesYesYesYesYesYesYesYes4,9304,9304,9284,928continuedonnextpageMobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh137Table6.4continued(1)(2)(3)(4)belowmedianhouseholdabovemedianhouseholdincomein2015incomein2015PanelCPercapitaPercapitaPercapitaPercapitafoodnonfoodfoodnonfoodexpenditureexpenditureexpenditureexpenditureMobilemoneyuser-0.0870.508-0.1070.182(0.203)(0.226)(0.121)(0.127)Interaction-0.0970.151-0.0360.107(0.059)(0.063)(0.039)(0.043)Rainfallshock0.027-0.115-0.000-0.086(0.047)(0.049)(0.033)(0.038)HouseholdFEYesYesYesYesDivisionYearYesYesYesYesCovariatesYesYesYesYesInteractionwithshockYesYesYesYesObservations4,9364,9364,9224,922FE=fixedeffect.Notes:Allcolumnsareestimatedby2SLS.Robuststandarderrorsclusteredbyhouseholdsinparentheses.Outcomevariablesareconvertedintologarithm.Significantatthe1%level.Significantatthe5%level.Significantatthe10%level.Theinteractiontermbetweenmobilemoneyuserandshocksisinstrumentedbytheinteractiontermbetweenshareofmobilemoneyusersandtheshocks,whichisexogenousinourmodel.Thefullregressiontableisavailableonrequest.Source:Calculatedbyauthors.6.4.2Mechanism:MobileMoneyandRemittingNetworkTheplausiblemechanismpresentedinthischapteristhatmobilemoneyimproveshouseholdresiliencebyfacilitatingeasierflowofremittancesfromfriends,relatives,andfamilyinotherlocationsrespondingtoarainfallshockand,thus,enablinghouseholdstosmoothconsumption.WeshowthiswithestimatesfromEquation(5)thatarereportedinTable6.5.WhiletheinteractiontermisstatisticallyinsignificantinColumn(1),thecoefficientsofboththeinteractiontermandtherainfallshockarestatisticallysignificantinColumn(2).Itindicatesthathouseholdsusingmobilemoneyservicesaremorelikelytoreceiveforeignremittanceinlargeramountsthroughmobilemoneyinresponsetotherainfallshock,comparedtononuserhouseholds.SimilartoTabetandoandMatsumoto138DigitalTransformationforInclusiveandSustainableDevelopmentinAsia(2020),itthereforeseemsthatmobilemoneyuseisleadinghouseholdstoengageinaninformalinsurancestructurewherehouseholdstransferandshareresourcesparticularlyintheeventofanegativeshock.TheresultisalsoconsistentwithJackandSuri(2014),SuriandJack(2016),Riley(2018),TabetandoandMatsumoto(2020),andBatistaandVicente(2020).Inaddition,weaddtotheliteraturebydistinguishingtheeffectofdomesticversusinternationalremittancesusingmobilemoneybynotingthatinternationalremittancesareamuchlargersourceofresilienceforhouseholdsinthefaceofshockscomparedtodomesticremittances.Itthereforecanbeassumedthattheadoptionofmobilemoneytechnologyisencouraginginternationalremittanceflowsbyreducingthetransactioncostsassociatedwiththischannel.Asaresult,ourfindingsemphasizetheimportanceofremittancesfromoverseasout-migrantsandcallsforfacilitatingcross-borderlabormigrationandeaseofregulationsencouragingfreeflowoftransfersintothecountry.Table6.5:MechanismforMobileMoneyRemittances(1)(2)ValueofdomesticValueofforeignremittancesremittances(deflated)(deflated)Mobilemoneyuser3.7e+04-6.6e+03(2.5e+04)(1.1e+05)Interaction–3.2e+038.5e+04(7925.436)(3.9e+04)Rainfallshock–2.4e+03–3.1e+04(6,309.794)(3.2e+04)DivisionXYearYesYesControlvariablesYesYesInteractionwithshockYesYesObservations9,8609,860Notes:Robuststandarderrorsclusteredbyhouseholdsinparentheses.Significantatthe1%level.Significantatthe5%level.Significantatthe10%level.Theinteractiontermbetweenmobilemoneyuserandshocksisinstrumentedbytheinteractiontermbetweenshareofmobilemoneyusersandtheshocks,whichisexogenousinourmodel.Fullregressiontableisavailableuponrequests.Source:Calculatedbyauthors.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh1396.5ConclusionandPolicyImplicationsPoorhouseholdsaremorevulnerabletonegativeeconomicshockssincetheyarelikelytofailtosmooththeirconsumptioninresponsetoshockssuchasrainfall.Bangladeshisextremelypronetofloodseveryyearandtheclimateriskisrisingduetoglobalclimatechange.Sinceweneedeasierandmoreaccessibleadaptationstrategiesagainstsuchshocks,mobilemoneyservicescanbeapreferredandsuitableoption.Itisanovelandrapidly-growingtechnologythatcanhelphouseholdsinsuretheirwelfareagainstclimaticshocksbygivingaccesstoremittancesfromotherlocationsnotaffectedbyshocks(Riley2018).Inthischapter,weprovidenewandapplicableevidenceontheconsequencesofmobilemoneyadoptiononhouseholdwelfareinthecontextofSouthAsiancountries.Tothisend,weuseanationallyrepresentativehouseholdpaneldatasetfromBangladeshandamonthlygranularprecipitationdatasetcollectedbytheBangladeshMeteorologyDepartment.Combiningthetwodatasetsenableustoestimatetheroleofmobilemoneytechnologyonconsumptionsmoothinginresponsetoobjectiveandsubjectiveshocks.Ourresultsshowthatlargerainfallshocksnegativelyaffectfoodandnonfoodconsumption,buttheadoptionofmobilemoneycanprovidemuchneededresiliencetohouseholdsinmitigatingthisimpactbyallowingtheeasierflowofremittances,especiallyfromforeigncountries.However,wedonotfindthatself-reportedshocksaffecthouseholdconsumption,andthatmobilemoneymitigatestheeffectofsuchself-reportedshocks.Wefindsignificantheterogeneityinourresults,withrespecttogeographyandwelfaredistribution.Wefindgeographicallydisadvantageddivisionsbenefitmorefrommobilemoneytechnologybymitigatingthenegativeeffectsofdroughtsonfoodconsumption.Moreover,regardingfoodconsumption,mobilemoneyworksasaninformalinsuranceagainstdroughtsforthepoorerhouseholds.Itindicatesthatpoorerhouseholds’livelihoodsandfoodconsumptionaremorelikelytorelyontheirownagriculturalproductionthatismorevulnerabletodroughts.However,mobilemoneyenablesbothpoorerandricherhouseholdsaffectedbyfloodstosmooththeirnonfoodconsumption.Wedeterminethemechanism,wheremobilemoneyenablesrisksharing,byincreaseinremittancesreceivedaftertherainfallshock,possiblyduetoareductionintransactioncosts.Wealsofindevidencethathouseholdsowningmobilemoneyaremorelikelytoreceiveoverseasremittancesinresponsetotherainfallshocksratherthandomesticremittances.140DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaOurfindingsshedlightontheimportanceofmobilemoneyservicesasinformalinsuranceandrisksharing,giventheincidenceofextremeclimateeventsrelatedtoclimatechange.Governmentsandstakeholdersshouldpromotetheexpansionofmobilemoneyservicessothatruralhouseholdscancheaply,quickly,andsafelyavailanoptiontocopewithfutureweathershocks.Moreover,mobilemoneyservicesmayhelpovercomespatialinequalitybybringingforthapro-pooreffect.Thediffusionofmobilemoneywouldhelppoorhouseholds,whoselivelihoodsmainlydependonagriculture,smooththeirfoodconsumption,andsharetheirrisksbyoverseasremittances.ThisisparticularlyimportantinthecontextofBangladesh,wheremanyvulnerableruralhouseholdsconfrontweathershocks.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh141ReferencesAbiona,O.,andM.Koppensteiner.2022.FinancialInclusion,Shocks,andPoverty.JournalofHumanResources57(2):435–464.https://doi.org/10.3368/jhr.57.2.1018-9796R1AgriculturalExtensioninSouthAsia.2018.AgriculturalDevelopmentinNorthWestBangladesh,23January.https://www.aesanetwork.org/agricultural-development-in-north-west-bangladesh/Ahmed,A.,andS.Tauseef.2022.ClimbinguptheLadderandWatchingOutfortheFall:PovertyDynamicsinRuralBangladesh.SocialIndicatorsResearch160:309–340.Ahmed,H.,andB.Cowan.2021.MobileMoneyandHealthcareUse:EvidencefromEastAfrica.WorldDevelopment141:105392.https://doi.org/10.1016/j.worlddev.2021.105392Aker,J.,R.Boumnijel,A.McClelland,andN.Tierney.2016.PaymentMechanismsandAntipovertyPrograms:EvidencefromaMobileMoneyCashTransferExperimentinNiger.EconomicDevelopmentandCulturalChange65(1):1–37.Barnett,B.,andO.Mahul.2007.WeatherIndexInsuranceforAgricultureandRuralAreasinLower-IncomeCountries.AmericanJournalofAgriculturalEconomics89(5):1241–1247.http://doi.org/10.1111/j.1467-8276.2007.01091.xBatista,C.,andP.Vicente.2020.ImprovingAccesstoSavingsThroughMobileMoney:ExperimentalEvidencefromAfricanSmallholderFarmers.WorldDevelopment129:104905.Benami,E.,andM.Carter.2021.CanDigitalTechnologiesReshapeRuralMicrofinance?ImplicationsforSavings,Credit,&Insurance.AppliedEconomicPerspectivesandPolicy,43(4):1196–1220.Cameron,A.,andP.Trivedi.2005.MicroecenometricsMethodsandApplications.NewYork,US:CambridgeUniversityPress.DiFalco,S.,M.Veronesi,andM.Yesuf.2011.DoesAdaptationtoClimateChangeProvideFoodSecurity?AMicro-PerspectivefromEthiopia.AmericanJournalofAgriculturalEconomics93(3):829–846.Hossain,M.,L.Qiam,M.Arshad,S.Shahid,S.Fahad,andJ.Akhter.2019.ClimateChangeandCropFarminginBangladesh:AnAnalysisofEconomicImpacts.InternationalJournalofClimateChangeStrategiesandManagement11(3):424–440.Islam,T.,D.Newhouse,andM.Yanez-Pagans.2021.InternationalComparisonsofPovertyinSouthAsia.AsianDevelopmentReview38(1):142–175.DOI:https://doi.org/10.1162/adev_a_00161142DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaJack,W.,andT.Suri.2014.RiskSharingandTransactionsCosts:EvidencefromKenya’sMobileMoneyRevolution.AmericanEconomicReview104(1):183–223.Khandker,S.2012.SeasonalityofIncomeandPovertyinBangladesh.JournalofDevelopmentEconomics97:244–256.Kono,H.,andK.Takahashi.2010.MicrofinanceRevolution:ItsEffects,Innovations,andChallenges.TheDevelopingEconomies48(1):15–73.http://doi.org/10.1111%2Fj.1746-1049.2010.00098.xMa,W.,andA.Abudulai.2020.ImpactofInternetUseonEconomicWell-beingofRuralHouseholds:EvidencefromChina.ReviewofDevelopmentEconomics24(2):503–523.Makate,C.,A.Angelsen,S.T.Holden,andO.T.Westerngen.2022.CropsinCrises:ShocksShapeSmallholders’DiversificationinRuralEthiopia.WorldDevelopment159:106054.http://doi.org/10.1016/j.worlddev.2022.106054Matsuura,M.,A.-H.-M.-S.Islam,andS.Tauseef.2023.MobilePhoneOwnership,IncomeDiversification,andPovertyReductioninRuralBangladesh.IDEDiscussionPaper,875.http://dx.doi.org/10.2139/ssrn.4176609Matsuura,M.,Y.-H.Luh,andA.-H.-M.-S.Islam.2023.WeatherShocks,LivelihoodDiversification,andHouseholdFoodSecurity:EmpiricalEvidencefromRuralBangladesh.AgriculturalEconomics54(4):455–470.https://onlinelibrary.wiley.com/doi/abs/10.1111/agec.12776Riley,E.2018.MobileMoneyandRiskSharingAgainstVillageShocks.JournalofDevelopmentEconomics135:43–58.GloblDataLab.2019.SubnationalHDI.https://globaldatalab.org/shdi/shdi/BGD/?levels=1%2B4&interpolation=1&extraplation=0&nearest_real=0&years=2019(accessed26May2023).Suri,T.andW.Jack.2016.TheLong-runPovertyandGenderImpactsofMobileMoney.Science354(6317):1288–1292.https://www.science.org/doi/10.1126/science.aah5309Suri,T.etal.2023.MobileMoney.VoxDevLit2(2).Tabetando,R.,andT.Matsumoto.2020.MobileMoney,RiskSharing,andEducationalInvestment:PanelEvidencefromRuralUganda.ReviewofDevelopmentEconomics24:84–105.https://onlinelibrary.wiley.com/doi/10.1111/rode.12644Turner,A.,andH.Annamalai.2012.ClimateChangeandtheSouthAsianSummerMonsoon.NatureClimateChange2:587–595.Vyas,S.,andL.Kumaranayake.2006.ConstructingSocio-economicStatusIndices:HowtoUsePrincipalComponentsAnalysis.HealthPolicyandPlanning21(6):459–468.MobileMoneyMitigatestheNegativeEffectsofWeatherShocks:ImplicationsforRiskSharingandPovertyReductioninBangladesh143WorldBank.2022.IndividualsusingtheInternet(%ofpopulation).WorldBankOpenData:https://data.worldbank.org/indicator/IT.NET.USER.ZS(accessed25November2023).Zheng,H.,Y.Zhou,andD.Rahut.2022.SmartphoneUse,Off-farmEmployment,andWomen’sDecision-makingPower:EvidencefromRuralChina.ReviewofDevelopmentEconomics27(3):1327–1353.https://onlinelibrary.wiley.com/doi/10.1111/rode.12966144DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaAppendixTableA6.1:TestofValidityoftheSelectionInstruments(1)(2)PercapitafoodPercapitanonfoodexpenditureexpenditureShareofhouseholds0.029–0.001adoptingmobilemoney(0.064)(0.062)intheunionHouseholdFEYesYesDivisionYearYesYesControlvariablesYesYesObservations5,0325,032FE=fixedeffect.Notes:Robuststandarderrorsclusteredbyhouseholdsinparentheses.Outcomevariablesareconvertedintoalogarithm.Significantatthe1%level.Significantatthe5%level.Significantatthe10%level.Source:Authors.7TheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChinaPinghanLiangandWeiZou7.1IntroductionThePeople’sRepublicofChina(PRC)istheworld’slargestonlineretailmarket,andtherapiddevelopmentofonlineretailhasbroughtprosperitytotheexpressdeliveryindustry.In2021,thebusinessvolumeofthePRC’sexpressdeliveryservicecompaniesreached108.3billionparcels,rankingfirstintheworldforconsecutiveyears,withabusinessrevenueofCNY1.0332trillion,becominganimportantpartofthemodernlogisticssystemandnationaleconomy.Thetremendousgrowthoftheexpressdeliveryindustryiscloselylinkedtotheflourishingdevelopmentofe-commerce.Recentstudieshavefoundasignificantimpactofe-commercedevelopmentonruralhouseholdconsumption(Coutureetal.2021).E-commerceisundoubtedlydrivenbylargedigitalplatforms,butitswidespreadpenetrationintodailylifereliesontheoperationofend-pointinfrastructuresuchasexpressdeliverypoints.So,howdoesthedevelopmentofthedigitaleconomyaffectthedevelopmentofexpressdeliveryservices?Thisstudyexaminestheimpactofdigitalfinancialdevelopmentontheestablishmentofexpressdeliverypointsbasedontheexpansionofdigitalinclusivefinance.ThestudycollectsdataontheaddressofexpressdeliverypointsthroughoutthePRCfrom2011to2019usingtheBaiduMapPointofInformation(POI)applicationandtheregistrationdatabaseofindustrialandcommercialenterprises.PrivateexpressdeliveryservicesinthePRCstartedinthe1990stoserveexports,andwereinitiallysetupincoastalcitiessuchasHangzhou,Shanghai,and145146DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaNingbotohandlecustomsdeclarationsforexport.Withtheintroductionofthefranchisesystemintheexpressdeliveryindustry,deliverypointsbegantoexpandacrossthecountry,especiallywiththerapiddevelopmentofe-commerceplatformssuchasTaobao,JD,andT-mall,aswellastheinnovationofmobilepaymenttechnology,animportantfeatureofdigitalfinanceinthePRC.TheempiricalanalysisutilizesthewidelyuseddigitalfinancialindexdevelopedbythejointworkoftheAntGroupandPekingUniversity.Itfoundthatbetween2011and2019,digitalfinancialdevelopmentsignificantlyincreasesthenumberofexpressdeliverypoints.Onaverage,forevery10percentagepointsincreaseinthedigitalfinancialindex,thetotalnumberofexpressdeliverypointsincreasesby3.16percentagepoints,andthenumberofnewlyestablishedexpressdeliverypointsinthatyearincreasesby3.81percentagepoints.Thestudyalsoexaminestheimpactofdifferentsubindexesofdigitalfinancialdevelopment,includingfinancialcoveragebreadthanddepth,anddigitalserviceconvenience,onthedevelopmentofexpressdeliveryoutlets,andfindsthatfinancialcoveragebreadthanddepthhavesignificantpositiveeffects.Toovercometheinfluenceofunobservabletime-variantomittedvariables,thisstudyconstructsanovelinstrumentalvariable(IV)forcity-leveldigitalfinancialdevelopment.WeexploitthepromotionofAlipay’sRedEnvelopefunctionin2016,whichsignificantlyextendsthepenetrationofAlipayacrossthecountry,andconstructanIVusingadifference-in-differenceapproach.Theinstrumentalvariableestimationresultsshowthatthedevelopmentofdigitalfinancesignificantlyincreasesthenumberofexpressdeliverypoints.Heterogeneityanalysisshowsthatdigitalfinancedevelopmentmainlypromotesthegrowthofexpressdeliverypointsinareaswithhighereducationlevels,betterinternetaccess,androadinfrastructure.Theeffectconcentratesintheeasternprovinces.Finally,thestudyfurtherexploresthechannelsthroughwhichdigitalfinanceinfluences.First,thedevelopmentofdigitalfinanceattractsinvestmentfrome-commercecompaniestoparticipateinthelogisticsindustry,therebyimprovinglogisticsdeliveryefficiencyandthesupplyofdeliverypoints.Second,digitalfinancehelpsactivatesocialconsumptionpotential,especiallyintheprosperityandriseofonlineconsumptionactivities,whichdrovethedevelopmentofexpressdeliveryoutletsonthedemandside.Finally,digitalfinancialdevelopmentexpandedfinancingchannels,alleviatingfinancingconstraintsforestablishingexpressdeliveryoutlets,andpromotingtheestablishmentofoutletsonthesupplyside.ThisTheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina147wasmorepronouncedinregionswhereaccesstoexistingfinancialresourceswaslowerandmorefavorablefortheincreaseofsmallandmedium-sizedexpressdeliveryenterpriseoutlets.Thischaptercontributesthreeaspectsofdigitalfinance.First,itcontributestotheunderstandingoftheimpactofdigitalfinanceoninfrastructure,providinganewperspectiveforunderstandingthecoordinateddevelopmentbetweene-commerceandofflineinfrastructure.Thisoffersnewinsightsforfurtherimprovingtheconstructionofnewinfrastructure,leveragingthesynergiesbetweendifferentareasofinfrastructure,andachievinghigh-qualitydevelopment,whichhaspracticalguidanceforadjustingthePRC’sdigitaleconomypolicies.Second,thischaptercontributestothestudyoffactorsaffectinginfrastructureconstruction.PreviousresearchhasmainlyfocusedonanalyzingtheinfluenceofgovernmentbehaviorinthePRC,suchasbenchmarkcompetition(Zhangetal.2007),central–localrelations(Ma2022),andpromotionincentives(WuandZhou2018)oninfrastructureinvestmentandconstruction.Thisstudyshowsthatmarket-orienteddigitalfinancedevelopmentdrivenbyprivateenterprisesalsoaffectstheexpansionofexpressdeliverypoints,therebycontributingtounderstandingthedrivingforceandeffectivenessofthePRC’sinfrastructuredevelopmentfromamarketperspective.Third,thePRC’sexpressdeliveryindustryhasbeenrankedfirstintheworldintermsofscaleforseveralconsecutiveyears,withservicerevenueexceedingCNY1trillion,becominganimportantelementinthePRC’sdevelopedlogisticssystem.However,researchonthisindustryisscarce(Wang,Lin,andQiu2022),andresearchonthedrivingforceandinfluencingfactorsofitsrapiddevelopmentisevenmorescarce.Thischapterinvestigatestheroleofdigitalfinancedevelopmentontheexpansionoftheexpressdeliveryindustry,providingtheoreticalsupportforunderstandingthedevelopmenttrendsandpatternsoftheexpressdeliveryindustry,andfurtherbuildinganefficientandsmoothcirculationsystemtoreducelogisticscosts.Theremainingsectionsofthischapterarearrangedasfollows.Section7.2introducestheinstitutionalbackgroundandaliteraturereview.Section7.3mainlydescribesthedescriptivestatisticsofdata,identificationstrategy,andvariables.Section7.4reportsthemaineffectestimationresults,Section7.5furtherdiscussesthemechanismthatcoordinatesthedevelopmentofdigitalfinanceandexpressdeliveryindustry,andSection7.6concludes.148DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.2BackgroundandLiterature7.2.1InstitutionalBackgroundTorevitalizetheruraleconomy,thePRC’scentralgovernmentproposedthelogisticsdevelopmentpolicyof“revitalizingcommoditycirculation,breakingtheurban–ruraldivideandregionalblockades,andexpandingcirculationchannels”in1983,whichbrokethenationalizedmonopolysystemofpostalindustrythathadlastedformorethan30years.Thankstothelooseningofstatecontroloverthepostalindustry,thePRC’sfirstprivateexpressdeliverycompany,ShentongExpress,wasestablishedinHangzhouin1992,mainlyprovidingcustomsdeclarationservicesforexportenterprises.In1995,expressdeliverycompaniesbegantoadoptafranchisingsystemwithintheindustry,thatis,“point-to-pointcontractingforhouseholds”.Thepersoninchargeofthedeliverypointonlyneedstopayacertaindepositinadvancetothecompany,andcontractforthedeliveryserviceofgoodswithinaspecificarea.ThisfranchisingstrategyhelpedleadingexpressdeliverycompaniestoquicklyexpandandseizethePRC’smailinglogisticsmarket(amongthem,thetotalmarketshareof“STO,ZTO,YTO,andSF”reachedmorethan60%(ThePaper2021).Therapidexpansionoftheexpressdeliveryindustryhasbenefitedfromthedevelopmentofdigitalfinance.Thedigitalfinanceapps,suchasWeChatPayandAlipay,havegreatlyimprovedtheaccessibilityandconvenienceoffinancialservices,therebydrivingthetrendofmassonlineshopping.Duringthe“DoubleEleven”periodin2021(1Novemberto11November,similarto“BlackFriday”intheUnitedStates),ChinaUnionPayprocessed27.048billiontransactions,withatotalamountofCNY22.32trillion,andthehighestbusinesspeakof965,000transactionspersecond.Thishugeamountofonlinetransactionsgenerated6.8billionexpressdeliverypackages.Itcanbeseenthatbecausedigitalfinancehasawiderreach,mostresidentswithmobilephonesoraccesstotheinternetcanenjoytheconveniencebroughtbydigitalfinance.Thisgreatlypromotestheprosperityofonlineshopping,andincreasesthequantityofgoodstransportationsignificantly.Thishasbroughtasteadystreamoforderstotheexpressdeliveryindustry,ultimatelypromotingitsrapiddevelopment.7.2.2LiteratureReviewExpressdeliverypointsineffectisakindofend-pointinfrastructure.Alargebodyofliteratureinvestigatesthedeterminantsofinfrastructureconstruction.ItiswidelyacknowledgedthatinfrastructureplaysakeyTheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina149roleineconomicgrowth(Démurger2001;EsfahaniandRamirez2003;Faber2014;Storeygard2016;Donaldson2018;Banerjee,Duflo,andQian2020;Baum-Snowetal.2020).TheexistingresearchinthePRCfocusesontheroleofofficialpromotionincentivesininfrastructure.ThestudybyZhangetal.(2007)showsthattheyardstickcompetitionamonglocalgovernmentsisanimportantfactorunderlyinginfrastructureinvestment.Wang,Zhang,andZhou(2020)demonstratetheimportanceofofficialpromotionincentivesinurbaninfrastructureconstruction.However,theroleoftheemergingdigitalfinanceininfrastructureconstructionisignored.Anemergingbodyofliteratureaddressestheeconomicimpactsofdigitalfinance.Previousstudiesonmobilepaymentuseinthebroaderfieldofdigitalfinancehaveextensivelyexaminedtherelevantstakeholderssuchasconsumersandmerchants(AuandKauffman2008;Kim,Mirusmonov,andLee2010;OzcanandSantos2015;Shaw2014;Shin2009).ThePRChasbeenattheforefrontofthedevelopmentofdigitalfinanceduetothepervasiveuseofonlinetradingplatformsandanunderdevelopedbankingsystemthatexcludeslargesegmentsoftheruralpopulationfromtraditionalbankcredit(Hauetal.2019).Zhangetal.(2020a,2020b,2020c),Hauetal.(2021),YangandZhang(2022)demonstratetheimpactofdigitalfinanceonhouseholdconsumption,inequality,entrepreneurshipgrowth,andfinancialinclusion.Furthermore,digitalfinancealsohassocialbenefitssuchasreducingthecrimerate(JiangandLiang2022).However,althoughitiswellknownthattherapiddevelopmentofe-commerceinthePRCreliesondigitalfinanceandexpressdeliveryindustry,thecausalimpactofdigitalfinanceonexpressdeliveryindustryisstillmissing.Digitalfinancecanpromoteexpressdeliveryindustryfromthesupplysideanddemandside.First,thedevelopmentofdigitalfinanceincreaseshouseholdconsumptionsinceitfacilitatespaymentandreducesthetimespentonpurchasing(YangandZhang2022).Theexpansionofconsumptioncreatesdemandforentrepreneurshipgrowth.Hence,digitalfinanceincreaseshouseholdconsumption,andrequiresmoreexpressdeliverypointstosatisfyhouseholddemand.Second,thedevelopmentofdigitalfinancecouldrelaxfinancialconstraintsofentrepreneurship,consequentlyimprovingthesupplyofexpressdeliverypoints.Aswedescribedbefore,theexpansionoftheexpressdeliveryindustryismainlythroughfranchising.Therefore,thestartingofnewdeliverypointsisaffectedbythefinancialconstraintsoffirmsandthedeliverypointmanagers.Thereisevidencethatthedevelopmentofdigitalfinancecouldreducethehouseholdbarriertofinancialservices,makefinanceinclusive,andrelaxthefinancialconstraintsforentrepreneurshipgrowth(Hauetal.2021).150DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.3DataSourceandEmpiricalDesign7.3.1DataThedataonexpressdeliverypointsarescrapedfromtheBaiduPOI(https://map.baidu.com/).Thesedatacoverthename,startingtime,address,nameofmanager,andcontactmethodofeveryexpressdeliverypoint.Tocomplementthesedata,wealsousetheregistrationdataofindustrialandcommercialenterprises,whichcoverstheregistrationinformationofallenterprisesinthePRC.1Weaggregatethestartingtimeandaddressofdeliverypointsintoacity-yearpanel.ThecoreexplanatoryvariableisthedigitalfinanceindexdevelopedbythejointeffortoftheAntFinanceGroupandPekingUniversity(Guoetal.2020).ThefundamentaldataarefromtheuniverseoftransactioninformationinAlipay,aleadingmobilepaymentplatforminthePRC.Thisindexcoversthecity-leveldevelopmentofdigitalfinance,andincludesthreesubindexes:coverage,depth,andservicesupport.CoveragemeasuresthenumberofAlipayaccountsandthetiedbankaccounts.DepthmeasurestheextentthatAlipayusersengageininvestment,loans,insurance,andotherfinancialservices.Servicesupportmainlymeasuresthefacilitationofpaymentandthecostofpayment.ThesedataarewidelyusedinthestudyofdigitalfinanceinthePRC(Zhangetal.2020a,2020b,2002c;YangandZhang2022)Thecontrolvariablesarethecity-leveleconomicandsocialcharacteristics,includinggrossdomesticproduct(GDP)percapita,averageincome,population,area,theshareofserviceindustry,shareofcollegegraduates,internetusersize,fixed-linephoneusers,mobilephoneusers,2fiscalpressure,andshareofinvestmentinGDP.ThesevariablesarefromthePRC’sCityStatisticalYearbooks,compliedbytheNationalBureauofStatisticsandpublishedbyChinaStatisticsPress,inthecorrespondingyears.Ourstudyperiodcovers2011to2019.Afterdeletingtheobservationswithmissingvariables,thefinalsampleisanon-balancepanelof284prefecture-levelcitiesfor9years.Table7.1presentsthedescriptivestatistics.1ThesedataarescrapedfromtheNationalEnterpriseCreditInformationPublicitySystem(https://www.gsxt.gov.cn/index.html).2ThemobilephoneusersreportedinthestatisticalyearbooksarecalculatedbasedonthenumberofSIMcardsissuedbythetelecommunicationserviceproviders.ItiscommonforPRCcustomerstoholdmultipleSIMcards.Thismayoverestimatethepenetrationofmobilephones,butitisalsotheonlyavailablemeasureonthesizeofmobilephoneusers.Similarproblemexistsfortheinternetuserbase.TheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina151Table7.1:DescriptiveStatisticsVariablesObsMeanMedianSDMinMaxDataSourceNumberof3,206158.791192.201,452BaiduPOI+deliverypoints42.20351registration3,20636.923dataofNumberofnewenterprisesdeliverypointsDigitalfinance2,989162.3169.264.325.0289.2Guoetal.index(2020)Coverageindex2,989151.5157.363.04.49287.6Depthindex2,988159.2155.167.114.7293.7Servicesupport2,989203.4237.679.816.5340.0indexGDPpercapita3,0035.164.213.340.6546.77PRC’sCity(CNY10,000)17.32Statistical2,6155.535.301.800.50YearbooksAverageincome(2012–2020)(CNY10,000)Population2,627440.9376.9304.603,392(10,000)Area(10,0002,6221.731.232.450.00140.73squarekm)Shareofcollege1,7021.80.952.40.005913graduates(%)Shareofservice2,6090.41409.91083industry(%)Fixed-linephone1,72782.252103.54926.4users(1,000)Mobilephone2,611446.9319463.8154076users(1,000)Internetusers2,60595.8158113.311,535.17(1,000)Budget2,6162.952.382.010.6520.6expenditure/budgetrevenueShareof1,7287976288.7220investmentinGDP(%)GDP=grossdomesticproduct,Obs=observations,POI=pointofinformation,PRC=People’sRepublicofChina,SD=standarddeviation.Source:Authors.152DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.3.2EmpiricalDesignOurbaselinemodelisthefollowingspecification:lnCSPit=β0+β1difit+β2controlit+γi+ηt+εit(1)InwhichlnCSPitisthelogarithmnumberofexpressdeliverypoints,orthenumberofnewexpressdeliverypoints,incityiinyeart.difitisthelogarithmofthedigitalfinanceindexofcityiinyeart.controlitisasetofcontrolvariables.γiiscityfixedeffecttocaptureanytime-invariantcity-levelunobservable,ηtisyearfixedeffect,tocapturecommontimevariantshocks.Thestandarderrorεitisclusteredatthecitylevel.WetakethestartingofRedEnvelopfunctionofAlipayintheendof2015asashock,andconstructanIV.IntheChineseNewYearin2015,thepromotionofRedEnvelopebyWeChat,theleadingsocialmediaappinthePRC,wasabigsuccess.WeChatuserscouldtransfermoneyfromtheirbankaccountstotheirWeChataccount,andputtheminan“envelope”,andsendtooneorseveralfriends.Asaresponsetothemaincompetitor,AlipaystartedtheRedEnvelopefunctionintheendof2015.Topromotethis,AlipayannouncedtodistributeCNY800millionintheseenvelopesasgiftstousersintheCCTVNewYear’sGalain2016(People.cn2015).ThisencouragedpeopletoopenAlipayaccounttowinthefreemoney.UntiltheChineseNewYearin2016(18February),intotal1.72billionAlipayRedEnvelopeswereused.Hence,weconsiderthismarketingactivityasanexogenousshocktothepenetrationofdigitalfinance.TheclosertoHangzhou,themorelikelythatthecitywasaffectedbythismarketingactivity.Ontheotherhand,thedistancetoHangzhouisexogeneousandtime-invariant,andcouldnotdirectlyaffectthedevelopmentofexpressdeliverypointsincurrentdecades.Hence,weusetheinteractiontermofdistancetoHangzhouandadummyindicatingwhethertheyearisafter2016astheIVfordigitalfinanceindex.7.4Results7.4.1BaselineTable7.2reportstheordinaryleastsquares(OLS)resultsbasedonequation(1).Column(1)usethenumberofexpressdeliverypointsasthedependentvariable.Column(2)and(3)addcityfixedeffectandyearfixedeffect,stepbystep.Column(4)furtherincludesasetofcity-levelcontrolvariables.Theestimatedcoefficientis0.316,andsignificantin1%level.Column(5)usesthenumberofnewexpressdeliverypointsasthedependentvariable,andthemagnitudeofcoefficientissimilarasTheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina153Column(4).Theresultsindicatethata10percentagepointsincreaseindigitalfinanceindexissignificantlyassociatedwitha3.16percentagepointsincreaseinthenumberofexpressdeliverypoints,and3.81percentagepointsincreaseinthenumberofnewdeliverypoints.Table7.2:BaselineResults(1)(2)(3)(4)(5)Numberofexpressdeliverypoints(logarithm)NumberofnewdeliveryDigitalfinanceindex2.0391.9930.2060.316points(log)(log)(0.03)(0.03)(0.10)(0.12)0.381(0.15)GDPpercapita0.0190.027(log)(0.08)(0.13)Averageincome0.0380.063(log)(0.23)(0.26)Population(log)0.6030.877(0.38)(0.54)Area(log)–0.443–0.531(0.26)(0.39)Shareofservice0.156–0.115industry(log)(0.54)(0.73)Shareofcollege1.6633.558graduates(log)(2.75)(3.55)Internetusers(log)0.0200.074(0.03)(0.06)Fixedlineusers(log)–0.050–0.138(0.06)(0.08)Mobilephoneusers0.1270.110(log)(0.08)(0.11)Fiscalpressure(%)0.017–0.032(0.02)(0.02)Investment/GDP0.1080.152(%)(0.08)(0.10)Cityfixedeffect3003YYYYYearfixedeffect0.668YYYObservations3003300316481648AdjustedR20.9170.9650.9520.777GDP=grossdomesticproduct,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.154DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable7.3reportstheresultswiththethreesubindexesofdigitalfinanceastheexplanatoryvariables,respectively.Westillusetheregression(1).Columns(1)–(3)showthatforevery10percentagepointsincreaseinthecoverageanddepthissignificantlyassociatedwith1.70percentagepointsand1.51percentagepointsincreaseinthenumberofexpressdeliverypoints,respectively.However,thecorrelationbetweenofservicesupportandexpressdeliverypointsisnotsignificant.Aswementionedbefore,servicesupportmainlyreferstothepenetrationofmobilepayment.Unlikefooddeliveryorsharedtravel,onlineshoppingrequiresthesupportofdigitalpaymentsuchasAlipay,butitdoesnotnecessarilyrelyonmobilepayment.Hence,sincethecoverageindexmainlyreferstotheusersizeofAlipay,itindicatesthatastherearemoreAlipayusers,therewouldbemoredemandforexpressdeliveryservice.Besides,asthedepthindexmeasurestheextentthatAlipayusersexploittheappforvariousfinancialservices,itsuggeststhatthevariousfinancialservicesprovidedbythedigitalfinanceplatformalsorelaxthefinancialconstraintsofentrepreneurshipgrowth,andhelpthestartingofexpressdeliverypoints.Table7.3:TheImpactofSubindexesofDigitalFinance(1)(2)(3)Numberofexpressdeliverypoints(log)Coverage(log)0.170(0.08)Depth(log)0.151(0.08)Servicesupport(log)–0.012(0.04)ControlsYYYCityfixedeffectYYYYearfixedeffectYYYObservations164416451648AdjustedR20.9520.9520.952R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.TheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina155Table7.4reportstheIVregressionresults.Column(1)reportstheresultwithoutthecontrolsofanycity-levelcharacteristics,andColumn(2)includescityfixedeffect,yearfixedeffect,andallcity-levelcontrols.Theestimatedcoefficientsare1.743and1.268,andsignificantunderatleast10%.ThissuggeststhatthepromotionofRedEnvelopebyAlipaysignificantlystimulatesthedevelopmentofdigitalfinance,andconsequentlyincreasesthenumberofexpressdeliverypoints.Table7.4:InstrumentalVariablesEstimationStage2(1)(2)Digitalfinanceindex(log)4.0621.268(0.34)(0.73)Stage1RedEnvelopefunction0.2180.105(0.02)(0.02)ControlsYCityfixedeffectYYearfixedeffectYF-stat76.55633.638Obs30101593AdjustedR20.6980.977Obs=observations,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.7.4.2HeterogeneityAnalysisWeconductfourheterogeneityteststoexaminethecontextthatdigitalfinanceaffectsthedevelopmentofexpressdeliverypoints.Educationallevelisanimportantfactorrestrictingtheabilityofasocietytoadapttothechangesinducedbythedevelopmentoftheinternet.Therefore,thehighertheeducationalattainmentofaregion,thelargerthesizeofhouseholdswhobenefitfromtheuseoftheinternet.Weusetheshareofcollegegraduatesincityresidentsinthebaseyear(2011)tomeasuretheeducationalattainmentofacity.Citiesaredividedashigheducationalattainmentandloweducational156DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaattainment,respectively,basedonthemedium.Columns(1)and(2)inTable7.5reportthesubsampleregressionresults.Itisdemonstratedthattheimpactofdigitalfinanceonthedevelopmentofexpressdeliverypointsisinsignificantincitieswithloweducationalattainment.Internetinfrastructureisaprerequisiteforhouseholdstoenjoythebenefitsofdigitalfinance.Therefore,weusetheinternetusersizein2011tocharacterizethedevelopmentofinternetinfrastructure.Basedonwhetheritisaboveorbelowthemedium,everycityiscategorizedashavingstronginternetinfrastructureorweakinternetinfrastructure.TheregressionresultsarereportedinColumns(3)and(4)inTable7.5.Itisshownthattheimpactofdigitalfinanceonexpressdeliverypointsissmallandinsignificantamongthecitieswithweakinternetinfrastructure.However,digitalfinancehassignificantimpactonthedevelopmentofexpressdeliverypointsincitieswithstronginternetinfrastructure.Thedevelopmentoftheexpressdeliveryindustryalsoreliesonroadinfrastructure(Shamdasani2021).Weusethelengthofroadinacityin2011tomeasurethestockofroadinfrastructure,andcategorizecitiesashavinghighroadinfrastructureorlowroadinfrastructure.Columns(5)and(6)inTable7.5showthatthedevelopmentofdigitalfinanceonlystimulatesthedevelopmentofexpressdeliverypointsincitieswithgoodroadinfrastructure,butinsignificantlyaffectsexpressdeliverypointsincitieswithrelativelyworseroadinfrastructure.Table7.5:HeterogeneityAnalysis(1)(2)(3)(4)(5)(6)(7)(8)(9)No.ofexpressdeliverypoints(log)HighLoweducationaleducationalStrongWeakHighroadLowroadinternetattainmentattainmentinternetinfrastructureinfrastructureEasternMiddleWesternDigital0.4210.1030.571–0.0570.4830.081.0420.136–0.003finance(0.21)(0.16)(0.18)(0.17)(0.19)(0.18)(0.24)(0.19)(0.21)index(log)ControlsYYYYYYYYYCityfixedYYYYYYYYYeffectYearfixedYYYYYYYYYeffectObs844804839809846802658629344AdjustedR20.9580.9440.9550.9400.9570.9440.9520.9590.957Obs=observations,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.TheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina157Columns(7)–(9)furtherexaminetheregionalheterogeneityoftheimpactofdigitalfinance.Astheresultsdemonstrate,thedevelopmentofdigitalfinanceonlysignificantlyleadstotheexpansionofexpressdeliverypointsintheeasternprovinces,e.g.,thosecoastaldevelopedprovinces.Theseareconsistentwithothercolumnsthattheexistinginfrastructureplaysakeyroleindeterminingtheeffectofdigitalfinance.7.4.3RobustnessTestTofurtherconfirmtherelationshipbetweendigitalfinanceandexpressdeliverypoints,weundertakeaplacebotestwiththenumberofpostalofficesasthedependentvariable.Asinothercountries,thepostalserviceinthePRCisfullynationalized,andprovidesabasicpubliccommunicationservice.Asearlyas1980ChinaPoststartedtheEMS,aserviceaimedatexpressdelivery,muchearlierthanotherleadingexpressdeliverycompaniesanddigitalfinance.Sincethepostserviceisnotmarket-oriented,itisexpectedthatdigitalfinanceshouldnothaveanyimpactonthegeographicdistributionofpostaloffices.Table7.6reportstheresults.Thecoefficientofdigitalfinanceisinsignificant,indicatingthatthenationalizeddeliveryservicedidnotrespondtothedevelopmentofdigitalfinance.Table7.6:RobustnessChecksPostaloffices(log)Digitalfinanceindex(log)–0.200(0.24)ControlsYCityfixedeffectYYearfixedeffectYAdjustedR20.881Obs1648Obs=observations,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.158DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.5Mechanisms7.5.1ImproveServiceEfficiencyInitially,AlipaywasestablishedbyAlibabaGrouptosupportonlinetransactionsinTaobao.Tofurtherimprovetheefficiencyofe-commerce,AlibabaGroupestablishedtheCainiaostationnetworkin2013toservecommunitiesandcampuses.AfeatureoftheCainiaostationisthatparcelsaretemporarilystoredinalockedmailbox,andreceiverscanuseatextedcodetogettheparcelatanyconvenienttime,beforeaspecifieddeadline.Thissubstantiallyreducestheburdenofcourierworkersandpromotestheefficiencyofexpressdelivery.Therefore,thoseexpressdeliverycompaniesthatestablishcooperationwiththeCainiaostationwouldgaininefficiency,anddevelopmorequickly.WeexaminetheimpactofstrategiccooperationbetweenanexpressdeliverycompanyandtheCainiaostation.Columns(1)and(2)inTable7.7reportthesubsampleregressionresults.ItshowsthatdigitalfinanceonlysignificantlyincreasesthenumberofexpressdeliverypointsbelongtothestrategicpartnersoftheCainiaostation,buthasinsignificantimpactonthosenon-partners.Further,weexaminewhethertheinvestmentofAlibabaGrouphasanyimpactonthedevelopmentofexpressdeliverycompanies.AlibabaGrouphasstockinvestmentinleadingexpressdeliverycompanies,suchasSTO,ZTO,YTO,invariousyears.WecollectthemonthlynationwidecomplaintratesofexpressdeliveryfromthewebsiteoftheStatePostBureau.3AsFigure7.1shows,afterAlibababecametheshareholderoftopexpressdeliverycorporations,thecomplaintrateoftheexpressdeliveryservicedeclines.Ontheonehand,thisinvestmentfacilitatestheconnectionbetweenonlineshoppingandexpressdelivery.Forinstance,in2015Alipaystartedanewfunctionenablingthesendingparcels,statuschecking,andpaymentinjustoneclick.AllthecompaniesreceivingAlibabaGroupinvestmentarecoveredbythisfunction.Ontheotherhand,theinvestmentofAlibabaGroupalsoincreasesthecapitalofthoseexpressdeliverycompanies,andhelpsthemtoexpand.Therefore,wedividedeliverycompaniesbasedonwhethertheyreceiveinvestmentfromAlibabaGroup.Columns(3)and(4)reportthesubsampleregressionresults.Itisshownthatalthoughexpressdeliverycompaniesbenefitfromthedevelopmentofdigitalfinance,thecompaniesreceivinginvestmentfromtheAlibabaGroupexhibitevenlargerexpansioninresponsetothedevelopmentofdigitalfinance.3https://www.spb.gov.cn/gjyzj/c100278/common_list.shtmlNo.ofvalidcomplaintspermillionexpressparcelsTheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina159Figure7.1:NationwideTrendofComplaintRatesofExpressDeliveryServiceTheestablishmentofRookiepoststationAlibabaownstakeinYTOExpressAlibabaownstakeinBestLogisticsAlibabaownstakeinZTOExpressmmmmmmmmmmmmmmmCalendarmonthlySource:Authors.Table7.7:ImproveServiceEfficiency(1)(2)(3)(4)StrategicNo.ofexpressdeliverypoints(log)NoAlibabapartnersofstockCainiaostationNon-partnersAlibabaholdingstock0.2420.421holding(0.12)(0.15)Digital0.0540.361finance(log)(0.12)(0.17)ControlsYYYYCityfixedeffectYYYYYearfixedYYYYeffect0.9100.9490.8880.954AdjustedR2Obs1698169816981698Obs=observations,R2=residualsquares.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.160DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.5.2PromoteConsumptionPreviousstudieshaveshownthatdigitalfinanceincreaseshouseholdconsumptionbyfacilitatingpayment(YangandZhang2022).Theincreaseinthescaleandscopeofhouseholdconsumptionstirsdemandforonlineshopping,aswellasexpressdeliveryservice.WeusethecitylevelconsumptionperGDPandconsumptionperpopulationasthedependentvariabletoexaminetheimpactofdigitalfinanceonconsumption.Table7.8reportstheregressionresults.Itshowsthat10percentagepointsincreaseindigitalfinanceisassociatedwith0.4percentagepointsincreaseintheshareofconsumptioninGDP,and1.65percentagepointsincreaseintheconsumptionpercapita.Thecoefficientsaresignificantunderatleast5%level.Table7.8:ConsumptionPromotion(1)(2)Consumption/GDP(log)GDPpercapita(log)Digitalfinanceindex(log)0.0400.165(0.02)(0.06)ControlsYYCityfixedeffectYYYearfixedeffectYYObs16511651AdjustedR20.9030.968GDP=grossdomesticproduct,Obs=observations,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.7.5.3FinancialConstraintsTheoperationofexpressdeliverypointsisusuallyintheformoffranchising,althoughtheyjointhenetworkoftheleadingexpressdeliverycompany,theykeeptheindependentlegalstatusasasmallormedium-sizedfirm.Hence,asothersmallfirms,thestartingofexpressdeliverypointsalsoneedstoovercomefinancialconstraints.Hauetal.(2019)usethebigdataofloansfromtheAntGroup(asubordinateTheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina161ofAlibabaGroup)toshowthatthedevelopmentoffintechinthePRCsubstantiallyalleviatesfinancialconstraintsforentrepreneurshipgrowth.Therefore,inareaswithbetterfinancialaccess,thealleviationeffectofdigitalfinanceisrelativelyweak,anditscontributiontothedevelopmentofexpressdeliverypointswouldberelativelysmall.Weusethenumberofbankbranchesin2011tomeasuretofinancialaccessinthebaseyear,andincludeaninteractiontermbetweendigitalfinanceindexandfinancialaccessinequation(1).Column(1)inTable7.9showsthatthemorebankbranchesinacity,theweakertheimpactofdigitalfinanceonthedevelopmentofexpressdeliverypoints.InColumns(2)and(3)weusethebankdepositpercapitaandthesizeofloananddepositinGDPtomeasuretheaccesstofinancialresources.Theresultsareconsistent.Theysuggestthatdigitalfinancecouldimprovefinancialaccesstoincreasethesupplyofexpressdeliverypoints.Table7.9:FinancialConstraint(1)(2)(3)No.ofexpressdeliverypoints(log)Digitalfinanceindexno.–0.047ofbankbranches(0.02)Digitalfinanceindexdeposit–0.118percapita(0.04)Digitalfinance–0.186index(deposit+loan)/GDP(0.13)Digitalfinanceindex(log)0.3491.2060.385(0.12)(0.34)(0.13)ControlsYYYCityfixedeffectYYYYearfixedeffectYYYObs162316461646AdjustedR20.9520.9530.952GDP=grossdomesticproduct,Obs=observations,R2=residualsquare.Notes:,,indicatesignificancelevelat1%,5%,and10%,respectively.Standarderrorsclusteredatthecitylevelareinbrackets.Source:Authors.162DigitalTransformationforInclusiveandSustainableDevelopmentinAsia7.6ConclusionThischapterevaluatestheeffectofdigitalfinanceonthedevelopmentofexpressdeliverypointsinthePRC.Bymatchingthedigitalfinanceindexwiththeaddressofexpressdeliverypoints,weshowthesignificantpromotioneffectofdigitalfinanceontheexpressdeliveryindustry.Moreover,digitalfinancepromotestheexpressdeliveryservicebyimprovingserviceefficiency,stimulatinghouseholdconsumption,andalleviatingfinancialconstraintsinstartingadeliverypoint.Theeffectofdigitalfinanceismoresalientintheareawithhighereducationlevel,betterinternetinfrastructure,andbetterroadinfrastructure.Thisalsosuggeststhecomplementaryroleoftransportationinfrastructureanddigitalinfrastructureinpromotingtheprosperityofexpressdeliveryservices.Aspartoflogisticsinfrastructure,thedevelopmentofexpressdeliveryismainlydrivenbythemarket,insteadoftheinitiativeofthestate.Hence,understandingthefactorsunderlyingthedevelopmentoftheexpressdeliveryserviceplaysaroleinunderstandingtheconstructionofawell-functioninglogisticssystem,andimprovingthesupplychainofaneconomy.Ourresultssuggesttheoften-ignoredroleofdigitalfinanceindeliveringprosperityofcertainindustries.Hence,toimprovethepenetrationofamodernlogisticssystem,itisimportanttocreatetheproperdemandforlogisticsservices,andimprovethesupplyoflogisticsservicesbyalleviatingthefinancialconstraintsandincreasingtheserviceefficiency.Moreover,weshouldalsotakecautionofthepotentialdigitaldividecausedbythedevelopmentofdigitalfinance.Thestatecouldhelptoovercomethedigitaldivideandmakethebeneficialoutcomesofawell-functioningsupplychaininclusivebyprovidinghighqualityeducation,andinvestingintraditionalinfrastructurethatisthefoundationofthedevelopmentofexpressdeliveryservices.TheRiseofDigitalFinanceandtheDevelopmentoftheExpressDeliveryIndustryinthePeople’sRepublicofChina163ReferencesAu,Y.A.,andR.J.Kauffman.2008.TheEconomicsofMobilePayments:UnderstandingStakeholderIssuesforanEmergingFinancialTechnologyApplication.ElectronicCommerceResearchandApplications7(2):141–164.Banerjee,A.,E.Duflo,andN.Qian.2020.OntheRoad:AccesstoTransportationInfrastructureandEconomicGrowthinChina.JournalofDevelopmentEconomics145:102442.Baum-Snow,N.,L.Brandt,V.Henderson,M.Turner,M.,andQ.Zhang.2020.DoesInvestmentinNationalHighwaysHelporHurtHinterlandCityGrowth?JournalofUrbanEconomics115:103124.Couture,V.,B.Faber,Y.Gu,andL.Liu.2021.ConnectingtheCountrysideviaE-commerce:EvidencefromChina.AmericanEconomicReview:Insights3(1):35–50.Démurger,S.2001.InfrastructureDevelopmentandEconomicGrowth:AnExplanationforRegionalDisparitiesinChina?JournalofComparativeEconomics29(1):95–117.Donaldson,D.2018.RailroadsoftheRaj:EstimatingtheImpactofTransportationInfrastructure.AmericanEconomicReview108:899–934.Esfahani,H.S.,andM.T.Ramírez.2003.Institutions,Infrastructure,andEconomicGrowth.JournalofDevelopmentEconomics70(2):443–477.Faber,B.2014.TradeIntegration,MarketSize,andIndustrialization:EvidencefromChina’sNationalTrunkHighwaySystem.ReviewofEconomicStudies81:1046–1070.Guo,F.,J.Wang,F.Wang,T.Kong,X.Zhang,andZ.Cheng.2020.MeasuringtheDevelopmentofChina’sDigitalInclusiveFinance:IndexCompilationandSpatialCharacteristics.ChinaEconomicQuarterly19(4):1401–1418.(inChinese)Hau,H.,Y.Huang,H.Shan,andZ.Sheng.2019.HowFinTechEntersChina’sCreditMarket.AERPapersandProceedings109:60–64.Hau,H.,Y.Huang,H.Shan,andZ.Sheng.2021.FinTechCreditandEntrepreneurialGrowth.SFIResearchPaperNo.21-47.Jiang,H.,andP.Liang.2021.LessCash,LessTheft?EvidencefromFintechDevelopmentinthePeople’sRepublicofChina.ADBIWorkingPaperNo.1282.Tokyo:AsianDevelopmentBankInstitute.Kim,C.,M.Mirusmonov,andI.Lee.2010.AnEmpiricalExaminationofFactorsInfluencingthe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ductinge-commerceforbothin-borderandcross-bordertrade.Theseeconomiesalsomakeuseofdigitalpaymentsystems(AlipayandPaytm,amongothers)topromoteandfacilitatedigitaltransactions.TheAsianeconomiesarealsoahuboffinancialtechnologyadvancementsanddatalocalization.Notonlythis,digitalinfrastructuredevelopmentandpolicymodelingfordigitaltradeareamongthetopagendaitemsintheAsianregion.Figure8.1:DigitalTradeinTermsofICTGoodsExportsbyRegion(%ofTotalGoodsExports)EastAsia&PaciﬁcMiddleEast&NorthAfrica(IDA&IBRDcountries)EuropeanUnionNorthAmericaLatinAmerica&CaribbeanSouthAsiaIBRD=InternationalBankforReconstructionandDevelopment,ICT=informationandcommunicationstechnology,IDA=InternationalDevelopmentAssociation.Source:WorldDevelopmentIndicators.https://databank.worldbank.org/source/world-development-indicators#(accessed11December2023.)DigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment169Consequently,theAsianeconomiesarerulingtheworldintermsofdigitaltrade.AscanbedelineatedinFigure8.1,EastAsiaandthePacificisthetopregionintermsofinformationandcommunicationstechnology(ICT)goodsexports(i.e.,aproxyfordigitaltrade),followedbySouthAsia.Likewise,Figure8.2depictsthatintermsofICTservicesexports(digitaltrade),theSouthAsianregionleadstheworld.Interestingly,theshareofICTservicesexportsintotalservicesexportsisaround50%intheSouthAsianregionaftertheCOVID-19pandemic.ItisinterestingtoreportthatdespiterisingtrendsofdigitaltradeintheAsianeconomies,theystillconfrontseveralchallengesthathinderdigitaltrade.Forinstance,measuringordefiningdigitaltradeisoneofthekeyissues(Gao2018).Similarly,theinternationaltraderulesrelatedtodigitaltradeareintheirearlystages,whiletheexistingrulesmightcausehindrancestosmoothandefficientdigitaltrade(Meltzer2019).Besides,researchershavebeenstrivingtoprobeothertriggersandbarrierstodigitaltrade.Figure8.2:DigitalTradeinTermsofICTServicesExportsbyRegion(%ofTotalGoodsExports)SouthAsiaNorthAmericaEuropeanUnionEastAsia&PaciﬁcLatinAmerica&CaribbeanSource:WorldDevelopmentIndicators.https://databank.worldbank.org/source/world-development-indicators#(accessed11December2023.)Whileexploringtheimpactfactorsofdigitaltrade,Ferracane,Lee-Makiyama,andVanDerMarel(2018)proposeadigitaltraderestrictivenessindexfor64countriesbasedonalmost100policymeasuresthatcanimpededigitaltrade.Thestudysegregatesthese100policy170DigitalTransformationforInclusiveandSustainableDevelopmentinAsiameasuresintofourbroadcategories:(i)tradingrestrictions,(ii)fiscalrestrictions,(iii)datarestrictions,and(iv)establishmentrestrictions.TheoutcomesofthestudyconcludethatsevenAsianeconomiesareamongthetop10restrictedcountriesintermsofdigitaltrade,withthePeople’sRepublicofChina(PRC)beingoneofthemostrestrictedcountriesintheworld.FollowingthemethodologyofFerracaneLee-Makiyama,andVanDerMarel(2018),Ferencz(2019)developedthedigitalservicestraderestrictivenessindex,whichishelpfultocomparethelevelofrestrictionsintermsofdigitalservicestradein44countries.ThestudyconcludesthatthePRCisoneofthemostdigitallyrestrictedcountriesforservicestrade.OtherAsianeconomiessuchastheRussianFederation,India,andIndonesiaarealsotop-restrictedcountries.Likewise,BiryukovaandMatiukhina(2019)reportthatdigitaltrade(proxiedbyICTservicesexports)inBrazil,theRussianFederation,India,thePeople’sRepublicofChina,andSouthAfrica(agroupknownastheBRICSeconomies)hasbeenupsurgingintermsofvolume;however,thecomparativeadvantageofICTservicesexportshaswitnessedadecreaseduetomultipleeconomicconditionssuchasunstableinflationandgrowth.AstudybyNathandLiu(2017)exploreswhetherICTdevelopmentimpactsICTservicesexportsandimports(i.e.,digitaltrade)in49selectedcountries.TheresultsconcludethatICTburgeoningupsurgesbothICTservicesexportsandimports(i.e.,digitaltrade)inagroupofselectedcountries.WhileexploringtheimpactofICTonservicesexportsandimports(i.e.,digitaltrade)foralmost200countries,LuongandNguyen(2021)revealthatICTpromotionhasapositiveimpactonICT-enabledexportsandimportsofservices(i.e.,digitaltrade).InthecaseoftheASEANeconomies,Tee,Tham,andKam(2020)revealthatICTfacilitationpromotesservicesexports,includingICT-enabledexports(i.e.,digitaltrade).Paralleltothis,VanderMarelandShepherd(2013)arguethatpolicymeasuresasgaugedbytheservicestraderestrictivenessindexnotonlyimpedenon-ICTenablesservicesexportsbutalsowaneICTservicesexports(i.e.,digitaltrade).KnellerandTimmis(2016)explorewhetherICThasacausaleffectondigitalandtraditionalservicestradeintheUnitedKingdom.ThestudyconcludesthatICTdoeshaveacausaleffectondigitaltrade,whereasnocausalrelationshiphasbeenobservedbetweenICTandtraditionalservicestrade.LeeandPang(2022)findthatICTdevelopmentimpactsbothexportsandimportsinservices(i.e.,digitaltrade).However,thestudyconcludesthatICTupsurgesexportsandwanesimports.Moreover,thevolumeofdigitaltradeincreasesprofoundlyinnetexportercountriescomparedtothenetimporters.Wang,Li,andWang(2021)findthatICTwanesthetradecostandupsurgesinnovation.Asaresult,digitaltradeintermsofICTservicesDigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment171exportswitnessedanincreaseinthecaseoftheBeltandRoadInitiativecountries.1Usingthefixedeffectsmodel,GaniandClemes(2016)reportthattheruleoflawandcontractenforcementaredriversofdigitaltrade.Cheng(2020)highlightsthattheexchangeratedoesnotaffectservicestrade,includingdigitaltrade,inthelong-run,whereasincomeisakeydeterminantofdigitaltradeinthecaseoftheUnitedStates.Likewise,Srivastava(2006)pointsoutthatforeigndirectinvestmentcreatesalltypesofserviceexports(i.e.,digitaltrade)inIndia.Likewise,NasirandKalirajan(2016)arguethathighereducationandICTprogresspromotedigitaltradeinASEANeconomies.TheexistingliteratureonthedeterminantsofdigitaltradeexploresICT,humancapital,income,exchangerate,andinstitutionalperformanceorqualityasimpactfactorsofdigitaltrade.However,therearemanyotherfactorsthataffectdigitaltrade.WhileconsideringthisinthecontextoftheAsianregion,itisworthnotingthatAsianeconomiesconfrontvariousissuesandchallengesthatmayaffectdigitaltrade.OneoftheissuesinAsianeconomiesisenergypoverty,whichcouldbeinterlinkedwithdigitaltrade.TheUnitedNationsDevelopmentProgrammedefinesenergypovertyas“thelackofsufficientoptionsinaccessingadequate,accessible,reliable,high-quality,clean,andenvironmentallybenignenergyservicestosustaineconomicdevelopment”(UNDP2010).Inadditiontothis,theInternationalEnergyAgencyexplainsthatenergypovertyis“alackofelectricityandheavyrelianceontraditionalbiomass”(Sovacool2013).Additionally,theAsianDevelopmentBankdefinesenergypovertyas“theabsenceofsufficientchoiceinaccessingadequate,affordable,reliable,high-quality,safeandenvironmentallybenignenergyservicestosupporteconomicandhumandevelopment”(Masud,Sharan,andLohani2007).EnergypovertyintermsofaccesstoelectricityisanacuteconcerninAsianeconomies.Table8.1elucidatesthatAsiaisthesecondmostpoorregionintermsofenergypoverty(i.e.,accesstoelectricity).Itcouldbehypothesizedthatenergypovertymayimpededigitaltradedirectlyorindirectly.ThelackofaccesstoelectricitydecreasesICTservicesexports(i.e.,digitaltrade)sinceenergyisabasicinputtoprovideICT-enabledservices.Consideringtheindirectimpactofenergypovertyondigitaltrade,itcanadverselyimpacteducation,andhealth,whichinturndiscouragesdigitaltrade.Therefore,itisinevitabletoprobewhetherenergypovertyimpactsdigitaltradeinAsia.1FordetailsontheBeltandRoadInitiative,pleaserefertohttps://www.cfr.org/backgrounder/chinas-massive-belt-and-road-initiative172DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable8.1:ComparisonofEnergyPovertybyRegionYearEastAsiaLatinMiddleNorthSouthSub-&PacificAmerica&East&AmericaAsiaSaharanCaribbeanNorthAfricaAfricaTotalEnergyPoverty(%ofthetotalpopulation)200092.2591.7292.4410056.3725.63200593.8593.5593.9810064.8729.30201095.5495.8595.6510073.1833.28201597.1097.2896.4510084.5439.01202098.0198.5197.2310095.7448.23RuralEnergyPoverty(%ofthepopulation)200088.2671.0782.8110045.1411.86200589.6376.9686.4510054.1913.14201092.3983.6589.2310063.9216.98201594.8289.0292.0910078.3618.57202096.2993.5593.8210093.6128.52UrbanEnergyPoverty(%ofthepopulation)200098.5898.4299.2710088.5961.66200598.7598.4798.9310090.9565.36201098.4999.1899.5210093.9468.32201598.8799.3699.4410097.2372.22202099.1399.6699.6410099.7278.18Source:WorldDevelopmentIndicators.https://databank.worldbank.org/source/world-development-indicators#(accessed11December2023.)AnotherprimeissueinAsiaisunemployment.SinceAsiaisthelargestregionbypopulationwithinadequateorinefficientresourcestoemploytheentirelaborforce,therateofunemploymentinAsiaremainshigh.Figure8.3explainstheunemploymentrateindifferentregionsoftheworld.AscanbeseenfromFigure8.3,asignificantportionofthelaborforceisunemployedinAsia.Unemploymentmightaffectdigitaltradeindifferentdimensions.Thehigherunemploymentratepropelsindividualstoworkatlowerwages,which,inturn,upsurgescompetitivenessandescalatesICTservicesexports(i.e.,digitaltrade).Also,unemploymentleadstoalowerlevelofincome,therebyskillandhumancapitallevelsareexpectedtoplunge.Asaresult,digitaltrade(intermsofICTservicesexports)candecrease.Therefore,itisimperativeDigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment173toscrutinizetheimpactofunemploymentondigitaltradefromtheAsianperspective.Basedonthisbackdrop,thisempiricalstudyaimstodiscernwhetherdigitaltradeissusceptibletoenergypovertyandunemploymentinAsia.Hence,weuseapaneldatasetconsistingofeightAsianeconomies:thePRC,Pakistan,theRussianFederation,Singapore,theRepublicofKorea,Malaysia,Japan,andthePhilippines,overtheperiod2000–2021.Focusingonthenoveltyofthisstudy,thisisthefirstempiricalstudythatinvestigateswhetherenergypovertyhasanyimpactondigitaltrade.Second,thisisthefirstattempttoprobetheeffectofunemploymentondigitaltrade.Figure8.3:UnemploymentRatebyRegion(%ofLaborForce)EastAsia&PaciﬁcLatinAmerica&CaribbeanMiddleEast&NorthAfricaNorthAmericaSouthAsiaSub-SaharanAfricaSource:WorldDevelopmentIndicators.https://databank.worldbank.org/source/world-development-indicators#(accessed11December2023.)Consideringthesignificanceofthisstudy,itisworthhighlightingthatthisresearchisconductedtoassistpolicymakers,businesses,andacademicians.ItwillhelppolicymakersformulatepoliciestoescalatedigitaltradeinAsia.Similarly,businessescanmanageemploymentandenergypovertytoupsurgetheirsalesrelatedtodigitaltrade.Finally,thecurrentinvestigationhelpsacademicianstounderstandtherelationshipbetweendigitaltrade,unemployment,andenergypovertyinAsia.Also,174DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaitwillopennewresearchavenuesfortheacademiciansinthislineofresearch.8.2ConceptualFrameworkWeelucidatethepotentialrelationshipbetweenenergypoverty,unemployment,anddigitaltradeinFigure8.4.Regardingtheenergypoverty–digitaltradenexus,thefirstchannelexplainsthedirectimpactofenergypovertyondigitaltrade.Sinceenergyisaninputofgoodsandservices,thelackofelectricitydiscouragestheproductionand/orprovisionofgoodsandservicesandhenceimpedesdigitaltrade(Adometal.2021).Thesecondchannelisthehealthandeducationeffect,whichnotesthatenergypovertyadverselyimpactshealthandeducationsincetheavailabilityofenergyisessentialforahealthylifestyleandtheprovisionofqualityeducation(Irwin,Hoxha,andGrépin2019;Oum2019).Ontheotherhand,poorhealthdiscouragesindividualstoprovideservices(Krauseetal.2013).Hence,wemayformulatethatenergypovertycanimpactdigitaltradethroughthehealthchannel.Moreover,thethirdchannelisthecostchannel,whicharguesthatenergypovertyupsurgesthetradecostsinceindividualsandbusinessesswitchtoanexpensivemodeofenergy.Thisimpedescompetitivenessandhencemitigatesdigitaltrade.Finally,energypovertyhaltstheuseofdigitaltechnologiessuchastheinternet,computers,andmobilephones.Onthecontrary,limiteduseofICTwanesdigitaltrade(NathandLiu2017;Tee,Tham,andKam2020).Next,varioustheoreticalchannelscanexplainthelinkagesbetweenunemploymentanddigitaltrade(Figure8.4).Forinstance,countrieswithhighunemploymentratesmayplacegreateremphasisonexportinggoodsandservicesasameansofgeneratingeconomicgrowthandcreatingjobs.ThiscouldleadtomoregovernmentsupportforthedevelopmentandpromotionofICT-enabledservicesexports.Also,unemploymentcanleadtoasurplusofskilledworkerswhomightbemorewillingtolearnnewskillsandtakeonjobsintheICT-enabledservicesindustry.Thiscanleadtothedevelopmentofastronglocaltalentpool,whichcanattractforeigncompaniesandincreaseexports.Highunemploymentratesmayleadtoincreasedentrepreneurshipandinnovationasindividualsseektocreatetheirownjobs.ThiscanresultinthedevelopmentofnewandinnovativeICT-enabledserviceswithexportpotential.Thereisalikelihoodthatanupsurgeinunemploymentforcesindividualstoseekotherjobsaccordingtotheirskills,sopeoplestartexportingtheirICT-basedservices.Finally,unemploymentallowspeopletoworkforlowerwages,which,inturn,reducesthecostofservices.Asaresult,competitivenessincreases,andhencedigitaltradeupsurgesovertime.DigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment175Figure8.4:ConceptualFrameworkEnergyUnemploymentPovertyHealthandDigitalGovernmentEducationTradeSupportEnergyEntrepreneurshipConsumptionandInnovationICTUseWagesandCostHighCostSkillsandLowCompetitivenessPositiverelationshipNegativerelationshipSource:Authors.8.3Methods8.3.1ModelandMethodologyToattaintheaimofthisempiricalstudy(i.e.,probingtheimpactofenergypovertyandunemploymentondigitaltrade),weadopttheexportdemandfunction.Theexportdemandfunctionnotesthatexportsrelyonincomeandexchangerates(Akoto2012;CheungandSengupta2013).Equation(1)yieldstheexportdemandfunction:DTRit=β0+β1WGDPit+β2REERit+Uit(1)Intheabove-mentionedequation,cross-sectionsandtimearedenotedbyiandt,respectively.DTRrepresentsdigitaltechnologytrade,anditismeasuredastheICTservicesexports.Next,WGDPrepresentstheworldglobalgrossdomesticproduct(GDP)(Afzal2006),whiletherealeffectiveexchangerateisdenotedbyREER(CheungandSengupta2013).Finally,Uitexpressestheerrorterm.Sincethefocusedvariablesinthisstudyincludeenergypoverty(EPOV)andunemployment(UNE),weembedthesevariablesintoequation(1)toformulateequation(2):176DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaDTRit=β0+β1WGDPit+β2REERit+β3EPOVit+β4UNEit+Uit(2)Equation(2)isthefinaleconometricequationweestimateinthisstudy.Regardingthemethodologytobefollowed,wemakeuseofpanelfixedandrandomeffectsmodelstoestimatethedependencyofDTRonEPOVandUNEinselectedAsianeconomies.8.3.2DataThecurrentempiricalinvestigationutilizesthepaneldatasetforeightAsianeconomiesfortheperiod2000–2021.Itisworthreportingthatthissample(countriesandperiod)isadoptedbasedondataavailability.ThedependentvariableofthisstudyisDTR,whichisproxiedbyICTservicesexports.ICTservicesexports(whicharealsodenotedasICT-enabledservices)mainlycontainfinancialservices,communicationservices,licensefees,computer/informationservices,personal/recreationalservices,andbusinessservices(e.g.,digitalmarketing,etc.)(NathandLiu2017).Also,DTRismeasuredincurrentUSdollars.Regardingthecontrolvariables,WGDPismeasuredinconstant2015$.Similarly,REERisarealeffectiveexchangerateindexwith2010asabaseyear.Withregardtofocusedvariables,EPOVismeasuredinthepercentageofthepopulationwithaccesstoelectricity,whereasUNEismeasuredinthepercentageofunemployedindividualsinthelaborforce,accordingtotheILO.Table8.2summarizessomekeyaspectsofthedata.Table8.2:SummaryofDataSymbolVariableProxyMeasurementDTRDigitaltradeICTservicesexportsCurrentUS$(BoP)WGDPWorldGDPPercapitaworldGDPConstant2015$REERRelativepricesRealeffective2010baseyearindexexchangerateindexEPOVEnergypovertyAccesstopopulationPercentageofthepopulationwithaccesstoelectricityUNEUnemploymentShareofPercentageofunemployedunemploymentpopulationinthelaborforceBoP=currentaccountbalance,DTR=digitaltechnologytrade,EPOV=energypoverty,REER=realeffectiveexchangerate,WDI=WorldDevelopmentIndicators,WGDP=worldgrossdomesticproduct,UNE=unemployment.Source:WorldDevelopmentIndicators.https://databank.worldbank.org/source/world-development-indicators#(accessed5May2023.)DigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment177Next,wetransformthedatasetintoanaturallogarithmicexpression.Also,Table8.3highlightscertaindescriptivestatistics.WGDPcontainsthelargestmeanvalueandstandarddeviation.Further,thewholedatasetisnegativelyskewed,excludingDTR,whichcontainsapositiveskewness.ThekurtosisvalueisthehighestforUNE.TheJarque-Berastatisticsclaimthatallvariablesfollowanon-normaldistributionexceptforDTR.Table8.3:DescriptiveStatisticsEPOVDTRREERUNEWGDPMean4.5421.474.591.339441.19Median4.6021.534.601.3459402.99Maximum4.6024.645.032.4111011.13Minimum4.2519.073.99–0.917868.56Std.Dev.0.111.250.160.48989.56Skewness–1.660.11–0.18–2.02–0.05Kurtosis4.062.544.1210.491.83Probability(0.00)(0.38)(0.00)(0.00)(0.00)DTR=digitaltechnologytrade,EPOV=energypoverty,REER=realeffectiveexchangerate,WGDP=worldgrossdomesticproduct,UNE=unemployment.Note:Datainbracketsrepresentthep-value,whileshowsthelevelofsignificanceat1%.Source:Authors’calculation.8.4Findings8.4.1TestingUnitRootThissectionrepresentstheempiricalfindingsabouttheimpactofenergypovertyandunemploymentondigitaltradeinAsia.Itisworthreportingthatwhilehandlingthepaneldataset,itisindispensabletodiscerntheunitroottocircumventspuriousregression(Syedetal.2022).Therefore,weapplytheIm,Pesaran,andShin(IPS)unitroottesttoprobetheorderofintegration.TheoutcomesfromtheIPStestaredelineatedinTable8.4.178DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTable8.4:UnitRootAnalysisVariableI(0)I(1)DTR(0.90)(0.00)REER(0.41)(0.00)WGDP(0.98)(0.00)EPOV(0.71)(0.00)UNE(0.31)(0.00)DTR=digitaltechnologytrade,EPOV=energypoverty,REER=realeffectiveexchangerate,WGDP=worldgrossdomesticproduct,UNE=unemployment.Notes:Datainbracketrepresentsthep-value,whereasnotesthelevelofsignificanceat1%.Source:Authors’calculation.Table8.4depictsthep-valuesretrievedfromtheIPStestatI(0)andI(1).AscanbeobservedthatweareunabletorejecttheH0(i.e.,theexistenceofaunitroot)atI(0),therebyweconcludethattheentiredatasetcontainsaunitrootatI(0).Onthecontrary,wecouldrejecttheH0foreachvariableatI(1).Hence,weclaimthateachvariableisstationaryatI(1).DuetothepresenceofstationarityatI(1),weusethefirstdifferenceofeachvariableforestimatingtherandomandfixedeffectsmodel.8.4.2FindingsfromRandomandFixedEffectsModelsThissubsectionrendersempiricalresultsfromrandomandfixedeffectsmodels.Table8.5containstheestimatedcoefficientsfromtheaforementionedmodels.Consideringtherandomeffectsmodel,itisworthnotingthateachregressorisstatisticallysignificant.Thisindicatesthatenergypoverty,unemployment,worldincome,andtherealeffectiveexchangerateimpactdigitaltrade.ThecoefficientofEPOVis–0.31,delineatingthata1%upsurgeinenergypovertywanesdigitaltradeby0.31%inselectedAsianeconomies.Thereexistmanytheoreticalchannelsthatcanhelptoexplainthisoutcome.Forinstance,itisempiricallyarguedthatenergypovertyharmshealth(Oum2019).Asaresult,poorhealthstatusdiscouragesindividualsfromworkingandexportingtheirservices.Ontopofthis,energypoverty(i.e.,unavailabilityofelectricity)directlydiscouragesindividualsandbusinessestoprovideICT-enabledservicesbecauseelectricityisakeyinputtoICT-enabledservicesexports.Next,theunavailabilityofelectricity(i.e.,energypoverty)propelsindividualsandbusinessesDigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment179toswitchtoalternativeenergysourcessuchassolarorwindenergy.ThisupsurgesthecosttoexportICTservicesandescalatesthepriceofICTserviceexports.ThelowlevelofcompetitivenessduetohigherpriceswanesICTservicesexports(i.e.,digitaltrade).Thesefindingsaresomehowconsistentwithpriorliterature.Forexample,Shahbaz(2015)reportsthattheunavailabilityofelectricityadverselyimpactsPakistan’sservicessector.Similarly,Abdisa(2018)concludesthattheunavailabilityofelectricityplungesfirms’productivity.GuptaandChauhan(2021)alsonotethatelectricityoutagescompelfirmsnottoenterexportmarkets.ThesefindingsaresomehowbackedbytheconclusionofChowdhuryetal.(2021),whoreportedthatthelackofelectricityplungeslaborproductivityandultimatelyimpedesthelaboroutputinPakistan.ThestudybyMensah(2018)alsonotesthatenergypoverty(i.e.,lackofelectricity)hasadverseimpactsonlaborproductivity,labordemand,andexportsatthefirmlevel.RegardingthefindingsrelatedtoUNE,itscoefficientis0.15.Thisimpliesthata1%surgeinunemploymentescalatesICTservicesexports(i.e.,digitaltrade)by0.15%.Severaltheoreticalaspectscanexplaintheseempiricaloutcomes.Forinstance,highunemploymentratescanleadtolowerwageratesforworkers(GreggandMachin2012),makingitmoreattractiveforcompaniestooutsourceworktocountrieswithlowerlaborcosts(Palvia2014).Table8.5:FindingsfromRandomandFixedEffectsModelsRandomEffectsModelFixedEffectsModelVariablecoefficientp-valueCoefficientp-valueEPOV–0.31(0.00)–0.30(0.02)UNE0.15(0.03)0.17(0.00)WGDP0.12(0.00)0.12(0.00)REER0.11(0.00)0.15(0.03)EPOV=energypoverty,REER=realeffectiveexchangerate,WGDP=worldgrossdomesticproduct,UNE=unemployment.Note:Datainbracketsdenotethep-value.andshowthelevelofsignificanceat5%and1%levelofsignificance,respectively.Source:Authors’calculation.ThiscanmakeICT-enabledservicesmoreaffordableforforeignbuyers,increasingdemandandexports.Highunemploymentratescanleadtoincreasedentrepreneurshipandinnovationasindividualsseektocreatetheirjobs(MahadeaandKaseeram2018;OECD2021).180DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaThiscanresultinthedevelopmentofnewandinnovativeICT-enabledservicesthathaveexportpotential(Cumming,Johan,andZhang2014).Also,unemploymentcanupgradeandimprovelaborskills(Weisskopf2006).Thiscanleadtothedevelopmentofastronglocaltalentpool,whichcanattractforeigncompaniesandincreaseexports.Thereisalikelihoodthatanupsurgeinunemploymentforcesindividualstoseekotherjobsaccordingtotheirskills,sopeoplestartexportingtheirICT-basedservices.ThisscenariohasbeenobservedduringtheCOVID-19outbreak,whereanenormousproportionofthepopulationlostjobs,andtheseunemployedpeoplestartedfreelancing,includingICTservicesexports(Simplilearn2023)Further,theUnitedNationsConferenceonTradeandDevelopmentalsoreportedthesamenotionthatduringtheCOVID-19pandemic(whenpeoplelosttheirjobs),ICTservicesexportswitnessedaprofoundincrease(UNCTAD2021).ItcouldbededucedthattheunemployedjoinedtheICTservicessectorand,hence,thereisapositiverelationshipbetweenunemploymentanddigitaltrade.ItisworthnotingthatICTservicesexportsinPakistanincreasedby26%amidtheCOVID-19outbreakbecausemillionsofpeoplelosttheirjobsintheformalsectors,andtheyinitiatedtheexportofICT-enabledservices(SouthAsianInvestorReview2020).Regardingthefindingsrelatedtocontrolvariables(i.e.,WGDPandREER),itisworthnotingthathigherworldGDPincreasestheDTR,whilethedepreciationoftheexchangeratealsoupsurgesDTR.Inparticular,a1%increaseintheworldGDPupsurgesICTservicesexportsby0.12%.Thisisawell-citedargumentthathigherglobalincomeescalatestheexportsofdomesticcountries(Afzal2006).Similarly,thedepreciationoftheexchangerateimprovesthecompetitivenessofexportinggoods/servicesbyloweringthecost,which,inturn,increasestheexportvolume(Ho2012).Regardingtheoutcomesfromthefixedeffectsmodel,thecoefficientsofEPOV,UNE,WGDP,andREERarestatisticallysignificant.Further,thecoefficientofEPOVandUNEis<0and>0,respectively.Thisrevealsthatenergypovertyimpedesdigitaltradewhereasunemploymentupsurgesit.ThisimpliesthatenergypovertydecreasesdigitaltradeinselectedAsianeconomies,whileunemploymentisthefactorthatescalatesdigitaltrade.Paralleltothis,thecoefficientofWGDPexplainsthattheworld’sincomepromotesdigitaltrade,whilethedepreciationoftherealeffectiveexchangerateincreasesdigitaltrade.Thefindingsfromboththerandomeffectsandfixedeffectsmodelsarealike.Thisdelineatesthatourresultsareinsensitivetothechoiceofmethodology.DigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment1818.4.3SensitivityAnalysisInthissubsection,weperformasensitivityanalysistorevealwhetherourbaselineresults(asreportedinSection8.4.2)arerobust.Tothisend,weadopttheshareofICTservicesexportsintotalservicesexportsasadependentvariable.ThefindingsarepresentedinTable8.6.Table8.6:SensitivityAnalysisRandomEffectsModelFixedEffectsModelVariableCoefficientp-valueCoefficientp-valueEPOV–0.12(0.00)–0.16(0.00)UNE0.05(0.04)0.05(0.00)WGDP0.03(0.00)0.11(0.00)REER0.09(0.01)0.06(0.00)EPOV=energypoverty,REER=realeffectiveexchangerate,WGDP=worldgrossdomesticproduct,UNE=unemployment.Notes:Thedatainbracketsdenotethep-value.andshowthelevelofsignificanceat5%and1%levelofsignificance,respectively.Source:Authors’calculation.AscanbeobservedfromTable8.6,thefindingsfrombothmodelsaresimilartothebaselineoutcomes.Thatis,EPOV,UNE,WGDP,andREERarefoundtobestatisticallysignificant.Inparticular,EPOVwanestheshareofICTservesexports(i.e.,DTR),whereasUNEimprovesit.Further,exchangeratedepreciationandworldGDPimprovetheshareofICTservicesexports(i.e.,DTR)intheselectedAsianeconomies.Theseresultsshowthatourkeyfindingsareinsensitive/robusttodataand/ormodelchanges.8.5ConclusionTheworldhaswitnessedanunprecedentedepisodeofdigitalconnectivityduringthe21stcentury.Thisopensnewavenuesforconsumers,producers,andgovernments.Asaresultofthisdigitalconnectivity,traditionaltradeisalsobeingtransformedintodigitaltrade,anditsvolumeisupsurgingatanincrediblepace.Further,itisevidentthatdigitaltradeisimmunetoglobaladverseshockssuchastheCOVID-19outbreak,amongothers.Therefore,itisimperativetopromote182DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaandfacilitatedigitaltrade.Hence,researchendeavorsarerequiredtoprobethetriggersandbarrierstodigitaltrade.Paralleltothis,theentireworld(particularlytheAsianregion)confrontssocioeconomicchallenges,suchasenergypovertyandunemployment.Therefore,itisessentialtodiscerntheimpactofenergypovertyandunemploymentondigitaltrade.Againstthisbackdrop,weinvestigatewhetherenergypovertyandunemploymenttriggerorwanedigitaltrade(i.e.,measuredinICTservicesexports)ineightselectedAsianeconomies.Theresultsfromtherandomandfixedeffectsmodelsrevealthatenergypovertydecreasesdigitaltrade.EnergypovertymightwaneenergyconsumptionanduseofICT.Asaresult,digitaltradeisexpectedtodecrease.Onthecontrary,unemploymentcouldpromoteentrepreneurship,whichmightincreasedigitaltrade.Unemploymentalsoplungeswages,whichattractsforeignersandhencedigitaltradewitnessesanincrease.Interestingly,unlikethetraditionalviewthathigherunemploymentimpedestrade,wefindthatunemploymentactsasatriggerandpromotesdigitaltrade.Further,weperformthesensitivityanalysisbyusingtheshareofICTservicesexportsintotalservicesexportsasadependentvariable.Theresultsretrievedfromthesensitivityanalysisaresimilartoourbaselineoutcomes.WeputforwardsomepolicysuggestionstoraisethevolumeofdigitaltradeinAsia.Basedontheresults,itcouldbeproposedtotakemeasurestodecreaseenergypoverty,whichinturnenhancesdigitaltrade.Hence,electricityshouldbeavailabletotheentirepopulation.Further,electricityloadsheddingshouldbereducedtoboostdigitaltrade.Governmentsshouldcontrolortakemeasurestoavoidpoweroutages.Next,energyinfrastructureshouldbeupgradedtoprovideaccesstoaffordableandreliableelectricitytoall.InAsiancountrieswhereelectricityshortagesareakeychallenge(e.g.,Pakistan,India,SriLanka,etc.)(Sovacool2013),alternativeenergysourcescouldbeprovided(e.g.,solarandwindenergy).Topromotealternativeenergysources,governmentsshouldgivesubsidiesonrenewableenergysources.Also,feed-intariffscouldbemanagedtopromotealternativeenergysources.Inlightofthepolicyrecommendationspertainingtounemployment,itisimperativetoacknowledgethatunemploymentcanbeperceivedasafortuitouscircumstanceindisguise.Policymakersshouldestablishsuitableavenuesforindividualswhoarecurrentlywithoutemploymenttoengageintheexportationoftheirservices.AsianeconomieshavethepotentialtoleverageonlineplatformssuchasUpwork,Fiverr,Amazon,andotherstoestablishvirtualworkspaces.Furthermore,itisimperativeforgovernmentstoimplementskill-enhancementinitiativesaimedatequippingtheunemployedworkforceDigitalTradeinAsia:TheRoleofEnergyPovertyandUnemployment183withdigitalproficiencies,therebyenablingthemtoengageintheexportationofICTservices.Withregardtofutureresearchaspects,otherAsianeconomiescouldbeincludedinthisanalysis(excludedhereduetotheunavailabilityofdata).TheICTservicesexportsisasubsetofdigitaltrade;therefore,anybetterproxyofdigitaltradeshouldbeusedinthefuturetoefficientlygaugethedigitaltrade.Thedynamicmodelscouldalsobeappliedtoinvestigatehowenergypovertyandunemploymentimpactdigitaltradeacrosstimehorizons.Inaddition,toprovideadeeperinsight,energypovertycanbesegregatedintourbanandruralenergypoverty.Similarly,unemploymentcanbesegregatedaccordingtogenderandage.184DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaReferencesAbdisa,L.T.2018.PowerOutages,EconomicCost,andFirmPerformance:EvidencefromEthiopia.UtilitiesPolicy53:111–120.Adom,P.K.,F.Amuakwa-Mensah,M.P.Agradi,andA.Nsabimana.2021.EnergyPoverty,DevelopmentOutcomes,andTransitiontoGreenEnergy.RenewableEnergy178:1337–1352.Afzal,M.2006.CausalitybetweenExports,WorldIncomeandEconomicGrowthinPakistan.InternationalEconomicJournal20(1):63–77.Akoto,W.2012.OntheNatureoftheCausalRelationshipsbetweenForeignDirectInvestment,GDPandExportsinSouthAfrica.JournalofInternationalDevelopment28(1):112–126.Anser,M.K.,B.N.Adeleye,M.I.Tabash,andA.KTiwari.2022.ServicesTrade–ICT–TourismNexusinSelectedAsianCountries:NewEvidencefromPanelDataTechniques.CurrentIssuesinTourism25(15):2388–2403.Biryukova,O.G.V.,andA.I.Matiukhina.2019.ICTServicesTradeintheBRICSCountries:SpecialandCommonFeatures.JournaloftheKnowledgeEconomy10:1080–1097.Cheng,K.M.2020.CurrencyDevaluationandTradeBalance:EvidencefromtheUSServicesTrade.JournalofPolicyModeling42(1):20–37.Cheung,Y.W.,andR.Sengupta.2013.ImpactofExchangeRateMovementsonExports:AnAnalysisofIndianNon-financialSectorFirms.JournalofInternationalMoneyandFinance39:231–245.Chowdhuryetal.2021.UnveilingtheNexusbetweenAccesstoElectricity,FirmSizeandSME’sPerformanceinBangladesh:NewEvidenceusingPSM.Energies14(20):6493.Cumming,D.,S.Johan,andM.Zhang.2014.TheEconomicImpactofEntrepreneurship:ComparingInternationalDatasets.CorporateGovernance:AnInternationalReview22(2):162–178.Danish,S.Khan,andN.Haneklaus.2023.SustainableEconomicDevelopmentAcrossGlobe:TheDynamicsbetweenTechnology,DigitalTradeandEconomicPerformance.TechnologyinSociety72:102207.Ferracane,M.F.,H.Lee-Makiyama,andE.VanDerMarel.2018.DigitalTradeRestrictivenessIndex.EuropeanCenterforInternationalPoliticalEconomy,April.Ferencz,J.2019.TheOECDDigitalServicesTradeRestrictivenessIndex.Gani,A.,andM.D.Clemes.2016.DoestheStre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handdevelopmentandtheparticipation.However,mostconclusionswerederivedfromcross-sectionalanalyseswithlimitedobservations,possiblyresultinginendogeneityissuesandbiasedestimation.Againstthisbackdrop,thisstudyaimstoelucidatethelinkbetweendigitalizationandGVCparticipationatthefirmlevel,focusingonSMEsinAsiaandthePacific.Itemploysprobitandtobitregressionanalysesusingcross-sectionaldatafromtheWorldBank’sEnterpriseSurveysspanning28countriesand23,551firmsfor2008–2018.Theindustryaverageofdigitalization(i.e.,emailandwebsiteadoptions)isadoptedasinstrumentalvariables(IVs)tocorrectendogeneityissues.Thus,solvingendogeneityissues,thecurrentstudycontributestotheliteratureby(i)distinguishingtheimpactofdigitalizationonGVCparticipationbetweenSMEsandlargefirms,(iii)differentiatingtheimpactofdigitalizationonGVCparticipationprobability(dummyvariableswithdifferentGVCdefinitions)andGVCparticipationlevelmeasuredbyGVCindex,whichhasnotbeenusedinthepreviousstudies,and(iii)providingtheanalysisofdigitalreadinessandfirm-levelGVCparticipationinthecontextofAsiaandthePacific.Thefindingsfrombothmodelsindicatethatfirms,especiallySMEswithdigitalconnectivitythroughemailandwebsiteuse,aremorelikelytoengageinGVCsandexhibithigherGVCparticipation.Thisfindingisconsistentwithpreviousresearch,highlightingtheroleofdigitaltechnologiesinfacilitatinginternationalmarketaccessandenhancingsupplychains.However,theimpactofdigitalizationonGVCparticipationdiffersbetweenSMEsandlargefirms.Basicdigitaltechnology,suchasemailsandwebsites,ismorebeneficialforSMEsinincreasingtheirTheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific191GVCparticipation.Incontrast,theeffectsofdigitalizationonGVCparticipationdiminishforlargefirms,likelyduetotheirwidespreadadoptionofbasicdigitaltechnology.ThestudyalsoconfirmsthatsmallerfirmsandSMEsfacegreaterchallengesparticipatinginGVCs.Foreignownership,certificates,andcreditaccessalsopositivelyinfluenceGVCparticipation.9.2CurrentSituationofFirms’GVCParticipationandDigitalizationinAsiaandthePacific9.2.1GVCParticipationThecriticalroleofSMEscannotbeoverlookedsincetheyrepresentmostfirmsanddomesticemploymentinAsiaandthePacificandcontributesignificantlytothenationalgrossdomesticproduct(GDP),accountingfor20%–50%(APEC2020).Despitetheireconomiccontributions,SMEsfacelimitationsinparticipatingininternationaltradeandGVCs.TheAsia-PacificEconomicCooperation(APEC)(2020)reportsthatapproximately97%ofcompaniesinAsiaandthePacificareSMEs.Still,theirexportvolumemakesuponly35%orlessofthetotalexport,causinganunequaldistributionofbenefitsandnewopportunitiesemergingfromGVCparticipationbetweenSMEsandlargeenterprises.Inaddition,thedigitaldividebetweensmallandlargefirmscanexacerbatetheirunequalbenefitandopportunitydistribution(Antràs2020;Korwatanasakul2020).Table9.1presentsvariouspatternsofforeigntradeengagementamongglobalfirms,asderivedfromtheWorldBank’sEnterpriseSurveys.SMEsaccountfor86%ofthesamplefirms,aligningwiththeWorldBank’sestimateindicatingthat90%ofbusinessesareSMEs(WorldBank2020).AmongGVCfirms,SMEscompriseasignificantproportionof67%,althoughmostSMEs(47%)primarilyfocusonprocurementandsaleswithinacountry(Column1).Only18%ofSMEsparticipateinGVCs,equivalentto15%ofthetotalsample,whereas53%oflargeenterprisesareinvolvedinGVCs(Column7ofworld’spercentagebyfirmtype).Consistentwiththeglobaltrend,firmsinAsiaandthePacificfollowsimilarengagementpatternsinforeigntrade.Nevertheless,SMEsinthisregionarelessactivetojoinGVCsandarerelativelymoreconcentratedindomesticmarketsandprocurement.Column7ofAsiaandthePacific’spercentagebyfirmtyperevealsthat12%ofSMEsareconsideredGVCfirms,lowerthantheglobalaverage(18%).Column1192DigitalTransformationforInclusiveandSustainableDevelopmentinAsiashowsthatmostSMEs(61%)areconcentratedindomesticprocurementandsales.ThisdescriptiveanalysissuggeststhatSMEsinAsiaandthePacificmayhavegreaterdifficultiesjoiningGVCsthanthoseinotherregionsorindirectlyengageinGVCs.TheindirectGVCengagementistypicallycharacterizedbyactivitiesfromlowvalue-addedtiersandprocurementandsalesthroughlocalintermediaryfirmstoconnectwithmultinationalenterprisesorGVCfirms(KorwatanasakulandIntarakumnerd2020).Figure9.1alsohighlightsSMEs’challengesinjoiningGVCsduetotheirlimitedSMEinvolvementinforeigntrade.TheshareofGVCfirmsandSMEsparticipatinginGVCs,orGVCSMEs,inAsiaandthePacific,remainsmoderateat19%and8%,respectively.ThesectoraldistributionofGVCSMEsmirrorsthatofgeneralGVCfirms(Figure9.2).MostcountriesintheAsia-Pacificregionhavespecializationprimarilyinlabor-intensiveandlow-value-addedproductionactivities,suchasrawmaterialproduction,componentmanufacturing,andproductassembly;therefore,GVCfirmsarepredominantlyconcentratedinindustriesthatrequiresignificantlaborinputs,implyingthefactthatAsianandthePacificGVCfirmslackcapabilitiestoupgradetomorecapital-intensivevaluechains.ThelargestshareofAsiaandthePacific’sGVCfirmsisinthetextileandclothingindustry(36%),followedbythechemicalindustry(11%),thefoodindustry(10%),andtheelectricalindustry(9%).TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific193Table9.1:PatternsofEngagementinForeignTradebyFirmType7GVCFirm8123456(5+6)TotalSalesDomesticOOXOXOO/XExportsXXOOOOOInputsDomesticOOOOOOOImportsXOXXOOOWorldFirmtypeSME18,13110,6346532,5696816,2546,93538,922Non-SME9881,1501497075602,8773,4376,431Total19,11911,7848023,2761,2419,13110,37245,353%bySalesandInputsPatternFirmtypeSME94.890.281.478.454.968.566.985.8Non-SME5.29.818.621.645.131.533.114.2Total100100100100100100100100%byFirmTypeFirmtypeSME46.627.31.76.61.716.117.8100Non-SME15.417.92.311.08.744.753.4100Total42.226.01.87.22.720.122.9100AsiaandthePacificFirmtypeSME8,7642,2204421,2743311,3611,69214,392Non-SME6354181254393558991,2542,871Total9,3992,6385671,7136862,2602,94617,263%bySalesandInputsPatternFirmtypeSME93.284.277.974.448.260.257.483.4Non-SME6.815.822.125.651.839.842.616.6Total100100100100100100100100%byFirmTypeFirmtypeSME60.915.43.18.92.39.511.8100Non-SME22.114.64.415.312.431.343.7100Total54.415.33.39.94.013.117.1100O=Yes;X=No,GVC=globalvaluechain,SME=smallandmedium-sizedenterprises.Note:Columns1–6represent1)firmswithdomesticprocurementandsales;2)firmswithinputimports,domesticprocurement,anddomesticsales;3)firmswithdomesticprocurementandexports;4)firmswithdomesticprocurement,domesticsales,andexports;5)firmswithexports,inputimports,anddomesticprocurement;and6)firmswithdomesticprocurementandsales,inputimports,andexports.Source:Author’scalculationusingWorldBank’sEnterpriseSurveysdata.http://www.enterprisesurveys.org(accessed1June2023).194DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaFigure9.1:ShareofSMEs,GVCfirms,andSMEsengaginginGVCsbyRegion(%)CentralAsia..EastAsia&Paciﬁc...SouthAsia.Europe...LatinAmerica&Caribbean...MiddleEast&NorthAfrica....Sub-SaharanAfricaAsiaandthePaciﬁc...Non-AsiaandthePaciﬁc...World........ShareofSMEsengaginginGVCs()ShareofGVCﬁrms()ShareofSMEs()GVC=globalvaluechain,SME=smallandmedium-sizedenterprises.Note:EastAsia&PacificincludesCambodia,thePRC,Indonesia,theLaoPDR,Malaysia,Mongolia,Myanmar,PapuaNewGuinea,thePhilippines,SolomonIslands,Thailand,Timor-Leste,andVietNam.Asia&PacificincludesCentralAsia,EastAsia&Pacific,andSouthAsia.Source:Author’scalculationbasedontheWorldBank’sEnterpriseSurveys.http://www.enterprisesurveys.org(accessed1June2023).Figure9.2:SectoralDistributionofGVCFirmsintheAsiaandPacificRegion(%)Textiles,textileproducts,leatherandfootwear...Chemicalsandchemicalproducts.Foodproducts,beveragesandtobacco...Electricalmachineryandapparatus,nec.Rubberandplasticsproducts...Othernon-metallicmineralproducts.Machineryandequipment,nec...Fabricatedmetalproducts.Basicmetals...Motorvehicles,trailersandsemi-trailers.Manufacturingnec...Computer,Electronicandopticalequipment.Pulp,paper,paperproducts,printingandpublishing...Woodandproductsofwoodandcork.Coke,reﬁnedpetroleumproductsandnuclearfuel...Othertransportequipment.ShareofSMEsengaginginGVCs()ShareofGVCﬁrms()GVC=globalvaluechain,NEC=notelsewhereclassified,SME=smallandmedium-sizedenterprises.Source:Author’scalculationbasedontheWorldBank’sEnterpriseSurveys.(http://www.enterprisesurveys.org(accessed1June2023).TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific1959.2.2EvaluationofSMEDigitalReadinessFigures9.3and9.4highlighttheregionaldisparitiesindigitalizationandthedigitaldividebetweenSMEsandlargefirmsacrossdifferentregions.ThesedigitalchallengesSMEsmayexplainSMEs’lowerparticipationinGVCsthanlargefirms.Figure9.3presentsdataonemailadoptionratebyfirmsizeacrossdifferentregions,revealingnotabledifferencesbetweenlargefirmsandSMEs.Generally,largefirmshavehigheremailadoptionratescomparedtoSMEs.ThegapinadoptionratesisparticularlysignificantinAsiaandthePacific(27%),onlyafterAfrica.Figure9.4illustratesthatSMEshavesignificantlylowerwebsiteadoptionthanlargefirmsacrossregions.Onaverage,inAsiaandthePacific,thewebsiteadoptionrateforSMEsis41%,whileitstandsat81%forlargeenterprises.ThedisparitybetweenthewebsiteadoptionratesofSMEsandlargefirmsisevenbroaderthanemailadoptionrates.Creatingandmaintainingawebsitedemandsmoreadvancedcomputerskillsandhighermaintenancecosts,leadingSMEstorelyprimarilyoncost-effectivedigitaltechnologieslikeemail.Figure9.3:AdoptionRateofEmailbyRegionandFirmSize(%)CentralAsiaEastAsia&PaciﬁcSouthAsiaEuropeLatinAmerica&CaribbeanMiddleEast&NorthAfricaSub-SaharanAfricaAsiaandthePaciﬁcNon-AsiaandthePaciﬁcWorldShareofSMEsShareoflargeenterprisesSME=smallandmedium-sizedenterprises.Notes:EastAsia&PacificincludesCambodia,thePRC,Indonesia,theLaoPDR,Malaysia,Mongolia,Myanmar,PapuaNewGuinea,thePhilippines,SolomonIslands,Thailand,Timor-Leste,andVietNam.Asia&PacificincludesCentralAsia,EastAsia&Pacific,andSouthAsia.Source:Author’scalculationbasedontheWorldBank’sEnterpriseSurveys.http://www.enterprisesurveys.org(accessed1June2023).196DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaFigure9.4:AdoptionRateofWebsitebyRegionandFirmSize(%)CentralAsiaEastAsia&PaciﬁcSouthAsiaEuropeLatinAmerica&CaribbeanMiddleEast&NorthAfricaSub-SaharanAfricaAsiaandthePaciﬁcNon-AsiaandthePaciﬁcWorldShareofSMEsShareoflargeenterprisesSME=smallandmedium-sizedenterprises.Notes:EastAsia&PacificincludesCambodia,thePRC,Indonesia,theLaoPDR,Malaysia,Mongolia,Myanmar,PapuaNewGuinea,thePhilippines,SolomonIslands,Thailand,Timor-Leste,andVietNam.Asia&PacificincludesCentralAsia,EastAsia&Pacific,andSouthAsia.Source:Author’scalculationbasedontheWorldBank’sEnterpriseSurveys.http://www.enterprisesurveys.org(accessed1June2023).SMEsinAsiaandthePacificexhibitinadequatelevelsofdigitalreadiness,3asindicatedinFigure9.5.Thereissignificantroomforimprovement,withaconsiderablegapbetweentheircurrentdigitalreadinesslevelsandthedigitalreadinessfrontier,rangingfrom32and66points.Severaldomainsrequireattention,includingfinance,supportinginfrastructure,andlaborcapability.Incontrast,theyrelativelyexcelindigitalization,i.e.,telecommunicationsandemailadoption,whichareclosesttothedigitalreadinessfrontiercomparedtootherfactors.AsignificantmajorityofSMEsinAsiaandthePacificuseemailforcommunicationwithclientsandsuppliers(68%),and64%reportnoobstaclesintelecommunicationsfortheirdailyoperations.However,theadoptionrateofDigitalizationIII(website)islow,with3TheSMEDigitalReadinessIndexcomprisesfourkeydomainsencompassingvariousfactorscontributingtodigitaldevelopment:laborcapability,supportinginfrastructure,digitalization,andfinance(Korwatanasakul2020).TheindexiscalculatedbyaveragingscoresorstatisticsrelatedtoeachfactorordeterminingtheproportionofSMEsthathavenotfacedspecificchallenges.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific197Figure9.5:DigitalReadinessIndexofSMEsinAsiaandthePacificLaborI(Averageyearsofschooling)FinanceII(Financialaccess)LaborII(Highschoolcertiﬁcate)FinanceI(Credit/loan)LaborIII(Formaltrainingprogram)DigitalizationIII(Website)InfrastructureI(Powerstability)DigitalizationII(Email)InfrastructureII(Electricity)DigitalizationI(Telecommunications)AsiaandthePaciﬁcNon-AsiaandthePaciﬁcFrontierSMEs=smallandmedium-sizedenterprises.Source:Author’scalculationbasedontheWorldBank’sEnterpriseSurveys.http://www.enterprisesurveys.org(accessed1June2023).only41%havingawebsite.ThefindingsfromFigure9.5areconsistentwithFigures9.3and9.4,asSMEsarelessdigitallyready.9.3Methodology9.3.1DataTheanalysisinthisstudyisconductedatthefirmlevelusingpooledcross-sectionaldatafromtheWorldBank’sEnterpriseSurveys.Thedataspansfrom2008to2018andincludes28countriesand23,551firms.FollowingUrataandBaek(2021)andKorwatanasakulandPaweenawat(2021),twoindicatorsofGVCparticipationareconstructed:theGVCparticipationdummyandtheGVCparticipationindex.Ontheonehand,theGVCparticipationdummyidentifieswhetherafirmengagesinGVCsbasedonitspatternsofdirectandindirectinvolvementinforeigntradethroughsalesandinputprocurement,asshowninTable9.1.198DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaOntheotherhand,theGVCparticipationindexiscalculatedbymultiplyingtheratioofexportstototalsalesbytheratioofforeigninputtototalinput.Eachindicatorisusedasanindependentvariableintwoseparateanalyses,employingprobitandtobitmodels.Table9.2providessummarystatistics,anddataconstructionispresentedinTableA9(Appendix).Table9.2:SummaryStatisticsStandardMinimumMaximumVariableDescriptionObservationsMeandeviationvalueValue0.1873GVCGVCparticipationparticipation0.0613Equals1iftheestablishment18,3730.390201GVCparticipatesinGVCsand00.7320participationotherwise.TheGVCparticipation0.4757indexdummyindicateswhethera3.7446DigitalizationfirmjoinsGVCsbasedonthe0.8408Emailfirm’spatternsofdirectand13.6533indirectengagementinforeign18.7890Websitetradethroughsalesandinput5.4490procurement.0.4210Firmsize0.3669SMETheGVCparticipationindexis18,3710.34810.194201calculatedbymultiplyingtheratioLaborofexportstototalsalesandtheproductivityratioofforeigninputtototalinput.FirmageForeignEquals1iftheestablishmentuses23,4110.442901ownershipemailtocommunicatewithclientsSoleorsuppliersand0otherwise.proprietorshipCertificateEquals1iftheestablishmenthasits23,5210.499401ownwebsiteand0otherwise.CreditaccessFirmcharacteristicsNaturallogarithmofafirm’stotal23,5511.4283010.309full-timeemployees.1Equals1iftheestablishmenthas23,5510.36590lessthan200employeesand0otherwise.1)Naturallogarithmoflabor19,0672.72021.354826.8425productivitybasedonvalueadded.13.5661016220.69800100Numberofyearsinoperation.23,2720.4937010.482001Theshareofequityownedbya23,492foreignfirm(%).Equals1ifsoleproprietorshipand23,5760otherwise.Equals1ifownershipof23,262internationallyrecognizedqualitycertificationand0otherwise.Equals1iftheestablishment22,6160.476401hasalineofcreditorloanfromafinancialinstitutionand0otherwise.GVC=globalvaluechain,SME=smallandmedium-sizedenterprise.Source:Author.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific1999.3.2EstimationmethodsBasedonitsspecificcharacteristics,suchasdigitalconnectivity,firmsize,andtypesofownership,theprobitmodel(Equation1)usedinthisstudyaimstoestimatethelikelihoodofafirmbeingaGVCfirmoranon-GVCfirm.Moreover,thetobitmodel(Equation2)estimatestheeffectofdigitalizationontheGVCparticipationlevelmeasuredbytheGVCindex.Themodelspecificationsareasfollows:Probitmodel:Pr(GVCparticipationict=1Zict)=θ(β0+β1Digitalizationict+β2Xict+γc+σk+μt+ϵict)(1)Tobitmodel:GVCindexict=GVCindexict,if0<GVCindexict<1GVCindexict=0,ifGVCindexict≤0GVCindexict=1,ifGVCindexict≥1GVCindexict=β0+β1Digitalizationict+β2Xict+γc+σk+μt+ϵict(2)GVCparticipationictreferstotheGVCparticipationdummyindicatingwhetherafirmisengaginginforeigntradethroughsalesandinputprocurement.Incontrast,GVCindexictreferstothelevelofGVCparticipationestimatedfromthemultiplicationofexportstototalsalesandforeigninputtototalinputratios(UrataandBaek2021).ThevariableofinterestisDigitalizationictindicatingwhetherafirmusesemailsorwebsitesforfirmiincountrycandyeart.Xictrepresentsasetofcontrolvariables,includinglaborproductivity,firmage,foreignownership,governmentownership,femaleownership,creditaccess,andinternationallyrecognizedqualitycertificate.4Theestimationmodelsincludecountry-,industry-andtime-fixedeffectsandthedisturbanceterm,representedbyγc,σk,μt,andϵictrespectively.Robuststandarderrorsarealsousedintheestimations.Theproposedestimationmodelsmaysufferfromendogeneityissues.ParticipatinginGVCscausesfirmstoinvestinandutilizedigitalizationintheirbusinesspractices,resultinginreversecausality,whileunobservedvariablesmayalsoexistinthedisturbanceterms.Thisstudyemploysrecursivebivariateprobitandtobitmodelstoaddress4Thisstudy’smodelspecificationsfollowUrataandBaek(2021).Formorediscussiononthehypothesesbehindeachvariable,seeUrataandBaek(2021).200DigitalTransformationforInclusiveandSustainableDevelopmentinAsiatheendogeneityissuesbyutilizinganexogenousinstrumentfortheendogenousdigitalizationvariable.TheIVmodelspecificationsareasfollows:Recursivebivariateprobitmodel:FirststagePr(Digitalizationict=1Zict)=θ(β0+β1Digitalization_Industryict+β2Xict+γc+σk+μt+ϵict)(3)SecondstagePr(GVCparticipationict=1Zict)=θ(β0+β1Digitalization_Industryict+β2Xict+γc+σk+μt+ϵict)(4)Recursivebivariatetobitmodel:FirststageDigitalizationict=β0+β1Digitalization_Industryict+β2Xict+γc+σk+μt+ϵict(5)SecondstageGVCindexict=β0+β1Digitalization_Industryict+β2Xict+γc+σk+μt+ϵict(6)Followingthepreviousliterature(e.g.,Cette,Nevoux,andPy2021),theIV,Digitalization_Industryict,referstotheindustrymeanofemail(website)adoptionexcludingthefirm’suseofemail(website).TheIVmeasuresthedeviationoffirm-leveldigitalizationadoptionfromtheindustrymean.Theidentificationstrategyfollowsthelogicthatfirmswithadeviation,i.e.,adoptingdigitalization,aremoreexposedtoindustry-widetechnologyspillovers,implyingloweradoptioncosts,and,inturn,moreactivelyutilizedigitalizationtoparticipateinGVCsorincreasetheirGVCparticipationlevel.TheexogeneityoftheadoptedIVcomesfromtheinclusionoftheindustryfixedeffects(σk)intheestimationmodel.Whileσkcapturesalltheotherindustryspecificities(unobservedindustry-levelvariables),theonlydifferenceamongfirmsiseachfirm’sdigitalizationadoption(i.e.,emailandwebsiteadoptions).Sectoralspillovers,externaleconomiesofscale,andnetworkeffectsareunlikelytoaffectthevalidityoftheIV(Cette,Nevoux,andPy2021).Thus,theIVaddressesomittedvariablebiasandreversecausalitysimultaneously.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific2019.4EstimatedResults9.4.1ResultsDespitethehighercoefficientsofbivariateprobitandtobitestimates,theestimatedresultsfrombaselineregressionandthebivariateestimationwithinstrumentsareconsistent,exceptinColumn6,Table9.3.Theestimatedresultsoftheprobitandtobitmodels(Tables9.3and9.4)demonstratethatfirms,especiallySMEswithdigitalization,suchasemailandwebsiteadoption,aremorelikelytoengageinGVCsandhaveahigherdegreeofGVCparticipation(greaterGVCparticipationindex),consistentwiththepreviousliterature,(e.g.,Korwatanasakul2020;ReddyandSasidharan2021;andGopalan,Reddy,andSasidharan2022).Thisconfirmsthatadoptingdigitaltechnologiesenablesfirmstoaccessinternationalmarkets(LendleandOlarreaga2014;WTO2016)andfacilitatesconnectionswithdomesticandforeignsuppliersandconsumers,enhancingsupplyandvaluechains(Abel-Koch2016).Moreover,theestimatedresultsrevealtheheterogenouseffectsofdigitalizationonGVCparticipationbetweenSMEsandlargefirms.WhenintroducinginteractiontermsbetweenSMEsanddigitalization,theimpactofdigitalizationonGVCparticipationisgreaterforSMEs(Table9.3Column12andTable9.4Columns6and12).ThedifferenteffectsofdigitalizationbetweenSMEsandlargeenterprisesimplythatbasicdigitaltechnology,suchasemailsandwebsites,possiblyhelpsSMEsparticipateinGVCsandraisetheirGVCparticipationlevel.However,theheterogeneouseffectofemailadoptiononGVCparticipationtendencybetweenSMEsandlargefirmsisnotobservable(Table9.3Column6),whiletheimpactofwebsiteadoptiononGVCparticipationtendencydisappearsforlargefirms(Table9.4Column6).Digitalizationeffectsonlargefirms’GVCparticipationtendencydisappear,possiblybecausemostlargefirmshavealreadyinvestedinandcommonlyutilizedigitaltechnology,e.g.,websites,intheirbusinesses.Nevertheless,thecurrentestimatedresultsarenotsufficienttosupportthehypothesisexplaininglargefirms’situationand,inturn,warrantfurtherinvestigationwithricherdataonfirms’digitalization.Additionally,bothestimationmodelsindicatethatbeingasmallerfirmoranSMEnegativelycorrelateswithGVCparticipation,withtheestimatednegativecoefficientsbeingstatisticallysignificantandrobustacrossdifferentmodelspecifications.Thefindingssupportpreviousresearch,suchasArudchelvanandWignaraja(2015),KorwatanasakulandPaweenawat(2020),andVidavong,Thipphavong,andSuvannaphakdy(2017),arguingthatSMEsfacechallengesin202DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaparticipatinginGVCsduetotheirlimitedknowledge,technology,andinnovationcapacity.Regardingthecontrolvariables,foreignownership,certificate,andcreditaccessarestatisticallysignificantandpositivelyaffectGVCparticipation,highlightingtheimportanceofthesefactorsinpromotingfirms’GVCparticipation.Theirestimatedcoefficientsarealsorobustacrossdifferentmodelspecifications.Forinstance,firmswithahigherlevelofforeignownershipwouldexperienceknowledgeandtechnologytransfer,providingthemaccesstonewtechnology,innovation,managementexpertise,andinternationalnetworksand,inturn,makingthemmoreinclinedtoparticipateinGVCs.Incontrast,soleproprietorshipsnegativelyaffectGVCparticipationasfirmsareatadisadvantageinjoiningGVCswhentheylacksocialtiesandresources(Das,Roberts,andTybout2007).Table9.3:EffectsofEmailAdoptiononGVCParticipationDependentvariable:GVCparticipation(Dummy)Dependentvariable:GVCparticipationindexBaselineRegression:BivariateProbitEstimationBaselineRegression:BivariateTobitEstimationProbitEstimationwithInstrumentsTobitEstimationwithInstrumentsVariable123456789101112Digitalization(Email)0.5930.7310.3690.5601.4661.4470.2850.3560.0681.1041.1590.357Firmsize–0.052–0.051–0.152–0.148–0.084–0.175–0.023–0.023–0.063–0.145–0.126–0.099SME0.3030.3060.1240.0416SMExDigitalization(Email)–0.013–0.017–0.006–0.013Laborproductivity–0.656–1.041–0.511–0.530–0.272–0.579–0.114–0.447Firmage–0.038–0.155–0.041–0.155–0.016–0.066–0.025–0.063Foreignownership0.4040.0190.3220.253Soleproprietorship–0.159–0.153–0.067–0.061Certificate0.04790.04190.04200.04850.0140.0140.01620.01320.0134–0.0153–0.01870.003Creditaccess–0.012–0.012–0.012–0.012–0.011–0.011–0.005–0.005–0.005–0.007–0.006–0.005Constant0.0010.03890.03850.0010.0320.032–0.0321–0.014–0.015–0.0247–0.0174–0.014TheRoleofDigitalizationinFirms’GlobalValueObservationsChainParticipationinAsiaandthePacific203–0.024–0.023–0.023–0.023–0.022–0.022–0.010–0.010–0.010–0.009–0.009–0.0090.01170.01250.01240.01180.01140.01140.005700.006190.006180.004560.004600.00522–0.001–0.001–0.001–0.001–0.001–0.001–0.000–0.000–0.000–0.000–0.000–0.000–0.299–0.394–0.390–0.304–0.280–0.280–0.122–0.165–0.161–0.0448–0.0491–0.117–0.039–0.038–0.038–0.041–0.040–0.040–0.017–0.017–0.017–0.020–0.022–0.0180.2680.4030.4040.2670.2900.2910.09360.1510.1520.03350.04100.110–0.037–0.036–0.036–0.038–0.037–0.038–0.016–0.016–0.016–0.018–0.021–0.0170.1300.1700.1690.1290.1280.1280.02610.04480.04460.0050.0090.0325–0.032–0.031–0.032–0.032–0.031–0.031–0.014–0.014–0.014–0.014–0.014–0.013–4.020–2.421–2.076–3.973–2.644–2.627–1.715–1.074–0.800–1.574–1.334–0.882–0.464–0.468–0.487–0.493–0.475–0.493–0.216–0.217–0.224–0.213–0.222–0.21113,60613,60613,70513,65013,65013,65013,68113,68113,68117,97917,97917,979GVC=globalvaluechain,SME=smallandmedium-sizedenterprise.Notes:,,andindicatethatcoefficientsaresignificantatthe1%,5%,and10%levels,respectively.Robuststandarderrorsarereportedinparentheses.Allregressionsincludeindustry-country-andtime-fixedeffects.SMExDigitalization(email)indicatingtheinteractiontermbetweenthetwovariables.Source:Author.Table9.4:EffectsofWebsiteAdoptiononGVCParticipationDependentvariable:GVCparticipation(Dummy)Dependentvariable:GVCparticipationindex204DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaBaselineRegression:BivariateProbitEstimationBaselineRegression:BivariateTobitEstimationProbitEstimationwithInstrumentsTobitEstimationwithInstrumentsVariable123456789101112Digitalization(Website)0.3690.4700.0860.3820.558–0.1150.1360.186–0.06700.8650.9810.873Firmsize–0.037–0.037–0.077–0.204–0.185–0.245–0.016–0.016–0.031–0.252–0.222–0.18SME0.3060.3050.1310.043SMExDigitalization–0.013–0.024–0.006–0.027(Website)Labor–0.655–1.020–0.635–1.075–0.283–0.529–0.062–0.224productivityFirmage–0.038–0.075–0.059–0.097–0.017–0.031–0.056–0.055Foreign0.4690.4830.3220.239ownershipSole–0.083–0.086–0.034–0.032proprietorshipCertificate0.05370.04960.04980.05290.04630.05540.02030.01820.0180–0.006–0.012–0.0145–0.012–0.012–0.012–0.013–0.012–0.013–0.005–0.005–0.005–0.010–0.009–0.008Creditaccess–0.0100.0280.03–0.0100.0250.035–0.0178–0.015–0.0381–0.0309–0.0297–0.023–0.023–0.023–0.023–0.023–0.023–0.0359–0.010–0.010–0.010–0.010–0.010Constant0.0121–0.0100.006510.006260.005680.005900.005730.01200.01290.0127–0.0010.01300.0127–0.000–0.000–0.000–0.000–0.000Observations–0.001–0.001–0.001–0.001–0.0010.00590–0.187–0.179–0.0880–0.0936–0.0771–0.314–0.421–0.413–0.315–0.413–0.000–0.017–0.017–0.021–0.027–0.024–0.039–0.038–0.038–0.042–0.044–0.435–0.1350.1370.134–0.104–0.115–0.1430.2240.3510.3480.2190.325–0.044–0.017–0.016–0.016–0.063–0.068–0.054–0.038–0.037–0.037–0.058–0.0630.3960.05050.04990.0120.0160.0130.1360.1780.1760.1330.172–0.0660.0804–0.014–0.014–0.014–0.016–0.015–0.032–0.031–0.031–0.032–0.0320.182–0.016–0.877–0.671–1.071–0.867–0.722–1.686–1.374–3.128–1.636–0.0320.0293–0.243–0.244–0.253–0.243–0.248–3.205–0.511–0.516–0.553–0.542–1.260–0.01413,78013,78018,07518,07518,075–0.51113,77613,77613,74813,748–0.552–1.52313,77613,748–0.2413,780GVC=globalvaluechain,SME=smallandmedium-sizedenterprise.Notes:,,andindicatethatcoefficientsaresignificantatthe1%,5%,and10%levels,respectively.Robuststandarderrorsarereportedinparentheses.Allregressionsincludeindustry-country-andtime-fixedeffects.SMExDigitalization(email)indicatingtheinteractiontermbetweenthetwovariables.Source:Author.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific2059.5ConclusionThisstudyaddressesresearchgapsbyconductingempiricalanalysesontherelationshipbetweenGVCparticipationandfirms’digitalization,focusingonSMEsinAsiaandthePacific.Thestudyemploysbivariateprobitandtobitregressionswithinstrumentsusingcross-sectionaldatafromtheWorldBank’sEnterpriseSurveysspanning28countriesand23,551firmsfrom2008to2018.Theindustryaverageofdigitalization(i.e.,emailandwebsiteadoptions)isadoptedasIVstocorrectendogeneityissues.Theestimatedresultssuggestthatfirms,particularlySMEs,withdigitalconnectivity,suchasemailandwebsiteadoption,aremorelikelytoparticipateinGVCsandhaveahigherdegreeofGVCparticipation.Thisfindingalignswithpreviousstudies,indicatingthatadoptingdigitaltechnologiesenablesfirmstoaccessinternationalmarketsandenhancesupplychains.However,theeffectsofdigitalizationonGVCparticipationdifferbetweenSMEsandlargefirms.Basicdigitaltechnology,likeemailsandwebsites,appearstoassistSMEsinparticipatinginGVCsandincreasingtheirlevelofparticipation.Incontrast,theimpactofdigitalizationonGVCparticipationforlargefirmsdiminishes,likelyduetotheirwidespreaduseofbasicdigitaltechnology.ThestudyalsoconfirmsthatsmallerfirmsandSMEsfacechallengesparticipatinginGVCs.Otherfactors,suchasforeignownership,certificates,andcreditaccess,alsopositivelyinfluenceGVCparticipation.AccordingtotheSMEDigitalReadinessIndexandtheestimationresults,severalpolicymeasuresarecrucialforenhancingSMEdigitalizationandpromotingparticipationinGVCs.Thesemeasuresincludeimprovingaccesstofinance,promotingtechnologicalcapacity,enhancinglaborquality,andupgradingbasicinfrastructure.PolicymakersmustimplementcomprehensivestrategiesaddressingtheseareasandprioritizeSMEcapacitybuildingindigitaltechnology.AccesstofinancialresourcesshouldbeensuredtosupportSMEsinimprovingtheirdigitalinfrastructure,suchasstableelectricityandreliableinternetconnectivity,andtoenablethemtoinvestinadvanceddigitaltechnologies.Additionally,integratingIT-relatedcoursesintoschoolcurriculaandprovidingtrainingprogramstoenhancethedigitalliteracyofthecurrentworkforceareessentialsteps.CollaborationbetweenthepublicandprivatesectorsinresearchanddevelopmentinitiativesisnecessarytoupgradeSMEs’technologyanddigitalcapabilities.AholisticapproachisneededtoovercomedigitalbarriersandfacilitateSMEengagementinGVCs.Apotentiallimitationinouranalysisarisesfromtheissueofendogeneity,specificallythereversecausalitybetweenGVCparticipationandtotalrevenue.Consequently,itisessentialtoexercise206DigitalTransformationforInclusiveandSustainableDevelopmentinAsiacautionwheninterpretingourempiricalresultsandnottodrawanycausalrelationshipfromthem.Nonetheless,thisstudyservesasaninitialsteptowardsestablishingafoundationformorerobustfindingsregardingtheimpactofGVCparticipationonfirms’performanceatthefirmlevel.Futureresearchwithmorecomprehensivefirm-levelGVCdatacanenhancetheanalysistoaddressindirectGVCengagementanddifferentvalue-addedactivities,whicharenotaccountedforinthecurrentstudy.Moreover,duetodatalimitations,utilizingemailandwebsiteadoptionasproxiesofdigitalizationyieldsanalyticallimitations.Thus,thisstudyurgesmorecomprehensivefirm-leveldigitalizationdatacollectionthatenablesexploringalternativeandmorenuanceddigitalizationmeasuresandoffersaricherperspectiveonthelevelandnatureofdigitaltransformation.TheRoleofDigitalizationinFirms’GlobalValueChainParticipationinAsiaandthePacific207ReferencesAbel-Koch,J.2016.SMEs’ValueChainsAreBecomingMoreInternational–EuropeRemainsKey.KfWResearchPaperNo.137.Antràs,P.2020.ConceptualAspectsofGlobalValueChainsDiscussion.WorldBankEconomicReview34(3):551–574.APECStudyCentre.2017.StudyofSMEs’IntegrationintoGlobalValueChainsinServicesIndustries–FashionDesign.APECStudyCentre,theChineseUniversityofHongKong.Arudchelvan,M.,andG.Wignaraja.2015.SMEInternationalizationthroughGlobalValueChainsandFreeTradeAgreements:MalaysianEvidence.ADBIWorkingPaper515.Tokyo:AsianDevelopmentBankInstitute.Asia-PacificEconomicCooperation(APEC).2020.SmallandMediumEnterprises.https://www.apec.org/Groups/SOM-Steering-Committee-on-Economic-and-Technical-Cooperation/Working-Groups/Small-and-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India000007,1550002200707Indonesia01,05700001,0513550244001,051Kazakhstan01320019900000027KyrgyzRepublic0720010400001,90500LaoPDR00093000004100361Malaysia00000058302237260726Mongolia01000012000005601,907Myanmar000003520014223401,369Nepal00002440023,6071,325Pakistan00001,05100PapuaNewGuinea00000027Philippines086900001,036SolomonIslands00000041SriLanka003610000Tajikistan10000012300Thailand0000000Timor-Leste00000056Türkiye8270001,08000Uzbekistan10600012800VietNam06780000691Total1,1273,0933611,7784,5717,6453,565LaoPDR=LaoPeople’sDemocraticRepublic,PRC=People’sRepublicofChina.Source:Author.Conclusion:DigitalConnectivityandDigitalTrade—UnderstandingtheLinkagesandPolicyChallengesAmitenduPalit,DilRahut,SubhasisBera,andYixinYaoDigitalConnectivityinOverallConnectivityTheglobalshifttowarddigitalconnectivityforeconomicprogressfacespersistentchallengesknownasthedigitaldivide.Despiteefforts,disparitiesinaffordabilityandaccesspersist,especiallyintheAsiaandPacificregion.Whilesubsidiesaloneareinsufficient,bridgingthegaprequirestargetedinclusionefforts,emphasizingdigitalliteracyandfinancialskillsformicro,small,andmedium-sizedenterprises(MSMEs),andparticipatingincross-bordertrade.Whiledigitalizationreducesgeographicaldistances,italsocreatesopportunitiesforincomeandinvestments.Theimpactofdigitaltransformationisconditionalonthedigitalconnectivityandefficiencyofdigitalfinanceandtrade.Digitalconnectivityhasintroducedanewdimensionintheoverallideaandunderstandingofconnectivity.Thecomplexinterplayofdigitalconnectivity,economicgrowth,andsustainabilitydemandscomprehendingthetermconnectivity.Connectivityforcenturieshasbeentypicallyunderstoodasconnectionsformovementofgoods,services,andpeopleprovidedthroughlandorwaterroutes.Connectionsthrougheitherlandorsea,aresinglemode,orunimodalincharacter.Acombinationoflandandwaterroutesimpliesmultimodalconnectivity.Moderninfrastructureplansaremultimodalincharacterastheyinvolvethepassageofgoodsbyrailorroadfromthehinterlandtoseaportsforfurtherpassageacrossacountry’sborders.Similarmultimodalityisalsoobservedforthemovementofgoodsandpeoplefromthehinterlandtoairportsandfurtheroutward.211212DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTheattentionofglobaleconomicpolicybodies,suchastheWorldBank,theAsianDevelopmentBank,andtheWorldTradeOrganization(WTO)inthepasthasbeenonenablingfastertransitthroughlandandwaterroutes.Landroutesincluderoadlinksacrosscontiguousgeographiesandsovereignnationsforconnectingnationalandsubnationalterritories.Waterroutesprimarilycomprisemaritimeconnectionsacrossoceansandseas,linkingcontinents,regions,andsubregions.Thelogicofeconomicglobalizationandunrestrictedtradehasencouragedmulti-prongedeffortsforbringingdowncostsofthemovementofgoodsacrossborders.AnotableinitiativeinthisregardhasbeentheadoptionoftheWTO’stradefacilitationagreement(TFA)(WTO2014).Inforcesince22February2017,theTFAaimstocutscostsofthemovementofgoodsacrosslandandseabordersbyminimizingproceduresandharmonizingcooperationbetweencustomsandotherregulatoryagenciesofWTOmembers.Asofnow,156WTOmembers,outofatotalof164,haveratifiedtheTFA(WTO.n.d.).TradeFacilitation,NewFrameworks,andDigitalConnectivityTheintroductionandimplementationoftheTFAhasplayedanimportantpartindrawingattentiontothesignificantroleofdigitalizationinenhancingtradefacilitation.EffortsbytheTFAtocut“regulatorycholesterol”atthebordersbysimplifyingproceduresformovinggoodsincludeemphasisondigitalizationofclearanceprocesses.Thedigitalizationofcustomsoperations,transitioningtoelectronicfilingofdocuments(e.g.,paymentinvoices),andemergenceofsingleandcompositeoperableonlineplatformsthatintegrateallrequirementspertainingtothemovementofgoods,haslaidthefoundationforcountries,particularlydevelopingnations,forgettingdigitallyconnectedthroughtrade.Thedigitalizationofborderclearanceprocesseshasalsoestablishedthefoundationforgrowthoftradeagreementsthatfocusonmaximizingeconomicbenefitsthatcanbeobtainedfromdigitalconnectivity.TheAsiaandPacificregionhasseenthegrowthofsomesuchnotableframeworksinrecentyears.TheseincludetheDigitalEconomyPartnershipAgreementexecutedbySingapore,NewZealand,andChile(MTIn.d.);theUnitedKingdom–SingaporeDigitalEconomyAgreement(GOV.UK2022);andtheSingapore–AustraliaDigitalEconomyAgreement(DFATn.d.).Theseagreementsemphasizetheintegrationofnationaldigitaleconomiesthroughinteroperabilityofdigitalsystemsandprocesses.ThelatterarewiderangingandincludeConclusion:DigitalConnectivityandDigitalTrade—UnderstandingtheLinkagesandPolicyChallenges213e-invoicing,digitalpaymentsystems,paperlesstrade,establishmentofdigitalidentities,1andharmonizeddataprotectionandcybersecurityrules.Asmoredigitalrules-basedframeworksdevelop—bothmultilaterallyandregionally—digitalconnectivitywillexpandrapidlyacrossvariouspartsoftheworld.TheexpansionwillbeparticularlynoticeableintheAsiaandPacificregionduetotheevolutionofnewdigitalframeworks.Theconnectivityexpansionwillacceleratefromgreaterdigitalizationofbothnationalandcross-bordertradesystems.DigitalEconomyFrameworkAgreementThepushtowarddigitaltransformationhasbeenhastenedbyunanticipateddevelopmentslikethenovelcoronavirusdisease(COVID-19)pandemic.Indeed,thepandemichasprobablybeenthemostinstrumentalfactorinemphasizingtheimportanceofbuildingdigitalconnectivitycapacities,notjustatthenationallevel,butalsoamongenterprisesandhouseholds.Countrieshavebegunrespondingtotheurgencywithsignificantpolicyinitiatives.AprominentexampleofaregionaleffortinthisregardisthelaunchofnegotiationsfortheAssociationofSoutheastAsianNations(ASEAN)DigitalEconomyFrameworkAgreement(DEFA)(ASEAN2023).TheDEFAisexpectedtobeasignificantinitiativeinmultipleways.Asthefirstregionaldigitaleconomyframeworkagreement,itfocusesonanexhaustiverangeofissuesthatimpactdigitalconnectivityanddigitaltrade.Theseincludee-commerce,digitalpayments,dataflows,andcybersecurity.Theeffortisalsonoteworthyforitsfurtheremphasisondigitalinclusivity.ASEANisaheterogenousgroupingofeconomies,whichincludematureindustrialeconomies(e.g.,Singapore,Malaysia,Thailand)alongwithlow-income,leastdevelopedmembers(e.g.,Myanmar,Cambodia,theLaoPeople’sDemocraticRepublic).ThenationaldigitalcapacityofASEANmemberstatesshowslargevariations.Notwithstandingthesevariations,theDEFAaimstodigitallyconnecttheASEANregionbycreatingaframeworkofcommonrulesthatwouldsupportthegrowthofaregionaldigitaleconomy.Inthisregard,itsprogresswillprovideanilluminatingexperienceonevolutionofanexhaustivedigitalconnectivityframework.1Adigitalidentityisdescribedas“acollectionofelectronicallycapturedandstoredidentityattributesthatuniquelydescribeapersonwithinagivencontextandareusedforelectronictransactions.”Digitalidentitycanuniquelyidentifyindividuals,enterprises,andfinancialasserts(PECC2021).214DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaTheDigitalEconomyDigitalconnectivity,asexemplifiedbytheASEANDEFAandotherdigitaleconomypartnershipagreementsmentionedearlier,isfundamentaltothecreationofthedigitaleconomy.Thedigitaleconomybeingdevelopedbyexistingandupcomingrules-basedframeworksgeneratesdigitaltradethatfurthergeneratesmultipleeconomicopportunitiesandoveralleconomicgrowth.Theevolutionofthedigitaleconomy,asfacilitatedbydigitalconnectivity,haskeycharactersthatleadtosuchevolution.Theseincludeconnectivitybetweendigitallypowerednetworksforenablingtheflowofdigitaltrafficacrossvariouscountryandterritorialboundaries,andoperabilityamongnetworksforenablingvariousfunctions(e.g.,usingsmartphonesforbankingtransactions).Theothercriticalcharacterofthedigitaleconomyisdigitaltrade.Asmorecross-bordersystemsdigitalizeandmutuallyharmonizetobecomeinteroperable,digitalconnectivityemergesasthenextfrontiergenerationinoverallconnectivity.Itisassumingasmuchprominence,ifnotmore,aslandandwaterconnectivityinthewiderspaceofglobalinfrastructuredevelopment.Tradefacilitationeffortshaveensuredthatdigitalprocessesbecomeintegralinimprovingefficienciesofglobalandregionaltradetrafficthroughtraditionalmodesofconnectivitysuchaslandandwaterroutes.Thisisperhapsoneofthemajorreasonsbehindcontemporarylarge-scalecross-borderinfrastructureprojects,suchasthePeople’sRepublicofChina’sBeltandRoadInitiative(BRI)andtheIndia–MiddleEast–EuropeEconomicCorridor(IMEC),proposingdigitalconnectivityasimportantpartsoftheirframeworks.2Theassumptionbehindtheemphasisonstrongdigitalconnectivityistheroleitwillplayinfacilitatingdigitaltradeacrosstheseframeworks.DigitalConnectivityandDigitalTradeThegrowthofdigitalconnectivityhasbeeninseparablefromthatofdigitaltrade.Bothhavebeenfacilitatingandspurringthegrowthofeachother.Intuitively,thesharpgrowthindigitaltradeisperhapsthemostobviousindicatoroftheincreaseindigitalconnectivitythattheworldhasexperienced.Acloseunderstandingofdigitalconnectivityintermsofitsqualitativeandquantitativedimensionscanbeobtainedfrombetter2TheDigitalSilkRoadisanimportantcomponentoftheBRI.FormoredetailsseeCouncilonForeignRelations(n.d.)FormoredetailsontheIMEClaunchedattheG20Leaders’SummitinNewDelhi,seeWhitehouse.gov(2023).Conclusion:DigitalConnectivityandDigitalTrade—UnderstandingtheLinkagesandPolicyChallenges215knowledgeoftheempiricalcharacteristicsofgrowthindigitaltrade.Suchunderstanding,however,encountersconsiderablemethodologicaldifficulties.Despiteitsexplosivegrowth,itisnotsimpletocomprehendtheincreaseindigitaltradeinbaredetails.Thisisbecauseunlikephysicaltradeingoods,digitaltradeisnotnecessarilyvisible.Asaresult,theincreaseindigitaltradecannotbe“seenandfelt”likethewayalotofnondigitaltrade,particularlyingoods,canbefelt,andthereforecanbeunderstoodonlythroughitsstatisticalreflections.ComprehendingDigitalTradeUnderstandingdigitaltradeandmeasuringitarechallengingissues.Thechallengearisesfromtheconceptualdefinitionofdigitaltradeitself.Theprevalentdefinitionofdigitaltrade,asemployedbytheInternationalMonetaryFund(IMF),–theOrganisationforEconomicCo-operationandDevelopment(OECD),theUnitedNationsConferenceonTradeandDevelopment(UNCTAD),andtheWTO,is“……allinternationaltradethatisdigitallyorderedand/ordigitallydelivered”(IMF,OECD,UNCTAD,andWTO2023).Thedefinitionincludestradeingoodsandservicesthatareeitherdigitallyordered,ordigitallydelivered,orbothdigitallyorderedanddelivered.Astatisticalreflectionofthesedifferenttransactionalvarietiesoftraderequiresavailabilityofdatathatcapturesallofthem.Obtainingsuchdatainadisaggregatedformonacountryorproductbasisisdifficultasmanynationalstatisticalsystemsarenotequippedfortherequiredstatisticalreportinganddataorganization.Ontheotherhand,itisalsoimportanttounderstandthatdigitaltradeinaspecificgoodorserviceisapartoftheoverallglobaltradeinthatgoodorservice.Digitaltradeisdistinguishedfromnondigitaltradebythenatureofitstransaction.Inthisrespect,thechallengeofmeasuringdigitaltradearisesinseparatelyestimatingthetradethatisdigitallytransactedinlinewiththedefinitionsofdigitaltrade.Formanyquarters,thishasledtodigitaltradebeingconsideredequivalenttoe-commerce.E-commerce(electroniccommerce)isthebuyingandsellingofgoodsandservicesthroughonlineplatformsusingtheinternetandincludesdigitaldeliveryofgoodsandservices.Thescopeofe-commerceisexhaustiveinitscoverageoftransactionsandincludesthosebetweenbusiness-to-business(B2B),consumer-to-consumer(C2C),aswellasthosebetweenbusiness-to-consumer(B2C)andbusiness-to-government(B2G).Butitisdoubtfulwhetherthemeasurementofe-commerceisaneffectiveequivalentfordigitaltrade.216DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaDigitaltradeisamuchbroaderconceptandcannotbecapturedjustthroughasimpleequivalencewithe-commerce.Anaccurateunderstandingofitneedstoincludedigitalgoodsandservices(e.g.,software,e-publishing,cloudcomputing),digitaldeliveryofphysicalgoodsandservices,hardandsoftinfrastructurefordigitaltrade(e.g.,virtualnetworks,digitalidentities),digitaltransactions(e.g.,onlinepayments),protectionservices(e.g.,cybersecurity,privacy)anddigitaltechnologies(e.g.,5Gtechnology,artificialintelligence,3D-printing)(formoredetails,seePECC2021).MeasuringDigitalTradeTheIMF,OECD,UNCTAD,andWTOcollectiveeffortstomeasuredigitaltrade,asproposedintheHandbookonMeasuringDigitalTrade(IMF,OECD,UNCTAD,andWTO2023),hastakenonthetaskofmeasuringdigitaltradebysettingoutkeydefinitions,outliningamethodologyformeasuring,andproposingatemplateforreportingdigitaltradestatistics.Thedefinitionspertaintodistinguishingbetweendigitallyorderedtradeanddigitallydeliveredtrade.Digitallyorderedtradeincludestradeintheformofcross-bordersaleorpurchaseofgoodsandservicesthatareorderedonlinethroughspecificprocessesdesignedforthetransactions.This,byandlarge,astheconceptualunderpinningsindicate,issynonymouswiththeconceptofinternationale-commerce.However,sincedigitaltradeincludesdigitallydeliveredtradeaswell,itsmeasurementextendsbeyondthatofinternationale-commerce.Digitallydeliveredtraderepresentscross-bordersaleandpurchasetransactionsthataredeliveredremotelyoverdigitalnetworks(IMF,OECD,UNCTAD,andWTO2023).Thus,acompositestatisticalmeasureofdigitaltrademustincludedigitallyorderedtradeanddigitallydeliveredtradetherebymakingitnecessarytoobtaindataonboth.Indeed,inthisregard,astheHandbookonMeasuringDigitalTradepointsout,itisnecessarytoobtaindataontransactionscarriedoutthroughdigitalintermediationplatformsaswell.Theseplatformsfacilitatethesaleandpurchaseofgoodsandservicesbetweenbuyersandsellerswithoutowningthemandcontributesignificantlytooveralldigitaltrade.Thechallengeincompilingdataondigitaltradeistogatherinformationonanationalandglobalscaleinlinewiththereportingtemplate.Theissuebecomescomplexduetotheengagementofmultipleactorsinvolvedintransactions:business,government,consumers.Internationale-commerce,capturingdigitallyorderedtrade,needstobegleanedthroughdataonB2Btransactions.ThesecanbeobtainedConclusion:DigitalConnectivityandDigitalTrade—UnderstandingtheLinkagesandPolicyChallenges217throughbusinesssurveysthatgatherdataonincomesearnedbybusinessesthroughB2Btransactions.Thechallengethougharisesforthosetransactionswhereconsumers,suchashouseholds,areinvolved,astheseneedtobeassessedseparately.Fordigitallydeliveredtrade,itispossibletoobtainsomeideasthroughdataoncross-bordertradeindigitallydeliverableservices.Toalargeextentthesecanbeobtainedthroughsurveysofbusinessesengagedininternationaltradeinservices;buthouseholdsareimportantactorsindigitaltradetransactionsandposesimilarchallengesastheydoinmeasuringdigitallyorderedtrade.LookingAheadonIssuesTheexpansionindigitalconnectivity—andthatofdigitaltrade—hasbeenexceptionallyfast.Intheyearstocome,thepaceofexpansionisgoingtoincreaseevenmoreascountriesrapidlydigitalizeanddigitalrules-basedframeworkscomeupforestablishingwholesomedigitaleconomies.Withthepaceofdigitalconnectivityexpandingfast,severalcountries,particularlythedevelopingeconomiesoftheAsiaandPacificregion,willneedtoolsandsystemsforunderstandingthegrowthofdigitaltradeingreaterdetail.Theirdomesticcapacitymightlaginthisregard.Indeed,itisimportantforthesecountriestorealizethatitisnotenoughtodigitalizesystemsandprocessesformoreefficienttrade.Itisequallyimportanttomodernizestatisticalcapacitiesformeasuringdigitaltrade.Otherwise,digitalconnectivitymightremainavaguelyunderstoodevolutionanditsimplicationswouldbedifficulttounderstand.218DigitalTransformationforInclusiveandSustainableDevelopmentinAsiaReferencesASEAN.2023.ASEANLaunchesWorld’sFirstRegionwideDigitalEconomyFrameworkAgreement,3September.https://asean.org/asean-launches-worlds-first-regionwide-digital-economy-framework-agreement/CouncilonForeignRelations.n.d.AssessingChina’sDigitalSilkRoadInitiative.https://www.cfr.org/china-digital-silk-road/DepartmentofForeignAffairsandTrade(DFAT).n.d.Australia–SingaporeDigitalEconomyAgreement.https://www.dfat.gov.au/trade/services-and-digital-trade/australia-and-singapore-digital-economy-agreementGOV.UK.2022.UK–SingaporeDigitalEconomyAgreement,14June.https://www.gov.uk/government/collections/uk-singapore-digital-economy-agreementInternationalMonetaryFund(IMF),OrganisationforEconomicCo-operationandDevelopment(OECD),theUnitedNationsConferenceonTradeandDevelopment(UNCTAD),andtheWorldTradeOrganization(WTO).2023.HandbookonMeasuringDigitalTrade.SecondEdition.Geneva:WTO.https://www.wto.org/english/res_e/booksp_e/digital_trade_2023_e.pdfMinistryofTradeandIndustry(MTI),GovernmentofSingapore.n.d.DigitalEconomyPartnershipAgreement(DEPA).https://www.mti.gov.sg/Trade/Digital-Economy-Agreements/The-Digital-Economy-Partnership-AgreementPacificEconomicCooperationCouncil(PECC).2021.PECCSignatureProjectontheDigitalEconomy:PrimeronEconomicIntegrationIssuesPosedbytheDigitalEconomy,22October.https://pecc.org/resources/digital-economy/2705-pecc-signature-project-primer-on-economic-integration-issues-posed-by-the-digital-economyWhitehouse.gov.2023.MemorandumofUnderstandingonthePrinciplesofanIndia–MiddleEast–EuropeEconomicCorridor.https://www.whitehouse.gov/wp-content/uploads/2023/09/Project-Gateway-Multilateral-MOU.pdfWorldTradeOrganization(WTO).n.d.TradeFacilitationAgreementFacility:RatificationsList.https://www.tfafacility.org/notifications-ratifications/ratifications-list#:~:text=The%20Trade%20Facilitation%20Agreement%20entered,their%20acceptance%20of%20this%20Protocol____.2014.ProtocolAmendingtheMarrakeshAgreementEstablishingtheWorldTradeOrganization.WT/L/940,28November.https://docs.wto.org/dol2fe/Pages/SS/directdoc.aspx?filename=q:/WT/L/940.pdf&Open=True

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