DEPARTMENT OF POLITICAL SCIENCE

Master’s Thesis: 30 higher education credits

Programme: Master’s Programme in International Administration and Global Governance

Date: 2018-05-22

Supervisor: Kohei Suzuki

Words: 10 207

MOBILE MONEY AS A FINANCIAL INCLUSION TOOL FOR POVERTY REDUCTION A cross-country analysis of low- and middle-income countries

Emma Grimme Hallberg

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ABSTRACT

This thesis aims to examine if mobile money can act as a financial inclusion tool to

reducepovertylevelsinlow-andmiddle-incomecountries.Incontrasttothemajorityof

the literature within its research field, this thesis observes the relationship on an

aggregatecross-countrylevelinsteadofusingdataonanindividualorhouseholdlevel,

inordertofullyexaminetheimpactsonnationalpovertylevels.Theresearchquestion

is analyzed through theory about how financial market imperfections hinder

investmentsforthepoorandconsequentlycomplicatetheircapitalaccumulation,which

isnecessaryforpovertyreduction.Furthermoredoesthestudybuildontheoryofhow

technical innovations in the private sector can produce new financial services that in

turn enables financial inclusion, leading to a reduction in poverty levels. The overall

resultsdonot findsupport formyhypothesisofreducedpoverty levelsasaneffectof

mobilemoney,suggestingthatfurtherresearchisrequired.

Keywords:mobile-money,financialinclusion,poverty,technology,cross-country

analysis,low-incomecountries,middle-incomecountries

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Acknowledgement

IwouldliketoexpressmygreatestgratitudetowardsmysupervisorKoheiSuzukiwho

helpedmewithguidance,expertiseandencouragementalongtheway.

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INDEX

1.1.Introductionp.4

2.Literaturereviewandtheoreticalframeworkp.8

2.1.Clarificationofconcepts–mobilemoneyp.8

2.2.Previousresearchp.8

2.2.1.Previousliteratureonmobilemoneyp.8

2.2.2.Researchontechnologyfordevelopmentandfinancialinclusionp.12

2.2.3.Researchgapp.15

2.3.Theoreticalframeworkp.16

3.Researchdesignp.19

3.1.Datap.20

3.1.1.Financialinclusionandpovertylevelsp.20

3.1.2Complementarydatasourcesforcontrolvariablesp.21

3.2.Operationalizationandvariablesp.22

3.2.1.Dependentvariablep.22

3.2.2.Independentvariablesp.23

3.2.2.Controlvariablesp.23

3.3.1.Descriptivestatisticsp.26

3.4.Methodp.27

4.1.Resultsp.28

4.1.RobustRegressionResultsp.28

4.2.Futureresearchp.32

5.Conclusionp.33

6.Referencesp.35

7.Appendixp.39

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1.1Introduction

More than 2 billion people of the world’s population are today living excluded from

financial services (GPFI, 2016). That is almost half of theworld’s population that are

livingwithoutaccesstoabankaccountformoneydeposits,creditordebitcards,loans,

formalsavingsandinsurance,simplybecausetheyaretoopoortobeconsideredvalid

customersfortheformalbankingsector.Beingexcludedfrombasicfinancialservicesis

howevermorethanasimplematterofinconvenience,andhasconsequencesonbotha

micro-level forpeople’spossibilities toescapepoverty, andonamacro level affecting

economicandsocietaldevelopment(GPFI,2016;UNCTAD2014).Asaresult,theroleof

financial inclusion has over the last decade gained increasing attention on the

internationalagenda,andistodayconsideredakeyfactorfordevelopmentandpoverty

reduction,anchoredasanenablerfor7outof17oftheSustainableDevelopmentGoals

(ibid).

While there are multiple elements required for attaining financial inclusion, many

experts specifically stress the role of innovative businessmodels andnew technology

(GPFI,2016).Mobilephonesisonetechnologythatbymostmaynotbeconsideredasa

new technology, but new or not, is the mobile phone a technology that currently is

revolutionizingdevelopingcountriesasatoolinthefightagainstpoverty(GPFI,2016).

Yet, despite this increased attention for financial inclusion andmobilemoney, is still

most of the research focusing on effects ofmobilemoney on an individual household

level, rather than national poverty levels. There is to the best of my knowledge few

cross-national- studies examining the effect on poverty levels, and instead are the

studies that have been made, either observing household income or savings,

alternativelyonacountryleveloftenestimatingeffectsoneconomicgrowth,notliving

standards as poverty. This study therefore aims to contribute to the research on the

effectsofmobilemoneyonpovertylevelsthroughdoingacross-countrystudyonlow-

and middle-income countries. Through an OLS regression analysis the thesis

furthermoreaimstoanswertheresearchquestion‘Doestheuseofmobilemoneyservices

reducepovertylevelsinlowandmiddleincomecountries?’.Thefindingsdidhowevernot

managetofindanysupportformyhypothesisofadecreaseinpovertylevelsasaneffect

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of mobile money, suggesting that further research is necessary. Especially given the

timelydiscourseoffinancialinclusionandmobilephonesontheinternationalagenda.

In thedevelopmentdiscourse, themobilephone isarecognized tool in theway it can

transportaccess,toservicesandinformation,thatotherwisewouldremaininaccessible

for themarginalizedgroupsof society (Beck et al, 2015;Obijiofor, 2015;MercyCorps,

2017; Unwin, 2017). The fact that technology has the ability to transport services to

remoteplaces, reachingotherwiseexcludedgroups, is something thatparticularlyhas

becometrueinthecaseofmobilemoney.Mobilemoneyserviceswerefirstintroduceda

littlemore than ten years ago in SouthAfrica and thePhilippines, andhas since then

growntobeofferedin93countriesbymorethan270differentcompanies(Suri&Jack,

2016).With theKenyan success storyofmobilemoney companyM-Pesa showing the

worldhowmobilemoneycanopenupaccesstofinancialservicesforunbankedpeople,

andprovideopportunitiesforfacilitatingeveryday-bankingsolutionsalsoforthosewho

don’thaveabankaccount(Mbiti&Neil,2011;Suri&Jack,2016),theinterestforwhat

mobilemoneycanbringhasnotyetseenitspeak.

Holdingabankaccount isaccording to theWorldBank(2018)apotentialgateway to

other financial services such as savings, credits and insurance, and a mobile money

account, could be the stepping-stone for unbanked marginalized people into formal

banking services (Suri& Jack, 2016). Given the outburst ofmobilemoney services in

manydevelopingcountries,companiesaretodayofferingmoreservicesthantransfers,

payments and storage, such as savings, loans and insurance to its mobile money

customers(Suri&Jack,2016),andtheseservicesareessentialforliftingpeopleoutof

poverty(WorldBank,2018).Savingsforexample,areimportantforpeopletobeableto

dealwithunpredictedexpensesincaseofsickness,unemploymentorabadharvestyear

fortheself-employedfarmer.Usingamobilemoneyaccountanditsservicesimproves

householdcashflowvariationmanagementaswellasconsumptionsmoothing(Cullet

al, 2014), and has been suggested by several studies that it furthermore increases

householdsavings(Jack&Suri,2011).Culletal(2014)furthermorefindthatitforthe

poorest households without access to saving mechanisms, is more difficult to resist

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immediate spending temptations than for those with available saving instruments,

emphasizing the importance of formal saving services. Also loans and credits are

importantfacilities,thatfortheunbanked,arehardtogetapprovedwithoutanaccount

or credit history, leaving people not being granted the loan that eitherwould finance

theirchildren’sschoolingor the loanneededtostartupanewbusinessor invest ina

new cow for their farm. With the agriculture sector occupying a grand majority of

people in developing countries (ILO, 2013), insurance is furthermore another central

financialservicetoe.g.secureincomeduringbadcropyears.Inotherwordsaresavings

andincomenecessaryforpeopletoescapepoverty,andgiventheservicesthatmobile

money companies’ today offer, mobile money provides people with opportunities to

changetheirsavingsandincomes.

Financial inclusionthroughtechnologyandnewbusinessmodels likemobilemoneyis

thereforekeyforsustainabledevelopment(UNCTAD,2014).Severalstudieshavefound

supporting evidence for the positive impact of mobile money on saving habits,

household income and investments (Morawczynski & Pickens, 2009). Althoughmany

scholars have examined the impacts of mobile money, the studies are as previously

mentioned mainly focusing on economic effects on a household, or individual level

(Morawczynski&Pickens,2009;Mbiti&Neil,2011;Mbogo,2010),ratherthanpoverty

at a country level. Furthermore have previous research often been case studies on

specific countries, where particularly African countries have been the most present

(Mbiti & Neil, 2011; Mbogo, 2010; Must & Ludewig, 2010). Mobile money has been

aroundforroughlyadecadebynowandithasbecauseofthat,perhapsbeendifficultto

atearlierstagesexamineitsaffectsonpovertylevel,fortime-logreasons.Partlycanthe

scarcityofstudiesonmobilemoneyandpovertylevelsalsobeexplainedbythelackof

datainitsfield,somethingthattheGlobalFinancialIndexreleasedbytheWorldBankin

2017 seeks to address. This thesis aims therefore as mentioned to contribute to the

research fieldbyusing theGlobal Findexdata anddataonpoverty levels for a cross-

country data analysis of the effects of mobile money on poverty levels in low and

middle-incomecountries.

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The thesis will proceed as follows. Section 1 will encompass a literature review of

previousresearchfollowedbythetheoreticalframework.Insection2,themethodology,

data and variables will be explained, followed by a presentation and analysis of the

regressionresultsinsection4.Futureresearchwillfurthermorebeapartofsection4,

andfinallywillaconclusionofthisthesisbepresentedinsection5.

2.Literaturereviewandtheoreticalframework

Thissectionpresentsaclarificationofmobilemoney,aliteraturereviewinthefieldof

mobilemoneyandfinancialinclusionforpovertyreduction,includingachapteronthe

researchgap.Finallywillatheoreticalframeworkbepresented.

2.1.Clarificationofconcepts–mobilemoney

Mobilemoney refers to the servicewhere people can send, store and receivemoney

throughSMStextmessageservicesprovidedbyamobileoperator.Theserviceenables

people to exchange cash for “e-float” that then can be used to either pay bills or

transferredtoafriend’sorfamilymember’smobilemoneyaccount(Mbiti&Weil,2011).

Storedorreceivedmoneyinamobilemoneyaccountcanbewithdrawnasregularcash

at theusers convenience.Mobilemoneydoesnot requireone toholdabankaccount,

andisnotconnectedtoaformalbankaccountinanyway,asusersinsteadeithercharge

a refill-card with e-float, or exchange cash for e-float stored at their specific mobile

moneyaccount,dependingontheserviceprovider.

2.2.Previousresearch

2.2.1.Previousliteratureonmobilemoney

Intermsofpreviousliteratureontheeffectsofmobilemoney,M-Pesaisamobilemoney

servicethatparticularlyre-appears instudiesofdifferentkinds,and issomethingthat

accordingly shapes the literature in this research field. As within the development

discourse about mobile money, the Kenyan mobile payment service M-Pesa without

doubtisthemostacclaimedandmostdiscussedcaseinitsfield,thereareconsequently

numerousstudieson theeffectsof thisM-Pesaservice.Tenyearsafter itscommercial

release in2007,more than20millionusersweresubscribed to theM-Pesa service in

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Kenya forbothbusinessandperson-to-personpurposes,makingupalmosthalfof the

country’spopulation(Monks,2017).AlthoughM-Pesaisfarfromtheonlymobilemoney

service,with93differentcountriesoffering271differentmobilemoneyservicesatthe

endof2015(Suri&Jack,2016),thesuccessstoryofM-Pesaanditsinternationalacclaim

hasliteraturewise,resultedinplentyofcasestudiesonshort-termeconomicimpactsof

M-Pesa in Kenya at an individual level. These studies typically examine effects on

household income, capitalaccumulation, savingsandcredits, inaKenyancontext, and

have resulted inmany positive results (Morawczynski & Pickens, 2009;Mbiti & Neil,

2011; Suri & Jack, 2016). One study about customer usage and impacts made by

Morawczynski&Pickens(2009)revealsthatruralcitizens’incomesincreasedbyupto

30%afterstartingtouseM-Pesa.FurthermoredidusersstarttointegrateM-Pesainto

their saving portfolios, which in turn changed saving patterns by increasing the

frequency of money deposits with different saving mechanisms, often in order to

accumulate money for investments in rural homes such as e.g. buying a cow

(Morawczynski & Pickens, 2009). The increased number of deposits disclosed also a

demandformoresavingproducts(ibid)forthe ‘bottomofthetriangle’ thatotherwise

hastorelyoninformalsavingmechanisms.

Research on M-Pesa has furthermore found effects for the number of businesses

(Mbogo,2010)andatransitionfrominformaltoformalbanking(Mbiti&Neil,2011).In

one study on impacts of M-Pesa, Mbogo (2010) finds that increased usage of mobile

moneyhasapositive impactonthegrowthofmicro-businesses,whereoneof thekey

ingredients is the convenienceandaccessibilitymobilemoneyoffers.This remainsan

interesting finding as it highlights the role for innovative services reaching otherwise

financially excluded for societal development and growth, in this case reflected in

increasedmicro-businessesandi.e.alsoself-employment.Asimilarstudyconductedby

Mbiti&Neil (2011)examines theeconomic impactsofM-Pesa inKenyaandprovides

evidence,inlinewiththefindingsofMorawczynski&Pickens(2009),forhowincreased

usage of mobile money such as M-Pesa, lowers people’s usage of informal saving

mechanisms and increases formal banking probability, with M-PESA serving as a

steppingstoneintoformalbanking(Suri&Jack,2016).Inaddition,theresearchersfind

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thatmobilemoney andM-PESA improves “individual outcomes by promoting banking

andincreasedtransfers”(Mbiti&Neil,2011).

The economic impacts ofM-Pesa are furthermorediscussed in a studyby Jack& Suri

(2011),wheretheyreviewthepotentialeconomic impacts,onceagain,atahousehold

level.ThroughM-Pesa’sfacilitationofmoneytransferandsafestorage,theyarguethatit

also facilitates trade in theway itmakes iteasierandsafer tobuyandsellgoodsand

services. This is displayed through the example of paying electricity bills that for the

mobilemoneyusercansavealotoftimenothavingtowalkseveralkilometerstomake

thepayment,aswellasremovingthesafetyriskofcarryingthemoneyonhandwhile

goingtothepaymentstation(ibid).Thesafetyaspectcansecondlyincreasehousehold

savings (ibid), which also was confirmed in the studies by Morawczynski & Pickens

(2009)andMbiti&Neil(2011).AnothereffectonhouseholdleveldiscussedbyJack&

Sury (2011) is howmobile money services like M-Pesa that makes “transfers across

largedistancestriviallycheap”canboost investmentsandallocationofhuman,aswell

as physical capital. The authors argue that when transfers are cheap, fast and easy,

householdsmaybemorewillingtosendfamilymembersto“high-payingjobsindistant

locations(e.g.,thecapital),andtoinvestinskillsthatarelikelytoearnareturninsuch

places”outsideofhome(ibid).

Whilethereismuchevidenceontheeffectsofmobilemoneyinashort-termeconomic

perspective,suchasallocationofconsumptionsmoothingovertimebecauseofchanged

savinghabits(Morawczynski&Pickens,2009;Mbiti&Neil,2011;Jack&Suri,2011)and

increasedhousehold incomes (Morawczynski&Pickens,2009), theeffectsonpoverty

levelsremainshoweverlessstudied.Jack&Suri(2016)triestoaddressthisinastudy

wheretheyexamineifmobilemoneyservicescanincreaseconsumptionandliftpeople

out of poverty. Their results were positive and suggested that especially for female-

headedhouseholds,accesstomobilemoneyreducespoverty,providingfiguresforhow

194,000Kenyanhouseholdsintheirstudyhavebeenliftedoutofpoverty(Jack&Suri,

2016). Consistent with the authors’ previous works, do these findings furthermore

implythatreducedpovertyasaneffectofmobilemoneyservices,“derivefromamore

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efficientallocationoflabour,savingsandrisk”(ibid).Althoughnotonlyfemale-headed

householdswereexaminedinthestudy,weretheauthorsunabletofindanystatistically

significantestimatesformale-headedhouseholds(ibid).

AlsoMust & Ludewig (2010) argue for howmobilemoney can be used as a tool for

poverty reduction “by increasing savings rates, creating jobs, and increasingaccess to

financialproductsofferedbymicrofinanceinstitutions”intheirpolicypaperaboutcell

phone banking in developing countries. Linked to evidence supporting how financial

inclusionandaccesstofinancialserviceshasprovenveryefficientforpovertyreduction,

theauthorsexemplifyhowaccess to financialserviceshave ine.g.astudyonEthiopia

reduced five out of seventeen poverty determinants (Must & Ludewig, 2010). They

continuebydescribinghowmobilemoneycanbeusedtopurchasemicro-insurancefor

businesses(inKenyaforinstancecanfarmerspurchasecropinsurancethroughmobile

moneyandsolar-poweredweatherstations)anddecreasethecostofsavingsintheway

peoplecanstorewealthintheirmobilemoneyaccount.“Savingsisthecomplementto

credit;bothenablepeopletoaccumulatecapitalandsmooththeirconsumptionduring

times of need such as unemployment or drought.With credit, people acquire a lump

sumupfrontandthenpay itoffovertime.Withsavings, theyaccumulatecapitalover

timeinordertobuildalumpsum.Amobilemoneyaccountcanserveasaninexpensive

risk-freemeansofstoringwealth,analternativetostoringitintheformoflivestockor

ascashhiddeninthehome”(ibid).

Studies have also shown positive results ofmobilemoney on remittances,which is a

formof income thatmostmarginalizedpeopleare relyingon to someextent (Mbiti&

Neil, 2011). These are transfers that family members and relatives send from either

abroadorotherregionsinthehomecountry,suchasusuallyurbanareaswherethere

aremore jobs. As research has suggested,mobilemoney increases transfers between

peopleatalowercost(Mbiti&Neil,2011).Mobilemoneyhasthereforecontributedto

higher incomes through increased number or remittances (Jack & Suri, 2011), as it

becomes easier, cheaper and faster, enabling, often rural households, to fast receive

moneyfordifferentreasons(ibid).

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Anotherrelatedfieldscholarshavestudiedishowmobilephoneswithmobilemoneyas

one part of the ‘mobile phone concept’, is impacting poverty levels and livelihoods.

Somethingthatoftenisdonefromanaccesstoinformationperspective(Sifeetal2010).

InastudyestimatingtheeffectofmobilephonesonpovertyreductioninruralTanzania,

Sife et al (2010) found evidence formobile phones contributing to poverty reduction

andimprovedrurallivelihoods.Theauthorsexplainedtheirfindingsthroughexpansion

and strengthening of social networks (consistent with Jack & Suri, 2011), “increase

people’sabilitytodealwithemergencies;cutdowntravelcosts;maximizetheoutcomes

of necessary journeys; increase temporal accessibility; and amplify efficiency of

activities”(Sifeetal,2010).

AlthoughmanystudiesarefocusingontheAfricancontextaretherealsoothercountries

wheremobilemoneyimpactshavebeenexamined. Inastudyonmobilemoneyinthe

PhilippinesmadebyCGAP,GSMAandMcKinsey in2009, findingsshowedthat52%of

thesurveyrespondentsrequestedmoresavingservicesfromthemobilemoneyservices

(Pickens, 2009), which ones again mirrors previous suggestions on how mobile

paymentscanleadtoincreasedfinancial-deepening.Toholdanaccountwithabank,or

another kind of formal financial institution, is considered to be a gateway intomore

financial services (World Bank, 2018). With mobile money as a an alternative, less

expensivewayofsendingmoneyforpeoplewithoutaformalbankaccountthattheycan

send and receive money to, a mobile money account like M-Pesa, or refill-card, is

claimed by scholars to therefore be analyzed as an ‘alternative gateway’ to financial

services for financial inclusion, corresponding with the findings of saving behaviours

madebyMorawczynski&Pickens(2009),Mbiti&Neil,(2011)andJack&Suri(2011).

2.2.2.Researchontechnologyfordevelopmentandfinancialinclusion

Similarly to theamountofresearchoneffectsofM-Pesa, is thereaplentifulofstudies

examining different effects of technology for development. The concept of how

technologyanddigitalizationhavetheabilitytotransportservicestoremoteplacesand

marginalized groups, that without technology are unable to access these services or

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facilitiesneededfordevelopment,isforinstanceclaimedbymanyscholars(Becketal,

2015;Obijiofor,2015;MercyCorps,2017;Unwin,2017).Consequently,povertyreducing

impactsof technology ingeneral, isaverywellstudiedareawheremanystudieshave

been made on the positives technology can bring for development and poverty (e.g.

Dutton2013;CantoniandDanowski2015;Hannawithsummer2015;MansellandAng

2015,Proenza2015).

One of the main findings from previous literature is how technology creates new

opportunitiesforpeople.Becketal(2015)describesthetechnologyofmobilebanking

and innovative institutions as ‘one mechanism for closing the development gaps by

creating opportunities for access to finance by underserved regions and populations

segments’. Positive impacts of reduced transaction costs and increased reach on

development can furthermore be exemplified by the way it ‘can enable new business

modelstoaddressotherdevelopmentpriorities’(Culletal,2014),somethingthatthusin

turncanhelpcombatingpovertyinseveralways.AgoodexampleofthisishowM-PESA

that now reaches 80% of the population in Kenya (ibid), has led to “second-wave

innovationsthatusetheinfrastructureofM-PESA”likeM-KOPA(ibid).Inaninterview

withM-Pesa co-founderNickHughes, he describes how themobilemoney serviceM-

Pesa has open up ways for new business models like M-Kopa, building on customer

needsthroughsmarttechnology.“Morethan700millionslackaccesstoreliablepower

andspendmoneyonpoorqualityfueldependingontheirlimitedavailablecash.Mobile

money allows us to offer a new deliverymodel.Weproduce good quality solar energy

systems that are connected to themobilenetworkusingGSM technologyand thatwe

canmonitorremotely.Wethenallowcustomerstobuyunitsofcreditforaspecificsolar

devicebymakingsmallpaymentstous”.

Impactsoftechnologicalnewdeliverymodelsisfurthermoreexaminedinastudymade

by Diniz et al (2011) on the triggers and barriers for financial inclusion, where the

authorsevaluatedtheroleoftechnologyforfinancialinclusionintheuseofbranchless

banking, throughdigital information&communication technology (ICT) inBrazil.The

resultsshowedapositiveimpactonlocalsocio-economicdevelopment,withtheoverall

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conclusionthataccesstofinancialservicesbroughtbytechnologyisfundamentalforthe

low-income population to develop, but should also be accompanied by financial

educationinordertobesuccessful(ibid).

Although there are many positive views and studies on the effects of technology for

development, there are some critical arguments as well. Avergou (2010) argues that

despite claims that new technologies can improve the socioeconomic conditions of

peopleindevelopingcountries,itisoftenthecasethatthedevelopmentpotentialofnew

technologies is overrated. A problem that in turn, hinders wider constructive use of

technology is the absence of an enabling environment that promotes awareness,

understanding and acceptance of the values of new technologies (Obijiofor, 2015).

Furthermore argues Obijiofor (2015) for how there needs to be a larger focus on

regulatoryaspectsoftechnology,makingthelong-termfinanceagendaanextensiveone

for researchers and policy makers. Financial innovation in terms of new risk

managementtoolsandnewmarketsalsorequirenewinstitutionalarrangements(Beck

etal,2015).

A majority of the research on technology in general for development is furthermore

examinedthroughtheperspectiveofeconomicgrowth.Whilemanystudieshavebeen

made on the positives technology can bring for development in terms of economic

growth (e.g. Dutton 2013; Cantoni and Danowski 2015; Hanna with summer 2015;

Mansell and Ang 2015, Proenza 2015), few of them seem to, as mentioned by other

scholars (Avergou, 2010), address a critical approach towards the real effect of

technologyondevelopment(Unwin,2017).Unwin(2017)providesthereforemoreofa

criticalapproachwhenheinhisbookarguesthattechnologyhasbeenwaytoopraised

in thework fordevelopmentwithoneof themainproblemsbeing that technology in

itself cannot be viewed as a “silver bullet to fight poverty”. Many development

practitionershavehoweveralsobeenskepticalofthevalueoftechnologyindelivering

on the basic needs of poor people (Unwin, 2017). “A frequent refrain from such

practitioners is that poor andmarginalizedpeople need such things as security, food,

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waterclothingandshelterbeforetheycanthinkrealisticallyaboutthepotentialbenefits

oftechnology”(ibid).

Inadifferentstudyyetalsopresentingsomecritiqueabouttheeffectoftechnologyon

development,Toayama(2015)arguesforhowtherealsoneedstobemoreof“apeople-

centricviewofsocialchangetodeliverondevelopmentgoals”.Somethingthatisinline

withUnwin’s(2017)reasoningabouthowhefindsitproblematicthatmosttechnology,

according tohim, isproduced for thepoorandnotwith them.Unwin (2017) stresses

that the poor andmarginalizedmust be both stakeholders and partners in order for

technological innovations to fully improve livelihoodsandpoverty,notonlyusersand

customers. Particularly since technology never is neutral, “but always closely linked

withculture,societyandgovernmentpolicies”(Green2001,Lanier2011).

2.2.3.Researchgap

In the previous literature on impacts ofmobilemoney for poverty reduction, a large

focus has been on the short-term economic effects on individual household levels

(Morawczynski&Pickens,2009;Mbiti&Neil,2011;Mbogo,2010)ratherthanexploring

aggregate poverty reducing effects. There is to the best of my knowledge few cross-

national-studiesexaminingtheeffectonpovertylevels,andinsteadarethestudiesthat

have beenmade on a country level often estimating effects on economic growth, not

livingstandardsaspoverty.Moreoveraremanyofthestudiesontechnologieseffectson

development and poverty levels estimated through focusing on technology ormobile

phones in general (Dutton 2013; Cantoni and Danowski 2015; Hanna with summer

2015; Mansell and Ang 2015, Proenza 2015), rather than specifically mobile money

combined. Although there are studies estimating the impacts of mobile money on

poverty levels (Suri & Jack, 2016), these are often case studies on specific countries

(especially African countries where Kenya is extremely occurring) instead of cross-

countryanalysis.Thisthesisthereforeaimstocontributetotheresearchfieldofmobile

money with the main contribution being its cross-country perspective on national

povertylevels,ratherthanshort-termeffectsonanindividualleveloraspecificcountry.

Therehasasmentionedbeen fewcross-national-studies to thebestofmyknowledge

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andthisthesisthereforecontributestoresearchonmobilemoneyandpovertylevelsat

anaggregatelevel.

2.3.Theoreticalframework

The following section will present the theoretical framework used to explain the

theoreticalargumentsbehindhowmobilemoneycanimpactandreducepovertylevels.

Myargumentswillbuildonmoderndevelopmenttheoriesofaccesstofinance,financial

marketfrictionandcapitalaccumulationcombinedwithtechnicalinnovationprinciple.

This section will conclude with providing a hypothesis for the thereafter-succeeding

regressionoutputs.

ThefirstargumentformytheoreticalframeworkassumesinlinewiththeWorldBank

(2018) andMorawczynski & Pickens (2009) that capital accumulation for individuals

andhouseholdsareacrucialpartofpovertyreduction.Thisstatementbuildsontheory

that through different financial services, such asmobile account holding or payments,

credit, insurance and savings, people are supplied with opportunities to accumulate

capital(Morawczynski&Pickens,2009;Must&Ludewig2010;Mbiti&Neil,2011;Jack

&Suri,2011).Isupporttheideathathasbeensuggestedbymanyscholars(Suri&Jack,

2016;World Bank, 2018) that holding a bank account can serve as gateway to other

financial services, and that a mobile bank account is one form of bank account. I

furthermorebuildontheassumptioninlinewith(Morawczynski&Pickens,2009;Suri

& Jack,2011&2016), that financial servicessuchase.g. credits canprovide loans for

educationorentrepreneurship,savingscanbeusedforunexpectedexpenses,anddigital

payments plus storage can smooth consumption. That is to say that financial services

createopportunitiesforinvestmentstoimproveone’slivingconditions.Becauseofthis,

accumulation of capital is necessary for the poor to be able to invest in their human

capital, i.e. getting education, or invest in physical capital, which could be income

generatingactivitiessuchasfamilyfarmingorentrepreneurialbusinessoperationsofa

differentkind. Iwill thereforeargue for thatusing financialservices,mobilemoney in

thiscase,canfacilitatecapitalaccumulationforthemarginalizedandthereforereducing

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thepercentageofpeoplewhoarelivingunder1,90USDperday,consequentlyreducing

poverty.

Mysecondbuildingblockconcernsthevitalneedforincreasedfinancialaccessibilityasa

meansforpovertyreduction.Thissuggeststhatwhiletheuseoffinancialservicescanbe

consideredtoolsforcapitalaccumulationandfinancialmanagement,theseservicesare

yet,asstressedbymanyresearches(Dinizetal,2011)inmanylow-andmiddle-income

countriesonlyavailabletothealreadyrichelite.Financialexclusion is inmostcasesa

consequenceofwealthandincomeinequalities(Demirguc-Kuntetal,2008;WorldBank,

2018),whereonlythosewhoalreadyarerichareentitledtofinancialservicesaccording

tofinancialserviceproviders.Ithereforestressthatlackofaccesstofinancialmarkets

eliminatestheopportunitiestoaccumulatecapital.Initiativesforfinancialinclusionand

providing access to finance are consequently one important part of enabling capital

accumulationandinthepuzzlefordevelopment.

Thirdly, my theoretical reasoning incorporates theory about financial market friction

andhowmarket friction isonereasonforthe lackofaccessto financialmarkets forthe

marginalized,besidesmodelsofincomeinequalities.IheresupporttheWorldBankand

researchers reasoning on how financial market friction can cause poverty traps and

income inequalities (Demirguc-Kunt et al, 2008).Market frictions refer to transaction

costs and market imperfections that hinder the exchange of, in this case, financial

services or goods. Here, theoreticians’ stress how “financial market imperfections

determine the extent towhich thepoor canborrow to invest in schoolingorphysical

capital” (Demirguc-Kunt et al, 2008). Themarket imperfections represent in this case

the lack of information and transaction costs that lead to poor people being denied

accesstofinancialservices.

InabookwrittenbyGalorandZeira(1993)theauthorsdevelopamodeldescribinghow

“because of financial market frictions, poor people cannot invest in their education

despitetheirhighmarginalproductivityofinvestment”.Ahighmarginalproductivityof

investments refers to an individual’s incline tomake an, in this case, investment. The

18

modelassumesthatonlypeoplewhoinheritmorethanthecostofeducationwillfindit

economicallyprofitabletoinvestineducation,whilethosewhoaremoremarginalized

to begin with, will due tomarket friction find it too expensive and therefore remain

uneducatedandpoor,affectinglong-terminequality(Galor&Zeira,1993).InmycaseI

complywiththeirreasoningandalsoemphasizethat,asthepoorremainpoorbecause

they initially arepoor, their exclusion from formal financialmarketswill furthermore

continue todeny themaccess.Anadditionalassumption to this is furthermore that in

situationswherepeopleare forsomereasonrequiredto takea loan, thepoorhaveto

turntomoreexpensiveinformalalternatives,whichevenmightmakethemworseoffas

they once again are stuck one kind of poverty trap. Banarjee & Newman (1993)

developedasimilarmodelfromtheperspectiveofanindividual’soccupationalchoices,

whichstipulatesthatbecauseofmarketfrictionandimperfections,poorpeopleremain

wage earners in contrast to wealthier people who instead become entrepreneurs. In

other words do market friction and imperfections determine people’s occupational

situation,which in turndetermines theirpossibility to saveandcontrol risk, affecting

theirlivingconditions.Thesemodelsthusexemplifyhowlackoffinancialaccesscanbe

“the criticalmechanism for generating persistent income inequality or poverty traps”

(Demirguc-Kuntetal,2008),whichemphasizesthatmarketfrictionisonekeyobstacle

foraccessibility.

The following argument inmy frameworkwill suggest that theprivate sector plays a

significant role in providing financial accessibility and overcomingmarket friction. In

contrast to many researchers who treat market imperfections as given “and suggest

different redistributive policies to promote growth, focusing on schooling, saving or

fertility changes” (Demirguc-Kunt et al, 2008), I instead reason that there is amarket

gap in financial services for the marginalized that needs to be filled, which can be

addressedbytheprivatesectordevelopingnewservicesandproductsforthepoor.This

argumentcompliesmoreoverwiththeDemirguc-Kuntetal’s(2008)claimthatfinancial

marketimperfectionsneedtobeaddresseddirectly“throughputtingfinancialinclusion

initiatives at the frontline of the development agenda” (ibid), although froma private

sectorperspective. Inorderfortheprivatesectorto fill thismarketgap, I therefore in

19

linewith (Cull et al, 2011; GPFI, 2016) state that newbusinessmodels, products and

servicesneedtobedevelopedtoachieveaccessandescapepotentialtraps.

Finally,willmylastargumentbelinkedtothepreviousreasoningfortheprivatesector,

and the need to develop new business models and new services. In the light of e.g.

Solow’s growth model (Solow, 1956) and researchers within Information

Communication Technology (ICT), do I argue for technology as a necessary tool for

improved financial access and poverty reduction. With the power of technology can

informationandservicesbetransportedtoremoteplacesandthusreachnewusersand

actors that otherwise because of distances for instance. The final assumption formy

theoretical framework is thus that technology is the required tool for overcoming

obstacles hindering capital accumulation, financial exclusion and in-human living

standards,i.e.poverty.

In short, will I through theory about how financial market imperfections hinder

investments for the poor and consequently complicate their, for poverty reducing

necessary capital accumulation, argue for how technical innovations in the financial

sector can serve as a tool for financial inclusion with the result of reducing poverty

levels.Accordingly,thefollowinghypothesiscanbeassumed:

HI:Throughusageofmobilemoneyasatoolforfinancialinclusion,povertylevelscanbe

reduced

3.ResearchDesign

In thissectiontheresearchdesignwillbepresentedstartingwithadescriptionof the

data and its sources. Secondly, an explanation of the dependent variable will be

presented followed by one of the independent variables. Subsequently, the control

variableswillbepresented, followedbydescriptive statistics.Finallya chapteron the

methodwillconcludethissection.

20

3.1Data

In this section will the data used for the study be presented. The data originates

primarily from theWorld Bank but is in addition complemented by the Bertelsmann

StiftungtandtheHeritageFoundation.Belowwillathoroughdescriptionofthedataand

itsvariablesbepresented.

3.1.1.Financialinclusionandpovertylevels

Fordataonmobilemoneyandfinancialinclusion,thedatasetGlobalFinancialInclusion

hasbeencollectedfromtheWorldBank,whichwasgatheredinapartnershipwiththe

GallupWorldPoll(WorldBank,2017).TheGlobalFinancialInclusionsetwasreleasedin

2017,andspansfrom2011to2014dependingontheindicator(WorldBank,2017).The

datahasbeencollectedthroughinterviewswithmorethan150,000peopleofdifferent

nationalities,with randomly selectedadults fromage15 (ibid). It entails information

about theoutspreadof services suchasmobilepaymentsanddebitor credit cards to

someextent,butprimarilyfocusesonpatternsoftherespondents’habitudesforusing

financialservicesandoffersthusinformationabout“howindividualssave,borrow,make

payments,andmanagerisks”(ibid).Habitsareencounteredthroughiftherespondentin

theneartimehasforinstancesaved,takenaloanorinsurance,andhowpaymentshave

been made – be electronically, through mobile transfers or with cash. Prior to this

releasehastherebeenanextensivelackofdataonuserhabitsoffinancialservicesand

the data is since its release the world’s most “comprehensive database on financial

inclusion”(WorldBank,2017).

Thefulldataset includesdatafrom220differentcountriesandregionswhere70have

beenselectedforthisstudyastheyofferdataontherelevantindicatorsandcorrespond

withtheavailabledataonpovertylevels.Inordertoanalyzethephenomenaofmobile

money’s role for financial exclusion,only lowandmiddle-incomecountrieshavebeen

included from the data. A low-income country is according to the World Bank Atlas

method a countrywith a GNI per capita of less than 1,005USD and amiddle-income

countryrangingbetween1,006-12,235USD(WorldBankGroup,2018).Thelow-income

countries for2014were13,while themiddle-incomecountrieswere57whenputting

21

lowerandupper-middle-incometogether.Theoverallnumberofcountriesforthisstudy

isconsequently70andalistofallincludedcountriesforthisstudycanbefoundin7in

theappendix.

In addition to this data has also the dataset World Development Indicators been

downloadedfromtheWorldBank, inordertoobtaindataonpoverty levelsaswellas

for some control variables. This dataset is the World Bank’s primary dataset on

development, “and compiled from officially-recognized international sources” (World

Bank, 2018). It “presents the most current and accurate global development data

available, and includesnational, regional and global estimates” (ibid). The full dataset

containsdatafrom217differenteconomiesandoffers,dependingontheindicator,data

from1960-2017.Thefirstversionofthedatasetwasreleasedin2010butisupdatedon

aquarterlybasis,withthelastupdatemadein2018.

3.1.2Complementarydatasourcesforcontrolvariables

To be able to include some of the control variables I had to gather data from other

sources than theWorldBank. I thereforeuseddata for twoof thevariables fromThe

Bertelsmann Stiftung’s Transformation Index, which is gathered by the Bertelsmann

Stiftung. The complete dataset contains variables on quality of democracy, market

economy and political management in 129 transition and developing countries to

analyzeandevaluatepoliciesandpoliticalsteering(BertelsmannStiftung,2012).Both

cross-sectionalandtimeseriesdataisavailable,althoughonlythetimeseriesdata(TS)

has been selected for this study to match the variables from the World Bank data,

making country year the unit of analysis. Data is available on every other year from

2006until 2018, outofwhich the2012data is selected to correspondwith theother

controlvariablesfromtheWorldBankandHeritageFoundation.Thedecisiontoselect

data from2012 isbecausea fewof thecountries for thepoverty leveldataonlywere

available for2012, and I thereforenaturallydidnotwant to include control variables

from after-following years in regard to this. The data is collected through surveys

conductedineachreportedcountryandover300professionalacademicsarepartofthe

22

workfortheso-called ‘BTI-project’,BertelsmannTransformationIndex, (Bertelsmann

Stiftung,2012).

InadditiontotheBertelsmannStiftung’sTransformationIndexIalsouseddatafromthe

American think tank the Heritage Foundation’s Index of Economic Freedom for one

controlvariable.TheIndexofEconomicFreedomdatasetcontains12differentfreedom

variables related to economic and financial freedom on an aggregate level (Heritage

Foundation,2018).Thefulldatasetoffersdataon186countries,forhigh-,middle-and

low-income countries (ibid), out ofwhich70 of the low andmiddle-income countries

were selected tomatch theotherdata formystudy.Thedata is collectedyearly from

surveyinginthecountriesandhasbeencollectedforthelast20years(ibid).

3.2.Operationalizationandvariables

3.2.1.Dependentvariable

Tocapturetheeffectonpovertythedependentvariableusedforthisstudywillbe‘%of

povertygap’.ThisvariableisdownloadedfromtheWorldBankIndicatordatacatalogue

andmeasuresthepovertygapat1,90USDperdayadjustedby2011year’spurchasing

powerparity(PPP)(WorldBank,2018).Morespecificallydoesthevariablecapture“the

mean shortfall in income or consumption from the poverty line 1,90 dollars a day

(counting the non-poor as having zero shortfall), expressed as a percentage of the

povertyline”(ibid).Bymeasuringthepercentageofthepovertyline,i.e.gapinthiscase,

it enables us to reflect on the “depth of poverty” as well as its extent (QoG, 2018).

Furthermore,tolookatthepercentageofthepovertylineandnottheabsolutenumbers

of people living under the poverty line, I am able to observe the impact of the

phenomena, that it to say the independent variables, rather than what number of

individualsthatareaffected.Asthedatawasnotavailableforalllowandmiddleincome

countries the same year, 2014 is themain year but has been added data from 2012,

2013and2015toincludeasmanycountriesaspossible.

23

3.2.2.Independentvariables

Forthe independentvariables thedata isretrieved fromtheGlobalFinancial Inclusion

data,which is providedby theWorldBank in 2017. To observe the impact ofmobile

money technology, as it will be described in this thesis, there will be three different

independentvariablesusedfromthedatasetfollowedbyoneself-constructedbasedon

theotherthree.Thefirstonemeasuresmobilemoneypaymentsforgoodsorservicesin

the year of 2011, expressed as a percentage of the total populationwithin a country

fromtheageof15.Itcombinesthusthepercentageofpeopleusingamobilephonefor

paymentsandwillbenamedmobilepayments(1).Thesecondindependentvariablewill

bemobile transfers (2), measuring regular transfers made to another mobile money

user, also this onemeasured in percentage of thewhole population that in 2011 has

madeoneormoretransferstoe.g.arelative,neighbororfriend,usingamobilemoney

service.Thethirdvariable isthemobiletransferreceived (3)variable,whichexpresses

onlytransfersreceivedusingmobilemoney.Toonlyreceivetransfersaimstotargeta

maybe less active user of mobile money, that assumingly only uses it to receive

remittancesfromfamilymemberslivingandworkinginremoteareas.Thefirstvariable

ontheotherhandaimstotargetthemoreactivemobilemoneyuserthatobviouslyhas

takentheserviceastepfurtherandusesitforpaymentsofdifferentservicesandgoods.

My fourth and final independent variable is a combined term of using all three

previously mentioned mobile money services. This combined variable will be called

mobilemoney(4)andcapturesthe%ofapopulationthatusesallthreeoftheseservices.

Thefactthatall independentvariablesonlyreflecttheusagein2011isbecauseofthe

lackofdataonotheryears.

3.2.3.Controlvariables

Tocontrolforexternalexplanationsandpossiblesourcesofendogeneitytochangesin

thepovertygaps,differentcontrolvariablesareincludedatacountrylevel.Firstly,GDP

percapitawillbe includedsinceacountrywithahigherGDPpercapita isbelievedto

reflectahigherstandardofliving(Thebalance,2018)andisthereforelikelytoaffectthe

povertylevelsofthepeoplelivinginthatcountry.Thevariableisexpressedasanumber

and is downloaded from the World Bank. The observed year for the data is 2012.

24

Secondly, property rights will be added as studies have provided evidence for a

correlation between secured property rights and reduced poverty levels (Galiani &

Schargrodsky, 2010). Secured property, or land, rights is believed to increase

investmentsprimarilysinceitinaruralcontextsecuresownershipoflandandtheright

to exclude others from using it, which in turn boosts e.g. agricultural investments

(Goldstein & Udry, 2008). In an urban context, people are without secured property

rightsunwilling to leave theirhomes forwork,becauseof therisk that theirproperty

will be expropriated in their absence (Field, 2007). Property rights are therefore

importanttopreventpropertytheftandexpropriationbothbythestateandneighbours,

making it possible for people to leave their homes to engage in income generating

activities.Thevariableismeasuredasascaleofeachcountry’spropertyrightsranging

between 0 and 100, where 100 represents the “maximum degree of protection of

property rights” (Heritage Foundation, 2018). The original source is the Heritage

FoundationthatcollectedthisdatafortheirIndexofEconomicFreedom.Similartomy

firstcontrolvariableistheselectedyearofdata2012.

Moreover,istheintensityofconflictinacountryanotherfactorthatcanexplainpoverty

levels (Rohwerder, 2014; Justino/Justino & Verwimp 2010,2013; Kugler et al., 2013;

Nasser et al., 2014).Where there ismore conflict, people areunable to carryonwith

their everyday activities, such as e.g. going to work, as the conflict “damage

infrastructure, destroys assets, forcesdisplacement, increasesunemployment, reduces

spending on social services and cause death and injury to people” (Baddeley, 2011;

Addisonet al., 2010; Justino,2010;Nasseret al., JustinoandVerwimp,2013).Conflict

intensitywillthereforebecontrolledformeasuredonascalebetween1and10,where

10 represent the highest number of conflict. The word conflict does here encounter

social,ethnicandreligiousconflicts (BertelsmannStiftungt,2012).Thedatagenerates

from Bertelsmann Stiftungt that was collected for the Bertelsmann Transformation

Index in 2012. From the same source of data is also the subsequent control variable

social safety nets collected. A state with a stronger welfare regime that compensates

peopleforsocialriskswillbemoreabletomakearrangementsforitspeoplenothaving

toliveinpoverty(FoxetAl,2013),andhence,acontrolvariablewillbeincludedforthe

25

levelofsocialsafetynetsacountryhas,measuredonascalebetween1and10where10

represent the highest level of social safety nets. The variable is defined to assess

“whetherthereareavailablearrangementstocompensateforsocialrisks”(Bertelsmann

Stiftungt,2012).

Furthermore, does the level of unemployment represent opportunities to earn an

incomeandconsequentlyprovideforoneselfandone’sfamily.Inotherwordsisitlikely

toassumethatthelowerunemploymentthereisinacountry,thelowerthepossibility

thatpeopleare living inpoverty(Saunders,2002).Thevariableunemployment is thus

added and ismeasured as a percentage of the total labour forcewithin the observed

countrythatisunemployed.Thevariableusesdatafrom2012andoriginatesfromthe

World Bank. On the topic of opportunities is in addition to employment, education

anotherfactorthatcanaffectpovertylevels(Ferguson,Bovaird&Mueller,2007).Many

studies show that the higher level of educationpeople has, the lower risk of living in

poverty(ibid).Thevariablesecondaryeducationaimsthereforetocapturetheeffectof

gettingsecondaryschoolingcomparedtoonlyprimaryschooling.Itisexpressedasthe

enrolment rates as no sufficient data could be found on the completion of secondary

education.ThedataiscollectedfromtheWorldBankassecondhanddatabutoriginates

fromUNESCO.Enrolmentratesareforyear2012.

Additionally,willalsohealthlevelbecontrolledformeasuredasexpendituresonhealth.

Health isaccording tomanybotha causeanda consequenceofpoverty (WorldBank,

2014), leading to people being trapped in a ‘poverty andpoor health circle’. Through

controlling for health expenditures I therefore aim to control for how higher health

levels influence my dependent variable poverty levels instead of my independent

variablesaboutmobilemoney. Ihereassume that it is likelier fora country tohavea

healthier population if the country has higher expenditures on health, compared to

lowerlevelsofspending,andthereforecontrolforthis.Theoriginalsourceofdataisthe

WorldBankdatacatalogueandthedataisfrom2012.

26

Finally, in order to capture potential systematic differences between geographical

regions,Iwillalsoincluderegionalfixedeffectsthroughcreatingadummyvariablefor

eachgeographicalregion.Theseregiondummieswillbeconstructedasbinaryvariables

takingonvaluesof0or1,where1representsbelongingtothespecificregionalgroup

and0representsnotbelongingtotheregionalgroup.Countriesaregroupedtogetherin

five categories as: Europe (15 countries), Americas (19 countries), Asia & Pacific (23

countries),Middle East&NorthernAfrica (10 countries), and Sub-SaharanAfrica (40

countries).

3.3.1.Descriptivestatistics

Intable1aredescriptivestatisticsofallvariablespresented.Thetableentailsnumberof

observations(N),meanvalue,standarddeviation,aswellasaminimumandmaximum

valueforeachvariable.Regionaldummiesarenotpartofthetable.

Table1Datadescription

VariableName N Mean Std.Dev. Min MaxDependentVariable PovertygapUSD1,90 70 4,652857 8,004812 0 39 ExplanatoryVariables Mobilepayments 97 2,592525 4,133918 0 25,65222Mobilesendtransfers 97 4,572228 8.913239 0 60,478Mobilereceivedtransfers 96 6,584215 11.17235 0 66,6526Mobilemoney 96 4.593832 7.445234 0 46.85476 ControlVariables GDP/capita 105 3714.439 3234.754 300.6766 13467.1Propertyrights 107 42.98204 13.88306 5.2 83.8Conflictintensity 102 5.254902 1.983528 1 10Socialsafety 102 4.558824 1.691994 1 8Unemployment 107 7.813664 6.233672 .179 26.975Secondaryeducation 71 74.20345 25.87506 20.56177 123.0861Healthexpenditures 105 236.697 231.1481 16.63966 997.9314

27

3.4.1.Method

To examine the relationship between mobile money technology and poverty gaps in

low- and middle-income countries, the statistical method of ordinary least squares

(OLS) will be used for estimation. Both simple linear regression and multiple linear

regressions will be conducted in order to examine the research question, which one

dependingon thespecification.Thesamplesize is70,and inorder toaccount forany

multicollinearity in the independent variables robust checks will be carried out

throughout the regressions. The main models of simplest specification will be the

following:

1 𝑌! = 𝛽! + 𝛽!𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑠 + 𝛽! ∗ 𝑋! + 𝜀!

2 𝑌! = 𝛽! + 𝛽!𝑚𝑜𝑏𝑖𝑙𝑒 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑠 + 𝛽! ∗ 𝑋! + 𝜀!

3 𝑌! = 𝛽! + 𝛽!𝑚𝑜𝑏𝑖𝑙𝑒 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑒 + 𝛽! ∗ 𝑋! + 𝜀!

4 𝑌! = 𝛽! + 𝛽!𝑚𝑜𝑏𝑖𝑙𝑒 𝑚𝑜𝑛𝑒𝑦 + 𝛽! ∗ 𝑋! + 𝜀!

Themodelspresenttheoutcomesofthedifferentexplanatoryvariables,𝛽! ,onpoverty

gaps,𝑌! , in low-andmiddle-incomecountrieswhere𝛽!isaconstantinterceptionterm.

The interpretation of a negative 𝛽! would suggest that the estimated mobile money

servicehasareducingeffectonpoverty,whileapositivesuggestsinsteadanincreasing.

Theparameter𝛽! ∗𝑋! representsthedifferentcontrolvariablesthatfurtheronwillbe

added subsequently to develop the models. These include both control variables for

alternative explanations to a givenpoverty level, aswell as regional fixed effects that

willbeincludedinthefinalspecificationtocontrolforanyregionaldifferences.Finally,

the error term 𝜀! aims to capture unobserved variations that were not possible to

include such as e.g. personal characteristics of the respondents, but also factors

concerningthestudiedcountryorareathatIcouldnotcontrolfor.

Thissectionhaspresentedadescriptionofthedata,itssourcesandinformationabout

the different variables. Additionally,were descriptive statistics presented followed by

thestatisticalmethodfortheestimationofacorrelation.

28

4.1.Results

ThissectionpresentstheOLSregressionoutputsinTable2.Thetabledisplaywhether

thedifferentindependentvariablesformobilemoneycoefficientsaresignificant,andif

so, how they affect the outcome where a negative sign would imply a reduction in

povertywhileapositivewouldsuggesttheopposite.

4.1.1.RobustRegressionResults

To test my hypothesis whether usage of mobile money could serve as a financial

inclusion tool for a reduction in poverty gaps, I carried out multiple cross-country

regressions. First, with each mobile money service separately as the independent

variable thus using simple linear regression and then subsequently adding control

variablesonebyoneinmultiplelinearregressions.Thiswasfollowedbyanothersimple

linear regression using a combined term for all mobile money services as the

independent variable, with an additional control variable for each specification in a

multiple linear regression. Table 2 shows the coefficients formobile payments,mobile

transfers, mobile transfers received and the combined mobile money service variable

mobilemoneyfromrunningrobustregressions.

29

The obtained results for the first regression do not manage to yield any significant

estimates formobilepayments anddoesconsequentlyneither findanysupport formy

hypothesisofapotentialreductioninpovertygapsfromusageofmobilepayments.The

simplest specification for the analysis starts for all specifications with running the

explanatory variable on my dependent variable poverty gap where for every

specificationanadditionalcontrolvariableisaddeduntilthelastoneincludesthemall.

While a few of the control variables turned significant in some of the specifications,

socialsafetyandsecondaryeducation,thesedidnotaffectthelevelofsignificanceonthe

main explanatory variable mobile payments. The number of observations decreases

slightly in the first fewspecificationsanddrastically in the last two(VII)and(VIV).A

Table2OLSEstimatesoutput

Dependent

Variable:PovertyGapUSD1,90

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (VIV)

Mobilepayments -.005 -.076 -.057 -.015 -.021 -.054 -.051 .009 -.356 (.040) (.074) (.075) (.041) (.043) (.055) (.059) (.116) (.414)Mobiletransfers .008 -.015 .134* -.020 .125** .002 .010 .000 .072 (.032) (.042) (.070) (.033) (.057) (.042) (.043) (.034) (.176)Mobiletransfersreceive .008 -.176 -.169 -.186 -.102 -.083 -.062 -.085 -.142

(.254) (.226) (.229) (.233) (.227) (.230) (.233) (.307) (.725)Mobilemoney .005*** -.004*** .004*** .005*** .004*** .004*** .004*** .010*** -.002 (.001) (.001) (.001) (.000) (.001) (.001) (.001) (.002) (.004)GDP/capita Yes Propertyrights Yes Conflictintensity Yes Socialsafety Yes Unemployment Yes

Healthexpenditures Yes

Secondaryeducation Yes

Regionalfixedeffects Yes

N 64 64 64 62 62 62 62 46 34Robuststandarderrorsinparentheses.Levelofsignificance:***p<0,01,**p<0,05,*p<0,1

Constantsincludedinallspecificationsbutomittedfromthetable.

30

tableofallspecifications(I-VIV) includingcontrolvariablescanbe found in table3 in

theappendix.

Forthenextindependentvariablemobiletransferstheresultsshowthatneithermobile

transfers gained significant coefficients, with the exception of specification III andV,

where they temporarily turnedsignificant.Thisone-and two-star levelofsignificance

doeshoweverdisappearwhenaddingmorecontrolvariablesandconsequentlyremove

thepotentialimpactonpovertygapsthatmobiletransferscouldhave.Furthermoreare

the two significant coefficient positive,whichwould imply that thehigher% that use

mobile transfers would lead to an increase in poverty gaps, something that in turn

opposesmyhypothesisandtheoreticalarguments.Justlikeinthepreviousregressions,

observationscontinuetodropthroughoutthespecifications.Atableofallspecifications

(I-VIV)includingcontrolvariablescanbefoundintable4intheappendix.

Thecoefficientsforthethirdregression,mobiletransfersreceived,remainliketheones

inmobilepayments insignificant throughall specifications (I-VIV).Onceagainsomeof

thecontrolvariables(GDP/capita,Conflictintensity,Socialsafety,Secondaryeducation)

areincontrasttothemainexplanatoryvariablesignificantinacoupleofspecifications,

although this seems to haveno effect on the level of significance for the independent

variableobserved. In linewithpreviousregressions,observationsdecrease foralmost

everyspecification.TheseresultscanbeseeninTable5intheappendix,displayingall

resultsforspecification(I-VIV).

Finallythecombinedmainexplanatoryvariablemobilemoneyisexaminedinthefourth

multiple regression, forwhicha full tablewith theoutputsandsignificanceof control

variables canbe found in the appendix, table6.Unlikeprevious regressions, here the

coefficients remain significant all through the specifications until the very last one,

regionalcontrols,where througha fixedeffectsmodelonebyone,eachregion isheld

constant todetect any regionaldifferences.Whether losing the levelof significance in

(VIV)isduetoregionaldifferencesorsomethingdifferentmayhoweverbedifficultto

determinebecauseofthelargedropofobservations,resultinginanalmostnon-existing

31

sample size. Whether or not the loss of significance is a consequent of regional

differences, sample size or something else, it is regardlessworth commenting on the

signofthecoefficientforthosethataresignificantastheyallarepositive,whichinother

wordsisindisagreementtomyhypothesesandtheoreticalarguments.Standarderrors

canhowevernotbeconsideredparticularly large, inturnnot implyingthattheresults

shouldbetreatedwithspecialcautionbecauseofthis.

As previouslymentioned, robust regressionswere carriedout in order to account for

outliersandinfluentialobservations(IDRE,2017).Theactionofcontrollingforregional

differencesthroughfixedeffectswasfurthermorealsoanattempttoremovemoreofthe

potentialerrorvarianceandthusmaketheresultsmorerobust,astheregressionsgets

severaltreatments.Althoughthisbothprovedunsuccessful ingainingmoresignificant

results,therecanneverthelessbeotherissueshinderingsignificantcoefficients,where

one problem could be multicollinearity in some of the variables. To check for

multicollinearity among the variables I therefore tested the variance inflation factors

(VIF) for each variable, to find if it couldbe the case that somearehighly correlated,

especiallygivenmysmallsamplewheremulticollinearityisknowntobemostcommon

(2017). Inregard to10being ‘viewedasa threshold’ forhighcollinearity,noneof the

variablescouldhoweverbeconsideredhighlycorrelated,sincethehighestVIFvalueto

befoundforthefirstmobilepaymentsregressionwas8,81forGDP/capita.Alsotaking

intoconsiderationthatthe‘tolerance’shouldnotbebelow0,1,noneofthevariablesfell

below this number either although GDP/capitawas closewith a tolerance of 0,1135.

Similarly for the second, third and fourth regression examining themobile transfers,

mobiletransfersreceivedandthecombinedmobilemoneyvariables,noVIFvaluesof10

orabovewerefound.ThehighestvaluewasonceagainforGDPC/capitaandwas8,51;

8,48and8,48,withnovaluesoftolerancebelow0,1.Consequently,itwouldnotbelikely

that the variables could be predicted from one another, something that in turn could

haveincreasedstandarderrors.

Alternativeexplanationstothelackofsignificanceforthegrandmajorityoftheresults

arelikelytoberelatedtothesamplesize.Becauseofthelackofdataonpovertylevels

32

fromthesameyears,thesamplesizewasalreadystretchingitfromthebeginning.When

thefirstdropinobservationsappearedinthefirstspecificationforeachmobilemoney

independentvariable,andthencontinuedtodecreaseforeachone,itwouldbelikelyto

assumethat this isoneof themainreasonsbehindthis.Anotherexplanationcouldbe

becauseof time-logaspects, in thesensethata fewof thecountrydatawerecollected

rather short after themobilemoney datawas collected and therefore potentially too

closetobeabletodetectanydifference.Tocontinueonexplanationsbasedontime-log

aspects, it is alsoworthmentioning that once again because of poor data, all data on

poverty levelswere not from the exact same year, something that in turn could have

affectedthestatisticaloutcomes.Alsoselectionbiasnesscouldhaveafingerinthegame

as several of the countries that lacks data on poverty levels are categorized as low

incomecountries,andthereforecoulditbelikelytoassumethattherewouldbeahigher

prevalenceofpovertyinsuchcountries.

4.2.Futureresearch

Intheabsenceofmoredataonpovertylevelsasuggestionforfutureresearchistouse

proxy variables for poverty levels. Since poverty levels often are calculated based on

household income, savings and consumption expenditures (World Bank, 2017), one

suggestioncouldbetoforinstanceusehouseholdconsumptionexpendituresdataasa

proxy variable for poverty levels. This could be a good fit since according to several

studies(Wamuyu,2016)savingstendtobedownprioritizedinmanypoorhouseholds

in low income countries and because of this probably only play a small part in the

household economy, unless the household in question is from a richer quintile of the

populations, something that in turn could be controlled for through different control

variables. The main reason for suggesting proxies is to maintain the analysis on an

aggregate cross-country level, and thus be able to more look at the phenomenon of

mobilemoneyandpovertylevelsthanusingindividualdataonamicrolevel.Sincethere

already are many studies on mobile money, specifically M-PESA, on a

household/individuallevel,itremainsimportanttodosimilarstudieslikethisonetofill

theresearchgaponanaggregatecrosscountrylevel.Asnewdataismadeavailableon

33

poverty levels,household incomeandexpenditures,opportunities for furtherresearch

onanaggregatelevelwillcontinuetogrow.

5.Conclusion

Inthelightof financial inclusion’s increasedattentiononthedevelopmentagendaand

mobilemoneysuccessstorieslikeM-Pesa,thisthesishasaimedtoexaminetheeffectof

mobilemoneyasafinancialinclusiontoolforreducedpovertylevelsinlow-andmiddle-

income countries around the globe. In contrast to a previous study on the effects of

mobile money on household levels in a Kenyan perspective (Jack & Suri, 2016), the

obtainedresultsdidnotmanagetofindanysupportformyhypothesisthattheusageof

mobilemoney could serve as a tool for financial inclusion and consequently reducing

poverty levels. Although four different independent variables formobilemoneywere

runrobustregressionson,neitherfoundsupportformyhypothesis.Whilethemajority

of theoutputestimateswere insignificant,a fewalsoturnedtemporarilysignificant in

someoftheregressions,althoughtheseinsteadshowedapositivecoefficientsign,which

opposesmytheorythatmobilemoneycouldreducepoverty.Potentialexplanationsto

why no findings yielded support for my hypothesis can be discussed from different

viewpointswhereonereasoncouldbeduetotimelogissuesbetweentheindependent

anddependentvariables.Anotherreasoncouldbeaconsequenceoftherelativelackof

dataonpovertylevels,whichdidnotmakeitpossibletousepovertydatafromthesame

year for all countries. An additional explanation could furthermore be related to the

small sample size.Worth considering is yet that doing a cross-country analysis at an

aggregate level does limit the sample size one could have, as there only are a certain

numberofcountries intheworldthatcategorizesas low-ormiddle-incomecountries,

comparedtostudiesonamicrolevelthatcouldincludethousandsofobservations.An

alternative approach for future research could therefore be to use a multi-level

approach and combine aggregate cross-country data with data on individuals or

households. Most certainly are there however also other aspects that could be the

reasons behind my insignificant results, such as different uncertainties, unobserved

variationsandotherexogenousorendogenouscharacteristicsamongothers.

34

Onefactthatthisthesisdoespointtowardsishowevertheneedforemoreresearchon

mobilemoneyandpovertylevels.Derivedfromfindingsofmobilemoneyonincreased

household income (Morawczynski & Pickens, 2009), savings (ibid), self-started

businesses (Mbogo, 2010) together with the role of financial inclusion on the

development agenda and theories about capital accumulation, access to finance and

technology for new financial services, this certainly indicates possibilities for further

effectsofmobilemoney indevelopment.And tobeable toobserve thephenomenaof

mobilemoneyonpovertylevels,studiesfromcross-countryperspectivesareneeded.In

theabsenceofmoredata,onesuggestionisthereforetoinsteaduseproxyvariablesfor

poverty.Andasnewdataismadeavailableonpovertylevels,opportunitiesforfurther

researchonanaggregatelevelwillcontinuetogrow.

35

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39

7.Appendix

Table3

OLSEstimatesoutput

DV:PovertyGapUSD1,90 (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX)

Mobilepayments -.005 -.076 -.057 -.015 -.021 -.054 -.051 .009 -.356

(.040) (.074) (.075) (.041) (.043) (.0554) (.059) (.116) (.414)

GDP/capita -.000*** -.000** -.000 -.000 -.000 -.000 -.000 -.000

(.000) (.000) (.000) (.000) (.000) (.000) (.000) (.001)

Propertyrights -.034 -.017 -.003 .010 .012 -.003 .037

(.030) (.017) (.020) (.025) (.026) (.040) (.063)

Conflictintensity .038 -.080 -.098 -.076 .147 -.214

(.105) (.112) (.1375) (.150) (.241) (.350)

Socialsafety -.507** -.818*** -.837*** -.589 -.888

(.205) (.269) (.279) (.514) (.787)

Unemployment .081* .075 .042 .185

(.044) (.048) (.085) (.194)

Healthexpenditures .001 .002 .003

(.003) (.004) (.008)

Secondaryeducation -.091*** -.060

(.023) (.037)

Regionalfixedeffects Yes

N 64 64 64 62 62 62 62 46 34

Robuststandarderrorsinparentheses.Levelofsignificance:***p<0,01,**p<0,05,*p<0,1

Constantsincludedinallspecificationsbutomittedfromthetable.

40

Table4

OLSEstimatesoutput

DV:PovertyGapUSD1,90 (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX)

Mobiletransfers .008 -.015 .134* -.020 .125** .002 .010 .000 .072

(.032) (.042) (.070) (.033) (.057) (.042) (.043) (.034) (.176)

GDP/capita -.000** -.000** -.000 -.000 -.000 -.000 -.000 -.001*

(.000) (.000) (.000) (.000) (.000) (.000) (.000) (.001)

Propertyrights -.045 -.018 .012 .003 .007 .003 .124

(.034) (.017) (.033) (.024) (.024) (.018) (.075)

Conflictintensity .034 -.118 -.077 -.041 .207* -.080

(.104) (.182) (.131) (.140) (.105) (.397)

Socialsafety -1.000*** -.746*** -.756*** -.072 .569

(.330) (.257) (.262) (.225) (.944)

Unemployment .060 .051 .006 -.703**

(.041) (.043) (.034) (.311)

Healthexpenditures .002 .002 .013

(.003) (.002) (.008)

Secondaryeducation -.017 -

.116**

(.010) (.043)

Regionalfixedeffects Yes

N 64 64 64 62 62 62 62 46 33

Robuststandarderrorsinparentheses.Levelofsignificance:***p<0,01,**p<0,05,*p<0,1

Constantsincludedinallspecificationsbutomittedfromthetable.

41

Table5

OLSEstimatesoutput

DV:PovertyGapUSD1,90 (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX)

Mobiletransfersreceived .008 -.176 -.169 -.186 -.102 -.083 -.062 -.085 -.142

(.254) (.226) (.229) (.233) (.227) (.230) (.233) (.307) (.725)

GDP/capita -.001*** .001*** -.001*** -.001** -.001** -.001 -.001 -.002

(.000) (.000) (.000) (.000) (.000) (.001) (.001) (.001)

Propertyrights -.020 -.044 .066 .065 .072 .022 .065

(.086) (.091) (.099) (.099) (.100) (.117) (.163)

Conflictintensity -.701 -.922* -.886 -.780 -.273 -.581

(.560) (.545) (.551) (.581) (.708) (.897)

Socialsafety -2.414** -2.145* -2.194* -.291 -.432

(1.001) (1.086) (1.095) (1.518) (2.101)

Unemployment -.112 -.143 -.143 -.234

(.169) (.1780) (.226) (.445)

Healthexpenditures .007 .006 .014

(.011) (.012) (.020)

Secondaryeducation -.166** -.136

(.067) (.093)

Regionalfixedeffects Yes

N 64 64 64 62 62 62 62 46 34

Robuststandarderrorsinparentheses.Levelofsignificance:***p<0,01,**p<0,05,*p<0,1

Constantsincludedinallspecificationsbutomittedfromthetable.

42

Table6

OLSEstimatesoutput

DV:PovertyGapUSD1,90 (I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX)

Mobilemoney .005*** -.004*** .004*** .005*** .004*** .004*** .004*** .010*** -.002

(.001) (.001) (.001) (.000) (.001) (.001) (.001) (.002) (.004)

GDP/capita -.000* -.000** -.000 -.000 -.000 -.000 -.001 -.001

(.000) (.000) (.000) (.000) (.000) (.001) (.001) (.001)

Propertyrights -.046 -.013 .002 .003 .009 -.010 .038

(.032) (.018) (.025) (.027) (.029) (.029) (.069)

Conflictintensity .078 -.086 -.103 -.066 -.081 -.247

(.115) (.141) (.149) (.168) (.171) (.363)

Socialsafety -.657** -.791** -.869*** -.632* -.755

(.261) (.296) (.319) (.365) (.829)

Unemployment .039 .029 -.020 .134

(.046) (.051) (.058) (.188)

Healthexpenditures .003 .003 .005

(.003) (.003) (.007)

Secondaryeducation -.026 -.067

(.017) (.040)

Regionalfixedeffects Yes

N 63 63 63 61 61 61 61 45 34

Robuststandarderrorsinparentheses.Levelofsignificance:***p<0,01,**p<0,05,*p<0,1

Constantsincludedinallspecificationsbutomittedfromthetable.

43

Table7

Countries Incomelevel* Countries Incomelevel*Burundi Low Philippines Lower-middleBenin Low ElSalvador Lower-middleBurkinaFaso Low Tajikistan Lower-middleComoros Low Ukraine Lower-middleEthiopia Low Vietnam Lower-middleGuinea Low Yemen,Rep. Lower-middleHaiti Low Zambia Lower-middleLiberia Low Albania Upper-middleMadagascar Low Argentina Upper-middleNiger Low Bulgaria Upper-middleRwanda Low BosniaandHerzegovina Upper-middleTogo Low Belarus Upper-middleUganda Low Brazil Upper-middleArmenia Lower-middle China Upper-middleBangladesh Lower-middle Colombia Upper-middleBolivia Lower-middle CostaRica Upper-middleBhutan Lower-middle DominicanRepublic Upper-middleCoted'Ivoire Lower-middle Ecuador Upper-middleCameroon Lower-middle Croatia Upper-middleDjibouti Lower-middle Iran,IslamicRep. Upper-middleEgypt,ArabRep. Lower-middle Iraq Upper-middleGeorgia Lower-middle Kazakhstan Upper-middleGhana Lower-middle Mexico Upper-middleGuatemala Lower-middle Macedonia,FYR Upper-middleHonduras Lower-middle Montenegro Upper-middleIndonesia Lower-middle Mauritius Upper-middleKyrgyzRepublic Lower-middle Panama Upper-middleLaoPDR Lower-middle Peru Upper-middleSriLanka Lower-middle Paraguay Upper-middleMoldova Lower-middle Romania Upper-middleMyanmar Lower-middle RussianFederation Upper-middleMongolia Lower-middle Serbia Upper-middleMauritania Lower-middle Thailand Upper-middleNicaragua Lower-middle Turkey Upper-middlePakistan Lower-middle SouthAfrica Upper-middle*WorldBankAtlasmethod