EFFECT OF MOBILE MONEY TRANSFER SCHEME ON THE … · Mobile Money Transfer Scheme was the independent variable, meanwhile Economic Growth was the dependent variable and the proxies

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 10, Issue 12, December 2019, pp. 484-511, Article ID: IJMET_10_12_048

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=12

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication

EFFECT OF MOBILE MONEY TRANSFER

SCHEME ON THE ECONOMIC GROWTH OF

CAMEROON

Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke

Department of Accountancy, University of Nigeria Nsukka, Enugu Campus, Nigeria

ABSTRACT

This study evaluated the “Effect of Mobile Money Transfer Scheme on the

Economic Growth of Cameroon”. Mobile Money Transfer Scheme was the

independent variable, meanwhile Economic Growth was the dependent variable and

the proxies used were Gross Domestic Product (GDP) and Inflation Rate. The

population consisted of the two primary Mobile Money service providers in Cameroon

i.e. MTN Mobile Money and Orange Money. Explanatory Research Design was

employed in the Methodology. Secondary data was used for the study, and the data

were collected from the annual reports of Cameroon’s central bank (BEAC),IMF and

Knoema for Mobile Money, GDP and Interest Rate respectively. These data collected

wereanalysed using simple Linear Regression at 5% probability level of significance

with the aid of Statistical Package for the Social Sciences (SPSS), version 23.0. The

findings revealed that there is a weak positive insignificant correlation between

Mobile Money Transfer Scheme and Economic Growth in Cameroon i.e. GDP (r =

.162, alpha-significance is .520 at p > 0.05)and inflation rate (r = .385, alpha-

significance is .115 at p > 0.05). The research concludes that the reason why this

scheme does not currently have a material effect on Cameroon’s Economic Growth is

probably due to the fact that the industry is relatively new in the country, gradually

gaining grounds and so could potentially have a significant effect in the future. It

could also be due to the severe political instability plaguing Cameroon at the moment.

The study therefore recommends that the government should subsidise and also

implement policies which will favour the further penetration of mobile money service

even to the more remote parts of the country. It equally recommends that the

government should try to resolve the current, devastating political crisis so as to make

the environment safer to conduct business.

Keywords: Mobile Money, Mobile Money Transfer Service, Mobile Money Agent,

Economic Growth, Gross Domestic Product, Inflation, Life expectancy.

Cite this Article: Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and

Obioma Vivian Ugwoke, Effect of Mobile Money Transfer Scheme on the Economic

Growth of Cameroon. International Journal of Mechanical Engineering and

Technology 10(12), 2020, pp. 484-511.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=12

Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon


1. INTRODUCTION

Access to financial services is a crucial boost to social and economic development of a

country. Until recently in Cameroon, such services focused on the formal banking sector,

which traditionally does not open branches in low income and rural areas as their returns there

would not be able to justify their substantial operating costs. This meant that only wealthy and

urban citizens could enjoy such privileges. The rapid growth of the mobile network industry

led to half the world‟s population having at least one mobile subscription by 2014 (GSMA

Intelligence, 2015), with a total number of mobile subscriptions worldwide reaching more

than 7 billion by the close of 2015 (Sanou, 2015). These mobile devices offered a distribution

technology for mobile financial services for the unbanked. The initial goal of mobile money

was to enable the unbanked persons to be able to carry out person-to-person (P2P) money

transfer transactions, which were previously done through relatively unsecured physical

means such as bus agencies as well as travelling relatives and friends. This service, therefore,

brought people from the cash-based, „unbanked‟ economy to the modern system of „book-

entry money‟. Thus began the era of „banking the unbanked‟ (Klein & Mayer, 2011).

Worldwide, mobile money service is available in 93 countries today. The service is fast

overtaking the banking sector with the number of registered accounts in the world increasing

by 31% to 411 million in 2015 compared to the previous year. Mobile money providers are

processing an average of 33 million transactions per day. Mobile money services offering

International Money Transfer (IMT) saw the volume of cross-border remittances increase by

52% in 2015, compared to the previous year (GSMA, 2015). Indeed, the World Bank (2016)

referred to mobile money as a “success story” that is also a “regulatory minefield”. Africa has

a vast potential for growth in the telecoms industry, especially as there is only 47%

penetration so far. One of the fastest growing areas in the telecom industry happens to be

mobile money. This service makes it possible for mobile phone users to send and receive

money anywhere by facilitating transactions through their mobile phones. This is essential

especially in Africa which has poor infrastructure, and a vast majority of the people do not

have bank accounts (Paelo, 2014). So far, Kenya is the country where mobile money service

is most successful as it has been there since 2007, especially with the advent of M-PESA

provided by the Vodafone-owned Safaricom mobile network which has the largest market

share in the country. Though not yet fully embraced, it is also present in Nigeria thanks to

MTN Mobile Money (working in partnership with GT Bank), Glo Xchange, Paga and so on.

In Cameroon, less than 20% of the population has a bank account whereas the penetration

of mobile telephony is estimated at 80% (Cameroonweb, 2015). This could be because the

cost of mobile phones is becoming more and more affordable over the years (currently as low

as 5,000FCFA or $10) and subscribers do not necessarily need an expensive Smartphone or

internet connection on their phones to be able to use mobile money. Some of these phones

take even up to 2 or 3 SIM cards at once, meaning a customer could have multiple mobile

money accounts using the same phone. Three Mobile Network Operators (MNOs) - MTN,

Orange and Nexttel are in Cameroon. Two of these currently provide mobile money services.

MTN Mobile Money (also called MoMo) began in 2010 and Orange Money followed a year

later in 2011. Nexttel Possa will be launched soon. According to Media Intelligence (2016),

as of June 2016, there were about 6.8 million mobile money subscribers in Cameroon, with

1.5 million active users.

Cameroon currently has a population of about 23 million, and the mobile money market is

continuing to proliferate in the country. For instance, Tabi (2018) posits that the Governor of

the Bank of Central African States (BEAC) signed authorisation in 2018 permitting

SociétéGénérale Cameroun (SGC) bank to partner with YUP Cameroun in launching its

mobile money services within 12 months. Worthy of note is the fact that SGC had initially



launched a mobile money service called Monifone, but this was suspended in 2014 due to

competition and recurring conflicts with some telecommunication operators. This time

around, it has decided not to rely on any telecommunication operator, and instead chosen to

partner with YUP Cameroun (Business in Cameroon, 2018).Tabi (2018) states that official

statistics show that there are presently 34,114 mobile money service points in the CEMAC

(Economic and Monetary Community of Central Africa) zone, with Cameroon accounting for

70% (23,880) of them.

Mobile money transfer has a lot of advantages. It improves efficiency and effectiveness by

increasing the speed, safety and frequency of payments and decreasing the cost, paper work,

and processes of sending and receiving money even on off days and odd hours. Besides,

security is also a significant benefit here, as it eliminates the risk of theft. Cash inflow to rural

areas can be enhanced too. Increased money flow from Urban to rural dwellers can greatly

enhance economic growth. Those who partner with telecom providers to offer financial

services are called Mobile Money Agents (MMA) in this paper. It examines the effect of the

Mobile Money Transfer Scheme on the Economic Growth of Cameroon proxy by GDP and

Inflation rate. It has five parts made up of introduction, literature review, methodology,

presentation and analysis of findings and conclusion and recommendations.

1.2. Statement of the Problem

The ability to carry out financial transactions through a mobile phone has attracted MNOs

(especially in developing countries) to the financial services industry. They were able to

penetrate the market due to their comparatively quicker service and lower charges, compared

to the formal banking sector. In the case of Cameroon, MTN and Orange now offer Mobile

Money Transfer (MMT) services called MTN Mobile Money and Orange Money

respectively. The third, Nexttel has plans to launch its own called Nexttel Possa. According to

Paelo (2014), Cameroon had more mobile money subscribers than bank account holders by

the end of 2013. OCHA (2016) indicates that MTN Mobile Money formed a partnership with

Afriland First Bank whereby MTN manages the technical platform as well as the marketing

and distribution network, while Afriland issues the e-money and ensures compliance with the

financial regulations. It goes on to say the services include person-to-person (P2P), bill

payment and purchasing of goods and services from authorised retailers but do not

substantiate on any of these aspects. Bahri-Damon (2015) posits that Mobile Money services

in Cameroon enable Cameroonians to send and receive money anywhere within and outside

the country as well as pay their electricity bills, cable bills, insurance premiums, university

tuition fees and taxes. It equally creates a medium through which they can buy train tickets,

flight tickets, airtime and fuel. It even enables them to do shopping in their authorised

supermarkets. Some companies pay salaries to their employees using this means. Though

these numerous activities are going on in the field of mobile money transfer in Cameroon, the

economy of the country has been at a comatose and no study has attempted to determine the

effect of the scheme on her economic growth. Langaa (2012) only explored the social impact

of mobile money and mobile electronic transfer services among rural farmers in the North

West Region of Cameroon. Therefore there exists a gap in literature in this field despite the

need to find a solution to poor economic growth in Cameroon.

1.3. Objectives of the Study and Accompanying Research Questions and

Hypotheses

The general objective of this study was to assess the effect of Money Mobile Transfer Scheme

on the Economic Growth of Cameroon. Its specific objectives were to: (i) establish the effect

of Mobile Money Transfer Scheme on the GDP of Cameroon and (ii) ascertain the effect of



Mobile Money Transfer Scheme on the inflation rate of Cameroon. These objectives gave rise

the following research questions viz.(i) Does Mobile Money Transfer Scheme have a

significant effect on the GDP of Cameroon? (ii) Does Mobile Money Transfer Scheme have a

significant effect on the inflation rate of Cameroon? In tandem with the above therefore the

following two hypotheses were formulated namely: (i) Mobile Money Transfer Scheme has a

significant effect on the GDP of Cameroon and (ii) Mobile Money Transfer Scheme has a

significant effect on the inflation rate of Cameroon. This research is expected to be of great

benefit to the government, researchers, mobile money service providers and the general

public. For instance, while the government would utilize the findings for effective policy

formulation to make the scheme acceptable to all and sundry in the country, researchers will

find in it usable data and reference point for further researches. We acknowledge and

appreciate the paucity of work done in this area about the Cameroonian economy due to the

newness of the introduction of the scheme in the country (2010) relative to Kenya and other

African countries. Notwithstanding the above limitation, the study focused on MTN Mobile

Money and Orange Money which occupy almost the entire market, it covered a period of 16

years (2002 – 2017), that is 8 years before the advent of Mobile Money, and then 8 years

after.

2. LITERATURE REVIEW

2.1. Conceptual Framework

2.1.1. Mobile Money Transfer

Mobile Money was relatively unknown over a decade ago starting only in 2001. But it is

probably the phenomenal growth since 2007 of Kenya‟s M-Pesa system that has brought

mobile money to international popularity. Mobile money refers to financial transaction

services potentially available to any mobile phone user, including the under banked and

unbanked global poor who are not a profitable target for commercial banks. According to

Diniz, Albuquerque and Cernev (2011), Mobile Money is a digital repository of electronic

money developed and implemented on a mobile device, allowing peer-to-peer transaction

between users of the same service provider. The services allow electronic money transactions

over a mobile phone (Ernst & Young, 2009) while GSMA (2010) opines that Mobile Money

is a service in which mobile phone is used to access financial services, giving rise to

movement of value from a mobile wallet through a mobile phone. Common mobile financial

services offered through the mobile phone include bill payment, account transfers, domestic

and international Person-to-Person transfers, proximity payments at the point of sale, and

remote payments to purchase goods and services. The Mobile Network Operators (MNOs)

partner with banking institutions to be able to render these mobile money services. Mobile

Money is not Mobile Banking. Mobile Money does not require an internet connection and

works merely with codes. On the other hand, Mobile Banking requires the subscriber to have

a bank account, a Smartphone, download the app on the phone, and use internet connection to

do the electronic money transfer. How it works according Aaron (2015) is that an

individual/customer sets up an electronic money account with the mobile money service

provider (after providing identity documents) and then deposits cash in exchange for

electronic money. This electronic money can be stored or withdrawn as cash, or transferred

via a coded secure text message to others, without the customer having a formal bank account.

He further asserts that the scheme enables the use of mobile phones to pay bills, remit funds,

deposit cash, and make withdrawals using e-money issued by banks as well as non-bank

providers such as telecommunication companies. This service currently exists in many

developing countries today and is proliferating, especially in Africa. It serves several people

without access to banking services, known as the unbanked. According to Buckley, Greenacre



and Malady, (2015), it provides financial inclusion which has the potential of helping the

unbanked and low-income groups to save and borrow with a possible spiral effect of

investment in education and asset generating activities.

2.1.2. Economic Growth

Economic growth is an increase in the production and consumption of goods and services.

This happens when there is an increase in the multiplied product of population and per capita

consumption. The global economy grows as an integrated whole made up of agricultural,

extractive, manufacturing, and services sectors that require inputs and outputs. Economic

growth is often indicated by an increase in real Gross Domestic Product (GDP) or real Gross

National Product (GNP). Economic growth has been a primary, perennial goal of many

societies and most governments (Wilson et al., n.d). Haller (2012) posits that Economic

Growth is, in a limited sense, an increase of the national income per capita, and it has to do

with the analysis, especially in quantitative terms, of this process. It concentrates on the

functional relations between the endogenous variables; in the broader sense, it involves the

increase of the Gross Domestic Product(GDP), Gross National Product(GNP) and National

Income (NI), resulting in national wealth, including the production capacity, expressed in both

absolute and relative terms, per capita, as well as involving the structural modifications of

economy.

According to Haller (2012), Economic Growth can be positive, zero or negative. It is

positive when the annual average rhythms of the macro-indicators (especially GDP) are

higher than the normal rhythms of growth of the population and negative when it vice versa.

Then it is zero when the annual average rhythms of growth of the macro-economic indicators

(especially GDP) are equal to those of the population growth. Essentially, Economic growth

is the sustained increase in the welfare of an economy together with the ongoing changes in

that economy's industrial structure; public health, literacy, and demography; and distribution

of income (Habane, 2012). The ultimate goal of Economic Growth is to increase the standard

of living of the citizens by making them sustainably wealthier.

Researchers attribute economic growth to several factors – economic and non economic.

Economic factors range from natural resources, capital formation, technological progress,

human resources, population growth, social overheads, and entrepreneurship to

transformation of traditional agricultural society. Each has varying degrees of impact on

economic growth (Boldeanu & Constantinescu, 2015; Onyinye, Idenyi & Ifeyinwa, 2017;

Muchdie et al., 2016; Arabi & Abdalla, 2013; Nwosu, Dike & Okwara, 2014; Biktemirova et

al., 2015; Afghah, Raoofi and Hoshyar, 2014; Odetola & Etumnu, 2013; Sertoglu, Ugural and

Bekun, 2017). On the other hand, non economic factors also affect economic growth and

these are political, education, urbanization and religion (Younis, Lin, Sharahili and

Selvarathinam, 2008; Kotaskova et al., 2018; Odit, Dookhan and Fauzel, 2010; Arouri,

Youssef, Nguyen-Viet and Soucat, 2014; and Campante & Yangizawa-Drott, 2013). Long

religious induced holidays and other religious bigotry affect economic growth negatively, for

instance.

2.1.3. Gross Domestic Product (GDP)

GDP is the total market value of all final goods and services produced by all the people and

companies in a country, within a given period. It does not matter if they are citizens or

foreign-owned companies, as long as they are located within the country's boundaries; the

government counts their production as GDP (Amadeo, 2018). Fresh Forex (n.d) posits that

GDP includes the market cost of all goods and services produced on the territory of the state

by all branches of the economy purposed for consumption, accumulation or exporting for a

year. GDP is different from Gross National Product (GNP); as the latter is the total value of

goods and services produced by citizens of a particular country, no matter which part of the



world they reside in. Amadeo (2018) cites the World Bank for preferring to use Gross

National Income in place of GNP, as it gives a better picture and the difference between both

is insignificant. However, GDP per capita is the best index for comparing GDP between

countries as it is derived by dividing the GDP by the number of residents of a country. Real

GDP is usually lower than Nominal GDP, and most countries prefer to use it (Real GDP)

since it takes out the effects of inflation, exchange rates, and differences in population.

Essentially, GDP is an important economic analysis tool because it is the best instrument to

assess a country‟s economic health; it is a yardstick for several monetary policies.

2.1.4. Inflation Rate

Inflation rate according to Evans (2014) is a measure of a general increase of the price level in

an economy, as represented typically by a general price index, such as the Consumer Price

Index (CPI). The term indicates that many individual prices are rising simultaneously rather

than one or two isolated prices. The inflation rate has seven distinct thresholds viz: < 0%

Deflation; 0% - 2.5% Price Stability; 2.5% - 5% Moderate Inflation; 5% - 8% Serious

Inflation; 8% - 12% Self-Compounding Inflation; 12% - 20% Hyperinflation; and 20%+

Explosive Inflation. A healthy rate of inflation is considered positive since it encourages

consumption which can in turn stimulate the economy and create more jobs. However

inflation above the mild threshold is detrimental to economic growth and development.

2.2. Theoretical Framework

2.2.1. The Technology Acceptance Model (TAM)

TAM was postulated by Davis (1989). He believed that this model could be used as a tool to

predict acceptance of technology. This is because it demonstrates the relationship connecting

believe, attitude and action purpose. Holistically, TAM attempts to predict individuals‟

intentions toward using a technology based on its Perceived Usefulness (PU) and Perceived

Ease of Use (POEU). Accordingly, PU is the degree to which a person believes that using a

particular system would improve their job performance while PEOU is the degree to which a

person believes that using a particular system would be free of physical as well as mental

effort. Very importantly, some researches having individual‟s acceptance of mobile services

as their central research focus have used TAM to understand the adoption of different mobile

services (Hong et al., 2006; Bouwman et al., 2012; Pederson, 2003; Wang et al., 2006). This

suggests the possibility to predict users‟ acceptance and adoption of mobile services using

TAM constructs. However, Mathieson (1991) and Stern et al. (2007) argue that despite the

predictive ability of the constructs, they are not alone. In other words, PU and PEOU in TAM

are not sufficient enough to predict users‟ intentions.

2.2.2. Agency Theory

Jensen and Mecklin (1976) were among the propounders of the Agency Theory. The theory

focuses on the Principal – Agent - Delegation - of - Work paradigm using the contract. The

central idea here is that the Principal is too busy to do a given job and so hires an agent to

undertake the task for him. The issues that arise from this is that while the Principal and the

Agent work towards the same goal, they may not always have the same interest. The literature

on Agency Theory mostly focuses on methods and systems and their consequences that arise

to try to match the interests of both the Principal and the Agent. Mobile money transfer

definitely benefits from a proper analysis and application of the Agency theory as it

introduces middle men between the payer and receiver of money quite different from the bank

itself.

https://www.thebalance.com/gdp-by-country-3-ways-to-compare-3306012

https://www.thebalance.com/gdp-by-country-3-ways-to-compare-3306012

https://www.moneycrashers.com/best-ways-to-stimulate-the-united-states-economy-and-create-jobs/



2.2.3. Transaction Cost Theory

Transaction Cost Theory was initially developed by Coase (1937). He aimed at explaining

why certain activities, products, or services are carried out internally in firms, while others are

bought and sold in the marketplace. This implies that companies weigh the costs of

exchanging resources with the environment, against the bureaucratic costs of performing

activities in-house. If the external transaction costs are higher than the company‟s internal

bureaucratic costs, the company will be more profitable by performing activities in-house and

vice versa. Mobile money transfer definitely benefits from a proper analysis and application

of the transaction cost theory as it is envisaged that cost of transaction would drastically be

reduced through its adoption. Recall that Transaction Cost Theory recognizes that it is

unlikely to be economically optimal to obtain perfect knowledge and that even if this were

possible, the extra cost of obtaining extra information should be weighed against its extra

benefits (Baumol and Quandt, 1964). According to Williamson (1981), a transaction cost

occurs “when a good or serviceis transferred across a technologically separable interface".

From all the theories explained above, this study is anchored on the Theory of Technology

Acceptance Model (TAM). This is because a potential user‟s acceptance or rejection of this

new technology (mobile money) will depend on how easy and convenient they find it to use

the service. The more user-friendly the service, the more subscribers there will be and this

will therefore go a long way to enhance economic activities.

2.3. Empirical Review

Nyasimi (2016) examined the effect of Mobile Money Transfers on Economic Growth in

Kenya. Her study employed Explanatory Research Design which concentrated on “why”

questions by developing casual explanations. The dependent variable was Economic Growth

for the year 2007 to 2015 while the independent variables were Mobile Money Transfer

Agents, Mobile Money Transfer Customer Enrolments, Mobile Money Transfer Transaction

Frequency and Mobile Money Transfer Deposit Value. These secondary data were collected

from the Central Bank of Kenya (CBK) as well as the Kenya National Bureau of Statistics

(KNBS) Reports. After regressing the data, there was no co-integration between Economic

Growth and Mobile Money Agents, Customers, Frequency of Transfer as well as the Value of

Money Transferred. Instead the VAR modelling impulse response showed that; Number of

Agents, Customers and Frequency of Transactions have a long run real shock on Economic

Growth while both interest rate and exchange rate impact it negatively. She recommended the

need to intensify the adaption of Mobile Money Transfer Services among those who have not

adopted them while the Kenya government should consider the Mobile Money Transfer when

drafting its policies.

Habane (2012) established the relationship between mobile money transfers and economic

growth in Kenya using descriptive research design and correlation analysis. The target

population included six mobile phone service providers in Kenya which also provide mobile

money transfer services. The total amounts transferred through the mobile for the past five

years was collected and then correlated with the economic growth proxy, Gross Domestic

Product, measured by change in GDP. Data were sourced from the Annual Financial

Statements of the Central Bank of Kenya and the Mobile Phone Companies, and Kenya

National Bureau of Statistics. The study revealed that the amount of money transacted

through Mobile Money Transfers increased steadily from KSh 0.06 billion in 2007 on its

launch to KSh 118.08 billion by 2012. The growth was driven by the convenience offered by

the service as the service does not require an individual to have a bank account in order to

transact. Customers also transacted business on Mobile Money Transfer platform from

anywhere thus offering convenience. But it also found that there was a positive though



insignificant correlation between Economic Growth and Mobile Money Transfer in Kenya.

The study recommended that the policymakers should take Mobile Money Transfer into

account when drafting policies. This was due to the indirect relationship of Mobile Money

Transfer to Economic Growth through the provision of job opportunities, increased financial

deepening and financial inclusion.

Ssonko (2010) explored the role of Mobile Money Services (MMS) in enhancing

Financial Inclusion (FI). His study was inspired by the proliferation of mobile phones

amongst low-income earners, the prepaid billing system sensitive to users‟ incomes, embrace

of ICT by the government and the private sector that has enhanced e-commerce readiness of

Uganda, as well as the launch of three Mobile Money Services in the country. A Qualitative

Analysis of the web content of the three MMS providers was undertaken and concentrated on

issues related to services provided; transaction charges; number of registered customers;

number and volume of transactions; stakeholders; user interfaces and security; institutional

relationships; policy and regulation; as well as appropriateness of the current business

model(s). The results indicated that while the MMS has enormous potential to enhance FI, it

would require an open business model that involves all stakeholders to establish a truly

national solution. Moreover, the initial contribution of MMS to FI was in the enhancement of

money transfer by lowering the transaction costs for small volumes. He recommended that the

regulatory authorities need to establish a legal framework that does not suppress innovation

but ensures safety for customers‟ savings.

Mbogo (2010) investigated the success factors attributable to the use of mobile payments

by micro-business operators through a sample of 409 micro business entrepreneurs in Nairobi,

Kenya and applying the Theory of Technology Acceptance Model (TAM) which was

extended to include other factors to assist her in predicting success and growth in micro-

businesses. The results revealed that the convenience of the money transfer technology as

well as its accessibility, cost, support and security factors are related to behavioural intention

to use and actual usage of the mobile payment services by the micro businesses to improve

their success and growth.

Lee and Gardner (2010) analysed the impact of mobile phone penetration on economic

growth by estimating a fixed-effect dynamic panel model on 56 South Asian and Sub-Saharan

African countries from 1990 to 2008. Their results indicate that mobile phones are positively

correlated with economic growth and that the marginal contribution is even more significant

where the conventional fixed-line telecommunications infrastructure is poor.

Blauw and Franses (2011) also examined the impact of mobile telephone use on the

economic development of individual households in Uganda. They used unique cross-sectional

data obtained through personal interviews with heads of households (N=196) in Uganda.

Economic development was measured at the household level by the Progress out of Poverty

Index. They found strong evidence that mobile phone use positively impacts economic

development. Kamau (2012) established the relationship between agency banking and

financial performance of the banks in Kenya. An agency bank is a company or organisation

which acts in some capacity on behalf of another bank. Through a review of secondary data,

the study found that agency banking outlets had increased from 8,809 active agents in 2010 to

9,748 the following year, which is 2011. These specific agents facilitated a total volume of 8.7

million transactions which were valued at KSh 43.6 billion in 2011. Most of these

transactions mainly consisted of cash withdrawals and cash deposits carried out at the various

banking agency outlets. In their own case, Batista and Vicente (2012) designed and conducted

a field experiment to assess the impact of randomised mobile money dissemination in rural

Mozambique. For this purpose they benefited from the fact that mobile money was recently

launched in the country, allowing for the identification of a real control group. They found



clear adherence to the services from administrative and behavioural data in the treatment

group. Financial literacy and trust outcomes were positively affected by the treatment. They

showed behavioural evidence that the availability of mobile money increased the marginal

willingness to remit. Finally, they observed that mobile money substitutes traditional

alternatives for both savings and remittances.

In a study in Uganda still, Kamukama and Tumwine (2012) found that Ugandan

commercial banks were in a liquidity crisis, having fallen short of the Bank of Uganda‟s

threshold ratio of 20% but mobile money services alone accounted for 36.7% of liquidity

variance. They recommended that commercial banks should partner or enter into a joint

venture with mobile money operators so as to expand their physical reach into poor and rural

areas.

Wanyonyi and Bwisa (2013) sought to determine if the use of Mobile Money Transfer

Services in the Kitale municipality of Kenya, a rural town setting, had resulted in the success

and growth of microenterprises. Their study was based on a survey of 36 microenterprises,

from three major sectors of the Kenyan economy; agriculture, service and processing sectors.

Microenterprises that were studied were those that had been in existence for more than five

years and had experienced business without Mobile Money Transfer (MMT), before 2007

(when MMT was not yet in Kenya), and after that with it. Their study used a questionnaire

and Chi-Square analysis and found out that mobile money transfer for business-to-business

(B2B) transactions when making purchases from suppliers, and customer-to-business (C2B)

transfers when customers buy from the business as well as debt collection for credit sales

contributed to improved performance of the micro-enterprises. Similar finding was made by

Kirui et al. (2013) in the Agricultural sector in Kenya where the use of mobile phone –based

money transfer significantly increased the level of annual household input use by $42,

household agricultural commercialisation by 37% and annual household income by $224.

They concluded that mobile phone-based money transfer services in rural areas help to

resolve a market failure that farmers face; access to financial services. Frederick (2014)

examined the effect of Mobile Money usage on microenterprise profits in Zambia. She

employed an instrumental variable strategy using the type of mobile operator as the

instrument to address the selection bias in adoption, as Mobile Money services are at the

disposal of everyone. In this urban context, she found initial evidence of positive net marginal

benefits for microenterprises using mobile money, and she calculated bounds that range

between 36% and 74% increase in profits. Her study helped to fill the gaps in the emerging

microenterprise and free money literature and offered guidance to public and private

policymakers regarding this market segment. According to Chale (2014), small and medium

enterprises in Tanzania use Mobile Money services in different ways for business purposes,

which include sales transactions, efficiency in the purchase of stock, receiving payment,

payment of goods and services, savings as well as money transfer that influenced their

business growth. This also is closely aligned to the position of Makee et al. (2014) who

maintain that there is indeed a positive effect of the mobile phone transfer services

innovations on enterprise performance among hair-dressing, carpentry and cloth making

industries in Kitale town in Kenya as well as Aker et al. (2014) to the effect that households

receiving free transfers had higher diet diversity and children consumed more meals per day

in Niger. On adoptability of mobile money transfer services, Etim (2014) carpeted Nigeria for

low use of mobile phones for money transfers. But Tsilizani (2015) paints a different picture

for Malawi where she says that mobile money transfer is well adopted and it has helped in

poverty alleviation. In like manner, in Ghana, Bampoe (2015) found that the adoption of

mobile money transfer is affected by factors as perceived usefulness, perceived trust, social

influence and competitive intensity and recommends that different parties of interest for



mobile money should recognise and address these factors to increase its use and encourage its

general acceptance.

Other notable researchers in this field include Peruta (2015) who investigated the adoption

patterns of Mobile Money in emerging developing countries such as Kenya, Tanzania,

Uganda, Burundi and Rwanda, Saliu (2015) and Bank of Ghana (2017) in Ghana; Similu and

Oloko (2015), Kirui and Onyuma (2015) and Soi (2018) in Kenya; Madila and Msamba

(2016) in Tanzania; Islam et al. (2016) in the East African countries of Kenya, Tanzania and

Uganda; Mawejje and Lakuma (2017); Munyoro et al. (2017) in Zimbabwe; These

researchers extensively linked Mobile Money Transfer Scheme and Economic Growth though

from different sectors of the varying economies and point to the success of Mobile money

transfer scheme. However, none of them, to the best knowledge of the researchers have

assessed the effect of Mobile Money Transfer Scheme on the Economic Growth of

Cameroon, and this constitutes the gap that this research has filled.

3. METHODOLOGY

Explanatory Research Design was adopted. It concentrates on “why” questions and answers

involving the development of causal explanations (De Vaus, 2001) thus establishing cause

and effect between variables (Mugenda and Mugenda 2003). It is for 16 year period from

2002 to 2017; 8 years pre and 8 years after the introduction the scheme in Cameroon. The

secondary data were collected from the Annual Reports of Cameroon‟s Central Bank i.e.

Bank of Central African States also called BEAC (2018) for mobile money data, IMF (2018)

for the GDP of Cameroon, Global Economy (2018) for the unemployment rate of Cameroon,

and Knoema (2018) for the inflation rate of Cameroon. The population consisted of the two

principal Mobile Money Operators in Cameroon, i.e. MTN Mobile Money and Orange

Money. Regression Analysis was used while the applicable model for each of the hypotheses

is specified below:

H01: GDP = f(MMTS)……………………………………………………… (1)

Where: GDP = Gross Domestic Product (dependent variable)

MMTS = Mobile Money Transfer Scheme (MMTS)

H02: IR = f(MMTS)…………………………………………………………… (2)

Where: IR = Inflation Rate (dependent variable)

MMTS = Mobile Money Transfer Scheme (MMTS)

4. DATA PRESENTATION AND ANALYSIS

Table 4.1: GDP of Cameroon from 2002 - 2017

Year GDP, Current Price

(bn US$)

GDP, Current PPP

(bn US$)

Real GDP Growth

(%)

2002 11.6 35.6 4.2

2003 14.6 38.0 4.6

2004 17.4 41.7 6.8

2005 18.0 43.9 2.0

2006 19.4 46.8 3.5

2007 22.4 50.4 4.9

2008 26.5 53.2 3.5

2009 26.1 54.8 2.2

2010 26.2 57.3 3.4

2011 29.4 60.9 4.1

2012 29.1 64.9 4.5



Source: Open Data for Africa (2018) (citing IMF) Table 4.1 revealed

That the GDP of Cameroon experienced a fluctuation from 2002 to 2017. In other words,

it increased from 2002 – 2004 but declined in 2005. It rose again from 2006 – 2007 but

experienced a decline again in 2008 - 2009. In the same vein, it rose from 2010 to 2014 and

experienced another decline from 2015 – 2017. Therefore, the trend shows a persistent

fluctuation in the GDP of Cameroon. These trends are also shown by the time series plot

shown in Figure 4.1.

Figure 4.1: Showing the Trend in the GDP of Cameroon from 2002 – 2017

Source: Researcher‟s Analysis, 2018.

Table 4.2: Inflation Rate of Cameroon from 2002 - 2017

Year Inflation Rate (%) % Change

2002 2.8 -

2003 0.6 -78.6

2004 0.3 -50.0

2005 2.0 +566.7

2006 4.9 +145.0

2007 1.1 -77.6

2008 5.3 +381.8

2009 3.0 -43.4

2010 1.3 -56.7

2011 2.9 +123.1

2012 2.4 -17.2

2013 2.1 -12.5

2014 1.9 -9.5

2015 2.7 +42.1

2016 0.9 -66.7

2017 0.6 -33.3

Source: Knoema (2018)

2013 32.4 69.5 5.4

2014 35.0 74.9 5.9

2015 30.9 80.0 5.7

2016 32.2 84.6 4.5

2017 34.0 88.9 3.2



Table 4.2 revealed that the inflation rate of Cameroon also experienced a fluctuation from

2002 to 2017. In other words, it declined from 2002 – 2004, then increased from 2005 – 2006

but fell again from 2008 – 2010. It increased in 2011 but dropped from 2012 – 2014. It rose in

2015 but descended from 2016 – 2017. This detail is expressed graphically in Figure 4.2

Figure 4.2: Showing the Trend in the Inflation Rate of Cameroon from 2002 - 2017


Table 4.3: Showing the GDP and Inflation Rate of Cameroon BEFORE the Mobile Money Transfer

Scheme

Year MMTS GDP

(%)

Inflation

Rate

(%) Volume of

Transactions

Value of

Transactions

(FCFA)

Value of

Transactions

(US $)

2002 - - - 4.2 2.8

2003 - - - 4.6 0.6

2004 - - - 6.8 0.3

2005 - - - 2.0 2.0

2006 - - - 3.5 4.9

2007 - - - 4.9 1.1

2008 - - - 3.5 5.3

2009 - - - 2.2 3.0

Table 4.3 revealed that the GDP and the inflation rate fluctuated (increased and decreased)

repeatedly from 2002 – 2009, that is before the Mobile Money Transfer Scheme. These

details are expressed graphically in Figure 4.3



Figure 4.3: Showing the Trend in the GDP and Inflation Rate of Cameroon BEFORE the Mobile

Money Transfer Scheme


Table 4.4: Showing the GDP and Inflation Rate of Cameroon AFTER the Mobile Money Transfer

Scheme

Year MMTS GDP

(%)

Inflation

Rate

(%)

Volume of

Transacti

ons

Value of

Transactions

(FCFA)

Value of

Transactio

ns (US $)

2010 6,478 461,973 807 3.4 1.3

2011 35,359 1,485,327,981 2,594,110 4.1 2.9

2012 605,691 14,326,108,301 25,020,405 4.5 2.4

2013 2,131,267 106,919,195,069 186,733,305 5.4 2.1

2014 6,991,176 280,112,364,451 489,213,443 5.9 1.9

2015 13,768,731 307,589,225,761 537,201,507 5.7 2.7

2016 49,831,982 887,783,935,214 1,550,505,765 4.5 0.9

2017 210,276,929 3,412,970,418,636 5,960,718,706 3.2 0.6

NOTE: 1 FCFA = 0.00174649 US $

1 US $ = 572,578 FCFA

Table 4.4revealed that GDP of Cameroon increased from 2010 – 2014 but decreased from

2015 – 2017. However, the inflation rate of Cameroon fluctuated repeatedly from 2010 –

2017.These results are supported by the time series plot shown in Figure 4.4

GDP INFLATION RATE



Figure 4.4: Showing the Trend in the GDP and Inflation Rate of Cameroon AFTER the Mobile

Money Transfer Scheme


4.2. Test of Hypotheses

4.2.1. Test of Hypothesis One

H0: Mobile Money Transfer Scheme does not have a significant effect on the GDP of

Cameroon.

H1: Mobile Money Transfer Scheme has a significant effect on the GDP of Cameroon.

To test the hypothesis, the data on Tables 4.3 and 4.4were used. The goal was to

determine if the Mobile Money Transfer Scheme has a significant effect on the GDP of

Cameroon.

Regression Test for Hypothesis One

Table 4.5

Model Summaryb

Model r r2 Adjusted r

2

Std. The error of the

Estimate

1 .162a .026 -.035 1.25265

a. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME

b. Dependent Variable: GDP

Source: SPSS Analysis, 2018

Table 4.6

GDP INFLATION RATE

Coefficientsa

Model

Unstandardized Coefficients

Standardised

Coefficients

T Sig. B Std. Error Beta

1 (Constant) 4.337 .320 13.546 .000

MOBILE MONEY

TRANSFER SCHEME -8.522E-11 .000 -.162 -.658 .520

a. Dependent Variable: GDP

Source: Researcher‟s SPSS Analysis, 2018



Result: The result of the regression model shows that there is no statistically significant

relationship between mobile Money Transfer Scheme and the GDP of Cameroon (i.e. p >

0.05, @ 0.05 significant level). It also indicates that the strength or magnitude of the

relationship between the Mobile Money Transfer Scheme and the GDP of Cameroon is feeble

even though it is positive (r = .162). Also, the coefficient of determination (r2) which shows

the variance explained between Mobile Money Transfer Scheme and the GDP of Cameroon

indicates 2.6% shared variance (i.e. r2 expressed as a percentage). This implies that the

Mobile Money Transfer Scheme explains only 2.6% of the variance in the GDP. Furthermore,

the coefficient of the variable vis-à-vis Mobile Money Transfer Scheme (that is

0.00000000008522 with a p-value of .520) is also not significant when related with the GDP

of Cameroon for the period under study. Thus, the effect of the Mobile Money Transfer

Scheme on the GDP of Cameroon is not significant.

Decision Rule: If the p-value is less than 0.05, reject the null hypothesis and accept the

alternate hypothesis.

Decision: The result of the regression model shows that there is no statistically significant

relationship between Mobile Money Transfer Scheme and the GDP of Cameroon (r = .162,

alpha-significance is .520 at p > 0.05). Therefore, we accept the null hypothesis which states

that Mobile Money Transfer Scheme does not have a significant effect on the GDP of

Cameroon.

4.2.2. Test of Hypothesis Two

H0: Mobile Money Transfer Scheme has no significant effect on the inflation rate of

Cameroon

H1: Mobile Money Transfer Scheme has a significant effect on the inflation rate of Cameroon

To test the hypothesis, the data on Tables4.3 and 4.4were used. The goal was to determine

if the Mobile Money Transfer Scheme has a significant effect on the inflation rate of

Cameroon.

Regression Test for Hypothesis Two

Table 4.7

Model Summaryb

Model R r2 Adjusted r

2

Std. The error of the

Estimate

1 .385a .148 .095 1.42650


b. Dependent Variable: INFLATION RATE


Table 4.8


Coefficientsa

Model


Standardised

Coefficients

T Sig. B Std. Error Beta

1 (Constant) 2.469 .365 6.770 .000

Mobile Money Transfer

Scheme -2.462E-10 .000 -.385 -1.668 .115

a. Dependent Variable: INFLATION RATE



Result: The result of the regression model shows that there is no statistically significant

relationship between the Mobile Money Transfer Scheme and the inflation rate of Cameroon

(i.e. p > 0.05 at 0.05 significance level). It also indicates that the strength or magnitude of the

relationship between mobile Money Transfer Scheme and the inflation rate in Cameroon is

feeble even though it is positive (r = .385). Also, the coefficient of determination (r2) which

shows the variance explained between Mobile Money Transfer Scheme and the inflation rate

of Cameroonindicates 14.8% shared variance (i.e. r2 expressed as a percentage). This implies

that the Mobile Money Transfer Schemehelps to explain only 14.8% of the variance inthe

inflation rate of Cameroon. This is also an insignificant amount of variance explained.

Furthermore, the coefficient of the variable vis-à-vis Mobile Money Transfer Scheme (that is

0.0000000002462 with a p-value of .115) is also not significant when related with the

inflation rate of Cameroon for the period under study. This implies that the Mobile Money

Transfer Scheme cannot be used solely to predict the inflation rate of Cameroon as other

factors account for the remaining 85.2% of the variance unaccounted for.

Decision Rule: If the p-value is less than 0.05, reject the null hypothesis and accept the

alternate hypothesis.

Decision: The result of the regression model shows that there is no statistically significant

relationship between Mobile Money Transfer Scheme and the inflation rate of Cameroon (r =

.385, alpha-significance is .115 at p > 0.05). Therefore, we accept the null hypothesis which

states that Mobile Money Transfer Scheme has no significant effect on the inflation rate of

Cameroon.

4.3. Discussion of Results

For hypothesis one, the result of the test of hypothesis indicated that Mobile Money Transfer

Scheme has no significant effect on the GDP of Cameroon (r = .162, alpha-significance is

.520 at p > 0.05).Also, the value of r-squared implied that Mobile Money Transfer Scheme

only helps to explain only 2.6% of the variance in the GDP of Cameroon, and therefore

insignificant. This result or finding can be supported with the study of Nyasimi (2016) in

Kenya who revealed that there was no significant influence of Mobile Money Transfer

Scheme on Economic Growth. She also revealed that mobile money transfer deposit value

had a positive but insignificant relationship with economic growth in Kenya. This further

aligned with the result of Mawejje and Lakuma (2017) who found out that mobile money had

only a moderate positive effect in the Ugandan economy, partly because the service was

relatively new in the country and had not yet gained substantial ground in the economy.

In a similar vein, the study also agrees with the findings of Wilkison and Sundelelowotz

(2007) who argued that there are direct and indirect links between the exponential growth of

mobile telephony and the rate of economic growth in Africa. A possible explanation for this

finding could be seen in the work of Eriksson (2010) who stated that even though Mobile

Money Transfer Scheme could have positive effects with economic growth, such benefits

could be stifled by regulatory and initial investment barriers that could prevent the widespread

adoption of mobile money. In a practical sense, this could be said to be the situation in

Cameroon where the government has not deemed it fit to subsidise the development of more

local mobile money infrastructure or to adopt policies that would enable the formation of a

decentralised network of trusted mobile money agents.

For hypothesis two, the result indicated that the Mobile Money Transfer Scheme has no

significant effect on the inflation rate of Cameroon (r= .385, alpha-significance is .115 at p >

0.05). Besides, the value of the r-squared also implies that Mobile Money Transfer Scheme

helps to explain only 14.8% of the variance in the inflation rate in Cameroon, therefore

insignificant. This finding can be corroborated with the study of Habane (2012) who revealed



that there was a weak positive insignificant correlation between economic growth (inflation

rate) and mobile money transfer in Kenya as explained by the Pearson correlation coefficient

of +0.027 which was very low with the significance two-tailed test figure being 0.966 which

was greater than 0.05.

A possible explanation could be adduced from the findings of Pat (2018) who stated that

the major causes of inflation in Cameroon are increased money supply, increased input costs

of raw materials and wages as well as staggering exchange rate which has made the

Cameroon currency (FCFA) to become less valuable relative to foreign currency and has also

made imports to be more expensive toCameroonian consumers while simultaneously making

Cameroonian‟s exports cheaper to foreign consumers.

Generally, Mobile Money is an essential tool for poverty reduction since it bridges the gap

that exists between banks and poor households. Most banks do not find it economically

attractive to make banking infrastructure and financial services available in poor

communities. This is because high transaction costs relative to small transaction value sizes

make it unprofitable for banks to service this population. Similarly, poor people can be

reluctant to access formal financial services as a result of the inconvenience and high cost

involved in accessing these services relative to the more local and informal alternatives they

have traditionally used. Besides, some of them even mistrust these formal banking

institutions.For these reasons, around 2.5 billion adults in the world today are excluded from

the formal financial system and are subject to „financial exclusion.‟ This group of people are

referred to as the „unbanked.‟ Providing the unbanked with access to financial services,

known as „financial inclusion‟, is now recognised as an essential mechanism for alleviating

poverty and enhancing a country‟s broader economic development. Financial inclusion aims

to provide the unbanked,low-income households and small business with a range of financial

services which they can use to facilitate their consumption and protect themselves against

„economic shocks‟, such as illness, accidents, theft, and unemployment. An economic shock

can be severely detrimental to the unbanked‟s already vulnerable financial position, making it

more difficult for them to move out of poverty. In many less developed countries, economic

shocks can take a wide variety of forms beyond traditional financial or economic crisis; they

can also be health-related emergencies, crop failures, livestock deaths, and farming-equipment

expenses. Financial inclusion also aims to help the unbanked and low-income groups to save

and borrow which in turn can enable them to invest in education and asset generating

activities, such as enterprises. (Buckley, Greenacre and Malady, 2015)

5. SUMMARY OF FINDINGS, CONCLUSION AND

RECOMMENDATIONS

5.1. Summary of Findings

1. There was no significant relationship between Mobile Money Transfer Scheme and GDP in

Cameroon.

2. There was no significant relationship between Mobile Money Transfer Scheme and

Inflation Rate of Cameroon..

5.2. Conclusion

The importance of the Mobile Money Transfer Scheme in third world countries, where bank

accounts are unaffordable to most people, cannot be overemphasised. However given the fact

that this industry is a relatively new phenomenon in Cameroon, it has not yet attained the

level whereby it can have a significant effect on the economic growth of Cameroon. The

result of the study attests to this fact. One of the reasons that could account for this is due to

the current,severe political crisis in Cameroon which is slowing down most businesses.



5.3. Recommendations

1. The government of Cameroon needs to implement policies which will favour the further

penetration of the Mobile Money operations right into the remote interior parts of the country.

They can do so by giving incentives to MTN, Orange and Nexttel to install network poles in

those areas so that the use of Mobile Money can reach the places.

2. The government of Cameroon needs to resolve the current political crisis in the North West

Region and South West Region to facilitate the penetration of the Mobile Money services into

the interior villages of those regions. This will enable the service to be enjoyed by a greater

audience and thus boost the economic growth of the nation.

3. Increase in the tele-density of Cameroon. This could be done by subsidising the cost of

mobile phones to make it even more affordable to a wider population of the country.

4. The government needs to do more to ensure a more constant and stable supply of

electricity. This is because mobile phones are powered by electricity, and so blackouts will

only help to reduce the frequency of mobile money transactions as the phone‟s battery will be

running flat.

5. The government also needs to invest more in education so as to improve the literacy rate of

the country. This because the more literate the populace is, the greater exposure they will have

and thus will be able make better use of technology such as mobile money.

The significant contribution to knowledge in this study is filling the existing gap observed in

literature, which is absence of documented research on the effect of Mobile Money Transfer

Scheme on the Economic Growth of Cameroon.

5.4. Suggested Areas for Further Studies

This study is by no means exhaustive. The researcher has noted other possible areas for

further studies such as: Mobile Money Transfer Scheme vis-viz Interest Rate in Cameroon.

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APPENDIX

Regression

Notes

Output Created 20-OCT-2018 19:14:59

Comments

Input Data C:\Users\DELL\Desktop\MICHAEL'S

ANALYSIS\ANALYSIS FOR

MICHAEL - SPSS.sav

Active Dataset DataSet1

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data

File 18

Missing Value Handling Definition of Missing User-defined missing values are treated

as missing.

Cases Used Statistics are based on cases with no

missing values for any variable used.

Syntax REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R

ANOVA COLLIN TOL ZPP

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT GDP

/METHOD=ENTER VOT

/SCATTERPLOT=(*ZRESID

,*ZPRED).

Resources Processor Time 00:00:02.54

Elapsed Time 00:00:11.22

Memory Required 1420 bytes

Additional Memory Required

for Residual Plots 240 bytes

[DataSet1] C:\Users\DELL\Desktop\MICHAEL'S ANALYSIS\ANALYSIS FOR MICHAEL -

SPSS.sav

Variables Entered/Removeda

Model Variables Entered

Variables

Removed Method

1 MOBILE MONEY

TRANSFER SCHEMEb

. Enter


b. All requested variables entered.

Model Summaryb


2 Std. Error of the Estimate

1 .162a .026 -.035 1.25265


b. Dependent Variable: GDP



ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression .678 1 .678 .432 .520b

Residual 25.106 16 1.569

Total 25.784 17


b. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME

Coefficientsa

Model

Unstandardized

Coefficients

Standardised

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 4.337 .320 13.546 .000

MOBILE MONEY

TRANSFER SCHEME

-8.522E-

11 .000 -.162 -.658 .520

Coefficients

a

Model

Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1 (Constant)

MOBILE MONEY

TRANSFER SCHEME -.162 -.162 -.162 1.000 1.000


CollinearityDiagnosticsa

Model Dimension

Eigen

Value

Condition

Index

Variance Proportions

(Constant)

MOBILE MONEY

TRANSFER SCHEME

1 1 1.387 1.000 .31 .31

2 .613 1.504 .69 .69


Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 3.6171 4.3370 4.2556 .19977 18

Residual -2.33699 2.46301 .00000 1.21525 18

Std. Predicted Value -3.196 .408 .000 1.000 18

Std. Residual -1.866 1.966 .000 .970 18




Charts

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP


/NOORIGIN

/DEPENDENT EMPLOYMENT

/METHOD=ENTER VOT

/SCATTERPLOT=(*ZRESID ,*ZPRED).

Regression

Notes

Output Created 20-OCT-2018 19:22:57

Comments

Input Data C:\Users\DELL\Desktop\MICHAEL'

S ANALYSIS\ANALYSIS FOR

MICHAEL - SPSS.sav

Active Dataset DataSet1

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working

Data File 18

Missing Value Handling Definition of Missing User-defined missing values are

treated as missing.

Cases Used Statistics are based on cases with no

missing values for any variable used.



Syntax REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R

ANOVA COLLIN TOL ZPP


/NOORIGIN

/DEPENDENT INFLATION

/METHOD=ENTER VOT

/SCATTERPLOT=(*ZRESID

,*ZPRED).

Resources Processor Time 00:00:00.48

Elapsed Time 00:00:01.81

Memory Required 1420 bytes

Additional Memory

Required for Residual Plots 240 bytes

Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 MOBILE MONEY TRANSFER

SCHEMEb

. Enter


b. All requested variables entered.

Model Summaryb


2 Std. Error of the Estimate

1 .385a .148 .095 1.42650


b. Dependent Variable: INFLATION RATE


b. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME

Coefficientsa

Model


Standardised

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 2.469 .365 6.770 .000

MOBILE MONEY

TRANSFER SCHEME -2.462E-10 .000 -.385 -1.668 .115

ANOVAa

Model

Sum of

Squares Df Mean Square F Sig.

1 Regression 5.662 1 5.662 2.782 .115b

Residual 32.558 16 2.035

Total 38.220 17



Coefficientsa

Model

Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1 (Constant)

MOBILE MONEY

TRANSFER SCHEME -.385 -.385 -.385 1.000 1.000

CollinearityDiagnosticsa

Model Dimension Eigenvalue

Condition

Index

Variance Proportions

(Constant)

MOBILE MONEY

TRANSFER SCHEME

1 1 1.387 1.000 .31 .31

2 .613 1.504 .69 .69

Residuals Statisticsa

Minimum Maximum Mean Std.

Deviation

N

Predicted Value .3891 2.4686 2.2333 .57709 18

Residual -2.16856 2.83144 .00000 1.38391 18

Std. Predicted

Value

-3.196 .408 .000 1.000 18

Std. Residual -1.520 1.985 .000 .970 18


Charts



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EFFECT OF MOBILE MONEY TRANSFER SCHEME ON THE … · Mobile Money Transfer Scheme was the independent variable, meanwhile Economic Growth was the dependent variable and the proxies