M- PESA MOBILE MONEY SERVICE. Name: Zachariah Khadudu Odhiambo

Transcription

1 M- PESA MOBILE MONEY SERVICE Name: Zachariah Khadudu Odhiambo Roskilde University Spring 2014

2 Table of Contents 1.0 INTRODUCTION THE CONTEXT FOR MOBILE MONEY IN KENYA PROBLEM AREA CONCEPTS PHILOSOPHY OF SCIENCE PROJECT OUTLINE METHOD & METHODOLOGY RELIABILITY AND VALIDITY LITERATURE REVIEW CHOICE OF THEORY NETWORK POWER THEORY CORE- PERIPHERY THEORY DISCUSSION AND ANALYSIS M- PESA AS A NETWORK POWER NORTH EASTERN REGION AS A PERIPHERY M- PESA DISTRIBUTION M- PESA DOMINATION AND INCLUSION OF RURAL POPULATION CONCLUSION FINDINGS RECCOMENDATIONS ERROR! BOOKMARK NOT DEFINED. 2

3 List of Abbreviations AFI - Alliance for Financial Inclusion CGAP - Consultative Group to Assist the Poor CPT - Core Periphery Theory IC4D - Information and Communication for Development ICT - Information Communication Technology IMF - International Monetary Fund ITU - International Telecommunication Union PSR - Planning, Society and Resources UN - United Nations USA - United States of America USAID - United States Agency for International Development 3

4 Abstract The role of mobile money in financial inclusion is contested between proponents who argue that mobile money has had transformational impacts on financial inclusivity especially of the poor, and the critics who argue that the impact of mobile systems, even of successful ones like M- PESA is a potential yet to be considered transformational. In this project I argue that although there is a clear growth trend of M- PESA, this growth is uneven depending on the geographical location and as such there is gap of inclusivity between rural areas and urban areas. I take a multidispliary approach, using network power theory and core- periphery theory to argue that dominance of the market by M- PESA influence the inclusion of the populations in rural areas. 1.0 Introduction Financial system like banks enable people to transact, make savings, manage their financial risks and invest. When accessible and available, these services are especially beneficial to the poor and disadvantaged groups (Demirguc- Kunt &Klapper, 2012:1). Without inclusive financial systems, poor people must rely on their own limited savings to invest in or become entrepreneurs. (Ibid.) This limitation can lead to persistent inequality and slower economic growth (Ibid.). Despite the importance of financial inclusivity to economic development, financial services are not readily accessible especially to the poor and marginalized populations. In the first analysis of the Global Financial Inclusion database (2012), the World Banks puts a number of adults without a formal account at 2.5 Billion worldwide. This represents 36 percent of the world s population. The majority of these people live in the developing world. The need to increase financial inclusion to such people has led to emergence of alternative ways of financial systems such as mobile banking. 4

5 According to the World Bank s 2012 Information and Communication for Development report (IC4D) there are at- least 110 mobile money systems worldwide with more than 40 million users. The most well known is M- PESA 1 started in Kenya and is now operational in over six countries (World Bank, 2012:61). Despite the successes of M- PESA however, there are challenges and that emerge that need to be addressed if M- PESA and other mobile systems are to reach the majority the financially secluded populations. 1.1 The Context for Mobile Money in Kenya Before M- PESA was invented, many Kenyans did not have a formal financial service. One study puts the number of unbanked people in Kenya by 2007 at over 70 percent (Kimenyi &Ndungu, 2009). Another study puts the number of unbanked people by 2007 a little lower, at around 66 percent (Michaels, 2011). Regardless of the different methods used by these studies to arrive at these numbers, it is clear from both studies that two thirds of Kenyans did not have an account at a formal financial institution as at Before mobile money service came to be, those who lacked a formal financial service relied on alternative methods, often riskier and unreliable to carry out financial activities like sending money and saving. Culturally, in Kenya, financial assistance to relatives is a norm. For example, a young person working in the city would be expected to send some money to his parents or other relatives in the village. With no bank accounts, they relied on informal methods such as sending money through friends and other family members traveling to the village, or sending the money through bus drivers or taxi (matatu) 2 drivers to deliver the money. The farmers in the villages also relied on such methods to sell their goods through middlemen and receive payments. These methods were and to an extent remain the cheapest and possibly the riskiest as some, or all the money could be lost along the way (Mas & Morawczynski, 2009). 1 M is for mobile while PESA is the Swahili word for money. 2 This is a form of shared taxi that carries around fourteen passengers. 2 This is a form of shared taxi that carries around fourteen passengers. 5

6 Having identified the need for a more secure and reliable form of money saving and transfer, the largest mobile network operator, Safaricom (part of Vodafone, Group) launched M- PESA in March 2007 (Hughes & Lonie, 2007). The aim was to narrow the gap of financial exclusion and allow more people access some form of a financial service. The International Monetary Fund (IMF) notes that since its inception M- PESA has grown exponentially in Kenya from its pilot project covering 500 users to 14 million registered members (40% of Kenya s population) by April The service grew beyond the Kenyan boundaries to other countries including Tanzania and Uganda. 1.2 Problem Area From when it was establishment in March of 2007, M- PESA has been the subject of several researches, news reports and case studies in mobile money service. The arguments have generally been contested between those like Jack &Suri (2011), Mas & Morawczynski (2009), Hughes & Lonie (2007) who argue that M- PESA has had widespread transformational effects in facilitating financial inclusion to the poor, and those like Duncombe&Boateng ( 2009) who argue that assessment of M- PESA s impact on financial inclusion of the poor is still a nascent possibility yet to be optimally explored. The IMF, reports that the emergence of M- PESA enabled low- income earners who could not access the mainstream banking services to access the mobile telephone money services. Payments worth 80 billion Kenyan shillings (Appx. 1billion US dollars) equivalent to 31% of gross domestic product, GDP are channeled through M- PESA monthly (IMF, 2013:14). In the IMF working paper (WP/11/73), Andrianaivo & Kpodar (2011) argue that the mobile telephony reduces costs of transactions especially the costs of running physical bank branches. The increasing use of mobile telephony in developing countries has contributed to the emergence of branchless banking services, thereby improving financial inclusion. (Andrianaivo & Kpodar, 2011:7) However, despite these evidential successes of M- PESA in terms of growth and to some extend inclusion of the poor, other researches cite the monopolistic dominance of M- 6

7 PESA as problematic. Donovan (2012) writes that the dominance of M- PESA should be interrogated as it may have a bearing on inclusion of the poor by exerting its network power through means such as direct force, for instance by applying prohibitive costs of transaction effectively locking out the poor. In the theory of network power used in this project, Grewel (2008) argues that when a platform (such as M- PESA) grows and gains monopolistic dominance status, beyond a certain point, members of a small network are forced to adopt the standard of a dominant network or face isolation. As such some people unable to join the dominant network remain financially secluded. The growth of such network powers also appears to be uneven based on geographical locations. According to the Consultative Group to Assist the Poor (CGAP), the growth of financial services such as M- PESA has been uneven, favoring urban populations than the rural ones. These financial services have grown unevenly in Kenya with more growth taking place in population dense areas such as Nairobi and Mombasa than in the Arid and Semi Arid Lands (ASAL) in the North- Eastern part of the Country. When the World Food Program (WFP) attempted to use M- PESA services to distribute funds for food aid to the poor rural communities, they were unable to do so after finding out that in the rural areas network connectivity was not strong enough to process the payments (Zimmerman et al, 2014:6). This led to the organization reverting to other means to avail the funds to their beneficiaries. From analysis of these studies it is clear that despite the general success of M- PESA there is an imbalance in its growth between the urban and rural areas. Its growth as a means of financial inclusion has been more favorable to the urbanities than to the rural populations. This research project takes its point of departure from these two identified issues of concern; the dominance of M- PESA, and its uneven growth. With a focus on the rural areas of Kenya (taking the Northeastern region as an example of rural areas) the underlying assumption of this research is the premise that there is a correlation between the dominant power of M- PESA and its uneven growth between the urban and rural areas. Based on this premise the following problem formulation is made. 7

8 Problem Formulation: What effect does uneven growth of M- PESA as a network power have on the financial inclusivity of the populations in rural regions of Kenya? Working questions: 1. What data do I investigate for the dominance and uneven growth of M- PESA 2. What are the main arguments in the theories used as applies to Kenyan context? 3. What characterizes the rural areas in Kenya in terms of living standards and infrastructure? With 2.5 Billion people financial secluded, it is imperative that issues of barriers for their inclusion be addressed. Zuckerman (2010) like some other researchers argues that inclusion of the poor in the financial services is vital but challenges abound. For everyone engaged in empowering the poor, mobile phones are a critical platform for deploying tools and services. But these platforms have troublesome traits cost, control, and barriers to innovation. (Zuckerman, 2010:102). These troublesome traits call for attention. That is why research such as these is relevant. Duncombe & Boateng (2009) in their review of available researches in mobile money technology, called attention to the need for increased research that does not only focus on the design of the M- PESA, but also on its impact to society. Donovan (2012) recommends that success of M- PESA should be considered critically especially how its relation to freedom of choice and available alternatives services. Moving towards impact assessment includes an analytical and critical study of how M- PESA has grown to the levels it has reached. The formulated problem encompasses aspects of technology, human relations, and geographical locations. This project takes an interdisciplinary approach to address the formulated problem. The theories used will be drawn from Planning, Society and Resources (PSR) and from Sociology. As an infrastructural service, M- PESA is dependent on structures such as availability of network connectivity. Geographical locations therefore have a bearing on how well the service functions. From PSR, theories that seek to explain developmental issues in relation to geographical locales (urban or 8

9 rural) will be considered and used in this project. From sociological perspective, I argue that services such as M- PESA are platforms that allow networks of people to be connected. As these networks of people grow through a given platform, they increase the visibility and importance of that particular platform and they make it more attractive for others to adopt it. It is worth noting, that although this research draws from two subjects, the analysis in this project will be crosscutting through the subjects in an aim to integrate the two subjects. Integration is a critical part of an interdisciplinary project without integration, intersubjectivity becomes merely approaching the problem from two different subject perspective which would lead to multi- displinarity (Repko, 2012:98) rather than interdisciplinarity. 1.3 Concepts Network power: Constructed from two terms: network and power; this theory focuses on the way networks work and how they evolve (Brown, 2010). Grewel defines a network as an interconnected group of people linked to one another in a way that makes them capable of beneficial cooperation which can take various form including the exchange of goods and ideas (Grewel 2008:20). These interconnections between people are made possible by a standard such as M- PESA, which is a shared norm, practice or platform. He defines a network s power as the amount of real and potential influence that the standard exerts in relation to others. This concept of Network power will be described in details in the theory chapter and operationalized in the subsequent chapters. Financial Inclusion: Financial inclusion in the traditional sense refers to the state of having an account and an access to a formal financial institution. Formal financial institutions understood as including banks, commercial bank and microfinance institutions. Financial inclusion of a particular group of people, particular region, country or areas is indicated by factors such as penetrations rates and accessibility to loans (Andrianaivo & Kpodar, 2011). The term has been extended to include access to financial services provided by emerging technologies such as mobile money service and Internet based money service providers. 9

10 1.4 Philosophy of Science Like other scientific investigations, this project is built on some assumptions. Assumptions are part and parcel of scientific inquiry. Since it is impossible to have complete knowledge of a subject, assumptions are necessary for a scientific inquiry to proceed (Nachmias,1996). These assumptions Nachmias & Nachmias (1996:5) writes are unproven and unprovable (Ibid.). It will be noted in the project whenever such assumptions are made. This includes the assumptions made by the theories used this project: Network power theory and Core- Periphery Theory (CPT). Nachmias & Nachmias (1996) also notes that epistemology; the study of the foundations of knowledge, examines the nature of these assumptions and how they work in a scientific investigation. To help us understand the real world, it must be empirical that it must rely on perceptions, experiences and observations. (Nachmias & Nachmias 1996: 5). To ensure an accepted criteria for empirical objectivity (truth) (Ibid.) a scientist cannot make claims for objectivity until other scientists have verified his/her claims. (Ibid.). To this end this project has sought to use empirical data from peer reviewed sources, and from other sources such as World Bank documents which are generally acceptable in scholarly research. The peer- reviewed sources offer the benefit of intersubjectivity, which involves the sharing of observations and factual information among scientists (Ibid.) 1.5 Project Outline In the first chapter, I introduce the general background of this project. I start with the general situation of financial inclusion. Using data from World Bank and IMF I note through selected numerical figures the numbers of people worldwide who are financial excluded and the emerging technologies that seek to ensure more inclusivity. Mobile money technology has been spearheaded as one of the technologies that could help narrow the gap by ensuring more people, particularly poor people from developing countries have access to some form of financial services. 10

11 In the chapter I also identify the major arguments in the area of mobile money technology. M- PESA has been a successful transformational power, others argue that at best the impact of impact of M- PESA has yet to reach the transformational point at worst that it has created new problems such as uneven growth. In the second chapter I explain the empirical data used in this research. I describe the kind of material that I have selected and how I chose the kind of sources used in the project. As a secondary research I describe the types of available material: journals, conference papers, case studies, documents from organizations, country reports and other sources. I also argue in the second chapter, how reliability and validity have been achieved in the chosen data. The third chapter introduces the theories used in the project. One theory is the theory of Network Power as developed by David Singh Grewal. The concept of network power is the power that successful standards (such as M- PESA) posses when it enables cooperation among members of a network (Grewel 2008:10). The theory of network power seeks to explain globalization, social relations and technology. As people become more interconnected, we adopt same standards (such as social media, mobile money technology) that allow us to keep connected. As more people adopt a given standard, a dominant standard such as a monopoly may emerge, which may give rise to other new challenges both foreseen and unforeseen. Applied to the Kenya and M- MPESA, this theory helps to explain the dominance of M- PESA in the Kenyan mobile market. M- PESA as a standard is a platform that helps people in Kenya to be connected in financial transactional terms. The people connected in social ties such as family members or business partners are networks who can send and receive money through the common standard M- PESA. As more people join this particular standard, it grows rapidly to a dominant level. The other theory is the Core- Periphery Theory (CPT) developed by John Friedman. It states that the world can be divided majorly into four regions. In general terms these regions can be described as core or periphery regions. In continental terms, the theory argues, core regions, which are the rich countries such as USA, GERMANY and France, 11

12 attract labor from the periphery. This may be due to the high economic rates at the core compared to the periphery. At the national level, the core include metropolitan areas while the periphery are the rural areas. In this project, CPT theory is used to analyze the rural urban connection in terms of infrastructure and M- PESA growth levels in rural versus urban areas. In chapter 4, I carry out an analysis cross cutting Planning Society & Resources (PSR) theory of Core- Periphery and sociological theory.- Network power. I investigate the issues that arise from the dominance of the mobile money market by M- PESA and how this affects the rural populations. I cite the example of Northeastern region of Kenya as an example of a periphery (rural) area.. I argue that M- PESA has grown extensively to dominant level through several factors such as its attractiveness due to the big community it commands. I demonstrate that uneven growth of M- PESA in the rural and urban areas can be explained through Core- Periphery Theory of John Friedman (1966) and as operationalized in models by researchers such as Krugman (2005) and Venables (1995). I discuss how the dominance of M- PESA relates to its uneven growth and how this influences financial inclusion of the poor. In Chapter 6, I conclude the project. First I cite the findings achieved from the investigations and make recommendations for further research consideration 12

13 2.0 Method & methodology Nachmias & Nachmias (1996) argues that science must be empirical if it has to help us understand the real world. That is, it must rely on perceptions, experience and observations. (Ibid.p.6) That is what this project s empirical data choice seeks to achieve. To tap the perceptions of the role of technology network M- PESA, the experiences and observations as documented in the existing body of knowledge that includes books, journals, official documents such as World Bank and IMF reports, news articles, government documents, policy briefs and websites and then make an analysis and draw a conclusion. Because international organizations like the World Bank, IMF and UN s agencies have a keen interest in the mobile money technology, they have collected primary data, commissioned studies and researches. The statistical data analyzed in this project will be sourced from these organizations and other independent research organizations such as Microfinance Inclusion Exchange (MIX). These statistical data include M- PESA distribution in the Northeastern and Nairobi region. The statistical data also included the M- PESA agent ratios to the populations in urban and rural areas. The government of Kenya through its agencies such as the Central Bank of Kenya, Communications Commission of Kenya, Kenya Bureau of Statistics have also collected and published several statistical data that have been used by other researches, which will be referenced in this project. Industry practitioners such as AFI (Alliance for Financial Inclusion) and CGAP (Consultative Group to Assist the Poor), donor institutions such as USAID have also spearheaded development of research in mobile money technology. The literature review will therefore encompass academic sources (journals, working papers, and conference papers. M- PESA mobile service has also received sizeable attention in the international news media. Due to the credibility of some of the news organization they will be also used as reference sources for this project report. 13

14 2.1 Reliability and Validity In selecting the data for the project issues of reliability and validity were considered. Reliability is concerned with the question of whether the measures that are devised for concepts are consistent (Bryman, 2008:31). The question of reliability also concerns whether the sample can be replicated to a wider group than those who took part in the study. There are varying levels of reliability in this project. Reliability of sources in research refers to the consistency of a measure of a concept (Bryman, 2008:149). Sources used in this project from organizations such as World Bank, IMF, CGAP and USAID have been involved in collecting primary data involving large samples in rural and urban areas and carefully constructing research designs by use of repeated measurements. The mentioned organizations have devoted significant time, presence and financial resources which are some of the hindrances to independent researches (Duncombe & Boateng, 2009:24). They (organizations mentioned above) have used extensive and clear methodologies to undertake their researches. Despite critics arguing that these organizations are politically motivated, independent researchers have repeated their research methods and they are generally at acceptable reliability levels among researchers. Validity in research refers to whether the research measures what it claims to. it refers to the issue of whether an indicator (or set of indicators) that is devised to gauge a concept really measures that concept (Bryman, 2008:151). Researches such as Microfinance Information Exchange have collected primary data in both urban and rural from users and non- users of M- PESA to carry out an analysis. These researches have used triangulation methods to ensure validity of the studies. Researches particularly from practitioners have been less reliable due to constraints such as inability to access some remote rural areas in Kenya. Limited resources have also contributed to less elaborate sampling in the rural areas. In the urban areas however, both independent researches and donor- funded researches have been able to conduct primary researches by sampling both users and non- users of M- PESA. They 14

15 have employed methods such as methodological and data triangulation to ensure validity. From the selected materials in this project I have undertaken a triangulation of data methods by drawing from various sources both independent researches, donor and government sources for validity. 2.2 Literature Review As a secondary research, this project report is conducted based on the existing body of literature. As noted earlier, Duncombe and Boateng (2009) write that the existing literature on the subject of mobile money technology is majorly as a result of donor funded research, telecommunication and banking industry funded researchers, academic researches and independent consulting research. The strong link between the practitioners and the research community has facilitated the significant growth of research in mobile money technology. But it has also caused research to become too narrowly defined and largely atheoretical (Duncombe & Boateng, 2009). Independent researchers involvement has contributed to the shift in research to wider issues. The research in mobile money technologies is shifting from just focusing on structural issues of design and application to issues of identifying the needs (demand) of the poor and the impact of these services. This has raised interest in academic research in the subject. International monetary and policy organizations such as the World Bank and IMF have created huge databases including database on financial inclusion and population distribution. This is due to their mandate in policy formulations, involvement in monetary, developmental and financial issues. These organizations have commissioned case studies such as IMF case study (IMF, 2012).The organizations have also consistently produced country reports (IMF, 2013), case studies, and other country and international comparative analyses. As such a sizeable available literature on mobile money technology is from these sources. The general observation from the international organization sources is the focus on the design and functionality of M- PESA. They have also placed special focus on mobile money s financial inclusion aspect of the poor people in the developing world in general and in Kenya in particular. There is keen focus on the success of M- PESA and its study as 15

16 a case that could be reproduced elsewhere. However it is noted in these publications that despite the success in Kenya, mobile money technology has not picked as well in other countries such as Tanzania, compared to Kenya...the user uptake of the service in Tanzania has been much slower (Camner, Sjòblom & Pulver 2009) Donner, Jonathan, Tellez & Camilo (2008) focus their research on the impact to society of adopting innovations such as M- PESA. They note that for example that less attention has been paid to the social, economic and cultural contexts surrounding the use of the systems (Donner & Camilo 2008). There is also substantial number of academic journals and articles on the theme of mobile money transfer. I identified and reviewed 30 journal articles from academic journals. These were generally drawn from planning, economics, political economy, development studies and sociology. Most of the academic journals and essays used in this project are peer reviewed and/or have been numerously cited in other scholarly works. The academic sources generally take a more critical look at M- PESA and its impact. Most of the early ( ) research conducted on M- PESA took a futuristic approach. These researches considered the potential of M- PESA as a nascent possibility as opposed to an already observable feature. In their study Jack & Suri (2011) considered the mechanics of M- PESA and reviewed its potential economic impact (Jack &Suri 2011). Kendall et al (2011) identified the need for M- PESA to integrate its systems to other services such as banks. They also identified transactional issues and the need for mobile money providers, especially M- PESA, to lower integration costs (Kendall et al, 2011) Considering that the concept of mobile money is a rather recent concept, and with the influence of new online media, there are not many books published that directly address this concept. However there is substantial availability of online scholarly works of the concept. The online sources that include websites, online scientific journals and essays also form large part of this project. These online sources like the other literature 16

17 have been identified due to their relevance to the project. As an interdisciplinary project, sources have been drawn from sociology and PSR. Despite lack of academic books addressing the question of mobile money technologies, there are a number of academic books used in other sections of this study. These books are used mainly as reference points in the methodological and theoretical sections. They are also used as references in the general writing design and layout of the project report. Bitsch & Pedersen (2008) have inspired the introductory parts of this project; the problem area, problem formulation and conceptual segments. Nachmias & Nachmias (1996), Gerring (2012), Kuada (2012), Repko (2012) among other books have been used as reference points in different areas of this project. Grewel s book Network Power is the source of the network power theory. 17

18 3.0 Choice of Theory 3.1 Network Power Theory This research takes a theoretical- based approach. Both theories in the project are drawn from social sciences subjects of Sociology and PSR. The theories are interpretivistic in nature. Interpretivism challenges the conventional wisdom held by positivist theories that suggest that approaches in natural science can be applied to social sciences in study of social reality (Bryman,2008:16) Interpretivism challenges this view. Interpretivism is predicted upon the view that a strategy is required that respects the difference between people and the objects of natural sciences (Ibid.). Interpretivism requires the social scientist to grasp the subjective meaning of social actions. The theory of Network power developed by Grewel (2008) is applied to investigate how M- PESA as a network standard has attracted huge numbers of subscribers and become the dominant market leader. It further seeks to explain how the dominant standards socially and economically influence society. The major assumption in the network power theory is that standards are incompatible, which means someone can use either Standard A or Standard B, but not both at the same time. Given incompatibility, Network A can exert power over Network B, since users must adopt either standard A or Standard B to gain access to their respective networks. (Grewel, 2008:). The sheer size of the bigger Standard will make it more attractive that a smaller standard, This assumption that standards are incompatible holds for M- PESA especially in the first years of its formation. Interoperability otherwise referred to as compatibility is the acceptance of parallel or simultaneous standards to gain access to a given standard (Grewel, 2008:). Donovan (2012) writes that for a long time, M- PESA was not interoperable with other mobile networks. This meant one could only choose to use M- PESA or a different provider, as M- PESA systems did not allow for cross- network transactions. Interoperability would allow third parties to lower the cost of adopting M- PESA thus reduce the cost of adopters driven by its network power. This risk of lower costs for 18

19 adopting M- PESA would lead to reduced revenue for M- PESA hence its reluctance to adopt interoperability designs. In describing how network power works, Grewel uses the example of a language. A language is more valuable for us to learn if many other people are speaking it. In general therefore, the larger the network, the more powerful the standard underlying it will be- and the more pressure non- users will feel to adopt that standard. (Grewel 2008:10). When it was founded in 2007 M- PESA was the only mobile- based financial service. In 2009 YU- Cash entered the market, Orange money in (2010) and Airtel money in (2010). Due to lack of interoperability, the choice of one Standard meant the loss to another. If a member of YU Cash decided to adopt M- PESA to connect because it had a more people (Network Power), then this meant a loss of a member by YU. Table 1: Mobile money providers Company Mobile Subscriber 17.5 Million Mobile market share Date of mobile launch 69.89% March 2007 Mobile money subscribers Mobile money agents 15.5% 28, Million November 2010 (as Zain Zap) launched in August Million 6.37% November Million 8, ,000 3, Million 8.50% December ,000 5,400 Source: USAID (2011) As of November 2011 as shown in the table above, M- PESA dominated the market by subscription numbers and the amounts channeled through the service. This in effect 19

20 made it the dominant standard with a mobile money market of percent and mobile money transactions market share estimated at 99% (Michaels, 2011:8). The disparity between M- PESA and the other networks is so huge that monopolistic status is observable in M- PESA. The huge gap, Grewel argues, characterizes dominant Standards. As a given standard becomes dominant and moves to universality, it eclipses rival standards that formerly facilitated the same activity. It mays also prevent the emergence of alternative forms of coordination in the future. The choice to adopt the dominant Standard then becomes an increasingly coerced one, for the only options are to join it or face social isolation (Grewel 2008:12) In summary, Network Power Theory argues that the rise of globalization has led to emergence of shared forms of social platforms (Standards). These standards, enables sharing among and within networks displays Network Power, which means that a standard is more valuable when greater numbers of people use them, making them gain power, which can lead to elimination of alternative standards by absorbing the alternative standard s members. 3.2 Core- Periphery Theory John Friedman (1966) developed the Core- periphery- Theory (CPT). In the theory Friedman argues that regions in the world can be divide into four. Adopters of this theory have suggested that these four regions can broadly be narrowed down to two categories classified as being Core or Periphery. At an international level Krugman&Venerables write the Core and Periphery could be labeled global south and global north. Figure 1: Core- Periphery Theory model 20

21 Core Key industries, jobs, formal and alternative financial services Good infrastructure Centre for economic, political and so Spill- over effect Processed products Knowledge Periphery Few jobs Few/no industries, financial services Poor infrastrure Backwash effect Raw material Labour A model of Core- Periphery Theory showing the relationship between core and periphery. The theory is based on the assumption that development is a zero sum situation, where a gain by one part means a loss by another. The point of departure of this theory however is the argument that this zero sum situation runs circularly. Having exhausted their resources Core areas (developed world, or metropolitan cities like Nairobi) rely on resources from the Periphery areas (such as rural areas, northeastern region of Kenya) As the Core areas experience high economic growth and development, these spills over to the periphery. On the other hand the periphery populations will be attracted to the Core drawn to it by the infrastructure and high economic rates. This leads to a backwash effect, when the people in the periphery are attracted to the Core. 21

22 Figure 2: The Map of Kenya showing the Northeastern region and in the east and Nairobi in central Source: World Maps This theory is adopted in the Kenyan context. The urban regions and rural areas are assigned as being Core and Periphery consecutively. The Urban regions are the economic, political and social centers while peripheries are the marginalized rural areas (such as northeastern) with little or no direct political or economic influence. However this region has resources such as labor or natural resources that are usable in developing the Core regions. There is a relationship therefore between these two regions. The Core being dependent on the periphery for capital and labour while the excess of the core can spill over to the peripheries.. In the following chapter these theories (Network Power and Core- Periphery theories will used to analyze the Rural- Urban relationship in Kenya. 22

23 4.0 Discussion and Analysis As demonstrated in Chapter 3, two main observations can be made. One is the fact that M- PESA is the dominant mobile money provider in Kenya and therefore a dominant standard. The second observation is that the spread of populations in Kenya can be defined in terms of Core and Periphery as being rural and urban. It is noted that the majority of the population is in the rural areas. This Chapter integrates the analysis of uneven growth of M- PESA in Rural and Urban Areas and how it is affected by dominance of M- PESA. The chapter is therefore and integral discussion of Network Power and Core Periphery Theory 4.1 M- PESA as a Network Power Before delving into the discussion of how M- PESA dominates the market, it is important to describe how the service works. A customer who owns a Safaricom SIM card visits one of more than 40,000 Safaricom agents located across the country to register as an M- PESA customer, withdraw cash or deposit cash from ones M- PESA account (Safaricom, 2014). Customers can then use an application on their mobile phone to check their balance, send money to other people, pay bills and purchase mobile phone airtime (Morawczynski, Olga & Pickens, 2009). The customer visiting the Safaricom agent is required to show a valid identification (such as national identification card or a passport), all the transactions are fed into an intergrated mobile system and for every transaction undertaken the customer receives a confirmation text message (Mbiti & Weil, 2011) The theoretical description above of how M- PESA functions can be modeled as follows: A typical user of M- PESA in a Metropolitan city like Nairobi or Mombasa might receive his/her salary paid in cash or bank deposit. The user visits an M- PESA agent to load his/her M- PESA account which is their personal digital wallet (Donovan, 2012: 2648). The user then sends the value of that amount to a relative for example who lives in the Northeastern rural region of Kenya. The relative visits another M- PESA agent in the rural area to withdraw the money. A set amount for withdrawal is deducted from the amount the receiver receives. 23

24 The assumption here is that there is an M- PESA agent both in the urban area where the sender sends the money from and another at the rural area where the recipient withdraws. While this is a real possibility, the distribution of M- PESA agents in the rural and urban areas shows a huge difference making such a scenario untenable for many of those in the rural areas who cannot readily and easily access M- PESA for reasons such as long distances to the agent as will be discussed further on (4.2) The mobile network operator company Safaricom (part of Vodafone) launched M- PESA in Kenya in March In May 2008, 14 months after the launch, M- PESA in Kenya had 2.7 million registered users (Camner, Gunnar, Sjòblom,& Pulver, 2009). The number of users continued to steadily rise in the subsequent years and as early 2011 more than 14 million people were registered as M- PESA users (Donovan, 2012) and by the end of 2011, there were approximately 16 million users. Figure 1 below shows the trajectory of M- PESA users between April 2007 and November Figure 3: The number of M- PESA users Source: Donovan (2012) Grewel (2008) argues that the economies of scale drive the adoption of a particular standard as a result of positive feedback. Each new user who joins M- PESA standard, increases the desirability of that standard in the eyes of other potential users (Grewel, 24

25 2008:25). It is the positive feedback generated by the adoption of M- PESA that constitutes its power, the pull that the standard has (Ibid.). The premise of network power as Grewel observes is that the benefits that come from using one standard rather than another increases with the number of users. So the more people registered for M- PESA, rather than join existing banks or other standards, helped to build M- PESA as a dominant standard. Before M- PESA was founded, Kenyans relied on mostly informal ways of conducting financial services such as sending, receiving and saving money. The Alliance for Financial Inclusion, AFI ( 2010) describes the informal ways that Kenyans relied to transfer funds included sending money with trusted bus, travellers, relatives and to the few who had access to formal banking services, deposits. Table 2: Money Transfers before M- PESA Means of transfer Local Money International Money Transfers % Transfer % Sent with family/friend Through bus or matatu company Post Office money order Directly into bank account Using money transfer service 9 66 By cheque 4 8 Paid into someone s else account, who then passed it on 3 8 After M- PESA came into the market, there was a significant shift in the way money was transferred in Kenya as shown in the Figure below: 25

26 4.2 North Eastern Region as a Periphery One of the reasons cited for the success of M- PESA service has been the fact that growing urbanization has meant that there is an increased number of people from the Periphery- rural areas attracted to metropolitan cities such as areas as Nairobi and Mombasa. This establishes an important link between the migrants moving from the Periphery- rural areas to the Core- urban areas. Since they live behind their families, they become the link between the rural and the urban. This rural- urban ties are strong enough to require significant transfers (IMF, 2013:9). In the CPT theory, Friedman argues that the concentration of economy from the periphery to the core begins as a result of capital accumulation and industrial growth. The interregional mobility of labour and intensity of trade enormously. (Raagmaa, 2003:). Kenya s capital Nairobi as the economic and political center attracting people from across the country especially rural areas in search of work, studies and adventure. While Friedman s assumption of this theory is that a gain in either the Core or Periphery leads to a loss in the other, Krugman and Venables (2005) in their model of this CPT theory argues that this assumption is justifiable. On an international level Krugman notes that increased globalization, a close Integration of world markets, affect the real incomes of core and periphery nations. (Krugman &Venables, 1995:858). The UN population statistics note that the majority of Kenyans (75.6%) live in the rural areas while 24.4% live in the urban areas. This translates to 31 Million people in the rural areas and 10 million in the urban areas.(un data, 2011). Despite the majority of the populations living in the rural areas, financial services have been concentrated in the urban areas than in the rural areas. 26

27 4.3 M- PESA Distribution As seen in the graphical representation of M- PESA users growth in Figure 1, the number of agents had a positive correlation to the number of agents. Figure 2 below shows the upward growth of M- PESA agents countrywide. Figure 4: Number of M- PESA agents Regardless of the visible growth of M- PESA agents, some areas are well covered by M- PESA agents than others. The Micro Finance Information Exchange (2012) puts the total number of M- PESA agents in Nairobi region at Nairobi has a general population of 2.1 Million people. North Eastern region with a population of 1.3 Million people has only 117 M- PESA agents. With no indication of how many of these are adults, an assumption is made that these numbers represent the general population that may benefit the use of M- PESA. Using these numbers, the agent to population proportion for M- PESA in Nairobi is 450:1 (450 people for a single agent). The ratio for North Eastern is 11,110:1(11,110 people for a single agent). This huge difference shows the uneven distribution of M- PESA agents to the rural- urban areas. 27

28 These challenges of uneven growth of M- PESA, according to Mas & Ng weno (2010) are observable in early- stage development of payment systems. Rural areas in Northeastern Kenya are characterized by poor infrastructure and unevenly distribute populations and low- key economic activities. This makes it unattractive for extensive investment by firms including financial service providers such as M- PESA. Mas & Ng weno write that it is a chicken- and- egg trap In order to grow M- PESA had to attract both customers and stores in tandem. It is hard to sell the preposition to customers while there are few stores to serve them and equally hard to convince stores to sign up while there are a few customers to be had (Mas & Ng weno, 2010:). 4.4 M- PESA domination and Inclusion of Rural Population As mentioned, the rural populations in Kenya are unevenly distributed covering wide areas therefore less attractive to financial services such as M- PESA. The metropolitan centers like Nairobi and Mombasa on the other hand are densely populated therefore more attractive to elaborate mainstream financial systems like banks and microcredits, and alternative services such as M- PESA. In his study on how Kenyans use mobile money services to save, Morawczynski found that in rural areas, home bank was the most popular mechanism for savings (Morawczynski, 2009:10). This is the most preferred method for saving, as it is convenient and easily accessible. The unavailability of enough agents is also a contributing reason for the rural people choosing home banking as opposed to visiting agents who are unevenly distributed over long distances. (Ibid.) Gigler (2004) in his studies of urban rural ICT penetration finds that knowledge power is another reason that creates a barrier for the rural populations to access the financial services on the same level as urban populations. He argues that not only do rural communities lack access to information and knowledge but also that policymakers lack knowledge of poor and marginalized groups. (Gigler, 2004:4). In the Microfinance Information Exchange report (2012), M- PESA was the only mobile money service provider in Northeastern region. It enjoyed the monopoly of mobile 28

29 money services. As such M- PESA had the network power over the region. Grewel (2012) notes that the network power can be imposed as either direct force or indirect force. Direct force involves use of such things as imposition of costs or denying benefits (Grewel, 2012: 36). Indirect force is the opportunity cost that arises from non membership in a network. M- PESA service provider could not impose different rates in the Northeastern region compared to the urban region even though by being a monopoly it possessed the network power and the possibility to do so. Since M- PESA rates are standardized nationally, there is no evidence to suggest that different rates were applied to the Northeastern region different from Nairobi or Mombasa. To this extend, despite the theory of network power suggesting use of direct force such as costs by dominant networks, this was not observed in the case of M- PESA in the region. Other factors being equal, no part of population in the northeastern region was financially secluded by M- PESA because higher costs than usual rates were imposed to them and not to other users. 29

30 5.0 Conclusion So far, an analysis has been undertaken based on the working questions in the problem formulation. The analysis has focused on the dominance of M- PESA as a network standard, the uneven distribution of M- PESA in the rural and urban areas, the analysis of the rural and urban areas as Core and periphery regions based on the Friedmans CPT theory. In this chapter some findings from the analysis are presented followed by recommendations for further researches on mobile money technology. 5.1 Findings The analysis in this paper has shown that M- PESA is a dominant force whose growth has been uneven. The growth of M- PESA has been greater in the urban regions of Kenya than in the rural areas. This uneven growth has meant that the urban populations have stood better chances of adapting to M- PESA than the rural populations due to proximity. M- PESA agents are unevenly spread between urban regions and rural areas. The ratio of number of people per single M- PESA agent is much lower in the urban areas than in the rural areas. The urbanites are better served by M- PESA due to ease of access to the agents than the rural areas. There is higher possibility of the urbanites to be financially included by use of M- PESA than the rural populations who face barriers such as long distances to the agent. The theory of network power can explain how M- PESA exponentially growth qualify it as a network power exerting its influence on other standards and on networks. People are attracted to the M- PESA due to its big size. By joining M- PESA, which is the market leader, the individuals have a large network of people as compared to if the chose a smaller network. This theory fits the M- PESA model but with some reservations. For instance, despite being the dominant power and enjoying monopolistic status in the northeastern region, M- PESA cannot be less favorable to rural areas as compared to urban areas in terms of transactional fees as standard rates are used for M- PESA nationwide. 30

31 5.2 Recommendations M- PESA as a mobile money service provider presents huge opportunities for continued research. This research sought to study the growth of M- PESA, its dominance of the market and the influence it has on the financial inclusivity of the rural populations. The dominance of the service is clear, the uneven distribution of M- PESA agents is also clear in the northeastern region. However, rural Kenya covers other areas of the country apart from northeastern region. There is opportunity for further studies in those other rural areas of Kenya such as northern Kenya to determine how the presence of M- PESA is in those regions. Such studies together with this study focused on northeastern region can help in analyzing a clearer picture of the impact M- PESA has on financial inclusivity of the populations in rural areas. 31

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