Why has M-Pesa been so successful in Kenya, yet mobile money initiatives in other developing countries much less so? Recent Centre for Development Informatics research can help provide a systematic response.
M-money services have two core functionalities. Registered customers can convert between e-cash and real cash (typically at the physical premises of an m-money agent), and can transfer e-cash from their account to that of another account holder via SMS. They might use this to send money to family members or friends, or to pay a provider – anyone from a taxi driver to a local school – for goods and services.
M-Pesa was launched in Kenya in 2007. It has grown spectacularly: in mid-2012, there were 19.5 million m-money users in Kenya (83% of the adult population), transferring nearly US$8 billion per year (equivalent to 24% of GDP) – M-Pesa is responsible for more than 90% of these transfers. Transfers are growing at nearly 40% per year.
It’s not that m-money initiatives in other developing countries have failed: there are an estimated 250m users of m-money services in emerging markets. Just that they have not – yet – succeeded on anything like the scale of M-Pesa, with Kenya accounting for 30% of all emerging market m-money transactions in 2011. For example, a recent survey in South Africa found only 16% of respondents with a mobile money account. In Nigeria, only 3% of adults use mobile money. And Africa is the lead continent: outside the Phillipines, m-money has been very slow to catch on in Asia. In India, for example, Nokia quit the m-money business in 2012 after two years of failing to build a critical mass.
How do we explain the differences? University of Manchester research, based on six months of primary fieldwork conducted by Chris Foster, analysed the reasons M-Pesa has grown so fast in Kenya; reasons summarised in the model shown below:
Ongoing support from government – liberalisation of the mobile market; investment in infrastructure; light-touch regulation; facilitation of the initial pilot, etc – combined with strong consumer demand across all strata of society (itself partly fed by the instability and disruption following the disputed 2007 elections). These drove a virtuous circle:
- Competition between mobile sector firms pushed them to seek profits beyond the traditional middle-of-the-pyramid; answering the demand from the majority market of the country’s poor.
- The service was delivered via atomised distribution networks that reached right down into poor urban and rural communities; a network of nearly 50,000 agents by 2012.
- Those embedded intermediaries – essential in scaling any innovation to reach the base-of-the-pyramid – were given the flexibility to adapt business models, retailing patterns and service offerings so they met the specific and heterogeneous needs of their local customers. Effective knowledge channels allowed these innovations to filter back up to the lead firms, which then scaled those they found most useful; fuelling yet further growth.
Armed with this model, we can analyse the m-money weaknesses in other emerging markets. For example:
- Much lower levels of customer demand (put down to both culturo-institutional factors and more effective functioning of and access to existing financial services) combined with a more stringent regulatory regime are behind the slow growth rates in India.
- A much smaller number of intermediaries (agents) and a lack of innovation (e.g. to address cash float problems) is restricting growth of m-money in Uganda and Tanzania.
- Tighter regulation and the much small number of intermediaries has held back expansion of mobile money services in South Africa.
We are not the first to try to understand the different performance of M-Pesa vs. other countries (see e.g. Wolfgang Fengler, Amaka Okechukwu who both also note the value of Safaricom’s market domination). However, we hope that our model provides a clear and transferable framework for comparison, that can be used alongside more in-depth evidence from other countries to help understand their relative success or failure in mobile money.
If you see ways in which you think the model should be modified – based either on experiences in Kenya or elsewhere; then let us know . . .Follow @CDIManchester
How can we understand the impact that mobiles are having on the livelihoods of the poor?
We all know that mobile phone use has grown exponentially in developing countries. And that phones are having an increasing impact on the livelihoods of the poor by providing market prices, by supplying health information, by enabling financial transfers, etc.
But we know a lot less about how to conceptualise all this. Can we just pull some development studies ideas off-the-shelf? Or do we need to do more than this?
A new working paper in the Development Informatics series – “Understanding Mobile Phone Impact on Livelihoods in Developing Countries: A New Research Framework” – argues the livelihoods approach is a good starting point. But that it needs modification.
The livelihoods approach suggests four potential impacts of mobiles on the assets that underpin all livelihoods:
− Asset substitution: saving time and costs for journeys, but adding costs for mobile expenditure.
− Asset enhancement: greater efficiency in use of other assets e.g. for agricultural production or relationship-building.
− Asset disembodiment: the conversion of assets to digital form e.g. the codification of social contacts, or digitisation of money.
− Asset exchange/combination: e.g. the exchange of airtime or m-cash.
Important intermediaries – mobile operators, their agents, community-based organisations and NGOs, family and friends – help shape the extent and distribution of these impacts. These are also shaped by the three livelihood strategies to which the poor apply mobiles:
− Maintaining existing livelihoods and mitigating vulnerability: e.g. use of mobiles to maintain social networks that can assist in an emergency.
− Expanding and enhancing existing activities: e.g. using mobiles to obtain greater earnings from existing produce, to save more effectively, or to obtain greater remittances from existing social contacts.
− Diversifying into new activities: e.g. employment in the mobile sector, or use of mobiles to complete micro-work tasks.
These components of the livelihoods approach – assets, intermediating organisations and institutions, strategies – are therefore very useful in understanding the role of mobiles in development. But the approach also has four shortcomings.
i. Reconceiving assets. The assets pentagon was developed within the context of traditional agriculture, and it underplays recent understandings of the importance of networks, agency and capabilities in development. It would be better replaced by a three-way categorisation of assets:
− resource-based assets (RBA) that are tangible (physical, financial, natural capital);
− network-based assets (NBA) that derive from connections (social, political, cultural capital);
− cognitive-based assets (CBA) comprising human and psychological capital including competencies (knowledge, skills, attitudes).
ii. Incorporating information. Mobiles expose a truth that information is the lifeblood of development, and yet it is essentially ignored within the livelihoods framework. Information is essential to individuals’ awareness of, and ability to utilise, all assets; and the use of information requires other assets to turn it into decisions and livelihood strategies. Those processes need to be recognised within any understanding of livelihoods.
iii. Recognising bottom-up processes. The livelihoods framework tends to see intermediating processes and structures in macro-terms (government, laws, policies, culture). But diffusion and use of mobile has equally been shaped by more bottom-up processes including the functioning of specific market transactions, and user appropriations and adaptations within poor communities. The latter need to be recognised.
iv. Categorising impacts. If the core interest is impact of mobiles, the homogenising of that impact into a single “livelihood outcomes” box is not particularly helpful. Better to borrow from the ICT4D value chain and differentiate a broadening scale: from direct changes in behaviour, through process-level outcomes, to broader impacts on development goals.
Adapting the livelihoods framework on the basis of these four points, we arrive at the revised framework shown below, for use in conceiving and researching the impact of mobiles on livelihoods in developing countries:
The framework immediately helps to identify possible research questions:
− What is the effect of contextual factors – processes of globalisation, processes of technological innovation, population migration, etc – on the livelihoods impact of mobiles?
− How are markets and market processes shaping the impact of mobiles, including the tension between seeking to make markets more inclusive, and markets’ tendency towards exclusion and inequality?
− What exactly is the impact of mobiles on the substitution, enhancement/diminution, disembodiment, exchange and combination of livelihood assets at the household level?
− Are mobiles forging new forms of connection to the intermediating structures and processes that govern the enactment of livelihood strategies?
− What new livelihood strategies are mobiles enabling; how do they come into being and come to sustain; and what impact are they having?
− What factors mediate the conversion of mobile behavioural outputs into broader outcomes and development impacts?
No doubt there are many other questions that the framework can be used to identify and conceptualise.Follow @CDIManchester
ICTs bring benefits to those who have them and not to those who don’t. They therefore increase inequality. Right? Well . . . let’s see.
First question: what do you mean by “those who don’t have ICTs”?
We need something a bit more nuanced than a simple, binary digital divide, and can use instead a digital divide stack of four categories (see figure below):
- Non-Users: those who have no access to either ICTs or ICT-based information and services.
- Indirect Users: those who do not get hands-on themselves, but gain access to digital information and services via those who are direct users.
- Shared Users: those who do not own the technology, but who directly use ICT owned by someone else (a friend, workplace, ICT business, community, etc).
- Owner-Users: those who own and use the technology
Of course we would need to make transverse slices through the figure; potentially, one slice for each different type of ICT, but particularly noting many in developing countries would be in a different category level for mobiles compared to the Internet.
Second question: what’s the evidence on inequality?
It is relatively limited and often bad at differentiating which digital divide categories it’s talking about. However, we can find three types of evidence.
The Rich Get Richer; The Poor Get Poorer: situations in which some category of user gains a benefit from ICT while non-users suffer a disbenefit. For example, micro-producers of cloth in Nigeria who owned or had use of a mobile phone found they were gaining orders and income; micro-producers without mobile phone access found they were losing orders and income (to those who had phones). (See also work on growing costs of network exclusion.)
Development vs. Stasis: situations in which some category of user gains a benefit from ICT while non-users do not gain that benefit. For example, farmers in rural Peru who used a local telecentre were able to introduce improved agricultural practices and new crops, which increased their incomes. Those who did not use the telecentre just continued farming in the same way as previously.
Spillover Benefits: situations in which some category of user gains a benefit from ICT while non-users also gain a (lesser) benefit. One rather less-publicised outcome from the case of Keralan fishermen using mobile phones to check market prices is an example. Those fishermen without mobile phones saw their profit rise by an average Rs.97 (c.US$2) per day as a result of the general improvements in market efficiency and reduced wastage which phones introduced. This was about half the profit increase seen by phone owners and meant, even allowing for the additional costs, that returns to phone ownership were greater than those for non-ownership. However, it was a spillover benefit to non-ICT-users.
ICT4D research on spillovers to non-users specifically has been rare, with the main interests in non-users being to understand why they are non-users; and most spillover work being done between sectors or enterprises and/or focusing on the spillover of encouraging ICT adoption rather than more immediate benefits.
This does seem to be changing, perhaps because of the growth of mobile and related to earlier work on the externalities to non-users of arrival of rural telecommunications. Rob Jensen’s Kerala study found a second digital spillover: while fishermen’s revenues rose, the price per kg fell due to the increase in supply arising from less waste. Fish consumers (many likely non-users) now paid less than previously thanks to the mobile-induced efficiency gains. More directly, a study of M-PESA’s community effects in Kenya found its use providing positive financial, employment, security and capital accumulation externalities that affected both users and non-users within the community.
We also have a little evidence of spillover benefits from owner-users to indirect users:
- Follow-up work with Keralan fishermen found fish workers who will only get into a boat with a mobile phone-owner due to safety concerns, with these indirect users able to benefit from the owner should the boat get into difficulties. That paper’s author (personal email) also gives the example of an indirect user citing as a benefit being informed of – and able to curtail – his daughter’s illicit elopement via his boat owner’s phone.
- Research on farmers in Northern Ghana found those who did not themselves own or use mobiles benefitting from information passed on from phone owners, including more frequent meetings with agricultural extension officers; meetings that were coordinated by phone owners.
In all these cases, owner-users are benefitting more than the lower-category users to whom benefits spill over. That means – if you’ll forgive the pun – that in these cases ICTs are causing all boats to rise but the ICT-using boats to rise somewhat faster. Inequality may still grow; perhaps absolutely but not relatively.
I look forward to what appears to be forthcoming work by the Global Impact Study on non-user spillovers. However, this remains a poorly-understood and little-researched issue; one that needs a greater focus since it is central to understanding the digital divide and digital inequalities. It also has implications for practice; suggesting ICT4D projects should promote non-user spillovers as much as they promote ICT usage. As ever, your pointers to spillover research and practice are welcome.
 Smith, M. (2010) A Technology of Poverty Reduction for Non-Commercial Farmers? Mobile Phones in Rural North Ghana, BA dissertation, unpublished, University of Oxford, UK
Do ICTs contribute to economic growth in developing countries?
In the 1980s, Robert Solow triggered the idea of a productivity paradox, saying “You can see the computer age everywhere but in the productivity statistics.” And for many years there was a similar developing country growth paradox: that you could increasingly see ICTs in developing countries except in the economic growth data.
That is still largely true of computers and to some extent the Internet, but much less true overall as mobiles have become the dominant form of ICTs in development. In particular key studies such as those by Waverman et al (2005), Lee et al (2009), and Qiang (2009) have demonstrated a clear connection between mobiles and economic growth and/or between telecoms more generally and economic growth. They all address the “endogeneity” problem: that a correlation between telecoms (indeed, all ICTs) and economic growth is readily demonstrable; but that you then have to tease out the direction of causality: economic growth of course causes increased levels of ICTs in a country (we buy more tech as we get richer); you need to try to control for that, and separate out the interesting bit: the extent to which the technology causes economic growth.
The studies try to do this and show ICT investments cause economic growth, but they are all multi-country and provide no specific insights into the experiences of a particular developing nation. If you know of such data, do please contribute. Meanwhile, a recent edition of “Kenya Economic Update” provides an example. Some overall points:
- The ICT sector grew at an average of nearly 20% per year from 1999-2009 (by contrast, Kenya’s largest economic sector – agriculture – shrank by an annual average of nearly 2% per year).
- The number of phone subscriptions has grown from the equivalent of one per 1,000 adults in 1999 to the equivalent of nearly one per adult in 2010; Internet usage rates for 2010 were around four per ten adults.
- Person-to-person mobile money transactions at the end of 2010 were equivalent to around 20% of GDP with two of every three Kenyan adults being users.
But the report’s strongest claim is this: “ICT has been the main driver of Kenya’s economic growth over the last decade. … Since 2000, Kenya’s economy grew at an average of 3.7 percent. Without ICT, growth would have been a lackluster 2.8 percent—similar to the populaton growth rate—and income per capita would have stagnated”. So ICTs were responsible for 0.9 of the 3.7% annual GDP growth, and for all of Kenya’s GDP per capita growth. Put another way, ICTs were responsible for roughly one-quarter of Kenya’s GDP growth during the first decade of the 21st century.
Other nuggets from the report and from original World Bank data underlying the report:
- The “ICT sector” is actually the “posts and telecommunications” sector. Comparing figures from Research ICT Africa for mobile + fixed line + Internet/data services with those for the overall sector suggests that ICTs form by far the majority (likely greater than 90%) of that sector. For the ICT part of the sector, latest figures for 08/09 show mobile takes a 54.8% share, fixed line takes 39.5%, with 1.8% for Internet services and 3.8% for data services (not 100% due to rounding).
- The ICT sector in 2009 still represented only 5% of total Kenyan GDP (compared to 21% for agriculture/forestry), and growth has been volatile, at least as based on the recorded figures, ranging from 3.5% per year up to 66% per year during the first part of the decade, and from 7.9% to over 30% during the second part of the decade. Only tourism (hotels/restaurants) was more volatile. In six of the ten years of the 2000-2009 decade, though, ICT was Kenya’s fastest growing sector.
- In the first half of the decade, annual investments in mobile were higher than annual revenues; but the ratio has subsequently slipped to investment averaging around half of revenue. Investments in mobile during 2001/02 to 2009/10 are estimated at US$3.2bn (c.KSh250bn) and US$3bn in fixed phone services, with broadband, Internet and BPO investments adding perhaps another US$1bn.
- The ICT sector provided a more than six-times-greater contribution to Kenyan GDP in 2009 compared to 1999. Directly, the ICT sector contributed to 14% of the country’s GDP growth between 2000 and 2009 (at constant (i.e. not actual/current but accounting for inflation) prices, it grew from KSh13.7bn in 2000 to KSh71.8bn in 2009; GDP overall grew from KSh976bn to KSh1.382tn). So the World Bank’s calculation that ICTs contributed a quarter of GDP growth during the decade also include a specific, quantified assumption about ICTs triggering growth in other sectors, in particular the financial sector.
- Employment in the ICT sector is estimated to be around 100,000 in 2011 (c. 0.7% of the estimated 14m overall labour force). But ICT punches above its weight in other ways: changes in mobile prices at the start of 2011 were credited with both causing the Kenyan inflation rate to drop and with potentially derailing government constitutional talks due to the substantial knock-on effects in causing tax revenues to drop since phone companies now contribute such a significant proportion of government income.
So, overall, what do we have here? Some fairly solid evidence that ICT sector growth (predominantly due to mobiles) is making an important direct contribution to economic growth in this developing country. And some less clear evidence that the indirect GDP growth effect of ICTs may nearly double this. Thanks to mobile money, Kenya has seen a particularly strong take-up and economic role for ICTs, but it is fairly typical in terms of mobile investment, revenues, subscriber base, employment, etc. In that case, it’s not too much of an extrapolation to expect that ICTs will have contributed something like one quarter of GDP growth in many developing countries during the first decade of the 21st century. Evidence of ICT impact that development strategists and practitioners should be more aware of.Follow @CDIManchester
This entry reports findings from a survey of nearly 400 mobile phone users in The Gambia conducted by Fatim Badjie, who recently participated in Manchester’s MSc in ICTs for Development.
Its findings fall into six main areas:
Ownership and Costs: 83% of phone users owned their mobile; roughly 70% said that it was cheap to use a mobile.
Mobile Usage: 82% said the most-used facility on their phone was calls; 12% said it was texting; 3% said it was Internet browsing. Overall, 38% said the service they enjoyed most was texting; 15% said Internet browsing; 8% said conference calls; 5% said video calls. 47% share their mobile with other people, sharing with an average of 3.1 other people. That means, overall, the average mobile is used by 2.5 people (i.e. shared with 1.5 other people). On average, users said they used their mobiles 28 times per day, and two-thirds use their mobile at least 10 times per day.
Availability and Issues: roughly 60% of users said they always had a signal and that services were available even in “inconvenient” locations (though of course Gambia is a small country). Only 30% reported the mobile was always effective for communication and roughly one third reported they felt mobile use had become a burden to them – mostly financially but also socially or personally. For the 55% of users who wanted improvements, these almost all related to getting 100% network coverage in the country, or wanting cheaper prices.
Impacts and Benefits: 78% felt they benefited from having a mobile particularly due to low cost of calls. 31% felt having a mobile helped them to make or get money, for example through calls from customers to go and collect money owing or, more often, calling family/friends for money (“money calls”). 58% thus felt they had come to depend on their mobile, and 78% said they could not see themselves living without one.
In terms of male-female differences:
- No real difference in rates of ownership, rates and scope of phone sharing, difficulties experienced, or dependency on mobiles.
- Slight tendency for women to have been using fixed lines rather than telecentres as a prior means of communication.
- Women use mobiles a little more than men on average per day (28.6 vs. 26.6 times).
- Less use of mobiles for Internet browsing by women than men; more use of phones for texting.
- More men (38%) than women (24%) said the mobile helped them get money and resources, though women used phones proportionately more for “money calls” than men.
My commentary would be that, overall, this is a reminder of how mature the mobile market is getting in Africa with very high rates of ownership, very high rates of usage, and signs of movement beyond basic calls/SMS: at least 15% going online via their mobiles, at least 13% using video/conference calls. With roughly one-third saying they use mobiles to make or get money, it looks like quite a valuable financial tool: so embedded that nearly fourth-fifths of users couldn’t imagine life without it, including some who see mobiles as a “necessary burden”.
ITU estimates for 2009 (the year prior to the survey) there were 84 mobile subscriptions per 100 population in The Gambia. Even allowing for calculations to convert from subscription data to actual ownership and use (see earlier blog entry), this means phone users were by far the bulk of the Gambian population during this survey (so skews compared to the overall population will be present but probably limited). Given the rates of sharing reported it means that access to a mobile is virtually universal (though it must also mean that many people share their phone with others who already have one).
Noting exclusion from the survey of women (and men) who don’t use mobiles, there was relatively little difference in ownership and usage patterns between men and women. Is that, too, a sign of market maturity?
Finally, a reminder that, even in a small country there can be significant locational differences and that “market maturity” has a rural—urban axis. Users were surveyed in seven different parts of The Gambia but the table below compares some of the key findings for those surveyed in the capital, Banjul, and those surveyed in Bansang, a small town three-quarters of the way up-country.
|Banjul (urban)||Bansang (rural)|
|Cheap to use?||66%||84%|
|Access Internet via mobile||17%||0%|
|Use SMS texting||69%||4%|
|Share your mobile?||24%||86%|
|Average uses per day||39.8||6.7|
|Available in inconvenient locations?||75%||12%|
|Main problem (of those reporting a problem)||Cost (87%)||Network availability (98%)|
|Help you to get money/resources?||28%||40%|
|Calls for money||14%||44%|
|Live without mobile?||52%||20%|
The data show some not unexpected differences. In the rural location, there was much less ownership of mobiles and much more sharing; much less use of non-call services and generally much less daily use of the mobile. Network availability is more of an issue in the rural area, but the mobile seems to be more useful for getting money and far fewer users in the rural area can imagine life without it.
You can access the results of the survey by clicking here: they also include more Gambia-specific questions about operators, services, and awareness of institutions. Note the breakdown-by-location is very lengthy, and not provided in this document.
I’ve entered the ITU data on mobile phone penetration for all countries from 1998-2008 into a Google Docs spreadsheet, and then added the Motion Chart visualiser (the same engine made famous by Hans Rosling and TED, though they use the Gapminder Trendalyzer version).
Unfortunately, WordPress scripting rules mean I can’t post the active chart here. To access the spreadsheet data and Google Motion chart, you need to go to:
Screenshots below give an indicator of how you can visualise the data. The chart offers three main means to visualise (bubble, bar chart, and line graph) via tabs at the top right. You can change the axes and element colouring/size, and highlight individual countries. For bubble and bar, the main point of the chart is that you can click play (bottom left) and show how things change over time. (Note playback speed variation control, and also the ability to drag over and zoom in on parts of the chart.)
Not sure it adds a lot of analytic value but it’s engaging, helps give a sense of some overall trends, and identifies some interesting outliers. (Some older PCs and low-bandwidth connections will struggle to display.) I’ll repeat for other ITU data in later posts (e.g. broadband data visualisation here). You can find similar visualisation for mobile, Internet and a host of other development data at: http://devdata.worldbank.org/DataVisualizer (though currently up to 2007 only, no obvious access to underlying data, and the mobile data display doesn’t seem to work properly). And, finally, on a separate blog entry you can find a set of rough converters to change mobile phone subscription data to data on ownership, access, use and non-use.
What makes for good ICTs-for-development research?
The following represents a subjective answer – feel free to add your own ideas – based on reading and reviewing ICT4D research. I draw out three good practices and three good ideas, which can be epitomised by Rob Jensen’s paper on mobile phone use by Keralan fishermen.
The Good Practices
a) Audience Focus and Dissemination: good ICT4D research identifies, focuses on and targets its particular audience. Jensen is an academic economist. He did this research and he wrote up this research for other academic economists. He chose an appropriate channel – a leading economics journal – to reach that audience. (And then reached much further through having the work summarised in The Economist.)
b) Conceptual Foundation: good ICT4D research is founded on and structured around some conceptual framework or model. Without that, research struggles for coherence and consistency. With that, it is more likely to make a longer-term contribution. Jensen’s work is rooted in welfare economics theory, to which it also makes a contribution.
c) Rigorous Methods: good ICT4D research has a methodology, and rigorously applies appropriate research methods. It also explains the methodology, methods and their application to its readers. Good narratives about ICT4D wins hearts. Good quantitative statistics win minds. But too much ICT4D “research” falls down in between: methodology-less, wishy-washy qualitative data that wins nothing. Jensen’s research avoids this: it has a rigorous quantitative foundation built on shed-loads of longitudinal field data.
The Good Ideas
d) Speaking to Development: one of the seductions of the ICT4D field’s growth is to publish in ICT4D journals for an ICT4D audience. But one’s impact (and career trajectory!) can be greater if ICT4D’s parent disciplines are targetted. Most who do this have chosen one of the fractions of informatics (information systems, human-computer interaction, computer science). But longer-term impact of both research and ICT may be better-served by targetting development studies; the reference discipline for many of those working in development agencies. Jensen speaks to development: by drawing in particular on the ideas of Joe Stiglitz, his research can be seen as part of development economics; and as work that can make a connection with economists in international agencies. That’s why Jensen’s research is one of very, very few ICT4D studies that colleagues in development studies have heard of.
e) Researching Technology-In-Use: in his book, The Shock of the Old, David Edgerton argues we should not be so obsessed by novelty and by inventing new technology; instead we should look at the actual technologies already in use. Much ICT4D research fails this test, reporting some new prototype or pilot; oftentimes in which the authors have themselves had a hand. Jensen eschews this route. He did not try to create any new technology. He did not invent. He did not seek to innovate. Instead, he researched technology-in-use: the application of mobiles within a poor community to meet their particular needs (arguably an example of grassroots innnovation).
f) Researching Income-Generating Uses of ICTs: a fair chunk of ICT4D research looks at social development: health, education, governance, community empowerment, gender equality. But the number one need of the world’s poor (there’s a clue in the name) is money. Jensen focuses on this, studying the use of ICTs in productive micro-enterprise; investigating how mobiles increase income generation in poor communities. It therefore tells us how ICTs can directly contribute to economic growth and poverty alleviation.
Since MTN’s Mobile Money service was introduced in Uganda in March 2009, other network service providers – Uganda Telecom and Zain – have entered the market with similar money transfer products. In the opinion of Richard Mwami, MTN’s Mobile Money head, “mobile phones have created a new battleground for banking”. There is a strong belief that new services can transform the way in which the ordinary citizens of Uganda conduct their monetary transfers and payments.
MTN had 40,000 service subscribers by June 2009, with a relatively low average value for each transfer of US$35. A large proportion of these have been conducted ‘up-country’ outside of the capital city – Kampala; evidence that the service is attracting less well-off clients. The true impact has yet to be empirically demonstrated. However, a recent Working Paper from the University of Manchester’s Centre for Development Informatics provides some pointers to areas of potential and also possible constraints. Given Uganda’s reflection of broader patterns in both financial services and mobile usage, this should also tell us something about the situation in other African nations.
The paper shows participation in financial services in Uganda falls into four categories:
- Those who access and make use of the formal banking sector and who may hold deposit or savings accounts (18% of the adult population).
- Those who access semi-formal micro-finance institutions or savings and credit co-ops (3% of the adult population).
- Those who participate in informal sector financial services – ROSCAs (Rotating Savings and Credit Associations), ASCAs (Accumulating Savings and Credit Associations) and other community-based savings clubs and funds (17% of the adult population).
- A fourth group includes all those who are financially un-served and they constitute approximately 62% of the adult population (aged 15 and over).
Interestingly, the proportions estimated for financial service access seem to strongly mirror that for mobile phones. It is estimated that 20% of the adult population own a mobile phone, whilst 42% have access. Thus, 58% remain without meaningful access (based on 2007 data). This correlation between mobile phone ownership and formal sector financial service participation is also demonstrated in research conducted by Johnson & Nino-Zarazua (2007) who found that those who own a mobile phone are more likely to have a formal sector bank account by a factor of three than those who do not. MTN’s Mobile Money subscribers account for approximately 1.4% of the 2.9 million adults that bank in the formal or semi-formal sector. The make-up of the subscriber base is not known, but it might be assumed that all are mobile phone owners and a large proportion will already be banked.
The potential to expand the subscriber base for m-payments (and subsequently broader m-banking services such as accounts and credit) is large even among current mobile phone owners. As the working paper suggests, though, the constraints may also be significant – particularly amongst the financially un-served. These include:
- Lack of financial literacy – access to post-primary education is a key factor in building financial literacy (data from 2006 suggests that only 18.1% of the population attended secondary school). Lack of literacy skills has been mentioned as a reason for lack of use of text-based services in Uganda where only 10% of the poorest wealth quintile use SMS compared with 82% of the richest.
- Affordability – service costs are relatively low: MTN’s Mobile Money charges as little as US40 cents per transaction; comparing favourably with services such as M-PESA in Kenya. However, and despite strong declines, mobile usage and ownership costs remain high in Uganda. To illustrate, consider the cost of 100 minutes of network use as a percentage of GNI (Gross National Income) per capita. In Uganda this figure stood at 96% in 2007, compared with only 7% for South Africa. Handsets are also far from affordable by the majority. The extent to which the currently unbanked may be drawn into mobile phone ownership for the purpose of accessing m-payments services is likely to be highly price sensitive. For poor households, it may depend upon whether expenditure on mobile phone services is prioritised ahead of other essential expenditure.
- Organisational factors – for access to cash-in and cash-out facilities the services of local agents become essential. A key issue is not just the proximity of agents to communities that wish to use the service, but also trust in the individual agent concerned, as well as trust in the technology and the financial security of the service provider. New entrants such as mobile phone operators may be an an advantage here. In comparison, studies reviewed in the paper report a particularly low level of trust of existing financial service providers.
Reaching the unbanked will likely require ingenuity and innovation on the behalf of service providers. In the first instance, there is a need to more accurately define the extent of mobile phone ownership and use among this group; given that these are ever-rising. There is also a need to understand more fully how mobile phones are used by the poor. Evidence suggests that mobile is more likely to be used as a tool to communicate and coordinate cash transactions, rather than to deliver funds electronically. The extent and impact of use of airtime as a currency is also unknown.
If mobile networks are to facilitate cash transfers for the poor it will be necessary to enable access to services for those who do not own phones, and to those who do not have access within their immediate vicinity. This will require an intermediated solution and effective participation and inclusion of appropriate community-based groups in m-payments initiatives.
As widely reported, the number of mobile phone subscribers in the world passed the four billion mark at some point around the end of 2008, in a global population of around 6.7bn of whom about 80% (5.4bn) live in developing countries.
At first sight, that might suggest that 60% (4 / 6.7) of the world’s population has a mobile phone. Given (ITU stats) that the subscriber rate in industrialised countries is roughly 100 per 100 inhabitants, that might also suggest that 50% ((4 – 1.3) / 5.4) of those living in developing countries had a mobile phone at the start of 2009.
We arrive at similar figures if we extrapolate the ITU 2007 stats using the CAGR% for 2002-2007 (minus population growth for the per 100 inhabitants data), to get a 2008 figure:
- Africa: 409m mobile subscribers; 41 subscriptions per 100 inhabitants
- Americas minus US/Canada: 483m subscribers; 84 subscriptions per 100 inhabitants
- Asia minus Japan/S. Korea/Israel/Singapore: 1736m subscribers; 45 subscriptions per 100 inhabitants
- All developing countries: 2.63bn subscribers; 49 subscriptions per 100 inhabitants
BUT . . . These figures have a number of problems. I summarise below what little I have found: if you know more, please comment.
- Some individuals have multiple subscriptions
- Visitors (both foreigners (tourists, business visitors) and nationals who currently reside abroad) buy a subscription/card for their phone during a short-term visit
- Some people living in neighbouring countries may subscribe when they live close to the border within service range
- Subscriptions are counted for some period of days/months after the last use; some of these subscriptions may be on cards/phones that are now discarded
How big is this effect? Of course it varies, but a gratingly rough estimate is that in-country ownership is 75% of the subscription figure. Ewan Sutherland reports an EU-wide figure of 103 subscriptions per 100 population in 2006, but on-the-ground surveys indicating 79 per 100 own a mobile phone. Gillwald et al report 44 subscriptions per 100 population in South Africa in 2004, but on-the-ground surveys indicated 33 per 100 mobile phone ownership. de Silva et al (2008) surveyed in India in 2006 and found 9 mobiles owned per 100 respondents, compared to ITU data for the same period of 15 subscriptions per 100: a converter of 60%; in Sri Lanka, the converter was 85%. Wireless Intelligence produced a report indicating real penetration as a percentage of reported penetration for 2006 was: Romania (80%); Turkey (79%); South Africa (76%).
Within the EU, the ratios vary from about 50% (wealthy Luxembourg where multiple subscriptions are very common) to roughly 100% in France. In accession state Bulgaria, the figure was about 55%, perhaps due to it having so many foreign second home owners, nationals working overseas, and transit visitors. So the 75% estimate covers quite a wide variation that will depend on specific national conditions. We also need a lot more data from developing countries themselves (where there may be a fifth reason for overestimation according to James & Versteeg: those who own SIM cards but not phones).
Second, mobile subscription figures are underestimates of in-country mobile phone access and use for at least two reasons:
- Private mobile phones are shared with family, friends, neighbours, etc.
- Public mobile phones are accessible to large numbers of people.
How big is this effect? James & Versteeg cite an estimate of two users on average per privately-owned mobile phone on the basis of Vodafone Tanzania data; and 70 users on average per public mobile on the basis of Grameen Phone Bangladesh data. But they also note that levels of sharing seem to vary a lot between countries.
If we take the 75% figure and the “two users” figure, this would mean that usage rates of private mobile phones in developing countries are 1.5 times the cited subscription rates. So, for example, that would estimate in 2008 there were around 615m private mobile phone users in Africa: about two-thirds of the population. That’s also (citing GSM Association data) about the proportion of the population that was covered by a mobile signal.
Of course, that excludes those who use public mobiles and other public phones. Looking at de Silva et al’s Asia data, we might estimate that in the poorer developing countries, for every mobile phone owner, there are about three others who don’t own any phone, but find a way to access and use one. However, the figures vary wildly and the ratios decrease rapidly as mobile phone ownership rises. It may be better to lump all phone use together and ask our final question . . .
Levels of Non-Use
Thirdly, how many people still do not use a phone? In the 1990s, we circulated the much-quoted “fact” that half the world’s population had never made a phone call. How do things look now? Let us reduce the 350m in Africa who live outside cell phone coverage with Gillwald et al’s data that around 25% of rural populations in Africa had used public payphones in the past three months. We get roughly 250m non-phoners. About 40% of these will be under 15; we’ll exclude those, to reach 150m adult non-phoners.
In Asia, de Silva et al’s survey work from late 2006 suggested only 4.5% of adult lower-income group members in the countries they studied had not made a phone call in the previous 3 months. That suggests 120m non-phoners. And we might guess roughly the same proportion for Latin America, giving about 20m non-phoners there.
So, a very rough and ready estimate suggests about 300m adult non-phoners in developing countries. This number becomes larger if we start adding in children: around 10% of the developing world’s population (some 500m) is under 5; something like a further 1.5bn are aged 5-14. Many of them will have made phone calls, but many will not.
Finally, some additional confusions:
- The 75% conversion from subscriptions to ownership might get messed up by age demographics. Gillwald et al did draw from all age ranges to get their survey figures. It’s not clear if the EU and Bulgaria figures cited by Sutherland do the same, and the de Silva et al data does not seem to have done so. We can at least estimate that about 25% of Africa’s population and perhaps 20% of Asia’s and Latin America’s population might be seen as too young (10 or under) to own a mobile phone. That would mean, ironically, that the mobile subscription per capita figures and the actual ownership per capita of adult populations could be about the same.
- The basis for the figures is not totally clear, but in a number of surveys (some sub-Saharan African countries covered by Gillwald et al; some Asian countries covered by de Silva et al), the reported mobile ownership per capita figures were the same, or even higher, than the ITU-reported mobile subscriber per capita figure for that country. This may reflect the exact population from whom the survey data was gathered (e.g. more urban than the general population), but it may well also reflect just how ropey are the statistics on mobiles in developing countries, where ballpark figures and trends are all we can really talk about.
Is implementation of fiscal policy in developing countries being increasingly handled by mobile phone operators?
That’s what hit me from two presentations at the recent 1st m4d conference; in particular from Adam Denton’s keynote [2MB pdf] on behalf of the GSM Association. (This was full of interesting evidence-based nuggets, some of which I’ve added at the bottom of this post.)
My thoughts on government finances arise from:-
- a) the general poor performance of developing country governments in implementation of fiscal (tax and spending) policy;
- b) Mick Moore’s argument (e.g. in the book, “Taxation and State-Building in Developing Countries”: you can find on Google books) that state-building in developing countries is significantly undermined because governments don’t rely very much – if at all – on their citizens for taxes; as a result, governments find it easy to ignore citizens when making policy.
Mobile telephony could be changing this, albeit in an unplanned and as yet largely unrecognised way. How? Because mobile phone operators are key, and rapidly growing, contributors of tax revenues to government. They average 7% of tax receipts in Africa and, in some countries, are the single largest tax payer.
More importantly, and almost uniquely among tax sources (compared, say, to customs revenue or taxes on natural resource extraction), mobile phone companies derive their revenue from a large and increasing mass of the citizenry of a country (around 30% of the cost of mobile phone ownership goes to pay tax). They are, therefore, indirectly providing the tax connection between citizens and governments in developing countries, the absence of which Mick Moore and others have long lamented.
Previously, if governments wanted to raise or lower the tax burden on their citizens, they had few if any levers. Now they do. Assuming a competitive mobile market, changes in the tax burden on phone companies will directly affect large swathes of a country’s population. Raise taxes on mobile operators: you extract more money from millions of citizens. Lower taxes for the reverse effect.
The fiscal flipside could also apply. Katharine Vincent and Nick Freeland are working on a social protection project in Lesotho. Cash transfers to the poor are an increasingly-popular social protection tool to address poverty and wider vulnerabilities. However, delivery mechanisms are difficult. One possible solution? Get government to outsource delivery of the cash/credit to mobile operators, who would effect the transfer via mobiles. Mobile operators would thus be implementing government spending policy.
That may be some way in the future. However, we do need to recognise the extent to which mobile phone companies are mediating – and could mediate – fiscal relations between governments and citizens. This one more sign of “Development 2.0″: the way in which ICTs are reworking core development structures and processes.
Any other examples of phone operators as outsourced Treasuries would be welcome.
Other nuggets from Adam Denton:
- 10 years ago, Manhattan had more phone lines than all of Africa. Today, Africa has more phone lines than the US and Canada combined.
- GSMA states that every US$1.00 invested in mobile telephony generates an average US$0.80 in tax revenues for developing countries.
- GSMA predicts that 80% of broadband delivery in Africa will occur via mobile; but that may require release of spectrum currently used by analogue TV.
- Population coverage in Africa of mobile networks grew from 10% in 1999 to 60% in 2007, and is predicted to grow to 90% by 2012.
- Mobile operators provide 34% of the revenues for universal service funds but receive only 5% of disbursements (fixed line operators get the rest). In 2006, more than US$6bn in universal service funds was collected; only US$1.5bn was disbursed.
- (A personal conclusion): we know the menu for mobile and telecom and ICT policy quite well already; the interesting question today is not what should be in policy but how and why policy is made and implemented.
And, yes, I do realise that the GSM Association has a particular worldview. If you wish to challenge their figures with alternative evidence, please feel free to comment. If you’d like more of the same, then see: http://www.gsmworld.com/newsroom/index.htm