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How Whatsapp Strengthens Livelihoods of Women Farmers in Rural Zimbabwe

Whatsapp [1] (icon shown in Figure 1) is improving women farmer’s social capital – facilitating effective social networks in rural Zimbabwe.  We know that mobile technology use leads to information sharing – with the possibility of building social capital and leading to asset creation.  Some even argue that ultimately this can lead to better and sustainable livelihoods strategies.  There is talk, however, to suggest that many rural women in sub-Saharan Africa have not realised the benefits of mobile technology, despite widespread positive outcomes of mobile phone uptake in agricultural settings [2].  This is concerning, so exploring Zimbabwe’s situation is perhaps relevant and enlightening.

Figure 1: Whatsapp Icon

Whatsapp is facilitating access to support networks which better allow rural women farmers to pursue sustainable livelihoods in Zimbabwe.  Support networks (for example informal farming groups, church and savings’ clubs, as well as formal support from local NGOs and extension workers) are prevalent here.  In fact these links are particularly valuable in an environment (vulnerability context) which is typified by four factors.  First there are complex market trends (like flooded livestock markets and price fluctuations).  Then there are confounding financial shocks (like the lack of capital and abundant cash shortages).  Third are the challenging and extreme climatic shocks.  Fourth is the threat of disease (which is usually high and persistent throughout the year)[3].

Such characteristics are compounded by multiple role expectations on these rural women, and multifaceted, often contradictory structural relations.  Inequalities of access and women’s multiple competing roles limit opportunities [4], and so it seems reasonable to argue that social networks are central to mitigating vulnerability, which in turn enhance sustainable livelihoods prospects for Zimbabwean rural women livestock farmers.  In this sense, social media application Whatsapp is being used to [3] (see Figure 2):

a) Solve livestock problems, for example rural women are able to post/ send photos and videos of livestock to Whatsapp group members with common livestock interests, local vets and extension workers.

b) Help out in emergencies, allowing quick access to Whatsapp group forums to warn community members when livestock is stolen/ when disease threats arise, thereby efficiently coordinating emergency visits.

c) Build and strengthen women’s networks whereby women chat to each other and seek advice/ information through Whatsapp group forums.

Figure 2: A Zimbabwean Woman Using Whatsapp [3]

Essentially effective support is garnered through creating Whatsapp chat groups to openly communicate livestock issues.  Granted, some women do not have smart phones (largely due to cost), but it seems normal that an informed connection is never far off [3].  Also, in true Zimbabwean style, more experienced women farmers share experiences and knowledge with younger women farmers, serving as mentoring platforms where strong bonds are often formed through vulnerabilities and hardships.  A strong sense of togetherness and willingness to assist each other through these open channels ensues.

Whatsapp is accepted as a cheaper, useful and effective way of coordinating meetings (see Figure 3 – a photo with a group of livestock farmers brought together using Whatsapp).  A preferred form of communication, it enables rural women to inform each other, keep records of, and forward important (livestock) information.  It is perceived as being revolutionary in transforming communication amongst community members [3].

Figure 3: A Group Meeting in the Mashonaland East Province of Zimbabwe [3]

Given its apparent prominence in allowing economical flow of useful information, it is permissible to suggest that social networks accessed through Whatsapp are facilitating rural women’s pursuit of (diversified) livelihoods in an otherwise complex and challenging vulnerability context.  It would be useful to explore how the same/ similar mobile phone applications can be used to provide equal/ further access to key influential social and political networks [5] in order to abate the apparent perceived complex and contradictory structural relations and gender differences in such contexts. 

References 

[1]  Whatsapp is a cross-platform messaging and voice over IP service that allows users to send text messages, documents, images, and other media.  It also allows users to make voice and video callsChat groups can also be formed on the application.  Whatsapp (2018). Simple. Secure. Reliable messaging. Whatsapp [Online]. Available at: https://www.whatsapp.com/ [Accessed 28 November 2018].

[2]  Baird, T.D., and Hartter, J., (2017). Livelihood diversification, mobile phones and information diversity in Northern Tanzania. Land Use Policy, 67, pp.460-471.

[3]  Author’s Zimbabwean fieldwork data, August – September 2017.

[4]  Wyche, S., and Olson, J., (2018). Gender, Mobile, and Mobile Internet Kenyan Women’s Rural Realities, Mobile Internet Access, and “Africa Rising”. Information Technologies & International Development, 14, p.15.

[5]  Ruswa, G., (2007). The Golden Era?: Reflections on the First Phase of Land Reform in Zimbabwe. African Institute for Agrarian Studies.

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Categories: e-Agriculture, m4d Tags: , ,

Why M-Pesa Outperforms Other Developing Country Mobile Money Schemes

24 November 2012 16 comments

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[1] 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 . . .


[1] Foster, C. & Heeks, R (2012) Analysing policy for inclusive innovation: the mobile sector and base-of-the-pyramid markets in Kenya, paper presented at Globelics 2012, Hangzhou, 9-11 Nov [copy available on request: innov4dev@gmail.com]

Understanding Mobiles and Livelihoods

9 March 2012 3 comments

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.

Categories: m4d Tags: , ,

From Digital Divide to Digital Provide: Spillover Benefits to ICT4D Non-Users

31 August 2011 5 comments

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[1] 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.


[1] 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

Mobile Phone Use in West Africa: Gambian Statistics

30 January 2011 9 comments

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)
Ownership 100% 32%
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.

Mobile Phone Penetration: Google Motion Chart Data Visualisation

30 November 2009 7 comments

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:

http://spreadsheets.google.com/pub?key=tUzZsw5SoG_jXRDl6p8tRCg&single=true&gid=0&output=html

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.

Can Mobile Phones Bring Financial Services to Africa’s Poorest?

20 September 2009 1 comment

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.

Categories: m4d Tags: , , ,
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