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.Follow @CDIManchester
 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.