Mobiles for Impoverishment?

If you had to choose three words to sum up the future of ICT4D, they might well be “mobiles, mobiles, mobiles”.  And the way to that future is being more clearly indicated as the promise of mobiles-for-development research comes to fruition; reflected, for example, in the recent 1st “m4d” international research conference.


But such research is starting to throw up some perplexing – even worrying – findings about mobiles.  At its bluntest, such research suggests mobiles are doing more economic harm than good, and sometimes making poor people poorer.  Let’s have a look:-


a) Kurt DeMaagd’s “Pervasive versus Productive” paper analyses country-level data on mobiles and national productivity as measured by GDP.  He finds that, short-term, there is a negative association between investment in mobiles and GDP in developing countries, possibly because “mobiles represent a diversion of resources away from other productive uses”.


b) Kathleen Diga’s “Mobile Cell Phones and Poverty Reduction” dissertation (Ch.5) shows at the micro-level that some rural Ugandan households are sacrificing expenditure on purchased food (e.g. sugar, milk, flour) so they can pay for mobile airtime.  This includes households that “admit to some days of hunger in order to maintain the mobile phone”.  They are also diverting savings into mobile phone purchase and saving for airtime by foregoing attendance at social functions.


c) Hosea Mpogole, Hidaya Usanga and Matti Tedre’s “Mobile Phones and Poverty Allevation” paper at the m4d conference researches mobile use in rural Tanzania.  “48% of respondents reported that they sometimes substitute important needs (e.g. education, buying food, and clothes) for mobile phone ownership/usage”.  Modal monthly costs of mobile phone maintenance and use were US$10-20.  Mean costs were US$22.4: an average 19% of monthly income.  And, in a digital variant on the workload of water-carrying in rural Africa, many respondents were undertaking 3-7 kilometer walks 2-3 times per week in order to recharge their mobile batteries.


Very interesting research.  To which one might offer four responses.


First, I find all three pieces of research to be credible.  However, one should always mine into research methods: what exactly is being measured; exactly what questions are being asked, and what answers might respondents think they are being asked to give; what is the sample size; what assumptions are built into calculations; is the difference between correlation and causation recognised?


Second, we have research evidence of mobiles increasing incomes of the poor such as the studies on Keralan fishermen or Heather Horst and Daniel Miller’s work on mobiles in Jamaica or (from a mobiles-as-tools-of-production not tools-of-consumption perspective) studies of “umbrella people” and GrameenPhone operators.  We also have evidence of mobiles saving costs for the poor e.g. work on the informal sector in Nigeria.


Third, there is a bigger picture that this research recognises.  DeMaagd notes that, longer-term, mobile-associated GDP downticks seem to be replaced by upticks as “learning and integration with business processes” take place.  Diga echoes this macro-level explanation at the micro-level: households see short-term sacrifices as investments that will provide longer-term security and opportunity.


Fourthly, we need to explain a surprising finding in Mpogole et al’s work.  Less than 15% of mobile phone owners interviewed stated that the benefits of owning a mobile phone justified the costs.  Um . . . so if you believe that guys, why on earth do you own a mobile?


Diga’s research offers some insight but we can get much more from Harsha de Silva, Ayesha Zainudeen and Dimuthu Ratnadiwakara’s paper (earlier version as: “Teleuse on a Shoestring“) that looked at mobile use in poor communities in five Asian countries.


The most negative explanation is that mobiles represent one more step in the ingestion of the poor by the consumer society.  They are sacrificing food for (potentially economically-valueless) status and an identity of modernity, youth, urbanicity, etc that they believe mobile ownership brings.


Mobile owners may also be associating questions about financial benefits with direct, enterprise-based income generation via mobile: something that only a few achieve as yet.  They may thus set aside from their cost-benefit calculations the so-far key financial impact: savings from substitution of journey costs.


And finally, most research tells us that the poor are using mobiles for social more than business purposes.  This, again, they may set aside from their cost-benefit calculations.  Yet a) the social benefits, such as knowledge that help can be at hand in an emergency, are often highly rated when asked about directly; and b) there is no easy separation in reality of the social and the economic: social networks are often utilised by the poor to maintain or generate financial flows such as remittances or help during a crisis.


I think my overall conclusion (apart from the obvious: more research needed) would be two-fold:


– Setting aside the possibility of irrationality, the significant amounts being spent by the poor on mobiles indicate that phones have a significant value to the poor.  But that value is some rather complex mix of the financial, the economic, the psychological, the social and the symbolic.


– For some poor consumers, the financial benefits of mobiles outweigh the costs.  For some poor consumers they do not.  But we have long known (e.g. via the livelihoods framework) that “poverty” is not just about money and, hence, that poverty interventions and tools can usefully target more than just financial benefits.


What we see here, then, is not an argument to try to slam on the mobile brakes.  At most, we have an argument to invest more in sharing or building innovative uses of mobiles that are more directly connected to income generation.



Impact Assessment of ICT4D Projects

What have we got to show for the billions invested in ICT4D projects?


By and large, we’re not sure because relatively little impact assessment of ICT4D projects has been undertaken; and what has been undertaken often lacks clear framing and rigour.


Impact assessment is therefore pushing its way up the ICT4D agenda.  For example, a number of ICT4D agencies have IA programmes; perhaps the biggest being the joint Gates Foundation/IDRC IPAI programme.


As a feed-in to that programme, staff with the University of Manchester’s Centre for Development Informatics created a “Compendium on Impact Assessment of ICT-for-Development Projects”.  IDRC – the sponsor for its creation – has given permission for this Compendium to be shared, and it is attached here (2MB .doc file): idrc-ia-for-ict4d-compendium


The Compendium is arranged into three parts:

·        Overview – explains the basis for understanding impact assessment of ICT4D projects (including the ICT4D Value Chain), and the different assessment frameworks that can be used.

·        Frameworks – summarises a series of impact assessment frameworks, each one drawing from a different perspective.

·        Bibliography – a tabular summary of real-world examples of ICT4D impact assessment.


This is an ongoing work, and comments or pointers to similar resources are welcome.