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.

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[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

Actor-network theory (ANT) has been around since the 1980s, and significantly utilised in some disciplines, such as information systems.  But – oddly – it has hardly been applied at all in development studies, including within ICT4D research.  That is recently starting to change but to give some further impetus, we organised an international workshop in June 2011: “Understanding Development Through Actor-Network Theory”.  You can find online both a summary of the workshop and abstracts and presentations from the nine papers (the papers should appear in a journal special issue in 2012).

Actor-network theory began as a means to explain how science works, such as the operation of scientific laboratories and projects.  However, it has subsequently grown to be seen as a full-blown social theory.  In particular, ANT says three things.

First, it says, “Hey, sociologists, you’ve been so obsessed with humans that you’ve been ignoring all the objects in the world.  But those objects – documents, mobile phones, plants, websites, etc – play an important role; just like humans they shape the people and other objects around them. So ANT is going to treat them the same as people, and call them both ‘actors’.”

Second, it says, “Hey, sociologists, because you’ve been so obsessed with humans, you think that society and social contexts or social factors are what explains everything in life.  But you’re wrong.  In fact you’re so wrong you’ve got your basic equation of life the wrong way around.  You think that society explains what goes on in the world.  Nope.  What goes on in the world is what explains society.  So ANT is going to focus on the mechanics of life: the ways in which people and objects interact with each other.”

Third, it says, “Hey, more recent French-type sociologists, you’ve been so obsessed with breaking things apart to understand the bits of grammar and bits of history that made them that your idea of researching a clock would be to smash it to pieces with a hammer.  That is not how to research a clock.  To research a clock you need to understand how all the pieces got put together, following the network of people and objects that interacted in order to make that clock.  So ANT is going to focus on how networks are assembled.”

Much ANT writing is horribly obscure, so full of hideously complex sentences and words that the writers must surely have done this deliberately in the hope of avoiding Oscar Wilde’s dictum, “to be intelligible is to be found out”.  But, done well, ANT can tell a good story and even occasionally give you the sense that you are suddenly seeing the world in a whole new light.  A whole new light that – because it’s about dynamics and innovations and technology and networks – seems especially relevant to ICT4D.

A couple of good entry points – good because they each provide a fairly clear and portable conceptual framework that you can re-use in your own research – are:

-         Callon, M. (1986) Some elements of a sociology of translation: domestication of scallops and the fishermen of St Brieuc Bay, in: Power, Action and Belief, J. Law (ed.), Routledge & Kegan Paul, London, 196-233

-         Law, J. & Callon, M. (1992) The life and death of an aircraft: a network analysis of technical change, in: W.E. Bijker & J. Law (eds), Shaping Technology/Building Society, MIT Press, Cambridge, MA, 21-52

Also not too unreadable is Latour’s Reassembling the Social, though had Latour been shot half-way through the dialogue with a PhD student that is reported in the book, I can’t help feeling a verdict of justifiable homicide would have been returned.

Although, as noted, use of ANT in ICT4D research has been limited there have been enough examples, at least from developing country cases within the information systems field, that we get a sense of the questions ANT is good at answering:

-         How do you explain the trajectory of an ICT4D project?

-         What role does technology play in an ICT4D project?

-         How does power manifest itself in an ICT4D project?  How were apparently powerless actors able to influence the direction of an ICT4D project?  How was it that apparently powerful actors didn’t get their way on an ICT4D project?

-         How does a particular ICT4D innovation (be it a new technology or business model or idea) diffuse or scale-up or sink without trace?

-         How did a particular ICT4D impact or ICT4D policy come about?

If you’ve identified other ICT4D questions that are especially suitable for an ANT lens, then do contribute them.

If you want an example of applying ANT in ICT4D that also includes a reflection on the pros and cons of the theory, and some thoughts on applying it in your research, I can recommend:

-         Stanforth, C. (2007) Using actor-network theory to analyze e-government implementation in developing countries, Information Technology and International Development, 3(3), 35-60

There is also a discussion of the relation between ICT4D and ANT in:

-         Rubinoff, D.D. (2008) Towards an ICT4D geometry of empowerment: using actor-network theory to understand and improve ICT4D, in: Developing Successful ICT Strategies, M.H. Rahman (ed.), Information Science Reference, Hershey, PA, 133-154

And feel free to comment on other ICT4D literature that makes use of ANT.

If you would like to participate in discussions about ANT, you can join our online forum on LinkedIn at: http://www.linkedin.com/groups/ActorNetwork-Theory-in-Development-Studies-3995328

We will also be populating a group on Mendeley with reference details, and welcome contributions: http://www.mendeley.com/groups/1255941/actor-network-theory-in-development-studies/

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.

Development 2.0 is the ICT-enabled transformation of international development.  An earlier paper and blog entry discussed transformative Development 2.0 models and impacts.  This entry looks at a potential example; a case study of direct development and digital production from rural India.

The case is one of “socially-responsible outsourcing” (SRO): the use of – in this case IT – outsourcing contracts to drive livelihood benefits directly into poor communities.  I’ve already written up an impact analysis of SRO that delivered new jobs, incomes, skills and empowerment into poor urban communities in Kerala.

The case study analysed here also involved SRO to poor communities in India, but this time to telecentres in rural Bihar.  It contracted from city-based clients both data work (data entry, data editing, digitisation for three clients) and voice work (call centre-based service/technical support and tele-sales for six clients including the Govt of Bihar).  The full case report is available online.

The Development 2.0 promise is that it will bypass traditional development blockages to bring digital production – that is ICT-based productive work – to the bottom of the pyramid.  If that was the promise, what was the reality of this pilot project, run by the social enterprise Drishtee?

The first reality is that this is far from ‘direct development’.  It is a re-intermediated model of development that interposes two layers between urban clients and village production: a city-based head office that interacts direct with clients, a regional office based in a large village (6,000 inhabitants) in rural Bihar which can undertake both data and voice work and quality assurance of the third layer: individual telecentres in relatively remote villages where data (but not voice) work can be done.

The second reality is that the technical and human infrastructure in rural areas requires significant investments before it can get close to the promise of this type of Development 2.0.  The regional office (20 PCs, two printers, 512 kbps Internet connection via VSAT and ISDN, UPS and generator: see Figure 1) had to be created at a cost of US$13,000.  The village telecentres (at least two PCs, GPRS Internet link (114 kbps),and electricity plus back-up: see Figure 2) were within 35km of the regional office and were already in existence.  They had cost an average US$1,500 to set up with running costs (inc. loan costs, rent, telecoms, maintenance) of US$150 per month.  Some needed additional investment to ensure greater reliability of power supply.

Figure 1: Main rural outsourcing office

(Source: Drishtee)  

Figure 2: Village telecentre

(Source: Drishtee)

The staff who were to do the work in both the large and the remote village locations were selected from unemployed youth (presumed to be under 25 years old) who had some school education including English language skills and IT familiarity.  However, they all required two-three month training programmes covering IT, language, typing, and communication skills before there were seen as ready to participate in this particular part of the digital economy.  Even then, their initial accuracy rate for data work was around 75%, rising to 95% after about two months of work.  They still required the layered superstructure of quality control between them and the clients.

In all, the pilot project created 19 new jobs in the large village (regional office) and 5 overall in the village telecentres, with earnings of US$80 per month (for 25 days of eight-hour shifts; a pay level set at the top of the typical US$40-80 range for rural business process outsourcing work) when there was sufficient work.  In such circumstances, the telecentre owners could net US$90 per month from the SRO, thus strengthening telecentre sustainability.  In addition to the creation of jobs and incomes at the bottom of the pyramid, this project confirmed the findings of the Kerala SRO programme that there are key gains in skills and self-confidence.

If the message is that the BoP isn’t quite ready, but can be made ready, for this particular fraction of Development 2.0, the news from the top of pyramid is less cheery.  Having largely addressed the technical, skill and quality challenges of SRO, Drishtee’s main difficulty has been demand: getting enough clients.

They charge US$1 (Rs.45) per job hour for domestic clients, which is the going rate, and rural outsourcing has clear advantages over outsourcing to urban areas (c.35% cost advantage, and much lower staff turnover rates than the c.40% per year in urban locations).  But there have been difficulties of awareness of the rural/socially-responsible outsourcing model, and of trust of the model and of a new entrant into the field like Drishtee.

Scaling – even sustaining – this particular model is therefore difficult.  Experiences in Kerala show that both scalability and sustainability are achievable, but those all occurred within one large state government rather than via the more commercial sales and marketing approach that Drishtee must follow.

In conclusion, the Bihar pilot demonstrates that the benefits of the digital economy – specifically, ICT-based jobs – can be brought to rural areas, and can deliver livelihood benefits of income, skills, and empowerment.  The poor in rural communities therefore do not just have to be digital consumers, they can also be digital producers.

It is also an example of ICT helping bring new development actors into play; in this case a multi-layered social enterprise that provides a new form of intermediation between urban business and rural livelihoods.  It is disappointing that the same constraints we got bored of discussing in the 1980s – power, telecommunications, skills – are so deeply persistent.  And troubling that new constraints – trust, awareness, demand – may be holding back realisation of Development 2.0′s potential.  But increasing numbers of new intermediaries are bringing ICT-based SRO to poor urban and rural communities, so we can expect that realisation to increase in future.

Links: see also blog entry on BoPsourcing: Fighting or Fuelling Inequality?

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