ICT-for-Development Research: Size and Growth

How big is the ICT4D research field?  And is it growing or shrinking?

The first question is harder to answer.  I’ll offer an estimate based on conferences (which will only attract a sub-set of the field e.g. 350 attendees at ICTD2009, and 500 authors submitting papers) and my own contact lists.  On that basis, I estimate that, worldwide, several hundred academics and several thousand PhD researchers are working specifically on ICT4D topics.  They work alongside thousands of staff in donor agencies, national governments and private firms who occasionally contribute to research outputs.  Several thousand more academic staff, particularly in business/management and informatics, undertake occasional research in the field.

What about trends?  One way to measure is through the ISI Web of Knowledge which records books, all papers in a large number of journals and some conferences.  Searching for the term ‘ict4d’ produces only 25 results, almost all during 2007-2009; too few for any real analysis.  Searching for ‘ict*’ and ‘developing countr*’ produces 395 results.  A manual review suggests the great majority are ICT4D-relevant publications, and analysis shows the following trend (note results for 2009 are incomplete as papers are still being entered): 

This shows dramatic growth in ICT4D research during the “noughties”: a nearly 2000% increase from 1999 to 2008; an average 39% annual growth rate.

In part, this might be an “ICT effect” reflecting greater use of the term.  But it does also seem to reflect more general research growth in the area of development informatics.  Publications using ‘info*’ and ‘developing countr*’ grew by 80% from 1999-2008 (7% annual average); and the narrower band of publications using ‘information technolog*’ and ‘developing countr*’ grew by 153% from 1999-2008 (from 83 to 210; an 11% annual average).

Just to check there wasn’t a general research growth effect, a cross-check with ‘political’ and ‘developing countr*’ showed a couple of hundred items per year published, but only a 14% growth in the literature from 1999-2008 (1% annual average).  A better cross-check was with just ‘developing countr*’ which showed a 57% growth (5% annual); and just ‘ict*’ which showed a 126% growth (9% annual) during the same period.

From this data, then, ICT4D research publication is growing significantly faster than cognate research areas.

We can draw a similar conclusion of high growth by looking at ICT4D-specific journals.  In 1999, these produced 33 articles.  In 2009, they produced 182 articles; a 450% rise.  This rise was very much related to growth in the number of journals: two in 1999 and eleven today (though one has yet to produce its 2009 edition, and one only produces its first edition in 2010; none of these journals is covered by the ISI Web of Knowledge).

Given growth and items not covered by these two methods, this suggests at least 300 ICT4D journal articles will be published in 2010, and likely several hundred more under the general banner of development informatics in its broad sense.  Plus, of course, all of the books, reports and conference papers not to mention blogs, wikis and the like.  [See more consideration and detail on this in comment.]

That’s one hell of a change from 1987 when I first started academic work in the field and when, as I never tire of saying, the entire historical academic output on IT and development would fit on a single shelf of my bookcase.  And it indicates ICT4D research as a fast-growing field with all the pros (greater audience, more jobs, more collaborators, more new ideas, more impact) and cons (more to read, greater competition) that brings.

Indian IT Sector Statistics: 1980-2009 Time Series Data

The spreadsheet linked below provides time-series data for India’s IT industry: software, hardware and services revenue for both export and domestic markets.  Software export figures run from 1980; overall IT outputs from 1991; and detailed breakdown from 1998 including BPO (business process outsourcing) data from 2000.

- Link to PDF version of Indian IT Industry data with charts

- Link to XLS version of Indian IT Industry data via Google Docs

- Link to Google Doc spreadsheet of Indian IT Industry data

Although software and IT services tend to grab the headlines, other sub-sectors are significant: with hardware worth US$9.5bn (nearer US$12bn if one includes hardware design) and BPO worth more than US$13bn in 2008/09.  Total revenue for India’s IT industry in 2008/09 was US$73.4bn.

A number of charts are included in the PDF version.  These show, for example:

- The phenomenal growth rate of India’s software exports, with (ten-year rolling) average annual growth never dropping below 30%, and overall exports exceeding US$36bn in 2008/09:

- The much higher growth rate of Indian IT exports compared to production for the domestic market.  As a result, the share of exports in total IT output has risen from 19% in 1991/92 to 69% in 2008/09:

The source for the data used is a mixture of interviews in India and Department of Electronics/IT reports for the earlier data up to late 1990s; and the invaluable Dataquest (India) annual review of the IT industry (the “DQ Top 20″) from that point on.

Asian Livelihoods from Online Games: Past Phases and Future Directions for “Gold Farming”

“Gold farming” and “real-money trading” (RMT) are – respectively – the production and sale of online game virtual goods or services for real money.  They consist mainly of making and selling the virtual currencies used in games such as World of Warcraft; or of creating and selling powerful characters in those games (“power-levelling”).

Such actions are against the rules of the game.  Nevertheless, they provide paid employment for perhaps hundreds of thousands of young men, mainly based in urban areas in China.  A feature article in Scientific American summarises research from the University of Manchester on this emerging linkage between ICTs and development.  (See also our working paper on the topic.)

The research charts three eras of gold farming and RMT:

• Pre-History: first sales of virtual game items occurred during the 1980s and 1990s, building to a largely US-based cottage industry in which individuals made the equivalent of pocket money.
• Golden Age: from the late 1990s to mid-2000s, gold farming was a sector of super-profits as the number of online game players expanded rapidly, as eBay emerged to facilitate trading, and as production shifted to low-cost locations in Asia.  The result was creation of tens and then probably hundreds of thousands of new jobs, with the virtual currency and power-levelling services being sold to game players in the US, Europe and within Asia itself.
• Backlash and Beyond: from the mid-2000s, game companies began clamping down on what they saw as illegal activity, yet simultaneously new gold farmers and traders began flooding in.  As a result, gold farming profits were cut and it became a difficult and risky activity, though one that still appears to be growing despite the global economic crisis.

Non-purchasing players, game companies, and many Western commentators have built a chorus of disapproval against gold farming.  They try to brand it as illegal, exploitative, and linked to organised crime.

Although the evidence base on gold farming is much too limited, work at the Centre for Development Informatics seeks to show there is an alternative, developmental perspective.  Gold farming and RMT show novel ways in which ICTs can create new income streams for developing countries, new jobs (some for unemployed rural migrants), and new skills.  They may also be the early sign of a new business model for developing countries – “cybersourcing” – the outsourcing of activities that take place entirely within the virtuality of cyberspace.  Other examples would be welcome . . .

Broadband Penetration Over Time: Data Visualisation with Google Motion Chart, Gapminder and Excel

I’ve entered the ITU data on broadband penetration for all countries from 1998-2008 into a Google Docs spreadsheet, and then added the Motion Chart visualiser.

To access the spreadsheet data and Google Motion chart, go to: http://spreadsheets.google.com/pub?key=tuY4rmkYVhRxKCXiltg_obg&output=html

The screenshot below gives an example. Also given below are two screenshot summary graphs derived from overview data about diffusion rates for broadband, which can be found at: http://spreadsheets.google.com/pub?key=tCyqBGWg0E8qg6qG77ltZ2g&output=html.

The most useful statistics are absolute growth rates (weighted by population), which show growth having peaked in 2005 for the richest fifth of nations, but generally still rising for the others. The percentage growth rates have been steadily declining, but mainly because those growth figures are insanely high in the first few years of broadband diffusion given the very low base they start from.

You can find similar visualisations that you can cross-match with a host of other data categories (demographics, economic/social development, and ICT diffusion) using World Bank data (http://devdata.worldbank.org/DataVisualizer/) or a graph I’ve created at Gapminder: http://bit.ly/78IWkN. But these don’t go up to 2008, and you can’t see or access the underlying data.

Note the dynamic visualisation charts will not show up on slower PCs or Internet connections.

For similar data visualisation of mobile phone penetration, see my earlier blog entry at: http://ict4dblog.wordpress.com/2009/11/30/mobile-phone-penetration-google-motion-chart-data-visualisation/

Mobile Phone Penetration: Google Motion Chart Data Visualisation

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. 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).

Good Practice in ICT4D Research

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.

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

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.

ICT4D 2.0: Where Next for ICTs and Development?

Are we seeing a phase change in use of ICTs for international development?

The “ICT4D 2.0 Manifesto” (http://bit.ly/W6y4a) argues that we are; moving from phase 1 (late-1990s to late-2000s) to phase 2 (late-2000s on).

The paper outlines some of the emerging characteristics of ICT4D 2.0, based on research from the University of Manchester’s Centre for Development Informatics, and other sources.  Feel welcome to comment and add your own observations to this list:

a)    New Hardware Priorities: a need for innovation around low-cost, broad-reach terminals, telecommunications, and power.  A need to bring the hardware success story of the last decade – mobiles – even more centre stage.  The paper also discusses implications of broadband, cloud computing, and individualisation of hardware devices.

b)    New Application Priorities: the growth of participatory content creation, and the use of ICTs to create new income and employment for the world’s poor.  The paper also discusses implications of FOSS, and the growth of applications to address urban poverty, security, economic growth, and climate change.

c)     New Innovation Models: the growing need for – and potential of – innovation that moves beyond top-down, laboratory-type models.  This includes collaborative (para-poor) models that work alongside poor communities.  It also means greater attention to the grassroots (per-poor) innovation that is arising from within those communities.  The paper also discusses the new innovation intermediaries that are emerging in private and NGO sectors.

d)    New Implementation Models: based on the limitations of ICT4D 1.0 projects, there will be greater emphasis on sustainability, scalability and ICT4D project evaluation.  This will necessitate more process than blueprint approaches to implementation, and better techniques for closing design—reality gaps.  The paper also discusses new funding mechanisms and new organisation forms that are increasingly seen.

e)    New Worldviews: effective ICT4D 2.0 policies, strategies and projects will require “tribrid” champions.  They must understand enough about the three domains of computer science, information systems, and development studies to draw key lessons and to interact with and manage domain professionals.  Training programmes and working group formation must reflect this need.

The paper also discusses the need to move beyond ICT4D mainstreaming, to plan ICT4D policy structure and process as much as content, to engage with the growing “Development 2.0″ agenda, and to shape ICT4D research priorities accordingly.

Above all, it argues, ICT4D 2.0 will require a new worldview of the poor; no longer characterising them as passive consumers but, instead, seeing them relate to ICT as active producers and active innovators.

Attitudes to Science and Technology: Global South vs. Global North

Here’s an interesting piece of research on attitudes to science and technology in different countries from the Relevance of Science Education project that surveyed 14-16 year olds in 25 countries.  Countries covered were (roughly) low-income African (e.g. Uganda); low/mid-income Asian (e.g. India); high-income European (e.g. England).

There are three main findings:

- There is a significant inverse relationship between level of development (human development index score) and the rated importance and benefits of science and technology to society; though the decline from global South to global North is relatively small.

- There is a significant inverse relationship between level of development and desire to work with technology.  The differences are quite large: African and Asian youth are on average positively inclined; European youth negatively so.

- The gender gap in attitudes to science and technology is greater in industrialised countries than in developing countries; very significantly so in relation to getting a technology-related job.

There’s a generic conclusion.  Given the importance of S&T to economic growth, the global North is in big trouble unless it can keep importing science and technology graduates from the global South.

There’s specific conclusion no.1.  If you’re working on ICTs, focusing on ICT4D is a good bet: you’ll find a more receptive and faster-growing audience for research in developing countries; a more receptive and faster-growing training audience; and those might (er, ignoring the odd structural factor!) be more gender-balanced audiences.

There’s specific conclusion no.2.  Developing country audiences may be more techno-centric and less receptive to information systems-type approaches to ICT4D, which place less emphasis on the technology and which tend to be less optimistic about technology.

And there’s a question.  Why?  Why should it be that the poorer your country, the more positive you feel about and the more you want to work with technology?

Because you’ve been less exposed to technology?  Because you can see that technology makes a real, positive difference to your country’s problems?  Because . . . [fill in your answer here]

(My thanks to Roger Boyle for pointing out this survey.)

China Bans Gold Farming!! … Er … But In Fact It Hasn’t

The blogosphere has been awash with reports of the demise of gold farming (production and real-money sale of virtual currencies, items and accounts in online games), which is big business in China; worth US$1bn per year and perhaps more.  (Click here for the full analytical report on the history, size and trends in gold farming.)

A deep breath and a read of what was actually announced suggests otherwise.

This is a government restriction on the use of the quasi-Paypal-like currencies (mainly QQ coins) that are used extensively in China to pay for virtual game stuff.  As announced they can now only be used to pay for virtual stuff, and you can’t buy real things with them as game companies were allowing to happen, nor can you gamble.  This therefore is not about what gold farming clients do: use real money to buy these virtual currencies; it’s the mirror image.  And it’s not about the major trade in gold farming such as World of Warcraft, which relates to other types of virtual currency.  And it’s not about buying/selling in-game items.  And it’s not about the power-levelling of avatars.  Bottom line: it’s not about gold farming.

Two other things to say.  The Chinese government appears to be this very odd mixture of fantastically effective (think Olympic Games) and fantastically ineffective (think rules on piracy and intellectual property) when it comes to implementation.

Second, this mirrors quite closely something that happened in Korea around 2006 based around a game called “Sea Story”.  A huge amount of gambling and then illicit political payoffs arose around use of the Sea Story currency.  Government then banned trade in virtual currencies.  I’m not aware of any reports about damage to gold farming that resulted and – as might be the case in China – the legislation in Korea may have been as much about political posturing and being seen to be doing something (i.e spin) rather than an implemented reality.

Both these points remind us that announcement is not implementation.  If this regulation does come to fruition, it will relate to finance and defence of the RNB yuan.  Yes, it may affect some types of games in China but, no, it as yet appears unlikely to have much of an impact on gold farming.