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Understanding e-Waste Management in Developing Countries

2 February 2016 Leave a comment

Organisations are the largest consumers of ICTs and the largest producers of e-waste.  But what shapes their e-waste decisions?  Why do some recycle, others donate, and others dispose?

To understand this, research in the Centre for Development Informatics by Loga Subramanian first categorised four different e-waste strategies:

  • Indifferent: the organisation does not adopt any strategic position in relation to e-waste.
  • Reactive: the organisation adopts the minimum e-waste strategy necessary to react to its context.
  • Proactive: the organisation pushes its e-waste strategy ahead of the basic reactive minimum.
  • Innovative: the organisation sees e-waste as an opportunity and adopts an innovative strategy in order to address that opportunity.

eWaste Strategies

To explain these differences, a six-factor model was developed of e-waste strategy determinants.  Key external determinants were:

  • Government regulation: in particular the threat of fines and other costs associated with non-compliance with environmental regulations.
  • Peer pressure: especially where there is some form of sectoral association.
  • Client requirements: where these include a need for particular environmental standards or actions.
  • Corporate reputation/brand image: given environmental actions are seen to directly correlate to image and reputation.

Key internal determinants were:

  • Financial impact: the financial implications of e-waste decisions.
  • Organisational culture/leadership: the complex of values, beliefs, assumptions and symbols which organisational leaders promote and which shape all decisions and actions.

eWaste Strategy Determinants

Applying this model to India’s largest e-waste producer – the ICT sector – Loga found a significant difference in strategies between different organisations:

  • Very large firms adopted a proactive strategy, driven by significant internal and external pressures that reflected their position within global value chains.
  • By contrast, ICT sector SMEs were largely indifferent to e-waste, felt few external pressures due to their position within localised value chains, and typically used informal channels that produced some financial return on their scrap ICT.

Given these insights, what are the policy implications?  Current legislative approaches – transferred from the global North and based on the principle of extended producer responsibility – are unlikely to help.  e-Waste recyclers must be brought into the legislative and financial equation.  SMEs must be placed within the purview of legislation (they are currently exempt), and SME associations must place e-waste onto their agenda.

If you would like to know more, please refer to the journal article reporting this research, published in the journal, Information Technology for Development and available via open access: “Understanding e-Waste Management in Developing Countries”.

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Digital Dividends: Thoughts on the 2016 World Development Report

13 January 2016 1 comment

Some years back, helping run a session at the World Bank, I introduced myself to a table of participants.  “Oh yes”, came the sniffy response, “you’re the ICT failure guy”.  This was a World Bank that believed in – and heavily promoted – the development benefits of ICTs, and had little time for any contrary evidence.

Judging from this year’s World Development Report – “Digital Dividends” – that rose-tinted optimism has been replaced by a much more realistic, and somewhat downbeat, perspective on ICT4D; a perspective that’s emerged from strong engagement with the current evidence base.

Three Divides

WDR2016 is a tale of three divides.

The first is an impact divide: a gap between ICT’s widespread diffusion and its actual delivery of benefits – the “digital dividends” of the title.  As one would expect, the Report does a great job of laying out those dividends, particularly through pithy frameworks and graphics.  It shows the way in which ICT affordances of efficiency, inclusivity and innovation have driven productivity, growth and jobs in the economic sphere, and more capable and responsive governments in the political sphere.

Yet alongside the digital dividend has come:

  • a digital deficit: inequalities in the distribution of these benefits with a few “haves”, many “have nots”, and far more “have lesses”; and
  • digital ills: cybercrime and curtailment of online freedoms that sit beside the ICT4D unmentionable, online pornography.

The cause of these problems – at least the digital deficit – is the two other divides.

The digital divide is familiar territory: the problems of accessibility and affordability with customary prescriptions that mix competition and regulation, and at least a mention for the applicability problems that arise from digital illiteracy.  But of more interest is the strong recognition that a social divide is the main determinant of the pattern of ICT4D impacts: a gap between the regulations, skills, and institutions needed to deliver digital dividends for all vs. the actual regulations, skills and institutions present within developing countries.

Those who believe in a contextualised, socio-technical approach to ICT4D will nod along to all this.  Even the consequent prescriptions – “regulations that allow firms to connect and compete; skills that technology augments rather than replaces; and institutions that are capable and accountable” – while they have an expected flavour of neo-liberalism, constitute a broader digital policy agenda than often promoted in the past by the World Bank.

Digital Development

This broader agenda reflects a bigger picture issue: the Report is one more marker of the transition from “ICT4D” to “digital development”.  The absence of ICT4D (it gets no mentions save a bibliographic reference to one of my papers) in favour of digital development is more than just a change in terminology but – as I’ve written in an earlier report (see here for edited version) – reflects the slow change from ICTs being a tool that assists development to their being the platform that mediates development.

The agenda for digital development will be substantially shaped by the Sustainable Development Goals, with their three essentials of transformation, inclusion and sustainability:

  • As noted above, WDR2016 identifies how much ICTs have already delivered; how reality has so far undershot the transformative potential of ICTs, due to technical and social divides; but also what the solutions might be.
  • Inclusion – or rather lack thereof – is also a key Report theme, citing concentration of economic and political power, state and corporate control of citizens, and inequality of economic impacts. The Report’s focus on economic and political domains means it has much less to say about ICTs and inequalities in other domains such as social and family and cultural life.  It is also rather mixed in its perspective on ICTs and inclusion: at times arguing inequalities “persist, not because of digital technologies, but in spite of them”, but in other places explaining how ICTs have facilitated digital monopolies, automation of middle-income jobs, and digital authoritarianism.
  • Sustainability and its operationalisation through resilience gets a brief acknowledgement but – as I’ve noted in my “ICT4D2016” paper – much remains to be done to really get a grip on the coming e-sustainability and e-resilience agenda.

The practice of digital development will be substantially shaped by Development 2.0: the ICT-enabled innovations that challenge existing development structures and processes: users as digital producers, the power of the crowd, digital participation, network structures, data-intensive development, and open development.  In largely reviewing the existing evidence base, “Digital Dividends” has less to say about these.  But they are identifiable within the Report as part of the coming flow.

WDR1998/99 (“Knowledge for Development”) had an important impact in kick-starting ICT4D.  WDR2016 faces a different world – one far more mature, and perhaps a little jaded in its experience of ICTs and development, but it reflects this evolution well and will be a vital pointer for the “digital development” future.

 

Disclosure: I was an invited Advisory Panel member for WDR2016.

ICT4D’s 95:5 Rule

29 October 2014 4 comments

Should we have a “95:5 rule” for ICTs and development?

Typical consumption-related uses of ICTs touch 95% of people but make only a 5% difference to their livelihoods.  This covers “intensive” application of ICTs: their use to intensify an existing livelihood.  Examples include use of mobiles to bring market information to farmers; access to e-government at a local kiosk, substituting a journey to district headquarters; use of a website helping handicraft producers sell their goods; or use of email by a retailer in a low-income community.

Typical production-related uses of ICTs touch 5% of people but make a 95% difference to their livelihood.  This covers “extensive” application of ICTs: their use to extend the range of possible livelihoods, by created a new ICT-based livelihood.  Examples include the umbrella people selling mobile phone calls by the street; or a worker from a poor community undertaking data entry work; or a mobile money service agent.  So extensive ICT livelihoods only exist because of ICT and they fall into the ICT sector, broadly defined.

95-5 Graphic

A classic example is the comparison of two studies from Kerala, India.  The arrival of mobile phones in one fishing area led to an average 9% increase in profits for fishermen[1].  Given 75% of income in South Indian fishing households comes from fishing[2], that suggests ICT consumption increased household income by 7% on average.  Simultaneous to this, the Keralan government was engaged in setting up an IT impact sourcing initiative, outsourcing data entry and digitisation work to groups of women from below-poverty-line families[3].  These new ICT jobs led to an average 75% increase in household income.

As with most quantitative findings, these specific figures don’t exactly match 5% or 95% but an overall average may get closer.

Let’s first take evidence on intensive use.  Consumption-related evidence sometimes reports more than a 5% income increase[4].  But this must be set against other work that shows a less than 5% income increase[5] or no increase[6] or questions the limited time-scales or scope of studies that demonstrate income increases[7].  And it must also be set against the occasional study showing an exact match: “Internet users reported an increase of US$ 51.86 in labor income … 5.01% per year”[8].

Can we say that 95% of those living in the global South are digital ICT consumers?  We are certainly close to that point.  There were just over 90 mobile subscriptions per 100 citizens in developing countries in 2014[9].  We need to bump that down to take account of individuals with multiple subscriptions but bump it up again to take account of shared access[10].  The end result will be in the neighbourhood of 95%.

Turning to evidence on extensive use, many of those working in the ICT sector derive 100% of their income from their employment.  We could shade that down overall given some with ICT-based livelihoods will have other income sources.  The proportion of those working in the ICT sector is growing but typically less than 5% (e.g. 5.7% of employment in OECD countries[11] but generally much lower in less-wealthy countries[12]).  As an example, India’s ICT sector represents less than 1% of India’s workforce[13] but that must be multiplied by three given the estimate that two-thirds of India’s ICT jobs lie outside the formal ICT sector[14].  But that estimate may exclude a number of ICT-based livelihoods, so the result may at least be heading for 5%.  It is certainly increasing year-on-year.

Given these pulls in various different directions, an endpoint of 95%:5% is not unreasonable, and certainly all the evidence points to some form of strong Pareto-type distribution.

So what?

Mathematically, 5% of 95% has the same development effect as 95% of 5%.  That means these two uses of ICTs should be given equal emphasis by governments, development agencies, development informatics researchers, ICT4D practitioners, etc.

But at present they are not.  Intensive, consumption-related ICT application is given far, far more attention.  In future that needs to be rectified, with equal emphasis given to digital inclusion by improving existing livelihoods; and to digital inclusion by creating new ICT-based livelihoods.

[1] Jensen, R. (2007) The digital provide: information (technology), market performance and welfare in the South Indian fishers sector, The Quarterly Journal of Economics, 122(3), 879-924

[2] Sivasubramaniam, K. (1991) Kattumaram Fisheries and Fisherfolk, FAO, Bay of Bengal Programme, Madras

[3] Heeks, R. & Arun, S. (2010) Social outsourcing as a development tool: the impact of outsourcing IT services to women’s social enterprises in Kerala, Journal of International Development, 22(4), 441-454

[4] E.g. Aker, J.C. (2008) Does Digital Divide or Provide? The Impact of Cell Phones on Grain Markets in Niger, BREAD Working Papers (177), Bureau for Research and Economic Analysis of Development, Duke University, Durham, NC; Rizvi, S.M.H. (2011) LifeLines: livelihood solutions through mobile technology in India, in: Strengthening Rural Livelihoods, D.J. Grimshaw & S. Kala (eds), Practical Action Publishing, Rugby, UK, 53-70

[5] E.g. May, J., Dutton, V. & Munyakazi, L. (2011) Information and Communication Technologies as an Escape from Poverty Traps, PICTURE Africa Research Project, Nairobi; cited in Diga, K. (2013) Access and usage of ICTs by the poor, in: Connecting ICTs to Development, L. Elder, H. Emdon, R. Fuchs & B. Petrazzini (eds), Anthem Press, London, 117-136

[6] E.g. Aker, J.C. & Fafchamps, M. (2013) Mobile Phone Coverage and Producer Markets: Evidence from West Africa, Discussion Paper 9491, Centre for Economic Policy Research, London, UK

[7] E.g. Srinivasan, J. & Burrell, J. (2013) Revisiting the fishers of Kerala, India, in:  ICTD2013: Proceedings of the Sixth International Conference on Information and Communication Technologies and Development, J. Donner & T. Parikh (eds), 56-66

[8] Galperin, H., Mariscal, J. & Barrantes, R. (2014) The Internet and Poverty: Opening the Black Box, IDRC, Ottawa

[9] ITU (2014) ICT-Eye, International Telecommunication Union, Geneva

[10] Heeks, R. (2009) Beyond subscriptions: actual ownerships, use and non-use of mobiles in developing countries, ICT4DBlog, 22 Mar

[11] OECD (2014) ICT Employment (indicator), OECD, Paris

[12] OECD (2011) Size of the ICTsector, in: OECD Factbook 2011-2012, OECD, Paris; EC (2012) Information and Communications Technology (ICT) Sector, EU Skills Panorama, European Commission, Brussels

[13] NSSO (2013) Key Indicators of Employment and Unemployment in India, 2011-2012, National Sample Survey Office, Government of India, New Delhi; Nasscom (2014) India IT-BPM Overview, Nasscom, New Delhi.

[14] Nandi, R. (2014) Decent work and low-end IT occupation workers in Delhi, The Journal of Social Science and Humanity Research, 2(1), 9-23

e-Government Benefits And Costs: Why e-Gov Raises Not Lowers Your Taxes

29 September 2011 2 comments

Often, IT companies sell e-government to politicians, and politicians sell e-government to citizens on the promise that it will save money.  These claims regularly appear as “news” items, especially in IT- or government-related media.  This has in part encouraged the huge expenditure on e-government: a ballpark figure would be US$3 trillion during the first decade of the 2000s[1].

So here’s my question: “If e-government is so great at cutting costs, how come my taxes haven’t gone down?”

Of course, taxes depend on far more variables than just e-government.  But the simple answer to the question is “. . . because e-government does not save money, it costs money”.  That seems likely the case in the global North where e-government seeks to cut costs by replacing expensive humans with cheap technology.  It is most definitely going to be the case in the global South where the technology is more expensive and the humans are much cheaper.

Despite the obvious importance of the topic, good quality cost:benefit calculations are rare but can be found.  Six years of e-government in UK local government saw £3.90 billion of investment release just £0.97 billion of savings[2].  The aggregate cost:benefit ratio of e-government projects in Australia was 1.64:1[3].

Rarer still is good quality work from developing countries.[4]  However, a recently-published study of e-government in Bhutan by Mayumi Miyata provides a model for systematic and comprehensive evaluation of e-government costs and benefits. The case study focuses on the Road Safety and Transport Authority of Bhutan, which issues driving licences and vehicle registration documents.  This was traditionally a paper-based process, and often slow; particularly for driving licences which had to be sent by post from regional offices to the head office in Thimphu.  In the mid-2000s, an Internet-enabled database system was installed so the main information associated with these processes could be passed instantly between offices.  (This was therefore an “e-administration” application for use by government staff rather than an “e-services” application for use by citizens.)

Data for Miyata’s research was gathered both before and after the introduction of this e-government system including detailed observation and timing of work processes, a breakdown of departmental accounts, and a survey of citizens.  The “after” component was undertaken in 2007; two years after implementation of the system, allowing plenty of bedding-in time.

Activity-based costing showed that the direct labour cost for issuing licences and registrations fell 24% following introduction of e-government; from US$15,080 to US$11,530 per year[5].  For example, the direct cost of issuing one driving licence fell from US$1.57 to US$1.17.  This was achieved largely through a significant redesign and decentralisation of internal decision-making and workflow.

However, introduction of e-government brought additional costs – hardware, software, internet connectivity and the cost of IT staff – totalling US$11,080 per year (set-up costs being amortised over 10 years).  The only indirect saving was in reduced postal cost (US$720).  Thus, overall costs were US$15,800 per year before e-government; US$22,610 after e-government.  A rise of 43%.

We need to recognise some specific features of this case that make it typical of a least developed country:

  • the particularly low labour costs and high IT costs;
  • the relatively low volumes of transactions across which costs can be spread (the case is more akin to a local than national government in size);
  • the use of e-administration rather than a web-based self-service system which, while still requiring human back-office intervention, would automate some processes.

Miyata’s research thus provides a model that should be replicated for a broader set of examples.

On the other hand, Miyata’s work misses out three additional reasons why e-government globally fails to deliver cost savings:

  • the relatively high rate of e-gov project failure, the costs of which must be included in any overall cost:benefit accounting[6];
  • the learning curve – often of some years – that must be traversed before e-government applications can be used efficiently and effectively[7];
  • the need for government e-services to be run in parallel with existing face-to-face, phone and postal service channels in order to bridge the digital divide and avoid excluding large sections of the population from access to government services; public e-services thus being a supplement to, not substitute for, other channels[8].

Does this mean e-government is a waste of money, and we should ask for our US$3 trillion back?  No.  What it means is that e-government is not going to save money for government and help bring taxes down.  The benefits of e-government lie elsewhere.  Again, Miyata’s paper is a good illustration:

  • External savings: the lead time from application to receipt was reduced by minutes, weeks, even months for outlying offices.  Wait times in offices may also have come down.  Other studies report shorter waits and fewer journeys.  Saving of journeys can be monetised, and saving of citizen time might be (it depends how that saving is spent).  The key cost savings of e-government may thus be external not internal: for service users not administrators.
  • Internal control and accountability: e-government provided managers with greater oversight of work processes and staff.
  • Service quality and equity: citizens reported the quality of service and the fairness of treatment improved after introduction of e-government.

Other research shows further qualitative and external benefits delivered by e-government including: greater transparency of public services; greater accountability of public servants and politicians; reduced corruption; lower costs for business; greater attraction of foreign investment[9].  Please comment to add your own examples of evidence.

So e-government may not bring your taxes down, but – if properly designed and implemented – it will bring a positive economic and social return on investment.

 


[1] Heeks, R.B. (2006) Managing and Implementing eGovernment, Sage, London http://books.google.com/books?id=hRzAnMulatUC&dq; WITSA (2008) Digital Planet 2008, World IT Services Association, Kuala Lumpur, Malaysia; see: http://www.witsa.org/KL08/DigitalPlanet2008_ReportTables.pdf

[2] Kable (2005) Implementing Electronic Government 4, Kable, London

[3] Foley, P. & Ghani, S. (2007) The Business Case for e-Government, paper prepared for High-Level Seminar on Measuring and Evaluating E-Government, Dubai, 12-13 March http://www.oecd.org/dataoecd/44/42/38404094.pdf

[4] There is a good study of e-government projects in India but it was unable to capture cost data, so focuses only on benefits.

[5] These are costs for issuing just over 31,000 documents.  Note this excludes the cost of materials for licences/registrations, which was the same before and after e-government.

[6] Heeks, R.B. (2006) Managing and Implementing eGovernment, Sage, London http://books.google.com/books?id=hRzAnMulatUC&dq; Gauld, R. & Goldfinch, S. (2006) Dangerous Enthusiasms: E-Government, Computer Failure and Information System Development, University of Otago Press, Dunedin, New Zealand

[7] Poostchi, M. (2003) Implementing E-government, MBA dissertation, Carleton University, Ottawa, ON

[8] Helbig, N., Gil-Garcia, J.R. & Ferro, E. (2009) Understanding the complexity of electronic government, Government Information Quarterly, 26(1), 89-97

[9] Accenture (2004) eGovernment Leadership: High Performance, Maximum Value, Accenture, Dublin; Bhatnagar, S. & Singh, N. (2010) Assessing the impact of e-government: a study of projects in India, Information Technologies and International Development, 6(2), 109-127 http://itidjournal.org/itid/article/viewFile/523/231

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

The ICT4D Value Chain

28 December 2010 7 comments

ICT4D projects and policies can best be understood through a value chain model.  As shown in Figure 1 below, this builds on a standard input—process—output model to create a sequence of linked ICT-for-development resources and processes.  The model can be used for projects and policies in various ways: to trace their history; to analyse their content; to assess and evaluate.

The ICT4D value chain offers four main domains that can be the focus for historical or content analysis or evaluation:

  • Readiness: the systemic prerequisites for any ICT4D initiative; both the foundational precursors that we might conceptualise mainly at the national level such as ICT infrastructure, skills and policy; and the more specific inputs (both ‘hard’ and ‘soft’) that feed into any individual initiative.  Assessment could focus on the presence/absence of these resources and capabilities, or the strategy that converts precursors into inputs.
  • Availability: implementation of an ICT4D initiative turns the inputs into a set of tangible ICT deliverables; typical among which might be a telecentre or mobile phones.  Again, assessment can focus on either the delivered resources and/or the delivery process.
  • Uptake: the processes by which access to the technology is turned into actual usage; also noting that key concerns around this process and its ability to contribute to development have related to the sustainability of this use over time, and – for various innovations that are prototyped – the potential or actuality of scaling-up.  In practice, usage indicators are more often assessed than the various uptake processes.
  • Impact: which can be divided into three sub-elements:
    • Outputs: the micro-level behavioural changes associated with technology use.
    • Outcomes: the wider costs and benefits associated with ICT.
    • Development Impacts: the contribution of the ICT to broader development goals.

Figure 1: The ICT4D Value Chain

 

How has interest in these four domains changed over time?

One way to trace this is through key staging posts for the ICT4D community:

  • The Digital Opportunity Taskforce (DOTForce) arose from the 2000 G8 summit in Okinawa.  In 2001, it produced its “Digital Opportunities for All” report which encompassed four focal areas.  Three – readiness, connectivity and human capacity – were related only to the Readiness domain; and one – participation in e-networks – looked mainly at Readiness and Availability issues.
  • In 2003, the first World Summit on the Information Society was held in Geneva.  Its main report was, tellingly, entitled “Building the Information Society” and not surprisingly the main focus was on building ICT connection and access; again looking mostly at the Readiness and Availability domains.
  • The second World Summit on the Information Society was held in Tunis in 2005.  Unlike its predecessor, its agenda did start to talk about impact.  It still had a strong focus on precursors like financing and governance, but it included additional discussion about the application of ICTs, thus starting to encompass the Uptake and Impact domains.
  • The largest subsequent meeting was the GK3 event in Kuala Lumpur at the end of 2007.  It was shaped by twelve main sub-themes.  Analysing these shows a fairly even spread across the four domains, though with Impact by now the largest single focus, followed by Availability.

There has been no subsequent comparable single event in the area drawing together many thousands of participants as these staging posts did; rather, a growing number of smaller events drawing several hundreds.  However, a useful bellwether is the Information and Communications for Development Report produced by the World Bank.  In its 2009 edition, the ratio of mentions of ‘readiness’ to ‘impact’ was 1:35.

Such evidence is best seen as straws in the wind rather than definitive, but it does suggest a similar pattern to that seen in other areas of ICT application, and summarised in Figure 2.

Figure 2: Changing Focus of ICT4D Priorities Over Time

 

Whatever the exact shape of the graph, it reflects the relative lack of attention that has been paid to ICTs’ contribution to development until quite recently.  That is problematic because, as you move from left to right along the value chain, assessment becomes more difficult, more costly but also more valuable.  Of course there has been literature assessing the connection to development including the summary Compendium on Impact Assessment of ICT4D Projects, and the 2010 Journal of International Development policy arena: “Do Information and Communication Technologies (ICTs) Contribute to Development?“.

However, donor agencies, governments, academic departments and others must still do more to shift the focus of attention along the ICT4D value chain; and to demonstrate ICTs’ development impact.

ICTs in Mountain Regions: Impact Assessment

30 July 2010 4 comments

Mountain regions are home to one-tenth of the world’s population.  Yet they are also among the poorest, most-remote and most-excluded areas.  Can ICTs address these issues?

Maybe.  But, to date, there has been very little research on this: partly because mountain areas are the last places on earth to get connected; partly due to the lack of conceptual frameworks attuned to the specific conditions of these areas.

Manchester’s Centre for Development Informatics has published a working paper – Remoteness, Exclusion and Telecentres in Mountain Regions: http://bit.ly/Hvkk4 – which develops two simple frameworks.  One looks at the positive and negative impacts that ICTs have on resources moving into and out of mountain communities.  The other looks at the “information chain” (see below): the set of actions and complementary inputs required for information to have a resultant development impact.

 

Using these frameworks to analyse the impact of a telecentre set up within a poor community in the high Andes, we found ICTs enabling new and positive resource flows for the two key user groups: teenaged school students and young farmers.  These flows help to maintain social networks.  They also support information searches that have improved agricultural practice so long as other information chain resources have been available.  But non-use and ineffective use of the telecentre are found where information chain resources are lacking.

ICTs have some impact on intangible elements of remoteness.  In this particular example, they also offer access to some previously-excluded resources.  But they have not really addressed the systemic exclusions faced by mountain communities.  And they so far appear to be a technology of inequality; favouring those residents who begin with better resource endowments.

On this basis, we recommend that mountain ICT projects need to be:

  • Info-centric“: focusing less on the technology and more on the data that technology carries.
  • Chain-centric“: attending to the additional information chain resources – over and above technology and data – that are required in order to turn digital data into development results.
  • Socio-centric“: recognising that new information chain resources are mainly provided by individuals’ social contact networks.
  • Econo-centric“: being especially mindful of ICT uses that enable new or more productive income-generating activities.

But this work is just a small start: we need much more research to be done as ICTs diffuse into mountain communities; work that takes account of the specific geographies of those communities.

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