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Adding ICT4D To Your Curriculum

29 November 2017 Leave a comment

ICT4D – information and communication technology for development – courses are increasingly being added to the curriculum for undergraduate and postgraduate programmes in development studies, information systems, business studies, computer science, etc.  Why?

First, the size and growth of the ICT4D domain.  US$ billions are being spent each year: by individual users at the base of the pyramid; by telecom and digital firms reaching BoP markets; by governments and NGOs and social enterprises serving those markets; by donor agencies.  Annual growth rates – for mobile and smartphones, for broadband – are always double-digit.

Second, the urgency and morality of the goals that ICT4D serves.  ICT4D means using digital technology to address the world’s most pressing problems: poverty, inequality, disease, illiteracy, corruption, climate change, etc.

Third, the buzz of ICT4D innovation.  There is both a need and exciting agenda for innovation that engages students: not just technical developments but also new business and social models.

If you want to add an ICT4D course to your curriculum, it is now easy to do so.  The Routledge textbook supporting such a course, “Information and Communication Technology for Development (ICT4D)” is available in physical and e-book formats via: https://www.routledge.com/Information-and-Communication-Technology-for-Development-ICT4D/Heeks/p/book/9781138101814

The textbook uses extensive in-text diagrams, tables and boxed examples with chapter-end discussion and assignment questions and further reading.  Online resources (slide packs, session outlines and notes) support use for teaching and training purposes, and inspection copies can be requested for those planning to adopt the book as a core text.

The book is designed for use around a ten-session course, but can be modified for alternative course designs:

  1. Understanding ICTs and Socio-Economic Development
  2. Foundations of ICTs and Socio-Economic Development
  3. Implementing ICT4D
  4. ICTs and Economic Growth
  5. ICTs, Poverty and Livelihoods
  6. ICTs and Social Development
  7. e-Governance and Development
  8. ICTs and Environmental Sustainability
  9. The Future of ICT4D
  10. *Visit / External Speaker / Lab Session*
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Categories: Teaching ICT4D Tags: ,

Decent Digital Work and the FairWork Foundation

31 October 2017 Leave a comment

How can we improve standards for digital gig workers: those undertaking micro-work and online freelancing via platforms like Upwork and Mechanical Turk?

The recent research paper – “Decent Work and the Digital Gig Economy” – explains why such standards are needed.  With up to 70m workers worldwide registered for online work and growth rates of 20-30% per year, this is already a sizeable activity.  It is especially popular with the c.80% of workers based in middle- and low-income countries, who often see online work as better than local alternatives.  However, this ignores the chronic precarity and structural inequality associated with such work: damaging outcomes that will only spread if nothing is done.

But what should be done?

The paper develops an inventory of “Decent Digital Work” standards.  This is a comprehensive set of guidelines that integrates two things: first, the global decent work standards set by the ILO; second, the actions needed to address specific digital gig economy problems.

A key value for this inventory is as a comparator with other decent work initiatives.  For example, the paper analyses the way in which two major initiatives – SA8000, and the Ethical Trading Initiative – do and do not cover the requirements for decent digital work.

Below, a further comparison is undertaken, between the Decent Digital Work standards, and the criteria adopted by the FairWork Foundation; an initiative aiming to rate and certify gig economy platforms.  The table indicates those elements which are the same in both standards; those where a completely-different element is included; and those where there is some variation in the element.

From this, three things can be concluded:

a) A number of Decent Digital Work standards are absent in the FairWork Foundation certification criteria. Several of these relate to the broader context for work, would be outwith the scope of an individual platform, and therefore are not relevant to platform certification. However, those identified under “Employment” and “Work Conditions” can form part of a further discussion to consider their relevance to certification.

b) Some elements (e.g. around access to digital work opportunities, and accounting for worker costs other than unpaid time) speak to the particular conditions of gig workers from the global South. This is the location for the great majority of gig workers: already for digital gig workers; increasingly for physical gig workers. As such, the FairWork Foundation must ensure its global North origins do not skew its focus.

c) The FairWork Foundation should review how prevalent the non-competition and non-disclosure agreement problems are, and whether they are worth including. (Human review of task instructions – something unlikely to be practicable for most platforms – appears to have been dropped from later versions of the certification criteria; hence, its inclusion in brackets.)

As noted in the Decent Work and the Digital Gig Economy paper’s action research agenda, next steps here would be:

– Survey of worker, client and platform views about identified standards.

– A multi-stakeholder dialogue to agree a minimum set of certification standards and evaluation methods.

– Parallel research on the impact of standards and certifications in the gig economy, and analysing the costs and benefits of interventions such as standards and certifications at micro- and macro-level.

This is just one example of the application of the Decent Digital Work standards.  We hope you can identify other uses . . .

How Big Data Changes the Locus of Organisational Power

19 September 2017 Leave a comment

Big data can lead centres of power in organisations to move.  Recent research on this was undertaken in an Indian state electricity corporation (“Stelcorp”), reported in the paper, “Exploring Big Data for Development: An Electricity Sector Case Study from India”.

This found three shifts to be occurring, as illustrated.

Power Shifts Associated with Big Data

1. From Public to Private. Previously, Stelcorp was responsible for its own data and data systems. As a result of sectoral reforms, private firm “Digicorp” was brought in.  While de jure control remains with Stelcorp, de facto control has shifted to Digicorp.  Digicorp controls knowledge of the design, construction, operation and maintenance of the data systems; it operates those systems; and Stelcorp staff have been reduced to a clerical service role.  In theory, Digicorp could be replaced.  But as seen in other public-private partnerships, in practice there is an asymmetry of power and dependency that has locked in the private partner.

2. From Workers to Managers. With the introduction of online meters for bulk and urban electricity consumers, requirement for human meter-readers has fallen. As a result, during 2013-2016, 40% of meter-readers lost their jobs.  For the remainder, the writing is on the wall – online metering will spread throughout the rest of the electricity network, and their jobs will slowly but steadily be automated out of existence.  For those that remain, the data they collect is less critical than previously, as it forms a declining proportion of all meter data; and they have less control when reduced to just capturing data on hand-held devices (they barely own and access this data and do not use or regulate it).  As a result, Stelcorp managers are decreasingly resource-dependent on the meter-readers and power has shifted away from the latter and towards the former.

3. From Local to Central Managers. The advent of big data led to creation of a central Finance and Energy Management Unit (FEMU). Previously, managers at divisional and zonal levels would be accountable to their immediate superiors, typically within that level: it was those superiors who collected performance data on the managers and negotiated its implications, and those superiors who held power over their junior managers.  Data was relatively “sticky”; tending to be restricted to localised enclaves within the organisation.  This is no longer the case.

Now, all forms of data flow readily to FEMU.  It sees all that goes on within Stelcorp (at least to the extent reflected by current online data) and is able to drill down through zonal and divisional data to individual assets.  It holds regular performance meetings with Stelcorp managers, and has introduced more of an audit and performance management culture.  As a result, managers now largely see themselves as accountable to FEMU.

For further details, including the models of resource dependency and data-related power that underpin this analysis, please refer to the working paper on this topic.

Big Data and Electoral Politics in India

What happens when big data and big politics collide?  One answer arises from a recent study of big data in the electricity distribution sector in an Indian state: “Exploring Big Data for Development: An Electricity Sector Case Study from India”.

[1]

The state electricity corporation has introduced millions of online digital meters that measure electricity flow along the distribution network and down to the level of consumers.  Producing a large stream of real-time data, these innovations should have addressed a critical problem in India: theft / non-payment by consumers which creates losses up to one-third of all supplied power.  But they did not.  Why should that be?

Big data does reduce some losses: technical losses from electrical resistance and faults are down; payment losses from urban consumers are down.  But the big data era has seen an unprecedented expansion of rural electrification, and in rural areas, payment losses have risen to 50% or more.  In other words, the corporation receives less than half the revenue it should given the electricity it is supplying to rural areas.

The expansion in rural electrification has been mandated by politicians.  The high level of rural payment losses has been condoned by politicians, given significant positive association between levels of electricity non-payment and likelihood of seat retention at an election.

Is this the silencing of big data in the face of big politics: the capability for accurate metering and billing of almost all consumers simply being overridden by electoral imperatives?  Not quite, because big data has been involved via an offsetting effect, and an epistemic effect.

  1. Offsetting Effect. Big data-driven technical and urban consumer loss reductions have allowed the State Government to “get away” with its political approach to rural electrification. The two effects of technical/urban loss reduction and political loss increase have roughly balanced one another out; a disappointing aggregate outcome but one that just falls under the threshold that would trigger some direct intervention by the regulators or by Central Government.
  2. Epistemic Effect. Big data creates a separate virtual model of phemonena: a so-called “data double”. This in turn can alter the “imaginaries” of those involved – the mental model and worldviews they have about the phenomena – and the wider discourse about the phenomena.
    This has happened in India.  Big data has created a new imaginary for electricity, particularly within the minds of politicians.  Before big data, the policy paradigm was one that saw electricity in terms of constraint: geographic constraint such that not all areas could be connected, and supply constraint such that “load-shedding” – regular blackouts and brownouts – was regarded as integral.
    After big data, the new paradigm is one of continuous, high-quality, universal electricity.  Plans and promises are now based on the idea that all districts – and all voters – can have 24 x 7 power.

In sum, one thing we know of digital systems is that they have unanticipated consequences.  This has been true of big data in this Indian state.  Far from reducing losses, the data-enabled growth in electricity connectivity has helped fuel a politically-enabled growth in free appropriation of electricity.

For further details, please refer to the working paper on this topic.

[1] Credit: Jorge Royan (Own work) CC-BY-SA-3.0, via Wikimedia Commons https://commons.wikimedia.org/wiki/File:India_-_Kolkata_electricity_meters_-_3832.jpg

The Affordances and Impacts of Data-Intensive Development

What is special about “data-intensive development”: the growing presence and application of data in the processes of international development?

We can identify three levels of understanding: qualities, affordances, and development impacts.

A. Data Qualities

Overused they may be but it still helps to recall the 3Vs.  Data-intensive development is based on a greater volume, velocity and variety of data than previously seen.  These are the core differentiating qualities of data from which affordances and impacts flow.

B. Data Affordances

The qualities are inherent functionalities of data.  From these qualities, combined with purposive use by individuals or organisations, the following affordances emerge[1]:

  • Datafication: an expansion of the phenomena about which data are held. A greater breadth: holding data about more things. A greater depth: holding more data about things.  And a greater granularity: holding more detailed data about things.  This is accelerated by the second affordance . . .
  • Digitisation: not just the conversion of analogue to digital data but the same conversion for all parts of the information value chain. Data processing and visualisation for development becomes digital; through growth of algorithms, development decision-making becomes digital; through growth of automation and smart technology, development action becomes digital.  Digitisation means dematerialisation of data (its separation from physical media) and liquification of data (its consequent fluidity of movement across media and networks), which underlie the third affordance . . .
  • Generativity: the use of data in ways not planned at the origination of the data. In particular, data’s reprogrammability (i.e. using data gathered for one purpose for a different purpose); and data’s recombinability (i.e. mashing up different sets of data to get additional, unplanned value from their intersection).

C. Data-Intensive Development Impacts

In turn, these affordances give rise to development impacts.  There are many ways in which these could be described, with much written about the (claimed) positive impacts.  Here I use a more critical eye to select four that can be connected to the concept of data (in)justice for development[2]:

i. (In)Visibility. The affordances of data create a far greater visibility for those development entities – people, organisations, processes, things, etc. – about which data is captured. They can more readily be part of development activity and decision making.  And they can also suffer loss of privacy and growth in surveillance from the state and private sector[3].

Conversely, those entities not represented in digital data suffer greater invisibility, as they are thrown further into shadow and exclusion from development decision-making.

Dematerialisation and generativity also make the whole information value chain increasingly invisible.  Data is gathered without leaving a physical trace.  Data is processed and decisions are made by algorithms whose code is not subject to external scrutiny.  The values, assumptions and biases inscribed into data, code and algorithms are unseen.

ii. Abstraction. A shift from primacy of the physical representation of development entities to their abstract representation: what Taylor & Broeders (2015) call the “data doubles” of entities, and the “shadow maps” of physical geographies. This abstraction typically represents a shift from qualitative to quantitative representation (and a shift in visibility from the physical to the abstract; from the real thing to its data imaginary).

iii. Determinism.  Often thought of in terms of solutionism: the growing use of data- and technology-driven approaches to development.  Alongside this growth in technological determinism of development, there is an epistemic determinism that sidelines one type of knowledge (messy, local, subjective) in favour of a different type of knowledge (remote, calculable and claiming-to-be-but-resolutely-not objective).  We could also identify the algorithmic determinism that increasingly shapes development decisions.

iv. (Dis)Empowerment. As the affordances of data change the information value chain, they facilitate change in the bases of power. Those who own and control the data, information, knowledge, decisions and actions of the new data-intensive value chains – including its code, visualisations, abstractions, algorithms, terminologies, capabilities, etc – are gaining in power.  Those who do not are losing power in relative terms.

D. Review

The idea of functionalities leading to affordances leading to impacts is too data-deterministic.  These impacts are not written, and they will vary through the different structural inscriptions imprinted into data systems, and through the space for agency that new technologies always permit in international development.  Equally, though, we should avoid social determinism.  The technology of data systems is altering the landscape of international development.  Just as ICT4D research and practice must embrace the affordances of its digital technologies, so data-intensive development must do likewise.

[1] Developed from: Lycett, M. (2013) ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381-386; Nambisan, S. (2016) Digital entrepreneurship: toward a digital technology perspective of entrepreneurship, Entrepreneurship Theory and Practice, advance online publication

[2] Developed from: Johnson, J.A. (2014) From open data to information justice. Ethics And Information Technology, 16(4), 263-274; Taylor, L. & Broeders, D. (2015) In the name of development: power, profit and the datafication of the global South. Geoforum, 64, 229-237; Sengupta, R., Heeks, R., Chattapadhyay, S. & Foster, C. (2017) Exploring Big Data for Development: An Electricity Sector Case Study from India, GDI Development Informatics Working Paper no.66, University of Manchester, UK; Shaw, J. & Graham, M. (2017) An informational right to the city? Code, content, control, and the urbanization of information. Antipode, advance online publication http://onlinelibrary.wiley.com/doi/10.1111/anti.12312/full; Taylor, L. (2017) What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level, TILT, Tilburg University, Netherlands  http://dx.doi.org/10.2139/ssrn.2918779

[3] What Taylor & Broeders (2015) not entirely convincingly argue is a change from overt and consensual “legibility” to tacit and contentious “visibility” of citizens (who now morph into data subjects).

 

ICT4D Course Curriculum and Teaching Materials

The draft materials below are for use in conjunction with the textbook, “Information and Communication Technology for Development (ICT4D)” under publication as part of the Routledge Perspectives on Development series.

They are designed for use in a ten-session teaching module consisting of nine teaching sessions plus one external visit / speaker / lab session.  For each teaching session, a set of PowerPoint slides is provided including notes on session content and class exercises.

ICT4D Course Introduction

Session 1: Understanding ICT4D

Session 2: Foundations of ICT4D

Session 3: Implementing ICT4D

Session 4: ICTs and Economic Growth

Session 5: ICTs, Poverty and Livelihoods

Session 6: ICTs and Social Development

Session 7: e-Governance and Development

Session 8: ICTs and Environmental Sustainability

Session 9: The Future of ICT4D

If used as presented, the material is suitable for a two/three-hour interactive session including a mix of presentation, class questions and class exercises.  Alternatively, the presentation material only can be presented in a c.one-hour lecture session with exercises and discussion left for a separate c.one-/two-hour tutorial session.

Categories: Teaching ICT4D Tags: ,

The Demographics of Digital Development

13 April 2017 2 comments

Any emergent digital development paradigm will be shaped by three changing demographics of ICT usage: geographical, maturational and experiential.

Geographically, we have already moved from domination of the old Internet world (the US and Europe) to domination of the new Internet world (emerging nations of the global East and South), as summarised in the table below[1].  Use of digital technology in developing countries[2] now represents the majority not minority global experience.

 

Region % Share in 2001 % Share in 2017
RISING SHARE
Africa 1% 9%
Middle East 1% 4%
Latin America/Caribbean 5% 10%
Asia 32% 50%
FALLING SHARE
North America 30% 9%
Oceania 2% 1%
Europe 29% 17%

Regional Share of Global Internet Users (2001, 2017)

 

Maturationally, there are growing numbers of digital natives: defined as those 15-24 year olds with five or more years of online experience[3].  While only around one-fifth of the youth cohort in developing countries are digital natives (compared to four-fifths in the global North), youth in the global South as twice as likely to be digital natives as the total population, and so they have a disproportionate role which might be worth specific encouragement.  Given they see ICTs as more important and more beneficial than others do, and given they make proportionately greater use of digital technologies and of social networks, then engagement of digital natives – for example in education or politics – may be enhanced by ensuring there are effective digital channels in these sectors.

Experientially, ICT users are experiencing changes that include[4]:

  • Time-space compression: a shortening of timespans for activities moving towards Castells’ notion of “timeless time” in which biological and clock time are replaced by compressed, desequenced notions of time; and a new geography that replaces physical distance with virtual space so that individual experience moves from a “space of places” to a “space of flows”[5].
  • Public to private: moving from shared-use to individual-use models of ICT interaction. Voice communication is moving from public payphones to shared mobile phones to individually-owned mobile phones.  Internet access is moving from public access telecentres and cybercafés to semi-public home or work computers to personal mobile devices.  The digital experience thus becomes increasingly private and personal.
  • Fixed to mobile: as mobile devices become the dominant means of access to digital infrastructure and content.
  • Text/audio to audio-visual: while it may be premature to call the emergence of a post-literate society, increasing bandwidth and technical capabilities mean digital experiences can increasingly resemble rich, natural real-life experiences rather than the artificial restrictions of just text or just audio.

One can argue that all four cases, represent an increasing presence yet decreasing visibility of the digital as its mediation merges more seamlessly into everyday life and activities.  This growth-but-disappearance of mediation thus represents a final experiential trend – that digital technologies more-and-more intercede between us and our experiences, and yet we notice them doing this less-and-less.  If the medium is the message, our conscious awareness of the message may be diminishing.

All three of these trends – geographical, maturational and experiential – form the emerging background underlying digital development, which is the subject of a Development Informatics working paper: “Examining “Digital Development”: The Shape of Things to Come?”, and will be the topic for future blog entries.

[1] IWS (2017) Internet Usage Statistics, Internet World Stats http://www.internetworldstats.com/stats.htm

[2] http://www.oecd.org/dac/stats/daclist.htm

[3] ITU (2013) Measuring the Information Society 2013, International Telecommunication Union, Geneva http://www.itu.int/en/ITU-D/Statistics/Pages/publications/mis2013.aspx

[4] Barney, D. (2004) The Network Society, Polity Press, Cambridge, UK; Boettiger, S., Toyama, K. & Abed, R. (2012) Natural obsolescence of Village Phone, in: ICTD’12, ACM, New York, NY, 221-229; Molony, T. (2012) ICT and human mobility: cases from developing countries and beyond, Information Technology for Development, 18(2), 87-90; Ridley, M. (2009) Beyond literacy, in: Pushing the Edge, D.M. Mueller (ed), American Library Association, Chicago, IL, 210-213

[5] Castells, M. (2000) Materials for an exploratory theory of the network society, British Journal of Sociology, 51(1), 5-24

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