Big data and development in India. The hype and the reality

Many around the world celebrated the agreement of the Sustainable Development Goals (SDGs) and a new agenda for transformative development by 2030. But, practitioners and policy makers were left scratching their heads as to how they were going to monitor the detailed 169 targets and ever more numerous indicators, never mind understanding and achieving these goals.

It is in this context that we’re seeing a growth of interest in using data to help solve development problems. Indeed, we can say that the infrastructures now being built to support data are likely to become central to how we make development decisions in the future.

How will such data infrastructures shape our thinking about development over the next decade? What types of limitations and biases might they embed? How should they best be designed and implemented? It is these questions that we looked to explore in a recent paper [1] analysing big data use for development in India.

In this paper we dug into two cases where big data was being used to support wider development over commercial goals – the Bengaluru Metropolitan Transport Corporation (BMTC) and big data transport upgrades in Bengaluru, India; and Stelcorp (name changed), a state initiative using big data for improving electricity systems.

Digging into big data

Digging into these cases, we found that both of these initiatives were connected into longer, often decades-old histories of data collection and decision making. This meant that new data innovations were being introduced in an attempt to understand long running development problems. Thus, the main focus of BMTC was on using vehicle tracking and big data innovations to improve the notoriously unreliable city bus services.

We found that big data innovation allowed improved integration of rich information flows, and led to centralisation of decision making. In StelCorp, previously manually-collected meter data was now digitally-collected and aggregated (see images below). The supporting infrastructure allowed a near real-time analysis of the status of the electricity network, and was more effective at monitoring around failures and blackouts. A new central data centre played a growing role in processing and analysing this data. In BTMC, new bus transportation data was aggregated and fed in real-time to large screens in a “control centre” where activity was monitored by administrators.

Digitalisation in Stelcorp: Meters such as those on the left supply real time data about network usage. Even manual meter reading data is now often transferred through automated reading devices (right) to later be input into the system.

Beyond day-to-day monitoring, we also saw signs that the new data was feeding into more strategic decisions. In the electricity sector, for example, upgrades have been plagued by poor and politicised decision making, but the state-wide data from Stelcorp is now being used in upgrading decisions.

More conceptually, there is evidence that these initiatives are playing a role in supporting new forms of state commitments, or citizen interaction. BTMC has been associated with a ‘Smart City’ initiative and citizens interacting with a set of efficient urban services. Indeed, BTMC introduced a citizen mobile app for tracking bus routes which has had over 50,000 downloads. In the Stelcorp initiative, state political visions about “24/7 electricity” have in part emerged from the better data that allows improved management of the electricity system.

Limitations

Whilst big data has led to these operational, strategic and visionary advances, there were a number of concerns in these projects. One key concern raised was the quality of data being used in these projects, which was often incomplete, short-term, or skewed.

Most problematic was that data from marginal groups was difficult to obtain, so in Stelcorp, automated electricity data was mainly coming from cities, where rural data was still manually collected, and in both cases there was often the need for “data wrangling” before the data had value.

These data limitations pose questions of how representative the data being used is of the population. If certain measures are skewed towards those more affluent, data coming from those more marginal might then be seen as “nonconforming” or even deviant. Moreover, the way that the data is selected, measured and transformed in such systems will be important in determining what processes are made visible by data and what might remain in the shadows.

The Smart Cities Challenge: Such visions can be seen to be made viable by the growth of big data. However in reality big data projects often tend to have a narrower focus. Source: http://www.smartcitieschallenge.in/

There were also more general questions about the focus of big data projects. These projects were marketed and discussed under lofty development goals, but in implementation they were often quite narrow projects. BTMC, for all its discussion of smart cities and citizens, was far more focussed on stamping out corruption among bus employees than making the city’s public transport smart.

Further, in all these projects there is scant sharing of the new data produced. These projects have not been about the public shining a light on opaque mechanisms of decision making. In fact, with a growing number of public and private actors involved, mechanisms of decision making are becoming even less transparent.

Big data for development

Big data projects are in their infancy in countries like India, but as these cases show they are becoming important to support decision making on key development issues, not only at an operational level, but in strategic decision making and in supporting new visions of developmental partnerships between citizens, private sector and the state.

However, these initiatives rarely follow the vision of big data driving transformative changes. They so-far tend to use problematic data to enhance decision making. They also tend to focus on quite narrow aspects of problems in implementation over the bigger development problems that might be more impactful.

We also need to make sure that big data does not solely lead to technocratic solutions, or underplay the importance of integrating with a wider set of social and political activities for development – data showing electricity pilferage will have limited impact without solving the complexities of local politics of electricity in rural and slum areas, and data on public vehicle movements cannot replace the underfunding of urban transport.

[1] Heeks, R., Rakesh, V., Sengupta, R., Chattapadhyay, S. & Foster, C. (In press) Datafication, Value and Power in Developing Countries: Big Data in Two Indian Public Service Organisations. Development Policy Review.

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This is an adapted version of a blog originally posted on the Sheffield Institute of International Development (SIID) blog.

With thanks to Vanya Rakesh & Ritam Sengupta for their research in India and SIID and the University of Manchester for the small grant support for this work.

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Big Data and Urban Transportation in India

What effect are big data systems having on urban transportation?

To investigate this, the Centre for Internet and Society was commissioned by the Universities of Manchester and Sheffield, to conduct a study of the big data system recently implemented by the Bengaluru Metropolitan Transport Corporation (BMTC).  The “Intelligent Transport System” (ITS) took three years to reach initial operational status in 2016, and now covers the more than five million daily passenger journeys undertaken on BMTC’s 6,400 buses.

ITS (see figure below) processes many gigabytes of data per day via three main components: vehicle tracking units that continuously transmit bus locations using the mobile cell network; online electronic ticketing machines that capture details of all ticketing transactions; and a passenger information system with linked mobile app to provide details such as bus locations, routes and arrival times.

ITS Architecture (Mishra 2016)^[1]

At the operational level the system is functioning moderately well: the data capture and transmission components mainly work though with some malfunctions; and the passenger-facing components are present but have data and functionality challenges that still need to be fully worked-through.  Higher-level use of big data for tactical and strategic decision-making – optimising routes, reducing staff numbers, increasing operational efficiency – is intended, but not yet evidenced.

Just over a year since full roll-out, this is not unexpected but it is a reminder that big data systems take many years to implement: in this case, at least four years to get the operational functions working, and years more to integrate big data into managerial decision-making.

Nonetheless some broader impacts can already be seen.  Big data has changed the mental model – the “imaginary” – that managers and politicians have of bus transport in Bengaluru.  Where daily operations of the bus fleet and bus crews were largely opaque to management prior to ITS, now they are increasingly visible.  Big data is thus changing the landscape of what is seen to be possible within the organisation, and has already resulted in plans for driver-only buses, and a restructuring that is removing middle management from the organisation: a layer no longer required when big data puts central management in direct contact with the operational front line.

Big data is also leading to shifts in power.  Some of these are tentative: a greater transparency of operations to the general public and civil society that may receive a step change once ITS data is openly shared.  Others are more concrete: big data is shifting power upwards in the organisation – away from front-line labour, and away from middle managers towards those in central management who have the capabilities to control and use the new data streams.

For further details of this study, see Development Informatics working paper no.72: “Big Data and Urban Transportation in India: A Bengaluru Bus Corporation Case Study”.

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[1] Mishra, B. (2016) Intelligent Transport System (ITS), presentation at workshop on Smart Mobility for Bengaluru, Bengaluru, 10 Jun https://www.slideshare.net/EMBARQNetwork/bmtc-intelligent-transport-system

How Big Data Changes the Locus of Organisational Power

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.

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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.

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[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

Understanding e-Waste Management in Developing Countries

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.

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.

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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Indian IT/Software Sector Statistics: 1980-2015 Time Series Data

The spreadsheet linked below provides time-series data for India’s IT industry, updating data from an earlier blog entry on Indian IT data to 2009. Software export figures run from 1980; overall IT outputs from 1991; and detailed breakdown from 1998 including BPO (business process outsourcing) data from 2000.  Data from 2009/10 uses a different source, so changes from 2008/09 to 2009/10 are not reliable.

– Link to XLS version of Indian IT data via Google docs

Main headlines:

a) Indian Software Exports

a1) Indian software exports are huge – roughly US$75bn in 2014/15 (and c.US$100bn if BPO services are included) – and continuously registering double digit annual growth.

a2) But the overall pattern of growth is slowing: the ten-year annual growth average was c.40% in 2002; c.30% in 2008; c.20% in 2014.

a3) IT software/services’ share of total exports remains roughly static: it was just under 14% in 2003/04 and just under 15% in 2013/14[1].

a4) Market diversification for Indian software remains limited.  In the early 1990s, export destinations were: US (c.60-65%), UK (c.10%), other Europe (c.10%), Aus/NZ (c.5-10%), Asia (c.0-3%)[2].  Twenty years later in 2013-14, export destinations were: North America (63%), UK (13%), other Europe (11%), Aus/NZ (4%), Asia (6%)[3].

a5) Location of production has changed.  In the early 1990s, 75% of work took place on-site, 25% in India[4].  By 2013/14, it was said that 20% of work took place on-site, 80% in India[5].  This means that net foreign exchange earnings will have risen as a proportion of gross since offshore work requires much less foreign exchange outflow than on-site working.

a6) One source[6] claims that productivity (as measured by average revenue per employee) in the Indian software sector has risen from c.US$7,000 per head in the mid-1990s, to c.US$16,000 in the late 1990s, to US$38,000 in 2014.  But my own data[7] gives a completely different picture: that productivity in the 1990s was static at just over US$30,000 per head, and thus has risen very little during the 2000s: at best by 1-2 percentage points per year.

b) Domestic IT Production

b1) Although the Indian domestic IT market is large and growing, production for exports is growing faster than production for the domestic market.  As a result, the share of exports in total IT output has risen from 19% in 1991/92 to 49% in 2000/01 to 67% in 2007/08 to 81% in 2014/15.

b2) IT production for the Indian domestic market and domestic IT consumption are very different.  For example, domestic computer hardware production in 2013/14 was roughly US$3bn.  But domestic IT consumption was US$12.4bn[8].  In part, this may be because the two figures are counting different things (e.g. consumption figure includes peripherals, network kit, storage, etc).  But it likely also points to a high – and said to be growing – share of imports in Indian domestic IT consumption.

c) IT Sector Overall

c1) The IT sector overall in India represents just over 5% of GDP in 2014/15.

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[1] Mani, S. (2014) Emergence of India as the world leader in computer and information services, Economic & Political Weekly, XLIX(49), 51-61

[2] Heeks, R. (1996) India’s Software Industry, Sage, New Delhi

[3] ESC (2014) Computer Software/Services and ITeS Exports, Electronics and Computer Software Export Promotion Council, New Delhi www.escindia.in/uploads/SOFT1415.pdf

[4] Heeks (ibid.)

[5] RBI (2015) Survey on Computer Software & Information Technology Enabled Services Exports: 2013-14, Reserve Bank of India, New Delhi https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=15452

[6] Malik, A. & Nilakant, V. (2015) Context and evolution of the Indian IT industry, in: Business Models and People Management in the Indian IT Industry, A. Malik & C. Rowley (eds), Routledge, Abingdon, UK, 15-34

[7] Heeks (ibid.)

[8] Chawla, M. (2014) Indian IT hardware markets stands at $12.43bn, The Economic Times, 25 Jun http://articles.economictimes.indiatimes.com/2014-06-25/news/50856134_1_anwar-shirpurwala-biswapriya-bhattacharjee-indian-it

Development 2.0 Case Study: Socially-Responsible Outsourcing to Rural Indian Telecentres

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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BoPsourcing: Fighting or Fuelling Inequality?

BoPsourcing – the outsourcing of work to bottom-of-the-pyramid communities – is on the rise.  Outsourcing used to mean sub-contracting work from one big firm to another nearby.  Then, with offshoring, the contracts crossed borders.  With BoPsourcing, the contracts cross several income strata as well.

BoPsourcing initiatives can be found that are run by:

• Governments: such as the IT component of Kerala State’s Kudumbashree initiative which has created more than 2,500 jobs for women from below-poverty-line urban households
• Social Enterprises: such as Anudip Foundation which set up its first ‘MERIT’ (mass employment through rural IT) Centre in rural West Bengal in 2010.  (There are suggestions that some 200 BoPsourcing initiatives are currently being run by social enterprises and NGOs.)
• Private Firms: such Source Pilani which operates a 50-person business process outsourcing centre in Rajasthan

All these, and many other examples from India, are onshore BoPsourcing.  There are also offshore varieties, such as Digital Divide Data, which outsources from large US organisations to telecentres in Cambodian small towns and villages.

BoPsourcing has no necessary connection with ICTs.  Indeed, many agricultural value chains are arguably examples.  Here, though, my focus is on ICT-related outsourcing.  Examples of contracts include data entry, transcription, digitisation, call centres and ICT training.

One of the key concerns about outsourcing and developing countries has been the potential to fuel income and other inequalities.  I could already see this studying software offshoring to India in the 1980s: an economic shearing in which those involved saw their incomes pull far ahead of the bulk of the population.  More recent evidence suggests offshoring increases overall wage growth but also increases inequality.

BoPsourcing presents an obvious solution.  Channelling the benefits of outsourcing down to the poor can drive wealth creation for those on the lowest incomes, and serve to reduce inequalities.

So.  Job done.  We can add BoPsourcing to our list of great development solutions.

Well, not quite yet.  First because the evidence base is very weak.  Second because BoPsourcing comes in nearly as many varieties as Heinz.  Figure 1 summarises.

 Figure 1: Continuum of BoPsourcing Approaches

There are at least four conceivable models that form the continuum (but feel free to add your own evidence and ideas):

• Exploitative outsourcing seeks to bear down on wages and working conditions in order to minimise costs and maximise profits.  The result is an ICT sweatshop that does little to grow incomes, to deliver empowerment, or to reduce inequality.  At present this seems more of a bogeyman brandished by those at the other end of the continuum, than it is an evidence-based reality.  The potential, though, is certainly present with so many outsourcing firms seeking to drive down costs.
• Commercial outsourcing reflects, for example, the steady diffusion of outsourcing in India and other nations, from cities to large towns to small towns and beyond.  Whether this can yet be called BoPsourcing (e.g., forgive the pun, whether BPO is BoP) is unclear.  Quite likely commercial operators have to date only got as far as large towns.  But the migration trend is clear, and it will reach poorer towns and even villages soon enough.  As for inequality, the effect is likely to be as arguable as it is for outsourcing generally: evidence is contested and, unfortunately, fought more by economists pitting ever-more complex models against each other, than on the basis of field data.
• Ethical outsourcing (also known as socially-responsible outsourcing) takes commercial outsourcing and requires that it meets certain minimum standards; typically relating to labour practices but also starting to include environmental issues.  The International Association of Outsourcing Professionals has taken a lead on this.  This is likely to have some impact on inequality but, again, the extent to which such work really reaches the BoP as yet is questionable.
• Social outsourcing (also known as developmental outsourcing) differs from ethical outsourcing as fair trade differs from ethical trade.  Ethical outsourcing involves existing commercial players with either a commitment to or measurement of adherence to standards.  Social outsourcing involves new non-market intermediaries who sit between the client and the BoP sub-contractor.  Social outsourcing most definitely does reach the BoP; indeed that is its raison d’être.  It has already been shown to increase incomes, increase asset holdings, increase skills, and increase empowerment.  It is therefore likely to reduce inequality.

True ICT BoPsourcing is on the increase – you only have to monitor the growing number of initiatives to see that.  Much of the more commercial end of outsourcing has yet to get this far.  It’s more like MoPsourcing (i.e. middle of the pyramid) just now, but cost pressures, supply-demand gaps, ICT diffusion, and growing awareness of BoPsourcing mean this is changing.

The impact of these trends on inequality will depend on which outsourcing model comes to dominate the BoPsourcing business.  If social outsourcing wins, then so too will the poor.  If exploitative outsourcing wins, the opposite will be true.

In practice we may well see some messy combination of models, as social and commercial approaches intersect.  For the social outsourcing intermediaries, the lure of big clients, contracts and growth may pull them into bed with commercial operators.  Conversely, the operators will be attracted by the contacts, expertise and CSR-window dressing that social intermediaries provide.  The poor themselves will get jobs, skills and income.  Whether they will see the structural transformation necessary to really clobber inequality, only time will tell.

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.