Archive

Author Archive

An Applied Data Justice Framework for Datafication and Development

Data is playing an ever-growing role in international development.  But what lens can we use to analyse the impact of data on development?

The emerging field of “data justice” offers some valuable ideas but they have not yet been put together into a systematic and comprehensive framework.  My open-access paper – Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework, written with Satyarupa Shekhar – provides such a framework, as shown below.

The framework exposes five dimensions of data justice:

  • Procedural: fairness in the way in which data is handled.
  • Instrumental: fairness in the results of data being used.
  • Rights-based: adherence to basic data rights such as representation, privacy, access and ownership.
  • Structural: the degree to which the interests and power in wider society support fair outcomes in other forms of data justice.
  • Distributive: an overarching dimension relating to the (in)equality of data-related outcomes that can be applied to each of the other dimensions of data justice.

The dimensions can be used individually; for example, just to analyse data practices, or just to analyse the impact of context on new data systems in developing countries.  Or the model can be used holistically; for example, to understand the full development impact of a particular data initiative.

The Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework paper takes the latter route.  It analyses “pro-equity data initiatives” that were implemented by data activists in four cities: Chennai, Nairobi, Pune and Surakarta.  These initiatives specifically sought to address the data injustices suffered by slum dwellers and other marginalised groups; particularly their invisibility to urban planners and other external agencies.

Using the data justice lens, this research finds that new data flows do have a positive impact in counteracting the injustice of invisibility, but they disproportionately serve those with the motivation and power to use that data.  Results in terms of service improvements and epistemic change are beneficial for slum communities and other marginalised citizens, and these initiatives can be justified on that basis.

However, though there can be no exact calibration from qualitative research, it is likely that these pro-equity initiatives actually increase relative inequalities.  Ordinary community members have seen some benefits but external actors who find the data to match their agenda and capabilities, benefit more.  It is the latter who are more empowered to access, use and control the new data.

If you would like to know more about this research’s findings, framework and recommendations for practice, then take a look at the paper: https://www.tandfonline.com/doi/full/10.1080/1369118X.2019.1599039

Advertisements

Data, Platforms and Power

19 February 2019 Leave a comment

We know that digital platforms can be very powerful, but how does their use of data relate to power?

In three ways[1] that derive from the datafication and digitisation affordances of platforms:

  1. Addressing Information Failure. Platforms succeed in part by finding ways to overcome information failures in existing markets. These failures may be sources of power for incumbents. For example, estate agents (realtors) hold power in real estate markets due to information asymmetries; such as knowledge of house sale prices.  Real estate platforms put such data into the public domain, thus undermining the power of incumbents.  Information failures may also be a source of weakness in existing markets.  For example, riders with traditional taxi firms don’t know exactly when their cab will arrive.  Platforms provide such data and so, again, undermine incumbents.

 

  1. Mashing Up. As they deal with digitised data, platforms can gain power by integrating different data streams onto the platform. Real estate platforms integrate online information about neighbourhoods.  Ride-hailing platforms integrate online maps to show cab location and routes to riders and drivers.

 

  1. Controlling New Data. By digitising transactions and associated processes, platforms create, capture and control new data. This bolsters their power; typically by creating new information asymmetries: the platforms know things that others don’t.  Real estate platforms can monitor search behaviours of buyers to understand more about which features of house listings they value most.  Ride-hailing platforms understand spatio-temporal patterns of supply and demand alongside many other behavioural characteristics of riders and drivers.

 

This simple framework can usefully be applied in order to analyse the role of data in platforms, and its contribution to power.

 

[1] Categorisation and examples developed from Drouillard, M. (2017) Addressing voids: how digital start-ups in Kenya create market infrastructure. In: Digital Kenya, B. Ndemo and T. Weiss (eds). London: Palgrave Macmillan, 97–131

How Many Platform Workers Are There in the Global South?

29 January 2019 Leave a comment

In developing countries, there has been a rapid increase in the gig economy and in the presence of digital labour platforms: defined as “a set of digital resources – including services and content – that enable value-creating interactions between consumers and individual service-providing workers”[1].

But how many workers actually work for such platforms?

I am not going to provide a reliable answer to that question but I will give some kind of ballpark figure.

We start by dividing out two types of platform work: digital gig work that involves digitisable tasks like data entry, writing copy, web design, accounting, etc; and physical gig work that involves a physical task like taxi driving, food delivery, domestic work, etc.  A previous estimate[2], updated to account for growth, would be that there were something like 10 million active digital gig workers in the global South at the start of 2019 (and around ten times that number registered on digital labour platforms but with 90% of them inactive).

So how many physical gig workers are there?  I’m going to break this down by continent since the extent of physical gig work seems to vary significantly between the three main continents of the global South.

Africa

Calculations here are based on extrapolations from just two economies, and seek to take account of wealth and population[3].  Current research for the Fairwork project estimates around 30,000 physical gig workers in South Africa; about half in taxi-driving and the rest mainly in delivery and domestic work.  Estimates for Nigeria[4] plus re-use of some of the same ratios found in South Africa, suggest 20,000 such workers.  Accounting for GDP per capita and population suggests around 60 workers per US$1,000 GDP/capita and per 1 million population; i.e. per US$1bn GDP.  Multiplying up to the overall GDP of Africa produces an estimate of c.130,000 physical gig workers in Africa.  However, given there are at least 100,000 in Egypt alone, we can at least double that to 250,000.

Asia

Similar calculations can be undertaken in Asia, based on numbers associated with platforms in India and Indonesia.  Extrapolating from estimates for taxi-driving and food delivery platforms in India[5], I estimate around 2 million physical gig workers in India.  For Indonesia[6], the figure is closer to 1 million.  Accounting for GDP suggests around 800 workers per US$1bn of GDP.  Multiplying up to the overall GDP of Asia (excluding Japan) produces an estimate of roughly 18 million physical gig workers in developing Asia.

However, there is an alternative approach, which is to exclude China in this calculation, which produces a figure of 9 million, and then take at face value claims that Didi Chuxing employs 21 million physical gig workers in China[7].  This would lead to an estimate of 30 million physical gig workers in developing Asia.

Latin America

Here, I’ve taken a simpler approach based on some national and continent-wide estimates of taxi driving[8] and then re-using ratios from the South Africa work.  This produces an estimate of something like 2 million physical gig workers in Latin America.

Summary

The basis for these estimates is flimsy, and the extrapolations are worse, so please attach a strong health warning to this material.  Better still, come up with some improved statistics.  But my ballpark figure is that there are at least 30 million platform-based gig workers in the global South; 10 million digital and just over 20 million physical.  And that the figure could be more than 40 million, which would be around 1.5% of the global South workforce.

A proportion of these workers are not relying on this as their primary source of income.  For digital gig workers, this number is anything from two-thirds to a half[9].  It may be somewhat less for the physical gig economy, so another ballpark would be that around 15-20 million workers in developing countries are relying on digital platforms for their primary source of income.

(Annual turnover is an issue for another day but, globally and summing figures for the digital gig economy[10] and main physical gig platforms Uber[11] and Didi Chuxing[12], it must be at least US$50bn.)

 

[1] Adapted from Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and Infrastructures in the Digital Age, Information Systems Research, 29(2), 381-400

[2] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[3] An alternative approach would seek to extrapolate in terms of numbers of Internet users but that is correlated with GDP, and the figures still point to a strong under-representation of Africa in platform labour and strong over-representation of China.  Put another way, factors other than wealth and Internet access are needed to explain national differences in the proportions working in the platform economy.

[4] E.g. https://www.vanguardngr.com/2018/08/uber-monthly-passenger-base-in-nigeria-hits-267000/ and https://technext.ng/2018/08/17/max-ng-3-5-things-should-know-about-ride-hailing-platform/

[5] E.g. https://qz.com/india/1385653/uber-ola-drivers-pay-the-price-for-indias-fuel-price-rise/ and https://www.livemint.com/Companies/cYbdfsYk93HFhMuC0XgaNN/Swiggy-Zomato-hike-delivery-boy-salaries-as-competition-gro.html and https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/zomato-swiggy-and-ubereats-paying-higher-cash-on-delivery/articleshow/65142563.cms

[6] e.g. http://buscompress.com/uploads/3/4/9/8/34980536/riber_7-s1_sp_h17-051_59-67.pdf and https://www.thejakartapost.com/academia/2018/11/21/the-gig-economy-and-skills-traps-in-indonesia.html

[7] E.g. https://technode.com/2018/03/19/didi-1-5-billion-abs/ and https://www.sustainabletransport.org/archives/6317

[8] E.g. https://www.reuters.com/article/us-uber-brazil/uber-rival-apps-join-forces-in-brazil-to-stem-tide-of-regulation-idUSKBN1D71KE and https://www.ft.com/content/7bf04e08-1d63-11e8-aaca-4574d7dabfb6 and https://www.globalfleet.com/en/smart-mobility/latin-america/news/chile-imposes-regulations-ride-hailing-companies and https://www.forbes.com/sites/jonathanmoed/2018/12/20/is-uber-operating-illegally-in-its-fastest-growing-region/#74c69e161925

[9] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[10] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[11] E.g. https://www.cnbc.com/2018/08/15/uber-q2-2018-revenue-bookings-slow-slightly.html

[12] E.g. https://kr-asia.com/losing-300m-in-2017-didi-chuxing-wants-to-turn-a-profit-in-2018-amid-fierce-competition

Big Data and Urban Transportation in India

12 February 2018 Leave a comment

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”.

[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

Do Outsourcing Clients Want Decent Digital Work?

22 December 2017 Leave a comment

There are growing concerns that digital gig work – supplied by platforms like Mechanical Turk, Upwork, Freelancer, etc – falls short of decent work standards.  (For further details see the working paper, “Decent Work and the Digital Gig Economy”.)  To address this, and as discussed previously in this blog, there are plans to encourage new ethical standards.

But almost all evidence on this to date comes from workers.  The voices of only a few platforms have been heard, and there seems to be no evidence from clients.  Yet clients are central to decent digital work standards: if they create incentives for platforms to improve, that will be a powerful motivation.  Conversely, if clients don’t care, it removes a key driving force from the gig economy ecosystem.

So, what evidence can be found?

Here, I summarise Babin, R., & Myers, P. (2015) Social responsibility trends and perceptions in global IT outsourcing, Proceedings of the Conference on Information Systems Applied Research, v8, n3663.  This in turn summarises results from surveys conducted during 2009-2014 by the International Association of Outsourcing Professionals.

The survey was specifically about corporate social responsibility (CSR) in IT outsourcing.  So: a) it is not exactly about digital gig work but a broader category of outsourcing; b) the survey may encourage some level of “virtue signalling”: respondents wanting to appear more socially-responsible than they are in reality.  Nonetheless, it offers some relevant guidance about client attitudes to decent digital work.

In general terms, half the respondents were US-based; half were non-US; a fair reflection of gig work clients.  They ranged from SMEs to multinationals and just over half had a written CSR policy.  They are thus larger and more formally-CSR-inclined than the modal micro-enterprise client for digital gig work, but important given the increasing involvement of firms in gig outsourcing.

Key findings include the following:

– Nearly half “often” or “always” gave preference to outsourcing providers who had demonstrable CSR capability.

– Nearly two-thirds expected CSR consideration to become “more” or “much more” important in their future IT outsourcing.

– The largest factor in evaluating CSR capabilities of an outsourcing provider was its labour practices (see figure below).

Figure: Key factors in evaluating the CSR capabilities of an outsourcing provider, survey median (IAOP, 2009-14)

At least for this group of clients, then, the type of labour practices covered by proposed decent digital work standards were the top CSR issue; and CSR was quite widespread as a determinant in digital-related outsourcing (only 5% said they never used CSR as a determinant).

This gives some basis for believing – at least among larger clients for digital gig work – that an appetite exists for better employment and working conditions; an appetite that can encourage platforms to change.

Adding ICT4D To Your Curriculum

29 November 2017 1 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*
Categories: Teaching ICT4D Tags: ,

Decent Digital Work and the FairWork Foundation

31 October 2017 1 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 . . .

%d bloggers like this: