Latest Digital Development Outputs (Data, Humanitarianism, Labour, Platforms) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Humanitarianism; Digital Labour; Digital Platforms – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

The Rise of the Data Economy and Policy Strategies for Digital Development” (open access) by Shamel Azmeh, Christopher Foster & Ahmad Abd Rabuh, expands on policy debates around digital development.  It examines the emergence of the data economy and potentials of strategic policy and/or industrial policy in the global South.  Based on a global policy analysis, it identifies four key “policy pathways” by which countries can look to strategically capture value in the data economy.

DIGITAL HUMANITARIANISM

Digital Innovation by Displaced Populations: A Critical Realist Study of Rohingya Refugees in Bangladesh” by Faheem Hussain, P.J. Wall & Richard Heeks, uses a critical realist approach to understand the three mechanisms the underpin digital innovation by Rohingya refugees.

Lessons On The Digital World From The Charity Sector: The Corporate World Has A Lot To Learn” (open access) by Brian Nicholson, Lisa Kidston, Cris Sachikoyne & Dane Anderton, argues that African charitable organisations and those like the national Citizens Advice in England and Wales are leading the way when it comes to demonstrating exemplary digital leadership.

DIGITAL LABOUR AND DEVELOPMENT

Competing Institutional Logics in Impact Sourcing” by Fareesa Malik & Brian Nicholson, draws on the concepts of institutional logics to  present a case study of a USA-based IT outsourcing vendor “AlphaCorp” practising impact sourcing in a Pakistan subsidiary. The findings show that in cases where actors are located in diverse institutional contexts, competing interests determine the respective priority given to the welfare and market logics.

Digital Labour Platforms in Pakistan: Institutional Voids and Solidarity Networks” by Fareesa Malik, Richard Heeks, Silvia Masiero & Brian Nicholson, conceptualises the theoretical link between labour platforms and socio-economic development drawing on the notion of institutional voids and empirical fieldwork in Pakistan.

Risks and Risk-Mitigation Strategies of Gig Economy Workers in the Global South” by Tatenda Mpofu, Pitso Tsibolane, Richard Heeks & Jean-Paul Van Belle, analyses three strategies (platform-, driver- and driver group-led) that seek to mitigate the risks of ride-hailing work in Cape Town.

The Fairwork Foundation: Strategies for Improving Platform Work in a Global Context” (open access) by Mark Graham, Jamie Woodcock, Richard Heeks, Paul Mungai, Jean-Paul Van Belle, Darcy du Toit, Sandra Fredman, Abigail Osiki, Anri van der Spuy & Six M. Silberman, introduces the work of the Fairwork Foundation to rank and compare gig work platforms against a set of five decent work principles.

DIGITAL PLATFORMS AND DEVELOPMENT

Analysing Urban Platforms and Inequality Through a ‘Platform Justice’ Lens” by Richard Heeks & Satyarupa Shekhar, introduces a model of “platform justice” through which to analyse the impact of urban digital platforms.

Competing Logics: Towards a Theory of Digital Platforms for Socio-economic Development” by Silvia Masiero & Brian Nicholson, seeks to contribute to the nascent literature on platforms in development, unpacking a human-centred development logic as an alternative to the market logic that animates most of the platforms discourse and relying on it to lay the foundations for an emerging theory of platforms for development.

Digital Platforms, Surveillance and Processes of Demoralization” by Sung Chai, Brian Nicholson, Robert Scapens & Chunlei Yang, conceptualises the theoretical link between platforms and morality drawing on an interpretive study of a hotel in Vietnam to examine surveillance.

Delivering Urban Data Justice for “Smart Cities 2.0”

11 February 2021 Leave a comment

What new institutions are needed to ensure smart cities are also data-just cities?

Smart City 1.0 “is primarily focused on diffusing smart technologies for corporate and economic interests”.  Smart City 2.0 is “a decentralised, people-centric approach where smart technologies are employed as tools to tackle social problems, address resident needs and foster collaborative participation”.[1]

Given their people-centrism, a foundation for Smart Cities 2.0 must therefore be delivery of urban data justice: fairness in the way people are made visible, represented and treated as a result of the production of urban digital data.[2]

We already know the constituent parts of urban data justice, as shown in the figure below.[3]

But a key argument of this model is that data justice is significantly shaped by urban social structures.  If those structures are unjust then data practices and outcomes will likely be unjust.  How, then, do we create urban social structures more likely to deliver the data justice that is part of Smart City 2.0?

Setting aside more radical restructuring of the urban polity, three more incremental forms can play a role:

1. Living Labs

“Living labs employ a user-focused design environment, a strategy of co-creation, and, increasingly, an institutionalized space wherein citizens, administrators, entrepreneurs and academics come together to develop smartness into concrete applications. They help identify and join localized expertise, real-life testing and prototyping with strategic networking of resources to address challenges that cannot be solved by single cities or departments.”[4]  Located at the upstream end of the innovation cycle, living labs are well-placed to come up with new, just ways of applying urban data.[5]

2. Urban Data Trusts

Data trusts are “a legal structure that provides independent stewardship of data … an approach to looking after and making decisions about data in a similar way that trusts have been used to look after and make decisions about other forms of asset in the past, such as land trusts that steward land on behalf of local communities.”[6]  These can form an institutional superstructure to ensure justice in the ownership, sharing and use of data; particularly data gathered about urban citizens.[7]

3. Community Data Intermediaries

Community data intermediaries are “organizations that gather data relevant for neighborhood-level analysis and make the information available to community groups and local institutions”.  Alongside their key role in gathering data – for example via community mapping – CDIs may also have features of both living labs (innovating application of that data) and data trusts (acting as stewards of the data for communities).[8]

The devil here will be in the detail: how exactly are these entities structured and run?  Simply attaching a label to an organisation does not make it just, with critiques in circulation of living labs[9], urban data trusts[10], and community data intermediaries[11].  Nonetheless, it is these types of urban institutional innovation that will underlie delivery of data justice in Smart Cities 2.0.  I look forward to further examples of these and similar innovations.

 

[1] Trencher, G. (2019) Towards the smart city 2.0: empirical evidence of using smartness as a tool for tackling social challenges, Technological Forecasting and Social Change, 142, 117-128

[2] Adapted slightly from Taylor, L. (2017) What is data justice? The case for connecting digital rights and freedoms globally, Big Data & Society, 4(2), 2053951717736335

[3] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

[4] Baykurt, B. (2020) Are “smart” cities living up to the hype?, University of Massachusetts Amherst News, 1 May

[5] For a data justice perspective on the activities of one Living Lab in Kathmandu plus related organisations, see: Mulder, F. (2020) Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal, Journal of Contingencies and Crisis Management, 28(4), 432-445

[6] Hardinges, J. (2020) Data trusts in 2020, Open Data Institute, 17 Mar

[7] For more on urban civic data trusts, see: Kariotis, T. (2020) Civic Data Trusts, Melbourne School of Government, University of Melbourne, Australia

[8] For a guide on creating community data intermedaries and examples, see: Hendey, L., Cowan, J., Kingsley, G.T. & Pettit, K.L. (2016) NNIP’s Guide to Starting a Local Data Intermediary, NNIP, Washington, DC

[9] Taylor, L. (2020) Exploitation as innovation: research ethics and the governance of experimentation in the urban living lab. Regional Studies, advance online publication.

[10] Artyushina, A. (2020) Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto, Telematics and Informatics, 55, 101456

[11] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

Revisiting “Leapfrogging” in a Platformised World

11 January 2021 Leave a comment

What difference do digital platforms make to the long-standing argument about “leapfrogging” of development by developing countries?[1]

The idea that latecomer nations could accelerate their passage through development stages via use of new technology has been around for decades[2].  It was no surprise, then, that leapfrogging played at least some part in turn-of-the-century cheerleading for the role that ICTs could play in development[3].  And statistics bore out the concrete example of global South countries jumping fairly quickly to mobile phone-based telecommunications infrastructure during the 2000s and 2010s, having invested much less in relative terms in the previous generation of landline infrastructure than countries in the global North[4].

The flaw in much of the simplistic thinking about technology and leapfrogging is that technology never acts alone in development; it always forms part of a socio-technical system[5].  Lower-income countries might be able to move more quickly than higher-income countries to a more-recent generation of technology.  But they could not repeat the same trick with the social part of their systems.

One way of understanding why the “social part of their systems” constrained development was to identify institutional shortcomings – often called “institutional voids” – that particularly meant developing country markets could be inefficient, ineffective, incomplete and/or inequitable.  While ICTs always had institutional effects, these were limited, with lack of institutional change acting as the brake that prevented economic leapfrogging.

In the past few years, though, this picture has changed with the arrival of digital platforms as an important force in development.  Digital platforms much more readily fill institutional voids than prior ICT-based systems.  They not only provide cheaper and better information, they form the entire institutional infrastructure for new markets; not just the transactional infrastructure but the regulatory infrastructure as well[6].

So digital platforms, being much more complete socio-technical systems than earlier ICTs, can offer developing countries a route for leapfrogging.  Yes, local context matters and platform implementation can be a bumpy road so a platform is not quite “market in a box”.  But, for example, e-hailing platforms have helped dozens of Southern cities quickly improve taxi markets that were beset by insecurity, high costs, long wait times, etc – problems that had existed for years without resolution.

But if leapfrogging, at least in terms of some markets, is now more feasible; exactly what are developing countries leapfrogging to?  The new platform-based markets are more efficient, safer, with less opportunistic behaviour.  But they are also more unequal and less democratic as the platform becomes marketplace, manager, adjudicator, enforcer and regulator all rolled into one; eliminating roles for government, unions, and other stakeholders[7].

Platforms may be offering an opportunity for leapfrogging but they come with a caveat: be careful where you leap.


[1] With acknowledgements to Anne Njathi for asking the questions about leapfrogging that led to this post.

[2] See e.g. Goldschmidt, A. (1962) Technology in emerging countries. Technology and Culture, 3(4), 581-600.

[3] See e.g. World Bank (1998) World Development Report, World Bank, Washington, DC; InfoDev (2000) The Networking Revolution: Opportunities and Challenges for Developing Countries, World Bank, Washington, DC; Steinmueller, W. E. (2001) ICTs and the possibilities for leapfrogging by developing countries. International Labour Review, 140, 193.

[4] UNCTAD (2018) Leapfrogging: Look Before You Leap, UNCTAD, Geneva.

[5] See e.g. Wade, R.H. (2002) ‘Bridging the digital divide: new route to development or new form of dependency?’, Global Governance, 8, 443-466; Alzouma, G. (2005) Myths of digital technology in Africa: Leapfrogging development?. Global Media and Communication, 1(3), 339-356; Kenny, C. (2006) Overselling the Web?: Development and the Internet, Lynne Reiner Publishers, Boulder, CO

[6] Heeks, R., Eskelund, K., Gomez-Morantes, J.E., Malik, F. & Nicholson, B. (2020) Digital Labour Platforms in the Global South: Filling or Creating Institutional Voids?, GDI Digital Development Working Paper no.86, University of Manchester, UK

[7] Heeks, R., Eskelund, K., Gomez-Morantes, J.E., Malik, F. & Nicholson, B. (2020) Digital Labour Platforms in the Global South: Filling or Creating Institutional Voids?, GDI Digital Development Working Paper no.86, University of Manchester, UK

Latest Digital Development Outputs (Agriculture, Data, Social Media) from CDD, Manchester

18 November 2020 Leave a comment

Recent outputs – on Agricultural Platforms; Data-for-Development; Social Media and Education – from the Centre for Digital Development, University of Manchester:

AGRICULTURAL PLATFORMS:

Ag-Platforms in East Africa: National and Regional Policy Gaps” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag identifies national and regional governance deficits (gaps) in the diffusion of digital agricultural platforms, and consequently how Ag-platforms bridge national and regional policy gaps.

Platforms in Agricultural Value Chains: Emergence of New Business Models” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag explains the various models of digital agricultural platforms that exist, and provides policy-makers with a roadmap that supports the proliferation of sustainable Ag-platforms.

DATA-FOR-DEVELOPMENT:

Datafication, Value and Power in Developing Countries” by Richard Heeks, Vanya Rakesh, Ritam Sengupta, Sumandro Chattapadhyay & Christopher Foster analyses the implementation challenges and impact of big data on organisational value, sources of power, and wider politics.

Identifying Potential Positive Deviants Across Rice-Producing Areas in Indonesia: An Application of Big Data Analytics and Approaches” (open access) by Basma Albanna, Dharani Dhar Burra & Michael Dyer uses remote sensing and survey data to identify “positive deviant” rice-farming villages in Indonesia: those which outperform their peers in agricultural productivity.

The Urban Data Justice Case Study Collection” (open access) presents ten case studies analysing new urban data in Latin America, Africa and Asia from data justice/rights perspectives.  It also outlines a future research agenda on urban data justice in the global South.

SOCIAL MEDIA AND EDUCATIONAL DEVELOPMENT

WhatsApp-Supported Language Teacher Development: A Case Study in the Zataari Refugee Camp” (open access) by Gary Motteram, Susan Dawson & Nazmi Al-Masri through a thematic analysis of WhatsApp exchanges, explores how Syrian English Language teachers working in refugee camps in Jordan work collaboratively on teacher development.

Digital trade and global governance of the digital economy

30 October 2020 Leave a comment

It is easy to forget that a decade ago, the digital economy was tiny in most countries. This is changing, and in many regions accelerated by the COVID crisis. The global growth of the digital economy has, however, not been matched by the growth of domestic digital firms. Rather, we have seen a growth in digital firms located in technologically-leading economies, operating across multiple markets and often with limited local investments.

This state of affairs has important implications globally, especially for countries who are looking to ‘catch up’ with technologically leading nations. In these contexts, digital development is as much about how policies (at all levels) shape foreign digital firms as it is about nurturing domestic digital economies.

We explore these issues in a recent paper which discusses the way that policies around digital technologies and data flows are becoming entwined with international trade [1]. We specifically look to examine such debates through a political economy perspective. Perhaps when we first started researching this topic a few years ago, connecting digital development outcomes to global political economy was an obscure topic. But in an era of app bans and global trade wars driven by a desire to control advanced technologies, political economy approaches are becoming ever more important.

Global governance and digital trade

Emerging from the birth of the Internet, so-called “Internet Governance” (IG) organisations were designed to govern technical issues as the Internet expanded globally (e.g. IP address allocations and standards). While there have been attempts to bring broader economic and social issues under the IG umbrella, the lack of formal rule making power limited the political power of these organisations.

As cross-border flows of data have expanded globally, actors have sought to integrate the governance of digital technologies and data within rule making on trade, typically referred to as “digital trade”. The goal of trade agreements is to encourage free-trade across borders. Following this, digital trade chapters in trade agreements look to enforce “open” digital trade, for example in binding commitments to “free flows of data” across borders and rules to prevent signatories undertaking certain domestic policies around digital and data [2]. The CPTPP (11 nations bordering the Pacific) and the USMCA (US, Mexico, Canada) are examples of recent trade agreements that include chapters with binding “digital trade” rules.

Clearly digital technologies and data have overlaps with trade, particularly in areas like e-commerce. But it is not clear if trade agreements are the most appropriate place to globally regulate digital and data [3]. From a political economy perspective, one can associate the growth of digital trade with the power of technologically advanced nations such as the US and Japan who seek to more strictly govern global norms around digital, and push open data flows. These nations are strongly backed by lobbying of ‘big tech’ firms who see such open digital trade as central to their global expansion.

Trade agreements are powerful because they offer binding rules unlike other spaces of global governance. In addition, dispute settlement mechanisms in trade agreements mean that signatories who break rules can face serious consequences. Even for a nation with a small digital economy, trade agreements can mean that breaking digital trade rules will lead to retaliatory tariffs in other sectors.

Ultimately the inclusion of digital trade in trade agreements (regional and bilateral) is a first step to powerful nations establishing digital trade rules at a global level. This would be through digital trade agreements in the World Trade Organisation (WTO). Digital trade rules at the WTO are controversial, not only because they look to enforce open digital trade globally, but because they potentially override existing trade agreements where developing countries have negotiated exceptions (such as the General Agreement on Trade in Services – GATS). These exceptions are thrown into question if goods and services that are transmitted digitally are subject to new rules [4].

This is best illustrated in the case of financial services, where some developing countries are permitted in WTO agreements to impose regulations on foreign operators to support development. But when a financial service becomes an application which is delivered digitally, how this is regulated can become a grey area. Would developing countries be able to continue to legitimately impose barriers, or would they be prevented if digital trade rules were present?

Global conflicts around digital trade

The path to binding digital trade rules within trade agreements has not so far been a smooth path. There is significant divergence in positions across powerful countries, including in Europe and China who see more strategic approaches and policy around digital as being an important part of their future development.

Vocal opponents to digital trade have also come from developing countries, especially India, South Africa and the “WTO Africa Group” [5]. They have opposed such rules arguing that they would override previous trade agreements, and potentially limit them undertaking new types of “industrial policy” to catch up in the digital area [6].

So far these alliances, alongside the recent anti-international approach of the Trump administration, has meant that digital trade has moved slowly and mainly in regional and bilateral agreements. But this story is still unfolding, and the political economy of digital trade is liable to change rapidly in the future.

These tensions are not just of policy concern. From licencing apps in the gig economy, to supporting local data pools for community development, to taxing the digital economy. Digital trade touches on crucial future directions of digital development [2].


References

[1] Azmeh, S., Foster, C.G. & Echavarri, J. (2020) The International Trade Regime and the Quest for Free Digital Trade. International Studies Review, 22(3), pp. 671–692.

[2] For details on specific policies on digital trade, we have launched an accompanying website – The digital trade tracker tracks digital trade policy and its relevance to development

[3] Aaronson, S.A. (2016) The Digital Trade Imbalance and Its Implications for Internet Governance, Paper 25, Centre for International Governance Innovation, Waterloo, Canada.

[4] Kelsey, J. (2018) How a TPP-Style E-Commerce Outcome in the WTO Would Endanger the Development Dimension of the GATS Acquis (and Potentially the WTO). Journal of International Economic Law, 21(2), pp. 273–295.

[5] Foster, C. & Azmeh, S. (2018) The Digital Trade Agenda and Africa. Bridges Africa, 7(2). Available at https://infomediation.net/publication-the-digital-trade-agenda-and-africa/

[6] Foster, C.G. & Azmeh, S. (2020) Latecomer Economies and National Digital Policy: An Industrial Policy Perspective. Journal of Development Studies, 56(7), pp. 1247–1262.


Image Credit: Kofi Annan, Monhla Hlahla and Gao Xiqing – World Economic Forum on Africa 2012 – Wikimedia Commons – CC Attribution Sharealike

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

8 September 2020 Leave a comment

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.


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.

How Widespread are Digital Water Payments in Ghana?

Digital systems are seen as important elements in the governance and management of the water sector. For instance, systems such as digital meters, IoT applications, digital payments, etc can significantly improve aspects of water service delivery and access. But are these new technologies widely adopted as yet, particularly in the global South context?

The open access paper Diffusion of Electronic Water Payment Innovations in Urban Ghana. Evidence from Tema Metropolis” explores aspects of this question; looking specifically at uptake of electronic water payments (EWP) in Ghana. Drawing on data from water utility customers and the utility’s own database, three main conclusions emerged.

i. EWP adoption is very low (below 3%) though many utility customers were aware of these payment options. 

ii. The growth of EWP uptake in urban Ghana is rapid (annual growth rate of 41% from 2017-2018), but from a low base.

iii. Awareness and potential uptake of these payment options were significantly associated with customers’ age, employment status, income, and means of receiving monthly water bills. EWP awareness was higher among elderly customers perhaps since they constitute a larger portion of people with utility pipeline connections from the study. Also, awareness was higher among utility customers with higher income, those employed and those who receive their water bills through electronic channels i.e. SMS or email. 

Explanations of why adoption rates are low range from behavioural to transaction fees to technological challenges. However, mobile phone ownership and mobile money usage may not be significant predictors or barriers to EWP uptake given universal mobile phone ownership by customers, and widespread use of mobile money.

Some actions to take to improve adoption include:

  • Developing specific guidelines and engagements that target unaware sections of the population, particularly low-income customers through advertising of payment solutions etc. 
  • Understanding prevailing baseline characteristics of targeted customers before rollout of these innovations. Also, these innovations should be piloted before upscale.

Notwithstanding the barriers that currently exist, it can be seen from this example that digital innovations in the water sector are on the rise. Beyond understanding adoption issues, we will increasingly need better evidence on the impact of such innovations in the global South: not just digital payments but also applications across the water value chain, from water sourcing to end-use. I look forward to examining the experiences and impacts of these innovations in an ongoing project.

Context and Digital Start-Ups in the Global South

How does context affect new digital start-ups in the global South?

The open-access paper, “Embeddedness of Digital Start-Ups in Development Contexts” provides some answers, using the Triple Embeddedness Framework:

Based on a study of 19 digital start-ups and 20 other start-up ecosystem organisations, this research makes three main conclusions.

1. Hybrid Embeddedness. These young enterprises are hybrids that straddle multiple contexts:

– They are embedded in both their vertical product sector but also the cross-cutting digital economy.  Some successful start-ups borrow ideas or staff from other digital firms; helping them to innovate in their product sector.

– They are also embedded in both local and global contexts.  Some successful start-ups mimic business models from the US and draw financing and training from the US; and then use this to innovate within their country or region.

2. Optimal Embeddedness. The most-successful digital start-ups find a “Goldilocks”-style sweet spot in their relation to context. They are not so deeply embedded that they are trapped within existing institutions and unable to innovate.  But they are sufficiently embedded that they can draw knowledge, money, skills, etc from their context.

3. Global Peripherality. Some global South digital economies have a “semi-permeable membrane” between themselves and the global North. Ideas and other resources can flow in to assist digital start-ups, but they have some relative protection from external competition.

Practical implications include:

– The need for global South governments to keep building local digital sector institutions; particularly network intermediaries that link local and global digital economies

– The need for digital start-ups to self-analyse their embeddedness: understanding the extent of constraint and freedom imposed by embeddedness in both digital and product sectors

– The need for business methodologies from the global North, such as Lean Start-up, to be re-scoped to better incorporate the realities of global South contexts

We look forward to further work on context and the digital economy in the global South.

Positive Deviance: A Data-Powered Approach to the Covid-19 Response

Nations around the world are struggling with their response to the Covid-19 pandemic.  In particular, they seek guidance on what works best in terms of preventive measures, treatments, and public health, economic and other policies.  Can we use the novel approach of data-powered positive deviance to improve the guidance being offered?

Positive Deviance and Covid-19

Positive deviants are those in a population that significantly outperform their peers.  While the terminology of positive deviance is absent from public discourse on Covid-19, the concept is implicitly present at least at the level of nations.  In an evolving list, countries like New Zealand, Australia, Taiwan, South Korea and Germany regularly appear among those seen as most “successful” in terms of their relative infection or death rates so far.

Here we argue first that the ideas and techniques of positive deviance could usefully be called on more directly; second that application of PD is probably more useful at levels other than the nation-state.  In the table below, we summarise four levels at which PD could be applied, giving potential examples and also potential explanators: the factors that underpin the outperformance of positive deviants.

Level Potential positive deviants Potential PD explanators
Nation[i] Countries with very low relative infection or death rates
  • Early lockdown
  • Extensive testing
  • Use of contact-tracing incl. apps
  • Cultural acceptance of mask-wearing
  • Prior mandatory TB vaccination
  • Quality of leadership
Locality (Regions, Cities)[ii] Cities and regions with significantly slower spread of Covid-19 infection than peers
  • Extensive or innovative community education campaigns
  • Testing well in excess of national levels
  • Earlier-than-national lockdown
  • Extensive sanitisation of public transport
  • Quality and breadth of local healthcare
  • Quality of leadership
Facility (Hospitals, Health Centres)[iii] Health facilities with significantly higher recovery rates than peers
  • Innovative use of existing (scarce) healthcare technologies / materials
  • Innovative use of new healthcare technologies: AI, new treatments
  • Level of medical staff expertise and Covid-19-specific training
Health facilities with significantly lower staff infection rates than peers
  • Provision of high-quality personal protective equipment in sufficient quantity
  • Strict adherence to infection monitoring and control measures
  • Strict adherence to high-quality disinfection procedures
  • Innovative use of contact-free healthcare technologies: chat bots, robots, interactive voice response, etc
Individual[iv] Individuals in vulnerable groups who contract full-blown Covid-19 and survive
  • Psychological resilience
  • Physical fitness
  • Absence of underlying health conditions
  • Effective therapies
  • Genetics

 

At present, items in the table are hypothetical and/or illustrative but they show the significant value that could be derived from identification of positive deviants and their explanators.  Those explanators that are under social control – such as use of technological solutions or policy/managerial measures – can be rapidly scaled across populations.  Those explanators such as genetics or pre-existing levels of healthcare capacity which are not under social control can be built into policy responses; for example in customising responses to particular groups or locations.

Evidence from positive deviance analysis can help currently in designing policies and specific interventions to help stem infection and death rates.  Soon it will be able to help design more-effective lockdown exit strategies as these start to show differential results, and as post-lockdown positive deviants start to appear.

However, positive deviance consists of two elements; not just outperformance but outperformance of peers.  It is the “peers” element that confounds the value of positive deviance at the nation-state level.

Public discourse has focused mainly on supposedly outperforming nations [v]; yet countries are complex systems that make meaningful comparisons very difficult[vi]: dataset definitions are different (e.g. how countries count deaths); dataset accuracy is different (with some countries suspected of artificially suppressing death rates from Covid-19); population profiles and densities are different (countries with young, rural populations differing from those with old, urban populations); climates are different (which may or may not have an impact); health service capacities are different; pre-existing health condition profiles are different; testing methods are different; and so on.  Within all this, there is a great danger of apophenia: the mistaken identification of “patterns” in the data that are either not actually present or which are just random.

More valid and hence more useful will be application of positive deviance at lower levels.  Indeed, the lower the level, the more feasible it becomes to identify and control for dimensions of difference and to then cluster data into true peer groups within which positive deviants – and perhaps also some of their explanators – can then be identified.

Data-Powered Positive Deviance and Covid-19

The traditional approach to identifying positive deviants has been the field survey: going out into human populations (positive deviants have historically been understood only as individuals or families) and asking questions of hundreds or thousands of respondents.  Not only was this time-consuming and costly but it also becomes more risky or more difficult or even impractical during a pandemic.

Much better, then, is to look at analysis of large-scale datasets which may be big data[vii] and/or open data, since this offers many potential benefits compared to the traditional approach[viii].  Many such datasets already exist online[ix], while others may be accessed as they are created by national statistical or public health authorities.

Analytical techniques, such as those being developed by the Data-Powered Positive Deviance project, can then be applied: clustering the data into peer groups, defining the level of outperformance needed to be classified as a positive deviant, identifying the positive deviants, then interrogating the dataset further to see if any PD explanators can be extracted from it.

An example already underway is clustering the 368 districts in Germany based on data from the country’s Landatlas dataset and identifying those which are outperforming in terms of spread of the virus.  Retrospective regression analysis is already suggesting structural factors that may be of importance in positive deviant districts: extent and nature of health infrastructure including family doctors and pharmacies, population density, and levels of higher education and of unemployment.

This can then be complemented in two directions – diving deeper into the data via machine learning to try to predict future spread of the disease; and complementing this large-scale open data with “thick data” using online survey and other methods to identify the non-structural factors that may underlie outperformance.  The latter particularly will look for factors under socio-political control such as policies on lockdown, testing, etc.

Of course, great care must be taken here.  Even setting aside deliberate under-reporting, accuracy of the most basic measures – cases of, and deaths from Covid-19 – has some inherent uncertainties[x].  Beyond accuracy are the broader issues of “data justice”[xi] as it applies to Covid-19-related analysis[xii], including:

  • Representation: the issue of who is and is not represented on datasets. Poorer countries, poorer populations, ethnic minority populations are often under-represented.  If not accounted for, data analysis may not only be inaccurate but also unjust.
  • Privacy: arguments about the benefits of analysing data are being used to push out the boundaries of what is seen as acceptable data privacy; opening the possibility of greater state surveillance of populations. As Privacy International notes, any boundary-pushing “must be temporary, necessary, and proportionate”[xiii].
  • Access and Ownership: best practice would seem to be datasets that are publicly-owned and open-access with analysis that is transparently explained. The danger is that private interests seek to sequester the value of Covid-19-related data or its analysis.
  • Inequality: the key systems of relevance to any Covid-19 response are the economic and public health systems. These contain structural inequalities that benefit some more than others.  Unless data-driven responses take this into account, those responses may further exacerbate existing social fracture lines.

However, if these challenges can be navigated, then the potential of data-powered positive deviance can be effectively harnessed in the fight against Covid-19.  By identifying Covid-19 positive deviants, we can spotlight the places, institutions and people who are dealing best with the pandemic.  By identifying PD explanators, we can understand what constitutes best practice in terms of prevention and treatment; from public health to direct healthcare.  By scaling out those PD explanators within peer groups, we can ensure a much-broader application of best practice which should reduce infections and save lives.  And using the power of digital datasets and data analytics, we can do this in a cost- and time-effective manner.

The “Data-Powered Positive Deviance” project will be working on this over coming months.  We welcome collaborations with colleagues around the world on this exciting initiative and encourage you to contact the GIZ Data Lab or the Centre for Digital Development (University of Manchester).

This blogpost was co-authored by Richard Heeks and Basma Albanna and was originally published on the Data-Powered Positive Deviance blog.

 

 

[i] https://interestingengineering.com/7-countries-keeping-covid-19-cases-in-check-so-far; https://www.forbes.com/sites/avivahwittenbergcox/2020/04/13/what-do-countries-with-the-best-coronavirus-reponses-have-in-common-women-leaders; https://www.maskssavelives.org/; https://www.bloomberg.com/news/articles/2020-04-02/fewer-coronavirus-deaths-seen-in-countries-that-mandate-tb-vaccine

[ii] https://www.weforum.org/agenda/2020/03/how-should-cities-prepare-for-coronavirus-pandemics/; https://www.wri.org/blog/2020/03/covid-19-could-affect-cities-years-here-are-4-ways-theyre-coping-now; https://www.fox9.com/news/experts-explain-why-minnesota-has-the-nations-lowest-per-capita-covid-19-infection-rate; https://www.bbc.co.uk/news/world-asia-52269607

[iii] https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19; https://www.cuimc.columbia.edu/news/columbia-develops-ventilator-sharing-protocol-covid-19-patients; https://www.esht.nhs.uk/2020/04/02/innovation-and-change-to-manage-covid-19-at-esht/; https://www.med-technews.com/topics/covid-19/; https://www.innovationsinhealthcare.org/covid-19-innovations-in-healthcare-responds/; https://www.cnbc.com/2020/03/23/video-hospital-in-china-where-covid-19-patients-treated-by-robots.html; https://www.researchprofessionalnews.com/rr-news-new-zealand-2020-4-high-quality-ppe-crucial-for-at-risk-healthcare-workers/; https://www.ecdc.europa.eu/sites/default/files/documents/Environmental-persistence-of-SARS_CoV_2-virus-Options-for-cleaning2020-03-26_0.pdf

[iv] https://www.sacbee.com/news/coronavirus/article241687336.html; https://www.thelocal.it/20200327/italian-101-year-old-leaves-hospital-after-recovering-from-coronavirus; https://www.vox.com/science-and-health/2020/4/8/21207269/covid-19-coronavirus-risk-factors; https://www.medrxiv.org/content/10.1101/2020.04.22.20072124v2; https://www.bloomberg.com/news/articles/2020-04-16/your-risk-of-getting-sick-from-covid-19-may-lie-in-your-genes

[v] Specifically, this refers to the positive discourse.  There is a significant “negative deviant” discourse (albeit, again, not using this specific terminology) that looks especially at countries and individuals which are under-performing the norm.

[vi] https://www.bbc.co.uk/news/52311014; https://www.theguardian.com/world/2020/apr/24/is-comparing-covid-19-death-rates-across-europe-helpful-

[vii] https://www.forbes.com/sites/ciocentral/2020/03/30/big-data-in-the-time-of-coronavirus-covid-19; https://healthitanalytics.com/news/understanding-the-covid-19-pandemic-as-a-big-data-analytics-issue

[viii] https://doi.org/10.1002/isd2.12063

[ix] E.g. via https://datasetsearch.research.google.com/search?query=coronavirus%20covid-19

[x] https://www.medicalnewstoday.com/articles/why-are-covid-19-death-rates-so-hard-to-calculate-experts-weigh-in; https://www.newsletter.co.uk/health/coronavirus/coronavirus-world-health-organisation-accepts-difficulties-teasing-out-true-death-rates-covid-19-2527689

[xi] https://doi.org/10.1080/1369118X.2019.1599039

[xii] https://www.opendemocracy.net/en/openmovements/widening-data-divide-covid-19-and-global-south/; https://www.wired.com/story/big-data-could-undermine-the-covid-19-response/; https://www.thenewhumanitarian.org/opinion/2020/03/30/coronavirus-apps-technology; https://botpopuli.net/covid19-coronavirus-technology-rights

[xiii] https://privacyinternational.org/examples/tracking-global-response-covid-19; see also https://globalprivacyassembly.org/covid19/

Protecting Gig Workers During Covid-19: What Platforms Must Do

27 April 2020 1 comment

The estimated 50 million gig workers worldwide have been particularly hard-hit by the Covid-19 pandemic.  How are their platforms responding, and what more should platforms do?

Reports indicate half of gig workers have lost their jobs. Those still working perform functions essential to society, yet they have lost two-thirds of their income on average.  Many face the impossible choice between destitution and infection, as summed up by one worker: “either I’m starving or I’m dying of coronavirus”.

To investigate this further, the Fairwork project research team undertook a survey of platform response policies; as of April 2020, covering 120 platforms in 23 countries across Europe, North America, South America, Asia and Africa.  The report from this analysis – “The Gig Economy and Covid-19: Fairwork Report on Platform Policies” – categorises platform responses according to the five ‘Fairwork Principles’ that our ongoing action research uses to rate platforms against decent work standards:

  • Fair Pay: by far the most important issue for workers; yet only five platforms had direct policies to increase pay for those in work; more common were actions to maintain levels of business, like client fee waivers or expanded scope of services.
  • Fair Conditions 1 (Prevention): cut-and-paste hygiene guidance and contactless delivery (though not contactless collection) were the most widespread policies. Just over half of the platforms we checked said they were providing personal protection equipment (disinfectant or, less often, masks); workers report they often did not receive this.
  • Fair Conditions 2 (Illness): around half of the platforms said they were providing some payment for workers who were ill, but workers reported it could be hard to access and payments often fell well below national minimum wage equivalents.
  • Fair Contracts: the only response here, by a few platforms, has been to try to create a firewall around their current actions; still asserting an arm’s-length relation to workers as “independent contractors”.
  • Fair Management: a few companies are guaranteeing no loss of bonus or incentive levels despite temporary deactivation of workers, or are issuing statements against any attempt by clients to discriminate against certain worker groups.
  • Fair Representation: we found no evidence yet of any platform engagement with worker associations, despite a number of such groups setting out demands and even organising strikes.

Overall, we find widespread responses by platforms to the current pandemic with occasional examples of comprehensive and enlightened policies.  But there are a number of issues in most platforms’ responses to date:

  • There is a gap between rhetoric and reality: platforms have been far better at publicising responses than at actually delivering them to workers.
  • There is a skew in stakeholder focus: platform responses have served shareholders, investors and customers before workers, even though it is workers who form the foundation of all value for the platform.
  • There is a timidity: while governments have torn up ideologies and rulebooks, platforms have generally been only incremental in their response and have too often used the language of the get-out clause rather than that of the guarantee.

Platforms have loaded risks and responsibilities onto others: too many platforms interpret “wash your hands” less in terms of the virus and more in terms of their responsibilities to their workers; throwing that responsibility onto governments for financial support and onto individual workers for their own protection from coronavirus.

Finally, there is a gap between needs and policies: between what workers require in order to stay safe – free from poverty and free from infection – and what platforms are currently providing.  Our report therefore ends with a summary of platform policy recommendations, reproduced here:

Fairwork Principle Recommended Platform Action
1. Fair Pay ·      Rapid access to a minimum income (equivalent to at least the local living wage) for those unable to work due to fall-off in demand, legislative restrictions, or to pre-existing health vulnerabilities

·      Reduction in costs (e.g. platform commission/fees) or increase in per-gig payments for those still working but with reduced earnings

·      Additional hazard pay for those facing additional risks while working during the pandemic

·      Waiver (not deferral) of work-related costs such as loan repayments

·      Facilitated access to interest-free emergency loans

·      Plan for post-lockdown income recovery measures which may include higher per-gig payments or lower commission fees

·      Inclusion in income compensation and financial deferral schemes of all those who have worked for the platform during the past three months

2a. Fair Conditions (Prevention) ·      Regular, adequate, free provision of personal protection equipment: disinfectants, gloves and masks

·      Installation of physical barriers between driver and passengers in all ride-hailing cars

·      Fully contact-free supply chains (both collection and delivery) for delivery workers

·      Daily sanitisation of vehicles and upstream locations: warehouses, hubs, etc.

·      Free Covid-19 check-ups for workers and their families

2b. Fair Conditions (Illness) ·      Accessible sick pay from platforms that applies universally to all those unable to work while ill or quarantined or while providing essential care for sick family members, and which relates to pre-pandemic average earnings

·      Sick pay policies that specify precisely and openly how much workers will be paid, with simple application processes which do not impose onerous health documentation requirements that sick workers cannot meet

·      Extended sick pay for those workers hospitalised by Covid-19 infection

·      Provision of general medical insurance cover

·      Provision of life insurance cover or other death-in-service benefits

3. Fair Contracts ·      No temporary or permanent alteration of contracts during the period of the pandemic to the detriment of workers
4. Fair Management ·      Ensure all Covid-19-related communications are in a form that can be readily accessed and understood by all workers

·      Set up an accessible communications channel for workers for all issues relating to Covid-19; adequately staffed for rapid resolution of issues

·      Transparent reporting of policies, actions and funds initiated by platforms during the pandemic

·      Adhere to data privacy standards in collecting and sharing data about workers

·      No loss of incentives, bonus levels or future availability of jobs for those temporarily deactivated as a result of Covid-19

·      Public statements to customers and others that discrimination against certain worker groups during the pandemic will not be tolerated

5. Fair Representation ·      Formal receipt of, engagement with, and action on Covid-19-related demands from worker representatives

Our intention is to update our report as more platforms adopt such policies.  We would therefore welcome details of updates to existing platform policies, and addition of new platforms and countries.  These can be shared with us via: https://fair.work/contact/

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