Workshop on China’s Digital Expansion in the Global South

Credit: ASPI https://chinatechmap.aspi.org.au/

China is fast-emerging as a global digital superpower and has a rapidly-growing digital presence in other low- and middle-income developing countries of the global South.  Yet research to date has been relatively limited on this rising phenomenon which is having important economic, social, political and geopolitical impacts.

This online workshop – to be held 1000-1730 (UK time/BST) on Thursday 21st July 2022 – will present new findings based on primary research in the global South, and also provide a space to reflect on the agenda and collaborations for future research.

Register here for the workshop: https://zoom.us/meeting/register/tJAscu-pqTIqEtT-t6mRqxoE3K-nsFGS6Fwd

An opportunity to discuss the future research agenda and actions will follow these presentations:

1000-1200:

The Future Research Agenda on China’s Digital Expansion – Richard Heeks, Angelica Ospina, Chris Foster, Ping Gao, Xia Han, Nicholas Jepson, Seth Schindler & Qingna Zhou (University of Manchester)

Learning Along the Digital Silk Road? Technology Transfer, Power, and Chinese ICT Corporations in North Africa – Tin Hinane El Kadi (London School of Economics)

China’s Digital Expansion in Africa: South to South Cooperation or South Dominance? – Grace Wang (Stellenbosch University)

1300-1445:

Chinese Digital Platform Companies’ Expansion in the Belt and Road Countries – Yujia He (University of Kentucky)

Global Developments of Chinese E-commerce Livestreaming: Case of AliExpress and Lazada in Southeast Asia – Xiaofei Han (Carleton University)

Transnational Governance behind Chinese Platforms’ Overseas Content Moderation: A Case Study of TikTok’s Global Reach to Southern and South-eastern Asia – Diyi Liu (University of Oxford)

1500-1645:

The Chinese Surveillance State in Latin America? Evidence from Argentina and Ecuador – Maximiliano Vila Seoane (National Scientific and Technical Research Council, Argentina) & Carla Álvarez Velasco (Institute of Higher National Studies, Ecuador)

China’s Expansion in Brazilian Digital Surveillance Markets: Between Public Actors and Foreign Enterprises – Esther Majerowicz (Federal University of Rio Grande do Norte) & Miguel Henriques de Carvalho (Federal University of Rio de Janeiro)

Alibaba in Mexico: Adapting the Digital Villages Model to Latin America – Guillermo J. Larios-Hernandez (Universidad Anahuac Mexico)

1645-1730:

Future Research Agenda Activity

The workshop is co-hosted by the University of Manchester’s Centre for Digital Development and Manchester China Institute

Graphic credit: ASPI at https://chinatechmap.aspi.org.au/

Return of the “Third World”?

Are we seeing a return to the old notion of a “third world”?

Originating in the 1950s, the term “third world” was used to refer to those nations not aligned to either the bloc of Western democracies or the Eastern bloc of communist states.  Over time, and particularly since the end of the Warsaw Pact and dissolution of the Soviet Union, the term has fallen from use.

Recent events, though, may point to a revival in two senses.  First, politically.  Compare the two maps below: of first, second and third worlds in the 1970s[1]; and of reaction to Russia’s invasion of Ukraine in 2022[2].  Yes, there are plenty of differences but the neutral countries are almost all third world; and very few third world countries have taken a strongly-supporting or strongly-condemnatory stance.

Second, economically.  Resource procurement and global supply chains are being rethought.  Western democracies are seeking to delink from Russian energy[3], and Russia is turning East to find new sales outlets[4].  Russia and China are collaborating more closely on financial and other systems[5].  Western firms are considering moving supply chains closer to home, into domains that are both more secure and less abusive of human rights[6].  China’s “dual circulation” strategy presages less economic interaction with the West[7].  Overall, “democracies are banding together, as are autocracies”[8].

If there is some greater economic and political coalescence into a Western democratic bloc and an Eastern autocratic bloc, what are the implications for the “third world” of those countries outside those blocs?

Some will benefit as the blocs seek economic collaboration and political alliance.  Mexico, Vietnam, Indonesia and others are already benefitting, for example, from US firms’ search for non-Chinese production bases[9].  China’s Belt & Road Initiative and Western responses such as the US Build Back Better World initiative are competitively channelling infrastructure funding to lower-income countries[10].

Some states may be adept enough to play off the two blocs, squeezing concessions and enabling greater attention to local development goals and interests[11].  But many will come under pressure to pick a side, as seems particularly to be happening with Western pressure on states to turn away from Russia and China[12].  Third world history suggests, if they do this, then such states may then face attempts at destabilisation from the other bloc.

The world is different, more complex and more connected than it was during the era in which “Third World” arose as a concept.  The realities of first and second world bloc formation will likely be less than they might be.  Just as in the 1970s, and as the 2022 diagram above illustrates, many countries outside those blocs may be more aligned with one than the other.  But, nonetheless, echoes of the Third World are sufficiently strong to be taken seriously.

[1] https://commons.wikimedia.org/wiki/File:World_map_worlds_first_second_third.gif https://rgs-ibg.onlinelibrary.wiley.com/doi/full/10.1111/tran.12480

[2] https://www.economist.com/graphic-detail/2022/04/04/who-are-russias-supporters

[3] https://www.theguardian.com/world/2022/apr/04/pressure-mounts-germany-embargo-russian-energy-imports

[4] https://www.energypolicy.columbia.edu/research/qa/qa-china-russia-energy-relations-will-new-oil-and-natural-gas-deals-help-russia-weather-economic

[5] https://www.economist.com/leaders/2022/03/12/will-china-offer-russia-financial-help

[6] https://www.economist.com/business/2022/04/02/is-cancel-culture-coming-to-free-trade; https://economistchina.com/wp-content/uploads/North-American-supply-chains-Will-reshoring-actually-happen.pdf

[7] https://www.economist.com/china/2021/03/11/a-confident-china-seeks-to-insulate-itself-from-the-world

[8] https://www.economist.com/finance-and-economics/2022/03/19/globalisation-and-autocracy-are-locked-together-for-how-much-longer

[9] https://www.economist.com/finance-and-economics/2022/01/01/new-research-counts-the-costs-of-the-sino-american-trade-war; https://economistchina.com/wp-content/uploads/North-American-supply-chains-Will-reshoring-actually-happen.pdf

[10] https://www.firstpost.com/world/explained-as-g7-plans-build-back-better-world-heres-how-much-china-has-spent-on-belt-and-road-initiative-9732641.html

[11] https://rgs-ibg.onlinelibrary.wiley.com/doi/full/10.1111/tran.12480

[12] https://www.economist.com/leaders/2021/07/17/bidens-new-china-doctrine; https://www.aspistrategist.org.au/pressure-building-on-india-to-condemn-russian-invasion-of-ukraine/; https://www.business-standard.com/article/international/pakistan-under-western-pressure-to-condemn-russia-s-invasion-in-ukraine-122030700563_1.html

Latest Digital Development Outputs (Data, Labour, Platforms, Society, Ed Tech, MSc) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Labour; Digital Platforms; Digital Society; Ed Tech; MSc Programme – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Data Powered Positive Deviance: Combining Traditional and Non-Traditional Data to Identify and Characterise Development-Related Outperformers” (open access) by Basma Albanna, Richard Heeks, Julia Handl and colleagues from the DPPD project, presents a new methodology through which datasets can be used to identify “positive deviants” – those who outperform their peers in development – and to identify and scale the factors behind their outperformance.

Publication Outperformance among Global South Researchers: An Analysis of Individual-Level and Publication-Level Predictors of Positive Deviance” (open access) by Basma Albanna, Julia Handl & Richard Heeks, uses interviews, a survey and analysis of online datasets to identify those among a group of global South researchers who outperform their peers.  It identifies characteristics of both the high-performing researchers and their publications.

DIGITAL LABOUR

Systematic Evaluation of Gig Work Against Decent Work Standards: The Development and Application of the Fairwork Framework” (open access) by Richard Heeks, Mark Graham, Paul Mungai, Jean-Paul Van Belle & Jamie Woodcock, explains the development and application of the Fairwork framework, which is used worldwide to rate gig economy platforms against decent work standards.

Stripping Back the Mask: Working Conditions on Digital Labour Platforms during the COVID-19 Pandemic” (open access) by Kelle Howson, Funda Ustek-Spilda, Alessio Bertolini, Richard Heeks and other colleagues from the Fairwork project, analyses the Covid policies of 191 platforms in 43 countries. It finds some positive worker protections but also entrenchment of precarious work as platforms leverage the opportunities arising from the crisis.

DIGITAL PLATFORMS

Digital Platforms for Development” (open access) by Brian Nicholson, Petter Nielsen & Johan Saebo, provides an editorial introduction to a special issue of Information Systems Journal on the link between digital platforms and development processes.

Driving the Digital Value Network: Economic Geographies of Global Platform Capitalism” (open access) by Kelle Howson, Fabian Ferrari, Funda Ustek-Spilda, Richard Heeks and other colleagues from the Fairwork project, uses insights from global value chain and global production network frameworks to analyse power imbalances and value extraction across territories by gig economy platforms.

DIGITAL SOCIETY

“Toolkit for Measuring Digital Skills and Digital Literacy“ (open access) by authors at CSIS Indonesia, supported by Matthew Sharp, offers a comprehensive and original framework for measuring digital skills in Indonesia and other G20 countries. The toolkit incorporates insights from pilot individual and firm-level surveys on digital skills undertaken by CSIS in the Greater Jakarta area.

How can Smart City Shape a Happier Life? The Mechanism for Developing a Happiness Driven Smart City” by Huiying Zhu, Liyin Shen & Yitian Ren, introduces a Happiness Driven Smart City (HDSC) mechanism, composed of a three-layer structure and underpinned by a set of strategic measures. A case study shows the HDSC mechanism’s effectiveness in helping decision makers understand the status quo, strengths and weaknesses of smart city development in their context, so that their SC blueprint can be better aligned towards a happiness-driven direction.

ED TECH

The Effectiveness of Technology‐Supported Personalised Learning in Low‐and Middle‐Income Countries” (open access) by Louis Major, Gill Francis & Maria Tsapali, provides a meta-analysis examining the impact of students’ use of technology that personalises and adapts to learning level.

Evaluating Digital Personalised Learning Tools in Kenya: A New Research Study” (blog) by Becky Daltry, Louis Major and others, reports on a new research study to rigorously evaluate the integration of digital personalised learninginto Kenyan classrooms for young children, aged between 4-8 years old.

MSc PROGRAMME

Centre for Digital Development staff provide the core directorship and teaching for the University’s new MSc programme in Digital Development, which will launch in Sept 2022.

Distribution of Income from Motorcycle-Based Gig Work in Indonesia

When a consumer pays for motorcycle-based gig work, where does the money go?

Following the approach of an earlier, similar post on car ride-hailing,  and again using data gathered by the Fairwork Indonesia team in Jakarta, we can break this down using the generic model shown below:

a. Amount paid by customer: the service payment plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer service payment, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the service payment minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of tasks or being available for work consistently and/or at particular times.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two motorcycle-based gig work platforms (which were charging a 20% gross commission on the customer service payment plus a fee).  Figure 1 presents data for riders who own their own motorcycle (the majority of riders in our sample).  Figure 2 presents data for riders who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Shares of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a significant share – around two-thirds – of the total payment made by the customer.  Aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

ii. Fuel Costs: fuel makes up a very significant proportion of costs: around 80% of costs for bike owners; about half of costs for those who finance their motorcycle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iii. Financing vs. Owning: as expected, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income (non-owners’ net income was about 5% lower).  It’s not completely clear how this happens but one contributing factor is that workers who finance their bikes work longer hours in order to help towards earning the extra to cover their repayments: an average 78-hour week compared to a 66-hour week for those who owned their bikes.

iv. Bonuses and Platform Subsidies: as noted below, the figures here are calculated on the basis of 23.5% of rider income deriving from platform bonus payments.  The platform gross commission plus fee represent just over 32% of the customer payment; yet the platform’s net earning is 5% or 6% only.  In other words, and absent unknown factors, the platform is on average paying substantially more than its entire commission to workers.

On this basis, one can calculate the tipping point at which platforms earn nothing and are having to subsidise worker income from investment or other sources of capital.  As illustrated in Figure 3, for this instance, this will happen when worker bonuses make up more than 30% of their income.  Yet one can find examples in Indonesia where the effect of bonuses is to more than double workers’ basic pay (i.e. bonuses make up more than 50% of worker income).  In such circumstances platforms must be significantly subsidising gig work from capital. If this is widespread, it may help to explain why so many gig work platforms report operating losses.

Network effects – the greater value of a platform to users as more users participate – would predict the emergence of monopoly (single seller of services to customers) and monopsony (single buyer of services from workers).  Yet this has not happened in most gig economy markets – including those of Indonesia – which, instead, are oligopolies/oligopsonies, meaning there is competition between platforms for both customers and workers.  It is that competition which in part motivates the payment of bonuses to workers.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 25% of rider income is made up from tips and bonuses, of which tips make up 1.5%.  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of rider income.

– Fairwork data from South Africa showed riders’ net income to be 55% of the total customer payment, but this did not separately account for bonuses, which will increase the percentage.  Overall, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

Distribution of Income from Ride-Hailing in Indonesia

When a customer takes a taxi journey from a ride-hailing platform, where does the money go?

Using data gathered by the Fairwork Indonesia team in Jakarta, we can now break this down using the generic model shown below:

a. Amount paid by customer: the fare for the ride plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer fare, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the fare minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of rides or being available for work consistently and/or at particular times of peak demand.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two car ride-hailing platforms (which were charging a 20% gross commission on the customer fare plus a fee).  Figure 1 presents data for drivers who own their own vehicles (the minority of car taxi drivers in our sample).  Figure 2 presents data for drivers who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Worker Share of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a minority share – around one-third – of the total payment made by the customer.

ii. Large Business Share of the Pie: aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

iii. Fuel Costs: fuel makes up a very significant proportion of costs: around 90% of costs for vehicle owners, who spend more on fuel than they earn in net terms; about half of costs for those who finance their vehicle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iv. Financing vs. Owning: not surprisingly, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income.  It’s not completely clear how this happens but one contributing factor is that workers who finance their vehicles work longer hours in order to help towards earning the extra to cover their repayments: an average 70-hour week compared to a 65-hour week for those who owned their cars.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 15% of driver income is made up from tips and bonuses, of which tips make up 1.5% (i.e. one tenth of the extra).  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of driver income.

– Fairwork data from South Africa showed a similar financial distribution, with ride-hailing taxi drivers’ net income being 32% of the total customer payment.  However, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

Fairwork vis-à-vis ILO Decent Work Standards

Fairwork logoHow does the Fairwork framework of five decent work standards in the gig economy – fair pay, conditions, management, contracts, representation – compare to more conventional frameworks?

As explained in a recently-published paper, Fairwork is a simplified, revised and measurable version of the 11 elements of the International Labour Organization’s decent work agenda.

Comparing the two, as shown in the table above, Fairwork is not as comprehensive.  Some ILO elements not covered were seen as unrelated to Fairwork’s purpose.  For example, the contextual elements lie outside the control of platforms, and no evidence was found of child or forced labour.  Quantum of employment measures are not directly relevant to Fairwork’s aims though it would be informative to know if platforms are creating new work as opposed to just substituting for existing work.

While outside the scope of Fairwork’s principles, work security and flexibility were investigated via open questions in worker interviews.  Workers did raise the issues of flexibility and autonomy, as positive attributes of their gig economy work.  These supposed benefits are arguably more perceptual than real.  Hours of work are often determined by client demand and shaped by incentive payments offered by the platform to work at certain times or for certain shift lengths.  Work is recorded and managed via the app and platform to a significant extent.

In sum, the Fairwork framework covers the decent work-related issues identified in the research literature on platforms, and covers the majority of decent work elements within the ILO framework.  Its ratings could nonetheless be contextualised in a number of ways by adding in broader findings about national socio-economic context, about any creation of work and autonomy by the gig economy, about dimensions of inequality within and between gig sectors, and about longer-term job (in)security and precarity.

You can find more detail about this and other foundations for the Fairwork project in the open-access paper, “Systematic Evaluation of Gig Work Against Decent Work Standards: The Development and Application of the Fairwork Framework”; published in the journal, The Information Society.

The Rise of Digital Self-Exclusion

Digital ExclusionWhy are marginalised groups self-excluding from digital systems?

The digital exclusion problem used to be people outside the house unable to get in.  For example, the digital divide preventing groups from accessing the benefits of digital systems.

Recently, a new digital exclusion issue is arising: people deciding they’d rather stay outside the house.  Some examples . . .

1. Informal Settlement Residents

In researching for our paper, “Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework”, my co-author Satyarupa Shekhar identified this pattern among informal settlement residents:

“businesses such as schools and pharmacies in Kibera did not wish to be [digitally] mapped.  They feared visibility to the state might lead to closure if their location became known and their informal status or activities (e.g. sales of stolen drugs) were then discovered …

… Particular settlements in Chennai refused to participate in data-gathering.  They believed that drawing attention to their existence and informal status – being under the ‘gaze of the state’ – would increase likelihood of eviction”

2. Refugees

The recent Information Technology for Development paper “Identity at the Margins” finds self-exclusion among refugees in relation to registration on UNHCR digital ID systems:

“Some participants were so concerned about the potential consequences of data sharing that they avoided registering altogether. For example, a male Syrian refugee living with his family in a one-room apartment in Lebanon told us:

Everybody was registering with the UN, but we did not. We were suspicious and scared. We don’t know if the UN shares information with anyone, so that is why I did not share many things with them.”

3. Migrants

The chapter, “The Dilemma of Undocumented Migrants Invisible to Covid-19 Counting” in recent online book “Covid-19 from the Margins” outlines the dilemma of those undocumented migrants unwilling to register with health systems despite contracting Covid, for fear of this alerting other arms of government which would then deport them.

4. LGBTQ People

The report, “Privacy, Anonymity, Visibility: Dilemmas in Tech Use by Marginalised Communities” explains how some LGBTQ people in Kenya have been unwilling to use digital systems designed to help them report discriminatory violence because of fears that their identities would become known.

Analysis

In one sense there is nothing new here.  Individuals have for centuries sought to avoid being included in government censuses and other records: to avoid tax, to avoid being conscripted for war, etc.

The difference with digital is the ease with which data can be transmitted, leading particularly to a fear that it will find its way to the agencies of state security. This fear applies not just to data collection by other state agencies but also to NGOs (who were undertaking the community mappings in the first examples) and to international organisations like UNHCR.

Whereas incorporation into historical data systems such as the census offered no individual benefit, this is not true of the digital systems cited above.  In all these cases, the marginalised are foregoing direct benefits of incorporation – better community decision-making, access to UN assistance, access to healthcare – because these benefits are outweighed by the fear of perceived harm arising from visibility to particular arms of the state.

All this in turn can be understood in terms of data justice models such as the one below from “Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework”.  At a basic level, the perceived utility of exclusion from these digital systems outweighs the perceived benefits.  But these perception are themselves shaped by the structural and historical context:

– A lack of credible, known data rights for those in marginalised groups

– A structural relation of perceived powerlessness vis-à-vis the state

– A lack of institutions and resources with which that powerlessness could be counteracted

Unless those wider, deeper causes can be addressed, the marginalised will continue to self-exclude from digital systems.

Latest Digital Development Outputs (Data, Economy, Health, Platforms, Water) from CDD, Manchester

Using SmartphoneRecent outputs – on Data-for-Development; Digital Economy; Digital Health; Digital Platforms; Digital Water – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia” (open access) by Pete Jones, Jackie Carter, Jaco Renken & Magdalena Arbeláez Tobón, considers the importance of quantitative data skills development implied by the UN Sustainable Development Goals. The success of a partnership programme in the UK is used to explore how ‘data fellowships’ can fulfil some of the unmet capacity needs of the SDGs in a developing country context, Colombia.

Building Information Modelling Diffusion Research in Developing Countries” (open access) by Samuel Adeniyi Adekunle, Obuks Ejohwomu & Clinton Ohis Aigbavboa undertakes a literature review – including current and future research trends – on the adoption of building information modelling in developing countries.

DIGITAL ECONOMY / PLATFORMS

Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” (open read access) by Juan Erasmo Gomez-Morantes, Richard Heeks & Richard Duncombe demonstrates how the multi-level perspective approach can be used to analyse the lifecycle of digital platforms: the process of innovation, rapidity of scaling, and development impacts relating to resource endowments, institutional formalisation, and shifts in power.

Digital Platforms and Institutional Voids in Developing Countries” (open access) by Richard Heeks, Juan Erasmo Gomez-Morantes, Brian Nicholson and colleagues from the Fairwork project, analyses how digital platforms change markets through their institutional actions.  Using the example of ride-hailing, it finds platforms have formed a market that is more efficient, effective, complete and formalised.  At the same time, though, they have institutionalised problematic behaviours and significant inequalities.

Navigating a New Digital Era Means Changing the World Economic Order” (open access) by Shamel Azmeh, discusses the implications of digital shifts for global economic governance.

DIGITAL HEALTH

Cost-Effectiveness of a Mobile Technology-Enabled Primary Care Intervention for Cardiovascular Disease Risk Management in Rural Indonesia” by Gindo Tampubolon and colleagues demonstrates how to determine the economic impact of m-health.  It calculates the cost-effectiveness of a mobile-based health intervention at c.US$4,300 per disability-adjusted life year averted and US$3,700 per cardiovascular disease event avoided.

Delivering Eye Health Education to Deprived Communities in India through a Social Media-Based Innovation” by Chandrani Maitra & Jenny Rowley aims to develop understanding of the benefits of, and the challenges associated with the use of social media to disseminate eye health information in deprived communities in India.

Using a Social Media Based Intervention to Enhance Eye Health Awareness of Members of a Deprived Community in India” (open access) by Chandrani Maitra & Jennifer Rowley reports on a WhatsApp-based intervention to promote eye health communication in deprived settings. This research highlights the potential benefits of WhatsApp in increasing awareness on eye problems, amongst deprived communities where the disease burden remains very high.

DIGITAL WATER

Digital Innovations and Water Services in Cities of the Global South: A Systematic Literature Review” (open access) by Godfred Amankwaa, Richard Heeks & Alison Browne reviews the literature on digital and water in Southern cities.  It summarises findings to date on implementation and impact and sets out the future research agenda.

A Better Way to Research Digital Platforms

Juan Paper Word CloudIn a new European Journal of Development Research paper – “Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” – I and my co-authors argue that there is a better way to research digital platforms.

Digital platforms play an ever-growing role within international development, and a body of research has emerged as a result.  This research offers valuable insights but we find three lacunae:

– Current work collectively identifies a whole set of factors at micro-, meso- and macro-levels that shape the trajectory of digital platforms.  But no research to date can encompass all of the factors and levels.

– Current work has been narrow and a-historical: it analyses the platform but not the existing ways of organising or delivering the particular social, economic or political activity that the platform competes with.

– Current work looks at either implementation and growth of platforms, or at their impact, but not both.  Yet implementation, scaling and impact of platforms are inextricably intertwined.

Our paper therefore uses a different and more holistic approach.  Understanding digital platforms as socio-technical transitions, it uses the multi-level perspective (MLP: see summary diagram below) as its analytical framework.

Using this framework, it analyses a successful ride-hailing platform – EasyTaxi in Colombia.  Although there were some challenges in applying the MLP framework, it addressed the three shortcomings of earlier work:

– It covers the broad range of factors that shape platforms at micro-, meso- and macro-level.

– By focusing on transition, it encompasses both the before and after of platform introduction.

– It analyses the platform lifecycle from initial innovation, though implementation and growth, to impact.

Thus, for example, the MLP explains how prior context and profile of traditional taxi driving created the landscape of infrastructure and incentives behind rapid scaling of the platform.  It also explains development impact: how resource endowments shifted between stakeholders; the formation and formalisation of institutional forces; and the changing distribution of power in the market.

On this basis, we recommend use of the multi-level perspective to researchers wanting to fully understand implementation and impact of digital platforms.

Digital Platforms as Institutions

platforms-as-institutionsHow should we understand digital platforms from an institutional perspective?

The paper, “Digital Platforms and Institutional Voids in Developing Countries”, suggests a four-layer model of institutional forms, and illustrates this using ride-hailing platforms as an example.

Layer 1: Digital Institutions.  Platforms themselves are institutions into which digitised routines and rules have been designed based on the digital affordances of the platform. Ride-hailing examples include algorithmic decision-making such as driver—customer matching, or price setting.

Layer 2: Digitally-Enabled Institutions.  Some institutional functions rely on digitised routines and rules within the platform but involve human intermediation.  Ride-hailing examples include checks on driver credentials for market entry, or adjudication of deactivation decisions.

Layer 3: Business Model Institutions.  These are broader rules and routines determined by the platform company as part of its business model, which govern participation in the platform but which exist outwith the digital platform.  Ride-hailing examples include control over vehicle entry into the market, determination of driver employment status, or setting the balance of supply and demand.

Layer 4: Stakeholder-Relation Institutions.  These are the connections or disconnections to other market or domain institutions.  Ride-hailing examples include relations to external stakeholders such as government agencies and trade unions.

Analysis of field evidence from Colombia and South Africa suggests that the first two types of institution are associated with the filling of prior institutional voids, and with market improvements.  The latter two institutional forms are more related to the maintenance, expansion or creation of institutional voids, and to market inequalities.

We look forward to further work applying and revising this institutional model of platforms.