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.

ICT infrastructures, e-commerce and rural China’s Taobao villages

The development of information and communications technology (ICT) infrastructures such as internet, smartphones and online social networks has contributed to the rapid growth of e-commerce, which has gradually changed people’s lifestyles and begun to play an essential role in socioeconomic development. Though ICT infrastructures and e-commerce emerged first in urban areas, they are increasingly becoming a profound influencing factor for the development of rural society in addressing their conventional deprivations such as geographical isolation and information asymmetry. With appropriate human capital conditions, ICT infrastructures and e-commerce are facilitating new forms of economic activity and providing alternative development approaches in some rural communities. From this narrative, rural development has entered into a digital era, and the rural communities in the Global South, which used to be deprived and marginalised in terms of geographical locations and institutional settings, are more rapidly influenced by the emerging forces of ICT infrastructures and e-commerce.

While rural communities indeed sit in a vulnerable position in terms of upscaling services and digitalisation, paradoxically, the problem of physical remoteness and inadequate service provision could to a large extent be solved by promoting digital connectivity as a substitute for many of those services. However, a deadlocked situation is that remote rural areas especially lack the required digital connectivity, which has increased the risk of these areas falling even further behind in terms of service accessibility amid the digital transformation. In particular, the population sparsity of those remote rural communities leads to a higher unit cost for ICT services and infrastructures delivery. For this, government support and investment towards narrowing the “digital divide” between rural and urban areas (or informationally disadvantaged and advanced areas) are essential.

China is therefore a typical case in navigating the roles that ICT infrastructures and e-commerce play in reshaping rural society, where state investments into linking rural communities to digital services are significant. Since 2006, the Chinese central government has implemented a series of national initiatives for “village informatisation” and “rural digital development”, aiming to “informatise” and “digitalise” the rural communities in China. The major actions underpinned in these programs include the two aspects of “access” and “application”, namely, 1) to improve rural society’s access to internet and communication infrastructures (including telephone, television, and the internet), and 2) to provide various applications of internet and communication infrastructures (such as government websites, information services stations, agriculture-related websites and e-commerce portals). With the efforts devoted by the central and local government, the gap of internet coverage between urban and rural areas in China has been narrowed effectively. By June 2021, internet coverage in rural China reached 59.2% (the figure is 78.3% for urban China) and broadband speed has achieved urban-rural equality (CNNIC, 2021).

In line with linking ICT services to both urban and rural sectors, China has also made remarkable progress in e-commerce development. By June 2021, the number of internet users in China reached 1011 million, and the number of online e-commerce users reached 812 million, indicating that 80.3% of the country’s internet users have been engaging in e-commerce activities (CNNIC, 2021). Mobile Taobao, established by Alibaba Group, has become the world’s largest online e-commerce platform where customers can buy products, interact with e-traders, and share their content with friends and other users.

Amid the wave of ICT development, digital transformation and e-commerce growth, a new form of regional development based on online platforms has recently emerged in rural China, and some of the rural villages developing e-commerce activities by Taobao platforms are defined as a Taobao village if certain criteria are met: 1) the basic unit of trading venue is an administrative village; 2) the scale of annual e-commerce sales is above 10 million RMB (c.US$1.5m); 3) the number of active online stores is over 100 or the number of active online stores is more than 10% of the total number of local households. The first three Taobao villages emerged in Zhejiang, Jiangsu and Hebei provinces respectively in the year 2009, and by September 2020 there were in total 5,425 Taobao villages (appropriately 1% of the total number of villages in China) distributed in 28 provinces in China (see Figure 1 below, showing most to be located in the East and especially coastal regions of the country) (AliResearch, 2020). The booming of Taobao villages and townships has contributed to socioeconomic development in rural China, evidenced by the fact that for the single year of 2020, the development of Taobao villages and townships is assessed to have created more than 8.28 million job opportunities and achieved more than 1,000 billion RMB (c.US$150bn) sales, which is 50% of the overall online retail sales in rural China (AliResearch, 2020).

Figure 1: Spatial distribution of Taobao villages in China (aggregated in East region) (AliResearch, 2020)

The Taobao villages and rural China’s digital development in a broader sense have been at the forefront of the digital revolution that is taking place around the world today. E-commerce-engaged development patterns in rural China more generally, illustrate how the internet promotes inclusion, efficiency, and innovation for development (World Bank, 2016). Compared with the prosperous development of rural China’s e-commerce development, though existing research has made some initial attempts to unravel the development phenomenon of Taobao villages, more interdisciplinary research efforts are called for in order to explore how ICT infrastructures and e-commerce are embedded into the rural territories, and what insightful implications can be drawn from rural China’s e-commerce activities to help catalyse breakthrough development of rural areas in the wider context of the Global South.

Note: This blog is based upon the PhD research of Yitian Ren at The University of Manchester.

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.

Mapping research on digital water in developing countries: state of the art and future research agenda

There is no doubt that digital water — use of digital technologies in the water sector — is growing, with evidence of widespread adoption and use across countries.  Many water service providers are investing in new technologies not only to improve infrastructure performance and enable existing systems to operate more efficiently, but also to make new service delivery models possible.

However, it is sometimes difficult to distinguish between the hype and hope of digital technologies to tackle water sector challenges, especially in developing countries. It is in this context that we’re witnessing a growth of interest in the discussion, evidence case studies and research on digital water. How have digital water innovations (DWIs) been implemented and what has been the impact?  And how is the growing implementation of digital water innovations in global South cities researched? Our recent systematic review paper of urban DWI in the global South answers some of these questions for the first time by analysing a total of 43 papers.

The findings

Literature profiling: Findings demonstrate the relative recency and volatility of publication – first paper published in 2006, 67% of papers published in the last five years of the review, and more than one-third in the last two years – reflecting the relative recency of digital technologies being deployed in developing countries.  Also, research is strongly dominated by engineering with limited focus on social science and limited engagement with theorisation (40%) (see Figure 1). At present, the majority of the research focused on Africa (58%).

Figure 1. Disciplinary focus for reviewed papers

Scope of DWI implementation: In terms of type of digital technology implemented, the current literature focused heavily on data processing/visualisation such as geographic information systems and what we called “action support technologies” such as mobile payment systems. There is relatively little research on digital technologies applied at the upstream end of the value chain such as water sources, headworks and treatment. To date, research reflects a provider-centric view of innovation with no instances of user-driven or government-driven innovation or even co-design or participative approaches to implementation involving users or government. Indeed, almost all (93%) of papers discussed water service providers with much less focus on end-users/consumers (25%) as key stakeholders of relevance to the focal digital water innovations and research. Partly related, research has focused mainly on use of digital for on-grid water supply, with only limited studies looking specifically at digital water innovations for off-grid water users.

Scope of DWI impacts: Overall, there has been relatively limited focus on the impact of digital systems.  Just under half of papers reported something about the impact of the digital systems, but more than a third of these were solely speculative and almost all of the remainder reported just pilot or early-stage evidence. Where impact findings were presented, they were skewed towards benefits more than disbenefits: on average each paper talked about four different types of benefit but fewer than two types of disbenefit; and they were skewed towards the impact of one type of DWI:  action support technologies. There was no primary evidence-based research on the impacts of the great majority of digital water innovations including data capture, data processing and decision support technologies.

Digging down, research interest was clustered around benefits for users and water service providers, and benefits such as financial, operational and other service benefits.  This meant that evidence of broader impacts — such as environmental impact, or impact on inequalities — was limited and tended to extrapolate from individual studies. Nor has research yet engaged with the datafication of water; that is, the growing presence, use and impact of data in the water value chain.

What does this tell us about implementing and researching digital water?

In the review, we found a field of research that is still at a formative stage, which thus provides ready opportunity for future research on specific technology, implementation and impact priorities. This calls for rethinking digital water innovations, with future research attention on:

  • social science research including socio-political and inter-disciplinary socio-technical perspectives;
  • particular digital technologies with proven water-related potential such as data capture technologies (remote sensing, smart meters, SCADA, telemetry); data processing technologies (big data, data mining, machine learning, artificial intelligence, blockchain, virtual and augmented reality); and less-researched action support technologies (water ATMs, digital purification systems);
  • “upstream” water value chain technologies and technologies for off-grid and low-income users;
  • user- and government-centred or -participative innovation processes, including action research;
  • impact, including evidence of longer-term and broader impact;
  • interpretive and critical realist research, qualitative and mixed methods research; and
  • more explicit use of theory and conceptual frameworks including re-use of conceptualisations by other researchers.

It is our belief that researchers, technology implementers, utilities and policy makers will continue to engage with digital water innovations in the coming years. To contribute to this discussion, I am currently undertaking a research project on digital water innovation impact in urban Ghana, with some early results already emerging. Stay tuned!

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.

How Platforms Change Markets: The Lens of “Institutional Voids”

Void

Do digital platforms change markets for better or worse?

To help understand this, we used the lens of institutional voids in the World Development paper, “Digital Platforms and Institutional Voids in Developing Countries”.  This argues that markets don’t work properly because they have institutional shortcomings or voids: inadequate provision of information, limited matching of buyers and sellers, poor management of transactions, ineffective market regulation, etc.

A promise of digital platforms is that they will fill these voids and change markets for the good.  We investigated this using evidence from Colombia and from the South Africa Fairwork project on taxi markets before and after the advent of three e-hailing platforms: Bolt, EasyTaxi and Uber.

The “before” picture was far from perfect.  Institutional voids led to markets with problems including high costs, crime, insecurity, opportunism, informality and discrimination.  As predicted, the gig economy platforms filled some of the institutional voids that led to this profile.  This reduced costs and risks for both drivers and passengers, improved vehicle and service quality, and enabled employment for those excluded from the traditional market.

Yet, in contrast to past research on business and institutional voids in the global South, we found that void-filling is not all that platforms companies do.  They also maintain some voids, such as lack of information and lack of formal employment status for drivers.  They expand some voids, such as lack of information available to government.  And they create some voids by circumventing the regulatory roles performed by government agencies and driver collective bodies.

The core impact of these additional strategies is to increase the relative power of the platform company vis-à-vis other market stakeholders and to make the market much more unequal.  Going far beyond the typical role of business, platform companies have internalised the institutions for the entire gamut of market functions; collapsing an entire organisational field into themselves.  The previously-distributed and -dissipated institutional power that the platform companies have concentrated into themselves is thus unprecedented, particularly given the duopolistic nature of the markets that are often created.

Filling institutional voids is not wholly beneficial – our research also identified problems caused by the digitalisations and formalisations that platforms bring.  But our key recommendation is a need to identify and address the voids that these companies retain or make.  Actions needed include information provision to address customer–driver asymmetries; revitalised state control over market supply–demand imbalance; new legislation to address lack of employment rights for workers; and more effective worker collectivisation.

Our research represents a novel insight into the relation between platforms, institutions and markets, and we look forward to further work applying these ideas to other sectors and contexts.