Antecedents of Significant Digital Development Research

This post is a cheat because it’s actually summarising a paper on organisational – not digital development – research.

It’s by the leading organisational theorist – and confutation of nominative determinism – Richard Daft, and I read it just before I started my PhD.

Based on a survey of organisational researchers, its findings feel relevant to digital development.  Significant research . . .

– Is an outcome of the researcher’s involvement in the real world

– Is an outcome of the researcher’s own interests, resolve and effort

– Is chosen on the basis of intuition

– Is an outcome of intellectual rigour

– Reaches into an uncertain world to produce something that is clear, tangible and well-understood

– Focuses on real problems

– Is concerned with theory, with a desire for understanding and explanation

Not-so-significant research is the opposite: expedient, quick and easy, lacking personal commitment from the researcher, lacking theoretical thought and effort, and so on.

While planning and clarity mark out the latter stages of significant research, it is the outcome of an organic process of intuition, integration of ideas from different fields or chance meetings, that starts with uncertainty.  Precisely planned, tidy, clean and clearly-defined research most likely leads to small results (research funders please take note!).

That all seems to fit equally-well with digital development research but, of course, these criteria come from a researcher perspective, not that of other stakeholders.  See what you think.

If you’d like to read the paper, it’s not so easy to find:

 – Daft, R. L. (1984). Antecedents of significant and not-so-significant organizational research. In: T.S. Bateman & G.R. Ferris (eds), Method and Analysis in Organizational Research. Reston Publishing, Reston, VA, 3-14.

Or, there’s a firewalled update:

– Daft, R. L., Griffin, R. W., & Yates, V. (1987). Retrospective accounts of research factors associated with significant and not-so-significant research outcomes. Academy of Management Journal30(4), 763-785.

Income of Gig Work vs. Previous Job in Pakistan

Richard Heeks, Iftikhar Ahmad, Shanza Sohail, Sidra Nizamuddin, Athar Jameel, Seemab Haider Aziz, Zoya Waheed, Sehrish Irfan, Ayesha Kiran & Shabana Malik

Does the transition to gig work improve incomes in Pakistan?

Many workers join gig work platforms in the belief that their incomes will improve, but is this borne out in practice?  To investigate, the Centre for Labour Research interviewed 94 workers based on six platforms across three sectors: ride-hailing, food delivery, and personal care.

Of these, 51 were able to tell us what their previous monthly income had been in their most-recent employment prior to joining the platform[1].  Stated income varied from the equivalent of US$60 per month up to U$1,200 per month, and averaged US$220 per month[2].

After moving into gig work, average gross income was slightly higher, at US$240 per month but, as the graph below shows, there was a much more differentiated picture behind the average, with around 40% of respondents earning less gross income (red-bordered blue columns) than they had done previously.

However, as the graph also shows, things looked worse when comparing net income (orange columns).  For the great majority of prior jobs, work-related costs were small (only work-to-home transport, which we calculated based on typical commuting journeys in Pakistan to be just under US$18 per month; i.e. less than 10% of average gross income).  But for gig work – much of which relies on journeys by vehicle and continuous internet connectivity – the costs of petrol, maintenance and data eat heavily into gross income.  In addition, for some (only a few in our Pakistan sample) there are costs of renting their vehicle.[3]

These costs represented, on average, 65% of gross income and knocked average net income for gig workers down to just US$85 per month.  When we compare before-and-after for net income, then, we found more than 70% of our sample were earning less than in their previous job, and 45% earned over US$100 per month less.

This was especially an issue for ride-hailing drivers and it does reflect the particular circumstances during our interview period of late 2021 to early 2022: a drop-off in demand for travel due to Covid, and a steep rise in petrol prices.  Indeed, so bad was the problem that just over a fifth – 21 of the 94 – were reporting negative income.  That is, they were effectively paying to go to work as their costs exceeded their gross income; something to which the platforms responded in May 2022 by dropping the commission taken from drivers to 0%.

While recognising the challenging period for gig workers covered by our fieldwork, nonetheless, this does suggest that – by and large – gig work is not delivering the income boost that workers often hope for.  They may, for example, be lured by gross income figures, not realising how much lower net income will be.  Gig work does provide a livelihood – 40% of our sample were unemployed in the immediate period prior to joining – but it is not really fulfilling its promise.  It also falls far from decent work standards: five-sixths of those we interviewed took home less than a living wage.

If you’d like to know more, please refer to the 2022 Fairwork Report on Pakistan’s gig economy.


[1] Those who stated what their prior employment had been gave the following job descriptions: BPO operator, Teacher (2), Housekeeper, Shopkeeper, Gas company worker (2), Safety officer, Business person, Tanker driver, Ride-hailing driver with another platform (3), Traditional taxi driver (3), Farmer, Builder, Computer operator, Cook, Technician, Shop assistant, Domestic worker, Government worker

[2] This average is some way above the overall average earnings of US$140 per month but well below formal sector average monthly salary of US$480.

[3] For further detail, see this discussion of the breakdown of ride-hailing passenger payments.

Digital Inequality Beyond the Digital Divide

How can we understand digital inequality in an era of digital inclusion?

As the open-access journal paper, Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South” explains, the digital divide has been an essential and powerful concept that links digital systems with inequality.

But it is no longer sufficient.  A majority of the global South’s population now has internet access and is included in, not excluded from, digital systems.  Yet, as the figure below illustrates, that inclusion also brings inequalities – the small farmers in digital value chains losing out to large intermediaries; the gig workers whose value and data are captured by their platforms; the communities disempowered when they are digitally mapped.

Figure 1: From an Exclusion-Based to an Inclusion-Based Perspective on Digital Inequality

We need a new conceptualisation to explain this emerging pattern.  I refer to this as “adverse digital incorporation”, defined as inclusion in a digital system that enables a more-advantaged group to extract disproportionate value from the work or resources of another, less-advantaged group.

As shown below, I have inductively built a model of adverse digital incorporation, based around three aspects:

Figure 2. Conceptual Model of Adverse Digital Incorporation

Future digital development research can apply this model deductively to cases of digital inequality, and can further investigate the digitality of adverse digital incorporation. 

For digital development practitioners, the challenge will be to achieve “advantageous digital incorporation”: designing digital interventions that specifically and effectively reduce existing inequalities.  This means going beyond digital equity to digital justice: addressing the underlying and contextual causes of inequality not just its surface manifestations.

For further details, please refer to the paper; “Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South”.

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

Recordings of the presentations in the three main workshop session can be found at: https://www.youtube.com/playlist?list=PLjghFTNvDEIyEUpx7nlYqWDKeA5JkWczL

The workshop timetable is shown below:

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 was 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.