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”.

How Does Technology Affect Smart City Governance?

What is a Smart City?

A Smart City (SC) capitalises on technology, proper governance and collaborations between the various stakeholders to comprehensively promote city prosperity and eventually improve the quality of citizens’ lives.

Figure 1. Envisaging the smart city[1]

Cities are agglomerations of economic, social, and cultural benefits[2]. On the other hand, cities are increasingly confronted with issues such as diminishing public management efficiency, backward infrastructure, traffic congestion, environmental pollution, and general security concerns, among others.

The Smart City is a concept that has evolved around the world to solve urban problems and enhance urban development. Several municipalities, such as Cape Town, Ottawa, San Diego, Southampton, Barcelona, Seoul, and Shanghai, have developed SCs to serve citizens better and improve the quality of citizens’ lives.

What is Smart City Governance?

New governance patterns are required to manage SCs. The governance models for SCs could be divided into two categories:

  • Some of the governance models are technology-driven, focusing on the role of big data and technology.
  • Other governance models emphasise the human and institutional factors,  such as the role of governance structures, citizen-centricity, social capital, human resources and stakeholders.

At the intersection of these two, Smart City Governance (SCG) emerges mainly due to the growing roles of technology and human capabilities in the functioning of cities, which gives the government the opportunity to optimise the governance process and outcomes. A typical description of SCG is “crafting new forms of human collaboration through the use of ICTs to obtain better outcomes and more open governance processes” [3].

How does technology affect SCG?

The technology revolution has altered the city governance model. The impact of technology on governance models is roughly in two directions. One is to use technology to strengthen the government-centric bureaucratic model, and the other is to use technology to distribute decision-making power to more stakeholders.

  • Technology contributing to the concentration of power

The case in Shenzhen, China shows how technology can strengthen a top-down governance model. The Shenzhen government propagated a programmatic document for SCG, the Shenzhen Municipal New-Type Smart City Construction Master Plan, in 2018[4]. In this plan, the SC structure of Shenzhen includes three layers and two supports, as outlined in the figure below.

The primary layer is the SC Sensory Network System, which mainly includes sensor networks, communication networks, and computing storage centres; the middle layer provides support for government decision-making, which is composed of the Urban Big Data Centre and SC Operation and Management Centre; the top application layer includes four parts public services, public safety, urban governance and smart industries.

In this scenario, technology is the core element of governance and is used to strengthen the government’s decision-making and implementation capabilities. In this kind of governance model, technology is used to collect public management-related data and information, help make governmental decisions and finally reinforce the rationality and efficiency of government.

Figure 2. Shenzhen’s smart city structure [5]

  • Technology contributing to the decentralisation of power

On the other hand, technology may give impetus to the bottom-up governance model. For example, in the case of Amsterdam Smart City (ASC)[6], the Amsterdam Economic Board governs and funds it using an open web-based platform. This platform allows stakeholders to communicate and disseminate information in a fair and transparent manner. Furthermore, open-house programmes and open gatherings help citizens communicate and empower themselves. This case demonstrates how technological innovation has aided in the distribution of information and power to more stakeholders in ASC.

Figure 3. Amsterdam Smart City

In conclusion, data and information bestow stakeholders’ power and legitimacy in urban governance to a certain extent. From the standpoint of technology, the power distribution of data and information may affect the governance model towards decentralisation or concentration.

References

[1] https://www.arcweb.com/industries/smart-cities

[2] https://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/citiesoftomorrow/citiesoftomorrow_final.pdf

[3] Bolívar, M. P. R., & Meijer, A. J. (2016). Smart governance: Using a literature review and empirical analysis to build a research model. Social Science Computer Review, 34(6), 673–692. https://doi.org/10.1177/0894439315611088

[4] http://www.sz.gov.cn/zfgb/2018/gb1062/content/post_4977617.html

[5] Hu, R., (2019). The state of smart cities in China: The case of Shenzhen. Energies, 12(22), p.4375

[6] https://amsterdamsmartcity.com/

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/

The Organisational Context for Successful ICT4D Practitioners

How can their organisational context best support those who implement ICT4D projects?

People – designers, builders, operators, champions – are critical to the successful implementation of ICT4D projects.  The digital development organisations that employ these practitioners already know that.  But what they know far less about is how to create a supportive organisational context that will improve ICT4D practitioner performance and, hence, ICT4D project success rates.

I have therefore been undertaking field research in East Africa designed to tease out components of supportive context, based on interviews in five organisations which were a mix of NGOs and social enterprises.  To date, I have identified six “habits of highly-effective digital development organisations”:

1. Reinforcing Mission Congruence

The most-effective contexts were those in which ICT4D practitioners were given a clear sense of how their work fitted with the organisation’s wider mission, which typically related to social impact.  As well as giving practitioners the bigger picture of their contribution, this also helped create a unity of purpose with shared goals of making a difference.

2. Strong Non-Monetary Rewards

Money is tight in most digital development organisations but they can successfully motivate their practitioners with non-monetary rewards.  Flexibility on working hours and opportunities for work-life balance came up repeatedly in this category, alongside recognition from peers of one’s contribution.

3. Involvement in Monetary Reward-Setting

A role for non-monetary rewards does not mean money is unimportant – it is!  But just as important as the amount was the process by which pay was calculated.  Supportive contexts were those where pay was transparently calculated and openly discussed, and hence where ICT4D practitioners felt involved in the process of decision-making.

4. Support for Career Progression

To make their best contribution to ICT4D projects, practitioners needed to feel that they were making progress in their careers.  Though often backed by direct mentoring, organisational support here varied by career stage.  Early-career practitioners had a strong perceived need for skills development: not narrow task-specific skills but a broad and hybrid mix of technical and non-technical capabilities.  This worked best where their organisation offered them a mix of different roles but also ensured access to high-quality digital tools and infrastructure.  Mid-career professionals also wanted growth opportunities but they focused less on technical skills and more on being given the autonomy and responsibility to develop leadership capabilities.

5. Meeting Personal Goals

ICT4D practitioners give their best to their projects and their organisation when they have a perception of reciprocation; particularly in terms of being helped to achieve their personal goals.  Goals of social impact and skills-building for career progression were mentioned already, but supportive contexts could provide other things – networks of stakeholder relations to build social capital for the future, and facilitation of personal development projects.

6. Socio-Emotional Support

ICT4D often has a technical bias but practitioners worked best in cultures attuned to the human side of work, and in which they felt their whole selves were recognised.  These were organisations that were more like “families” than “well-oiled machines”; in which peers and managers cared about wellbeing and would take time to listen and engage with personal problems; and in which socialisation and hence a sense of belonging were actively encouraged.

These findings may themselves have some specificity to East African digital development organisations.  Each organisation may thus need to identify the dimensions of organisational support that will work with its particular ICT4D practitioners.  Nonetheless, these six habits should be a useful starting point for all organisations.

If you would like further details about the six habits, or my ongoing work using these to develop interventions for digital development organisations, then feel free to contact me: epiphania.kimaro[@]manchester.ac.uk

Photo credit: Gladness Mayenga

Global South researchers succeeding against the odds: how are they different?

Understanding the Context

How are some global South researchers able to overcome contextual constraints and become highly cited?

There is a clear research divide between the global South and the global North[1] in terms of research investment and capabilities. The average national expenditure on research and development in Southern countries is 0.38% compared to 1.44% in Northern countries[2]. The number of researchers per million population in 2017 was 713 in the global South and 4,351 in the global North[3]. This had implications on the volume and impact of scientific outputs produced by the global South in comparison to the global North. Excluding China and India, in 2018 global North countries produced an average of more than 35,000 scientific and technical journal articles per country while global South countries produced 4,000 journal articles per country, out of which less than 2% made it to the top 1% most cited articles globally. This can be partially explained by the lower levels of investment and English proficiency, smaller relative populations of researchers, institutional exclusion factors and/or biases against Southern researchers when it comes to accepting their papers in top tier journals or awarding grants.

Despite all of the aforementioned challenges, there are a few Southern researchers who are able to achieve better outcomes than their peers. Such researchers could provide valuable insights and lessons that might help to better understand and even mitigate the current North–South divide in research outputs and citation. This blog post will highlight some of the valuable insights emerging from our recently published study that attempted to uncover publication-level and individual-level factors underlying the outperformance of information systems researchers in Egypt.

The Method

 This study employed the “data-powered positive deviance” (DPPD) methodology that uses digital datasets to identify positive deviants (those performing unexpectedly well in a specific outcome measure that is digitally recorded, mediated or observed) and potentially also to understand the characteristics and practices of those positive deviants (PDs) if digitally recorded.

Three main steps were conducted to identify and characterise PDs, as shown in Figure 1:

  • In the Define step, we defined our study population and the performance indicators that will be used to assign a score for each researcher. The study population comprised 203 information system researchers in Egyptian public universities. Six well-known citation metrics (h-index, g-index, hc-index, hi-index, aw-index and m-quotient) were calculated for each researcher using Publish or Perish and Google Scholar bibliometrics. Several citation metrics were used to avoid putting certain groups at a disadvantage due to factors such as the length of their research career, the size of their research departments, the age of their papers or their publication strategies.
  • The Determine step aims at identifying the PDs based on the scores calculated in the previous step. In this study, PDs or outliers were defined as researchers who significantly outperformed their peers in at least one of the six citation metrics. The interquartile (IQR) method was used to identify those outliers based on their deviation from the median, i.e. lying beyond the 1.5*IQR added to the third quartile in at least one of the six citation metrics.
  • The third step, Discover, consists of three main stages. In Stage 1, primary data was collected through in-depth interviews from a sample of PDs to explore practices, attitudes and attributes that might distinguish them from non-PDs. During Stage 2, the key findings from Stage 1 plus other predictors of research performance drawn from the literature were used to design a survey tool. That survey then targeted the whole population and tested if the proposed differentiators were significantly different between the two groups. Finally, in Stage 3, the Scopus database was used as the basis for analysis of researcher publications; extending and validating some of the findings identified in the previous stages.

Figure 1: Summary of the applied DPPD method

 What we found

 A combination of data sources (interviews, surveys, publications) and analytical techniques (PLS regression, topic modelling) were used to identify significant predictors of positively-deviant information system researchers. One of the key findings was that PDs contributed to the creation of roughly half (48%) of the publications and achieved nearly double (1.7x) the total number of citations of non-PDs despite representing roughly one-eighth (13%) of the study population. While there were significant predictors of outperformance that are structural (e.g. gender, academic rank and role, workplace perceptions), our focus in this post is on highlighting factors that are transferable i.e. practices and strategies that are to some extent within the control of the individual researchers. Table 1 provides a summary of such factors.

Individual-Level Predictors

 

Positive Deviants

Non-Positive Deviants

Travelling abroad to obtain their PhD degree

More PDs got their PhDs from global North countries 

Fewer non-PDs got their PhDs from global North countries

International research collaborations

Frequently part of multi-country research teams 

Seldom part of multi-country research teams

Co-authorship

Published more papers with foreign reputable authors

Published fewer papers with foreign reputable authors

Securing research grants and travel funds 

Secured more grants and travel funds

Secured fewer grants and travel funds

Research approach

Less inclined to do radical research

More inclined to do radical research

Student supervisions

Supervised a larger number of postgraduate students

Supervised a smaller number of postgraduate students

Capacity development  

More PDs took scientific writing and English writing courses

Fewer non-PDs took scientific writing and English writing courses

Publication-Level Predictors

Length of paper

Longer papers

Shorter papers

Length of abstract

Longer abstracts

Shorter abstracts

Length of title

Longer titles

Shorter titles

Number of authors and affiliations

More authors and affiliations

Fewer authors and affiliations

Number of references

More references

Fewer references 

Publication type

More journal articles and fewer conference papers

More conference papers and fewer journal articles

Quality of journals

Higher SJR journals

Lower SJR journals

Publishers

Published more in Elsevier Journals

Published less in Elsevier Journals

Topics

PDs publish fewer papers covering business process management and neural networks and published more papers in wireless sensor networks and hardware systems

Non-PDs publish more papers covering business process management and neural networks and published fewer papers in wireless sensor networks and hardware systems

 Table 1: Significant transferable predictors of outperformance

The analysis also included a visualization of topic prevalence over time for the PD corpus and non-PD corpus as presented in Figure 2. It shows topics, such as Classification Models, where PDs were early movers and then they were followed by NPDs. There is a greater prevalence of Expert Systems and GIS-related topics in the PD corpus in comparison to the NPD corpus. Conversely, there is lower prevalence of Neural Networks and Business Process Management & Process Mining. There are also topics that had very similar proportions over time for both groups, such as Social Network Mining.

Figure 2: Topic proportions of PD corpus (left) and non-PD corpus (right) over time

 Implications for practice and policy

This analysis cannot, of course, guarantee that applying these factors more broadly would lead to the same outcomes achieved by PDs. Nonetheless, there would be value in individual Southern researchers reflecting on the research- and paper-related behaviours that have been shown associated with positively-deviant research profiles. For instance, Southern researchers work in contexts of resource limitation, hence, research grants and travel funds are of outmost importance. Including partners from Northern universities (as PDs do) increases the chances of securing the funds as those partners are more familiar with grant procurement processes and more experienced in writing proposals. Studying abroad also seems to put Southern researchers at an advantage as it does not just equip them with the technical know-how and the degree needed to pursue their academic careers, but also helps them establish channels of collaboration with their supervisors and their PhD granting universities, long after they returned to their home countries. Those long standing relationships provide further access to research grants either directly or via joint grant applications.

In terms of paper-related strategies, Southern researchers could avoid low-visibility local conferences and can select journals instead as they are more likely to deliver citations. Publishing with more authors (domestic and international) could also help pay for journal publication fees, with fees split across more authors or paid from overseas sources. Publishing with foreign authors could also help Southern researchers overcome the institutional biases[4] among editors, reviewers in single-blind or open review systems, and readers. PDs’ preference for working on established research areas rather than on radical research topics may also help in relation to institutional barriers, with research that builds incrementally on existing ideas and literature being more likely to be accepted for publication by referees, and cited by others working in the established area. Hence, Southern researchers seeking more citations could consider contributing to mainstream topics that build on existing work. Along the same lines, having multiple authors and affiliations increases the likelihood of citations, as each author has their own network and bringing those networks together can increase readership. Similarly, publishing papers with a larger number of references increases paper visibility through citation-based search in databases that allow it, such as Google Scholar, and through the “tit-for-tat” hypothesis i.e. authors tend to cite those who cite them.[5]

Higher education institutions and higher education policy makers may also reflect on the findings, and consider strategic implications for training, resource provision, collaborations, etc. For example, English and scientific/formal writing courses were associated with PD performance; such courses could be prerequisites for starting a PhD research. There could be more academic training designed around research grant writing and providing guidance on funding bodies that researchers can apply to. International research collaborations appeared as an important predictor of PDs; so, university senior managers and policy makers can explore ways to reduce barriers and increase opportunities for overseas PhD study, post-PhD return, and ongoing joint research projects with global North universities.

Citation rates are, of course, not the “be all and end all” of research: there are and should be other motivations and indicators of research. However, we hope the findings presented here can provide valuable “food for thought” for global South researchers.

 ________ 

[1] The terms “South” and “Southern” will be used to refer to countries classified as upper-middle income, lower-middle income, and low income. Accordingly, the terms “North” and “Northern” will be used to refer to countries that are members of the OECD (Organisation for Economic Co-operation and Development) or are classified as high-income economies by the World Bank based on estimates of gross national income per capita.

[2] Blicharska, M., Smithers, R. J., Kuchler, M., Agrawal, G. K., Gutiérrez, J. M., Hassanali, A., Huq, S., Koller, S. H., Marjit, S., Mshinda, H. M., & Masjuki, H. (2017). Steps to overcome the North-South divide in research relevant to climate change policy and practice. Nature Climate Change, 7(1), 21–27.

[3] World Bank. (2020). Science & Technology Indicators. World Bank.

[4] Karlsson, S., Srebotnjak, T., & Gonzales, P. (2007). Understanding the North-South knowledge divide and its implications for policy: A quantitative analysis of the generation of scientific knowledge in the environmental sciences. Environmental Science and Policy, 10(7–8), 668–684.; Gibbs, W. W. (1995). Lost science in the third world. Scientific American, 273(2), 92–99.; Leimu, R., & Koricheva, J. (2005). What determines the citation frequency of ecological papers? Trends in Ecology & Evolution, 20(1), 28–32.

[5] Webster, G. D., Jonason, P. K., & Schember, T. O. (2009). Hot topics and popular papers in evolutionary psychology: Analyses of title words and citation counts in evolution and human behavior, 1979–2008. Evolutionary Psychology, 7(3), 147470490900700300.

 

The Benefits of Mobile Phone Applications to Women Livestock Keepers in Zimbabwe

Pfavai Nyajeka and Richard Duncombe

Mobile phone applications have offered much value in the livelihoods of women in rural Zimbabwe.  Research conducted in resettlement areas during 2017 and 2018 used mixed methods to collect data on samples of women livestock keepers (Figure 1) who were household-heads (HHHs) or non-household heads (NHHs), providing an understanding of the unique forms of hardship that are imposed on married, single, divorced or widowed women in their pursuance of livelihoods.  The research investigated how women farmers used mobile phones to strengthen their position in livestock keeping and mitigate their vulnerability.

Figure 1. An Interview with a Woman Livestock Keeper in the Mashonaland East Province of Zimbabwe

Zimbabwe, in common with other sub-Saharan African countries, was experiencing a revolution in digital communications prior to and up until the end of the study period; but Zimbabweans, and particularly those in rural areas, remained disadvantaged due to poor electrical grid connections and digital connectivity compared with some other sub-Saharan African countries (Table 1).

Table 1. Digital Landscape: Selected Indicators for 2018

Country/Region % of rural population with access to electricity % of population using the Internet Mobile cellular subscriptions (per 100 inhabitants) (a) Secure internet servers (per 1 million inhabitants)  
Botswana 24 58 150 134
Kenya 58 19.5 96 217
South Africa 67 62.4 160 12,032
Zimbabwe 19 25 89 47
sub-Saharan Africa 22 29 94 794

Sources: Human Development Report (2019) and ITU (2018) Indicators Database; see: World Bank Open Data | Data

(a) including accounts with mobile money service providers.

Women livestock keepers in resettlement areas (Mashonaland East and Midlands) pursued their livelihoods within a challenging vulnerability context, typified by adverse climatic conditions, volatile markets and lack of support services (Figure 2).  Their ability to participate in local economic development was also constrained by their position within the largely patriarchal social structures that govern livestock keeping in Zimbabwe.

Figure 2. Community Meeting Place for Women Livestock Keepers in the Midlands Province of Zimbabwe

Use of mobile phones enabled the women to resolve problems quickly, saving time that could be more profitably spent on other income generating activities.  One HHH commented… “no one likes to be constantly travelling distances to chase buyers or debtors, so you find that a lot of women livestock farmers in this area depend on their mobile phones to remind buyers or debtors about upcoming livestock sales and money owed.  A lot of the time constant mobile phone reminders are enough.  Even when the person on the other end does not answer the phone or respond to a message or post, seeing that missed call, or text, or post, is often enough to put pressure on debtors.  Some (women) will post a reminder on social media group forums such as WhatsApp.  You find that this is very effective and frees up time and money for them (women livestock keepers) to focus their energies on other things”.

WhatsApp was used for group messaging and exchanging of photos and short videos related to problems or threats to livestock.  WhatsApp was particularly useful in instances when livestock farmers used group chats to coordinate an emergency veterinary department’s visit.  One focus group participant in the Midlands province (Figure 3) stated… “we as women farmers can communicate quickly… this also allows us to get advice on livestock disease outbreaks.  Although some women do not have smart phones, due to the expense, everyone knows someone who has access to information through community WhatsApp groups… no one in the community is left out as the message can be spread quickly, meaning we are quickly able to manage disease and risks” (Respondent 49).

Figure 3. A Group Meeting with Woman Livestock Keepers in the Midlands Province of Zimbabwe

In addition to WhatsApp, locally designed applications such as Kurima Mari[1]provided farmers with information on livestock management, livestock market updates and information on crop production, with English, Shona and Ndebele language options.  Another platform service was EcoFarmer[2]– introduced in 2013 as a ‘weather indexed insurance business’ and EcoCash[3]– a mobile payment solution for Econet customers that let farmers carry out financial transactions and pay bills.

The survey suggested a high degree of independent information searching on behalf of married women.  Phones enabled women livestock keepers to enquire about market prices either directly or through the app, ascertain where livestock demand was, quantities, and agreed periods of payment, before travelling to market.  

The survey results also showed significant usage of mobile banking apps (such as EcoCash).  Many women moved to mobile banking due to the cash shortages, but most also viewed mobile money as the safest means of transferring money and conducting transactions.  Mobile banking fees were generally lower compared to bank charges, and some farmers were able to make and receive payments and gain access to credit more easily.  

Some key findings from the study include…

  • A largely positive picture of the use of mobile phones amongst women livestock keepers.  Everyday use of mobile phones and applications has brought considerable benefits associated with better overall communications, helping to meet rural women farmers’ information needs in a timely manner.
  • A divergence of the results according to whether the woman livestock keeper is a HHH or NHH.  HHHs tend to be more active in relation to income generation due to not having to defer to the waged husband in the household.  The use of the phone tends to reinforce and strengthen this income earning activity for HHHs, both in relation to livestock keeping and other income earning opportunities.
  • Various limits and social pressures are placed on the NHHs in the use of their phones, thus restricting the ability of NHHs to accrue the full benefits of phone use.  The ability of NHHs to link with new social networks and other livestock intermediaries is limited.
  • Despite the potential benefits, the cost of accessing information with mobile phones could be prohibitive, even when considering the relatively low initial cost of buying (mostly) second-hand phones.  In part this is dealt with by opting for cheaper phone data bundles that facilitate use of web-based applications such as WhatsApp.

The results of this research will be presented at the International Conference on Information and Communication Technologies and Development (ICTD 2022) in Seattle between June 27th – 29th and published in the Conference proceedings.  International Conference on Information & Communication Technologies and Development (ictd.org)


[1] Kurima Mari is a family farming Knowledge Platform which gathers and digitized quality information on family farming from all over the world; including national laws and regulations, public policies, best practices, relevant data and statistics, researches, articles and publications. Kurima Mari – Apps on Google Play

[2] EcoFarmer provides farmers, government, contracting companies, NGOs and farmer unions a range of digital solutions to assist productivity across the agriculture value chain. Launched in 2013 as a weather indexed insurance and micro insurance product with an SMS based advisory service it has evolved to offering diversified services like Vaya Tractor, logistics, warehousing, cold chain, Hay Bailing, combine harvesting and soil testing. Farmers register to access the application by paying a small charge.  Services for Farmers – EcoFarmer

[3] EcoCash is a mobile payment solution for Econet customers in Zimbabwe. It facilitates financial transactions, like sending money, the purchase of prepaid airtime or data and payments for goods and services, using a mobile phone. http://www.ecocash.co.zw/about

 

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.

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.