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

8 September 2020 Leave a comment

Many around the world celebrated the agreement of the Sustainable Development Goals (SDGs) and a new agenda for transformative development by 2030. But, practitioners and policy makers were left scratching their heads as to how they were going to monitor the detailed 169 targets and ever more numerous indicators, never mind understanding and achieving these goals.

It is in this context that we’re seeing a growth of interest in using data to help solve development problems. Indeed, we can say that the infrastructures now being built to support data are likely to become central to how we make development decisions in the future.

How will such data infrastructures shape our thinking about development over the next decade? What types of limitations and biases might they embed? How should they best be designed and implemented? It is these questions that we looked to explore in a recent paper [1] analysing big data use for development in India.

In this paper we dug into two cases where big data was being used to support wider development over commercial goals – the Bengaluru Metropolitan Transport Corporation (BMTC) and big data transport upgrades in Bengaluru, India; and Stelcorp (name changed), a state initiative using big data for improving electricity systems.

Digging into big data

Digging into these cases, we found that both of these initiatives were connected into longer, often decades-old histories of data collection and decision making. This meant that new data innovations were being introduced in an attempt to understand long running development problems. Thus, the main focus of BMTC was on using vehicle tracking and big data innovations to improve the notoriously unreliable city bus services.

We found that big data innovation allowed improved integration of rich information flows, and led to centralisation of decision making. In StelCorp, previously manually-collected meter data was now digitally-collected and aggregated (see images below). The supporting infrastructure allowed a near real-time analysis of the status of the electricity network, and was more effective at monitoring around failures and blackouts. A new central data centre played a growing role in processing and analysing this data. In BTMC, new bus transportation data was aggregated and fed in real-time to large screens in a “control centre” where activity was monitored by administrators.

Digitalisation in Stelcorp: Meters such as those on the left supply real time data about network usage. Even manual meter reading data is now often transferred through automated reading devices (right) to later be input into the system.

Beyond day-to-day monitoring, we also saw signs that the new data was feeding into more strategic decisions. In the electricity sector, for example, upgrades have been plagued by poor and politicised decision making, but the state-wide data from Stelcorp is now being used in upgrading decisions.

More conceptually, there is evidence that these initiatives are playing a role in supporting new forms of state commitments, or citizen interaction. BTMC has been associated with a ‘Smart City’ initiative and citizens interacting with a set of efficient urban services. Indeed, BTMC introduced a citizen mobile app for tracking bus routes which has had over 50,000 downloads. In the Stelcorp initiative, state political visions about “24/7 electricity” have in part emerged from the better data that allows improved management of the electricity system.

Limitations

Whilst big data has led to these operational, strategic and visionary advances, there were a number of concerns in these projects. One key concern raised was the quality of data being used in these projects, which was often incomplete, short-term, or skewed.

Most problematic was that data from marginal groups was difficult to obtain, so in Stelcorp, automated electricity data was mainly coming from cities, where rural data was still manually collected, and in both cases there was often the need for “data wrangling” before the data had value.

These data limitations pose questions of how representative the data being used is of the population. If certain measures are skewed towards those more affluent, data coming from those more marginal might then be seen as “nonconforming” or even deviant. Moreover, the way that the data is selected, measured and transformed in such systems will be important in determining what processes are made visible by data and what might remain in the shadows.

The Smart Cities Challenge: Such visions can be seen to be made viable by the growth of big data. However in reality big data projects often tend to have a narrower focus. Source: http://www.smartcitieschallenge.in/

There were also more general questions about the focus of big data projects. These projects were marketed and discussed under lofty development goals, but in implementation they were often quite narrow projects. BTMC, for all its discussion of smart cities and citizens, was far more focussed on stamping out corruption among bus employees than making the city’s public transport smart.

Further, in all these projects there is scant sharing of the new data produced. These projects have not been about the public shining a light on opaque mechanisms of decision making. In fact, with a growing number of public and private actors involved, mechanisms of decision making are becoming even less transparent.

Big data for development

Big data projects are in their infancy in countries like India, but as these cases show they are becoming important to support decision making on key development issues, not only at an operational level, but in strategic decision making and in supporting new visions of developmental partnerships between citizens, private sector and the state.

However, these initiatives rarely follow the vision of big data driving transformative changes. They so-far tend to use problematic data to enhance decision making. They also tend to focus on quite narrow aspects of problems in implementation over the bigger development problems that might be more impactful.

We also need to make sure that big data does not solely lead to technocratic solutions, or underplay the importance of integrating with a wider set of social and political activities for development – data showing electricity pilferage will have limited impact without solving the complexities of local politics of electricity in rural and slum areas, and data on public vehicle movements cannot replace the underfunding of urban transport.

[1] Heeks, R., Rakesh, V., Sengupta, R., Chattapadhyay, S. & Foster, C. (In press) Datafication, Value and Power in Developing Countries: Big Data in Two Indian Public Service Organisations. Development Policy Review.


This is an adapted version of a blog originally posted on the Sheffield Institute of International Development (SIID) blog.

With thanks to Vanya Rakesh & Ritam Sengupta for their research in India and SIID and the University of Manchester for the small grant support for this work.

How Widespread are Digital Water Payments in Ghana?

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

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

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

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

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

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

Some actions to take to improve adoption include:

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

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

Context and Digital Start-Ups in the Global South

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

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

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

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

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

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

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

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

Practical implications include:

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

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

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

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

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

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

Positive Deviance and Covid-19

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

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

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

 

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

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

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

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

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

Data-Powered Positive Deviance and Covid-19

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

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

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

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

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

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

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

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

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

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

 

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

27 April 2020 1 comment

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

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

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

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

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

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

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

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

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

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

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

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

·      Facilitated access to interest-free emergency loans

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

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

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

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

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

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

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

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

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

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

·      Provision of general medical insurance cover

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

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

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

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

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

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

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

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

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

Second round of Fairwork’s yearly platform ratings in South Africa launched!

The Fairwork South Africa 2020 report highlights the precarious nature of work in the South African gig economy. This research is particularly timely in light of the ongoing COVID-19 crisis, which has brought the risks faced by front-line gig workers into sharp relief.

The uncertainty that has gripped the world in the wake of the COVID-19 pandemic will especially impact the most vulnerable groups in our society. That includes those in casual or insecure employment, who face two possibilities: a (likely untenable) loss in income if they choose or are required to self-isolate, or ongoing exposure to the virus through the front-line nature of their work. Today the Fairwork Project is releasing a set of scores which evaluate gig economy platforms that operate in South Africa, such as Uber, SweepSouth, and OrderIn against a set of fair work standards. In the current circumstances, our findings about the situation of gig workers in South Africa are more relevant than ever.

The gig economy has flourished in South Africa, and with it, we are seeing a radical shift in how work is organised. Digital labour platforms hold the potential to reduce our sky high unemployment and inequality. However, there is growing evidence that platform workers worldwide face unfair work conditions, and lack the benefits and protections afforded to employees. To understand the state of gig work in South Africa, Fairwork, a collaboration between the Universities of Oxford, Cape Town, the Western Cape and Manchester, assessed eleven of the country’s largest digital labour platforms against five principles of fairness – fair pay, fair conditions, fair contracts, fair management, and fair representation – and gave them each a fairness rating out of ten.

GetTOD, SweepSouth, and NoSweat are tied at the top of this year’s league table with eight out of ten points. The fairness scores aim to help South Africans understand which digital platforms are committed to providing decent work.

Fairwork’s research on shortcomings in worker protections in the gig economy is even more relevant and urgent in light of the COVID-19 pandemic. Gig workers such as rideshare drivers and delivery couriers will play an essential role over the coming weeks and months – enabling access to transport services, and facilitating a continuous supply of food and other necessities to those who are self-isolating. That means that those workers are more vulnerable to exposure to COVID-19. However, if they need to self-isolate, they face severe financial insecurity.  If they are unable financially to self-isolate, they could also unfortunately spread the virus. Without unemployment benefits or sick pay, gig workers have no safety net.

Platforms and governments need to ensure that gig workers and those who are currently financially unable to stay at home are protected. Uber South Africa has indicated that it will follow the international company policy of compensating workers required to self-isolate for 14 days. However, we await details of exactly who will be covered, and to what extent.

With regard to Fairwork’s other findings, almost all platforms operating in South Africa were found to pay at least the minimum wage. However, when workers’ expenses (such as petrol and transport costs) were taken into account, evidence could only be found that six out of the eleven platforms paid workers above the minimum wage.

Growing numbers of South Africans find work in the gig economy, and digital platforms are frequently heralded as a solution to mass unemployment, as they allow those who typically face barriers to employment to find work more easily. Thirty percent of the gig workers who spoke to Fairwork were unemployed before getting jobs with their respective platforms. However, there is also evidence that some people are moving from secure work into insecure gig work, and seeing reductions in income.

The employment challenge facing South Africa is not simply the quantity of jobs but also the quality of jobs being created. Across contexts, Fairwork’s research has shown that gig workers face low pay, dangerous work conditions, opaque algorithmic management structures, and barriers to organising and bargaining collectively. However, decent work and job creation are not mutually exclusive. This is why, by bringing workers and other stakeholders to the table, Fairwork is developing an enforceable code of basic worker rights that are compatible with sustainable business models.

This is the second annual round of Fairwork Project ratings for South African platforms, and the impact is beginning to build.

Fairwork engages directly with platform managers to suggest avenues for improvement, and one of their accomplishments includes securing guarantees from two platforms – NoSweat and GetTOD – that all jobs they post will pay above the living wage, calculated at 6,800 South African Rand per month.

Furthermore, after working with the Fairwork Project, GetTOD has publicly announced its willingness to engage and negotiate with a union or workers’ association, including this in its terms and conditions. This is a commendable step to ensure fair worker representation. Having a voice and collective power in the workplace is essential for workers if they wish to move away from exploitative relationships.

Fairwork seeks to furnish consumers with enough information to be intentional about the platforms they choose to interact with, thus contributing to pressure on platforms to improve their working conditions and their scores. In addition, Fairwork engages with policy makers and governments to advocate for extending appropriate legal protections to all platform workers, irrespective of their legal classification.

Finally, and most importantly, they work with workers and workers’ organisations to develop and  continually refine their principles to remain in line with their needs. Ultimately, the project aims to support workers in collectively asserting their rights.

The current health crisis brings to light the essential role that gig workers play in our society, in service provision, infrastructure, and care. These workers are often working with little protections and low pay. COVID-19 is quickly revealing the injustice and unsustainability of the status quo.

Download the full report here.

A more accessible PDF is also available for users with screen-readers.

We would love to hear your thoughts on the report, or on our broader work – if you’d like to get in touch, head over to our Contact page, or email us.

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(This is a re-post of the original Fairwork blog posted by Srujana Katta.)

Rural Resilience Impact of ICTs-in-Agriculture

28 January 2020 Leave a comment

What impact do ICT-in-agriculture projects have on rural resilience?

To cope with short-term shocks (e.g. conflict, economic crisis) and long-term trends (e.g. climate change), rural areas in developing countries must become more resilient.  Yet we currently know very little about the impact that information and communication technologies (ICTs) can have on resilience-building.

To address this knowledge gap, we undertook a systematic literature review of 45 ICT4Ag cases from Africa and Asia.  We sought to understand both what the resilience impact of ICTs is, and why.

Measuring resilience using the RABIT (Resilience Assessment Benchmarking and Impact Toolkit) framework, current reported evidence suggests ICTs are strengthening rural resilience far more than weakening it.  But the impact is highly uneven.  Household resilience is built far more than community resilience, and there is a strong differential impact across different resilience attributes: equality in particular is reported as being undermined almost as much as enhanced.

In order to explain these outcomes, we developed a new conceptual model (as shown below) of the relationship between ICTs and resilience.  It highlights the importance of individual user motivations, complementary resources required to make ICT4Ag systems support resilience, and the role of wider systemic factors such as institutions and structural relations.

We make a series of recommendations for resilience policy and practice:

  • More equal focus on both household- and community-level resilience.
  • More attention to the resilience-weakening potential of ICTs.
  • Ensuring perceived utility of digital applications among rural users.
  • Encouraging use of more complex ICT4Ag systems.
  • Looking beyond the technology to make parallel, complementary changes in resource provision and development of rural institutions and social structures.

We also draw conclusions about the conceptualisation of resilience: the need for better incorporation of agency and power, and greater clarity on resilience system boundaries and indicators.

Overall, for those seeking to strengthen rural resilience through use of ICTs, the paper – “Impact of ICTs-in-Agriculture on Rural Resilience in Developing Countries” – offers new frameworks, new evidence, new practical guidance and a research agenda.

Ride-hailing Platform Asymmetries between Riders and Driver-partners in Lagos, Nigeria.

Platform companies, with the help of data, have the potential to formalise the processes of transport systems in global South cities, especially in African cities which are characterised with informal processes. For ride-hailing platforms, this will help improve safety and security for both drivers and riders and reduce the likelihood of workers who attempt to evade formal processes such as tax payments because information is accurately recorded and managed by algorithms. In our contemporary world today, data is important for several tech platforms including Uber, Airbnb and social media platforms. This process is known as datafication, which is defined as a significant feature of modern social life and society through “the drive to turn vast amounts of activity and human behaviour into data points that can be tracked, collected and analysed”1. However, there have been growing concerns about its impacts on society and user-groups at large – in this case, on platform workers/drivers in Lagos.

Registration on platforms like Uber and Bolt (Taxify) is asymmetrical for both workers and riders in Lagos. For workers, it is a stringent process which involves them providing critical details such as home address (which can be verified), vehicle details (plate numbers and vehicle numbers), licenses and at least two guarantors. For riders, registration on the platform is as simple as inputting a contact number, card details and home addresses (which cannot be verified). This has an implication on drivers’ safety, work ethic and motivation because they feel unequally treated. When data is not being equally represented on the platforms, it puts riders and platform companies in a greater position of power. Talking about drivers’ challenges, platform driver and Union president stated that:

Box 1 – Data misrepresentation
“To this present time we have 30 -35 of drivers been killed by riders because they don’t profile them well, many riders don’t use their correct names and don’t put correct information and they are collecting cars and killing people and that’s why we need government to regulate things, to open an account. Either you use BVN or use national ID or migrate their information with the road safety (use the driver’s license), so they are can know the perpetrator of these evil acts”2.

From observation, a rider can register with many accounts in search of trip discounts/bonuses or in rare instances when they may have been blocked by the platform. This makes it difficult to track riders who may have defaulted. It takes a 4.6/5 and 4.5/5-star rating for drivers to be blocked on Uber and Bolt respectively in Lagos, but until summer of 2019, there were no clear rating thresholds to discourage bad rider behaviours3. This has however not been observed in Lagos, especially on the Bolt platform, indicating a low barrier of entry and further perpetuating bad rider behaviours which demotivates drivers from working. Mr AY’s suggestion could be helpful in ensuring that riders are more accountable on platforms such that they are equally aware that bad behaviours cannot be tolerated.

The communication between platform companies and drivers in comparison to riders is also perceived as unfair to drivers, particularly during a conflict with a rider; algorithmic misinterpretation or network issue which can lead to an undercharged fare or overcharged fare especially on the Bolt platform (see figure 1); or drivers who are blocked without any clear explanation (see box 2). However, drivers highlighted that Uber is more organised and, in many cases, are more likely to reimburse drivers that may have been undercharged. This is hardly the case on the Bolt platform.

Figure 1: Undercharged fare for a Bolt trip
Source: Nigerian Platform drivers forum.
Box 2 – Poor driver relations and algorithmic misinterpretation
“…When I do fare review on Bolt, it takes them years to get back to me. Some riders will pay you that N500 (£1.1); some will tell you that it is promo and there is nothing you can do (for example see figure 1) …They have bad customer relations in our case. They would not check properly when there is a complaint from a rider but be so quick to block drivers. But in a reverse scenario, platforms respond quickly to riders” 4.
 
“The way they block drivers is something else, although some of our drivers are also funny too. But they should look at the history of the driver. Possibly, the driver might not be the issue, but you can call the driver and talk to the person…”. To summarise, While Uber does a good job sometimes in communicating and resolving issues, Mr Raz, went ahead to state how a rider reported him for being cheated for a trip which was originally N400 (£0.8), but the rider went to multiple destinations which was not accounted for. When he reported to Uber, only a fraction of his complaints was resolved 5.

These asymmetries can be resolved if algorithmic processes are closely supervised and communicated to drivers to ensure clarity. As Mr Raz advocates “… sometimes we need to interface more apart from texting on the app. The human angle should come in”. While it is a business that benefits massively from riders, drivers are equally important and should be treated fairly. The future of work hinges on the efficiency and transparency of platforms that might ensure that its processes are clear enough to create a fair working environment for its workers while meeting the needs of its customers.

References.

  1. Dencik, L., Jansen, F., Meltcafe, P. (2019). A conceptual framework for approaching social justice in an age of datafication.
  2. Fieldwork Interview with Platform Union President in Lagos Mr Ay, 2018
  3. Paul, K. (2019). Uber to ban riders with low ratings: will you pass the test? The Guardian. 1 June
  4. Fieldwork Interview with Platform Worker Mr Ed, 2019
  5. Fieldwork Interview with Platform Worker Mr Raz, 2018

Addressing Institutional Voids in Nigeria’s Agricultural Finance Markets through Agri-finance Platforms

5 December 2019 Leave a comment

In my previous blog “Crowdfarming: Platform-enabled Investment in Nigerian Agriculture”, I talked about how digital platforms are being mainstreamed into agricultural finance markets in Nigeria. This blog describes how digital platforms are addressing some of the underlying problems which have constrained rural farmer’s access to agricultural finance thereby creating gaps which manifest as institutional voids in agricultural finance markets. Historically, agricultural finance markets in Nigeria have been characterised by problems in accessing, disbursing and repaying agricultural credit (Akinola, 2013). Specifically, problems relating to accessing agricultural finance by rural farmers stem from three key issues:

  1. Low budgetary allocation to the agricultural sector: Given that agriculture contributes and average of 32% to the Nigeria’s GDP, the budgetary allocation to the sector continues to fall short of the recommended allocation stipulated in the Maputo agreement (PwC, 2018). The Maputo agreement signed in 2003 recommends that the Nigerian government should dedicate 10% of its yearly budgetary allocation to agriculture (NEPAD, 2003). This has however not been the case as Nigeria’s highest percentage budgetary allocation to agriculture since 2003 was 2.23% in 2018 and has dropped to 1.56% in 2019 (Adanikin, 2018). This is still far from the recommended 10% which is deemed necessary for the growth and development of the sector.
  2. Low level of credit extension from commercial banks: Aside the government allocation of finance to agriculture, financial institutions such as banks also tend to allocate less of their lending to agriculture when compared to other productive sectors such as oil and gas, manufacturing and real estate (PwC, 2018 ) (Figure 1). An underlying reason for this low commercial bank extension of credit is because over time banks have become averse to lending to farmers due to high rates of defaults (Akinola, 2013).

Figure 1 - Credit Extension by Sector in Nigeria

Figure 1: Commercial bank credit extension to economic sectors in Nigeria (PwC, 2018)

  1. Unstructured (rural) agricultural investment environment: Asides government funding schemes in partnership with financial institutions, agriculture, unlike other sectors, has not been packaged in a form that investors – both institutional and individual – can engage with easily. While investors might be able to find some agricultural investment opportunities in the Nigerian stock market, these are usually investments in large scale agricultural corporations and not rural agricultural enterprises – which still account for the larger share of agricultural businesses in Nigeria. Mechanisms which could enable individual investors to directly engage with rural agriculture have been largely unstructured and not opened to the general public.

Institutional voids arise in the absence or weakness of market institutions which perform intermediating functions that improve the efficiency of market activities at a lower cost (Khanna and Palepu, 2005). The problems discussed above can be understood as manifestations of voids in agricultural markets which have come about due to the absence of intermediating institutions; those should effectively facilitate agricultural investment procedure by matching the demand and supply of agricultural finance.

Although research on the use of platform to address constraints in accessing agricultural finance is still nascent, there is however anecdotal evidence that suggests that digital platforms have been mainstreamed into agricultural finance markets by innovators who are using platforms to crowdsource agricultural finance for rural agricultural enterprises in Nigeria (Akeredolu, 2019). These platform-enabled businesses have been able to package rural agriculture into ‘investable units’ which are made available to the general public to invest through mobile or web applications thereby tapping into a new pool of agricultural finance (the crowd) outside conventional sources of agricultural finance. Specifically, these agri-finance platforms address institutional voids which manifest as poor access to agricultural finance by:

  1. Serving as intermediaries who efficiently match demand and supply of agricultural finance: These platform-enabled agribusinesses addresses constraints to assessing agricultural finance by intermediating between farmers – who need finance, and investors – who have money to invest in agriculture. Using a digital platform enables these businesses to gather investment funds from large numbers of people in order to fund larger numbers of rural enterprises. Therefore, the use of a digital platform is now attracting new sources of finance into the sector which were previously not accessible by rural farmers.
  2. Ensuring loan repayment through complementary offline intermediation: These platform-enabled businesses also perform other non-platform intermediating functions to ensure that crowdsourced funds are efficiently used by rural farmers and repaid. This is achieved through close monitoring of agricultural enterprises that have benefitted from crowdsourced funds. For instance, primary data collected from 21 Thrive Agric’s platform users – showed a 100% repayment rate for funds crowdsourced through the platform. This is also supporting the re-branding of rural agriculture from a venture with low credibility to a legitimate and trustworthy investment opportunity for investors both within and outside Nigeria.
  3. Improving farmer-identity and visibility through data gathering: Through their on-boarding activities, which entail identifying credible farmers who crowdsourced funds will be invested in, they gather farmers’ bio-, geospatial-, socio-economic and farm enterprise- data. This will improve the confidence of financial institutions in extending finance to rural farmers through the platform. Gathering these data also reduces the transaction cost incurred by financial institutions in extending credit to farmers. As a result, these platform-enabled businesses are able to access high volumes of agricultural finance, not only from individual investors, but also from financial institutions such as commercial banks due to improved farmer-identification procedures.

Although agri-finance platforms in Nigeria have the potential to ensure increased access to finance by rural agricultural, there is still the question of the sustainability of this model, especially in light of uncertainties regarding the formalisation of crowdsourcing as a channel for accessing agricultural finance in Nigeria. For instance, the securities and allied matters act 2004; and the investments and securities act 2007 both limit private companies from inviting the public to subscribe to company units or raising capital from the general public (Uwaleke, 2018). Aside this restriction, the Security and Exchange Commission still has no specific policy provision for crowdsourcing activities in Nigeria. As a result, although it has becoming widely accepted as an investment channel, crowdsourcing is still a bit of a grey area to investors.

Therefore, although the use of digital platforms is opening up agricultural finance markets to new participants and attracting new streams of finance into rural agriculture; further research is needed to understand the long term sustainability of platform-enabled agri-business as well as the broader developmental implications of agri-finance platforms in Nigeria’s agricultural finance markets.

References

Adanikin, O. (2018) 2019 Budget: 16 Years after, Nigeria fails to implement Maputo Declaration on Agrc, food security [online], Available https://www.icirnigeria.org/2019-budget-16-years-after-nigeria-fails-to-implement-maputo-declaration-on-agric-food-security/ [Date accessed: 27/11/19]

Akeredolu, D. (2019) Crowdfunding in Nigeria: Investing in Agriculture [online], Available: https://businessinnigeria.com.ng/crowdfunding-in-nigeria-agriculture/ [Date accessed: 27/11/19]

Akinola, F. (2013) The challenges of agricultural finance in Nigeria: Constraints to sustainable agricultural and economic revival. International Journal of Business and Social Research, 3(5): 234-244.

Khanna, T, and Palepu, K. G “Spotting Institutional Voids in Emerging Markets.” Harvard Business School Background Note 106-014, August 2005

NEPAD (2003) AU 2003 Maputo Declaration on Agriculture and Food Security [Online], Available: https://www.nepad.org/caadp/publication/au-2003-maputo-declaration-agriculture-and-food-security [Date accessed: 27/11/19]

PwC (2018) Evaluating Agriculture Finance in Nigeria: Towards the US$1 trillion African food market by 2030 [Online], Available: https://www.pwc.com/ng/en/assets/pdf/evaluating-agric-finance-nigeria.pdf [Date accessed: 27/11/19]

Uwaleke, U. (2018) Equity crowdfunding: An idea whose time has come, Punch Newspaper [Online], Available: https://punchng.com/equity-crowdfunding-an-idea-whose-time-has-come/ [Date accessed: 27/11/19]

ICTs and Precision Development: Towards Personalised Development

5 November 2019 Leave a comment

Are ICTs about to deliver a new type of socio-economic development: personalised development?

ICTs can only have a significant development impact if they work at scale; touching the lives of thousands or better still millions of people.  Traditionally, this meant a uniform approach where everyone gets to use the same application in the same way.

Increasingly, though, ICTs have been enabling “precision development”: increasingly-precise in terms of who or what is targeted, what is known about the target, and the specificity of the associated development intervention.  The ultimate end-point would be “personalised development”: interventions customised to each individual.

Elements of digitally-enabled individualisation have already emerged: farmers navigating through web- or IVR-based systems to find the specific information they need; micro-entrepreneurs selecting the m-money savings and loan scheme and level that suited them.  But there is still rigidity and constraints within these systems.

Though we are far from its realisation, the potential for truly personalised development is now emerging.  For example:

  • Personalised Learning: “a methodology, according to which teaching and learning are focused on the needs and abilities of individual learners”[1]. ICTs are integral to personalised learning and technology-enabled personalisation has had a demonstrable positive impact on educational performance[2].
  • Precision Agriculture: though around as a concept for at least two decades, precision agriculture is only now starting to find implementations – often still at pilot stage – in the global South[3]. Combining data from on-ground sensors and remote sensing, precision agriculture provides targeted guidance in relation to “seeds, fertilizers, water, pesticides, and energy”.  The ultimate intention is that guidance will be customised to the very specific soil, micro-climate, etc. parameters of individual farms; even smallholder farms.
  • Personalised Healthcare: diagnosis and treatment may appear personalised but typically involve identifying which illness group a person belongs to, and then prescribing the generic treatment for that group. This is becoming more accurate with improvements in electronic health records that provide a more person-specific history and context[4].  Precision medicine prescribes even more narrowly for the individual; typically based on genetic analysis that requires strong digital capabilities.  Though at early stages, this is already being implemented in developing countries[5].

ICTs are thus leading us on a precision development track that will lead to personalised development.  The promise of this can be seen in the examples above: individualised information on learning level, farm status, or health status that then enables a much more effective development intervention.

It will be interesting to log other examples of “ICT4PD” as they emerge . . .

[1] Izmestiev, D. (2012). Personalized Learning: A New ICT-Enabled Education Approach, UNESCO Institute for Information Technologies in Education, Moscow.

[2] Kumar, A., & Mehra, A. (2018). Remedying Education with Personalized Learning: Evidence from a Randomized Field Experiment in India, ResearchGate.

[3] Say, S. M., Keskin, M., Sehri, M., & Sekerli, Y. E. (2018). Adoption of precision agriculture technologies in developed and developing countriesThe Online Journal of Science and Technology8(1), 7-15.

[4] Haskew, J., Rø, G., Saito, K., Turner, K., Odhiambo, G., Wamae, A., … & Sugishita, T. (2015). Implementation of a cloud-based electronic medical record for maternal and child health in rural KenyaInternational Journal of Medical Informatics84(5), 349-354.

[5] Mitropoulos, K., Cooper, D. N., Mitropoulou, C., Agathos, S., Reichardt, J. K., Al-Maskari, F., … & Lopez-Correa, C. (2017). Genomic medicine without borders: Which strategies should developing countries employ to invest in precision medicine? Omics: A Journal of Integrative Biology21(11), 647-657.

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