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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.

Follow us on Facebook and Twitter to keep up to date with Fairwork.

(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.

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.

An Applied Data Justice Framework for Datafication and Development

Data is playing an ever-growing role in international development.  But what lens can we use to analyse the impact of data on development?

The emerging field of “data justice” offers some valuable ideas but they have not yet been put together into a systematic and comprehensive framework.  My open-access paper – Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework, written with Satyarupa Shekhar – provides such a framework, as shown below.

The framework exposes five dimensions of data justice:

  • Procedural: fairness in the way in which data is handled.
  • Instrumental: fairness in the results of data being used.
  • Rights-based: adherence to basic data rights such as representation, privacy, access and ownership.
  • Structural: the degree to which the interests and power in wider society support fair outcomes in other forms of data justice.
  • Distributive: an overarching dimension relating to the (in)equality of data-related outcomes that can be applied to each of the other dimensions of data justice.

The dimensions can be used individually; for example, just to analyse data practices, or just to analyse the impact of context on new data systems in developing countries.  Or the model can be used holistically; for example, to understand the full development impact of a particular data initiative.

The Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework paper takes the latter route.  It analyses “pro-equity data initiatives” that were implemented by data activists in four cities: Chennai, Nairobi, Pune and Surakarta.  These initiatives specifically sought to address the data injustices suffered by slum dwellers and other marginalised groups; particularly their invisibility to urban planners and other external agencies.

Using the data justice lens, this research finds that new data flows do have a positive impact in counteracting the injustice of invisibility, but they disproportionately serve those with the motivation and power to use that data.  Results in terms of service improvements and epistemic change are beneficial for slum communities and other marginalised citizens, and these initiatives can be justified on that basis.

However, though there can be no exact calibration from qualitative research, it is likely that these pro-equity initiatives actually increase relative inequalities.  Ordinary community members have seen some benefits but external actors who find the data to match their agenda and capabilities, benefit more.  It is the latter who are more empowered to access, use and control the new data.

If you would like to know more about this research’s findings, framework and recommendations for practice, then take a look at the paper: https://www.tandfonline.com/doi/full/10.1080/1369118X.2019.1599039

Data, Platforms and Power

19 February 2019 Leave a comment

We know that digital platforms can be very powerful, but how does their use of data relate to power?

In three ways[1] that derive from the datafication and digitisation affordances of platforms:

  1. Addressing Information Failure. Platforms succeed in part by finding ways to overcome information failures in existing markets. These failures may be sources of power for incumbents. For example, estate agents (realtors) hold power in real estate markets due to information asymmetries; such as knowledge of house sale prices.  Real estate platforms put such data into the public domain, thus undermining the power of incumbents.  Information failures may also be a source of weakness in existing markets.  For example, riders with traditional taxi firms don’t know exactly when their cab will arrive.  Platforms provide such data and so, again, undermine incumbents.

 

  1. Mashing Up. As they deal with digitised data, platforms can gain power by integrating different data streams onto the platform. Real estate platforms integrate online information about neighbourhoods.  Ride-hailing platforms integrate online maps to show cab location and routes to riders and drivers.

 

  1. Controlling New Data. By digitising transactions and associated processes, platforms create, capture and control new data. This bolsters their power; typically by creating new information asymmetries: the platforms know things that others don’t.  Real estate platforms can monitor search behaviours of buyers to understand more about which features of house listings they value most.  Ride-hailing platforms understand spatio-temporal patterns of supply and demand alongside many other behavioural characteristics of riders and drivers.

 

This simple framework can usefully be applied in order to analyse the role of data in platforms, and its contribution to power.

 

[1] Categorisation and examples developed from Drouillard, M. (2017) Addressing voids: how digital start-ups in Kenya create market infrastructure. In: Digital Kenya, B. Ndemo and T. Weiss (eds). London: Palgrave Macmillan, 97–131

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