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Posts Tagged ‘Data-for-Development’

Latest Digital Development Outputs (Data, Economy, Health, Platforms, Water) from CDD, Manchester

Using SmartphoneRecent outputs – on Data-for-Development; Digital Economy; Digital Health; Digital Platforms; Digital Water – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia” (open access) by Pete Jones, Jackie Carter, Jaco Renken & Magdalena Arbeláez Tobón, considers the importance of quantitative data skills development implied by the UN Sustainable Development Goals. The success of a partnership programme in the UK is used to explore how ‘data fellowships’ can fulfil some of the unmet capacity needs of the SDGs in a developing country context, Colombia.

Building Information Modelling Diffusion Research in Developing Countries” (open access) by Samuel Adeniyi Adekunle, Obuks Ejohwomu & Clinton Ohis Aigbavboa undertakes a literature review – including current and future research trends – on the adoption of building information modelling in developing countries.

DIGITAL ECONOMY / PLATFORMS

Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” (open read access) by Juan Erasmo Gomez-Morantes, Richard Heeks & Richard Duncombe demonstrates how the multi-level perspective approach can be used to analyse the lifecycle of digital platforms: the process of innovation, rapidity of scaling, and development impacts relating to resource endowments, institutional formalisation, and shifts in power.

Digital Platforms and Institutional Voids in Developing Countries” (open access) by Richard Heeks, Juan Erasmo Gomez-Morantes, Brian Nicholson and colleagues from the Fairwork project, analyses how digital platforms change markets through their institutional actions.  Using the example of ride-hailing, it finds platforms have formed a market that is more efficient, effective, complete and formalised.  At the same time, though, they have institutionalised problematic behaviours and significant inequalities.

Navigating a New Digital Era Means Changing the World Economic Order” (open access) by Shamel Azmeh, discusses the implications of digital shifts for global economic governance.

DIGITAL HEALTH

Cost-Effectiveness of a Mobile Technology-Enabled Primary Care Intervention for Cardiovascular Disease Risk Management in Rural Indonesia” by Gindo Tampubolon and colleagues demonstrates how to determine the economic impact of m-health.  It calculates the cost-effectiveness of a mobile-based health intervention at c.US$4,300 per disability-adjusted life year averted and US$3,700 per cardiovascular disease event avoided.

Delivering Eye Health Education to Deprived Communities in India through a Social Media-Based Innovation” by Chandrani Maitra & Jenny Rowley aims to develop understanding of the benefits of, and the challenges associated with the use of social media to disseminate eye health information in deprived communities in India.

Using a Social Media Based Intervention to Enhance Eye Health Awareness of Members of a Deprived Community in India” (open access) by Chandrani Maitra & Jennifer Rowley reports on a WhatsApp-based intervention to promote eye health communication in deprived settings. This research highlights the potential benefits of WhatsApp in increasing awareness on eye problems, amongst deprived communities where the disease burden remains very high.

DIGITAL WATER

Digital Innovations and Water Services in Cities of the Global South: A Systematic Literature Review” (open access) by Godfred Amankwaa, Richard Heeks & Alison Browne reviews the literature on digital and water in Southern cities.  It summarises findings to date on implementation and impact and sets out the future research agenda.

Latest Digital Development Outputs (Data, Humanitarianism, Labour, Platforms) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Humanitarianism; Digital Labour; Digital Platforms – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

The Rise of the Data Economy and Policy Strategies for Digital Development” (open access) by Shamel Azmeh, Christopher Foster & Ahmad Abd Rabuh, expands on policy debates around digital development.  It examines the emergence of the data economy and potentials of strategic policy and/or industrial policy in the global South.  Based on a global policy analysis, it identifies four key “policy pathways” by which countries can look to strategically capture value in the data economy.

DIGITAL HUMANITARIANISM

Digital Innovation by Displaced Populations: A Critical Realist Study of Rohingya Refugees in Bangladesh” by Faheem Hussain, P.J. Wall & Richard Heeks, uses a critical realist approach to understand the three mechanisms the underpin digital innovation by Rohingya refugees.

Lessons On The Digital World From The Charity Sector: The Corporate World Has A Lot To Learn” (open access) by Brian Nicholson, Lisa Kidston, Cris Sachikoyne & Dane Anderton, argues that African charitable organisations and those like the national Citizens Advice in England and Wales are leading the way when it comes to demonstrating exemplary digital leadership.

DIGITAL LABOUR AND DEVELOPMENT

Competing Institutional Logics in Impact Sourcing” by Fareesa Malik & Brian Nicholson, draws on the concepts of institutional logics to  present a case study of a USA-based IT outsourcing vendor “AlphaCorp” practising impact sourcing in a Pakistan subsidiary. The findings show that in cases where actors are located in diverse institutional contexts, competing interests determine the respective priority given to the welfare and market logics.

Digital Labour Platforms in Pakistan: Institutional Voids and Solidarity Networks” by Fareesa Malik, Richard Heeks, Silvia Masiero & Brian Nicholson, conceptualises the theoretical link between labour platforms and socio-economic development drawing on the notion of institutional voids and empirical fieldwork in Pakistan.

Risks and Risk-Mitigation Strategies of Gig Economy Workers in the Global South” by Tatenda Mpofu, Pitso Tsibolane, Richard Heeks & Jean-Paul Van Belle, analyses three strategies (platform-, driver- and driver group-led) that seek to mitigate the risks of ride-hailing work in Cape Town.

The Fairwork Foundation: Strategies for Improving Platform Work in a Global Context” (open access) by Mark Graham, Jamie Woodcock, Richard Heeks, Paul Mungai, Jean-Paul Van Belle, Darcy du Toit, Sandra Fredman, Abigail Osiki, Anri van der Spuy & Six M. Silberman, introduces the work of the Fairwork Foundation to rank and compare gig work platforms against a set of five decent work principles.

DIGITAL PLATFORMS AND DEVELOPMENT

Analysing Urban Platforms and Inequality Through a ‘Platform Justice’ Lens” by Richard Heeks & Satyarupa Shekhar, introduces a model of “platform justice” through which to analyse the impact of urban digital platforms.

Competing Logics: Towards a Theory of Digital Platforms for Socio-economic Development” by Silvia Masiero & Brian Nicholson, seeks to contribute to the nascent literature on platforms in development, unpacking a human-centred development logic as an alternative to the market logic that animates most of the platforms discourse and relying on it to lay the foundations for an emerging theory of platforms for development.

Digital Platforms, Surveillance and Processes of Demoralization” by Sung Chai, Brian Nicholson, Robert Scapens & Chunlei Yang, conceptualises the theoretical link between platforms and morality drawing on an interpretive study of a hotel in Vietnam to examine surveillance.

Delivering Urban Data Justice for “Smart Cities 2.0”

11 February 2021 Leave a comment

What new institutions are needed to ensure smart cities are also data-just cities?

Smart City 1.0 “is primarily focused on diffusing smart technologies for corporate and economic interests”.  Smart City 2.0 is “a decentralised, people-centric approach where smart technologies are employed as tools to tackle social problems, address resident needs and foster collaborative participation”.[1]

Given their people-centrism, a foundation for Smart Cities 2.0 must therefore be delivery of urban data justice: fairness in the way people are made visible, represented and treated as a result of the production of urban digital data.[2]

We already know the constituent parts of urban data justice, as shown in the figure below.[3]

But a key argument of this model is that data justice is significantly shaped by urban social structures.  If those structures are unjust then data practices and outcomes will likely be unjust.  How, then, do we create urban social structures more likely to deliver the data justice that is part of Smart City 2.0?

Setting aside more radical restructuring of the urban polity, three more incremental forms can play a role:

1. Living Labs

“Living labs employ a user-focused design environment, a strategy of co-creation, and, increasingly, an institutionalized space wherein citizens, administrators, entrepreneurs and academics come together to develop smartness into concrete applications. They help identify and join localized expertise, real-life testing and prototyping with strategic networking of resources to address challenges that cannot be solved by single cities or departments.”[4]  Located at the upstream end of the innovation cycle, living labs are well-placed to come up with new, just ways of applying urban data.[5]

2. Urban Data Trusts

Data trusts are “a legal structure that provides independent stewardship of data … an approach to looking after and making decisions about data in a similar way that trusts have been used to look after and make decisions about other forms of asset in the past, such as land trusts that steward land on behalf of local communities.”[6]  These can form an institutional superstructure to ensure justice in the ownership, sharing and use of data; particularly data gathered about urban citizens.[7]

3. Community Data Intermediaries

Community data intermediaries are “organizations that gather data relevant for neighborhood-level analysis and make the information available to community groups and local institutions”.  Alongside their key role in gathering data – for example via community mapping – CDIs may also have features of both living labs (innovating application of that data) and data trusts (acting as stewards of the data for communities).[8]

The devil here will be in the detail: how exactly are these entities structured and run?  Simply attaching a label to an organisation does not make it just, with critiques in circulation of living labs[9], urban data trusts[10], and community data intermediaries[11].  Nonetheless, it is these types of urban institutional innovation that will underlie delivery of data justice in Smart Cities 2.0.  I look forward to further examples of these and similar innovations.

 

[1] Trencher, G. (2019) Towards the smart city 2.0: empirical evidence of using smartness as a tool for tackling social challenges, Technological Forecasting and Social Change, 142, 117-128

[2] Adapted slightly from Taylor, L. (2017) What is data justice? The case for connecting digital rights and freedoms globally, Big Data & Society, 4(2), 2053951717736335

[3] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

[4] Baykurt, B. (2020) Are “smart” cities living up to the hype?, University of Massachusetts Amherst News, 1 May

[5] For a data justice perspective on the activities of one Living Lab in Kathmandu plus related organisations, see: Mulder, F. (2020) Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal, Journal of Contingencies and Crisis Management, 28(4), 432-445

[6] Hardinges, J. (2020) Data trusts in 2020, Open Data Institute, 17 Mar

[7] For more on urban civic data trusts, see: Kariotis, T. (2020) Civic Data Trusts, Melbourne School of Government, University of Melbourne, Australia

[8] For a guide on creating community data intermedaries and examples, see: Hendey, L., Cowan, J., Kingsley, G.T. & Pettit, K.L. (2016) NNIP’s Guide to Starting a Local Data Intermediary, NNIP, Washington, DC

[9] Taylor, L. (2020) Exploitation as innovation: research ethics and the governance of experimentation in the urban living lab. Regional Studies, advance online publication.

[10] Artyushina, A. (2020) Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto, Telematics and Informatics, 55, 101456

[11] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

Latest Digital Development Outputs (Agriculture, Data, Social Media) from CDD, Manchester

18 November 2020 Leave a comment

Recent outputs – on Agricultural Platforms; Data-for-Development; Social Media and Education – from the Centre for Digital Development, University of Manchester:

AGRICULTURAL PLATFORMS:

Ag-Platforms in East Africa: National and Regional Policy Gaps” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag identifies national and regional governance deficits (gaps) in the diffusion of digital agricultural platforms, and consequently how Ag-platforms bridge national and regional policy gaps.

Platforms in Agricultural Value Chains: Emergence of New Business Models” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag explains the various models of digital agricultural platforms that exist, and provides policy-makers with a roadmap that supports the proliferation of sustainable Ag-platforms.

DATA-FOR-DEVELOPMENT:

Datafication, Value and Power in Developing Countries” by Richard Heeks, Vanya Rakesh, Ritam Sengupta, Sumandro Chattapadhyay & Christopher Foster analyses the implementation challenges and impact of big data on organisational value, sources of power, and wider politics.

Identifying Potential Positive Deviants Across Rice-Producing Areas in Indonesia: An Application of Big Data Analytics and Approaches” (open access) by Basma Albanna, Dharani Dhar Burra & Michael Dyer uses remote sensing and survey data to identify “positive deviant” rice-farming villages in Indonesia: those which outperform their peers in agricultural productivity.

The Urban Data Justice Case Study Collection” (open access) presents ten case studies analysing new urban data in Latin America, Africa and Asia from data justice/rights perspectives.  It also outlines a future research agenda on urban data justice in the global South.

SOCIAL MEDIA AND EDUCATIONAL DEVELOPMENT

WhatsApp-Supported Language Teacher Development: A Case Study in the Zataari Refugee Camp” (open access) by Gary Motteram, Susan Dawson & Nazmi Al-Masri through a thematic analysis of WhatsApp exchanges, explores how Syrian English Language teachers working in refugee camps in Jordan work collaboratively on teacher development.

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.

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/

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

Analysing the Perceptions of Digital Gig Workers: Mining Emotions from Job Reviews

In a previous post, we provided a discussion of how the analysis of user-generated content (e.g. comments/posts on social media and/or job review sites) can help in understanding perceptions of digital gig workers. The prevailing assumption is that generally, digital gig workers contend with non-standard working conditions, e.g. the lack of social security coverage, long working hours, lower salaries, and the lack of benefits. Nevertheless, it is believed that digital gig workers in the Global South in particular perceive their jobs as being better than local benchmarks (i.e. office-based work).

To test the above assumptions, we developed and employed automatic text analysis methods to answer the following research questions:

  • How do digital gig workers feel about their jobs?
  • Which topics pertaining to decent work standards do they frequently talk about?
  • Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

We hereby present the results of analytics in the way of answering the questions above.

Firstly, we collected online posts published by digital gig workers from Glassdoor, a web-based platform for sharing reviews of companies and their management. Focussing on reviews of the digital gig companies Upwork, Fiverr and Freelancer, we retrieved a total of 567 reviews, 297 of which include geographic metadata (i.e. the geographic location associated with the account/profile of the user posting a review). For our text analysis, we made use of the Pro and Con fields that each review came with.

Based on the NRC Emotion Lexicon, a dictionary-based emotion detection method (implemented in the R statistical package) was applied on the reviews, classifying them according to Robert Plutchik’s eight basic emotions: Joy, Trust, Fear, Surprise, Sadness, Anticipation, Anger, and Disgust. We then grouped the reviews as either coming from the Global North or the Global South based on the geographic metadata attached to them. Shown in the figure below are the 15 most frequent emotion-bearing words found within reviews, represented according to the emotions they express. Bars in amber correspond to words prevalent in reviews from the Global North (GN) while those in blue pertain to those in reviews from the Global South (GS). 

Riza GNGSemotion

It can be observed that there are more words within GS reviews containing emotions that are clearly positive. All of the 15 words associated with Trust were found more often in GS reviews. Furthermore, 10 and 8 words associated with Joy and Anticipation, respectively, were more frequent in GS reviews. These results support the belief that digital gig workers in the Global South (GS) do express positive feelings towards their jobs.

Meanwhile, our results show that digital gig workers from both GN and GS express negative emotions. On the one hand, GS reviews were the source of 11 and 10 words associated with Anger and Fear, respectively. On the other hand, 15 and 11 words associated with Sadness and Disgust, respectively, were contained in GN reviews. This suggests that generally speaking, digital gig workers do have to contend with less than ideal working conditions, which in turn trigger such negative emotions.

Finally, 10 words associated with Surprise came from GN, 5 from GS. It is worth noting though that this particular emotion can either be negative or positive depending on context.

These results are but “teasers” to the full results of our automated analysis. Further details including the topics/themes towards which such emotions are targeted, as well as answers to the second and third research questions stated above, will be presented by Dr Victoria Ikoro in the upcoming 3rd Annual ICT4D North Workshop to be held in the Management School of the University of Liverpool on the 6th June 2019.

 

Measuring the Broadband Speed Divide using Crowdsourced Data

Digital applications and services increasingly require high-speed Internet connectivity. Yet a strong “broadband divide” exists between nations [1,2]. We try to understand how big data can be used to measure this divide. In particular, what new measurement opportunities can crowdsourced data offer?

The broadband divide has been widely measured using subscription rates. However, the broadband speed divide measured using observed speeds has been less explored due to the lack of data in the hands of regulators and statistical offices. This article focuses on measuring the fixed-network broadband speed divide between developed and developing countries, exploring the benefits and limitations of using new crowdsourced data.

To this aim we used measurements from the Speedtest Global Index, generated by Ookla using data volunteered by Internet users verifying the speed of their Internet connections [3]. These crowdsourced tests allow this firm to estimate monthly measurements of the average upload and download speeds at the country level.

The dataset used for this analysis comprised monthly data, from January to December 2018, for a total of 120 countries. Using the income and regional categorisations set by the World Bank we identified 64 developing countries and 54 developed countries in seven regions. Complete data for only two of the least developed countries were available so these were not included in the analysis.

The following table presents the download and upload speed averages on the fixed network, aggregated by region and level of development, and the totals for all the countries in our final sample (n=118), while the figure below shows the download and upload speeds aggregated by level of development.

Table 1. Average upload and download speed by region and development level, fixed network. January – December 2018 (Mbps)

Note: Unweighted averages
Source: Author calculations using data from Ookla’s Speedtest Global Index [3]

Figure 1. Average upload and download speed by level of development, fixed network. January – December 2018 (Mbps)

-Download speeds. We observe that the divide between developed and developing countries is pronounced with average download speeds for the latter being around one-third of the former. However, the divide is also evident within regions: in the developed world, countries in North America have speeds three-times higher than those in the Middle East. Within the developing countries those in Europe & Central Asia have the highest download speeds and those in the Middle East & North Africa have the lowest. Overall, download speeds are much lower in the developing world, thus creating an important impediment to the use of data-intensive digital applications and services.

-Upload speeds. We identify that overall there is an existing divide between developed and developing countries similar in magnitude to the one observed in download speeds. However, when looking at the group of developing countries we see that regional rankings are different compared to those identified using download speeds: the East Asia & Pacific region ranks first and North America ranks third – the latter with speeds that are two-thirds of their download speeds. Across regions, upload speeds are always slower in the developing world, and again the Middle East & North Africa region ranks at the bottom; but the divide between download and upload speeds is lower in the developing world. Considering that faster upload speeds are also required in a data-intensive era, the majority of the countries are far from the ideal of having faster networks with synchronous speeds.

Some benefits and limitations are identified when measuring the broadband speed divide using this type of crowdsourced data.

-Benefits. First, the availability of these types of data allows us to measure the broadband speed divide between developed and developing countries using observed instead of theoretical speeds. Second, these measurements are openly available on a website that can be accessed by the general public at no cost. Third, the divide can be measured and tracked over time more frequently than when using survey or administrative data. Finally, this site reports both download and upload speeds which are important to measure in a data-intensive era.

-Limitations. Even if there are data available for a good number of countries there are no complete data about the least developed countries, leaving behind this group. Also, there might be some bias in the production of data as crowdsourced measurements might be coming from ICT-literate individuals in certain countries [4]. Finally, from this source it is not possible to access complete datasets with additional data points such as the number of observations, medians, and latencies for each country.

These findings derive from a broader research project that, overall, is researching use of big data for measurement of the digital divide.  Readers are welcome to contact the author for details of that broader project: luis.riveraillingworth@manchester.ac.uk

References

[1] ITU (2018). Measuring the Information Society Report 2018. Geneva, Switzerland: International Telecommunication Union.

[2] Broadband Commission (2018). The State of the Broadband: Broadband catalyzing sustainable development. Geneva, Switzerland: Broadband Commission for Sustainable Development.

[3] Ookla. (2018). Speed Test Global Index [Online]. Available: http://www.speedtest.net/global-index/about [Accessed 01/03/2019]

[4] Bauer, S., Clark, D. D. & Lehr, W. (2010). Understanding broadband speed measurements. In,TPRC 2010. Available at SSRN: https://ssrn.com/abstract=1988332

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