What goes up, must come down : the role of open data in improving aid accountability

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1 What goes up, must come down : the role of open data in improving aid accountability By ROBINSON, Susannah Elizabeth (United Kingdom) ABSTRACT Open data and technology are increasingly seen as a way of improving communication between aid donors and recipients. There is however a recurring problem in the feedback loop connecting open data collection and application in development programmes. Technology is increasing the capacity of central authorities to collect data, but failing to disseminate this knowledge back down to communities in targeted national policy. The following essay looks at how technology can be used to improve both the collection and application of data in development programmes related to primary health care in developing countries. Its potential role in overseas aid for the recent outbreak of Ebola virus will be used as a case study. The approach highlights the need to engage communities by ensuring their input influences government and donor health programmes, emphasising the Communication as much as the Information aspect of ICTs in development. By doing this, a self-perpetuating feedback loop will be created where health need shapes health supply. This minimizes the wasting of limited aid resources on programmes which are medically or contextually irrelevant to a community. What goes up, must come down, Susanne Elisabeth ROBINSON 1

2 What goes up, must come down : the role of open data in improving aid accountability INTRODUCTION War, said Napoleon, is 90% information. The assessment holds as true in the fight for global development as it did on the battlefields of Austerlitz. We currently live in what is referred to as the information age, where information and communication technologies (ICTs) have significantly increased the rate at which information can be gathered. The CEO of Google, Eric Schmidt, recently opened a conference in California with the observation that every 48 hours we now create as much information as we did from the dawn of civilization up until This deluge of digital information, or big data as it is commonly known, is widely believed to be capable of improving state and aid agency ground-level information on the development challenges facing communities. Recently the focus has shifted to the role of open data : data collected directly from the public using open access technologies which are free and accessible to a high percentage of the population. This includes open-access platforms, social media, and mobile phones. Rapid advances in technology innovation and affordability have strengthened the ability of local inhabitants to use these channels to engage directly with authorities and report on community needs or problems. It also allows aid and development agencies to collect and analyse local data on the specific requirements of communities, helping them create targeted aid programmes. Its role in citizen feedback is similarly helpful for ensuring donor aid is used appropriately and efficiently, and for identifying instances of corruption. As a process where public input controls information, open data is often seen by technology optimists as the democratization of knowledge. A problem with this reading is the question of just how open the bulk of information is to would-be users. Increased physical access to information offered by ICTs in developing countries does not necessarily mean the information is accessible in the sense of being clear, comprehensible, or user-friendly. In addition, as the well-worn quote from Einstein goes, information is not knowledge. To make information gathered by open technology relevant, there need to be systems for ordering and analysing the data. It is a matter of harnessing the outpouring of big data and transforming it from lists of figures into relevant knowledge which can be used in project planning, a process known as structuring the data. The risk otherwise is of a system where information is given to central authorities via collaborative networks and feedback mechanisms, but never returned to citizens in policies which respond to the needs identified by the initial data. In order to fulfil the expectations of accountability, transparency and citizen empowerment presented by open data, overseas development assistance needs to focus not only on using technology to gather better quality data for aid projects, but also on how to render this data usable to communities and state authorities how to make the data actionable. This essay will identify some of the problems with the current use of open data in development, and how these could be resolved. It will first provide a basic overview of the current uses made of open data in development broadly. This will be followed by a specific analysis of open technology models in primary health care, identifying obstacles affecting their use value in aid programmes and offering some practical suggestions on how these could be overcome. As a practical example the recent Ebola virus What goes up, must come down, Susanne Elisabeth ROBINSON 2

3 outbreak in Western Africa will be used as a case study to demonstrate how to maximise the impact of overseas development assistance using open data. The essay will conclude with policy recommendations. OPEN DEVELOPMENT Successful examples of open technology in development exist in a variety of sectors, including health, education, humanitarian aid and political governance. Open source software platforms such as Ushahidi have been used to collect and visually map data on events requiring external assistance. Such platforms typically gather information from citizen reports submitted online or via SMS, a technique known as crowdsourcing; it also uses other components such as geotagging, where geographical metadata is attached to a report to identify its provenance or reference. These opensource platforms are simple, free, and can be adapted by users: all essential elements of their success. They have been used to monitor election fraud in Kenya, identify humanitarian needs in areas hit by natural disasters, record civic crime rates and unrest, and report corruption. Other programmes have used open source software to track the results of aid programmes, ensuring aid delivery and accountability and reducing corruption. The World Bank for instance used text messaging (SMS) feedback from lorry drivers in Nepal to evaluate whether grants intended for road repairs had been used according to state guarantee. 1 There are however two weak points in the current use made of open data in development. The first is a tendency to assume that the data collection alone is sufficient. Open source technology and the opportunities it offers for collecting big data does address a systemic problem with poor data in development. 2 Its ability to store individual preferences offers development organizations an opportunity to figuratively take the pulse of communities. 3 Household surveys are too expensive to carry out regularly, and by the time the data has been collected, written up and published, it is usually out of date. Digital data, in contrast, is real-time, meaning the information gathered remaining relevant to the corresponding reality it is trying to capture the period during which the data, in a sense, remains real. The aim of real-time data is to facilitate the accurate mapping of service needs at a given time in a given place. The use of open data should therefore be within programme design. Collecting information on hurricane damage or levels of violence in a specific area has minimal value unless the data can be built into an aid response targeting the problems identified. The second critical point often overlooked in open data initiatives is the need to make data accessible to citizens. This not only improves local awareness of issues, but crucially allows community members to use information from centralized, reliable sources to draw state and international attention to problems in their own communities. Aside from fomenting citizen empowerment, it also creates incentives for participation by making locals feel that their input can 1 Closing the Feedback Loop: Can Technology Amplify Citizen Voices?, World Bank 2014, p.17 2 For a good overview, see Sandfur and Glassman s 2014 paper on The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics Center for Global Development, Washington, D.C. 3 Big Data for Development: Challenges and Opportunities, UN Global Pulse 2014, p.9 What goes up, must come down, Susanne Elisabeth ROBINSON 3

4 lead to a change in existing services. 4 Open data needs to make data usage a two-way process, with information fed from communities into government policy for those same communities. Like Newton s third law of motion, what goes up, must come down. The real question then is how to leverage open data in development to improve its use value for action, not just information. We need a new kind of data revolution, one where data revolves between communities and authorities. It should be provided by citizens, centrally aggregated and analysed, and the results returned to the communities who provided the raw data. Open data in development needs to resemble the use of data made by the Swedish medical statistician Dr Hans Rosling: revolutionising the listener s knowledge using facts they already knew, but presented in a way which opens up their meaning. The aim of the following section is to identify ways of achieving this in the context of public health in developing countries. OPEN DATA IN HEALTH Every year billions of dollars are given to developing countries in the form of development assistance for health (DAH). Between 1990 and 2007 total DAH quadrupled from $5.6 billion to $21.8 billion. 5 Yet the needs of national health systems in developing countries continue to outpace donations. The realisation is dawning that in order to keep up, DAH needs to modify its approach and focus on increasing spending efficiency to maximise the health value of each dollar given. Aid agencies and non-profit organizations (NGOs) have already begun using open data to improve aid delivery in a variety of contexts. The aim is to incorporate citizen feedback and reporting into aid projects to improve their speed and efficacy. This has included using data tracking to identify the worst-hit areas following natural disasters such as the 2010 earthquake in Haiti, monitor disease trends, and identify health needs of specific communities according to demand. Another dominant interest has been in the predictive power of large-scale digital data sets, the legendary big data. According to research from the US Center for Disease Control, mining online health data can help detect disease outbreaks before confirmed diagnoses or policy confirmation. 6 Even private sector companies have bought into these efforts: Google Dengue Trends measures online search term frequency of dengue fever symptoms and enquiries to detect disease activity across the world, whilst Twitter has been used to predict flu outbreaks in the US. 7 The benefits of incorporating open data into DAH projects are significant. In health, open data can be used to move donor focus beyond country averages to local or even individual problems, crucial for providing targeted aid programmes which rely on local context. An example of how open data can achieve this is by fostering the use of real-world data (RWD) instead of clinical data in evaluating health needs and solutions. RWD is typically less expensive and easier to obtain than clinical trial data, and is as accurate, if not more so given its direct link to the health problem being examined. It 4 World Bank ibid., p.5 5 Financing Global Health 2009: Tracking Development Assistance for Health, Institute of Health Metrics and Evaluation, p.6 6 UN ibid., p.11 7 Eke, P.I. Using Social Media for Research and Public Health Surveillance Journal of Dental Research What goes up, must come down, Susanne Elisabeth ROBINSON 4

5 use needs careful regulating to ensure consistency and accuracy, but more use of RWD could change health intervention design by shifting interventions from a one-size-fits-all approach to tailored treatment programmes. The advantages of this are that it avoids immunity build-up from drug overuse, the wasting of medical resources, and a degeneration of faith in donor medical assistance. Other benefits are that real-time reporting offered by open data ensures health information is more accurate: suspect figures can be identified and questioned almost instantly, such as the memorable Number of Male Caesareans in one health worker report in Nairobi. 8 The increased response speed is similarly important in health because of the rate at which infections and outbreaks can develop. Health policy needs to move in real-time to be at all effective, and open data may be the only channel capable of keeping up with a virus. 9 Finally, using open data to disseminate knowledge means patients are able to share their experiences of different clinics, diseases and treatments. This can facilitate a sense of trust in medical aid interventions such as vaccine programmes by transforming them from a foreign to a familiar experience, and helps dispel fears over aid motives which can disrupt interventions dramatically, as was seen in the 2003 boycott of polio vaccine programmes in Nigeria. 10 There are several issues which are often raised regarding the use of open data in health policy. The most heavily debated is that of privacy. As in all instances of personal data collection, there are legitimate concerns over what data will be accessible to which organizations, and whether patients will have a say in how their own data is used. In order to encourage trust, it will be important to ensure open data providers feel a sense of control over the use made of their health information. Studies have shown that when patients have access to their own health data, they are significantly more likely to be willing to share the data with aid organizations. 11 Interestingly, in an era where personal details are habitually shared online bank account details for online purchases, website cookies tracking site preferences it almost seems there may be more resistance to data sharing by private companies and other institutions which are keen to protect their data from competitors. In public health, data ownership might well become the twenty-first century s answer to the pharmaceutical patent debate. Too much of this kind of privacy at an institutional level undermines the collaborative benefits of open data: it stifles innovation and prevents the dissemination of relevant knowledge to create accurate and timely aid responses to key health problems. Reliability is the other main concern for open data in development. Coming from individualised sources, all information amassed by open source software is open to subjectivity bias and unintentional human error. Inaccurate self-diagnosis of a disease, based on the identification of common symptoms, is a good example of how accidental false data can be produced. That said, there is a case to be made for open data actually increasing the reliability of health information. Open source platforms allow a real-word peer review of all information submitted about a health 8 First-person account from Dr Anifalaje Adibusoye, from his experience working with health information systems in Nigeria. 9 UN ibid., p The polio immunization drive was brought to a standstill in July 2003 as religious and political leaders in northern Nigeria responded to fears that the vaccines were deliberately contaminated with anti-fertility agents and the HIV virus. A full account is given by Maryam Yahya in her 2007 article Polio vaccines No thank you! : barriers to polio eradication in Northern Nigeria. 11 Pickard, T.K. and Swan, M. (2014) Big Desire to Share Big Health Data: a Shift in Consumer Attitudes to Share Personal Information. AAAI Spring Symposium Series, Stanford University. What goes up, must come down, Susanne Elisabeth ROBINSON 5

6 event, by allowing the inhabitants of an area to assess the accuracy of claims made by others. 12 This helps minimize instances of misinformation, as well as reducing the risk of corruption from elite minorities annexing aid finances for projects which never happen. FROM PASSIVE TO ACTIVE From a development angle, the aim of incorporating open data into public health programmes should be to take advantage of the benefits offered by the technology namely speed, accessibility, and contextualization. The point is not to use it as a purely administrative device to simply enlarge government data banks. Open data is part of the means, not the end. At present there is a systemic problem with the digitalization of health information systems (HIS) in developing countries, with data being routinely submitted by individuals or community health workers but ignored by policy makers. Dr Shirin Madon records a typical instance of a survey for improving village health facilities in rural India, where first-hand patient opinions were collected and sent to the national authorities to help them target the facilities of most need. The result was silence and a community sense of frustration and neglect: we feel like students who sat an exam and never got the results. 13 Aside from failing to uphold its potential for more participatory health care for patients, an absence of data use also obstructs aid agency cooperation. It contributes to the silo-ing of health projects in areas where collaborative efforts would lead to a more efficient use of aid resources. The way to confront the one-way data street is to begin creating clear feedback loops between aid agencies and communities. The etymological roots of the word data reside in the Latin for thing given : data was passive. A development use of data needs to become active, by making the information provided by the data able to be used by aid organizations. There are several examples where this is beginning to be acknowledged as a key function of open data in development. DevTrac is a project run by AidData in Uganda which coordinates government public investment and feedback from local community members. 14 It uses an online database to track aid projects across the country and display the results in a visual map which anyone can access to see what is happening where. The aim is to integrate top-down and bottom-up data and compare the results to see where there are gaps in assistance need and assistance provide. This is in stark contrast to the current system where a lack of information coordination can lead to geographical regions or population sub-groups being excluded from aid-funded support. A good example of this was produced by Transparency International, who used an open source platform to overlap two different data sets on World Bank poverty relief projects and areas of lowest GDP per capita in Colombia. It displayed a clear negative correlation between the two: the Bank s projects for health, education and sanitation were most scarce in the areas of greatest poverty. 15 Open data was used to identify a problem in both cases, but only in AidData was this done with government and donor support to then address the problem by re-directing project funding. The two cases highlight the 12 Lustig, R. Why were we fooled by the fake Syria blog? BBC news, 13 June Madon, S. et al. Can mobile phones help control neglected tropical diseases? Experiences from Tanzania. Social Science and Medicine 2014, p Staid, M. Beyond Open Data in Uganda: How do we make citizen feedback more actionable? AidData blog, 2 July Fagan, C. Does open data make development more accountable? The case of Colombia. Transparency International, 13 December 2012 What goes up, must come down, Susanne Elisabeth ROBINSON 6

7 core problem of open data usage in development, which interestingly links to a broader challenge facing the use of research on development issues a failure to translate it into concrete action. In a blog entry on this topic, Roy Trivedy and Mike Battcock show that large aid organizations such as the United Nations Development Project are increasingly aware of the danger of impotent information: the main challenge facing the future of data in development work is ensuring it is actually used. 16 Open data adopts an alternative meaning: opening up data. Various pilot studies have looked at ways of using open data to improve aid allocation and impact. The problem is that at the moment the majority of these are small-scale and run by individual NGOs without government involvement. In order to leverage the full potential of open data for future aid programmes, open source systems need to be integrated into national health systems instead of working in isolation. The aid industry could do a lot to change this. So far the dominant interest has been in using open technologies in the later stages of a project cycle, namely in the implementation phase to improve the running of the programme, or to improve data on project evaluation. This focus could be redirected from how to make data banks, to how to make them useful in the ground-level projects themselves. Data visualization is a good start. Interactive maps are far more informative to the average lay user than Excel datasets because they boil down the core message of the information: people can quite literally see the problem. Yet this approach still fails to guarantee that the data is then incorporated into an appropriate response. Open technology also has strong potential use value for continuous assessment of a project, once again providing that local feedback is adapted into the project design as it occurs and not simply kept for post hoc analysis. This would promote downward accountability and increase aid impact by helping to maximise resource efficiency and community trust. Using open data as a channel for continuous communication between donors and recipients would increase health programme efficiency and reliability. By using an appropriate online open source platform, agencies could chart disease trends and needs in the area in which they operate by encouraging locals to submit reports via SMS or social media; alternatively community health workers could collect responses where individuals have no direct access to open technologies. This would let international donors see in real-time exactly what health requirements are needed by which community. Responses could then be aggregated and analysed to see whether the results correspond with aid activity in a given area; if not, the data could be used to tailor existing programmes, or begin new ones. This approach to disease surveillance has already been used in rural Kenya to monitor and report animal diseases on 1,600 farms. 17 In addition, open data could be used as the basis for identifying gaps in health worker knowledge in order to provide appropriate training programmes. Direct interaction between aid agencies and citizens could also facilitate the decentralization of funding sources. When local health authorities are given control over funding allocation, the results have been shown to address local health priorities more efficiently than when 16 An Open Data Revolution, But What s Next? Oxfam, 18 th December London International Development Centre Maasai vets carry out disease surveillance of 86,000 animals with google mobile phones. Online blog, What goes up, must come down, Susanne Elisabeth ROBINSON 7

8 funding decisions are made by central state authorities. 18 Community reporting on health service improvements would counter the increased threat of corruption that this devolution of spending control might represent, in a similar way to the Nepalese driver reporting mentioned previously. Finally, agencies could work to promote community access to data on local health issues, to promote health-seeking behaviours and community activism on areas of particular need. However in their critique of open data in development, Parminder Singh and Anita Gurumurthy make the valid point that citizens may not always want data access in itself. What they want are the development resources which those results should bring, such as better access to sanitation or more employment opportunities. 19 It is the responsibility of development agencies to ensure that better knowledge translates to more effective programmes. CASE STUDY: EBOLA The easiest way of demonstrating what the future of open data in health aid could look like is by example. The following section will use the example of the ongoing Ebola outbreak in Western Africa to demonstrate a scenario in which open technology could be used to strengthen aid efforts. The main traits which have traditionally made the Ebola virus difficult to contain are that it is highly infectious, difficult to diagnose (many of the symptoms fever, diarrhoea, etc. are common to a number of other local diseases), and controlling its spread requires sociocultural behavioural changes. The rate of infection and lack of vaccine or antidote means that any work in an infected area is dangerous, and in the most severely affected countries Liberia, Guinea and Sierra Leone public health infrastructure varies from weak to non-existent. 20 This makes mobilizing human and medical resources a challenge. Furthermore, local suspicion towards international aid workers creates a barrier to aid efforts. 21 To many communities, the aid workers themselves represent the harbingers of disease, since wherever they appear Ebola deaths also occur. 22 The ensuing lack of trust between aid workers and locals reduces collaboration and faith in any health advice aid workers provide. These factors have made it a real challenge for international public health organizations and aid agencies to gather sufficient data on the extent of the Ebola outbreak, such as tracking new cases, survival rates and epidemiological trends in the spread of the virus. Data managers have already been called for in the World Health Organization (WHO) Ebola Virus Disease Outbreak Response Plan 18 Madon, S. and Krishna, S. The Dynamics of Accountability in New Democratic Spaces: A Case Study of Village Health and Sanitation Committees in Karnataka. London School of Economics, forthcoming publication. 19 Sing and Gurumurthy, ICTD Is it a New Species of Development? IT for Change perspective paper, Ebola outbreak poses threat to African economies. Financial Times, July It is for instance unclear whether fruit bats are the main vector, if other animals can also be carriers, the degree of proximity required for the virus to be transmitted from one person to another, and how long the virus can survive outside a vector or victim, such as in a bodily fluid. 22 A reaction also observed in previous Ebola epidemics. See Barry Hewlett and Bonnie Hewlett s study Ebola, Culture and Politics: the Anthropology of an Emerging Disease (2007) for a good summary of anthropological readings of community responses to international aid during outbreaks of the disease. What goes up, must come down, Susanne Elisabeth ROBINSON 8

9 as one of the most urgent needs of the outbreak for understanding transmission triggers. 23 In addition, confusion over the disease vectors and spreading mechanisms has led to a proliferation of rumours on ways to avoid contracting the virus. The WHO has already been forced to issue warnings on the fallacy of eating a raw onion daily as a prevention technique. Such lapses in medicallysupported knowledge sound risible but are potentially fatal to individuals and communities who put trust in these methods. According to the WHO, winning the fight against Ebola is explicitly linked to defeating the myths and misconceptions surrounding the disease: it is, in the most literal of senses, vital. 24 Open data can facilitate contact between communities and public health organizations, and improve information flows between the two. Firstly, open data reporting by community health workers and individuals on the ground could improve public health surveillance. By creating an online database aggregating SMS reports from individuals or community health workers, the national health system could strengthen its surveillance of the disease. The Boston-based Healthmap project is already using a web of informal online data sources to create a realtime map of the disease hotspots. By creating a direct channel between locals and aid agencies but minimising person-to-person contact, open data would increase community willingness to report suspected Ebola cases, since it would no longer involve accepting foreign aid workers into their community. Local aid workers could then be sent to assess the potential cases and treat patients who are found to be positive. This would also increase reporting rates by avoiding suspect and verified cases to be grouped together in hospital and clinic quarantine, raising the likelihood of non-ebola cases becoming infected with the virus a risk which for obvious reasons reduces the willingness of all patients to self-report, from the very human hope that they are not Ebola carriers and a consequent fear of all real victims. Where patients or their families possess mobile phones, health workers can give patients and those caring for them advice on treatment until they are able to be transferred to a proper clinic minimizing contact with family member by explaining that the illness will infect them if they hug or kiss their sick family member. Additionally, geotagging could be used to help meet non-ebola health needs which have been badly affected by the outbreak. Fear of contagion in Sierra Leone and Liberia for example has led to increased rates of women choosing home births for fear of infection in hospitals. 25 To reduce the additional risk of birth complications, a system could be put in place where expectant mothers send an SMS to a toll-free number when the first contractions begin, and a health worker from the nearest clinic would be dispatched to the mother s address to oversee the delivery. As a counterpart to data collection, there is an obvious role for open access technology in aid response. Fear of infection has been one of the greatest barriers to engaging health workers in the fight against Ebola, and direct exposure endangers the few specialists with expert knowledge of the disease. By using open data to link with citizens, doctors and health workers can respond to multiple health questions more rapidly and effectively than by using clinic visits, whilst ensuring they themselves are at minimal risk of infection. By setting up a central database which can collect simple online or SMS messages from local inhabitants in affected areas, aid agencies could also pinpoint 23 WHO Ebola Virus Disease Outbreak Response Plan, WHO and the Governments of Guinea, Liberia and Sierra Leone, July Busting the myths about Ebola is crucial to stop the transmission of the disease in Guinea, WHO Press release April WHO declares Ebola outbreak an international public health emergency. The Guardian, 2014 What goes up, must come down, Susanne Elisabeth ROBINSON 9

10 demand for information on specific health concerns regarding prevention and management of the virus. A free hotline set up by the Ministry of Health in Guinea for questions on Ebola currently receives between calls daily, indicating limited alternative sources for this kind of basic information on transmission and prevention. 26 Finally, there is the question of delivery. One of the toughest challenges facing the Ebola response is in getting interventions to where they are most needed. Building citizen reporting into aid programmes will improve aid delivery by allowing programmes to identify real-time health needs. SMS and online reporting could be used to strengthen communication between communities and aid agencies by identifying specific food and medical needs in high-risk areas. At the time of writing, Monrovia s West Point slum has already been sealed to prevent further spread of the virus, making contact with those inside essential to gauge exactly what basic supplies are needed and when. Similar models have already been used to meet local aid needs in humanitarian emergencies such as the 2010 Haiti earthquake. 27 A final major concern is the general lack of relevant medical resources in affected areas: sanitation supplies, isolation equipment, and protective gear for health workers. Direct SMS contact with local health clinics would enable aid organizations to identify the most urgent requirements for individual areas. This will avoid wasting limited resources through misunderstanding of a community s needs. Parallel examples of this community-centric approach have been documented in Karnataka, India, where devolving power to village-level health committees saw local needs addressed more effectively than when supplies were centrally allocated. 28 CONCLUSION As the scenario above demonstrates, open data and its associated technologies have a clear role to play in improving the current model of aid delivery: by closing the feedback loop between donors and recipients. In the broader context of aid, the data revolution should mean that what comes from communities returns to them, in a cycle of mutual information disclosure and response. Donor agencies need to expand their use of open data models to make citizen feedback part of the design process as well as impact assessment. This will increase community trust in foreign aid programmes by making recipients feel the content is directly relevant to their individual situation, rather than imposed externally. The beauty of open data technology is its simplicity. It is not a question of reinventing aid, but of reinventing the use made of development data in aid programmes. New models of innovation in aid need to avoid falling into the binary of seeing programmes as either top-down or bottom-up. The design and implementation of aid programmes should be a collaborative effort between donors and recipients, using the strengths of each to maximise an intervention s effectiveness. To achieve this, if there is one technology which the aid industry should focus on over the next decade, it is the mobile phone. Mobile penetration rates across the developing world have soared in 26 Busting the myths about Ebola, ibid. April The Ushahidi platform is the best example of this. 28 Madon and Krishna, ibid. What goes up, must come down, Susanne Elisabeth ROBINSON 10

11 the past 2-3 years and are continuing to rise, with a number of low or middle income countries seeing subscriptions rates of over 100%. 29 Whilst there are still gaps in access, this leap in mobile ownership is a clear opportunity to use the technology for its primary purpose of communication, to improve interaction between donors and communities. Open-access programmes can be adapted to support basic SMS or more advanced smartphone databases and interactive platforms. The technology is sufficiently normalized for its use to be acceptable among most communities, and its application in development has seen a proliferation of successful projects in health, education, agriculture and human rights. The aid industry should not overlook the mobile s potential as a data collection tool, but as this essay has demonstrated nor should it limit its understanding of the mobile to this function. The technology exemplifies the principles of two-way communication on which aid programmes should be built in order to maximise their developmental impact. Napoleon s ultimate success in battle lay in his talent for mobilizing troops. It is entirely possible that the success of aid in the next 10 years will depend on a different kind of mobilizing. 29 Mobile subscriptions per 100 people: World Bank Databank, What goes up, must come down, Susanne Elisabeth ROBINSON 11

12 Referenes Benjamin, S., Bhuvaneswari, R. Rajan, P. and Manjunatha (2007) Bhoomi, e-governance, Or, An Anti-Politics Machine Necessary to Globalize Bangalore? CASUM working paper, September. [http://casumm.files.wordpress.com/2008/09/bhoomi-e-governance.pdf] CrisisNet (2014) Mapping Violence in Syria. CrisisNet blog, 3 June [http://blog.crisis.net/syrian-social-media-journalists-secret-weapon-in-the-crisis-data-revolution/] Davies, Tim (2014). Open Data in Developing Countries Emerging insights from Phase I. Open Data in Developing Countries Working Papers, Berlin: The World Wide Web Foundation. Eke, P.I. (2011) Using Social Media for Research and Public Health Surveillance Journal of Dental Research 90.9 Fagan, C. (2012) Does open data make development more accountable? The case of Colombia. 13 December, Transparency International [http://blog.transparency.org/2012/12/13/does-open-data-make-development-more-accountablethe-case-of-colombia/] Heeks (2005) Free and Open Source Software: a Blind Alley for Developing Countries? [http://www.seed.manchester.ac.uk/medialibrary/idpm/working_papers/di/dig_briefing/digbriefin g1foss.pdf] Hewlett, B.S. and Hewlett, B.L. (2007) Ebola, Culture and Politics: the Anthropology of an Emerging Disease. Thomas Wadsworth: USA. International Telecommunications Union (2014) The World in 2014: ICT Facts and Figures. Online publication [http://www.itu.int/en/itu-d/statistics/pages/facts/default.aspx] Paul, M.J. and M. Dredze (2011) You Are What You Tweet: Analyzing Twitter for Public Health. Johns Hopkins University: Baltimore (http://www.cs.jhu.edu/%7empaul/files/2011.icwsm.twitter_health.pdf) Smith, M., Reilly, K. and Benkler, Y. (2014) Open Development: Networked Innovations in International Development. International Development Research Centre, MIT Press: USA. The Guardian mothers choosing home births from fear of ebola infection in hospitals: Gigler, B.S., Custer, S., Bailur, S., Dodds, E., Asad, S. and Gagieva, E. (2014) Closing the Feedback Loop: Can Technology Amplify Citizen Voices. World Bank: Washington D.C. Institute for Health Metrices and Evaluation (2009) Financing Global Health 2009: Tracking Development Assistance for Health. University of Washington: Seattle. What goes up, must come down, Susanne Elisabeth ROBINSON 12

13 London International Development Centre (2010) Maasai vets carry out disease surveillance of 86,000 animals with google mobile phones. Online article. [http://www.lidc.org.uk/node/80] Madon, S., Amaguru, J.O., Malecela, M.N. and Michael, E. (2014) Can mobile phones help control neglected tropical diseases? Experiences from Tanzania. Social Science & Medicine, 102. pp Madon, S., Krishna, S. and Michael, E. (2010) Health Information Systems, Decentralisation and Democratic Accountability. Public Administration and Development, 30, pp Madon, S. and Krishna, S. (forthcoming) The Dynamics of Accountability in New Democratic Spaces: A Case Study of Village Health and Sanitation Committees in Karnataka. Working paper, London School of Economics: London Paul, M.J. and M. Dredze (2011) You Are What You Tweet: Analyzing Twitter for Public Health. Republican Center for Language and Speech Processing at Johns Hopkins University. Cached copy available online. [http://www.cs.jhu.edu/~mpaul/files/2011.icwsm.twitter_health.pdf] Pickard, T.K. and Swan, M. (2014) Big Desire to Share Big Health Data: a Shift in Consumer Attitudes to Share Personal Information. AAAI Spring Symposium Series, Stanford University. Signorini, Alessio, Alberto M. Segre, and Phillip M. Polgren (2011) The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLos One 6.5 World Health Organization (2014) Busting the myths about Ebola is crucial to stop the transmission of the disease in Guinea. April bulletin [http://www.who.int/features/2014/ebola-myths/en/] --- (2014b) Ebola Virus Disease Outbreak Response Plan in West Africa. July, WHO: Geneva. [http://www.who.int/csr/disease/ebola/outbreak-response-plan/en/] Sandefur, J. and Glassman, A. (2014) The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics. Working paper 373, Center for Global Development: Washington, D.C. Sing, A. and Gurumurthy, P.J. (2009) ICTD Is it a New Species of Development? IT for Change, Perspective Paper. Online open-access publication. [http://idl-bnc.idrc.ca/dspace/bitstream/10625/41794/1/ pdf] Staid, M. (2014) Beyond Open Data in Uganda: How do we make citizen feedback more actionable? AidData blog, 2 July. Trivedy, R. and Battcock, M. (2013) An Open Data Revolution, But What s Next? Oxfam Policy and Practice blog, 18 th December. [http://policy-practice.oxfam.org.uk/blog/2013/12/open-data-revolution] What goes up, must come down, Susanne Elisabeth ROBINSON 13

14 World Bank (2014) Mobile subscriptions per 100 people: World Bank Databank [http://data.worldbank.org/indicator/it.cel.sets.p2] World Economic Forum (2014) Re-thinking Personal Data: A New Lens for Strengthening Trust. Prepared in partnership with A. T. Kearney. World Economic Forum: Geneva (2012) Big Data Big Impact: New Possibilities for International Development. World Economic Forum and Vital Wave Consulting: Geneva. World Health Organization (2014b) Busting the myths about Ebola is crucial to stop the transmission of the disease in Guinea. WHO press release April Available online [http://www.who.int/features/2014/ebola-myths/en/] United Nations (2012) Big Data for Development: Challenges and Opportunities UN Global Pulse: New York. Online publication. [http://www.unglobalpulse.org/sites/default/files/bigdatafordevelopment- GlobalPulseMay2012.pdf] Yahya, M. (2007) Polio vaccines No thank you! : barriers to polio eradication in Northern Nigeria. African Affairs: 106 (423): What goes up, must come down, Susanne Elisabeth ROBINSON 14

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