Kenya passes data protection law crucial for tech investments


George Obulutsa and Duncan Miriri at Reuters: “Kenyan President Uhuru Kenyatta on Friday approved a data protection law which complies with European Union legal standards as it looks to bolster investment in its information technology sector.

The East African nation has attracted foreign firms with innovations such as Safaricom’s M-Pesa mobile money services, but the lack of safeguards in handling personal data has held it back from its full potential, officials say.

“Kenya has joined the global community in terms of data protection standards,” Joe Mucheru, minister for information, technology and communication, told Reuters.

The new law sets out restrictions on how personally identifiable data obtained by firms and government entities can be handled, stored and shared, the government said.

Mucheru said it complies with the EU’s General Data Protection Regulation which came into effect in May 2018 and said an independent office will investigate data infringements….

A lack of data protection legislation has also hampered the government’s efforts to digitize identity records for citizens.

The registration, which the government said would boost its provision of services, suffered a setback this year when the exercise was challenged in court.

“The lack of a data privacy law has been an enormous lacuna in Kenya’s digital rights landscape,” said Nanjala Nyabola, author of a book on information technology and democracy in Kenya….(More)”.

Are Randomized Poverty-Alleviation Experiments Ethical?


Peter Singer et al at Project Syndicate: “Last month, the Nobel Memorial Prize in Economic Sciences was awarded to three pioneers in using randomized controlled trials (RCTs) to fight poverty in low-income countries: Abhijit Banerjee, Esther Duflo, and Michael Kremer. In RCTs, researchers randomly choose a group of people to receive an intervention, and a control group of people who do not, and then compare the outcomes. Medical researchers use this method to test new drugs or surgical techniques, and anti-poverty researchers use it alongside other methods to discover which policies or interventions are most effective. Thanks to the work of Banerjee, Duflo, Kremer, and others, RCTs have become a powerful tool in the fight against poverty.

But the use of RCTs does raise ethical questions, because they require randomly choosing who receives a new drug or aid program, and those in the control group often receive no intervention or one that may be inferior. One could object to this on principle, following Kant’s claim that it is always wrong to use human beings as a means to an end; critics have argued that RCTs “sacrifice the well-being of study participants in order to ‘learn.’”

Rejecting all RCTs on this basis, however, would also rule out the clinical trials on which modern medicine relies to develop new treatments. In RCTs, participants in both the control and treatment groups are told what the study is about, sign up voluntarily, and can drop out at any time. To prevent people from choosing to participate in such trials would be excessively paternalistic, and a violation of their personal freedom.

less extreme version of the criticism argues that while medical RCTs are conducted only if there are genuine doubts about a treatment’s merits, many development RCTs test interventions, such as cash transfers, that are clearly better than nothing. In this case, maybe one should just provide the treatment?

This criticism neglects two considerations. First, it is not always obvious what is better, even for seemingly stark examples like this one. For example, before RCT evidence to the contrary, it was feared that cash transfers lead to conflict and alcoholism.

Second, in many development settings, there are not enough resources to help everyone, creating a natural control group….

A third version of the ethical objection is that participants may actually be harmed by RCTs. For example, cash transfers might cause price inflation and make non-recipients poorer, or make non-recipients envious and unhappy. These effects might even affect people who never consented to be part of a study.

This is perhaps the most serious criticism, but it, too, does not make RCTs unethical in general….(More)”.

African countries are missing the data needed to drive development


David Pilling at the Financial Times: “When statisticians decided to track how well African countries were doing in moving towards their 2030 UN sustainable development goals, they discovered a curious thing: no one had the faintest idea. More accurately, on average, African governments keep statistics covering only about a third of the relevant data. To be fair, the goals, which range from eradicating poverty and hunger to creating sustainable cities and communities, are overly complicated and sometimes unquantifiable.

The millennium development goals that they superseded had eight goals with 21 indicators. The SDGs have 17, with 232 indicators. Yet statisticians for the Mo Ibrahim Foundation, which compiled the report, are on to something. African states don’t know enough about their people. 

In this age of mass surveillance, that might seem counterintuitive. Surely governments, not to mention private companies, have too much information on their citizenry? In fact, in many African nations with weak states, big informal economies and undocumented communities, the problem is the reverse. How many people are there in Nigeria? What is the unemployment rate in Zimbabwe? How many people in Kibera, a huge informal settlement in Nairobi, have access to healthcare? The answers to such basic questions are: we don’t really know.  Nigeria last conducted a census in 2006, when the population — a sensitive topic in which religion, regionalism and budget allocations are messily intertwined — came out at 140m. These days it could be 180m or 200m. Or perhaps more. Or less.

President Muhammadu Buhari recently complained that statistics quoted by international bodies, such as those alleging that Nigeria has more people living in absolute poverty than India, were “wild estimates” bearing “little relation to facts on the ground”. The riposte to that is simple. Work out what is happening and do something about it. Likewise, unemployment is hard to define, let alone quantify, in a broken economy such as Zimbabwe’s where cited jobless statistics range from 5 to 95 per cent. Is a struggling subsistence farmer or a street-side hawker jobless or gainfully employed?

For that matter what is the status of a government employee who receives her salary in a useless electronic currency?  According to Seth Berkley, chief executive of the Vaccine Alliance, keeping tabs on unregistered people in the sprawling “slums” of Africa’s increasingly massive megacities, is harder than working out what is going on in isolated villages. If governments do not know whether a person exists it is all too easy to ignore their rights — to healthcare, to education or to the vote. The Mo Ibrahim Foundation found that only eight countries in Africa register more than 90 per cent of births. Tens of millions of people are literally invisible. Mr Ibrahim, a Sudanese billionaire, calls data “the missing SDG”….(More)”

Data gaps threaten achievement of development goals in Africa


Sara Jerving at Devex: “Data gaps across the African continent threaten to hinder the achievement of the Sustainable Development Goals and the African Union’s Agenda 2063, according to the Mo Ibrahim Foundation’s first governance report released on Tuesday.

The report, “Agendas 2063 & 2030: Is Africa On Track?“ based on an analysis of the foundation’s Ibrahim index of African governance, found that since the adoption of both of these agendas, the availability of public data in Africa has declined. With data focused on social outcomes, there has been a notable decline in education, population and vital statistics, such as birth and death records, which allow citizens to access public services.

The index, on which the report is based, is the most comprehensive dataset on African governance, drawing on ten years of data of all 54 African nations. An updated index is released every two years….

The main challenge in the production of quality, timely data, according to the report, is a lack of funding and lack of independence of the national statistical offices.

Only one country, Mauritius, had a perfect score in terms of independence of its national statistics office – meaning that its office can collect the data it chooses, publish without approval from other arms of the government, and is sufficiently funded. Fifteen African nations scored zero in terms of the independence of their offices….(More)”.

How to ensure that your data science is inclusive


Blog by Samhir Vasdev: “As a new generation of data scientists emerges in Africa, they will encounter relatively little trusted, accurate, and accessible data upon which to apply their skills. It’s time to acknowledge the limitations of the data sources upon which data science relies, particularly in lower-income countries.

The potential of data science to support, measure, and amplify sustainable development is undeniable. As public, private, and civic institutions around the world recognize the role that data science can play in advancing their growth, an increasingly robust array of efforts has emerged to foster data science in lower-income countries.

This phenomenon is particularly salient in Sub-Saharan Africa. There, foundations are investing millions into building data literacy and data science skills across the continent. Multilaterals and national governments are pioneering new investments into data science, artificial intelligence, and smart cities. Private and public donors are building data science centers to build cohorts of local, indigenous data science talent. Local universities are launching graduate-level data science courses.

Despite this progress, among the hype surrounding data science rests an unpopular and inconvenient truth: As a new generation of data scientists emerges in Africa, they will encounter relatively little trusted, accurate, and accessible data that they can use for data science.

We hear promises of how data science can help teachers tailor curricula according to students’ performances, but many school systems don’t collect or track that performance data with enough accuracy and timeliness to perform those data science–enabled tweaks. We believe that data science can help us catch disease outbreaks early, but health care facilities often lack the specific data, like patient origin or digitized information, that is needed to discern those insights.

These fundamental data gaps invite the question: Precisely what data would we perform data science on to achieve sustainable development?…(More)”.

Why policy networks don’t work (the way we think they do)


Blog by James Georgalakis: “Is it who you know or what you know? The literature on evidence uptake and the role of communities of experts mobilised at times of crisis convinced me that a useful approach would be to map the social network that emerged around the UK-led mission to Sierra Leone so it could be quantitatively analysed. Despite the well-deserved plaudits for my colleagues at IDS and their partners in the London School of Hygiene and Tropical Medicine, the UK Department for International Development (DFID), the Wellcome Trust and elsewhere, I was curious to know why they had still met real resistance to some of their policy advice. This included the provision of home care kits for victims of the virus who could not access government or NGO run Ebola Treatment Units (ETUs).

It seemed unlikely these challenges were related to poor communications. The timely provision of accessible research knowledge by the Ebola Response Anthropology Platform has been one of the most celebrated aspects of the mobilisation of anthropological expertise. This approach is now being replicated in the current Ebola response in the Democratic Republic of Congo (DRC).  Perhaps the answer was in the network itself. This was certainly indicated by some of the accounts of the crisis by those directly involved.

Social network analysis

I started by identifying the most important looking policy interactions that took place between March 2014, prior to the UK assuming leadership of the Sierra Leone international response and mid-2016, when West Africa was finally declared Ebola free. They had to be central to the efforts to coordinate the UK response and harness the use of evidence. I then looked for documents related to these events, a mixture of committee minutes, reports and correspondence , that could confirm who was an active participant in each. This analysis of secondary sources related to eight separate policy processes and produced a list of 129 individuals. However, I later removed a large UK conference that took place in early 2016 at which learning from the crisis was shared.  It appeared that most delegates had no significant involvement in giving policy advice during the crisis. This reduced the network to 77….(More)”.

‘Digital colonialism’: why some countries want to take control of their people’s data from Big Tech


Jacqueline Hicks at the Conversation: “There is a global standoff going on about who stores your data. At the close of June’s G20 summit in Japan, a number of developing countries refused to sign an international declaration on data flows – the so-called Osaka Track. Part of the reason why countries such as India, Indonesia and South Africa boycotted the declaration was because they had no opportunity to put their own interests about data into the document.

With 50 other signatories, the declaration still stands as a statement of future intent to negotiate further, but the boycott represents an ongoing struggle by some countries to assert their claim over the data generated by their own citizens.

Back in the dark ages of 2016, data was touted as the new oil. Although the metaphor was quickly debunked it’s still a helpful way to understand the global digital economy. Now, as international negotiations over data flows intensify, the oil comparison helps explain the economics of what’s called “data localisation” – the bid to keep citizens’ data within their own country.

Just as oil-producing nations pushed for oil refineries to add value to crude oil, so governments today want the world’s Big Tech companies to build data centres on their own soil. The cloud that powers much of the world’s tech industry is grounded in vast data centres located mainly around northern Europe and the US coasts. Yet, at the same time, US Big Tech companies are increasingly turning to markets in the global south for expansion as enormous numbers of young tech savvy populations come online….(More)”.

Social Systems Evidence


Social Systems Evidence is the world’s most comprehensive, continuously updated repository of syntheses of research evidence about the programs, services and products available in a broad range of government sectors and program areas (e.g., climate action, community and social services, economic development and growth, education, environmental conservation, education, housing and transportation) as well as the governance, financial and delivery arrangements within which these programs, services and products are provided, and the implementation strategies that can help to ensure that these programs, services and products get to those who need them. The content contained in Social Systems Evidence covers the Sustainable Development Goals, with the exceptions of the health part of goal 3 (which is already well covered by databases such as ACCESSSS for clinical evidence, Health Evidence for public health evidence, and Health Systems Evidence for the governance, financial and delivery arrangements, and the implementation strategies that determine whether the right programs, services and products get to those who need them).

The types of syntheses in Social Systems Evidence include evidence briefs for policy, overviews of systematic reviews, systematic reviews, systematic reviews in progress (i.e. protocols for systematic reviews), and systematic reviews being planned (i.e. registered titles for systematic reviews). Social Systems Evidence also contains a continuously updated repository of economic evaluations in these same domains.

Documents included in Social Systems Evidence are identified through weekly electronic searches of online bibliographic databases (EBSCOhost, ProQuest and Web of Science) and through manual searches of the websites of high-volume producers of research syntheses relevant to social-system program and service areas (see acknowledgements below).

For all types of documents, Social Systems Evidence provides links to user-friendly summaries, scientific abstracts, and full-text reports (if applicable and when freely available). For each systematic review, Social Systems Evidence also provides an assessment of its methodological quality, and links to the studies contained in the review.

While SSE is free to use and does not require that users have an account, creating an account will allow you to view more than 20 search results, to save documents and searches, and to subscribe to email alerts, among other advanced features. You can create an account by clicking ‘Create account’ on the top banner (for desktop and laptop computers) or in the menu on far right of the banner (for mobile devices).

Social Systems Evidence can save social-system policymakers and stakeholders a great deal of time by helping them to rapidly identify: a synthesis of the best available research evidence on a given topic that has been prepared in a systematic and transparent way, how recently the search for studies was conducted, the quality of the synthesis, the countries in which the studies included in the synthesis were conducted, and the key findings from the synthesis. Social Systems Evidence can also help them to rapidly identify economic evaluations in these same domains…(More)”.

Counting on the World to Act


Home report cover

Report by Trends: “Eradicating poverty and hunger, ensuring quality education, instituting affordable and clean energy, and more – the Sustainable Development Goals (SDGs) lay out a broad, ambitious vision for our world. But there is one common denominator that cuts across this agenda: data. Without timely, relevant, and disaggregated data, policymakers and their development partners will be unprepared to turn their promises into reality for communities worldwide. With only eleven years left to meet the goals, it is imperative that we focus on building robust, inclusive, and relevant national data systems to support the curation and promotion of better data for sustainable development. In Counting on the World to Act, TReNDS details an action plan for governments and their development partners that will enable them to help deliver the SDGs globally by 2030. Our recommendations specifically aim to empower government actors – whether they be national statisticians, chief data scientists, chief data officers, ministers of planning, or others concerned with evidence in support of sustainable development – to advocate for, build, and lead a new data ecosystem….(More)”.

Sharing Private Data for Public Good


Stefaan G. Verhulst at Project Syndicate: “After Hurricane Katrina struck New Orleans in 2005, the direct-mail marketing company Valassis shared its database with emergency agencies and volunteers to help improve aid delivery. In Santiago, Chile, analysts from Universidad del Desarrollo, ISI Foundation, UNICEF, and the GovLab collaborated with Telefónica, the city’s largest mobile operator, to study gender-based mobility patterns in order to design a more equitable transportation policy. And as part of the Yale University Open Data Access project, health-care companies Johnson & Johnson, Medtronic, and SI-BONE give researchers access to previously walled-off data from 333 clinical trials, opening the door to possible new innovations in medicine.

These are just three examples of “data collaboratives,” an emerging form of partnership in which participants exchange data for the public good. Such tie-ups typically involve public bodies using data from corporations and other private-sector entities to benefit society. But data collaboratives can help companies, too – pharmaceutical firms share data on biomarkers to accelerate their own drug-research efforts, for example. Data-sharing initiatives also have huge potential to improve artificial intelligence (AI). But they must be designed responsibly and take data-privacy concerns into account.

Understanding the societal and business case for data collaboratives, as well as the forms they can take, is critical to gaining a deeper appreciation the potential and limitations of such ventures. The GovLab has identified over 150 data collaboratives spanning continents and sectors; they include companies such as Air FranceZillow, and Facebook. Our research suggests that such partnerships can create value in three main ways….(More)”.