What are hidden data treasuries and how can they help development outcomes?


Blogpost by Damien Jacques et al: “Cashew nuts in Burkina Faso can be seen growing from space. Such is the power of satellite technology, it’s now possible to observe the changing colors of fields as crops slowly ripen.

This matters because it can be used as an early warning of crop failure and food crisis – giving governments and aid agencies more time to organize a response.

Our team built an exhaustive crop type and yield estimation map in Burkina Faso, using artificial intelligence and satellite images from the European Space Agency. 

But building the map would not have been possible without a data set that GIZ, the German government’s international development agency, had collected for one purpose on the ground some years before – and never looked at again.

At Dalberg, we call this a “hidden data treasury” and it has huge potential to be used for good. 

Unlocking data potential

In the records of the GIZ Data Lab, the GPS coordinates and crop yield measurements of just a few hundred cashew fields were sitting dormant.

They’d been collected in 2015 to assess the impact of a program to train farmers. But through the power of machine learning, that data set has been given a new purpose.

Using Dalberg Data Insights’ AIDA platform, our team trained algorithms to analyze satellite images for cashew crops, track the crops’ color as they ripen, and from there, estimate yields for the area covered by the data.

From this, it’s now possible to predict crop failures for thousands of fields.

We believe this “recycling” of old data, when paired with artificial intelligence, can help to bridge the data gaps in low-income countries and meet the UN’s Sustainable Development Goals….(More)”.

How randomised trials became big in development economics


Seán Mfundza Muller, Grieve Chelwa, and Nimi Hoffmann at the Conversation: “…One view of the challenge of development is that it is fundamentally about answering causal questions. If a country adopts a particular policy, will that cause an increase in economic growth, a reduction in poverty or some other improvement in the well-being of citizens?

In recent decades economists have been concerned about the reliability of previously used methods for identifying causal relationships. In addition to those methodological concerns, some have argued that “grand theories of development” are either incorrect or at least have failed to yield meaningful improvements in many developing countries.

Two notable examples are the idea that developing countries may be caught in a poverty trap that requires a “big push” to escape and the view that institutions are key for growth and development.

These concerns about methods and policies provided a fertile ground for randomised experiments in development economics. The surge of interest in experimental approaches in economics began in the early 1990s. Researchers began to use “natural experiments”, where for example random variation was part of a policy rather than decided by a researcher, to look at causation.

But it really gathered momentum in the 2000s, with researchers such as the Nobel awardees designing and implementing experiments to study a wide range of microeconomic questions.

Randomised trials

Proponents of these methods argued that a focus on “small” problems was more likely to succeed. They also argued that randomised experiments would bring credibility to economic analysis by providing a simple solution to causal questions.

These experiments randomly allocate a treatment to some members of a group and compare the outcomes against the other members who did not receive treatment. For example, to test whether providing credit helps to grow small firms or increase their likelihood of success, a researcher might partner with a financial institution and randomly allocate credit to applicants that meet certain basic requirements. Then a year later the researcher would compare changes in sales or employment in small firms that received the credit to those that did not.

Randomised trials are not a new research method. They are best known for their use in testing new medicines. The first medical experiment to use controlled randomisation occurred in the aftermath of the second world war. The British government used it to assess the effectiveness of a drug for tuberculosis treatment.

In the early 20th century and mid-20th century American researchers had used experiments like this to examine the effects of various social policies. Examples included income protection and social housing.

The introduction of these methods into development economics also followed an increase in their use in other areas of economics. One example was the study of labour markets.

Randomised control trials in economics are now mostly used to evaluate the impact of social policy interventions in poor and middle-income countries. Work by the 2019 Nobel awardees – Michael Kremer, Abhijit Banerjee and Esther Duflo – includes experiments in Kenya and India on teacher attendancetextbook provisionmonitoring of nurse attendance and the provision of microcredit.

The popularity, among academics and policymakers, of the approach is not only due to its seeming ability to solve methodological and policy concerns. It is also due to very deliberate, well-funded advocacy by its proponents….(More)”.

Responsible Data for Children


New Site and Report by UNICEF and The GovLab: “RD4C seeks to build awareness regarding the need for special attention to data issues affecting children—especially in this age of changing technology and data linkage; and to engage with governments, communities, and development actors to put the best interests of children and a child rights approach at the center of our data activities. The right data in the right hands at the right time can significantly improve outcomes for children. The challenge is to understand the potential risks and ensure that the collection, analysis and use of data on children does not undermine these benefits.

Drawing upon field-based research and established good practice, RD4C aims to highlight and support best practice data responsibility; identify challenges and develop practical tools to assist practitioners in evaluating and addressing them; and encourage a broader discussion on actionable principles, insights, and approaches for responsible data management.

The Digital Roadmap


Report by the Pathway for Prosperity Commission: “The Digital Roadmap presents an overarching vision for a globally connected world that both delivers on the opportunities presented by technology, and limits downside risks. Importantly, it also sets out how this vision can be achieved.

Craft a digital compact for inclusive development

Embracing country-wide digital change will be disruptive. Navigating it requires coordinated action. Reconfiguring an economy will result in some resistance. The best way to achieve buy-in, and to balance trade-offs, is through dialogue: the private sector and civil society in its broadest sense (including community leaders, academia, trade unions, NGOs, and faith groups). The political economy of upheaval is difficult, but change can be managed with discussions that are inclusive of multiple groups. These dialogues should result in a national digital compact: a shared vision of the future to which everyone commits. The Pathways Commission has supported three countries – Ethiopia, Mongolia and South Africa – as they each developed country-wide digital strategies, using the Digital Economy Kit.

Put people at the centre of the digital future

Rapid technological affects peoples’ lives.Failure to put people at the centre of social and economic change can lead to social unrest. The pace and intensity of change means it’s all the more important that people are at the centre of the digital future – not the technology. This requires equipping people to benefit from opportunities, while also protecting them from the potential harms of the digital age. Governments should take responsibility for ensuring that vocational education is truly useful for workers and for business in the digital age. The private sector needs to be involved in keeping curricula up to date.

Build the digital essentials

Digital products and services cannot be created in a vacuum – essential components need to be in place: physical infrastructure, foundational digital systems (such as digital identification and mobile money), and capital to invest in innovation. These are the basic ingredients needed for existing firms to adopt more productive technologies, and for digital entrepreneurs to build and innovate. Having reliable infrastructure and interoperable systems means that firms and service providers can focus on their core business, without having to build an enabling environment from scratch.

Reach everyone with digital technologies

If technology is to be a force for development for everyone, it must reach everyone.Just over half of the world’s population is connected to a digital life; for the rest, digital opportunities don’t mean much. Without digital connections, people can’t participate in digital work platforms, benefit from new technologies in education, or engage with government services online. Women, people with lower levels of education, and people in poverty are usually those who lack digital access. Reaching everyone requires looking beyond current business models. The private sector needs to design for inclusion, ensuring the poorest and most marginalised consumers, to ensure they are not left even further behind.

Govern technology for the future

The unprecedented pace of change and emergence of new risks in the digital era (such as algorithmic bias, cybersecurity, and threats to privacy) are creating headaches for even the most well-resourced countries. For developing countries, the challenges are even bigger. Digital technologies fundamentally shape what people do and how they do it: freelancers may face algorithms that determine chances to get hired. Banks might face a financial system with heightened risk from new, non-bank deposit holders. These issues, and many others, require new and adaptive approaches to decision-making. Emerging global norms will need to consider the needs of developing countries….(More)”.

Citizen science and the United Nations Sustainable Development Goals


Steffen Fritz et al in Nature: “Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution….(More)”.

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)”.