Book of the World Bank Group and the Inter-American Development Bank: “The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policymakers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluation and the best ways to use impact evaluations to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation. The handbook is divided into four sections: Part One discusses what to evaluate and why; Part Two presents the main impact evaluation methods; Part Three addresses how to manage impact evaluations; Part Four reviews impact evaluation sampling and data collection. Case studies illustrate different applications of impact evaluations. The book links to complementary instructional material available online, including an applied case as well as questions and answers. The updated second edition will be a valuable resource for the international development community, universities, and policymakers looking to build better evidence around what works in development….(More Resources) (Download here)”
The mAgri Design Toolkit
“The mAgri Design Toolkit is a collection of instructions, tools, and stories to help develop and scale mobile agriculture products by applying a user-centered design approach.
Many mAgri services that have launched in emerging markets have suffered from low user adoption, despite coming from leading mobile network operators and value-added service (VAS) providers. This toolkit is one of the outcomes of a partnership between the GSMA mAgri Programme and frog, and provides operational guidance on how to bring the user-centred design approach into the product development process to better connect mAgri services with the needs of farmers and other key actors in the ecosystem….(More)”
Responsible Data in Agriculture
Report by Lindsay Ferris and Zara Rahman for GODAN: “The agriculture sector is creating increasing amounts of data, from many different sources. From tractors equipped with GPS tracking, to open data released by government ministries, data is becoming ever more valuable, as agricultural business development and global food policy decisions are being made based upon data. But the sector is also home to severe resource inequality. The largest agricultural companies make billions of dollars per year, in comparison with subsistence farmers growing just enough to feed themselves, or smallholder farmers who grow enough to sell on a year-by-year basis. When it comes to data and technology, these differences in resources translate to stark power imbalances in data access and use. The most well resourced actors are able to delve into new technologies and make the most of those insights, whereas others are unable to take any such risks or divert any of their limited resources. Access to and use of data has radically changed the business models and behaviour of some of those well resourced actors, but in contrast, those with fewer resources are receiving the same, limited access to information that they always have.
In this paper, we have approached these issues from a responsible data perspective, drawing upon the experience of the Responsible Data community1 who over the past three years have created tools, questions and resources to deal with the ethical, legal, privacy and security challenges that come from new uses of data in various sectors. This piece aims to provide a broad overview of some of the responsible data challenges facing these actors, with a focus on the power imbalance between actors, and looking into how that inequality affects behaviour when it comes to the agricultural data ecosystem. What are the concerns of those with limited resources, when it comes to this new and rapidly changing data environment? In addition, what are the ethical grey areas or uncertainties that we need to address in the future? As a first attempt to answer these questions, we spoke to 14 individuals with various perspectives on the sector to understand what the challenges are for them and for the people they work with. We also carried out desk research to dive deeper into these issues, and we provide here an analysis of our findings and responsible data challenges….(More)”
How to advance open data research: Towards an understanding of demand, users, and key data
Danny Lämmerhirt and Stefaan Verhulst at IODC blog: “…Lord Kelvin’s famous quote “If you can not measure it, you can not improve it” equally applies to open data. Without more evidence of how open data contributes to meeting users’ needs and addressing societal challenges, efforts and policies toward releasing and using more data may be misinformed and based upon untested assumptions.
When done well, assessments, metrics, and audits can guide both (local) data providers and users to understand, reflect upon, and change how open data is designed. What we measure and how we measure is therefore decisive to advance open data.
Back in 2014, the Web Foundation and the GovLab at NYU brought together open data assessment experts from Open Knowledge, Organisation for Economic Co-operation and Development, United Nations, Canada’s International Development Research Centre, and elsewhere to explore the development of common methods and frameworks for the study of open data. It resulted in a draft template or framework for measuring open data. Despite the increased awareness for more evidence-based open data approaches, since 2014 open data assessment methods have only advanced slowly. At the same time, governments publish more of their data openly, and more civil society groups, civil servants, and entrepreneurs employ open data to manifold ends: the broader public may detect environmental issues and advocate for policy changes, neighbourhood projects employ data to enable marginalized communities to participate in urban planning, public institutions may enhance their information exchange, and entrepreneurs embed open data in new business models.
In 2015, the International Open Data Conference roadmap made the following recommendations on how to improve the way we assess and measure open data.
- Reviewing and refining the Common Assessment Methods for Open Data framework. This framework lays out four areas of inquiry: context of open data, the data published, use practices and users, as well as the impact of opening data.
- Developing a catalogue of assessment methods to monitor progress against the International Open Data Charter (based on the Common Assessment Methods for Open Data).
- Networking researchers to exchange common methods and metrics. This helps to build methodologies that are reproducible and increase credibility and impact of research.
- Developing sectoral assessments.
In short, the IODC called for refining our assessment criteria and metrics by connecting researchers, and applying the assessments to specific areas. It is hard to tell how much progress has been made in answering these recommendations, but there is a sense among researchers and practitioners that the first two goals are yet to be fully addressed.
Instead we have seen various disparate, yet well meaning, efforts to enhance the understanding of the release and impact of open data. A working group was created to measure progress on the International Open Data Charter, which provides governments with principles for implementing open data policies. While this working group compiled a list of studies and their methodologies, it did not (yet) deepen the common framework of definitions and criteria to assess and measure the implementation of the Charter.
In addition, there is an increase of sector- and case-specific studies that are often more descriptive and context specific in nature, yet do contribute to the need for examples that illustrate the value proposition for open data.
As such, there seems to be a disconnect between top-level frameworks and on-the-ground research, preventing the sharing of common methods and distilling replicable experiences about what works and what does not….(More)”
World leaders must invest in better data on children
Press Release: “UNICEF is calling on world leaders to invest in better data on children, warning in a new analysis that sufficient data is available only for half of the child-related Sustainable Development Goals indicators.
The UNICEF analysis shows that child-related data, including measures on poverty and violence that can be compared, are either too limited or of poor quality, leaving governments without the information they need to accurately address challenges facing millions of children, or to track progress towards achieving the Goals….
Examples of missing data:
• Around one in three countries does not have comparable measures on child poverty.
• Around 120 million girls under the age of 20 have been subjected to forced sexual intercourse or other forced sexual acts. Boys are also at risk, but almost no data is available.
• There is a shortage of accurate and comparable data on the number of children with disabilities in almost all countries.
• Universal access to safe drinking water is a fundamental need and human right. We have data about where drinking water comes from, but we often don’t know how safe it is.
• Nine out of 10 children are in primary school, yet crucial data about how many are learning is missing.
• Every day 830 mothers die as a result of complications related to childbirth. Most of these deaths are preventable, yet there are critical data gaps about the quality of maternal care.
• Stunting denies children a fair chance of survival, growth and development. Yet 105 out of 197 countries do not have recent data on stunting.
• One in two countries around the world lack recent data on overweight children.
…UNICEF is calling for governments to invest in disaggregated, comparable and quality data for children, to adequately address issues including intergenerational cycles of poverty, preventable deaths, and violence against children….(More)”
Data Love: The Seduction and Betrayal of Digital Technologies
Book by Roberto Simanowski: “Intelligence services, government administrations, businesses, and a growing majority of the population are hooked on the idea that big data can reveal patterns and correlations in everyday life. Initiated by software engineers and carried out through algorithms, the mining of big data has sparked a silent revolution. But algorithmic analysis and data mining are not simply byproducts of media development or the logical consequences of computation. They are the radicalization of the Enlightenment’s quest for knowledge and progress. Data Love argues that the “cold civil war” of big data is taking place not among citizens or between the citizen and government but within each of us.
Roberto Simanowski elaborates on the changes data love has brought to the human condition while exploring the entanglements of those who―out of stinginess, convenience, ignorance, narcissism, or passion―contribute to the amassing of ever more data about their lives, leading to the statistical evaluation and individual profiling of their selves. Writing from a philosophical standpoint, Simanowski illustrates the social implications of technological development and retrieves the concepts, events, and cultural artifacts of past centuries to help decode the programming of our present….(More)”
Combining Satellite Imagery and Machine Learning to Predict Poverty
From the sustainability and artificial intelligence lab: “The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide.
In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. Check out the short video below for a quick overview and then read the paper for a more detailed explanation of how it all works….(More)”
Recent Developments in Open Data Policy
Presentation by Paul Uhlir: “Several International organizations have issued policy statements on open data policies in the past two years. This presentation provides an overview of those statements and their relevance to developing countries.
International Statements on Open Data Policy
Open data policies have become much more supported internationally in recent years. Policy statements in just the most recent 2014-2016 period that endorse and promote openness to research data derived from public funding include: the African Data Consensus (UNECA 2014); the CODATA Nairobi Principles for Data Sharing for Science and Development in Developing Countries (PASTD 2014); the Hague Declaration on Knowledge Discovery in the Digital Age (LIBER 2014); Policy Guidelines for Open Access and Data Dissemination and Preservation (RECODE 2015); Accord on Open Data in a Big Data World (Science International 2015). This presentation will present the principal guidelines of these policy statements.
The Relevance of Open Data from Publicly Funded Research for Development
There are many reasons that publicly funded research data should be made as freely and openly available as possible. Some of these are noted here, although many other benefits are possible. For research, it is closing the gap with more economically developed countries, making researchers more visible on the web, enhancing their collaborative potential, and linking them globally. For educational benefits, open data assists greatly in helping students learn how to do data science and to manage data better. From a socioeconomic standpoint, open data policies have been shown to enhance economic opportunities and to enable citizens to improve their lives in myriad ways. Such policies are more ethical in allowing access to those that have no means to pay and not having to pay for the data twice—once through taxes to create the data in the first place and again at the user level . Finally, access to factual data can improve governance, leading to better decision making by policymakers, improved oversight by constituents, and digital repatriation of objects held by former colonial powers.
Some of these benefits are cited directly in the policy statements themselves, while others are developed more fully in other documents (Bailey Mathae and Uhlir 2012, Uhlir 2015). Of course, not all publicly funded data and information can be made available and there are appropriate reasons—such as the protection of national security, personal privacy, commercial concerns, and confidentiality of all kinds—that make the withholding of them legal and ethical. However, the default rule should be one of openness, balanced against a legitimate reason not to make the data public….(More)”
Open data, transparency and accountability
Topic guide by Liz Carolan: “…introduces evidence and lessons learned about open data, transparency and accountability in the international development context. It discusses the definitions, theories, challenges and debates presented by the relationship between these concepts, summarises the current state of open data implementation in international development, and highlights lessons and resources for designing and implementing open data programmes.
Open data involves the release of data so that anyone can access, use and share it. The Open DataCharter (2015) describes six principles that aim to make data easier to find, use and combine:
- open by default
- timely and comprehensive
- accessible and usable
- comparable and interoperable
- for improved governance and citizen engagement
- for inclusive development and innovation
One of the main objectives of making data open is to promote transparency.
Transparency is a characteristic of government, companies, organisations and individuals that are open in the clear disclosure of information, rules, plans, processes and actions. Transparency of information is a crucial part of this. Within a development context, transparency and accountability initiatives have emerged over the last decade as a way to address developmental failures and democratic deficits.
There is a strong intersection between open data and transparency as concepts, yet as fields of study and practice, they have remained somewhat separate. This guide draws extensively on analysis and evidence from both sets of literature, beginning by outlining the main concepts and the theories behind the relationships between them.
Data release and transparency are parts of the chain of events leading to accountability. For open data and transparency initiatives to lead to accountability, the required conditions include:
- getting the right data published, which requires an understanding of the politics of data publication
- enabling actors to find, process and use information, and to act on any outputs, which requires an accountability ecosystem that includes equipped and empowered intermediaries
- enabling institutional or social forms of enforceability or citizens’ ability to choose better services,which requires infrastructure that can impose sanctions, or sufficient choice or official support for citizens
Programmes intended to increase access to information can be impacted by and can affect inequality. They can also pose risks to privacy and may enable the misuse of data for the exploitation of individuals and markets.
Despite a range of international open data initiatives and pressures, developing countries are lagging behind in the implementation of reforms at government level, in the overall availability of data, and in the use of open data for transparency and accountability. What is more, there are signs that ‘open-washing’ –superficial efforts to publish data without full integration with transparency commitments – may be obscuring backsliding in other aspects of accountability.
The topic guide pulls together lessons and guidance from open data, transparency and accountability work,including an outline of technical and non-technical aspects of implementing a government open data initiative. It also lists further resources, tools and guidance….(More)”
Data for Policy: Data Science and Big Data in the Public Sector
Innar Liiv at OXPOL: “How can big data and data science help policy-making? This question has recently gained increasing attention. Both the European Commission and the White House have endorsed the use of data for evidence-based policy making.
Still, a gap remains between theory and practice. In this blog post, I make a number of recommendations for systematic development paths.
RESEARCH TRENDS SHAPING DATA FOR POLICY
‘Data for policy’ as an academic field is still in its infancy. A typology of the field’s foci and research areas are summarised in the figure below.
Besides the ‘data for policy’ community, there are two important research trends shaping the field: 1) computational social science; and 2) the emergence of politicised social bots.
Computational social science (CSS) is an new interdisciplinary research trend in social science, which tries to transform advances in big data and data science into research methodologies for understanding, explaining and predicting underlying social phenomena.
Social science has a long tradition of using computational and agent-based modelling approaches (e.g.Schelling’s Model of Segregation), but the new challenge is to feed real-life, and sometimes even real-time information into those systems to get gain rapid insights into the validity of research hypotheses.
For example, one could use mobile phone call records to assess the acculturation processes of different communities. Such a project would involve translating different acculturation theories into computational models, researching the ethical and legal issues inherent in using mobile phone data and developing a vision for generating policy recommendations and new research hypothesis from the analysis.
Politicised social bots are also beginning to make their mark. In 2011, DARPA solicited research proposals dealing with social media in strategic communication. The term ‘political bot’ was not used, but the expected results left no doubt about the goals…
The next wave of e-government innovation will be about analytics and predictive models. Taking advantage of their potential for social impact will require a solid foundation of e-government infrastructure.
The most important questions going forward are as follows:
- What are the relevant new data sources?
- How can we use them?
- What should we do with the information? Who cares? Which political decisions need faster information from novel sources? Do we need faster information? Does it come with unanticipated risks?
These questions barely scratch the surface, because the complex interplay between general advancements of computational social science and hovering satellite topics like political bots will have an enormous impact on research and using data for policy. But, it’s an important start….(More)”