Strengthening CRVS Systems to Improve Migration Policy: A Promising Innovation

Curated on Posted on by Stefaan Verhulst

Blog by Tawheeda Wahabzada and Deirdre Appel: “Migration is one of the most pressing issues of our time and innovation for migration policy can take on several different shapes to help solve challenges. It is seen through radical technological breakthrough such as biometric identifiers that completely transform the status quo as well as technological disruptions like mobile phone fund transforms that alter an existing process. There is also incremental innovation, or the gradual improvement of an existing process or institution even. Regardless of where the fall on the spectrum, their innovative applications are all relevant to migration policy.

Incremental innovation for civil registration and vital statistics (CRVS) systems can greatly benefit migrants and the policymakers trying to help them. According to World Health Organization, a well-functioning CRVS system registers all births and deaths, issues birth and death certificates, and compiles and disseminates vital statistics, including cause of death information. It may also record marriages and divorces. Each of these services brings a world of crucial advantages. But despite the social and legal benefits for individuals, especially migrants, these systems remain underfunded and under functioning. More than 100 low and middle-income countries lack functional CRVS systems and about one-third of all births are not registered. This amounts to more than one billion people without a legal identity leaving them unable to prove who they are and creating serious barriers to access health, education, financial, and other social services.

Throughout countries in Africa, there are great differences in CRVS coverage, where birth coverage ranges from above 90 percent in some North African countries to under 50 percent across several countries in different regions; and with death registration having greater gaps with either no information or lower coverage rates. For countries with low functioning CRVS systems, potential migrants from these countries could face additional obstacles in obtaining birth certificates and proof of identification….(More)”. See also https://data4migration.org/blog/

Morocco finds a new source of policy expertise — its own citizens

Curated on Posted on by Stefaan Verhulst

Participo: “This spring saw the release of a long-awaited report by the Commission Spéciale sur le modèle de developpement (CSMD), created in 2019 by His Majesty King Mohammed VI….

“Blue ribbon” commissions to tackle thorny issues are nothing new. But the methods employed by Morocco’s CSMD, and the proposals which resulted from them, point the way toward an entirely new approach to governance in the Middle East and North Africa (MENA) region.

Morocco’s new model of development was created through methods of collective intelligence, an emerging science that explores how groups can outperform individuals in learning, decision making, and problem-solving.

It is an ability that has long defined our species, from coordinated bands of hunters on the savannah to the networks of scientists that develop coronavirus vaccines. A complex environment has conditioned humans to pool their knowledge to survive. But collective intelligence doesn’t just happen; for the “wisdom of crowds” to emerge, a group must be organized in the right way, with the right methods and tools….

Beginning in January 2020, the CSMD launched a broad national consultation open to all Moroccan citizens, aimed at harnessing a wide variety of expertise from local communities, government, NGOs, and the private sector.

Its multi-channel approach was designed to reflect four indicators that studies suggest are critical to producing collective intelligence: a diversity of participants and information sources; a critical mass of contributions; a sufficiently rich exchange of information at each “touch point”; and an effective process to synthesize contributions into a coherent whole.

The CSMD created an online platform with opportunities to give quick feedback (“What is one thing you want to change about Morocco?”), as well as more detailed proposals on themes like health care and territorial inequality. A social media campaign reached an estimated 3.2 million citizens, with dozens of “participatory workshops” live-streamed on Facebook and YouTube.

To seek out the knowledge of those least connected to these channels, the CSMD conducted 30 field visits to struggling urban districts, universities, and remote villages in the High Atlas mountains. These field visits featured learning sessions with social innovators and rencontres citoyennes (“citizen encounters”) where groups of 20 to 30 local residents, balanced by age and gender, shared stories and aspirations….(More)”.

Old Cracks, New Tech

Curated on Posted on by Stefaan Verhulst

Paper for the Oxford Commission on AI & Good Governance: “Artificial intelligence (AI) systems are increasingly touted as solutions to many complex social and political issues around the world, particularly in developing countries like Kenya. Yet AI has also exacerbated cleavages and divisions in society, in part because those who build the technology often do not have a strong understanding of the politics of the societies in which the technology is deployed.

In her new report ‘Old Cracks, New Tech: Artificial Intelligence, Human Rights, and Good Governance in Highly Fragmented and Socially Stratified Societies: The Case of Kenya’ writer and activist Nanjala Nyabola explores the Kenyan government’s policy on AI and blockchain technology and evaluates it’s success.Commissioned by the Oxford Commission for Good Governance (OxCAIGG), the report highlights lessons learnt from the Kenyan experience and sets out four key recommendations to help government officials and policy makers ensure good governance in AI in public and private contexts in Kenya.

The report recommends:

  • Conducting a deeper and more wide-ranging analysis of the political implications of existing and proposed applications of AI in Kenya, including comparisons with other countries where similar technology has been deployed.
  • Carrying out a comprehensive review of ongoing implementations of AI in both private and public contexts in Kenya in order to identify existing legal and policy gaps.
  • Conducting deeper legal research into developing meaningful legislation to govern the development and deployment of AI technology in Kenya. In particular, a framework for the implementation of the Data Protection Act (2019) vis-à-vis AI and blockchain technology is urgently required.
  • Arranging training for local political actors and researchers on the risks and opportunities for AI to empower them to independently evaluate proposed interventions with due attention to the local context…(More)”.

Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access

Curated on Posted on by Stefaan Verhulst

Paper by Nathan Ratledge et al: “In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments….(More)”.

Big Data in Biodiversity Science: A Framework for Engagement

Curated on Posted on by Stefaan Verhulst

Paper by Tendai Musvuugwa, Muxe Gladmond Dlomu and Adekunle Adebowale: “Despite best efforts, the loss of biodiversity has continued at a pace that constitutes a major threat to the efficient functioning of ecosystems. Curbing the loss of biodiversity and assessing its local and global trends requires a vast amount of datasets from a variety of sources. Although the means for generating, aggregating and analyzing big datasets to inform policies are now within the reach of the scientific community, the data-driven nature of a complex multidisciplinary field such as biodiversity science necessitates an overarching framework for engagement. In this review, we propose such a schematic based on the life cycle of data to interrogate the science. The framework considers data generation and collection, storage and curation, access and analysis and, finally, communication as distinct yet interdependent themes for engaging biodiversity science for the purpose of making evidenced-based decisions. We summarize historical developments in each theme, including the challenges and prospects, and offer some recommendations based on best practices….(More)”.

Designing data collaboratives to better understand human mobility and migration in West Africa

Curated on Posted on by Stefaan Verhulst


“The Big Data for Migration Alliance (BD4M) is released the report, “Designing Data Collaboratives to Better Understand Human Mobility and Migration in West Africa,” providing findings from a first-of-its-kind rapid co-design and prototyping workshop, or “Studio.” The first BD4M Studio convened over 40 stakeholders in government, international organizations, research, civil society, and the public sector to develop concrete strategies for developing and implementing cross- sectoral data partnerships, or “data collaboratives,” to improve ethical and secure access to data for migration-related policymaking and research in West Africa.

BD4M is an effort spearheaded by the International Organization for Migration’s Global Migration Data Analysis Centre (IOM GMDAC), European Commission’s Joint Research Centre (JRC), and The GovLab to accelerate the responsible and ethical use of novel data sources and methodologies—such as social media, mobile phone data, satellite imagery, artificial intelligence—to support migration-related programming and policy on the global, national, and local levels. 

The BD4M Studio was informed by The Migration Domain of The 100 Questions Initiative — a global agenda-setting exercise to define the most impactful questions related to migration that could be answered through data collaboration. Inspired by the outputs of The 100 Questions, Studio participants designed data collaboratives that could produce answers to three key questions: 

  1. How can data be used to estimate current cross-border migration and mobility by sex and age in West Africa?
  2.  How can data be used to assess the current state of diaspora communities and their migration behavior in the region?
  3. How can we use data to better understand the drivers of migration in West Africa?…(More)”

Mapping Africa’s Buildings with Satellite Imagery

Curated on Posted on by Stefaan Verhulst

Google AI Blog: “An accurate record of building footprints is important for a range of applications, from population estimation and urban planning to humanitarian response and environmental science. After a disaster, such as a flood or an earthquake, authorities need to estimate how many households have been affected. Ideally there would be up-to-date census information for this, but in practice such records may be out of date or unavailable. Instead, data on the locations and density of buildings can be a valuable alternative source of information.

A good way to collect such data is through satellite imagery, which can map the distribution of buildings across the world, particularly in areas that are isolated or difficult to access. However, detecting buildings with computer vision methods in some environments can be a challenging task. Because satellite imaging involves photographing the earth from several hundred kilometres above the ground, even at high resolution (30–50 cm per pixel), a small building or tent shelter occupies only a few pixels. The task is even more difficult for informal settlements, or rural areas where buildings constructed with natural materials can visually blend into the surroundings. There are also many types of natural and artificial features that can be easily confused with buildings in overhead imagery.

In “Continental-Scale Building Detection from High-Resolution Satellite Imagery”, we address these challenges, using new methods for detecting buildings that work in rural and urban settings across different terrains, such as savannah, desert, and forest, as well as informal settlements and refugee facilities. We use this building detection model to create the Open Buildings dataset, a new open-access data resource containing the locations and footprints of 516 million buildings with coverage across most of the African continent. The dataset will support several practical, scientific and humanitarian applications, ranging from disaster response or population mapping to planning services such as new medical facilities or studying human impact on the natural environment….(More)”.

Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

Curated on Posted on by Stefaan Verhulst

Paper by Emily Aiken et al: “The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth.

Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date….(More)”.

Enhancing teacher deployment in Sierra Leone: Using spatial analysis to address disparity

Curated on Posted on by Stefaan Verhulst

Blog by Paul Atherton and Alasdair Mackintosh:”Sierra Leone has made significant progress towards educational targets in recent years, but is still struggling to ensure equitable access to quality teachers for all its learners. The government is exploring innovative solutions to tackle this problem. In support of this, Fab Inc. has brought their expertise in data science and education systems, merging the two to use spatial analysis to unpack and explore this challenge….

Figure 1: Pupil-teacher ratio for primary education by district (left); and within Kailahun district, Sierra Leone, by chiefdom (right), 2020.

maps

Source: Mackintosh, A., A. Ramirez, P. Atherton, V. Collis, M. Mason-Sesay, & C. Bart-Williams. 2019. Education Workforce Spatial Analysis in Sierra Leone. Research and Policy Paper. Education Workforce Initiative. The Education Commission.

…Spatial analysis, also referred to as geospatial analysis, is a set of techniques to explain patterns and behaviours in terms of geography and locations. It uses geographical features, such as distances, travel times and school neighbourhoods, to identify relationships and patterns.

Our team, using its expertise in both data science and education systems, examined issues linked to remoteness to produce a clearer picture of Sierra Leone’s teacher shortage. To see how the current education workforce was distributed across the country, and how well it served local populations, we drew on geo-processed population data from the Grid-3 initiative and the Government of Sierra Leone’s Education Data Hub. The project benefited from close collaboration with the Ministry and Teaching Service Commission (TSC).

Our analysis focused on teacher development, training and the deployment of new teachers across regions, drawing on exam data. Surveys of teacher training colleges (TTCs) were conducted to assess how many future teachers will need to be trained to make up for shortages. Gender and subject speciality were analysed to better address local imbalances. The team developed a matching algorithm for teacher deployment, to illustrate how schools’ needs, including aspects of qualifications and subject specialisms, can be matched to teachers’ preferences, including aspects of language and family connections, to improve allocation of both current and future teachers….(More)”

Text Your Government: Participatory Cell Phone Technology in Ghana

Curated on Posted on by Stefaan Verhulst

Article by Emily DiMatteo: “Direct citizen engagement can be transformed with innovative technological tools. As communities search for new ways to connect citizens to democratic processes, using existing technological devices such as cell phones can reach a number of citizens—including those typically excluded from policy processes. This occurred in Ghana when a technology startup and social enterprise called VOTO Mobile (now Viamo) created polling and information sharing software that uses mobile phone SMS texts and voice calls. Since its founding in 2010, the Ghana-based company has worked to use mobile technology to advance democratic engagement and good governance through new communication channels between citizens and their government.

Previous methods to overcome public participation challenges in Ghana include using public radio. However, when VOTO Mobile evaluated technological capabilities in several districts, cell phones offered a new way to engage. The option to contact citizens via text or voice call also helped remove certain barriers to participation in political processes, including distance, language and literacy. In 2012-2013, VOTO Mobile facilitated a project called the, “Mobile for Social Inclusive Government,” to increase citizen engagement and participation. The project used the company’s software to disseminate local information and conduct citizen surveys in four Ghanaian districts: Tamale, Savelugu, Wa and Yendi. VOTO Mobile partnered with civil society organizations including Savana Signatures, GINKS and Amplify Governance, as well as District Assemblies in local district governments.

Participant selection for the project utilized pre-existing District Assembly membership data across the four districts to contact citizens to participate. This outreach also was supplemented by the project’s partner organizations and ultimately involved more than 2,000 participants. In using VOTO Mobile’s technological platform of interactive text and voice call surveys, the project gathered feedback from citizens as they shared concerns with their local government. There was a large focus on input from marginalized populations across the districts including women, young people and people with disabilities. In addition to the cell phone surveys, the platform enabled online consultations between citizens and local district officials in place of face-to-face visits.

As a result, local district governments were able to crowdsource information directly from citizens, leading to increased citizen input in subsequent policy formulation and planning processes….(More)”.