Repository of 80+ real-life examples of how to anticipate migration using innovative forecast and foresight methods is now LIVE!

Launch! Repository of 80+ real-life examples of how to anticipate migration using innovative forecast and foresight methods is now LIVE!

BD4M Announcement: “Today, we are excited to launch the Big Data For Migration Alliance (BD4M) Repository of Use Cases for Anticipating Migration Policy! The repository is a curated collection of real-world applications of anticipatory methods in migration policy. Here, policymakers, researchers, and practitioners can find a wealth of examples demonstrating how foresight, forecast and other anticipatory approaches are applied to anticipating migration for policy making. 

Migration policy is a multifaceted and constantly evolving field, shaped by a wide variety of factors such as economic conditions, geopolitical shifts or climate emergencies. Anticipatory methods are essential to help policymakers proactively respond to emerging trends and potential challenges. By using anticipatory tools, migration policy makers can draw from both quantitative and qualitative data to obtain valuable insights for their specific goals. The Big Data for Migration Alliance — a join effort of The GovLab, the International Organization for Migration and the European Union Joint Research Centre that seeks to improve the evidence base on migration and human mobility — recognizes the importance of the role of anticipatory tools and has worked on the creation of a repository of use cases that showcases the current use landscape of anticipatory tools in migration policy making around the world. This repository aims to provide policymakers, researchers and practitioners with applied examples that can inform their strategies and ultimately contribute to the improvement of migration policies around the world. 

As part of our work on exploring innovative anticipatory methods for migration policy, throughout the year we have published a Blog Series that delved into various aspects of the use of anticipatory methods, exploring their value and challenges, proposing a taxonomy, and exploring practical applications…(More)”.

Private tech, humanitarian problems: how to ensure digital transformation does no harm

Report by Access Now: “People experiencing vulnerability as a consequence of conflict and violence often rely on a small group of humanitarian actors, trusted because of their claims of neutrality, impartiality, and independence from the warring parties. They rely on these humanitarian organisations and agencies for subsistence, protection, and access to basic services and information, in the darkest times in their lives. Yet these same actors can expose them to further harm. Our new report, Mapping Humanitarian Tech: exposing protection gaps in digital transformation programmes, examines the partnerships between humanitarian actors and private corporations. Our aim is to show how these often-opaque partnerships impact the digital rights of the affected communities, and to offer recommendations for keeping people safe…(More)”.

Defending the rights of refugees and migrants in the digital age

Primer by Amnesty International: “This is an introduction to the pervasive and rapid deployment of digital technologies in asylum and migration management systems across the globe including the United States, United Kingdom and the European Union. Defending the rights of refugees and migrants in the digital age, highlights some of the key digital technology developments in asylum and migration management systems, in particular systems that process large quantities of data, and the human rights issues arising from their use. This introductory briefing aims to build our collective understanding of these emerging technologies and hopes to add to wider advocacy efforts to stem their harmful effects…(More)”.

Selecting Anticipatory Methods for Migration Policy: Eight Key Elements To Consider

Blog by Sara Marcucci, Stefaan Verhulst, and Alina Menocal Peters: “Over the past several weeks, we’ve embarked on a journey exploring anticipatory methods for migration policy. Our exploration has taken us through the value proposition, challenges, taxonomy, and practical applications of these innovative methods. In this concluding blog, we unveil eight key considerations that policymakers’ may want to consider when choosing an anticipatory method for migration policy. By dissecting these factors, our intent is to equip decision-makers to navigate the complexities inherent in selecting anticipatory methodologies. 

  1. Nature and Determinants of Migration

When addressing migration policy challenges, the multifaceted nature of the type of migration is important when selecting anticipatory methods. Indeed, the specific challenges associated with anticipating migration can vary widely based on the context, causes, and characteristics of the movement. The complexity of the question at hand often determines the selection of methods or approaches. For instance, managing the integration of displaced populations following a conflict involves intricate factors such as cultural adaptation, economic integration, and community dynamics. If the question is about understanding the inferences and drivers that can predict migration patterns, methods like Cross-impact Analysis or System Dynamics Modeling can prove to be valuable. These can facilitate a comprehensive assessment of interdependencies and potential ripple effects, offering policymakers insights into the dynamic and interconnected nature of challenges associated with migration…(More)…See also Special Series on Anticipating Migration.

Measuring Global Migration: Towards Better Data for All

Book by Frank Laczko, Elisa Mosler Vidal, Marzia Rango: “This book focuses on how to improve the collection, analysis and responsible use of data on global migration and international mobility. While migration remains a topic of great policy interest for governments around the world, there is a serious lack of reliable, timely, disaggregated and comparable data on it, and often insufficient safeguards to protect migrants’ information. Meanwhile, vast amounts of data about the movement of people are being generated in real time due to new technologies, but these have not yet been fully captured and utilized by migration policymakers, who often do not have enough data to inform their policies and programmes. The lack of migration data has been internationally recognized; the Global Compact for Safe, Orderly and Regular Migration urges all countries to improve data on migration to ensure that policies and programmes are “evidence-based”, but does not spell out how this could be done.

This book examines both the technical issues associated with improving data on migration and the wider political challenges of how countries manage the collection and use of migration data. The first part of the book discusses how much we really know about international migration based on existing data, and key concepts and approaches which are often used to measure migration. The second part of the book examines what measures could be taken to improve migration data, highlighting examples of good practice from around the world in recent years, across a range of different policy areas, such as health, climate change and sustainable development more broadly.

Written by leading experts on international migration data, this book is the perfect guide for students, policymakers and practitioners looking to understand more about the existing evidence base on migration and what can be done to improve it…(More)”. (See also: Big Data For Migration Alliance).

Innovation in Anticipation for Migration: A Deep Dive into Methods, Tools, and Data Sources

Blog by Sara Marcucci and Stefaan Verhulst: “In the ever-evolving landscape of anticipatory methods for migration policy, innovation is a dynamic force propelling the field forward. This seems to be happening in two main ways: first, as we mentioned in our previous blog, one of the significant shifts lies in the blurring of boundaries between quantitative forecasting and qualitative foresight, as emerging mixed-method approaches challenge traditional paradigms. This transformation opens up new pathways for understanding complex phenomena, particularly in the context of human migration flows. 

Innovation in Anticipation for Migration: A Deep Dive into Methods, Tools, and Data Sources

Second, the innovation happening today is not necessarily rooted in the development of entirely new methodologies, but rather in how existing methods are adapted and enhanced. Indeed, innovation seems to extend to the utilization of diverse tools and data sources that bolster the effectiveness of existing methods, offering a more comprehensive and timely perspective on migration trends.

In the context of this blog series, methods refer to the various approaches and techniques used to anticipate and analyze migration trends, challenges, and opportunities. These methods are employed to make informed decisions and develop policies related to human migration. They can include a wide range of strategies to gather and interpret data and insights in the field of migration policy. 

Tools, on the other hand, refer to the specific instruments or technologies used to support and enhance the effectiveness of these methods. They encompass a diverse set of resources and technologies that facilitate data collection, analysis, and decision-making in the context of migration policy. These tools can include both quantitative and qualitative data collection and analysis tools, as well as innovative data sources, software, and techniques that help enhance anticipatory methods.

This blog aims to deep dive into the main anticipatory methods adopted in the field of migration, as well as some of the tools and data sources employed to enhance and experiment with them. First, the blog will provide a list of methods considered; second, it will illustrate the main innovative tools employed, and finally it will provide a set of new, non-traditional data sources that are increasingly being used to feed anticipatory methods…(More)”.

Towards a Taxonomy of Anticipatory Methods: Integrating Traditional and Innovative Methods for Migration Policy

Towards a Taxonomy of Anticipatory Methods: Integrating Traditional and Innovative Methods for Migration Policy

Blog by Sara Marcucci, and Stefaan Verhulst: “…In this week’s blog post, we delineate a taxonomy of anticipatory methods, categorizing them into three distinct sub-categories: Experience-based, Exploration-based, and Expertise-based methods. Our focus will be on what the practical applications of these methods are and how both traditional and non-traditional data sources play a pivotal role within each of these categories. …Experience-based methods in the realm of migration policy focus on gaining insights from the lived experiences of individuals and communities involved in migration processes. These methods allow policymakers to tap into the lived experiences, challenges, and aspirations of individuals and communities, fostering a more empathetic and holistic approach to policy development.

Through the lens of people’s experiences and viewpoints, it is possible to create and explore a multitude of scenarios. This in-depth exploration provides policy makers with a comprehensive understanding of these potential pathways, which, in turn, inform their decision-making process…(More)”.

Anticipating the Future: Shifting Paradigms

Blog by Sara Marcucci and Stefaan Verhulst: “…Migration is a dynamic phenomenon influenced by a variety of factors. As migration policies strive to keep pace with an ever-changing landscape, anticipating trends becomes increasingly pertinent. Traditionally, in the realm of anticipatory methods, a clear demarcation existed between foresight and forecast. 

  • Forecast predominantly relies on quantitative techniques to predict future trends, utilizing historical data, mathematical models, and statistical analyses to provide numerical predictions applicable to the short-to-medium term, seeking to facilitate expedited policy making, resource allocation, and logistical planning.
  • Foresight methodologies conventionally leaned on qualitative insights to explore future possibilities, employing expert judgment, scenario planning, and holistic exploration to envision potential future scenarios. This qualitative approach has been characterized by a more long-term perspective, which seeks to explore a spectrum of potential futures in the long run.

More recently, this once-clear distinction between quantitative forecasting and qualitative foresight has begun to blur. New methodologies that embrace a mixed-method approach are emerging, challenging traditional paradigms and offering new pathways for understanding complex phenomena. Despite the evolution and the growing interest in these novel approaches, there currently exists no comprehensive taxonomy to guide practitioners in selecting the most appropriate method for their given objective. Moreover, due to the state-of-the-art, there is a need for primers delving into these modern methodologies, filling a gap in knowledge and resources that practitioners can leverage to enhance their forecasting and foresight endeavors…(More)”.

Anticipating the Future: Shifting Paradigms
Anticipating the Future: Shifting Paradigms

The Good and Bad of Anticipating Migration

Article by Sara Marcucci, Stefaan Verhulst, María Esther Cervantes, Elena Wüllhorst: “This blog is the first in a series that will be published weekly, dedicated to exploring innovative anticipatory methods for migration policy. Over the coming weeks, we will delve into various aspects of these methods, delving into their value, challenges, taxonomy, and practical applications. 

This first blog serves as an exploration of the value proposition and challenges inherent in innovative anticipatory methods for migration policy. We delve into the various reasons why these methods hold promise for informing more resilient, and proactive migration policies. These reasons include evidence-based policy development, enabling policymakers to ground their decisions in empirical evidence and future projections. Decision-takers, users, and practitioners can benefit from anticipatory methods for policy evaluation and adaptation, resource allocation, the identification of root causes, and the facilitation of humanitarian aid through early warning systems. However, it’s vital to acknowledge the challenges associated with the adoption and implementation of these methods, ranging from conceptual concerns such as fossilization, unfalsifiability, and the legitimacy of preemptive intervention, to practical issues like interdisciplinary collaboration, data availability and quality, capacity building, and stakeholder engagement. As we navigate through these complexities, we aim to shed light on the potential and limitations of anticipatory methods in the context of migration policy, setting the stage for deeper explorations in the coming blogs of this series…(More)”.

Can Google Trends predict asylum-seekers’ destination choices?

Paper by Haodong Qi & Tuba Bircan: “Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration…(More)”.