This piece by Stefaan Verhulst, Damien Jusselme, Matteo Fontana, Martina Belmonte, and Anna Rosinska was originally published on  the blog for Data & Policy (cambridge.org/dap), a peer-reviewed open access journal exploring the interface of data science and governance. See also related Call for Papers on the Data & Policy Journal site.

With the rising complexity and diversity of migration patterns worldwide, it is clear that our understanding of this phenomenon must evolve to effectively address emerging challenges and opportunities. Today, migration holds significant potential for countries of origin, transit, and destination, and thus necessitates innovative approaches to policy formulation. Anticipatory methods — including early warning systems, nowcasting, forecasting, horizon scanning, and foresight methods, among others — are proving to be potent tools in deciphering this complex issue and providing critical insights for forward-looking policy actions.

The influx of environmental and political crises, coupled with the rise of ‘smart’ technologies and a rapidly transforming work environment, have prompted a renewed interest in anticipatory tools. These tools, powered by big data and artificial intelligence, offer new avenues to predict and better understand migration trends. They also enable policymakers to incorporate data-driven insights into decision-making processes, transforming the way migration policies are designed and implemented.

In light of this, the open-access Data & Policy journal at Cambridge University Press, in collaboration with the Big Data for Migration Alliance, is delighted to announce a call for papers for a special collection focused on the theme: ‘Anticipating Migration for Policymaking: Forecasting, Foresight, and Other Forward-Looking Methods To Inform Migration Policy’.

The primary goal of this special collection is to explore how new data sources, technologies, and anticipatory techniques can assist in identifying and predicting migration trends. Further, it aims to elucidate how these predictive methods can aid decision-makers in formulating effective migration policies while maintaining the necessary legal and regulatory safeguards to protect human rights and data privacy.

We are looking for contributors who can provide a comprehensive, multidisciplinary, and practical approach to the utilization of data and anticipatory methods for migration research and policymaking. In addition, we are looking for papers that offer valuable insights, practical tools, and policy recommendations for researchers, policymakers, and practitioners to enhance their understanding and application of data-driven techniques in migration policy.

This special collection acknowledges the constant change and diversity among migrants and seeks to aid in understanding who they are, where they come from, where they are going, and why they are on the move. Such understanding is crucial in establishing and enhancing the frameworks required for effective evaluation, monitoring, and governance of migration.

The collection welcomes papers on a variety of topics including: advances in forecasting models for different types of migration; the role of foresight techniques in developing possible migration scenarios; integrating quantitative and qualitative information in formulating forecasts; ethical and human rights-respecting collection, analysis, and use of data; and much more.

We look forward to your submissions.Deadline for abstract submission is 28 July with a deadline for full submission papers on 3 November 2023. Articles will be published upon acceptance and added to a curated page for the collection.

An editorial and blog reflecting on the contributions will be published at a later date.

Submission details:

• Abstracts (28 July) should be sent to dataandpolicy@cambridge.org
• Full submissions (3 November) should be submitted to https://mc.manuscriptcentral.com/dataandpolicy

Templates for full papers and other pertinent details are on the Data & Policy Instructions for Authors.

About the Authors

The special collection will be edited by members of the Big Data for Migration Alliance, an initiative led by the IOM’s Global Migration Data Analysis Centre, the European Commission’s Knowledge Centre on Migration and Demography, and The GovLab at New York University. The team consists of Stefaan Verhulst, Damien Jusselme, Matteo Fontana, Martina Belmonte, and Anna Rosinska.

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