Abstract
Migration—including but not limited to forced migration—is one of the greatest concerns of the twenty-first century, and is only likely to grow in importance over the coming years. Yet our understanding of the current situation, the causes, and consequences of population movements and what solutions work remain limited. In this piece, we argue that this lack of understanding is part of shortcoming that could, at least in part, be addressed through the targeted analysis of datasets dispersed across stakeholders in governments, the private sector, and civil society. Data collaboratives, an emerging form of public–private partnership that allows for collaboration across sectors and actors, have the potential to break down data siloes to the end of improving our understanding of the drivers of migration and facilitating better decision-making by those active in the space. By taking three recommended steps—mapping and documenting data collaboratives, identifying and nurturing “data stewards,” and developing data responsibility frameworks—actors in the migration field could unlock the value of data held across sectors and improve the lives of migrants, refugees and those affected by the movement of populations across borders.
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Notes
- 1.
Several have indicated, however, that the push–pull factors research has a series of limitations both on a conceptual and on a methodological level. See, for instance, [26].
- 2.
In his seminal paper, “The Market for Lemons,” economist George A. Akerlof argued that information asymmetry can lead to market failure, due to one party having more information about the characteristics and values of goods. The 2008 financial crisis is partly fueled by credit sellers having more information about the credit product.
- 3.
The rate of migration globally has not increased substantially (as a % of the world’s population) but migration has risen to the top of countries’ political agenda due to recent large flows of migrants and refugees, particularly in certain regions such as Europe.
- 4.
See, for instance, this comparative overview at the Migration Data Portal of the different types of migration data and the current gaps and limitations: https://migrationdataportal.org/themes/migration-data-sources.
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- 6.
See [36] for discussion of one methodology for inferring gender in call detail records.
- 7.
The Summary Report of the Workshop can be found in: https://bluehub.jrc.ec.europa.eu/bigdata4migration/workshop-outcome. Some of the reflections and examples listed in this paper emerged from that Workshop, which is summarized in [32].
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“Accelerating Medicines Partnership (AMP),” DataCollaboratives.org, http://datacollaboratives.org/cases/accelerating-medicines-partnership-amp.html.
- 12.
“Beeline Crowdsourced Bus Service,” DataCollaboratives.org, http://datacollaboratives.org/cases/beeline-crowdsourced-bus-service.html.
- 13.
“DigitalGlobe imagery assists UNHRC in tracking Sudanese refugees,” https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/65/DG-REFMAP-CS_WEB_0.pdf.
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Acknowledgments
The authors would like to thank Michele Vespe and Marzia Rango for their excellent suggestions and peer review; and members of the GovLab research team, Audrie Pirkl, Michelle Winowatan, and Mastoureh Sadeghnia for their research assistance.
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Verhulst, S.G., Young, A. (2019). The Potential and Practice of Data Collaboratives for Migration. In: Salah, A., Pentland, A., Lepri, B., Letouzé, E. (eds) Guide to Mobile Data Analytics in Refugee Scenarios. Springer, Cham. https://doi.org/10.1007/978-3-030-12554-7_24
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