Circular Temporary Labour Migration (CTLM) is being promoted as an innovative and viable way of regulating the flow of labour migrants. Based on a specific empirical case study, we identify an unexpected outcome of CTLM programmes: the emergence of a new empirical migrant category, the circular labour migrant, which is as yet theoretically unnamed and lacks recognition by public institutions. We argue that, to date, there have been two historical phases of circular labour migration: one with total deregulation and another with partial regulation, involving private actors supported by public institutions. In a developed welfare state context, it would be normatively pertinent to expect a step towards a third phase, involving the institutionalization of this new trend in mobility by the formulation of a public policy. Current legal, political, social, and economic frameworks have to be reassessed in order to recognise the category of the circular labour migrant.
Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.