From Uncertainty to Policy: A Guide to Migration Scenarios is a book that focuses on forecasting international migration, with an emphasis on how states can estimate future migration trends. The case examples focus on Europe, defined as the ‘EU+ system’, consisting of the 27 Member States of the European Union, as well as Iceland, Liechtenstein, Norway, Switzerland, and the UK. It serves both as an academic overview of the state of the art in quantitative future‑oriented migration studies and a practical guidebook for those interested in short‑term forecasting of forced migration crises or the impact of migration on longer‑term population projections. The edited volume consists of 12 chapters divided into five parts: 1) Foundations; 2) Dealing with Epistemic Uncertainty: Concepts, Drivers, and Data; 3) Adapting to Aleatory Uncertainty: Scanning the Future with Scenarios; 4) From Migration Scenarios to Evidence‑Informed Policies; and 5) Conclusions. The chapters are authored by an interdisciplinary team of 30 researchers involved in the Horizon 2020 project QuantMig: Quantifying Migration Scenarios for Better Policy (2020–2023, www.quantmig.eu). The book includes insights from statistics, migration research, sociology, geography, and demography, among others. In my opinion, it is a useful source for policymakers interested in forecasting migration.
International migration is a key driver of population change, yet both in‑ and out‑migration are significantly more difficult to estimate than fertility or mortality. This is because migration is notable for its complexity and uncertainty, which makes predicting future migration trends difficult. If the reader of this book expects to find an easy solution to this dilemma, they are in for a disappointment. However, the book guides the reader through the various complexities of migration and explains how uncertainty operates at both the micro (individual) and macro (population) levels. The book stresses that uncertainty comes in two varieties. First is epistemic uncertainty, which refers to factors that are potentially knowable and, therefore, reducible as we learn more in the future. Second is aleatory uncertainty, which refers to factors that are unknowable and intrinsically random and, therefore, are things that we cannot influence or fully estimate (see also Susmann et al. 2022).
Part I of the book provides the foundations and key arguments of the book that are further developed later. First, Jakub Bijak (Chapter 1) provides a solution for estimating future migration trends based on quantitative migration scenarios that are adjusted as new information arises. The scenarios should consider, for example, all available and relevant data on migration drivers in countries of origin, transit, and destination and the available estimates of migration flows. These scenarios reflect the consequences of assumptions regarding the future trajectories of international migration. This is followed by a chapter by Jakub Bijak and Mathias Czaika (Chapter 2), who provide clear guidelines for constructing migration scenarios: defining the purpose of the scenario and its time horizon, estimating uncertainty, selecting appropriate methodology and data, and communicating the results. Because of the uncertainty involved even in the best possible scenarios, the reader is reminded of two things: For the creators of these estimates, it is important to avoid the illusion of precision; and for the users of them, it is key to avoid the illusion of control.
Part II continues to elaborate on epistemic uncertainty in migration scenarios and the conceptual challenges in forward‑looking migration studies. The chapter by Mathias Czaika, Heidrun Bohnet, Federica Zardo, and Jakub Bijak (Chapter 3) serves as a concise summary of what migration is, who migrants are, what drives people to migrate internationally, and what the impacts of migration policies are. The authors conclude that good‑enough, feasible solutions in future migration scenarios are the best one can aim for, given the complexity of the issue at hand (see also Czaika & Bijak 2020). The following chapter by Marta Bivand Erdal, Helga de Valk, Jackline Wahba, and Jakub Bijak (Chapter 4) dives deeper into migration drivers across time and space to provide an overview of what we know about why people decide to move abroad. It builds on the aspirations and capabilities model (Carling & Schewel 2018) and our current understanding of migration decision‑making (e.g. Hagen‑Zanker & Mallett 2023). It highlights migration drivers both in countries of origin and destination, including factors such as economic development, conflict and violence, and environmental factors. Both of these chapters are highly useful for students and other readers interested in what current research tells us about these key questions in migration research.
Part III continues to deepen the discussion of uncertainty; the gaze shifts from epistemic uncertainty – i.e. the knowable facts, such as past trends, current estimates, and leading indicators – to unknowable factors that we will never discover before they wreak havoc in our migration scenarios. From the chapter by Emily Barker and Jakub Bijak (Chapter 6), the reader learns what model‑based, expert‑based, probabilistic, descriptive, continuous, and discrete uncertainty are and how their impact can be minimised when estimating future migration flows in the short‑ and long‑term. Michaela Potančoková, Helga de Valk, Rafael Costa, Michaël Boissonneault, and Jakub Bijak (Chapter 7) present efforts to combine qualitative and quantitative approaches (Boissonneault et al. 2020) and show how methods such as factorial‑survey experiments and microsimulations can be used to create more holistic migration scenarios.
Part IV moves from migration scenarios to evidence‑informed policies and discusses the pitfalls of knowledge exchange between research and policy, for example. This section of the book is, unfortunately, not as coherent as the previous three sections. Yet, it does continue to elaborate on the key sources of uncertainty, including data problems, incidence and prevalence of migration, and different types of flows, and it shows that current forecasts can adequately predict the trajectory of ongoing crises but are less adept at pinpointing the arrival of new crises that have yet to emerge (see also Czaika & Bijak 2020). The section’s final substantive chapter by Ann Singleton (Chapter 11) is a rather philosophical take on the context and ethics of migration scenarios. It argues for decolonising migration studies, challenging pre‑existing data categories, and reflexivity in being open to different forms of knowledge. It raises important questions, but reads a bit as an afterthought to an otherwise rather quantitatively oriented book that aims to address policy challenges in migration forecasting, a practice that states regularly engage in.
In part V, Jakub Bijak (Chapter 12) outlines the most important takeaway messages of the book that summarise the findings of the QuantMig project (see also Bijak et al. 2023). According to Bijak, the edited volume provides a blueprint for exploring future migration flows across a range of time horizons. He notes three gaps or challenges that need to be considered when researchers or policymakers aim to set realistic migration scenarios to address policy challenges: 1) gaps in current knowledge, data, and methods; 2) gaps in policies and decisions; and 3) communication gaps. Bijak argues that an understanding of these is required for meaningful migration scenarios to be developed and revised as more information becomes available.
In conclusion, the book is a concise and highly recommendable, welcome overview of what is currently known about forecasting possible migration futures. However, I do have a negative comment concerning the graphs and images of the book, which, at least in the printed version, are too small to be perfectly legible. This reduces their information value. However, this criticism does not diminish the importance of the overall contribution that the book makes in the field of migration research and policy. The recognition that uncertainty is intrinsic to migration forecasting highlights a larger policy dilemma: how to design migration governance systems that are resilient to the unexpected. For us migration scholars, a crucial task is to try to communicate the limits of forecasting to policymakers. Migration governance that seeks to control or deter unwanted humanitarian migration often rests on misplaced assumptions of predictability – an assumption that research such as this can challenge.
Competing Interests
The author has no competing interests to declare.