Laboratory

Migration and Mobility

At a Glance Projects Publications Team

Research Area

Measuring and Modeling Migration and Mobility

Migration has emerged as a central focus in demographic research because it represents one of the most dynamic and least predictable drivers of contemporary population change. As a result, rigorous analysis of migration has become indispensable for accurately interpreting demographic trends, for informing policy responses to evolving population challenges, and for building population projections.

Yet, despite its importance, the empirical foundations for studying migration remain surprisingly weak. Within demography, migration is typically measured using official sources such as censuses, population registers, and administrative records. However, these data are often incomplete in terms of coverage, rely on inconsistent definitions of migrants and migration events, and vary substantially in terms of the collection methods used across countries. This fragmentation severely constrains the comparability and reliability of migration statistics at the international level, creating persistent challenges for estimating migration flows, developing robust models of human mobility, and rigorously testing competing theoretical frameworks. Without more consistent, comprehensive, and harmonized data, progress in understanding the determinants and consequences of migration remains fundamentally limited. Strengthening the empirical basis of migration measurement is therefore not merely a technical task, but a prerequisite for meaningful theoretical and methodological advances in the study of human mobility.

A first objective of projects in this research area is to overcome the fragmented and inconsistent nature of existing international migration statistics. By integrating multiple data sources, including population registers, surveys, and censuses, and by applying Bayesian statistical modeling, we generate harmonized estimates of bilateral migration flows over time. A key outcome of this effort is the development of the Human Migration Database, a new data infrastructure designed to provide consistent, transparent, and high-quality estimates of international migration. Crucially, these estimates are constrained to remain consistent with net migration estimates derived via the residual method using data from the Human Mortality Database and the Human Fertility Database. By establishing a harmonized and methodologically sound empirical foundation, the Human Migration Database will enable more rigorous comparative research and significantly advance the testing and development of migration theories.

Second, migration models, particularly gravity-type models, have long been used to describe patterns of human mobility by drawing analogies to physical laws, where flows are determined largely by population size and distance. While these models can approximate trends, they often lack strong predictive capacity because they rely on simplified assumptions and overlook the complex social, political, and economic drivers of migration. Factors such as policy changes, conflict, social networks, and gender differences are either poorly captured or entirely absent. As a result, the ability of these models to anticipate future migration flows is limited. A key goal of this research area is therefore to bridge this gap by systematically incorporating insights from migration theory into formal models. By embedding concepts such as network effects, aspirations and capabilities, and structural constraints, these projects aim to develop more nuanced models that not only fit past data, but also offer improved predictive performance in real-world scenarios.

A third goal of this research area is to incorporate digital trace data to improve the estimation of migration rates through timely nowcasting, and to apply these methods to the analysis of migration dynamics in the context of crises and shocks, such as armed conflicts, environmental disasters, and public health emergencies. Traditional migration statistics are often released with significant delays, making them ill-suited for capturing rapidly evolving dynamics. In contrast, digital traces, such as geolocated social media activity, mobile phone records, and online search behavior, offer near real-time signals of population movement. However, these data are noisy and selective, and they are not collected with demographic inferences in mind, which makes their integration into estimation frameworks both challenging and necessary. Projects in this research area aim to develop methods that systematically incorporate such data into migration models, combining their immediacy with the robustness of established statistical approaches.

Projects of this Research Area