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This talk presents a Bayesian framework for estimating international migration flows using imperfect and partially observed data. Migration statistics are often fragmented across sources and time, with substantial undercounting and measurement error, limiting their usefulness for understanding migration systems. Focusing on migration corridors between the EU-27 and the UK from 2011 to 2022, I show how traditional demographic data can be integrated with digital trace data to improve the estimation of migrant stocks and, subsequently, migration flows.
The approach proceeds in two stages. First, Bayesian hierarchical models are used to estimate observed and unobserved migrant stocks by combining census-based sources with social media data. Second, these stock estimates are used as inputs to derive consistent estimates of migration flows across corridors and over time. The talk introduces key concepts such as migration systems, corridors, and the stock–flow relationship, and illustrates how Bayesian modelling enables uncertainty quantification and comparability across data sources. I conclude by discussing implications for migration research and official statistics.
Please join either in person or online. For in-person attendees, the talk will be preceded by a light lunch at 12.15pm.
