Francesco Rampazzo , Saïd Business School, University of Oxford
Jakub Bijak, University of Southampton
Agnese Vitali, University of Trento
Ingmar Weber, Qatar Computing Research Institute
Emilio Zagheni, Max Planck Institute for demographic Research
The accurate estimation of international migration is hampered by a lack of timely and comprehensive data as well as varying measures and definitions. In this study, we complement traditional data sources for the United Kingdom with social media data. Our aim is to understand whether information from digital traces can help measure international migration. We use the Bayesian framework proposed in the Integrated Model of European Migration to combine data from the Labour Force Survey (LFS) and Facebook Advertising Platform to study the number of European migrants in the UK, aiming to produce estimates of European migrants closer to their true number, which is the quantity of migrants known if our collection system were able to perfectly measure all the migrants (Disney 2015). We then disaggregate the estimates by age and sex using a multinomial-Dirichlet-Dirichlet model. The overarching model is thus divided into a theory-based model of migration, a measurement error model, and an age/sex distribution model. We review the quality of the LFS and Facebook data, paying particular attention to the biases of these sources. The model estimates suggest that there are more European migrants than measured by the official estimates in 2018: our model suggests a 22% undercount, although with considerable uncertainty. The final version of the paper will include as well the last 2019 estimates from the Office for National Statistics. Several sensitivity analysis techniques are used to evaluate the quality of the model. We discuss the advantages and limitations of this approach.
Presented in Session 67. Migration Measures: Methodological Issues