Xinyi Kou - Estimating international bilateral migrations: an agent-based model approach

  • Presenting author: Xinyi Kou (University of Manchester)

  • Authors: Xinyi Kou, Arkadiusz Wisniowski, Natalie Shlomo, Eduardo Fe Rodriguez

  • Session: C03D - Migration - Wednesday 14:00-15:30 - Erika-Weinzierl Hall

  • Slides: PDF

The study of population dynamics has always been at the centre of public policy and planning due to its vital role in human society. Because of the low fertility and mortality in Europe, international migration is becoming an increasingly important factor in shaping population structures for European countries. Among all population components, international migration is the most challenging element to predict, and it plays a key role in population change and population geographic redistribution. International migration studies suffer from variety of underlying uncertainties, including the accuracy of data measurement, different migration drivers and the irreducible forecast uncertainties. Traditional statistical models, such as time series model, have been applied to predict international migration flows. However, these approaches perform poorly when migration data are limited, they fail to explain the mechanisms of migration process. To gain a better insight of migration behaviours, we present an agent-based model to estimate and explain international migrations. The ABM theoretical model adopts the theory of planned behaviour (TPB) as its migration decision rules to estimate migration behaviours, integrating with simulation modules simulating demographic changes to increase the model prediction accuracy. The resulting theoretical model has been applied to measure the bilateral migration flows among Poland, Germany, and United Kingdom. For the estimation purpose, we utilise a Gaussian Process emulator to calibrate the model with migration stock data, and then perform the sensitivity analysis. The proposed modelling framework shows its ability to estimate migration patterns. This research shifts the focus from macro-level migration numbers to micro-level individual migration decisions and generates a ‘real-world’ quantity of interest.