Modelling skilled and less‐skilled internal migrations in China, 2010–2015: Application of an eigenvector spatial filtering hurdle gravity approach

Abstract

AbstractIn consideration of the issue of network autocorrelation and zero‐inflated migration data, the study constructs an eigenvector spatial filtering (ESF) hurdle gravity model (ESF HGM) to examine the determinants of China's skilled and less‐skilled internal migrations between 2010 and 2015. In our case, the ESF technique effectively reduces network autocorrelation bias, while the hurdle approach enhances the model prediction on the probability of zeros. Results from the ESF HGM have illustrated significant differences in the determinants between skilled and less‐skilled migrations. It is found that the gravity factors (population sizes at origins and destinations; migration distance), regional industrial structure, unemployment rate, and education level are more related to the migration of less‐skilled people. However, the migration of skilled people is more affected by the wage disparity, natural comforts, and medical services of a region. Our results have also highlighted the differences in some factors (employment rate; education level at origins) between the probability of having migration and the migration volume.