How socioeconomic and environmental factors impact the migration destination choices of different population groups in China: an eigenfunction-based spatial filtering analysis

Abstract

Understanding how different factors impact migration destination choices is one of the main research themes in demographic studies. The current study uses relative intrinsic attractivity (RIA) as a measure for a place’s attractivity and attempts to apply an eigenfunction-based spatial filtering (ESF) approach to investigate the relationships between a place’s attractivity and 12 socioeconomic and natural condition factors in China at the prefecture level. Results suggest that the ESF approach may provide a potentially more robust way to account for how various factors impact different groups of people’s migration destination choices than non-spatial and spatial autoregressive models. The ESF approach is able to adequately address the spatial effects on data analysis when using geographic data and provide easily interpretable results. Places with better accessibility by roads, better economic opportunities (jobs and wages), and cooler average annual temperature are more attractive to all subgroups of migrants. Different sub-groups of migrants, however, are also attracted to places with different priorities and characteristics. The current study uses an ESF approach for the first time to investigate how factors impact different groups of people’s migration destination choices.