Using WeChat Data to Understand Residential Mobility of Mainland China-Born Migrants in Australia: A Segmented Assimilation Perspective

Abstract

Successive waves of international migration have rendered the residential patterns of cities a variegated patchwork of ethnical communities and spaces. Various theories attempt to account for this diversity, with particular regard to the motivations and drivers that explain the settlement pathways of individuals and households vis-a-vis co-ethnic residents in their city. This study provides a refined understanding of how the settlement pathways of international migrants are shaped by their residential mobility after arrival in the destination city. Applying a ‘segmented assimilation’ model, we assert that the residential mobility of international migrants is accompanied by the formation of both ethnic concentration and deconcentration simultaneously, the extent of which is determined by migrant-specific characteristics and neighbourhood features. To illustrate this, we examine the residential mobility of Mainland China (MC)-born migrants in Australia since settlement. We analyse primary survey collected by WeChat, a mobile application, to track residential mobility over the course of individuals’ settlement in Australia, the principal motives behind each move, and the determinants of successive residential moves. The findings indicate that residential moves correspond to three phases — the first tied to certain migrant-specific characteristics and in particular English proficiency, the second tied to education/career and residential environment, and the third driven by ‘optimisation’, which reflects a desire to co-locate with friends and family, or to be closer to work and transport options. Segmented assimilation, as reflected by residential mobility, is stratified within migrant concentrations and distinguished by space, and this non-linear process is associated with a combination of personal preference, migrants’ socioeconomic characteristics, and residential features.