APA Monthly Webinar

Please join us for a special APA Webinar presented by PhD Borrie Prize winner, Gin Wu.

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Abstract:
Internal migration, defined as a change of region of residence within national borders, has become one of the main mechanisms to redistribute populations between regions, thereby profoundly influencing the composition of subnational populations and regional economic development while allowing individuals to meet their personal needs and aspirations. Despite significant advances in internal migration research in recent years, most studies are still based on a dichotomy between migrants and non-migrants, which conceals the extent of repeat migration. In addition, increasing research attention has been directed toward examining subjective and non-economic outcomes of migration while the economic benefits of internal migration are widely documented. Leveraging the maturation of longitudinal surveys and methodological advances, this presentation systematically establishes numerical and social significance of repeat migration by examining the diversity and complexity of individual migration trajectories in Australia and Europe. It does so by jointly considering the timing, frequency, direction, and distance of migration across key periods of the life course. This presentation further explores the long-term association between internal migration trajectories with non-economic outcomes such as subjective well-being and social networks. Findings suggest that successive migrations cumulatively shape life satisfaction, with significant differences emerging across different types of migration trajectories, which reveals a nuanced and complex picture of the outcomes of repeat migration.

Bio:
Jing Wu is a PhD student at the University of Queensland where she works on establishing the diversity of repeat internal migration trajectories and their long-term associations with life outcomes over the life course. Trained as an economist, she has used her strong analytical skills to study internal migration longitudinally using both survey and administrative data and a range of advanced analytical techniques such as sequence and cluster analysis and machine learning techniques.