Article: Spatial Patterns and Determinants of Inter-county Migration in California: A Multilevel Gravity Model Approach.
Population research and policy review
2023 Volume 42, Issue 3, Page(s) 40
Abstract: Understanding migration patterns and their determinants is crucial for population estimation and resource allocation for policymakers. Utilizing residential mobility data collected by the Department of Motor Vehicles, this present study provides a ... ...
Abstract | Understanding migration patterns and their determinants is crucial for population estimation and resource allocation for policymakers. Utilizing residential mobility data collected by the Department of Motor Vehicles, this present study provides a spatiotemporal analysis of inter-county migration in California for the period 2014-2021. We use multilevel gravity models to address the hierarchical nature of migration data and the effects of migration flows sharing common origins, destinations, and regions, providing a substantively complete examination of push and pull forces affecting migration. Our findings show that populous counties in Southern California and the San Francisco Bay Area represent the largest origins and destinations, despite a systemic decline in intra-state migration. Migration is strongly associated with population size, geographic proximity (i.e., distance and contiguity), job availability, and industrial composition similarity between origins and destinations. Our findings also highlight the contribution of shared origins, destinations, and regions in explaining the systematic variation of migration flows. Counties vary more in the number of migrants they attract than the number they send. The purposed multilevel modeling approach is useful in identifying place-specific influences on migration and in improving estimation accuracy. Supplementary information: The online version contains supplementary material available at 10.1007/s11113-023-09782-2. |
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Language | English |
Publishing date | 2023-04-26 |
Publishing country | Netherlands |
Document type | Journal Article |
ZDB-ID | 2018638-1 |
ISSN | 1573-7829 ; 0167-5923 |
ISSN (online) | 1573-7829 |
ISSN | 0167-5923 |
DOI | 10.1007/s11113-023-09782-2 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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