Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/328739 
Year of Publication: 
2023
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 11 [Issue:] 4 [Article No.:] 114 [Year:] 2023 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
Using microdata from Statistics Canada's Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada's immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic.
Subjects: 
spatial characteristics
immigration
race
non-standard employment
temporary employment
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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