THE SOCIAL AND SPATIAL DIVISIONS OF PRECARIOUS LABOR

Abstract

The dissertation is composed of four manuscripts, positioned within the field of economic geography. Manuscript one broadly examined how precarious forms of employment (PFEs) are spatially patterned within multiple scales and across a range of geographies. The results suggested that different PFEs exhibited distinct spatial patterns across space and scale. For example, temporary and involuntary part-time work was more prevalent in Atlantic Canada and became gradually less prevalent moving westward. In contrast, part-time employment and employment in multiple jobs were more common in western Canada than in central and Atlantic Canada. The results also confirmed that all PFEs (except for involuntary-part-time work) were more common in rural and small-town areas, and less common in large urban areas. Second, using logistic regression models, results showed that the prevalence of PFEs was reinforced by factors such as immigration status, gender, age, education, and income. These models further confirmed that spatial patterns of PFEs were robust in finer scales i.e. CMAs (census metropolitan areas) and urban/rural geographies even when controlling for socio-demographic and socio-economic effects.

Manuscripts two and three builds on the findings in manuscript one by examining how PFEs are spatially patterned across social locations of gender and immigration status, respectively. Results showed that the east-west and urban-rural patterns observed in manuscript one were partially distorted when the analyses were disaggregated by gender and immigration status. The robustness of these spatial distortions was confirmed using logistic regression models. The fourth manuscript sought to understand the spatial characteristics influencing the spatial variations of temporary employment using ordinary least squares (OLS) regression models. Key findings revealed that CMA/CAs (census metropolitan areas/census agglomerations) characterized by large shares of manufacturing, utility, and management occupations were significantly negatively associated with temporary employment. Conversely, CMA/CAs with high shares of sales and service occupations were positively associated with temporary employment. Generally, population characteristics (measured by metropolitan areas characterized by a high share of Asian immigrants, low-income earners, and employment insurance beneficiaries) contributed more to explaining positive temporary employment estimates than industry characteristics.