Defense Date


Document Type


Degree Name

Master of Science



First Advisor

Victor Chen


In the US, labor market conditions vary by demographics and space. Though space has been shown to be highly related to labor force outcomes, lower-level analyses are under-utilized as statistical workforce analyses tend to rely on national or state-level data. This study addresses variability in labor markets by taking a spatial approach, using cluster analysis and logistic regression to explore variations in labor market variables across census tracts in Virginia. Cluster analysis reveals several hot and cold spots of key labor market indicators: rates of marriage, poverty, educational attainment, and income. In many cases, poor social conditions, such as low median income and educational attainment, overlap with high disability and low marriage rates. Patterns within these measures are analogous between urban and rural areas, suggesting a potential convergence between urban and rural labor markets. Logistic regression provides mixed support for conventionally supported labor market trends. While race controls are found to be insignificant therein, disability rates are a critical predictor for a low labor force participation rate. Suggestions are made for future research in rural labor economics.


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Available for download on Sunday, May 18, 2025