Segmented Intersectionality and Job Loss: Unpacking Race, Gender, and Occupational Segregation

Objective This study introduces a segmented intersectionality approach to enhance intersectionality theory and empirical research. It investigates involuntary job loss among full‐time U.S. workers, emphasizing how race and gender interact within a segregated occupational structure. Methods Using data from the 2000–2022 Current Population Survey, logistic regression models estimate job loss probabilities based on race, gender, age, education, occupation, and industry. Decomposition analysis evaluates the extent to which observed disparities result from group differences in characteristics versus differences in returns to those characteristics. Results Latina women and Black youth face significantly higher probabilities of involuntary job loss than other groups, even after controlling for relevant covariates. Disparities between men and women and between Latino and White workers are largely attributable to differences in occupational, industrial, and educational characteristics. In contrast, the Black–White gap is mainly driven by unequal returns to these characteristics. Men generally have higher job loss rates than women, due primarily to gender‐based occupational segregation. Conclusion Involuntary job loss is shaped by both structural inequalities and differential labor market returns. While occupational segregation explains disparities for some groups, others face systemic devaluation of their attributes. These findings highlight the segmented nature of intersectionality in the labor market.