Date of Award
Doctor of Philosophy
Research on diversity in the workplace is expanding to include multiple categories including, race, gender, and LGBT identities. Government agencies should have a workforce that represents a diverse public. This is a challenge because agencies and their employees are not immune to the social processes that produce inequalities. A central question in public administration is how to improve practices in the recruitment, management, and retention of a diverse workforce. I theorized that employee perceptions are affected by inequalities based on the intersections of race, gender, and sexuality. The purpose of this study was to determine if employee perceptions were influenced by intersectional identity patterns. The dominant framework in the literature proposes that the separate and additive effects of stigma, stereotypes, and bias influence employee perceptions at work. Intersectionality challenges this approach by conceptualizing social identity as the combination of identity categories. I provide a methodology for comparing the difference in fit between additive and intersectional models.The findings of this study demonstrate the greater explanatory power of intersectionality through replication and extension of Sabharwal et al.'s (2019) study of turnover intention. The replication analysis finds errors in the original models that, when corrected, provide stronger evidence for their hypotheses. Tests indicate that the intersectional model fit the data better than the additive model. The models show that patterns of turnover intention are conditional, shaped by intersectional combinations of race, gender, and sexuality. The patterns of diversity revealed in these models show that the effect of diversity is complex—it is more than the sum of its parts. The results change our understanding of diversity inclusion within the federal workforce, employee retention, and satisfaction. I discuss recommendations for managers in government agencies seeking to improve diversity inclusion practices in the federal workforce.
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