JOURNAL ARTICLE

Deconstructing Burnout at the Intersections of Race, Gender, and Generation in Local Government.

  • Published In: Journal of Public Administration Research & Theory, 2023, v. 33, n. 1. P. 186 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Barboza-Wilkes, Cynthia J; Le, Thai V; Resh, William G 3 of 3

Abstract

This article empirically investigates how intersectional identities—specifically the combined effects of gender, race, and generational cohort—influence employee burnout among local government workers in two large California cities. Using Conservation of Resource (COR) theory and applied intersectionality, the study disaggregates burnout into three dimensions: emotional exhaustion, depersonalization, and loss of personal accomplishment, revealing that younger women of color are particularly vulnerable but that burnout experiences vary significantly across different socio-demographic groups and burnout dimensions. The findings demonstrate that single-axis analyses (examining gender or race alone) obscure important disparities, while intersectional approaches uncover nuanced vulnerabilities that differ by race, gender, and generation. The study highlights the need for culturally competent, multidimensional diversity management strategies in public organizations to better support an increasingly diverse workforce and improve employee well-being and public service outcomes.

Additional Information

  • Source:Journal of Public Administration Research & Theory. 2023/01, Vol. 33, Issue 1, p186
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2023
  • ISSN:1053-1858
  • DOI:10.1093/jopart/muac018
  • Accession Number:161161446
  • Copyright Statement:Copyright of Journal of Public Administration Research & Theory is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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