Volume 37, Issue 1 p. 241-259
ORIGINAL ARTICLE

Gender representation in police organizations: Do upper-level and street-level female bureaucrats differ in their roles?

Sunyoung Pyo

Corresponding Author

Sunyoung Pyo

Department of Public Administration, The Catholic University of Korea, Bucheon, Gyeonggi-do, South Korea

Correspondence

Sunyoung Pyo, Department of Public Administration, The Catholic University of Korea, Bucheon, Gyeonggi-do, South Korea.

Email: [email protected]

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Hongseok Lee

Hongseok Lee

Department of Public Administration and Policy, State University of New York (SUNY), Albany, New York, USA

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First published: 17 January 2023
Citations: 1

Abstract

Some representative bureaucracy literature has suggested that increasing the share of female bureaucrats, particularly in supervisory positions, would not have a substantial influence on producing policy outcomes in favor of female clients because of the potential impact of organizational socialization. However, there has been little empirical investigation into how passive female representation across hierarchical ranks and its interaction influence policy outcomes for women. By analyzing data on 360 local U.S. police organizations in 2013 and 2016, we found that a higher proportion of female officers in first-line supervisory level positions was negatively linked to arrest performance for rape offenses, while a higher share of female street-level officers was not significantly linked to arrest performance for the same offenses. However, we noticed a positive interactive effect of the proportion of female street-level officers and female officers at the intermediate-level on rape arrests. These results imply that promoting female representation across different hierarchical ranks in police organizations will help to ensure the benefits of representative bureaucracy.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in Harvard Dataverse at https://doi.org/10.7910/DVN/LTNFRU.

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