JOURNAL ARTICLE

Interracial Marriage and the U.S. Military: A Test of Status Exchange and Own Race Preferences.

  • Published In: Armed Forces & Society (Sage Publications Inc.), 2024, v. 50, n. 2. P. 383 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Houseworth, Christina A. 3 of 3

Abstract

This article investigates the roles of racial social distance and individual identity in shaping interracial marriage patterns between Black and White individuals in the U.S. military, using interracial marriage as a proxy for social distance. Analyzing 2015–2019 American Community Survey data, the study applies status exchange theory—which posits that racial dissimilarity is compensated by differences in education—and own race preferences to compare military and civilian populations. Findings indicate that the military exhibits lower racial group boundaries, particularly among Black male–White female couples, with less evidence of status exchange and smaller educational differences between intermarried and intramarried couples. Conversely, Black women in the military show stronger own race preferences, potentially due to a more favorable marriage market within the military context. These results suggest that military service influences racial identity and partner selection dynamics differently than civilian life, highlighting structural factors affecting racial inequality in intimate relationships.

Additional Information

  • Source:Armed Forces & Society (Sage Publications Inc.). 2024/04, Vol. 50, Issue 2, p383
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0095-327X
  • DOI:10.1177/0095327X221123811
  • Accession Number:175845224
  • Copyright Statement:Copyright of Armed Forces & Society (Sage Publications Inc.) is the property of Sage Publications Inc. 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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