JOURNAL ARTICLE

Implicit Black identification among White non-Hispanic respondents and support for Black reparations.

  • Published In: Oxford Review of Economic Policy, 2024, v. 40, n. 3. P. 657 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Craemer, Thomas 3 of 3

Abstract

This article examines the political feasibility of Black reparations in the United States through the lens of "relational identity theory," which integrates economic utility theory and psychological self-expansion theory to explain how individuals can include outgroups within their self-concept. Contrary to dominant Western theories of self-interest and ingroup-favouritism—which predict limited support for reparations given African Americans’ minority status—empirical data from reaction-time experiments (n=1,594 White non-Hispanic respondents) reveal that implicit identification with African Americans strongly predicts support for reparations among White non-Hispanics. This implicit Black identification is associated with feelings of closeness, warmth, and empathic concern, and those identified as significant implicit identifiers hold race-related political opinions between Black and other White respondents. The findings suggest that outgroup solidarity, as conceptualized by relational identity theory, may enable political support for minority rights like reparations despite majority rule, with implications for understanding intergroup relations and conflict resolution beyond the U.S. context.

Additional Information

  • Source:Oxford Review of Economic Policy. 2024/09, Vol. 40, Issue 3, p657
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:0266-903X
  • DOI:10.1093/oxrep/grae024
  • Accession Number:181096020
  • Copyright Statement:Copyright of Oxford Review of Economic Policy 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.