JOURNAL ARTICLE
Federalism and the Governance of Contested Capital Cities: Comparing Addis Ababa and Brussels.
Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2024, v. 59, n. 2. P. 470 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Gemechu, Milkessa Midega 3 of 3
Abstract
This article examines the governance of contested federal capital cities through a comparative analysis of Brussels, Belgium, and Addis Ababa (Finfinne), Ethiopia, both of which serve as seats of multiple intranational and supranational governments. It highlights how, contrary to federal theory that promotes the diffusion of power across multiple capitals, these cities remain dominant primate capitals marked by historical linguistic and ethnic contestations—Frenchification in Brussels and Amharization in Addis Ababa—rooted in their unitary state legacies. Brussels functions as a bilingual city-state hosting four federated entities and the European Union, operating under a consociational democracy, while Addis Ababa is a monolingual, hybrid federal capital located within Oromia Regional State, governed through authoritarian power-sharing and hosting the African Union. The article argues that the presence of supranational institutions complicates traditional federal capital classifications and suggests that both cities could benefit from more inclusive language policies and greater local autonomy to ease intergroup tensions and better reflect federal principles.
Additional Information
- Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2024/03, Vol. 59, Issue 2, p470
- Document Type:Article
- Subject Area:History
- Publication Date:2024
- ISSN:0021-9096
- DOI:10.1177/00219096221113585
- Accession Number:175500839
- Copyright Statement:Copyright of Journal of Asian & African Studies (Sage Publications, Ltd.) 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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