JOURNAL ARTICLE
Navigating Conflicting Incentives: Discursive Strategies of Political Parties in Germany's Cooperative Federalism.
Published In: Publius: The Journal of Federalism, 2024, v. 54, n. 4. P. 656 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Souris, Antonios; Kropp, Sabine; Nguyen, Christoph 3 of 3
Abstract
This article examines how political parties in Germany navigate the conflicting incentives of cooperation and self-interest inherent in federal systems through their parliamentary discourses during the COVID-19 crisis. Using a qualitative content analysis of 212 debates and 4,524 coded statements, the study identifies five distinct discursive strategies—three self-serving ("finding a scapegoat," "passing responsibility," and "self-praise") and two cooperative ("showcasing federal cooperation" and "highlighting the value of federalism"). Findings show that parties more integrated into intergovernmental bodies and coalition governments tend to adopt cooperative discourse and softer self-serving strategies, while opposition parties and excluded parties like the right-wing Alternative for Germany (AfD) rely more on confrontational scapegoating. The study also highlights the role of Germany's vertically integrated party system in fostering intra-party solidarity across federal levels, mitigating blame games, and notes that cooperative norms persisted during early pandemic phases but declined amid prolonged crisis pressures. These insights contribute to understanding the adaptability and robustness of cooperative federalism under stress and suggest avenues for comparative research in other federal systems.
Additional Information
- Source:Publius: The Journal of Federalism. 2024/10, Vol. 54, Issue 4, p656
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2024
- ISSN:0048-5950
- DOI:10.1093/publius/pjae024
- Accession Number:180267242
- Copyright Statement:Copyright of Publius: The Journal of Federalism 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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