JOURNAL ARTICLE
The 2022 Elections: A Test of Democracy's Resilience and the Referendum Theory of Midterms.
Published In: Political Science Quarterly (Oxford University Press / USA), 2023, v. 138, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Jacobson, Gary C 3 of 3
Abstract
The article analyzes the 2022 U.S. midterm elections, focusing on how American democracy withstood the electoral test better than the traditional referendum theory predicted. Despite President Joe Biden's low approval ratings and high inflation, Democrats lost far fewer House seats than models based on economic and presidential approval fundamentals forecasted. Key factors altering the expected referendum outcome included former President Donald Trump's promotion of election denial ("the big lie") influencing Republican primaries, and the Supreme Court's Dobbs decision overturning Roe v. Wade, which mobilized Democratic and independent voters around threats to democracy and abortion rights. The electorate exhibited unprecedented partisan loyalty and continuity with the 2020 presidential vote, limiting cross-party defections and reinforcing deep political polarization. While Trump's endorsements and election denial claims generally hurt Republican candidates in competitive races, Democrats' strong campaign financing and issue framing helped them retain control of the Senate and mitigate losses in the House, underscoring the complex interplay of national issues and entrenched partisan identities in shaping the 2022 electoral outcomes.
Additional Information
- Source:Political Science Quarterly (Oxford University Press / USA). 2023/03, Vol. 138, Issue 1, p1
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2023
- ISSN:0032-3195
- DOI:10.1093/psquar/qqad002
- Accession Number:164203182
- Copyright Statement:Copyright of Political Science Quarterly (Oxford University Press / USA) 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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