Changes in Americans' Racial Attitudes Have Increased Support for Welfare.
Published In: Social Science Quarterly (Wiley-Blackwell), 2025, v. 106, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Vilbig, Karyn 3 of 3
Abstract
Objective: This article documents changes in Americans' attitudes toward redistributive policy since 2012 and explores the extent to which we can rightfully credit the increases in support for government aid to shifts in racial attitudes as opposed to other concurrent forces. Methods: Using the 2016–2020 GSS panel, I employ fixed‐effects models to quantify how within‐person changes in racial attitudes are related to support for redistributive policies in 2020. I then perform KOB decomposition analysis using the 2012 and 2020 ANES to determine how much of the change in Americans' support for redistributive policies can be explained by changes in the values and/or effects of their racial attitudes. Results: Americans have indeed exhibited large increases in their support for redistributive policies since 2012, and a sizable portion of these increases can be attributed to changes in their racial attitudes and the effects of these racial attitudes. These changes are strongest among Democrats but can even be found among Republicans. Conclusion: In the past, many Americans opposed government aid because they believed it benefitted Black people. Today, many Americans support government aid for precisely the same reason. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Social Science Quarterly (Wiley-Blackwell). 2025/01, Vol. 106, Issue 1, p1
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2025
- ISSN:0038-4941
- DOI:10.1111/ssqu.13483
- Accession Number:183867460
- Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell 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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