Taxation and Resentment: Race, Party, and Class in American Tax Attitudes by Andrea Louise Campbell
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ID: 322025
2026
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Abstract
The federal government's core functions are taxing, spending, and regulating. For decades, scholars have primarily focused on attitudes toward government spending, yet public attitudes toward taxation have received surprisingly little systematic attention. Andrea Louise Campbell's Taxation and Resentment addresses this gap with a comprehensive and analytically rigorous examination of how ordinary Americans think about the tax system. Campbell documents a pronounced principle-policy gap: Americans in the abstract endorse progressive taxation and believe the wealthy should pay their fair share, yet majorities, including working- and middle-class households who would benefit most from progressive redistribution, oppose the income and estate taxes that actually do the redistributive work. Support clusters instead around payroll and sales taxes, the most regressive elements of the system. This central finding offers a revealing complement to decades of research showing that Americans are symbolically conservative but operationally liberal, endorsing limited government in the abstract while supporting specific programs like Social Security and Medicare (Ellis and Stimson, Ideology in America, 2012). Campbell documents the mirror image on taxation: Americans are symbolically liberal, endorsing progressive principles, but operationally conservative, opposing the specific taxes that would put those principles into practice. Together, these paired asymmetries illuminate a deeper incoherence in American public opinion, one with significant consequences for democratic accountability. When symbolic and operational preferences systematically diverge in opposite directions across the spending and revenue sides of fiscal policy, political elites gain enormous latitude to claim popular mandates for nearly any position they choose to advance.
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openalex_W7170042828
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| Authors | Chris Faricy |
| Journal | political science quarterly |
| Year | 2026 |
| DOI |
10.1093/psquar/qqag051
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| Keywords | Keywords not found |
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