JOURNAL ARTICLE

Local Fiscal Response to State Preemption: A Case Study of Massachusetts' Proposition 2½ Tax Referendum.

  • Published In: Publius: The Journal of Federalism, 2024, v. 54, n. 4. P. 763 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Shu; Wu, Yonghong 3 of 3

Abstract

This article examines how local governments in Massachusetts respond to state fiscal preemption—specifically tax and expenditure limits (TELs) imposed by Proposition 2½—through direct democracy mechanisms such as tax referenda. Using data from 320 municipalities between 2010 and 2021, the study analyzes the two-stage referendum process involving local officials who propose overrides or exclusions to property tax limits and voters who approve them. Findings indicate that municipalities are more likely to seek tax referenda when fiscal slack resources (e.g., cash reserves, budget stabilization funds, and excess levy capacity) are low, while higher property tax burdens discourage initiating referenda. The study also highlights that voter approval is less influenced by fiscal need or tax burden factors and more by local political culture, and that federal policies like the 2017 Tax Cuts and Jobs Act affect the salience of property tax deductions in referendum decisions. Overall, the research underscores the complex interplay between fiscal constraints, local autonomy, and voter behavior in the context of state-imposed tax limits.

Additional Information

  • Source:Publius: The Journal of Federalism. 2024/10, Vol. 54, Issue 4, p763
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2024
  • ISSN:0048-5950
  • DOI:10.1093/publius/pjae007
  • Accession Number:180267236
  • 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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