JOURNAL ARTICLE
Addressing the Challenges of Rural Local Governments: Perceptions of State Assistance.
Published In: Publius: The Journal of Federalism, 2025, v. 55, n. 1. P. 89 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Helpap, David J 3 of 3
Abstract
This article examines the perceptions of rural local government officials in Wisconsin regarding state assistance in addressing community challenges, using survey data from 236 respondents across 42 rural counties. Findings reveal strong support for additional state resources or policy changes, particularly increased funding, relaxed state-imposed levy limits, and simplified grant application processes, while also expressing concerns about current financial support levels and regulatory constraints. Rural governments with higher expenditures, poorer financial conditions, greater agricultural employment, and older populations are more likely to view additional state assistance as beneficial, whereas those in manufacturing-dependent areas are less likely to do so. The study highlights the complex and sometimes conflictual nature of state–local relationships in rural contexts, emphasizing the need for nuanced state policies that consider fiscal capacity, economic structure, and demographic challenges. It also calls for further research across multiple states and deeper exploration of specific types of state assistance to better support rural local governments within the American federal system.
Additional Information
- Source:Publius: The Journal of Federalism. 2025/01, Vol. 55, Issue 1, p89
- Document Type:Article
- Subject Area:Sociology
- Publication Date:2025
- ISSN:0048-5950
- DOI:10.1093/publius/pjae038
- Accession Number:181970273
- 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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