JOURNAL ARTICLE
Financial Oversight Boards in the U.S. Federal System: Insights from the Puerto Rican Debt Crisis.
Published In: Publius: The Journal of Federalism, 2023, v. 53, n. 2. P. 201 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: López-Santana, Mariely 3 of 3
Abstract
This article examines the governance and political dynamics of Financial Oversight Boards (FOBs) in U.S. localities facing fiscal distress, with a particular focus on the Puerto Rican Financial Oversight and Management Board (commonly known as la junta) established under the Puerto Rico Oversight, Management, and Economic Stability Act (PROMESA) of 2016. It highlights how the Puerto Rican FOB, created to manage the island's $72 billion debt restructuring—the largest in U.S. bond market history—has raised significant legal and political challenges by constraining local autonomy and democratic governance within Puerto Rico's unique territorial status. Through analysis of legal cases and political conflicts, the article shows that while the FOB holds extensive budgetary and fiscal powers, it is not considered a federal entity but a local one, yet remains largely unaccountable to elected officials, leading to contentious relations and prolonged restructuring processes. The study situates the Puerto Rican case within broader U.S. practices of fiscal oversight in distressed municipalities, emphasizing the tensions between expert-led financial management and democratic accountability, and calls for clearer institutional designs to balance these competing demands.
Additional Information
- Source:Publius: The Journal of Federalism. 2023/04, Vol. 53, Issue 2, p201
- Document Type:Article
- Subject Area:History
- Publication Date:2023
- ISSN:0048-5950
- DOI:10.1093/publius/pjac037
- Accession Number:163024103
- 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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