JOURNAL ARTICLE
The Efficiency-Equity Trade-off in a Federal System: Local Financing of Schools and Student Achievement.
Published In: Publius: The Journal of Federalism, 2023, v. 53, n. 2. P. 174 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lastra-Anadón, Carlos X; Peterson, Paul E 3 of 3
Abstract
This article examines the trade-off between efficiency and equity in funding local public services, focusing specifically on K-12 education in the United States. Using nationally representative data from the National Assessment of Educational Progress (NAEP) between 1990 and 2017, the study finds that a higher share of education funding from local revenue sources is associated with modest increases in average student achievement (about 0.05 standard deviations per 10 percentage points increase in local funding) but also with widening socio-economic status (SES) achievement gaps (about 0.03 standard deviations). The analysis employs ordinary least squares (OLS), event study models of court-ordered school finance reforms, and geographic regression discontinuity designs, all of which support the existence of this efficiency-equity trade-off. The study further identifies “voice” (local preferences influencing spending priorities) and “exit” (families’ ability to choose districts) mechanisms as moderators that amplify efficiency gains but also contribute to greater inequities. These findings have implications for intergovernmental grant policies, suggesting that shifting funding from local to higher government levels may reduce achievement disparities but at the cost of overall educational performance.
Additional Information
- Source:Publius: The Journal of Federalism. 2023/04, Vol. 53, Issue 2, p174
- Document Type:Article
- Subject Area:Education
- Publication Date:2023
- ISSN:0048-5950
- DOI:10.1093/publius/pjac034
- Accession Number:163024101
- 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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