JOURNAL ARTICLE
Identifying Teacher Salary Spiking and Assessing the Impact of Pensionable Compensation Reforms in Illinois.
Published In: Journal of Education Human Resources, 2024, v. 42, n. 3. P. 323 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Goldhaber, Dan; Grout, Cyrus; Holden, Kristian L. 3 of 3
Abstract
This article focuses on the phenomenon of salary spiking in defined benefit (DB) pension plans, particularly within Illinois teacher pension systems. Salary spiking refers to sharp, end-of-career increases in pensionable compensation that inflate retirement benefits, potentially creating unfunded liabilities borne by taxpayers. The authors develop an empirical method to identify salary spiking by detecting statistically significant deviations in an employee's final years of compensation from their prior salary trajectory. Applying this method to the Teacher Retirement System of Illinois (TRSIL) and the Chicago Teachers' Pension Fund (CTPF), they find that about 40–50% of TRSIL employees exhibit salary spiking near retirement, compared to roughly 20–26% in CTPF, reflecting differing employer incentives. The study also shows that Illinois's 2005 policy capping pensionable compensation growth and billing employers for excess compensation reduced salary spiking prevalence and associated pension costs by an estimated $71 million per cohort. The authors note their approach identifies deviations in compensation regardless of intent, acknowledging that some increases may be incidental rather than pension-driven, but all have financial implications for pension systems.
Additional Information
- Source:Journal of Education Human Resources. 2024/07, Vol. 42, Issue 3, p323
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2024
- ISSN:2562-783X
- DOI:10.3138/jehr-2022-0038
- Accession Number:184509182
- Copyright Statement:Copyright of Journal of Education Human Resources is the property of University of Toronto Press 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.