Measuring potential gains from mergers: An application of cost sub-additivity to Taiwan Higher Education
Tsu-tan Fu,
Cliff J. Huang and
Wei-Hsin Kong ()
Additional contact information
Tsu-tan Fu: Soochow University
Cliff J. Huang: Vanderbilt University
Wei-Hsin Kong: National Ilan University
Journal of Productivity Analysis, 2025, vol. 64, issue 3, No 9, 379-391
Abstract:
Abstract This paper applies the concept of cost sub-additivity and utilizes the aggregate cost function proposed by Färe and Karagiannis (2023) to model the potential gains from mergers. We specifically assess potential gains by relaxing the assumption of identical reference input prices, allowing for a more accurate measurement of merger benefits. The potential gains are then decomposed into technical efficiency, allocative efficiency, and an additional input price effect. To estimate these gains and their components, we employ two empirical methods: the parametric stochastic frontier analysis (SFA) and the nonparametric data envelopment analysis (DEA). Using data from the Taiwan Ministry of Education, we evaluate potential merger gains across three categories: completed mergers, proposed mergers from 2021–2024, and hypothetical target mergers.
Keywords: Cost Sub-additivity; Mergers; Stochastic Frontier; Data Envelopment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11123-025-00775-1 Abstract (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:64:y:2025:i:3:d:10.1007_s11123-025-00775-1
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2
DOI: 10.1007/s11123-025-00775-1
Access Statistics for this article
Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski
More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().