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.
