Moral hazard in data envelopment analysis benchmarking

Abstract

This paper delves into the concept of moral hazard in data envelopment analysis (DEA) benchmarking. The moral hazard issue emerges when decision-making units (DMUs) conceal their actions in the application of best practices, driven by the costs involved and the possibility of incomplete reimbursement. This issue remains unexplored in DEA benchmarking because previous studies assume that applying best practices is straightforward once these practices have been identified. Therefore, we postulate the presence of information asymmetry pertaining to the optimal production possibilities of DMUs, and regard applying best practices in benchmarking as a moral hazard issue. To address this issue, we formulate an incentive game and propose efficient contracts, where DEA Russell-like measures are first employed to describe DMUs’ effort levels. We prove applying best practices is the dominate strategy equilibrium of the incentive game with the implementation of efficient contracts. By exploring moral hazard in DEA benchmarking, this paper recommends the managers to incorporate considerations of information asymmetry when embarking on benchmarking activities.