DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans

TL;DR

This paper extends Data Envelopment Analysis (DEA) to evaluate performance in pay-for-performance incentive plans, ensuring attainable targets aligned with improvement strategies, demonstrated through a case study of Spanish universities.

Contribution

It introduces a DEA-based model that incorporates incentive plan goals into performance benchmarking, aligning targets with strategic improvement efforts.

Findings

DEA targets reflect attainable performance levels.

The model aligns incentives with strategic goals.

Application to Spanish universities demonstrates effectiveness.

Abstract

Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The approach proposed ensures that the evaluation of performance of decision making units (DMUs) that follow the implementation of incentive plans, is made in terms of targets that are attainable, as well as representing best practices. A model is developed that adjusts the benchmarking to the goals through the corresponding payment of incentives, thus DEA targets are established taking into consideration the improvement strategies that were set out in the incentive plans. To illustrate, we examine an application concerned with the financing of public Spanish universities.