Random Versus Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals

Abstract

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

Citation

Wang, Z. and Zelenyuk, V. (2024), "Random Versus Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals", Parmeter, C.F., Tsionas, M.G. and Wang, H.-J. (Ed.) Essays in Honor of Subal Kumbhakar (Advances in Econometrics, Vol. 46), Emerald Publishing Limited, Leeds, pp. 371-413. https://doi.org/10.1108/S0731-905320240000046013