Persistent and Transient Inefficiency of Australian States and Territories in Providing Public Hospital Services: An Application of Bayesian Stochastic Finite Mixture Frontier Analysis

Antony Andrews (), Sean Kimpton and Omphile Temoso ()
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Antony Andrews: School of Economics, Faculty of Business, Economics and Law at AUT University
Omphile Temoso: Department of Resource and Agricultural Economics, University of New England (Australia)

No 2021-02, Working Papers from Auckland University of Technology, Department of Economics

Abstract: Current literature on public hospital efficiency in Australia only reveals information on how efficient public hospitals are in the short run. The presence of persistent technical inefficiency arising from long-term systemic problems and government-related regulatory constraints does not appear to have been addressed. Using yearly panel data for the period 2002-2018 on eight Australian states and territories, this study incorporates the measure of both transient and persistent technical inefficiency while controlling for unobserved heterogeneity to obtain a more precise measure of technical efficiency. The results of this study indicate that the technical inefficiency among public hospitals in Australia is persistent rather than transient based on state and territory level data. This implies that policymakers need to formulate comprehensive policies involving a longer time horizon that focuses on reducing the persistence in inefficiency among public hospitals in Australia. The study also calls on policymakers and regulators to disclose hospital-level data to researchers in order to gain further insight into the causes of persistence in inefficiency to formulate effective policies.

Keywords: stochastic frontier analysis; Australian hospitals; technical efficiency; persistent inefficiency; frontier estimation; Bayesian; STAN (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-eff
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Journal Article: Persistent and Transient Inefficiency of Australian States and Territories in Providing Public Hospital Services: An Application of Bayesian Stochastic Finite Mixture Frontier Analysis (2021)
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