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Stochastic Frontier Analysis for Healthcare, with Illustrations in R

Author

Listed:
  • Robin C. Sickles

    (Department of Economics, Rice University, Houston, TX 77251-1892, USA)

  • Zhichao Wang

    (School of Economics, University of Queensland, Brisbane, Qld 4072, Australia)

  • Valentin Zelenyuk

    (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia)

Abstract

In this chapter, we provide a brief overview of the stochastic frontier analysis (SFA) in the context of analysing healthcare, with a focus on hospitals, where it has received most attention. We start with the classical SFA model of Aigner, Lovell and Schmidt (1977) and then consider many of its popular extensions and generalizations in both cross-sectional and panel data (mainly published in Journal of Econometrics, Journal of Business & Economic Statistics and Journal of Productivity Analysis). We also briefly discuss semi-parametric and non-parametric generalizations, spatial frontiers, and Bayesian SFA. Whenever possible, we refer the readers to various applications of these general methods to healthcare, and for hospitals in particular. Finally, we also illustrate some of these methods for real data on public hospitals in Queensland, Australia, as well as provide practical guidance and references for their computational implementations via R.

Suggested Citation

  • Robin C. Sickles & Zhichao Wang & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis for Healthcare, with Illustrations in R," CEPA Working Papers Series WP052022, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:177
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    File URL: https://economics.uq.edu.au/files/35634/WP052022.pdf
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    References listed on IDEAS

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    1. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    2. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    3. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    4. Vitaliano, Donald F. & Toren, Mark, 1994. "Cost and efficiency in nursing homes: a stochastic frontier approach," Journal of Health Economics, Elsevier, vol. 13(3), pages 281-300, October.
    5. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Valentin Zelenyuk & Zhichao Wang, 2023. "Random vs. Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals," CEPA Working Papers Series WP052023, School of Economics, University of Queensland, Australia.
    2. Tsionas, Mike G. & Patel, Pankaj C., 2023. "Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency," International Journal of Production Economics, Elsevier, vol. 260(C).

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    Keywords

    Stochastic frontier analysis; R; healthcare; hospital; Queensland;
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