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Aggregate efficiency of industry and its groups: the case of Queensland public hospitals

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  • Bao Hoang Nguyen

    (University of Queensland)

  • Valentin Zelenyuk

    (University of Queensland)

Abstract

In this paper, we explore the efficiency of different groups of hospitals in Queensland, Australia, focusing on teaching and non-teaching hospitals, by adapting the most recent developments on statistical analysis of aggregate efficiency. We focus on the two approaches: the bootstrap approach proposed by Simar and Zelenyuk (J Appl Econ 22(7):1367–1394, 2007) and the central limit theorems recently developed by Simar and Zelenyuk (Oper Res 66(1):137–149, 2018), (Eur J Oper Res, 2020). To adapt these developments, we extend the central limit theorems to the context where there are several sub-groups in the population. Using real data on Queensland public hospitals, we found that teaching hospitals are significantly less efficient than non-teaching hospitals when benchmarking is done with respect to the constant returns to scale frontier, but are significantly more efficient when benchmarking with respect to the variable returns to scale frontier.

Suggested Citation

  • Bao Hoang Nguyen & Valentin Zelenyuk, 2021. "Aggregate efficiency of industry and its groups: the case of Queensland public hospitals," Empirical Economics, Springer, vol. 60(6), pages 2795-2836, June.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:6:d:10.1007_s00181-020-01994-1
    DOI: 10.1007/s00181-020-01994-1
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    Cited by:

    1. Léopold Simar & Valentin Zelenyuk & Shirong Zhao, 2023. "Further improvements of finite sample approximation of central limit theorems for envelopment estimators," Journal of Productivity Analysis, Springer, vol. 59(2), pages 189-194, April.
    2. Bao Hoang Nguyen & Shawna Grosskopf & Jongsay Yong & Valentin Zelenyuk, 2022. "Activity based funding reform and the performance of public hospitals: The Case of Queensland, Australia," CEPA Working Papers Series WP082022, School of Economics, University of Queensland, Australia.
    3. 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.
    4. Valentin Zelenyuk, 2021. "Performance Analysis: Economic Foundations & Trends," CEPA Working Papers Series WP162021, School of Economics, University of Queensland, Australia.
    5. Ali Homayoni & Reza Fallahnejad & Farhad Hosseinzadeh Lotfi, 2022. "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR, Springer, vol. 20(4), pages 567-602, December.
    6. Bao Hoang Nguyen & Léopold Simar & Valentin Zelenyuk, 2021. "Data Sharpening for improving CLT approximations for DEA-type efficiency estimators," CEPA Working Papers Series WP142021, School of Economics, University of Queensland, Australia.
    7. Bao Hoang Nguyen & Zhichao Wang & Valentin Zelenyuk, 2023. "Efficiency of Queensland Public Hospitals via Spatial Panel Stochastic Frontier Models," CEPA Working Papers Series WP102023, School of Economics, University of Queensland, Australia.
    8. Subal C. Kumbhakarⓡ & Emir Malikovⓡ & Christopher F. Parmeterⓡ, 2021. "Applications of efficiency and productivity analysis: editors’ introduction," Empirical Economics, Springer, vol. 60(6), pages 2657-2663, June.
    9. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    10. Färe, Rolf & Karagiannis, Giannis, 2022. "Aggregating Farrell efficiencies without value data: The case of hospitals," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).

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    More about this item

    Keywords

    Hospitals; Aggregate efficiency; Envelopment estimators; Bootstrap; Central limit theorems;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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