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PAggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives

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Abstract

Data envelopment analysis (DEA) has been widely recognised as a powerful tool for performance analysis over the last four decades. The application of DEA in empirical works, however, has become more challenging, especially in the modern era of big data, due to the so-called `curse of dimensionality'. Dimension reduction has been recently considered as a useful technique to deal with the `curse of dimensionality' in the context of DEA with large dimensions for inputs and outputs. In this study, we investigate the two most popular dimension reduction approaches: PCA-based aggregation and price-based aggregation for hospital efficiency analysis. Using data on public hospitals in Queensland, Australia, we find that the choice of price systems (with small variation in prices) does not significantly affect the DEA estimates under the price-based aggregation approach. Moreover, the estimated efficiency scores from DEA models are also robust with respect to the two different aggregation approaches.

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  • Bao Hoang Nguyen & Valentin Zelenyuk, 2020. "PAggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives," CEPA Working Papers Series WP112020, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:154
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    File URL: https://economics.uq.edu.au/files/23820/WP112020.pdf
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    Cited by:

    1. Samira El Gibari & Trinidad Gómez & Francisco Ruiz, 2022. "Combining reference point based composite indicators with data envelopment analysis: application to the assessment of universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4363-4395, August.
    2. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    3. Kok Fong See & Shawna Grosskopf & Vivian Valdmanis & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP072021, School of Economics, University of Queensland, Australia.

    More about this item

    Keywords

    Hospital efficiency; big wide data; DEA; PCA-based aggregation; price-based aggregation;
    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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