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Accounting For Heterogeneity In The Measurement of Hospital Performance

Author

Listed:
  • Philippe K Widmer
  • Peter Zweifel

    (Socioeconomic Institute University of Zurich, Switzerland)

  • Mehdi Farsi

    (Department of Management Technology and Economics ETH Zurich, Switzerland)

Abstract

With prospective payment of hospitals becoming more common, measuring their performance is gaining in importance. However, the standard cost frontier model yields biased efficiency scores because it ignores technological heterogeneity between hospitals. In this paper, efficiency scores are derived from a random intercept and an extended random parameter frontier model, designed to overcome the problem of unobserved heterogeneity in stochastic frontier analysis. Using a sample of 100 Swiss hospitals covering the years 2004 to 2007 and applying Bayesian inference, significant heterogeneity is found, suggesting rejection of the standard cost frontier model. Estimated inefficiency decreases even below the 14 percent reported by Hollingsworth (2008) for European countries. Accounting for unobserved heterogeneity would make hospitals rated below 85 percent efficiency according to the standard model gain up to 12 percentage points, serving to highlight the importance of heterogeneity correction in the estimation of hospital performance.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Philippe K Widmer & Peter Zweifel & Mehdi Farsi, 2010. "Accounting For Heterogeneity In The Measurement of Hospital Performance," Economics Discussion / Working Papers 10-21, The University of Western Australia, Department of Economics.
  • Handle: RePEc:uwa:wpaper:10-21
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    File URL: https://www.business.uwa.edu.au/__data/assets/pdf_file/0019/1326106/10-21_Accounting_for_Heterogeneity_in_the_Measurement_of_Hospital_Performance.pdf
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    Cited by:

    1. Maria Olivares & Heike Wetzel, 2011. "Competing in the Higher Education Market: Empirical Evidence for Economies of Scale and Scope in German Higher Education Institutions," Economics of Education Working Paper Series 0070, University of Zurich, Department of Business Administration (IBW).
    2. Philippe Widmer, 2015. "Does prospective payment increase hospital (in)efficiency? Evidence from the Swiss hospital sector," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(4), pages 407-419, May.
    3. Vincenzo Atella & Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Giorgia Marini, 2012. "Cost-containment policies and hospital efficiency: evidence from a panel of Italian hospitals," CEIS Research Paper 228, Tor Vergata University, CEIS, revised 13 Apr 2012.
    4. Michael M Havranek & Josef Ondrej & Stella Bollmann & Philippe K Widmer & Simon Spika & Stefan Boes, 2022. "Identification and assessment of a comprehensive set of structural factors associated with hospital costs in Switzerland," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-17, February.
    5. Widmer, Philippe K., 2016. "SwissDRG: Ein Vergütungssystem mit ungleichen finanziellen Risiken für die Spitäler?," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 70(3), pages 210-226.
    6. Philippe K. Widmer & Maria Trottmann & Peter Zweifel, 2018. "Choice of reserve capacity by hospitals: a problem for prospective payment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(5), pages 663-673, June.
    7. Alejandro Arvelo-Martín & Juan José Díaz-Hernández & Ignacio Abásolo-Alessón, 2019. "Hospital productivity bias when not adjusting for cost heterogeneity: The case of Spain," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
    8. Michael M. Havranek & Josef Ondrej & Philippe K. Widmer & Stella Bollmann & Simon Spika & Stefan Boes, 2023. "Using exogenous organizational and regional hospital attributes to explain differences in case‐mix adjusted hospital costs," Health Economics, John Wiley & Sons, Ltd., vol. 32(8), pages 1733-1748, August.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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