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Measuring regional public health provision

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  • Halkos, George
  • Tzeremes, Nickolaos

Abstract

This paper using Data Envelopment Analysis (DEA) evaluates the performance of public health services of the Greek prefectures. The efficiency levels of the Greek prefectures are compared and analyzed in a regional context. With the use of bootstrap techniques and conditional full frontier applications the paper shows that higher levels of GDP per capita and population density increase the prefectures’ performance of public health provision. In addition population density affects more the prefectures’ performance compared to the levels of GDP per capita. Finally, it appears that Greek prefectures with GDP per capita levels of 25000 to 30000 € and those with population density levels between 150 and 200 residents per square kilometre have significantly higher efficiency levels of public health provision.

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File URL: http://mpra.ub.uni-muenchen.de/23762/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 23762.

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Date of creation: 13 Sep 2008
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Handle: RePEc:pra:mprapa:23762

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Related research

Keywords: Public health; Performance measurement; Conditional DEA; Bootstrap techniques; Kernel density estimation.;

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References

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  1. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, 09.
  3. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
  4. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
  5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  7. Bruce Hollingsworth & P.J. Dawson & N. Maniadakis, 1999. "Efficiency measurement of health care: a review of non‐parametric methods and applications," Health Care Management Science, Springer, vol. 2(3), pages 161-172, July.
  8. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
  9. Karkazis, John & Thanassoulis, Emmanuel, 1998. "Assessing the effectiveness of regional development policies in Northern Greece using data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 32(2), pages 123-137, June.
  10. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
  11. Bruce Hollingsworth & Andrew Street, 2006. "The market for efficiency analysis of health care organisations," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1055-1059.
  12. Bruce Hollingsworth, 2008. "The measurement of efficiency and productivity of health care delivery," Health Economics, John Wiley & Sons, Ltd., vol. 17(10), pages 1107-1128.
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