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Estimating the technical efficiency of health care systems: A cross-country comparison using the directional distance function

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  • Cheng, Gang
  • Zervopoulos, Panagiotis D.

Abstract

Economic activity produces not only desirable outputs but also undesirable outputs. Undesirable outputs are usually omitted from efficiency assessments (i.e., applications of Data Envelopment Analysis) which fail to express the true production process. The directional distance function model has been used for handling asymmetrically both desirable and undesirable outputs in the assessment process. In the present paper, we apply a generalized directional distance function to measure the efficiency of the health systems of 171 countries. We incorporate both desirable and undesirable outputs into the efficiency assessment without transforming the latter type of outputs into inputs or into their inverse form, as is done in most of the extant studies that deal with the measurement of health efficiency. The methodology that we apply introduces a modified definition of the efficiency score which yields results consistent with those obtained from radial DEA models. In addition, our results are independent of the length of the direction vector.

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  • Cheng, Gang & Zervopoulos, Panagiotis D., 2014. "Estimating the technical efficiency of health care systems: A cross-country comparison using the directional distance function," European Journal of Operational Research, Elsevier, vol. 238(3), pages 899-910.
  • Handle: RePEc:eee:ejores:v:238:y:2014:i:3:p:899-910
    DOI: 10.1016/j.ejor.2014.05.007
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    5. Tao, Xueping & Wang, Ping & Zhu, Bangzhu, 2016. "Provincial green economic efficiency of China: A non-separable input–output SBM approach," Applied Energy, Elsevier, vol. 171(C), pages 58-66.
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    7. Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 0. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 0, pages 1-30.
    8. Marinko Škare & Danijela Rabar, 2016. "Measuring Economic Growth Using Data Envelopment Analysis," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 18(42), pages 386-386, May.
    9. Monica Giancotti & Annamaria Guglielmo & Marianna Mauro, 2017. "Efficiency and optimal size of hospitals: Results of a systematic search," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-40, March.
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    11. Guccio, C. & Lisi, D. & Martorana, M.F. & Pignataro, G., 2020. "Incorporating quality in the efficiency assessment of hospitals using a generalized directional distance function approach," Health, Econometrics and Data Group (HEDG) Working Papers 20/17, HEDG, c/o Department of Economics, University of York.
    12. Muñuzuri, Jesús & Muñoz-Díaz, María-Luisa, 2019. "Use of DEA to identify urban geographical zones with special difficulty for freight deliveries," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    13. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    14. Bonasia, Mariangela & Kounetas, Konstantinos & Oreste, Napolitano, 2020. "Assessment of regional productive performance of European health systems under a metatechnology framework," Economic Modelling, Elsevier, vol. 84(C), pages 234-248.
    15. Kounetas, Kostas & Zervopoulos, Panagiotis, 2017. "Annex I and non-Annex I countries’productive performance revisited using a generalized directional distance function under a metafrontier framework: Is there any convergence-divergence pattern for tec," MPRA Paper 80904, University Library of Munich, Germany.
    16. Falavigna, G. & Ippoliti, R., 2020. "The socio-economic planning of a community nurses programme in mountain areas: A Directional Distance Function approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    17. Bampatsou, Christina & Halkos, George, 2019. "Economic growth, efficiency and environmental elasticity for the G7 countries," Energy Policy, Elsevier, vol. 130(C), pages 355-360.
    18. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    19. Amado, Carla A.F. & São José, José M.S. & Santos, Sérgio P., 2016. "Measuring active ageing: A Data Envelopment Analysis approach," European Journal of Operational Research, Elsevier, vol. 255(1), pages 207-223.
    20. Zhang, Bin & Lu, Danting & He, Yan & Chiu, Yung-ho, 2018. "The efficiencies of resource-saving and environment: A case study based on Chinese cities," Energy, Elsevier, vol. 150(C), pages 493-507.
    21. Kounetas, Konstantinos & Zervopoulos, Panagiotis D., 2019. "A cross-country evaluation of environmental performance: Is there a convergence-divergence pattern in technology gaps?," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1136-1148.
    22. Halkos, George & Petrou, Kleoniki Natalia, 2018. "A critical review of the main methods to treat undesirable outputs in DEA," MPRA Paper 90374, University Library of Munich, Germany.

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