Journal of Medical Systems

, Volume 35, Issue 5, pp 981–989 | Cite as

The Impact of Non-Discretionary Factors on DEA and SFA Technical Efficiency Differences

  • Nick Kontodimopoulos
  • Nikolaos D. Papathanasiou
  • Angeliki Flokou
  • Yannis Tountas
  • Dimitris Niakas
Original Paper


The purpose of this study was to examine if factors of the external operating environment can explain differences in technical efficiency derived from Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). In a sample of 124 dialysis facilities, technical efficiency was compared according to ownership, region, years in operation and size. With second-stage Tobit regression, DEA and SFA efficiency was regressed against these environmental factors to determine their potential for predicting technical efficiency, as well as the efficiency differences between the two frontier methods. DEA expectedly generated lower mean efficiency scores than SFA (68.2% vs. 79.4%, P < 0.001), due to the “random effects” term computed by the latter, in addition to “true” inefficiency. This finding was consistent for the subgroups formed on the basis of the environmental factors. Half the variation in the DEA-SFA efficiency differences was explained by environmental factors. This suggests that in addition to market instabilities, luck, and other related phenomena, decision-makers in their effort to determine optimal resource allocation, should point their attention to the potentially useful insight provided by environmental factors.


Data envelopment analysis Stochastic frontier analysis Technical efficiency Operating environment 


  1. 1.
    Fried, H. O., Schmidt, S. S., and Yaisawarng, S., Incorporating the operating environment into a nonparametric measure of technical efficiency. J. Prod. Anal. 12:249–267, 1999.CrossRefGoogle Scholar
  2. 2.
    Lovell, C. A. K., Production frontiers and productive efficiency. In: Fried, H. O., Lovell, C. A. K., and Schmidt, S. S. (Eds.), The measurement of productive efficiency: techniques and applications. Oxford University Press, New York, 1993.Google Scholar
  3. 3.
    Hollingsworth, B., Non-parametric and parametric applications measuring efficiency in health care. Health Care Manag. Sci. 6:203–218, 2003.CrossRefGoogle Scholar
  4. 4.
    Worthington, A. C., Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Med. Care. Res. Rev. 61:135–170, 2004.CrossRefGoogle Scholar
  5. 5.
    Hollingsworth, B., Dawson, P., and Maniadakis, N., Efficiency measurement of health care: a review of non-parametric methods and applications. Health Care Manag. Sci. 2:161–172, 1999.CrossRefGoogle Scholar
  6. 6.
    Emrouznejad, A., Parker, B. R., and Tavares, G., Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences 42:151–157, 2008.CrossRefGoogle Scholar
  7. 7.
    Ruggiero, J., Performance evaluation when non-discretionary factors correlate with technical efficiency. Eur. J. Oper. Res. 159:250–257, 2004.MATHCrossRefGoogle Scholar
  8. 8.
    Fried, H. O., Lovell, C. A. K., Schmidt, S. S., and Yaisawarng, S., Accounting for environmental effects and statistical noise in data envelopment analysis. J. Prod. Anal. 17:157–174, 2002.CrossRefGoogle Scholar
  9. 9.
    Hoff, A., Second stage DEA: comparison of approaches for modeling the DEA score. Eur. J. Oper. Res. 181:425–435, 2007.MATHCrossRefGoogle Scholar
  10. 10.
    Wooldridge, J. M., Econometric analysis of cross section and panel data. MIT Press, Cambridge, MA, 2002.MATHGoogle Scholar
  11. 11.
    Rosko, M. D., Impact of internal and external environmental pressures on hospital inefficiency. Health Care Manag. Sci. 2:63–74, 1999.CrossRefGoogle Scholar
  12. 12.
    Chu, H. L., Liu, S. Z., and Romeis, J. C., Does the implementation of responsibility centers, total quality management, and physician fee programs improve hospital efficiency? Evidence from Taiwan hospitals. Med. Care 40:1223–1237, 2002.CrossRefGoogle Scholar
  13. 13.
    Pilyavsky, A. I., Aaronson, W. E., Bernet, P. M., Rosko, M. D., Valdmanis, V. G., and Golubchikov, M. V., East-west: does it make a difference to hospital efficiencies in Ukraine? Health Econ. 15:1173–1186, 2006.CrossRefGoogle Scholar
  14. 14.
    Kooreman, P., Nursing home care in The Netherlands: a nonparametric efficiency analysis. J. Health Econ. 13:301–316, 1994.CrossRefGoogle Scholar
  15. 15.
    Rosko, M. D., Chilingerian, J. A., Zinn, J. S., and Aaronson, W. E., The effects of ownership, operating environment, and strategic choices on nursing home efficiency. Med. Care 33:1001–1021, 1995.CrossRefGoogle Scholar
  16. 16.
    Zavras, A. I., Tsakos, G., Economou, C., and Kyriopoulos, J., Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network. Data Envelopment Analysis. J. Med. Syst. 26:285–292, 2002.CrossRefGoogle Scholar
  17. 17.
    Kontodimopoulos, N., Moschovakis, G., Aletras, V., and Niakas, D., The relationship between eligible service population and efficiency in primary health care providers in Greece. Cost Eff. Resour. Alloc. 5:14, 2007.CrossRefGoogle Scholar
  18. 18.
    Linna, M., Nordblad, A., and Koivu, M., Technical and cost efficiency of oral health care provision in Finnish health centres. Soc. Sci. Med. 56:343–353, 2003.CrossRefGoogle Scholar
  19. 19.
    Chilingerian, J. A., Evaluating physician efficiency in hospitals: a multivariate analysis of best practices. Eur. J. Oper. Res. 80:548–574, 1995.MATHCrossRefGoogle Scholar
  20. 20.
    Ozgen, H., and Ozcan, Y., A national study of efficiency for dialysis centers: an examination of market competition and facility characteristics for production of multiple dialysis outputs. Health Serv. Res. 37:711–732, 2002.CrossRefGoogle Scholar
  21. 21.
    Gerard, K., and Roderick, P., Comparison of apparent efficiency of haemodialysis satellite units in England and Wales using data envelopment analysis. Int. J. Tech. Assess. Health Care 19:533–539, 2003.CrossRefGoogle Scholar
  22. 22.
    Kontodimopoulos, N., and Niakas, D., Efficiency measurement of hemodialysis units in Greece with data envelopment analysis. Health Policy 71:195–204, 2005.CrossRefGoogle Scholar
  23. 23.
    Ozgen, H., and Ozcan, Y., Longitudinal analysis of efficiency in multiple output dialysis markets. Health Care Manag. Sci. 7:253–261, 2004.CrossRefGoogle Scholar
  24. 24.
    Kontodimopoulos, N., and Niakas, D., A 12-year analysis of Malmquist total factor productivity in dialysis facilities. J. Med. Sys. 30:333–342, 2006.CrossRefGoogle Scholar
  25. 25.
    Charnes, A., Cooper, W. W., and Rhodes, E., Measuring efficiency of decision-making units. Eur. J. Oper. Res. 3:429–444, 1978.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Banker, R. D., Charnes, A., and Cooper, W. W., Models for estimating technical and scale efficiencies in data envelopment analysis. Manag. Sci. 30:1078–1092, 1984.MATHCrossRefGoogle Scholar
  27. 27.
    Charnes, A., Cooper, W. W., Seiford, L., and Stutz, J., A multiplicative model for efficiency analysis. Socio-Economic Planning Sci. 6:223–224, 1982.CrossRefGoogle Scholar
  28. 28.
    Charnes, A., Cooper, W. W., Golany, B., and Seiford, L., Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J. Econometrics 30:91–107, 1985.MATHMathSciNetCrossRefGoogle Scholar
  29. 29.
    Charnes, A., Haag, S., Jaska, P., and Semple, J., Sensitivity of efficiency classifications in the additive model of data envelopment analysis. Int. J. Sys. Sci. 23:789–798, 1992.MATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Aigner, D. J., Lovell, C. A. K., and Schmidt, P., Formulation and estimation of stochastic frontier production function models. J. Econometrics 6:21–37, 1977.MATHMathSciNetCrossRefGoogle Scholar
  31. 31.
    Meeusen, W., and van den Broeck, J., Efficiency estimation from Cobb-Douglas production functions with composed error. Int. Econ. Rev. 18:435–444, 1977.MATHCrossRefGoogle Scholar
  32. 32.
    Kumbhakar, S. C., and Lovell, C. A. K., The estimation of technical efficiency. In: Stochastic frontier analysis (pp. 63–130). Cambridge University Press, 2000.Google Scholar
  33. 33.
    Jondrow, J., Lovell, C. A. K., Materov, S., and Schmidt, P., On the estimation of technical efficiency in the stochastic frontier production function model. J. Econometrics 19:233–238, 1982.MathSciNetCrossRefGoogle Scholar
  34. 34.
    Smith, P., Model misspecification in data envelopment analysis. Ann. Oper. Res. 73:233–252, 1997.MATHCrossRefGoogle Scholar
  35. 35.
    Held, P. J., and Pauly, M. V., Competition and efficiency in the end stage renal disease program. J. Health Econ. 2:95–118, 1983.CrossRefGoogle Scholar
  36. 36.
    Coelli, T., Rao, D. S., and Battese, G., An introduction to efficiency and productivity analysis. Kluwer, Boston, 1998.MATHCrossRefGoogle Scholar
  37. 37.
    Aletras, V., Kontodimopoulos, N., Zagouldoudis, A., and Niakas, D., The short-term effect on technical and scale efficiency of establishing Regional Health Systems and General Management in Greek NHS hospitals. Health Policy 83:236–245, 2007.CrossRefGoogle Scholar
  38. 38.
    McCallion, G., Glass, J. C., Jackson, R., Kerr, C. A., and McKillop, D. G., Investigating productivity change and hospital size: a nonparametric frontier approach. Appl. Econ. 32:161–174, 2000.CrossRefGoogle Scholar
  39. 39.
    Kazley, A. S., and Ozcan, Y. A., Electronic medical record use and efficiency: a DEA and windows analysis of hospitals. Socio-Economic Planning Sci. 43:209–216, 2009.CrossRefGoogle Scholar
  40. 40.
    Sheldon, T. A., and Smith, P. C., Equity in the allocation of health care resources. Health Econ. 9:571–574, 2000.CrossRefGoogle Scholar
  41. 41.
    Reinhard, S., Lovell, C. A. K., and Thijssen, G. J., Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res. 121:287–303, 2000.MATHCrossRefGoogle Scholar
  42. 42.
    Margari, B. B., Erbetta, F., and Petraglia, C., Regulatory and environmental effects on public transit efficiency: a mixed DEA-SFA approach. J. Reg. Econ. 32:131–151, 2007.CrossRefGoogle Scholar
  43. 43.
    Jacobs, R., Alternative methods to examine hospital efficiency: data envelopment analysis and stochastic frontier analysis. Health Care Manag. Sci. 4:103–115, 2001.CrossRefGoogle Scholar
  44. 44.
    Kontodimopoulos, N., Papathanasiou, N.D., Tountas, Y., and Niakas, D., Isolating managerial inefficiency from external influences across dialysis facilities. Online Early in J Medical Sys. doi:10.1007/s10916-009-9252-2.
  45. 45.
    Kooreman, P., Data envelopment analysis and parametric frontier estimation: Complementary tools. J. Health Econ. 13:345–346, 1994.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Nick Kontodimopoulos
    • 1
  • Nikolaos D. Papathanasiou
    • 2
  • Angeliki Flokou
    • 1
  • Yannis Tountas
    • 1
    • 2
  • Dimitris Niakas
    • 1
  1. 1.Faculty of Social SciencesHellenic Open UniversityPatrasGreece
  2. 2.Center for Health Services Research, Department of Hygiene and Epidemiology, Medical SchoolUniversity of AthensAthensGreece

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