Multilevel flexible specification of the production function in health economics
Previous studies on hospitals’ efficiency often refer to quite restrictive functional forms for the technology (Aigner et al., 1977, J. Econom., 6, 21–37) . In this paper, referring to a study about some hospitals in Lombardy, we formulate convenient correctives to a statistical model based on the translogarithmic function—the most widely used flexible functional form (Christensen et al., 1973, Rev. Econ. Stat., 55, 28–45). More specifically, in order to take into consideration the hierarchical structure of the data (as in Gori et al., 2002, Stat. Appl., 14, 247–275), we propose a multilevel model, ignoring for the moment the one-side error specification, typical of stochastic frontier analysis (Aigner et al., 1977, J. Econom., 6, 21–37). Given this simplification, however, we are easily able to take into account some typical econometric problems as, e.g. heteroscedasticity. The estimated production function can be used to identify the technical inefficiency of hospitals (as already seen in previous works), but also to draw some economic considerations about scale elasticity, scale efficiency and optimal resource allocation of the productive units. We will show, in fact, that for the translogarithmic specification it is possible to obtain the elasticity of the output (regarding an input) at hospital level as a weighted sum of elasticities at ward level. Analogous results can be achieved for scale elasticity, which measures how output changes in response to simultaneous inputs variation. In addition, referring to scale efficiency and to optimal resource allocation, we will consider the results of Ray (1998, J. Prod. Anal., 11, 183–194) to our context. The interpretation of the results is surely an interesting administrative instrument for decision makers in order to analyse the productive conditions of each hospital and its single wards and also to decide the preferable interventions.
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|Date of creation:||2005|
|Publication status:||Published in IMA Journal of Management Mathematics, 2005, vol. 16, pages 383–398|
|Contact details of provider:|| Postal: Via Bicocca degli Arcimboldi 8, 20126 Milano|
Web page: http://www.statistica.unimib.it
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- Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1973. "Transcendental Logarithmic Production Frontiers," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 28-45, February.
- Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
- Chambers,Robert G., 1988. "Applied Production Analysis," Cambridge Books, Cambridge University Press, number 9780521314275, January.
- Kim, H Youn, 1992. "The Translog Production Function and Variable Returns to Scale," The Review of Economics and Statistics, MIT Press, vol. 74(3), pages 546-552, August.
- Grant, James H., 1993. "The translog approximate function : Substitution among inputs in manufacturing evaluated at sample means," Economics Letters, Elsevier, vol. 41(3), pages 235-240.
- Ryan, David L. & Wales, Terence J., 2000. "Imposing local concavity in the translog and generalized Leontief cost functions," Economics Letters, Elsevier, vol. 67(3), pages 253-260, June.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.