Email this article Critical comparison of the main methods for the technical efficiency
Abstract
The Technical Efficiency is a basic tool to determine the factors that slow down the production. TE aims at evaluating and comparing the operating performance of a set of production units, such as Companies, Offices, Hospi- tals, Banks, Schools, Transport Systems, etc. This paper, after an overview of the literature regarding the methodologies for measuring the Technical Ef- ficiency, compares critically the two main approaches, the Data Envelopment Analysis (DEA) and the Stochastic Frontier Analysis (SFA). These method- ologies are also discussed within an original application that targets to study the efficiency of European Countries with respect to the Gross Domestic Product (GDP).
References
S.N. Afriat, Efficiency Estimation of Production Functions, International Economic Review, 13, (1972), pp. 568-598.
D.J. Aigner and S.F. Chu, On Estimating the Industry Production Function, American Economic Review, 58, (1968), pp. 826-839.
D.J. Aigner, C.A.K. Lovell and P. Schmidt, Formulation and Estimation of Stochastic Frontier Production Function Models, Journal of Econometrics, 6, (1977), pp. 21-37.
R.D. Banker, Estimating Most Productive Scale Size Using Data Envelopment Analysis, European Journal of Operational Research, 17, (1984), pp. 35-44.
R. D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in Data Evelopment Analysis, Managment Science, 30, (1984), no. 9, pp. 1078-1092.
G.E. Battese, Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics, Agricultural Economics, 7, (1992), pp. 185-208.
G.E Battese and G.S. Corra, Estimation of a production frontier model: with application to the pastoral zone of eastern Australia, Australian Journal of Agricultural and Resource Economics, 21, (1977), no. 3, pp. 169–179.
G.E Battese and T.J. Coelli, Prediction of firm level technical efficiencies with generalized frontier production function and panel data, Journal of Econometrics, (1988).
P.W. Bauer, Recent developments in the econometrics estimation of frontiers, Journal of econometrics, 46, (1990), pp. 39-56.
A. Charnes, W.W. Cooper and E. Rhodes, Measuring the Efficiency of decision Making Units, European Journal of Operation Research, 2, (1978), pp. 429-444.
A. Charnes, W.W. Cooper and E. Rhodes, Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through, Management Science, 27. (1981), No. 6, pp. 668-697.
L. R. Christiansen, D.W. Jorgenson and L.J. Lau, Transcendental Logarithmic Production Frontiers, Review of EcOnomics and Statistics, 65, (1971), pp. 28-45.
C. Cobb, and P. Douglas, A Theory of Production, American Economic Review Supplement to, 18, (1928), pp. 139-65.
T.J. Coelli, D.S. Prasada Rao and G.E. Battese, An Introduction to efficiency and productivity analysis, Kluwer Academic Publisher, Boston, (1998).
T.J., Coelli, D.S.P. Rao, C.J. O’Donnell and G.E Battese, An introduction to efficiency and productivity analysis, Springer, (2005).
W.W. Cooper, R.G. Thompson and R.M. Thrall, Introduction: Extensions and New Developments in DEA, Annals of Operations Research - Extensions and New Developments in Data Envelopment Analysis, 66, (1996), pp. 3-45.
J. Cordeiro, J. Sarkis, D. Vasquez and J. Dijkshoorn, A stochastic Frontier Analysis of estimates and correlates of the efficiency of solid waste management in welsh SMEs, GIN conference, Leeuwarden, Netherlands, (2008). Available at https://gin.confex.com/gin/responses/2008N/207.ppt.
W.E. Diewert, An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function, Journal of Political Economy, 79, (1971), pp. 481-507.
M.J. Farrell, The measurement of Productive Efficiency, Journal of the Royal Statistical Society Series A, 120, (1957), pp. 253-290.
H.O. Fried, C.A.K. Lovell and P. Schmidt, The Measurement of Productive Efficiency: techniques and applications, Oxford University Press, (eds.) (1993).
F.R. Forsund, C.A.K. Lovell and P. Schmidt, A survey of frontier production functions and of their relationship to efficiency measurement, Journal of econometrics, 13, (1980), pp. 5-25.
F.R. Forsund, A Comparison of Parametric and Non-parametric Efficiency Measures: The Case of Norwegian Ferries, Journal of Productivity Analysis, 3, (1992), pp. 25-43.
A.R. Gallant, On the Bias in Flexible Functional Forms and an Essentially Unbiased Form, Journal of Econometrics, 15, (1981), pp. 211-45.
W.H. Greene, Maximum Likelihood Estimation of Econometric Frontier Functions, Journal of Econometrics, 13, (1980), pp. 27-56.
B.H. Gong and R.C. Sickles, Finite Sample Evidence on the Performance of Stochastic Frontiers and Data Envelopment Analysis Using Panel Data, Journal of Econometrics, 51, (1992), pp. 259-84.
G.D. Hutcheson, Ordinary Least-Squares Regression. In L. Moutinho and G. D. Hutcheson, The SAGE Dictionary of Quantitative Management Research, (2011), pp. 224-228.
S.C. Kumbhakar and C.K. Lovell, Stochastic frontier analysis, Cambridge University Press, (2000).
K.P. Kalirajan, and R.T. Shand, Frontier Production Functions and Technical Efficiency, (2002).
L. F. Lee, A Test for Distributional Assumption for the Stochastic Frontier Functions, Journal of Econometrics, (1983).
W. Meeusen, and J. Van Den Broeck, Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error, International Economic Review, 18, (1977), pp. 435-44.
L. McMillan Melville, and C. Wing, University Efficiency: A Comparison of Results from Stochastic and Non-Stochastic Methods, (2003).
O.B. Olesen, Some unsolved problems in Data envelopment Analysis – a survey, International Journal of Production Economics, 26, (1995), no. 2, pp. 103119.
J.A. Olson, P. Schmidt and D.M. Waldman, A Monte Carlo Study of Estimator of Stochastic Frontier Production Functions, Journal of Econometrics, (1980).
P. Orbanz and Y.W. Teh, Encyclopedia of Machine Learning , Springer, (2010).
F. Porcelli, Measurement of technical efficiency. A brief survey on parametric and non-parametric techniques, (2009), Available at http://www.warwick.ac.uk/fac/soc/ economics/staff/ phd_students/porcelli_dea_sfm.pdf.
S.C. Ray, Data envelopment analysis: theory and techniques for economics and operations research, Cambridge University Press, (2004).
J. Richmond, Estimating the efficiency of production, International economic Review, 15, (1974), no. 2, pp. 515-521.
L.M. Seiford, Data Envelopment analysis: the evolution of the state of the art (1978-1995), Journal of Productivity Analysis, 7, (1996), no. 2-3, pp. 99-137.
J.K. Sengupta, Data Envelopment Analysis for Efficiency Measurement in the Stochastic Case, Computers and Operations Research, 14, (1987), no. 2, pp. 117-129.
P. Schmidt, On the Statistical Estimation of Parametric Frontier Production Functions, Review of Economics and Statistics, (1976).
P. Schmidt, Frontier Production Functions, Econometric Reviews, (1985).
L. Simar and P. Wilson, Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models, Management Science, 44, (1998), no. 11, pp. 49-61.
R.E. Stevenson, Likelihood functions for generalised stochastic frontier estimation, Journal of Econometrics, 13, (1980), pp. 57-66.
E. Thanassoulis, Introduction to the Theory and Application of Data Envelopment Analysis – A foundation text with integrated software, Boston: Kluwer Academic, (2001).
J. Van Den Broeck, G. Koop, J. Osiewalski, and M. Steel, Stochastic frontier models: A Bayesian perspective, Journal of Econometrics, 61, (1994), pp. 273-303.
C.B. Winsten, Discussion on Mr. Farrell’s Paper, Journal of the Royal Statistical Society A, 120, (1957), pp. 282-284.
Full Text: pdf
