Abstract
Data envelopment analysis (DEA) developed originally as a set of techniques for measuring the relative efficiency of a set of decisionmaking units (DMUs), when the price data for inputs and outputs are either unavailable or unknown. These techniques are nonparametric in the sense that they are entirely based on the observed input-output data. The statistical aspects of the data set are almost ignored by the traditional DEA models and in this sense they are far from nonparametric. Over the last two decades the DEA models have been widely applied in management science and operations research literature and the theoretical formulations of DEA have been generalized in several directions as follows:
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(i)
Various types of DEA models have been formulated which clarify the concepts of technical and allocative efficiency and their link with the concept of Pareto efficiency in economic theory,
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(ii)
Log-linear and nonlinear formulations have extended the linear DEA models, and generalized the concepts of increasing, decreasing or constant returns to scale as applied to multiple-output and multiple-input cases, and
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(iii)
Sources of inefficiency identified through the DEA models have been incorporated in regression models in various ways.
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References
Banker, R.D., Charnes, A. and W.W. Cooper (1984), “Some Models for Estimating Technical and Scale Inefficiencies in DEA,” Management Science, Vol. 30, pp. 1078–1092.
Banker, R.D. and R.M. Thrall (1992), “Estimation of Returns to Scale Using Data Envelopment Analysis,” European Journal of Operations Research, Vol. 62, pp. 74–84.
Bjurek, H., Hjalmarsson, L. and F.R. Forsund (1990), “Deterministic Parametric and Nonparametric Estimation of Efficiency in Service Production: A Comparison,” Journal of Econometrics, Vol. 46, pp. 213–227.
Charnes, A., Cooper, W.W. and E. Rhodes (1978), “Measuring the Efficiency of Decision Making Units,” European Journal of Operations Research, Vol. 2, pp. 429–444.
Charnes, A., Cooper, W.W. and L. Seiford (1982), “A Multiplicative Model for Efficiency Analysis,” Socio-economic Planning Science, Vol. 16, pp. 223–224.
Cooper, W.W., Huang, Z. and S.X. Li (1994) “Satisficing DEA Models Under Chance Constraints,” paper to be published.
Dalen, D.M. (1983), “Testing for Technical Change with Data Envelopment Analysis,” Memorandum No. 16, Department of Economics, University of Oslo.
Farrell, M.J. (1957), “The Measurement of Productive Efficiency,” Journal of Royal Statistical Society, Series A, Vol. 120, pp. 253–290.
Fried, H.O., Knox-Lovell, C.A. and S.S. Schmidt, eds. (1993), The Measurement of Productive Efficiency: Techniques and Applications, New York: Oxford University Press.
Greene, W.H. (1990), “A Gamma Distributed Stochastic Frontier Model,” Journal of Econometrics, Vol. 46, pp. 141–163.
Johansen, L. (1972), Production Functions, Amsterdam: North Holland.
Leibenstein, H. (1966), “Allocative Efficiency vs. Xefficiency,” American Economic Review, Vol. 56, pp. 392–415.
Nishimizu, M. and J.M. Page (1982), “Total Factor Productivity Growth, Technological Progress and Technical Efficiency Change: Dimensions of Productivity Change in Yugoslavia, 1965–78,” Economic Journal, Vol. 92, pp. 920–936.
Norsworthy, J.R. and S.L. Jang (1992), Empi ri cal Measurement and Analysis of Productivity and Technological Change, Amsterdam: North Holland.
Peleg, B. and M.E. Yaari (1975), “A Price Characterization of Efficient Random Variables,” Econometrica, Vol. 43, pp. 283–292.
Sahal, D. (1976), “The Multidimensional Diffusion of Technology,” in Technological Substitution, New York: Elsevier.
Sengupta, J.K. (1986), “Efficiency Measurement in Nonmarket Systems through Data Envelopment Analysis,” in Stochastic Optimization and Economic Models, Dordrecht: Kluwer Academic Publishers.
Sengupta, J.K. (1988), “Recent Nonparametric Measures of Productive Efficiency,” in Econometrics of Planning and Efficiency, Dordrecht: Kluwer Academic Publishers.
Sengupta, J.K. (1990), “Structural Efficiency in Stochastic Models of Data Envelopment Analysis,” International Journal of Systems Science, Vol. 21, pp. 1047–1056.
Sengupta, J.K. (1992), “On the Price and Structural Efficiency in Farrell’s Model,” Bulletin of Economic Research, Vol. 44, pp. 281–300.
Sengupta, J.K. (1994), “Estimating Efficiency by Cost Frontiers: Comparison of Parametric and Nonparametric Methods,” to be published in Applied Economics Letters.
Solow, R.M. (1957), “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics, Vol. 39, pp. 312–320.
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Sengupta, J.K. (1995). Theory of DEA Models. In: Dynamics of Data Envelopment Analysis. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8506-4_1
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DOI: https://doi.org/10.1007/978-94-015-8506-4_1
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