Abstract
This study examines the effect of sample size on the mean productive efficiency of firms when the efficiency is evaluated using the non-parametric approach of Data Envelopment Analysis. By employing Monte Carlo simulation, we show how the mean efficiency is related to the sample size. The paper discusses the implications for international comparisons. As an application, we investigate the efficiency of the electricity distribution industries in Australia, Sweden and New Zealand.
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References
Adolphson, D. L., G. C. Cornia, and L. C. Walters. (1990). “A United Framework for Classifying DEA Models.” Operational Research 90, 647–657.
Banker, R. D. (1989). “Econometric Estimation and Data Envelopment Analysis.” Research in Government and Nonprofit Accounting 5, 231–243.
Banker, R. D., A. Charnes, W. W. Cooper, and A. Maindiratte. (1988). “A Comparison of DEA and Translog Estimates of the Production Frontiers using Simulated Observations from a Know Technology.” In A. Dogramaci, and R. Fare (eds), Application of Modern Production Theory: Efficiency and Production.
Banker, R. D., W. M. Gadh, and W. L. Gorr. (1993). “A Monte Carlo Comparison of Two Production Frontier Estimation Methods: Correlated Ordinary Least Squares and Data Envelopment Analysis.” European Journal of Operational Research 69, 332–343.
Caves, R. E., and D. R. Barton. (1990). Efficiency in U.S. Manufacturing Industries. The MIT Press.
Charnes, A., W. W. Cooper, and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2, 429–444.
Charnes, A., W. W. Cooper, and E. Rhodes. (1981). “Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Fellow Through.” Management Science 27, 668–697.
Charnes, A., W.W. Cooper, and R. M. Thrall. (1991). “A Structure for Classifying and Characterizing Efficiency and Inefficiency in Data Envelopment Analysis.” Journal of Productivity Analysis 2, 197–237.
Diewert, E. (1993). “Data Envelopment Analysis: A Practical Alternative?” Paper Presented to Swan Consultants (Canberra) Pty Ltd Conference.
Forsund, F. R., and L. Hjalmarsson. (1987). Analyses of Industrial Structure: A Putty-Clay Approach. Stockholm: The Industrial Institute of Economic and Social Research, Stockholm.
Gong, B-H., and R. C. Sickles. (1992). “Finite Sample Evidence on the Performance of Stochastic Frontiers and Data Envelopment Analysis using Panel Data.” Journal of Econometrics 51, 259–284.
Hjalmarsson, L., and A. Veiderpass. (1992). “Efficiency and Ownership in Swedish Electricity Retail Distribution.” Journal of Productivity Analysis 3, 7–23.
Johnson, N. L., and S. Kotz. (1978). Distributions in Statistics: Continuous Multivariate Distributions. John Wiley and Sons, Inc.
Nunamaker, T. R. (1985). “Using Data Envelopment Analysis to Measure the Efficiency of Non-Profit Organizations: A Critical Evaluation.” Managerial and Decision Economics 6, 50–58.
Smith, P. (1993). “Misspecification Bias in Data Envelopment Analysis.” Discussion Paper, Department of Economics and the Related Studies. University of York, York.
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Zhang, Y., Bartels, R. The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis 9, 187–204 (1998). https://doi.org/10.1023/A:1018395303580
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DOI: https://doi.org/10.1023/A:1018395303580