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How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

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  • Perelman, Sergio
  • Santín, Daniel

Abstract

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb-Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.

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  • Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
    • Handle: RePEc:eee:ejores:v:199:y:2009:i:1:p:303-310
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    3. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    4. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.
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    6. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    7. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    8. Andor, Mark Andreas & Bernstein, David H. & Parmeter, Christopher F. & Sommer, Stephan, 2023. "Internal meta-analysis for Monte Carlo simulations," Ruhr Economic Papers 997, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Khezrimotlagh, Dariush, 2022. "Simulation designs for production frontiers," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1321-1334.
    10. Krüger, Jens J., 2012. "A Monte Carlo study of old and new frontier methods for efficiency measurement," European Journal of Operational Research, Elsevier, vol. 222(1), pages 137-148.
    11. Kumbhakar, Subal C., 2012. "Specification and estimation of primal production models," European Journal of Operational Research, Elsevier, vol. 217(3), pages 509-518.
    12. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    13. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study," Operational Research, Springer, vol. 23(3), pages 1-33, September.
    14. Kohl, Sebastian & Brunner, Jens O., 2020. "Benchmarking the benchmarks – Comparing the accuracy of Data Envelopment Analysis models in constant returns to scale settings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1042-1057.
    15. Lars-Erik Borge & Marianne Haraldsvik, 2009. "Efficiency potential and determinants of efficiency: an analysis of the care for the elderly sector in Norway," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 16(4), pages 468-486, August.
    16. Zervopoulos, Panagiotis, 2012. "Dealing with small samples and dimensionality issues in data envelopment analysis," MPRA Paper 39226, University Library of Munich, Germany.
    17. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    18. Zaiwu Gong & Xiaoqing Chen, 2017. "Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    19. Julie Harrison & Paul Rouse & Jamie Armstrong, 2012. "Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models," Journal of Productivity Analysis, Springer, vol. 37(3), pages 261-276, June.
    20. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    21. Zarrin, Mansour & Brunner, Jens O., 2023. "Analyzing the accuracy of variable returns to scale data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1286-1301.
    22. José Manuel Cordero & Cristina Polo & Daniel Santín & Gabriela Sicilia, 2016. "Monte-Carlo Comparison of Conditional Nonparametric Methods and Traditional Approaches to Include Exogenous Variables," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 483-497, October.
    23. Mellah, Thuraya & Ben Amor, Tawfik, 2016. "Performance of the Tunisian Water Utility: An input-distance function approach," Utilities Policy, Elsevier, vol. 38(C), pages 18-32.

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