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Efficiency and benchmarking with directional distances: a data-driven approach

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Daraio, Cinzia Simar, Léopold [UCL]

In efficiency analysis the assessment of the performance of Decision-Making Units (DMUs) relays on the selection of the direction along which the distance from the efficient frontier is measured. Directional Distance Functions (DDFs) represent a flexible way to gauge the inefficiency of DMUs. Permitting the selection of a direction towards the efficient frontier is often useful in empirical applications. As a matter of fact, many papers in the literature have proposed specific DDFs suitable for different contexts of application. Nevertheless, the selection of a direction implies the choice of an efficiency target which is imposed to all the analysed DMUs. Moreover, there exist many situations in which there is no a priori economic or managerial rationale to impose a subjective efficiency target. In this paper we propose a data-driven approach to find out an ‘objective’ direction along which to gauge the inefficiency of each DMU. Our approach permits to take into account for the heterogeneity of DMUs and their diverse contexts that may influence their input and/or output mixes. Our method is also a data-driven technique for benchmarking each DMU. We describe how to implement our framework and illustrate its usefulness with simulated and real data sets.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès libre
Publication date 2016
Language Anglais
Journal information "The journal of the Operational Research Society" - Vol. 67, p. 928-944 (2016)
Peer reviewed yes
Publisher Pergamon Press (Basingstoke)
issn 0160-5682
e-issn 1476-9360
Publication status Publié
Affiliation UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords DEA ; benchmarking ; Directional Distance Functions ; non-parametric estimation ; heterogeneity ; performance
Links
  1. Afriat S. N., Efficiency Estimation of Production Functions, 10.2307/2525845
  2. Aly Hassan Y., Grabowski Richard, Pasurka Carl, Rangan Nanda, Technical, Scale, and Allocative Efficiencies in U.S. Banking: An Empirical Investigation, 10.2307/2109710
  3. Anderson TW (2003). An Introduction to Multivariate Statistical Analysis. 3rd edn, John Wiley & Sons, Inc.: New Jersey.
  4. Banker R. D., Charnes A., Cooper W. W., Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, 10.1287/mnsc.30.9.1078
  5. Banker Rajiv D., Morey Richard C., Efficiency Analysis for Exogenously Fixed Inputs and Outputs, 10.1287/opre.34.4.513
  6. Bogetoft Peter, 10.1023/a:1018985313272
  7. Chambers Robert G., Chung Yangho, Färe Rolf, Benefit and Distance Functions, 10.1006/jeth.1996.0096
  8. Chambers R. G., Chung Y., Färe R., Profit, Directional Distance Functions, and Nerlovian Efficiency, 10.1023/a:1022637501082
  9. Charnes A., Cooper W.W., Rhodes E., Measuring the efficiency of decision making units, 10.1016/0377-2217(78)90138-8
  10. Charnes A., Cooper W. W., Rhodes E., Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through, 10.1287/mnsc.27.6.668
  11. Cooper WW, Seiford LM and Tone K (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer Science and Business Media: New York.
  12. Daraio C and Simar L (2007). Advanced Robust and Nonparametric Methods in Efficiency Analysis. Methodology and Applications. Springer: New York.
  13. Daraio Cinzia, Simar Léopold, Directional distances and their robust versions: Computational and testing issues, 10.1016/j.ejor.2014.01.064
  14. Debreu Gerard, The Coefficient of Resource Utilization, 10.2307/1906814
  15. Deprins D, Simar L and Tulkens H (1984). Measuring labor inefficiency in post offices. In: Marchand M, Pestieau P and Tulkens H (eds). The Performance of Public Enterprises: Concepts and Measurements. North-Holland: Amsterdam, pp 243–267.
  16. DI MARZIO MARCO, PANZERA AGNESE, TAYLOR CHARLES C., Non-parametric Regression for Circular Responses : Non-parametric circular regression, 10.1111/j.1467-9469.2012.00809.x
  17. Dyson Robert G., Strategic development and SWOT analysis at the University of Warwick, 10.1016/s0377-2217(03)00062-6
  18. Emrouznejad Ali, Parker Barnett R., Tavares Gabriel, Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA, 10.1016/j.seps.2007.07.002
  19. Farrell M. J., The Measurement of Productive Efficiency, 10.2307/2343100
  20. Färe Rolf, Grosskopf Shawna, 10.1023/a:1007844628920
  21. Färe Rolf, Grosskopf Shawna, Margaritis Dimitri, Efficiency and Productivity: Malmquist and More, The Measurement of Productive Efficiency and Productivity Change (2008) ISBN:9780195183528 p.522-622, 10.1093/acprof:oso/9780195183528.003.0005
  22. Färe R., Grosskopf S., Whittaker G., Directional output distance functions: endogenous directions based on exogenous normalization constraints, 10.1007/s11123-012-0333-8
  23. Fisher N. I., Lee A. J., Regression Models for an Angular Response, 10.2307/2532334
  24. Gould A. Lawrence, A Regression Technique for Angular Variates, 10.2307/2528567
  25. Hall Peter, Li Qi, Racine Jeffrey S., Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors, 10.1162/rest.89.4.784
  26. Kerstens K, Mounir A, Van de Woestyne I, Benchmarking mean-variance portfolios using a shortage function: the choice of direction vector affects rankings!, 10.1057/jors.2011.140
  27. Kneip A, Simar L and Wilson PW (2013). Testing hypothesis in nonparametric models of production. Discussion paper 2013/48, Institut de Statistique, UCL.
  28. Koopmans TC (ed). (1951). An analysis of production as an efficient combination of activities. In: Activity Analysis of Production and Allocation. Cowles Commision for Research in Economics, Monograph 13, John-Wiley: New-York.
  29. Korhonen Pekka, Tainio Risto, Wallenius Jyrki, Value efficiency analysis of academic research, 10.1016/s0377-2217(00)00050-3
  30. Lee Chia-Yen, Meta-data envelopment analysis: Finding a direction towards marginal profit maximization, 10.1016/j.ejor.2014.01.026
  31. Li Q and Racine JS (2007). Nonparametric Econometrics. Princeton University Press: Princeton, Cambridge.
  32. Luenberger David G., Benefit functions and duality, 10.1016/0304-4068(92)90035-6
  33. Pagan Adrian, Ullah Aman, Nonparametric Econometrics, ISBN:9780511612503, 10.1017/cbo9780511612503
  34. Peyrache Antonio, Daraio Cinzia, Empirical tools to assess the sensitivity of directional distance functions to direction selection, 10.1080/00036846.2010.526582
  35. Presnell Brett, Morrison Scott P., Littell Ramon C., Projected Multivariate Linear Models for Directional Data, 10.1080/01621459.1998.10473768
  36. Reder M. W., An Alternative Interpretation of the Cobb-Douglas Function, 10.2307/1905678
  37. Shephard RW (1970). Theory of Cost and Production Function. Princeton University Press: Princeton, NJ.
  38. Simar Léopold, 10.1023/a:1027308001925
  39. Simar Léopold, Wilson Paul W., Estimation and inference in two-stage, semi-parametric models of production processes, 10.1016/j.jeconom.2005.07.009
  40. Simar Léopold, Vanhems Anne, Probabilistic characterization of directional distances and their robust versions, 10.1016/j.jeconom.2011.10.002
  41. Simar Léopold, Vanhems Anne, Wilson Paul W., Statistical inference for DEA estimators of directional distances, 10.1016/j.ejor.2012.02.030
  42. Simar Léopold, Wilson Paul W., Inference by the m out of n bootstrap in nonparametric frontier models, 10.1007/s11123-010-0200-4
  43. Simar Léopold, Wilson Paul W., Statistical Approaches for Non-parametric Frontier Models: A Guided Tour : Non-parametric Frontier Models, 10.1111/insr.12056
  44. Thanassoulis Emmanuel, Portela Maria C. S., Despić Ozren, Data Envelopment Analysis: The Mathematical Programming Approach to Efficiency Analysis, The Measurement of Productive Efficiency and Productivity Change (2008) ISBN:9780195183528 p.251-420, 10.1093/acprof:oso/9780195183528.003.0003
  45. Wilson PW (1993). Detecting outliers in deterministic nonparametric frontier models with multiple outputs. Journal of Business and Economic Statistics 11 (3): 319–323.
  46. Zofio Jose L., Pastor Jesus T., Aparicio Juan, The directional profit efficiency measure: on why profit inefficiency is either technical or allocative, 10.1007/s11123-012-0292-0
Bibliographic reference Daraio, Cinzia ; Simar, Léopold. Efficiency and benchmarking with directional distances: a data-driven approach. In: The journal of the Operational Research Society, Vol. 67, p. 928-944 (2016)
Permanent URL http://hdl.handle.net/2078.1/171386