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Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models

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  • Julie Harrison
  • Paul Rouse
  • Jamie Armstrong

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  • 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.
    • Handle: RePEc:kap:jproda:v:37:y:2012:i:3:p:261-276
    DOI: 10.1007/s11123-011-0239-x
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    1. Ruggiero, John, 1998. "Non-discretionary inputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 111(3), pages 461-469, December.
    2. Finn R. Forsund, 2002. "Categorical Variables in DEA," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(1), pages 33-44, April.
    3. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    4. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Yun Zhang & Robert Bartels, 1998. "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, Springer, vol. 9(3), pages 187-204, March.
    7. Muniz, M. A., 2002. "Separating managerial inefficiency and external conditions in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(3), pages 625-643, December.
    8. Subhash C. Ray, 1991. "Resource-Use Efficiency in Public Schools: A Study of Connecticut Data," Management Science, INFORMS, vol. 37(12), pages 1620-1628, December.
    9. Matthias Staat, 2001. "The Effect of Sample Size on the Mean Efficiency in DEA: Comment," Journal of Productivity Analysis, Springer, vol. 15(2), pages 129-137, March.
    10. Haas, David A. & Murphy, Frederic H., 2003. "Compensating for non-homogeneity in decision-making units in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 144(3), pages 530-544, February.
    11. Ray, Subhash C., 1988. "Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation," Socio-Economic Planning Sciences, Elsevier, vol. 22(4), pages 167-176.
    12. Ruggiero, John, 2004. "Performance evaluation when non-discretionary factors correlate with technical efficiency," European Journal of Operational Research, Elsevier, vol. 159(1), pages 250-257, November.
    13. Ruggiero, John, 1996. "On the measurement of technical efficiency in the public sector," European Journal of Operational Research, Elsevier, vol. 90(3), pages 553-565, May.
    14. Rajiv D. Banker, 1992. "Selection of efficiency evaluation models," Contemporary Accounting Research, John Wiley & Sons, vol. 9(1), pages 343-355, September.
    15. Banker, Rajiv D. & Chang, Hsihui, 2006. "The super-efficiency procedure for outlier identification, not for ranking efficient units," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1311-1320, December.
    16. 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.
    17. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    18. Löber, Gerrit & Staat, Matthias, 2010. "Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique," European Journal of Operational Research, Elsevier, vol. 202(3), pages 810-818, May.
    19. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    20. Yu, Chunyan, 1998. "The effects of exogenous variables in efficiency measurement--A monte carlo study," European Journal of Operational Research, Elsevier, vol. 105(3), pages 569-580, March.
    21. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    22. Boaz Golany, 1988. "Note---A Note on Including Ordinal Relations Among Multipliers in Data Envelopment Analysis," Management Science, INFORMS, vol. 34(8), pages 1029-1033, August.
    23. Bhattacharyya, Arunava & Lovell, C. A. K. & Sahay, Pankaj, 1997. "The impact of liberalization on the productive efficiency of Indian commercial banks," European Journal of Operational Research, Elsevier, vol. 98(2), pages 332-345, April.
    24. A Basso & S Funari, 2003. "Measuring the performance of ethical mutual funds: a DEA approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 521-531, May.
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    Cited by:

    1. Laura López Torres & Diego Prior, 2014. "Measuring school demand in the presence of spatial dependence. A conditional approach," Investigaciones de Economía de la Educación volume 9, in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 4, pages 117-141, Asociación de Economía de la Educación.
    2. Castro Massimo Finocchiaro & Guccio Calogero, 2015. "Bottlenecks or Inefficiency? An Assessment of First Instance Italian Courts’ Performance," Review of Law & Economics, De Gruyter, vol. 11(2), pages 317-354, July.
    3. 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.
    4. Huguenin, Jean-Marc, 2015. "Adjusting for the environment in DEA: A comparison of alternative models based on empirical data," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 41-54.
    5. Paolo Liberati & Raffaele Lagravinese & Giuliano Resce, 2017. "How Does Economic Social And Cultural Status Affect The Efficiency Of Educational Attainments? A Comparative Analysis On Pisa Results," Departmental Working Papers of Economics - University 'Roma Tre' 0217, Department of Economics - University Roma Tre.
    6. Calogero Guccio & Marco Ferdinando Martorana & Luisa Monaco, 2016. "Evaluating the impact of the Bologna Process on the efficiency convergence of Italian universities: a non-parametric frontier approach," Journal of Productivity Analysis, Springer, vol. 45(3), pages 275-298, June.
    7. 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.
    8. López-Torres, Laura & Nicolini, Rosella & Prior, Diego, 2017. "Does strategic interaction affect demand for school places? A conditional efficiency approach," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 89-103.

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    More about this item

    Keywords

    Data envelopment analysis; Simulation; Non-discretionary inputs; Environmental modeling; C63; D24;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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