IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v213y2011i1p119-123.html
   My bibliography  Save this article

The impossibility of convex constant returns-to-scale production technologies with exogenously fixed factors

Author

Listed:
  • Podinovski, Victor V.
  • Bouzdine-Chameeva, Tatiana

Abstract

The extensions to the variable (VRS) and the constant (CRS) returns-to-scale models developed by Banker and Morey are considered among the main approaches to the incorporation of exogenously fixed factors in models of data envelopment analysis (DEA). Recently, Syrjänen showed that the Banker and Morey CRS technology is not convex. Taking into account that its subset VRS technology is explicitly assumed convex, this observation leads to difficulties with explaining the fundamental production assumptions of the CRS extension. Motivated by the example of Syrjänen, the contribution of this paper is twofold. First, we show that the nonconvex Banker and Morey CRS technology is nevertheless a suitable reference technology for the assessment of scale efficiency. Second, we ask if a convex technology could be constructed that would "correct" the nonconvexity of the CRS technology of Banker and Morey. The answer to this is negative: one consequence of assuming both convexity and ray unboundness with fixed exogenous factors is that we can always "mix-and-match" discretionary and nondiscretionary factors taken from different units, arriving at totally unrealistic production plans. This demonstrates that generally there exists no meaningful convex CRS technology with exogenously fixed factors that can be used in its own right, apart from its use as a reference technology in the measurement of scale efficiency.

Suggested Citation

  • Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2011. "The impossibility of convex constant returns-to-scale production technologies with exogenously fixed factors," European Journal of Operational Research, Elsevier, vol. 213(1), pages 119-123, August.
  • Handle: RePEc:eee:ejores:v:213:y:2011:i:1:p:119-123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(11)00214-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    3. C. Lovell & Shawna Grosskopf & Eduardo Ley & Jesús Pastor & Diego Prior & Philippe Eeckaut, 1994. "Linear programming approaches to the measurement and analysis of productive efficiency," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(2), pages 175-248, December.
    4. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    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. Timo Kuosmanen, 2005. "Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1077-1082.
    7. Syrjanen, Mikko J., 2004. "Non-discretionary and discretionary factors and scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 158(1), pages 20-33, October.
    8. V V Podinovski, 2004. "Bridging the gap between the constant and variable returns-to-scale models: selective proportionality in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(3), pages 265-276, March.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. Timo Kuosmanen & Victor Podinovski, 2008. "Weak Disposability in Nonparametric Production Analysis: Reply to Färe and Grosskopf," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(2), pages 539-545.
    11. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    2. Podinovski, Victor V. & Kuosmanen, Timo, 2011. "Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions," European Journal of Operational Research, Elsevier, vol. 211(3), pages 577-585, June.
    3. Syrjanen, Mikko J., 2004. "Non-discretionary and discretionary factors and scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 158(1), pages 20-33, October.
    4. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    5. Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2019. "Cone extensions of polyhedral production technologies," European Journal of Operational Research, Elsevier, vol. 276(2), pages 736-743.
    6. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
    7. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "Scale characteristics of variable returns-to-scale production technologies with ratio inputs and outputs," Annals of Operations Research, Springer, vol. 318(1), pages 383-423, November.
    8. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "The structure of production technologies with ratio inputs and outputs," Journal of Productivity Analysis, Springer, vol. 57(3), pages 255-267, June.
    9. Lee, Boon L. & Worthington, Andrew C., 2014. "Technical efficiency of mainstream airlines and low-cost carriers: New evidence using bootstrap data envelopment analysis truncated regression," Journal of Air Transport Management, Elsevier, vol. 38(C), pages 15-20.
    10. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.
    11. Brennan, Shae & Haelermans, Carla & Ruggiero, John, 2014. "Nonparametric estimation of education productivity incorporating nondiscretionary inputs with an application to Dutch schools," European Journal of Operational Research, Elsevier, vol. 234(3), pages 809-818.
    12. 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.
    13. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
    14. Alexandre Marinho & Simone de Souza Cardoso & Vivian Vicente de Almeida, 2009. "Avaliação da Eficiência Técnica dos Países nos Jogos Olímpicos de Pequim – 2008," Discussion Papers 1394, Instituto de Pesquisa Econômica Aplicada - IPEA.
    15. Pham, Manh D. & Zelenyuk, Valentin, 2019. "Weak disposability in nonparametric production analysis: A new taxonomy of reference technology sets," European Journal of Operational Research, Elsevier, vol. 274(1), pages 186-198.
    16. Mahmoudi, Reza & Emrouznejad, Ali & Shetab-Boushehri, Seyyed-Nader & Hejazi, Seyed Reza, 2020. "The origins, development and future directions of data envelopment analysis approach in transportation systems," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    17. Ruggiero, John, 1998. "Non-discretionary inputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 111(3), pages 461-469, December.
    18. Papaioannou, Grammatoula & Podinovski, Victor V., 2023. "Production technologies with ratio inputs and outputs," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1164-1178.
    19. Reilly, Allison C. & Davidson, Rachel A. & Nozick, Linda K. & Chen, Thomas & Guikema, Seth D., 2016. "Using data envelopment analysis to evaluate the performance of post-hurricane electric power restoration activities," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 197-204.
    20. OA Carboni & P. Russu, 2014. "Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model," Working Paper CRENoS 201401, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:213:y:2011:i:1:p:119-123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.