IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-81-322-2253-8_2.html
   My bibliography  Save this book chapter

Data Envelopment Analysis for Performance Evaluation: A Child’s Guide

In: Benchmarking for Performance Evaluation

Author

Listed:
  • Subhash C. Ray

    (University of Connecticut)

  • Lei Chen

    (Jianghan University)

Abstract

In this paper we offer a simple exposition of the neoclassical production theoretic foundations of Data Envelopment Analysis (DEA). The concepts of technical efficiency (both input and output oriented), scale efficiency, and cost efficiency are explained, and the corresponding DEA models are described in detail. We offer step-by-step instruction on how to write the codes for solving various DEA models using the Solver option in the widely accessible MS Excel software. An important feature of this paper is a detailed exposition of how to write various Visual Basic Macro programs for solving DEA problems. We also describe the non-convex free disposal hull (FDH) procedure and the second-stage regression analysis that seeks to account for variation in measured efficiency scores due to external factors.

Suggested Citation

  • Subhash C. Ray & Lei Chen, 2015. "Data Envelopment Analysis for Performance Evaluation: A Child’s Guide," Springer Books, in: Subhash C. Ray & Subal C. Kumbhakar & Pami Dua (ed.), Benchmarking for Performance Evaluation, edition 127, chapter 0, pages 75-116, Springer.
    • Handle: RePEc:spr:sprchp:978-81-322-2253-8_2
    DOI: 10.1007/978-81-322-2253-8_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, September.
    2. 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.
    3. 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.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    5. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, February.
    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. Giuseppe Coco & Raffaele Lagravinese & Giuliano Resce, 2020. "Beyond the weights: a multicriteria approach to evaluate inequality in education," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(4), pages 469-489, December.
    2. Maha Kalai, 2019. "Nonparametric Measures of Capacity Utilization of the Tunisian Manufacturing Industry: Short- and Long-Run Dual Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 318-334, March.

    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. Stefan Seifert, 2016. "Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation," Discussion Papers of DIW Berlin 1571, DIW Berlin, German Institute for Economic Research.
    2. Md Ali & K. Klein, 2014. "Water Use Efficiency and Productivity of the Irrigation Districts in Southern Alberta," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2751-2766, August.
    3. Jaime Bonet-Morón & Jhorland Ayala-García, 2016. "La brecha fiscal territorial en Colombia," Documentos de trabajo sobre Economía Regional y Urbana 235, Banco de la Republica de Colombia.
    4. Ioannis E. Tsolas, 2020. "Benchmarking Wind Farm Projects by Means of Series Two-Stage DEA," Clean Technol., MDPI, vol. 2(3), pages 1-12, September.
    5. Yun Liao, 2024. "Super-efficiency and Stock Market Valuation: Evidence from Listed Banks in China (2006 to 2023)," Papers 2407.14734, arXiv.org.
    6. Subhash C. Ray, 2014. "Data Envelopment Analysis: An Overview," Working papers 2014-33, University of Connecticut, Department of Economics.
    7. Alboghdady, Mohamed Altabei, 2014. "Nonparametric Model For Measuring Impact Of Inputs Density On Egyptian Tomato Production Efficiency," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 2(4), pages 1-10, October.
    8. Frenda, Antonio & Sepe, Enrica & Scippacercola, Sergio, 2021. "Efficiency analysis of social protection expenditure in the Italian Regions," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    9. 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.
    10. Sarkis, Joseph & Cordeiro, James J., 2012. "Ecological modernization in the electrical utility industry: An application of a bads–goods DEA model of ecological and technical efficiency," European Journal of Operational Research, Elsevier, vol. 219(2), pages 386-395.
    11. Subhash C. Ray, 2018. "Data Envelopment Analysis with Alternative Returns to Scale," Working papers 2018-20, University of Connecticut, Department of Economics.
    12. G. Thomas Sav, 2012. "Efficiency Estimates and Rankings Employing Data Envelopment and Stochastic Frontier Analyses: Evaluating the Management of U.S. Public Colleges," Information Management and Business Review, AMH International, vol. 4(8), pages 444-452.
    13. Aparicio, Juan & Kapelko, Magdalena & Zofío, José L., 2020. "The measurement of environmental economic inefficiency with pollution-generating technologies," Resource and Energy Economics, Elsevier, vol. 62(C).
    14. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    15. Musshoff, Oliver & Hirschauer, Norbert & Herink, Michael, 2009. "Bei welchen Problemstrukturen sind Data-Envelopment-Analysen sinnvoll? Eine kritische Würdigung," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 58(02), pages 1-11, February.
    16. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    17. W. Cooper & C. Lovell, 2011. "History lessons," Journal of Productivity Analysis, Springer, vol. 36(2), pages 193-200, October.
    18. Ljubica Nedelkoska, 2010. "Occupations at risk: The task content and job stability," Jena Economics Research Papers 2010-024, Friedrich-Schiller-University Jena.
    19. Hirofumi Fukuyama & Hiroya Masaki & Kazuyuki Sekitani & Jianming Shi, 2014. "Distance optimization approach to ratio-form efficiency measures in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 42(2), pages 175-186, October.
    20. Jaime Bonet‐Morón & Jhorland Ayala‐García, 2020. "The territorial fiscal gap in Colombia," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(1), pages 7-24, February.

    More about this item

    Keywords

    Efficiency; Linear programming; Benchmarking; Excel solver; D2;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact

    Statistics

    Access and download statistics

    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:spr:sprchp:978-81-322-2253-8_2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.