IDEAS home Printed from https://ideas.repec.org/p/uct/uconnp/2005-54.html
   My bibliography  Save this paper

The Validity of Input Aggregation in DEA Models: A Statistical Test

Author

Listed:
  • Subhash C. Ray

    (University of Connecticut)

  • Kankana Mukherjee

    (Worcester Polytechnic Institute)

Abstract

We address the problem of input aggregation in DEA models within a broader framework and provide a direct link between input aggregation in DEA on the one hand and the test of parameter restrictions implied by input aggregation in an explicitly specified production function on the other. We show that when input prices vary across firms, the DEA LP problems for measuring efficiency scores of individual firms from the aggregated model have to be appropriately modified. An empirical application of the revised model using data from Indian manufacturing sector reveals that the validity of aggregating production and non-production workers into a composite labor input using Banker's F-test cannot be rejected.

Suggested Citation

  • Subhash C. Ray & Kankana Mukherjee, 2005. "The Validity of Input Aggregation in DEA Models: A Statistical Test," Working papers 2005-54, University of Connecticut, Department of Economics, revised Nov 2006.
  • Handle: RePEc:uct:uconnp:2005-54
    Note: The paper has benefited from comments received from participants at the 2006 INFORMS Annual Meeting.
    as

    Download full text from publisher

    File URL: https://media.economics.uconn.edu/working/2005-54r.pdf
    File Function: Full text (revised version)
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Varian, Hal R, 1984. "The Nonparametric Approach to Production Analysis," Econometrica, Econometric Society, vol. 52(3), pages 579-597, May.
    4. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    Full references (including those not matched with items on IDEAS)

    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. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    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. Subhash C. Ray, 2018. "Data Envelopment Analysis with Alternative Returns to Scale," Working papers 2018-20, University of Connecticut, Department of Economics.
    5. Kuosmanen, T. & Post, G.T., 2001. "Non-Parametric Tests for Firm Efficiency in Case of Errors-in-Variables," ERIM Report Series Research in Management ERS-2001-06-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    7. 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.
    8. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2015. "Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 244(2), pages 511-518.
    9. Fadzlan Sufian & Muzafar Shah Habibullah, 2010. "Bank-specific, Industry-specific and Macroeconomic Determinants of Bank Efficiency," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(4), pages 427-461, November.
    10. Cesaroni, Giovanni & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2017. "Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration," European Journal of Operational Research, Elsevier, vol. 259(2), pages 576-586.
    11. repec:pra:mprapa:54437 is not listed on IDEAS
    12. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    13. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.
    14. Rudra Bahadur SHRESTHA & Wen-Chi HUANG & Shriniwas GAUTAM & Thomas Gordon JOHNSON, 2016. "Efficiency of small scale vegetable farms: policy implications for the rural poverty reduction in Nepal," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(4), pages 181-195.
    15. Ray, Subhash C. & Jeon, Yongil, 2008. "Reputation and efficiency: A non-parametric assessment of America's top-rated MBA programs," European Journal of Operational Research, Elsevier, vol. 189(1), pages 245-268, August.
    16. Alperovych, Yan & Amess, Kevin & Wright, Mike, 2013. "Private equity firm experience and buyout vendor source: What is their impact on efficiency?," European Journal of Operational Research, Elsevier, vol. 228(3), pages 601-611.
    17. Banker, Rajiv D. & Chang, Hsihui & Lee, Seok-Young, 2010. "Differential impact of Korean banking system reforms on bank productivity," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1450-1460, July.
    18. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    19. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    20. Giokas, Dimitris I., 2001. "Greek hospitals: how well their resources are used," Omega, Elsevier, vol. 29(1), pages 73-83, February.
    21. Mazumdar, Mainak & Rajeev, Meenakshi & Ray, Subhash C., 2012. "Sources of Heterogeneity in the Efficiency of Indian Pharmaceutical Firms," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 47(2), pages 191-221.

    More about this item

    Keywords

    Banker's F-test; data envelopment analysis; input aggregation; parameter restrictions;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L6 - Industrial Organization - - Industry Studies: Manufacturing

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:uct:uconnp:2005-54. 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: Mark McConnel (email available below). General contact details of provider: https://edirc.repec.org/data/deuctus.html .

    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.