IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/37965.html
   My bibliography  Save this paper

Selecting between different productivity measurement approaches: An application using EU KLEMS data

Author

Listed:
  • Giraleas, Dimitris
  • Emrouznejad, Ali
  • Thanassoulis, Emmanuel

Abstract

Over the years, a number of different approaches were developed to measure productivity change, both in the micro and the macro setting. Since each approach comes with its own set of assumptions, it is not uncommon in practice that they produce different, and sometimes quite divergent, productivity change estimates. This paper introduces a framework that can be used to select between the most common productivity measurement approaches based on a number of characteristics specific to the application/dataset at hand; these were selected based on the results of previous simulation analysis that examined the accuracy of different productivity measurement approaches under different conditions. The characteristics in question include input volatility through time, the extent of technical inefficiency and noise present in the dataset and whether the parametric approaches are likely to suffer from functional form miss-specification and are examined using a number of well-established diagnostics and indicators. Once assessed, the most appropriate approach can be selected based on its relative accuracy under these conditions; accuracy can in turn be assessed using simulation analysis, either previously published or designed specifically to emulate the characteristics of the application/dataset at hand. As an example of how this selection framework can be implemented in practice, we assess the productivity performance of a number of EU countries using the EU KLEMS dataset.

Suggested Citation

  • Giraleas, Dimitris & Emrouznejad, Ali & Thanassoulis, Emmanuel, 2012. "Selecting between different productivity measurement approaches: An application using EU KLEMS data," MPRA Paper 37965, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:37965
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/37965/1/MPRA_paper_37965.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    2. Portela, Maria C.A.S. & Thanassoulis, Emmanuel, 2010. "Malmquist-type indices in the presence of negative data: An application to bank branches," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1472-1483, July.
    3. Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 2004. "A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 153(3), pages 624-640, March.
    4. James Odeck, 2007. "Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2617-2630.
    5. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    6. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    7. Massimo Del Gatto & Adriana Di Liberto & Carmelo Petraglia, 2011. "Measuring Productivity," Journal of Economic Surveys, Wiley Blackwell, vol. 25(5), pages 952-1008, December.
    8. Resti, Andrea, 2000. "Efficiency measurement for multi-product industries: A comparison of classic and recent techniques based on simulated data," European Journal of Operational Research, Elsevier, vol. 121(3), pages 559-578, March.
    9. Diewert, W E, 1992. "The Measurement of Productivity," Bulletin of Economic Research, Wiley Blackwell, vol. 44(3), pages 163-198, July.
    10. 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.
    11. van Ark, Bart, 1998. "Productivity," Journal of the Japanese and International Economies, Elsevier, vol. 12(2), pages 171-174, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Data envelopment analysis; Productivity and competitiveness; Simulation; Stochastic Frontier Analysis; Growth accounting;

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    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:pra:mprapa:37965. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.