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Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data

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  • James Odeck
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    Abstract

    This article compares data envelopment analysis and stochastic frontier analysis to assess efficiency and productivity growth of Norwegian grain producers. Previous studies have dealt with either one of them and less of both. For the assessment of productivity growth or regress, Malmquist productivity indices are derived from both approaches. The data cover a 10-year period. We find consistency between the approaches to the extent that: (1) there are potentials for efficiency improvements, but the magnitudes depends on the model applied and by segmentation of the data set, (2) there has been a productivity improvement in the sector, on average in the interval 30-38% in the period studied and (3) technical change has had the greatest impact on productivity, indicating that producers have a tendency to catch-up with the front runners. In general, policy-makers are warned not to be indifferent with respect to the approach used for efficiency and productivity measurement as these may give different results.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840600722224
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Economics.

    Volume (Year): 39 (2007)
    Issue (Month): 20 ()
    Pages: 2617-2630

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    Handle: RePEc:taf:applec:v:39:y:2007:i:20:p:2617-2630

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    Web page: http://www.tandfonline.com/RAEC20

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    Cited by:
    1. Boon Lee, 2011. "Productivity, Technical and Efficiency Change in Singapore's Services Sector, 2005 to 2008," School of Economics and Finance Discussion Papers and Working Papers Series 269, School of Economics and Finance, Queensland University of Technology.
    2. Murali, Palanichamy & Balakrishnan, Raghupathy & Puthira Prathap, Duraisamy & Karpagam, Chidambara & Govindaraj, Gurrappa Naidu, 2012. "Productivity Improvement in Sugarcane Farming in Tamil Nadu (India): Parametric and Non-Parametric Analysis," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126346, International Association of Agricultural Economists.
    3. Kopsakangas-Savolainen, Maria & Svento, Rauli, 2011. "Observed and unobserved heterogeneity in stochastic frontier models: An application to the electricity distribution industry," Energy Economics, Elsevier, vol. 33(2), pages 304-310, March.
    4. 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.
    5. Olusegun, Ajetomobi Joshua, 2012. "Productivity Growth of ECOWAS Common Crops: A Tale of Two Competing Frontier Methods of Analysis," 2012 Eighth AFMA Congress, November 25-29, 2012, Nairobi, Kenya 159404, African Farm Management Association (AFMA).

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