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Measuring Regional Productivity Differences in the Australian Wool Industry: A Metafrontier Approach

  • Villano, Renato A.
  • Fleming, Euan M.
  • Fleming, Pauline

Using panel data, we estimate technology gaps for four distinct sheep-producing regions in Eastern Australia (Northern New South Wales, Central and South-Eastern New South Wales, South-Western New South Wales and South-West Victoria) that reflect spatial environmental and technological differences in wool production. A deterministic stochastic metafrontier production function model is estimated that envelops the stochastic frontiers of the four regions. This metafrontier approach enables us to estimate the environment-technology gap ratio that reflects these spatial differences in the environment and variations in production technologies in the wool enterprise for benchmarked farmers in each region. As a result, a more accurate estimation is possible of changes in total factor productivity on farms in the different regions. The major findings are that environment-technology gaps do exist between regions but they are relatively small. Greater variation is apparent within regions. Variation in technical efficiency seems to depend on the harshness of the production environment and whether consultancy advice is regularly received by the benchmarking group.

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File URL: http://purl.umn.edu/6036
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Paper provided by Australian Agricultural and Resource Economics Society in its series 2008 Conference (52nd), February 5-8, 2008, Canberra, Australia with number 6036.

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Date of creation: 2008
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Handle: RePEc:ags:aare08:6036
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  1. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
  2. C.J. O'Donnell & W.E. Griffiths, 2004. "Estimating State-Contingent Production Frontiers," Department of Economics - Working Papers Series 911, The University of Melbourne.
  3. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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