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Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates

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  • I. Fraser

    ()

  • W. Horrace

    ()

Abstract

A balanced panel of data is used to estimate technical efficiency, employing a fixed-effects stochastic frontier specification for wool producers in Australia. Both point estimates and confidence intervals for technical efficiency are reported. The confidence intervals are constructed using the multiple comparisons with the best (MCB) procedure of Horrace and Schmidt (1996, 2000). The confidence intervals make explicit the precision of the technical efficiency estimates and underscore the dangers of drawing inferences based solely on point estimates. Additionally, they allow identification of wool producers that are statistically efficient and those that are statistically inefficient. The data reveal at the 95% level that twenty-one of the twenty-six wool farms analyzed may be efficient. Copyright Kluwer Academic Publishers 2003

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Bibliographic Info

Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 20 (2003)
Issue (Month): 2 (September)
Pages: 169-190

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Handle: RePEc:kap:jproda:v:20:y:2003:i:2:p:169-190

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Web page: http://www.springerlink.com/link.asp?id=100296

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Keywords: production functions; stochastic frontier; multiple comparisons; wool;

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Cited by:
  1. Lohr, Luanne & Park, Timothy A., 2009. "Labor Pains: Valuing Seasonal versus Year-Round Labor on Organic Farms," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(2), August.
  2. Tai-Hsin Huang & Tong-Liang Kao, 2006. "Joint estimation of technical efficiency and production risk for multi-output banks under a panel data cost frontier model," Journal of Productivity Analysis, Springer, vol. 26(1), pages 87-102, August.
  3. Lohr, Luanne & Park, Timothy A., 2006. "Technical Efficiency of U.S. Organic Farmers: The Complementary Roles of Soil Management Techniques and Farm Experience," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 35(2), October.

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