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.
Similar content being viewed by others
References
ABARE.(1999). Australian Farm Surveys Report.Financial Performance of Australian Farms.1996-97 to 1998-99.ABARE, Canberra.
Ahmad, M. and B. E. Bravo-Ureta.(1996). “Technical Efficiency Measures for Dairy Farms Using Panel Data: A Comparison of Alternative Model Specifications.” Journal of Productivity Analysis 7, 399–415.
Aigner, D. J., C. A. K. Lovell and P. Schmidt.(1977).“Formulation and Estimation of Stochastic Frontier Production Frontiers Functions.”Journal of Econometrics 6, 21–37.
Battese, G. E. and T. J. Coelli.(1988). “Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data.”Journal of Econometrics 38, 387–399.
Battese, G. E. and G. S. Corra.(1977).“Estimatio n of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia.” Australian Journal of Agricultural Economics 21, 169–179.
Battese, G. E., A. Heshmati and L. Hjalmarsson. (2000).“Efficiency of labor Use in the Swedish Banking Industry: A Stochastic Frontier Approach.” Empirical Economics 25, 623–640.
Chapman, L., V. B. Rodriguez and S. Harrison. (1999). “Influence of Resource Quality on Productivity of Wool Producing Farms.” pp. 37–41. Australian Farm Surveys Report, ABARE, Canberra.
Coelli, T. J. (1995). “Recent Developments in Frontier Modeling and Efficiency Measurement.”Australian Journal of Agricultural Economics 39, 219–245.
Coelli, T. J. and G. E. Battese. (1996).“Identificat ion of Factors Which Influence the Technical Inefficiency of Indian Farmers.” Australian Journal of Agricultural Economics 40, 103–128.
Coelli, T. J., D. S. Prasada Rao and G.E. Battese.(1998). An Introduction to Efficiency and Productivity Analysis. Boston, Dordrecht and London: Kluwer Academic Publishers.
Cornwell, C. and P. Schmidt.(1995). “Production Frontiers and Efficiency Measurement.” In L. Matyas and P. Sevestre (eds.), Econometrics of Panel Data: Handbook of Theory and Applications, 2nd Edition, Boston: Kluwer Academic Publishers.
Edwards, D. G. and J. C. Hsu. (1983). “Multiple Comparisons with the Best Treatment.” Journal of the American Statistical Association 78, 965–971, Corrigenda (1984).79, 965.
Edwards, G. (1997).“The Economics of Restricting Exports of Wool.” Paper presented to the 26th Annual Conference of Economists, University of Tasmania, Hobart.
Fan, S. (1991).“Effects of Technological Change and Institutional Reform on Production Growth in Chinese Agriculture.” American Journal of Agricultural Economics 73, 266–275.
Fraser, I. M. and D. Cordina. (1999).“An Application of Data Envelopment Analysis to Irrigated Dairy Farms in Northern Victoria, Australia.” Agricultural Systems 59, 267–282.
Fraser, I. M. and P. Hone.(2001).“Farm-Level Efficiency and Productivity Measurement Using Panel Data: Wool Production in South-West Victoria.” Australian Journal of Agricultural and Resource Economics 45, 215–232.
Glasser, G. J. and R.F. Winter. (1961). “Critical Values of the Coefficient of Rank Correlation for Testing the Hypothesis of Independence.” Biometrika 48, 444–448.
Greene, W. H. (1997). “Frontier Production Functions.” In H. Pesaran and P. Schmidt (eds.) Handbook of Applied Econometric, Vol II-Microeconomics.London: Basil Blackwell.
Haszler, H., G. Edwards, A. Chisholm and P. Hone. (1996). “The Wool Debt, the Wool Stockpile and the National Interest: Did the Garnaut Committee Get it Right?'' Economic Record 72, 260–271.
Hone, P., G. Edwards and I. M. Fraser. (1999). “Agricultural Land Retirement and Biodiversity Policy.” Agenda 6, 211–224.
Horrace, W. C. (1998). “Tables of Percentage Points of the k-Variate Normal Distribution for Large Values of k.” Communications in Statistics: Simulation and Computation 27, 823–831.
Horrace, W. C. and P. Schmidt.(1996). “Confidence Statements for Efficiency Estimates from Stochastic Frontier Models.” Journal of Productivity Analysis 7, 257–282.
Horrace, W. C. and P. Schmidt.(2000). “Multiple Comparisons with the Best, With Economic Applications.” Journal of Applied Econometrics 15, 1–26.
Kalirajan, K. P. and R. T. Shand. (1999).“Frontier Production Functions and Technical Efficiency Measures.” Journal of Economic Surveys 13, 149–172.
Kim, Y. H.(1992).“The Translog Production Function and Variable Returns to Scale.” The Review of Economic and Statistics 74, 546–552.
Kim, Y. and P. Schmid t. (1999).“Marginal Comparisons with the Best and the Efficiency Measurement Problem.” Unpublished manuscript, Michigan State University.
Kingwell, R., A. Bathgate and M. O'Connell. (1999). “Wool in Western Australia. Research, Development and Extension.” Australian Agribusiness Review 7, 1–11.
Kleit, A. N. and D. Terrell. (2001).“Measuring Potential Efficiency Gains from Deregulation of Electricity Generation: A Bayesian Approach.” Review of Economics and Statistics 83, 523–530.
Koop, G., J. Osiewalski and M. F. Steel. (1997). “Bayesian Efficiency Analysis Through Individual Effects: Hospital Cost Frontiers.” Journal of Econometrics 76, 77–105.
Kumbhakar, S. C. (1990). “Production Frontiers, Panel Data and Time-Varying Technical Inefficiency.” Journal of Econometrics 46, 201–212.
Kumbhakar, S. C. and C. A. K. Lovell. (2000). Stochastic Frontier Analysis. Cambridge; New York and Melbourne: Cambridge University Press.
Lawrence, D. and P. Hone. (1981). “Relative Economic Efficiency in the Australian Grazing Industry.” Review of Marketing and Agricultural Economics 49, 7–23.
Lee, Y. H. and P. Schmidt.(1993).A Production Frontier Model with Flexible Temporal Variation in Technical Efficiency. In H. O. Fried, C. A. K. Lovell and S. S. Schmidt (eds.), The Measurement of Productive Efficiency.New York: Oxford University Press.
Meeusen, W. and J. van den Broeck. (1977). “Efficient Estimation from Cobb-Douglas Production Functions with Composed Error.” International Economic Review 18, 435–444.
Park, B. U. and L. Simar. (1994). “Efficient Semiparametric Estimation in a Stochastic Frontier Model.” Journal of the American Statistical Association 89, 929–936.
Patterson, A., L. Beattie and P. Floyd. (1998). “South West Victorian Monitor Farm Project. Summary of Results 1997-98.” Catchment and Agriculture Services, Department of Natural Resources and Environment, Hamilton.
Pitt, M. M. and L. F. Lee.(1981). “The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry.” Journal of Development Economics 9, 43–64.
Schmidt, P. and R. C. Sickles.(1984). “Production Frontiers and Panel Data.” Journal of Business and Economic Statistics 2, 367–374.
Simar, L. (1992). “Estimating Efficiencies from Frontier Models with Panel Data: A Comparison of Parametric, Non-Parametric and Semi-Parametric Methods with Bootstrapping.” Journal of Productivity Analysis 3, 167–203.
Simar, L. and P. W. Wilson.(1998). “Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models.” Management Science 44, 49–61.
Simar, L. and P. W. Wilson.(2000). “Statistical Inference in Nonparametric Frontier Models: The State of the Art.” Journal of Productivity Analysis 13, 49–78.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Fraser, I.M., Horrace, W.C. Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates. Journal of Productivity Analysis 20, 169–190 (2003). https://doi.org/10.1023/A:1025180205923
Issue Date:
DOI: https://doi.org/10.1023/A:1025180205923