Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches

Technical, allocative and economic efficiency measures are derived for a sample of swine producers in Hawaii using the parametric stochastic efficiency decomposition technique and nonparametric data envelopment analysis (DEA). Efficiency measures obtained from the two frontier approaches are compared. Firm-specific factors affecting productive efficiencies are also analyzed. Finally, swine producers' potential for reducing cost through improved efficiency is also examined. Under the specification of variable returns to scale (VRS), the mean technical, allocative and economic efficiency indices are 75.9%, 75.8% and 57.1%, respectively, for the paramettic approach and 75.9%, 80.3% and 60.3% for DEA; while for the constant returns to scale (CRS) they are 74.5%, 73.9% and 54.7%, respectively, for the parametric approach and 64.3%, 71.4% and 45.7% for DEA. Thus the results from both approaches reveal considerable inefficiencies in swine production in Hawaii. The removal of potential outliers increases the technical efficiencies in the parametric approach and allocative efficiencies in DEA, but, overall, contrary to popular belief, the results obtained from DEA are found to be more robust than those from the parametric approach. The estimated mean technical and economic efficiencies obtained from the paramettic technique are higher than those from DEA for CRS models but quite similar for VRS models, while allocative efficiencies are generally higher in DEA. However, the efficiency rankings of the sample producers based on the two approaches are highly correlated, with the highest correlation being achieved for the technical efficiency rankings under CRS. Based on mean compaiison and rank correlation analyses, the return to scale assumption is found to be crucial in assessing the similarities or differences in efficiency measures obtained from the two approaches. Analysis of the role of various firm-specific factors on productive efficiency shows that farm size has strong positive effects on efficiency levels. Similarly, farms producing market hogs are more efficient than those producing feeder pigs. Based on these results, by operating at the efficient frontier the sample swine producers would be able to reduce their production costs by 38-46% depending upon the method and returns to scale considered. © 1999 Elsevier Science B.V. All rights reserved.

[1]  Gary D. Ferrier,et al.  Measuring cost efficiency in banking: Econometric and linear programming evidence , 1990 .

[2]  T. Coelli RECENT DEVELOPMENTS IN FRONTIER MODELLING AND EFFICIENCY MEASUREMENT , 1995 .

[3]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[4]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[5]  P. Leung,et al.  Economic analysis of size and feed type of swine production in Hawaii , 1997 .

[6]  A. Heshmati,et al.  DEA, DFA and SFA: A comparison , 1996 .

[7]  Kaliappa Kalirajan,et al.  The importance of efficient use in the adoption of technology: A micro panel data analysis , 1991 .

[8]  R. Kopp,et al.  The decomposition of frontier cost function deviations into measures of technical and allocative efficiency , 1982 .

[9]  G. Battese,et al.  A model for technical inefficiency effects in a stochastic frontier production function for panel data , 1995 .

[10]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[11]  E. G. Dunn,et al.  Technical Efficiency, Managerial Ability and Farmer Education in Guatemalan Corn Production: A Latent Variable Analysis , 1995, Agricultural and Resource Economics Review.

[12]  Subhash C. Ray,et al.  Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation , 1988 .

[13]  B. Jacobsen,et al.  Reducing non-allocative costs on Danish dairy farms: Application of non-parametric methods , 1993 .

[14]  Tim Coelli,et al.  A Guide to Frontier version 4. 1: A Computer Program for Stochastic Frontier Production and Cost Fu , 1996 .

[15]  R. Evenson,et al.  Efficiency in agricultural production: The case of peasant farmers in eastern Paraguay , 1994 .

[16]  A. U.S.,et al.  FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS , 2001 .

[17]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[18]  S. Kumbhakar,et al.  A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms , 1991 .

[19]  Jean-Paul Chavas,et al.  Spatial allocation and the shadow pricing of product characteristics , 1998 .

[20]  P. Leung,et al.  Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis , 1996 .

[21]  Boris E. Bravo-Ureta,et al.  Dairy Farm Efficiency Measurement Using Stochastic Frontiers and Neoclassical Duality , 1991 .

[22]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[23]  T. Weyman-Jones,et al.  Productive and Allocative Inefficiencies in U.K. Building Societies: A Comparison of Non-parametric and Stochastic Frontier Techniques , 1996 .

[24]  Michael Aliber,et al.  AN ANALYSIS OF ECONOMIC EFFICIENCY IN AGRICULTURE: A NONPARAMETRIC APPROACH , 1993 .