Hostname: page-component-77f85d65b8-zzw9c Total loading time: 0 Render date: 2026-04-18T05:50:11.746Z Has data issue: false hasContentIssue false
  • English
  • Français

Cotton Farmers' Technical Efficiency: Stochastic and Nonstochastic Production Function Approaches

Published online by Cambridge University Press:  15 September 2016

Kalyan Chakraborty ,
Sukant Misra  and
Phillip Johnson
Kalyan Chakraborty
Affiliation:
Department of Accounting and CIS, College of Business, Emporia State University, Emporia, Kansas
Sukant Misra
Affiliation:
College of Agricultural Sciences and Natural Resources, Texas Tech University
Phillip Johnson
Affiliation:
Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, Texas
Get access Rights & Permissions [Opens in a new window]

Abstract

Technical efficiency for cotton growers is examined using both stochastic (SFA) and nonstochastic (DEA) production function approaches. The empirical application uses farm-level data from four counties in west Texas. While efficiency scores for the individual farms differed between SFA and DEA, the mean efficiency scores are invariant of the method of estimation under the assumption of constant returns to scale. On average, irrigated farms are 80% and nonirrigated farms are 70% efficient. Findings show that in Texas, the irrigated farms, on average, could reduce their expenditures on other inputs by 10%, and the nonirrigated farms could reduce their expenditures on machinery and labor by 12% and 13%, respectively, while producing the same level of output.

Keywords

cottondata envelopment analysisstochastic frontiertechnical efficiency

Information

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 31 , Issue 2 , October 2002 , pp. 211 - 220
Copyright
Copyright © 2002 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Aigner, D. J., Lovell, C. A. K., and Schmidt, P. (1977). “Foundation and Estimation of Stochastic Frontier Production Function Models.” Journal of Econometrics 6, 21–37.CrossRefGoogle Scholar
Ali, A. I., and Seiford, L. M. (1993). “The Mathematical Programming Approach to Efficiency Analysis.” In Fried, H. O., Lovell, C. A. K., and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency: Techniques and Applications (pp. 120–159). New York: Oxford University Press.CrossRefGoogle Scholar
Banker, R. D. (1993). “Maximum Likelihood, Consistence, and Data Envelopment Analysis: A Statistical Foundation.” Management Science 39, 1265–1273.CrossRefGoogle Scholar
Banker, R. D. (1996). “Hypothesis Tests Using Data Envelopment Analysis.” Journal of Productivity Analysis 7, 139–159.CrossRefGoogle Scholar
Banker, R. D., Chames, A., and Cooper, W. W. (1984). “Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis.” Management Science 30, 1078–1092.CrossRefGoogle Scholar
Battese, G. E., and Coelli, T. J. (1988). “Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data.” Journal of Econometrics 38, 387–399.CrossRefGoogle Scholar
Battese, G. E., and Coelli, T. J. (1992). “Frontier Production Functions, Technical Efficiency, and Panel Data: With Application to Paddy Farmers in India.” Journal of Productivity Analysis 3, 153–169.CrossRefGoogle Scholar
Battese, G. E., and Corra, G. S. (1977). “Estimation of the Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia.” Australian Journal of Agricultural Economics 21, 169–179.CrossRefGoogle Scholar
Battese, G. E., and Hassan, S. (1998). “Technical Efficiency of Cotton Farmers in Vehari District of Panjab, Pakistan.” Working paper, School of Economics, University of New England, Annidale, Australia.Google Scholar
Bauer, P. W. (1990). “Recent Developments in the Economic Estimation of Frontiers.” Journal of Econometrics 46, 39–56.CrossRefGoogle Scholar
Brooks, N. L. (2001, October). “Characteristics of Production Costs of U.S. Cotton Farms.” Statistical Bulletin No. 974–2, USDA/Economic Research Service, Washington, DC.Google Scholar
Chakraborty, K., Biswas, B., and Lewis, W. C. (2001). “Measurement of Technical Efficiency in Public Education: A Stochastic and Nonstochastic Production Function Approach.” Southern Economic Journal 67, 889–905.Google Scholar
Chakraborty, K., and Mohapatra, S. (1997). “Dynamic Productivity, Efficiency, and Technical Innovation in Education: A Mathematical Programming Approach Using Data Envelopment Analysis.” Study Paper No. 97–08, Economic Research Institution, Utah State University, Logan.Google Scholar
Chames, A., Cooper, W. W., and Rhodes, E. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operations Research E-2, 429–444.Google Scholar
Coelli, T. J. (1992). “A Computer Program for Frontier Production Function Estimation: Frontier Version 2.0.” Economic Letters 39, 29–32.CrossRefGoogle Scholar
Coelli, T. J. (1995a). “Recent Developments in Frontier Modeling and Efficiency Measurement.” Australian Journal of Agricultural Economics 39, 219–245.CrossRefGoogle Scholar
Coelli, T. J. (1995b). “Estimators and Hypothesis Tests for a Stochastic Frontier Function: A Monte Carlo Analysis.” Journal of Productivity Analysis 6, 247–268.CrossRefGoogle Scholar
Coelli, T. J. (1996). “A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation.” Working Paper No. 96/07, Center for Efficiency and Productivity Analysis, Department of Econometrics, University of New England, Annidale, Australia.Google Scholar
Coelli, T. J., and Perelman, S. (1999). “A Comparison of Parametric and Non-parametric Distance Functions: With Application to European Railways.” European Journal of Operations Research 117, 326–339.CrossRefGoogle Scholar
Coelli, T. J., Rao, D. S. P., and Battese, G. (1998). An Introduction to Efficiency and Productivity Analysis. Boston: Kluwer Academic Press.CrossRefGoogle Scholar
Debreu, G. (1951). “The Coefficient of Resource Utilization.” Econometrica 19, 273–292.CrossRefGoogle Scholar
Domazlicky, B. R., and Weber, W. (1997). “Total Factor Productivity in the Contiguous United States, 1977–86.” Journal of Regional Science 37, 213–233.CrossRefGoogle Scholar
Fare, R., and Grosskopf, S. (1995). “Nonparametric Tests of Regularity, Farrell Efficiency, and Goodness of Fit.” Journal of Econometrics 69, 415–425.CrossRefGoogle Scholar
Fare, R., Grosskopf, S., and Lovell, C. A. K. (1994). Production Frontiers. New York: Cambridge University Press.Google Scholar
Farrell, M. J. (1957). “The Measurement of Productive Efficiency.” Journal of the Royal Statistical Society, Series A 120, 253–281.CrossRefGoogle Scholar
Ferrier, G. D., and Lovell, C. A. K. (1990). “Measuring Cost Efficiency in Banking: An Econometric and Linear Programming Evidence.” Journal of Econometrics 46, 229–245.CrossRefGoogle Scholar
Forsund, E. R., and Hjalmarsson, L. (1979). “Generalized Farrell Measure of Efficiency: An Application to Milk Processing in Swedish Dairy Plants.” Economic Journal 89, 294–315.CrossRefGoogle Scholar
Forsund, E. R., Lovell, C. A. K., and Schmidt, P. (1980). “A Survey of Frontier Production Functions and Their Relationship to Efficiency Measurement.” Journal of Econometrics 13, 5–25.CrossRefGoogle Scholar
Greene, W. H. (1980). “On the Estimation of a Flexible Frontier Production Model.” Journal of Econometrics 13, 27–56.CrossRefGoogle Scholar
Greene, W. H. (1993). “The Econometric Approach to Efficiency Analysis.” In Fried, H. O., Lovell, C. A. K., and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency: Techniques and Applications (pp. 68–119). New York: Oxford University Press.CrossRefGoogle Scholar
Helmers, G., Weiss, A., and Shaik, S. (2000, January 29-February 2). “Impact of Climate Change on Efficiency and Productivity in Cotton and Wheat Producing Regions” Paper presented at the annual meeting of the Southern Agricultural Economic Association, Lexington, KY.Google Scholar
Koopmans, T. C. (1951). Activity Analysis of Production and Allocation. New York: John Wiley.Google Scholar
Kumbhakar, S. C., Ghosh, S., and McGuckin, J. T. (1991). “A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms.” Journal of Business and Economic Statistics 9, 279–286.Google Scholar
Lovell, C. A. K. (1993). “Production Frontiers and Productive Efficiency.” In Fried, H. O., Lovell, C. A. K., and Schmidt, S. S. (eds.), The Measurement of Productive Efficiency: Techniques and Applications (chapter 1). New York: Oxford University Press.Google Scholar
Lovell, C. A. K., and Schmidt, P. (1988). “A Comparison of Alternative Approaches to the Measurement of Productive Efficiency.” In Dogramaci, A. and Fare, R (eds.), Applications of Modern Production Theory: Efficiency and Productivity (pp. 3–55). Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Meeusen, W., and van den Broeck, J. (1977). “Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error.” International Economic Review 18, 435–444.CrossRefGoogle Scholar
Ruggiero, J., and Vitaliano, D. (1999). “Assessing the Efficiency of Public Schools Using Data Envelopment Analysis and Frontier Regression.” Contemporary Economic Policy 17, 321–331.CrossRefGoogle Scholar
Schmidt, P. (1986). “Frontier Production Functions.” Econometric Review 4, 289–328.CrossRefGoogle Scholar
Seiford, L. M., and Thrall, R. M. (1990). “Recent Developments in DEA: The Mathematical Programming Approach to Frontier Analysis.” Journal of Econometrics 46(1–2), 7–38.CrossRefGoogle Scholar
Texas Tech University. (1998). Standardized Performance Analysis (SPA) Database. Database of revenues and expenditures for cotton farms in the Texas High Plains. Maintained by the Department of Agricultural and Applied Economics, Texas Tech University, Lubbock.Google Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. (1996–2000). Texas Agricultural Statistics. USDA/NASS, Austin, TX.Google Scholar