Hostname: page-component-cc8bf7c57-n7qbj Total loading time: 0 Render date: 2024-12-11T23:02:46.407Z Has data issue: false hasContentIssue false

Technical Efficiency, Managerial Ability and Farmer Education in Guatemalan Corn Production: A Latent Variable Analysis

Published online by Cambridge University Press:  15 September 2016

N.G. Kalaitzandonakes
Affiliation:
Department of Agricultural Economics, University of Missouri, Columbia MO 65211
E.G. Dunn
Affiliation:
Department of Agricultural Economics, University of Missouri, Columbia MO 65211

Abstract

In this study it is argued that conflicting empirical results on the relationship between technical efficiency and education may be in part attributable to difficulties in the measurement of key variables. Calculation of technical efficiency with three alternative frontier methods for a sample of Guatemalan corn farms resulted in significant differences both in the average technical efficiency of the sample and the efficiency rankings of individual farms. Furthermore, following two-step procedures where technical efficiency is regressed against a set of explanatory variables, it is shown that the choice of efficiency measurement technique can alter the importance of education as a contributing factor to increased technical efficiency. An alternative approach is presented for investigating the relationship between education and efficiency while accounting for difficulties in the measurement of conceptual variables and measurement errors.

Type
Articles
Copyright
Copyright © 1995 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.)

References

Aigner, D., Lovell, C.K., and Schmidt, P.Formulation and Estimation of Stochastic Production Function Models,” Journal of Econometrics 6 (1977): 2137.Google Scholar
Anderson, J., and Gerbing, D.The Effects of Sampling Error on Convergence, Improper Solutions and Goodness of Fit Indices for Maximum Likelihood Confirmatory Analysis. Psychometrica 49 (1984): 155–73.Google Scholar
Antle, J., and Goodger, W. 1984. “Measuring Stochastic Technology: The Case of Tulave Milk Production.American Journal of Agricultural Economics 66342–50.Google Scholar
Arminger, G., and Schoenberg, R.Pseudo Maximum Likelihood Estimation and a Test for Misspecification in Mean and Covariance Structure Models.” Psychometrica 54 (1989): 409–25.Google Scholar
Banker, R.D., Charnes, A., Cooper, W., and Maindiratta, A. 1986. “A Comparison of DEA and Translog Estimates of Production Frontiers Using Simulated Observations from a Known Technology.” In Dogramaci, A. and Fare, R., eds. Applications of Modern Production Theory: Efficiency and Productivity Kluwer, Boston.Google Scholar
Bollen, K.A. 1989. Structural Equations with Latent Variables. New York, John Wiley.Google Scholar
Boomsma, A. 1982. “The Robustness of LISREL against Small Sample Sizes in Factor Analysis Models.” In Jorskog, K.G. and Wold, H., eds. Systems Under Indirect Observation Part I Amsterdam, North Holland, pp 149–73.Google Scholar
Browne, M.Asymptotic Distribution Free Methods in Analysis of Covariance Structures.” British Journal of Mathematical and Statistical Psychology 37 (1984): 6283.Google Scholar
Bravo-Ureta, B., and Pinheiro, A. 1993. “Efficiency Analysis of Developing Country Agriculture: A Review of the Frontier Literature.Agricultural and Resource Economics Review 22: 88101.CrossRefGoogle Scholar
Dunn, E.G. 1992. “The FUNDACEN Experience: Factors for Success and Failure in a Guatemalan Land Purchase-Sale Program.Land Tenure Center Research Paper No. 107. Madison, Wisconsin.Google Scholar
Dunn, E.G. 1991. “Institutions and Technical Efficiency on Farms in Guatemalan Land Purchase-Sale Programs.” Ph.D. diss., University of Wisconsin-Madison.Google Scholar
Fane, G.Education and the Managerial Efficiency of Farmers.” Review of Economics and Statistics 57 (1975): 452–61.CrossRefGoogle Scholar
Farrell, M.J.The Measurement of Production Efficiency.” Journal of Royal Statistical Society 120 (1957): 257–81.Google Scholar
Ferrier, G.D., and Lovell, C.A.K.Measuring Cost Efficiency in Banking. Econometric and Linear Programming Evidence.” Journal of Econometrics 46 (1991): 229–45.Google Scholar
Forsund, F., Lovell, C.A.K., and Schmidt, P.A Survey of Frontier Production Functions and of their Relationship to Efficiency Measurement.” Journal of Econometrics 13 (1980): 525.Google Scholar
Gabrielson, A. 1975. “On Estimating Efficient Production Functions” Working Paper A-85, Chr. Michelsen Institute, Department of Humanities and Social Sciences, Bergen Norway.Google Scholar
Hoch, I.Estimation of Production Function Parameters Combining Time Series and Cross-Section Data.” Econometrica 30 (1962): 3453.CrossRefGoogle Scholar
Huffman, W.E.Allocative Efficiency: The Role of Human Capital.” Quarterly Journal of Economics 91 (1977): 5779.CrossRefGoogle Scholar
Jondrow, J., Lovell, C.K., Materov, K., and Schmidt, P.On the Estimation of Technical Inefficiency in the Frontier Stochastic Production Function Model.” Journal of Econometrics 19 (1982): 233–38.CrossRefGoogle Scholar
Kalaitzandonakes, N.G., Wu, S., and Ma, J.The Relationship between Technical Efficiency and Firm Size Revisited.” Canadian Journal of Agricultural Economics 40 (1992): 427–42.Google Scholar
Kalirajan, K.The Importance of Efficient Use in the Adoption of Technology: A Micro Panel Data Analysis.” Journal of Productivity Analysis 2 (1991): 113–26.Google Scholar
Kumbhakar, S., Ghosh, S., and McGuckin, J.A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Industry.” Journal of Business and Economic Statistics 9 (1991): 279–86.Google Scholar
Lockheed, M.E., Jamison, D., and Lau, L.J.Farmer Education and Farm Efficiency: A Survey.” Economic Development and Cultural Change 29 (1981): 3776.CrossRefGoogle Scholar
Lovell, C.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 Kluwer, Boston.Google Scholar
Meeusen, W., and Van den Broeck, J.Efficiency Estimation from Cobb Douglas Production Functions with Composed Error.” International Economic Review 18 (1977): 435–44.Google Scholar
Mundlak, Y.Empirical Production Function Free of Management Bias.” Journal of Farm Economics 43 (1961): 4456.CrossRefGoogle Scholar
Phillips, J.M.A Comment on Farmer Education and Farm Efficiency.” Economic Development and Cultural Change 36 (1987): 637–41.Google Scholar
Seiford, L., and Thrall, R.Recent Developments in DEA the Mathematical Programming Approach to Frontier Analysis.” Journal of Econometrics 46 (1990): 738.CrossRefGoogle Scholar
Shephard, R.W. 1970. The Theory of Cost and Production Functions, Princeton University Press.Google Scholar
Stefanou, S.E., and Saxena, S.Education, Experience, and Allocative Efficiency: A Dual Approach.” American Journal of Agricultural Economics 70 (1988): 338–45.Google Scholar
Tanaka, J., and Huba, G.Confirmatory Hierarchical Factor Analyses of Psychological Distress Measures.” Journal of Personality and Social Psychology 46 (1984): 621–35.Google Scholar
Thomas, A., and Tauer, L.Linear Input Aggregation Bias in Non-Parametric Technical Efficiency Measurement.” Canadian Journal of Agricultural Economics 42 (1994): 7786.CrossRefGoogle Scholar
Weersink, A., Turvey, C., and Godah, A.Decomposition Measures of Technical Efficiency for Ontario Dairy Farms.” Canadian Journal of Agricultural Economics 38 (1990): 439–56.CrossRefGoogle Scholar
Welch, F.Education in Production.” Journal of Political Economy 78 (1970): 3559.Google Scholar
White, H.A Heteroskedasticity-Consistent Covariance Matrix Estimator and Direct Test of Heteroskedasticity.” Econometrica 48 (1980): 817–38.Google Scholar