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Self-Dual Stochastic Production Frontiers and Decomposition of Output Growth: The Case of Olive-Growing Farms in Greece

Published online by Cambridge University Press:  15 September 2016

Giannis Karagiannis  and
Vangelis Tzouvelekas
Giannis Karagiannis
Affiliation:
Department of Economics at the University of Ioannina, Greece
Vangelis Tzouvelekas
Affiliation:
Department of Economics at the University of Ioannina, Greece
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Abstract

This paper provides a decomposition of output growth among olive-growing farms in Greece during the period 1987–1993 by integrating Bauer's (1990) and Bravo-Ureta and Rieger's (1991) approaches. The proposed methodology is based on the use of self-dual production frontier functions. Output growth is attributed to the size effect, technical change, changes in technical and input allocative inefficiency, and the scale effect. Empirical results indicate that the scale and the input allocative inefficiency effects, which were not taken into account in previous studies on output growth decomposition analysis, have caused a 7.3% slowdown and a 11.0% increase in output growth, respectively. Technical change was found to be the main source of TFP growth while both technical and input allocative inefficiency decreased over time. Still though, a 56.5% of output growth is attributed to input growth.

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 30 , Issue 2 , October 2001 , pp. 168 - 178
Copyright
Copyright © 2001 Northeastern Agricultural and Resource Economics Association 

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References

Ahmad, M. and Bravo-Ureta, B.E. 1995. “An Econometric Decomposition of Dairy Output Growth.” American Journal of Agricultural Economics 77: 914–21.Google Scholar
Aigner, D.J., Lovell, C.A.K. and Schmidt, P. 1977. “Formulation and Estimation of Stochastic Frontier Production Function Models.” Journal of Econometrics 6: 21–37.Google Scholar
Arnade, C. 1998. “Using a Programming Approach to Measure International Agricultural Efficiency and Productivity.” Journal of Agricultural Economics 49: 67–84.Google Scholar
Batra, R.N. and Ullah, H. 1974. “Competitive Firm and the Theory of the Input Demand under Uncertainty.” Journal of Political Economy 82: 537–48.Google Scholar
Battese, G.E. and Coelli, T.J. 1995. “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data.” Empirical Economics 20: 325–32.CrossRefGoogle Scholar
Bauer, P.W. 1990. “Decomposing TFP Growth in the Presence of Cost Inefficiency, Nonconstant Returns to Scale, and Technological Progress.” Journal of Productivity Analysis 1: 287–99.Google Scholar
Bravo-Ureta, B.E. and Rieger, L. 1991. “Dairy Farms Efficiency Measurement Using Stochastic Frontiers and Neoclassical Duality.” American Journal of Agricultural Economics 73: 421–28.Google Scholar
Bureau, J.C., Fare, R. and Grosskopf, S. 1995. “A Comparison of Three Nonparametric Measures of Productivity Growth in European and United States Agriculture.” Journal of Agricultural Economics 46: 309–26.Google Scholar
Chan, M.W.L. and Mountain, D.C. 1983. “Economies of Scale and the Tornqvist Discrete Measure of Productivity.” Review of Economics and Statistics 65: 663–67.Google Scholar
Coelli, T.J. 1992. “A Computer Program for Frontier Production Function Estimation: Frontier Version 2.0.” Economics Letters 39: 29–32.Google Scholar
Coelli, T.J. 1995. “A Monte Carlo Analysis of the Stochastic Frontier Production Function.” Journal of Productivity Analysis 6: 247–268.Google Scholar
Fan, S. 1991. “Effects of Technological Change and Institutional Reform on Production Growth in Chinese Agriculture.” American Journal of Agricultural Economics 73: 266–75.Google Scholar
Fare, R. and Lovell, C.A.K. 1978. “Measuring the Technical Efficiency of Production.” Journal of Economic Theory 19: 150–62.CrossRefGoogle Scholar
Farrell, M.J. 1957. “The Measurement of Productive Efficiency.” Journal of Royal Statistical Society Series A 120: 253–281.Google Scholar
Fulginiti, L.E. and Perrin, R.K. 1993. “Prices and Productivity in Agriculture.” Review of Economics and Statistics 75: 471–82.Google Scholar
Fulginiti, L.E. and Perrin, R.K. 1997. “LDC Agriculture: Nonparametric Malmquist Productivity Indexes.” Journal of Development Economics 53: 373–90.Google Scholar
Fulginiti, L.E. and Perrin, R.K. 1998. “Agricultural Productivity in Developing Countries.” Agricultural Economics 19: 45–51.Google Scholar
Kalaitzandonakis, N.G. 1994. “Price Protection and Productivity Growth.” American Journal of Agricultural Economics 76: 722–32.Google Scholar
Kalirajan, K.P. and Shand, R.T. 1997. “Sources of Output Growth in Indian Agriculture.” Indian Journal of Agricultural Economics 52: 693–706.Google Scholar
Kalirajan, K.P., Obwona, M.B. and Zhao, S. 1996. “A Decomposition of Total Factor Productivity Growth: The Case of Chinese Agricultural Growth Before and After Reforms.” American Journal of Agricultural Economics 78: 331–38.Google Scholar
Kopp, R.J. 1981. “The Measurement of Productive Efficiency: A Reconsideration.” Quarterly Journal of Economics 96: 477–503.Google Scholar
Kopp, R.J. and Diewert, W.E. 1982. “The Decomposition of Frontier Cost Function Deviations into Measures of Technical and Allocative Efficiency.” Journal of Econometrics 19: 319–31.Google Scholar
Kodde, D.A. and Palm, F.C. 1986. “Wald Criteria for Jointly Testing Equality and Inequality Restrictions.” Econometrica 54: 1243–48.CrossRefGoogle Scholar
Kumbhakar, S.C. and Lovell, C.A.K. 2000. Stochastic Frontier Analysis, New York: Cambridge University Press.Google Scholar
Lambert, D.K. and Parker, E. 1998. “Productivity in Chinese Provincial Agriculture.” Journal of Agricultural Economics 49: 378–92.Google Scholar
Lovell, C.A.K. 1996. “Applying Efficiency Measurement Techniques to the Measurement of Productivity Change.” Journal of Productivity Analysis 7: 329–40.Google Scholar
Piesse, J., Thirtle, C. and Turk, J. 1996. Efficiency and Ownership in Slovene Dairying: A Comparison of Econometric and Programming Techniques.” Journal of Comparative Economics 22: 1–22.Google Scholar
Piesse, J., Thirtle, C. and van Zyl, J. 1996. “Effects of the 1992 Drought on Productivity in the South African Homelands: An Application of the Malmquist Index.” Journal of Agricultural Economics 47: 247–54.Google Scholar
Schmidt, P. and Lovell, C.A.K. 1979. “Estimating Technical and Allocative Inefficiency Relative to Stochastic Production and Cost Frontiers.” Journal of Econometrics 9: 343–66.Google Scholar
Schmidt, P. and Lovell, C.A.K. 1980. “Estimating Stochastic Production and Cost Frontiers when Technical and Allocative Inefficiency are Correlated.” Journal of Econometrics 13: 83–100.Google Scholar
Stevenson, R.E. 1980. “Likelihood Functions for Generalized Stochastic Frontier Estimation.” Journal of Econometrics 13: 58–66.Google Scholar
Tauer, L.W. 1993. “Short-run and Long-run Efficiency of New York Dairy Farms.” Agricultural and Resource Economics Review 22: 1–9.Google Scholar
Tauer, L.W. 1998. “Productivity of New York Dairy Farms Measured by Nonparametric Malmquist Indices.” Journal of Agricultural Economics 49: 234–49.Google Scholar
Thirtle, C., Piesse, J. and Turk, J. 1996. “The Productivity of Private and Social Farms: Multilateral Malmquist Indices for Slovene Dairying Enterprises.” Journal of Productivity Analysis 7: 447–60.Google Scholar
Wu, Y. 1995. “Productivity Growth, Technological Progress and Technical Efficiency Change in China: A Three Sector Analysis.” Journal of Comparative Economics 21: 207–29.Google Scholar
Zellner, A., Kmenta, J. and Dreze, J. 1966. “Specification and Estimation of Cobb-Douglas Production Function Models.” Econometrica 34: 784–95.Google Scholar