Parametric Decomposition of the Malmquist Index in Output-Oriented Distance Function: Productivity in Chinese Agriculture

DOI: 10.4236/me.2014.51009   PDF   HTML     3,303 Downloads   5,100 Views   Citations

Abstract

This paper decomposes the Malmquist productivity index into several assembling components: technical change (further break down into technical change magnitude, input bias and output bias), technical efficiency change, scale efficiency change, and output-mix effect. A translog output distance function is chosen to represent the production technology and each component of the Malmquist index is computed using the estimated parameters. This parametric approach allows us to statistically test the hypothesis regarding different components of the Malmquist index and the natural of production technology. The empirical application in Chinese agriculture shows that the average productivity grows at 2 percent per year during 1978-2010. This growth is mostly driven by technical change, which is found to be neutral.

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B. Yu, X. Liao and H. Shen, "Parametric Decomposition of the Malmquist Index in Output-Oriented Distance Function: Productivity in Chinese Agriculture," Modern Economy, Vol. 5 No. 1, 2014, pp. 70-85. doi: 10.4236/me.2014.51009.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] D. W. Caves, L. R. Christensen and W. E. Diewert, “The Economic Theory of Index Numbers and the Measurement of Input, Output and Productivity,” Econometrica, Vol. 50, 1982, pp. 1393-1414.
http://dx.doi.org/10.2307/1913388
[2] R. Färe, S. Grosskopf, M. Norris and Z. Zhang, “Productivity Growth, Technical Progress and Efficiency Change in Industrialized Countries,” American Economic Review, Vol. 84, No. 1, 1994, pp. 66-83.
[3] R. Färe, E. Grifell-Tatjé, S. Grosskopf and C. A. K. Lovell, “Biased Technical Change and the Malmquist Productivity Index,” The Scandinavian Journal of Economics, Vol. 99, No. 1, 1997, pp. 119-127.
http://dx.doi.org/10.1111/1467-9442.00051
[4] B. M. Balk, “Scale Efficiency and Productivity Change,” Journal of Productivity Analysis, Vol. 15, No. 3, 2001, pp. 159-183. http://dx.doi.org/10.1023/A:1011117324278
[5] C. A. K. Lovell, “The Decomposition of Malmquist Productivity Indexes,” Journal of Productivity Analysis, Vol. 20, 2003, pp. 437-458.
http://dx.doi.org/10.1023/A:1027312102834
[6] S. Ray, “Measuring Scale Efficiency from the Translog Multi-Input, Multi-Output Distance Function,” Economics Working Papers 2003-25, Department of Economics, University of Connecticut, 2003.
[7] R. Färe, S. Grosskopf and P. Roos, “Malmquist Productivity Indexes: A Survey of Theory and Practice,” In: R. Färe, S. Grosskopf and R. R. Russell, Eds., Index Numbers: Essays in Honour of Sten Malmquist, Kluwer Academic Publishers, Boston, 1998.
http://dx.doi.org/10.1007/978-94-011-4858-0_4
[8] H. Fuentes, E. Grifell-Tatje and S. Perelman, “A Parametric Distance Function Approach for Malmquist Productivity Index Estimation,” Journal of Productivity Analysis, Vol. 15, 2001, pp. 79-94.
http://dx.doi.org/10.1023/A:1007852020847
[9] C. J. Pantzios, G. Karagianis and V. Tzouvelekas, “Parametric Decomposition of the Input-oriented Malmquist Productivity Index: With an Application to Greek Aquaculture,” Journal of Productivity Analysis, Vol. 36, No. 1, 2011, pp. 21-31.
http://dx.doi.org/10.1007/s11123-010-0202-2
[10] L.Orea, “Parametric Decomposition of a Generalized Malmquist Productivity Index,” Journal of Productivity Analysis, Vol. 20, 2002, pp. 437-458.
[11] T. J. Coelli and S. Perelman, “Technical Efficiency of European Railways: A Distance Function Approach,” Applied Economics, Vol. 32, No. 15, 2000, pp. 1967-1976.
http://dx.doi.org/10.1080/00036840050155896
[12] K. Kounetas and K. Tsekouras, “Measuring Scale Efficiency Change Using a Translog Distance Function,” International Journal of Business and Economics, Vol. 69, No. 1, 2007, pp. 63-69.
[13] G. Battese and T. Coelli, “A Model for Technical Efficiency Effects in a Stochastic Frontier Production Function for Panel Data,” Empirical Economics, Vol. 20, 1995, pp. 325-332.
http://dx.doi.org/10.1007/BF01205442
[14] “China Statistical Yearbook, National Bureau of Statistics of China,” China Statistics Press, Beijing, Various Years.
[15] Y. Zhang and B. Brummer, “Productivity Change and the Effects of Policy Reform in China’s Agriculture since 1979,” Asian-Pacific Economic Literature, Vol. 25, No. 2, 2011, pp. 131-150.
http://dx.doi.org/10.1111/j.1467-8411.2011.01307.x
[16] C. J. O’Donnell and T. Coelli, “A Bayesian Approach to Imposing Curvature on Distance Functions,” Journal of Econometrics, Vol. 126, No. 2, 2005, pp. 493-523.
http://dx.doi.org/10.1016/j.jeconom.2004.05.011
[17] A. D. Alene, V. M. Manyong and J. Gockowski, “The Production Efficiency of Intercropping Annual and Perennial Crops in Southern Ethiopia: A Comparison of Distance Functions and Production Frontiers,” Agricultural Systems, Vol. 91, 2006, pp. 51-70.
http://dx.doi.org/10.1016/j.agsy.2006.01.007
[18] R. Färe and D. Primont, “Multi-Output Production and Duality: Theory and Applications,” Kluwer Academic Publishers, Boston, 1995.
http://dx.doi.org/10.1007/978-94-011-0651-1
[19] A. Nin-Pratt, B. Yu and S. Fan, “The Total Factor Productivity in China and India: New Measures and Approaches,” China Agricultural Economic Review, Vol. 1, No. 1, 2009, pp. 9-22.
http://dx.doi.org/10.1108/17561370910915339
[20] T. J. Coelli, P. D. S. Rao and G. E. Battese, “An Introduction to Efficiency and Productivity Analysis,” Kluwer, Boston, 1998.
http://dx.doi.org/10.1007/978-1-4615-5493-6

  
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