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Crecimiento y la paradoja de la productividad: Una estimación en la forma de state-space con componentes no observables para el sector agropecuario argentino, 1955-2003

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  • Luis Lanteri

    (Banco Central de Argentina)

Abstract

We estimate the total factor productivity for argentine agriculture over the period 1955 to 2003. One method of quantifying the impact of productivity is the use of growth accounting index numbers (Divisia index). However, some papers (see, for example, Hsieh, 2000) show that if technological change is not Hicks neutral then conventional total factor productivity index is not a satisfactory measure of this indicator, since that observed cost shares conflates the contribution of factor accumulation to output growth with that of technological change. In this paper, cost share equation system in the form of state-space model with latent variables are used to detect technological biased and to estimate the total factor productivity adjusted.

Suggested Citation

  • Luis Lanteri, 2005. "Crecimiento y la paradoja de la productividad: Una estimación en la forma de state-space con componentes no observables para el sector agropecuario argentino, 1955-2003," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 20(1), pages 53-78.
  • Handle: RePEc:emx:esteco:v:20:y:2005:i:1:p:53-78
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    1. A. Bailey & K. Balcombe & J. Morrison & C. Thirtle, 2003. "A comparison of proxy variable and stochastic latent variable approaches to the measurement of bias in technological change in south african agriculture," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 12(4), pages 315-324.
    2. Esposti, Roberto & Pierani, Pierpaolo, 1997. "The Source of Technical Change in Italian Agriculture: A Latent Variable Approach," Staff Papers 200593, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    3. Olmstead, Alan L & Rhode, Paul, 1993. "Induced Innovation in American Agriculture: A Reconsideration," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 100-118, February.
    4. Chambers,Robert G., 1988. "Applied Production Analysis," Cambridge Books, Cambridge University Press, number 9780521314275, October.
    5. Archibald, Sandra O. & Brandt, Loren, 1991. "A flexible model of factor biased technological change An application to Japanese agriculture," Journal of Development Economics, Elsevier, vol. 35(1), pages 127-145, January.
    6. Felipe, Jesus & McCombie, J. S. L., 2001. "Biased Technical Change, Growth Accounting, and the Conundrum of the East Asian Miracle," Journal of Comparative Economics, Elsevier, vol. 29(3), pages 542-565, September.
    7. Stevenson, Rodney, 1980. "Measuring Technological Bias," American Economic Review, American Economic Association, vol. 70(1), pages 162-173, March.
    8. David K. Lambert & J.S. Shonkwiler, 1995. "Factor Bias under Stochastic Technical Change," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 578-590.
    9. Bailey, Alastair & Irz, Xavier T. & Balcombe, Kelvin George, 2003. "An Appliation Of The Stochastic Latent Variable Approach To The Correction Of Sector Level Tfp Calculations In The Face Of Biased Technological Change," 2003 Annual Meeting, August 16-22, 2003, Durban, South Africa 25842, International Association of Agricultural Economists.
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