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Systematized and Path-independent Measurement of Biased Technical Change


  • Tsunehiro Otsuki

    () (Osaka School of International Public Policy, Osaka University)


The sample-specificity and path-dependence of the data envelopment analysis (DEA) based technical change index as a component of Malmquist indexes prevent us from obtaining overall and systematic information on technical change. This paper develops a pathindependent method to estimate technical change using a systematized set of controlled input- output vectors and visualization of the DEA frontiers. The application to the panel datasets of agricultural production in the Brazil Amazon in 1975-1995 indicates non-Hicks-neutral technical change, with crossings of frontiers in both the 1975-1985 and the 1985-1995 periods. The alternative measure of overall technical change shows that moderate technological progress may have occurred on the whole in 1975-1995. The results also show heterogeneous trends across products. The mean of the sample-specific technical change scores are found to be quite different from the overall technical change measure.

Suggested Citation

  • Tsunehiro Otsuki, 2009. "Systematized and Path-independent Measurement of Biased Technical Change," Discussion Papers in Economics and Business 09-11, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
  • Handle: RePEc:osk:wpaper:0911

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    References listed on IDEAS

    1. Fare, Rolf, et al, 1997. " Biased Technical Change and the Malmquist Productivity Index," Scandinavian Journal of Economics, Wiley Blackwell, vol. 99(1), pages 119-127, March.
    2. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    3. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    4. Chen, Yao & Iqbal Ali, Agha, 2004. "DEA Malmquist productivity measure: New insights with an application to computer industry," European Journal of Operational Research, Elsevier, vol. 159(1), pages 239-249, November.
    5. David R. Lee & Christopher B. Barrett & John G. McPeak, 2006. "Policy, technology, and management strategies for achieving sustainable agricultural intensification," Agricultural Economics, International Association of Agricultural Economists, vol. 34(2), pages 123-127, March.
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    More about this item


    Data envelopment analysis; Path dependence; Agricultural intensification;

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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