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Estimation of firm-specific technological bias, technical change and total factor productivity growth: a dual approach

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  • Subal Kumbhakar
  • Shinichiro Nakamura
  • Almas Heshmati

Abstract

This paper deals with modeling firm-specific technical change (TC), and technological biases (inputs and scale) in estimating total factor productivity (TFP) growth. Several dual parametric econometric models are used for this purpose. We examine robustness of TFP growth and TC among competing models. These models include the traditional time trend (TT) model and the general index (GI) model. The TT and the GI models are generalized to accommodate firm-specific TC and technological bias (in inputs and output). Both nested and non-nested tests are used to select the appropriate models. Firm-level panel data from the Japanese chemical industry during 1968- 1987 is used as an application.

Suggested Citation

  • Subal Kumbhakar & Shinichiro Nakamura & Almas Heshmati, 2000. "Estimation of firm-specific technological bias, technical change and total factor productivity growth: a dual approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 162-173.
    • Handle: RePEc:taf:emetrv:v:19:y:2000:i:4:p:162-173
    DOI: 10.1080/07474930008800483
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    References listed on IDEAS

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    1. Baltagi, Badi H & Griffin, James M, 1988. "A General Index of Technical Change," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 20-41, February.
    2. Atkinson, Scott E & Cornwell, Christopher, 1994. "Parametric Estimation of Technical and Allocative Inefficiency with Panel Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 231-243, February.
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    Cited by:

    1. Sauer, Johannes & Graversen, Jesper T. & Park, Timothy A., 2006. "Breathtaking or Stagnating? - Productivity, Technical Change and Structural Dynamics in Danish Organic Farming," 2006 Annual meeting, July 23-26, Long Beach, CA 21481, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Hossain, A K M Nurul & Serletis, Apostolos, 2020. "Technical change in U.S. industries," Economic Modelling, Elsevier, vol. 91(C), pages 579-600.
    3. Almas Heshmati & Subal C. Kumbhakar, 2014. "A general model of technical change with an application to the OECD countries," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 23(1), pages 25-48, January.
    4. Ghulam, Yaseen & Dhruva, Kamini, 2024. "Banking sector reforms in a challenging environment: An emerging financial market experience," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1074-1096.
    5. Almas Heshmati, 2003. "Productivity Growth, Efficiency and Outsourcing in Manufacturing and Service Industries," Journal of Economic Surveys, Wiley Blackwell, vol. 17(1), pages 79-112, February.
    6. Heshmati, Almas, 2002. "Productivity measurement in Swedish departments of gynecology and obstetrics," Structural Change and Economic Dynamics, Elsevier, vol. 13(3), pages 315-336, September.
    7. Chia-Hung Sun, 2005. "Productivity growth in East Asian manufacturing: a fading miracle or measurement problem?," Journal of International Development, John Wiley & Sons, Ltd., vol. 17(1), pages 1-19.
    8. Kumbhakar, Subal C., 2002. "Decomposition of technical change into input-specific components: a factor augmenting approach," Japan and the World Economy, Elsevier, vol. 14(3), pages 243-264, August.

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