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Measuring Total Factor Productivity, Technical Change And The Rate Of Returns To Research And Development

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  • Sang V Nguyen
  • Edward C Kokkelenberg

Abstract

Recent research indicates that estimates of the effect of research and development (R&D) on total factor productivity growth are sensitive to different measures of total factor productivity. In this paper, we use establishment level data for the flat glass industry extracted from the Census Bureau's Longitudinal Research Database (LRD) to construct three competing measures of total factor productivity. We then use these measures to estimate the conventional R&D intensity model. Our empirical results support previous finding that the estimated coefficients of the model are sensitive to the measurement of total factor productivity. Also, when using microdata and more detailed modeling, R&D is found to be a significant factor influencing productivity growth. Finally, for the flat glass industry, a specific technical change index capturing the learning-by-doing process appears to be superior to the conventional time trend index.

Suggested Citation

  • Sang V Nguyen & Edward C Kokkelenberg, 1991. "Measuring Total Factor Productivity, Technical Change And The Rate Of Returns To Research And Development," Working Papers 91-3, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:91-3
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    File URL: https://www2.census.gov/ces/wp/1991/CES-WP-91-03.pdf
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    References listed on IDEAS

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    1. Link, Albert N, 1980. "Firm Size and Efficient Entrepreneurial Activity: A Reformulation of the Schumpeter Hypothesis," Journal of Political Economy, University of Chicago Press, vol. 88(4), pages 771-782, August.
    2. Maddison, Angus, 1987. "Growth and Slowdown in Advanced Capitalist Economies: Techniques of Quantitative Assessment," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 649-698, June.
    3. Diewert, Walter E & Wales, Terence J, 1987. "Flexible Functional Forms and Global Curvature Conditions," Econometrica, Econometric Society, vol. 55(1), pages 43-68, January.
    4. Hall, Robert E, 1988. "The Relation between Price and Marginal Cost in U.S. Industry," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 921-947, October.
    5. Donald Siegel & Frank R Lichtenberg, 1989. "Using Linked Census R&D-Lrd Data To Analyze The Effect Of R&D Investment On Total Factor Productivity Growth," Working Papers 89-2, Center for Economic Studies, U.S. Census Bureau.
    6. Berndt, Ernst R & Khaled, Mohammed S, 1979. "Parametric Productivity Measurement and Choice among Flexible Functional Forms," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1220-1245, December.
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    Cited by:

    1. Shadbegian, Ronald J. & Gray, Wayne B., 2005. "Pollution abatement expenditures and plant-level productivity: A production function approach," Ecological Economics, Elsevier, vol. 54(2-3), pages 196-208, August.
    2. Hall, Bronwyn H. & Mairesse, Jacques & Mohnen, Pierre, 2010. "Measuring the Returns to R&D," Handbook of the Economics of Innovation, Elsevier.
    3. Wayne B Gray & Ronald J Shadbegian, 1994. "Pollution Abatement Costs, Regulation And Plant-Level Productivity," Working Papers 94-14, Center for Economic Studies, U.S. Census Bureau.
    4. Emi Nakamura & Masao Nakamura (presenter) & Takanobu Nakajima, 2004. "Measuring Firms’ R&D Effects on Technical Progress: Japan in the 199," Econometric Society 2004 Far Eastern Meetings 414, Econometric Society.
    5. Kaloyan Ganev, 2005. "Measuring Total Factor Productivity: Growth Accounting for Bulgaria," GE, Growth, Math methods 0504004, EconWPA, revised 21 Apr 2005.
    6. Lucia Foster & Cheryl Grim, 2010. "Characteristics of the Top R&D Performing Firms in the U.S.: Evidence from the Survey of Industrial R&D," Working Papers 10-33, Center for Economic Studies, U.S. Census Bureau.

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    Keywords

    CES; economic; research; micro; data; microdata; chief; economist;

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