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R&D, productivity and market value

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  • Bronwyn H. Hall

    (Institute for Fiscal Studies and University of California, Berkeley)

Abstract
Measuring the private returns to R&D requires knowledge of its private depreciation or obsolescence rate, which is inherently variable and responds to competitive pressure. Nevertheless, most of the previous literature has used a constant depreciation rate to construct R&D capital stocks and measure the returns to R&D, a rate usually equal to 15 per cent. In this paper I review the implications of this assumption for the measurement of returns using two different methodologies: one based on the production function and another that uses firm market value to infer returns. Under the assumption that firms choose their R&D investment optimally, that is, marginal expected benefit equals marginal cost, I show that both estimates of returns can be inverted to derive an implied depreciation rate for R&D capital. I then test these ideas on a large unbalanced panel of US manufacturing firms for the years 1974 to 2003. The two methods do not agree, in that the production function approach suggests depreciation rates near zero (or even appreciation) whereas the market value approach implies depreciation rates ranging from 20 to 50 per cent, depending on the period. The concluding section discusses the possible reasons why this is true.

Suggested Citation

  • Bronwyn H. Hall, 2006. "R&D, productivity and market value," IFS Working Papers W06/23, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:06/23
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    File URL: http://www.ifs.org.uk/wps/wp0623.pdf
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    References listed on IDEAS

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    Cited by:

    1. Bronwyn H. Hall & Grid Thoma & Salvatore Torrisi, 2006. "The market value of patents and R&D: Evidence from European firms," KITeS Working Papers 186, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Nov 2006.
    2. Jovanovic, Boyan, 2009. "Investment options and the business cycle," Journal of Economic Theory, Elsevier, vol. 144(6), pages 2247-2265, November.
    3. Ugur, Mehmet & Trushin, Eshref & Solomon, Edna, 2016. "Inverted-U relationship between R&D intensity and survival: Evidence on scale and complementarity effects in UK data," Research Policy, Elsevier, vol. 45(7), pages 1474-1492.
    4. Ivan Lugovoi & Dimitrios A. Andritsos & Claire Senot, 2022. "Novelty and scope of process innovation: The role of related and unrelated manufacturing experience," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3877-3895, October.
    5. Marin, Giovanni, 2014. "Do eco-innovations harm productivity growth through crowding out? Results of an extended CDM model for Italy," Research Policy, Elsevier, vol. 43(2), pages 301-317.
    6. Alessandro Sterlacchini & Francesco Venturini, 2013. "Boosting Manufacturing Productivity Through R&D: International Comparisons with Special Focus on Italy," Journal of Industry, Competition and Trade, Springer, vol. 13(2), pages 187-208, June.
    7. Kyriakos Drivas & Claire Economidou & Elena Ketteni & Konstantina Kottaridi, 2021. "Firms’ knowledge investment and market responses," Empirical Economics, Springer, vol. 61(5), pages 2363-2394, November.
    8. Alston, Julian M. & Pardey, Philip G. & Ruttan, Vernon W., 2008. "Research Lags Revisited: Concepts and Evidence from U.S. Agriculture," Staff Papers 50091, University of Minnesota, Department of Applied Economics.
    9. Esposti, Roberto, 2008. "Why Should Regional Agricultural Productivity Growth Converge? Evidence from Italian Regions," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 43955, European Association of Agricultural Economists.

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