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The generation and exploitation of technological change: market value and total factor productivity

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Abstract

In this paper we articulate and test the hypothesis that TFP is a reliable and relevant measure of firm’s innovation capabilities, and, as such, accounts for Tobin’s q indicator. With this aim, we investigate empirically the relationship between firm level total factor productivity and the Tobin’s q. Measuring Tobin’s q allows inferring the actual value of knowledge capital from stock market valuation. We use a panel of companies listed on UK and the main continental Europe financial markets (Germany, France and Italy) for the period 1995–2005. Our results confirm that TFP is a reliable indicator of firm’s innovative capabilities. When we control for firm’s R&D investments and intangible assets, the effects of TFP on market value remain highly significant. This suggests that TFP is a broader measure of innovation capability than R&D is. The validation of the Tobin’s q and TFP relationship has important implications concerning firm’s technological innovation measurement.

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Notes

  1. Tangible&intangible assets include all assets of the firm, both tangible and intangible. In Cockburn and Griliches (1988) and subsequent works, these assets are referred to as tangible capital or tangible assets. Hall et al. (2005) name them physical capital. In empirical analyses then different approaches are used in order to compute this variable: total fixed assets is used in Cockburn and Griliches (1988); net plant and equipment, inventories, and investments in unconsolidated subsidiaries, intangibles, and other in Hall et al. (2005); sum of property, plant, and equipment, inventory, and net working capital in Megna and Kloch (1993); total tangible assets in Hall and Oriani (2006). We chose the tangible&intangible assets definition for stressing that in our model we mean to include both tangible and intangible assets.

  2. These data were originally published and described in Van Ark (1995).

  3. As reviewed in Hall et al. (2010) two major approaches have been followed in order to estimate production functions. The first is the primal approach that estimates a production function using quantities as inputs and measures technical progress by means of total factor productivity. The second is the dual approach where technology is represented by a dual cost function that is used to derive the factor demand and the output supply equations. Our estimation of the production function follows the first approach. Yet, while the approach described in Hall et al. (2010) assumes that in the linear model the log of technical progress can be written as the sum of a firm-specific effect ωi and a time effect λt, in our approach the residual of the linear model representing TFP is assumed to be the sum of a productivity shock ωit, introducing an explicit behavioral hypothesis in the estimation procedure, and a random shock.

  4. A 15% depreciation rate is the most common value used in the literature. As a robustness check we also experimented with alternative (higher) depreciation rates. We found that the depreciation rate value makes little difference in empirical estimations as already discussed in Griliches and Mairesse (1984).

  5. Different assumptions on g did not significantly affect our results.

  6. We owe this comment to a Referee.

  7. We are indebted to Bronwyn Hall for crucial suggestions on this point.

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Acknowledgments

The authors wish to thank Bronwyn Hall, Raffaele Paci and Fabiano Schivardi for useful suggestions. Preliminary versions have been presented at CRENoS-DECA seminar series, University of Cagliari, and the AFSE 2009 tematic meeting, Sophia Antipolis. We are grateful for the useful comments of many participants. The financial support of the Collegio Carlo Alberto and the Dipartimento di Economia dell’università di Torino is acknowledged. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 216813.

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Authors and Affiliations

  1. Dipartimento di Economia, Università di Torino, Torino, Italy

    Cristiano Antonelli

  2. CRENoS, Università di Cagliari, Cagliari, Italy

    Alessandra Colombelli

  3. BRICK (Bureau of Research in Innovation Complexity and Knowledge), Collegio Carlo Alberto, Via Real Collegio, 30, 10024, Moncalieri, Torino, Italy

    Cristiano Antonelli & Alessandra Colombelli

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  1. Cristiano Antonelli
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  2. Alessandra Colombelli
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Correspondence to Alessandra Colombelli.

Appendices

Appendix 1

See Table 10.

Table 10 Sectoral concordance table
Full size table

Appendix 2: Robustness check, TFP series constructed under the assumption of constant returns to scale in production

See Tables 11, 12, and 13.

Table 11 Basic market value OLS, NLLS and FE regressions with dependent variable = log(q)
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Table 12 Market value OLS, NLLS and FE regressions with dependent variable = log(q) controlling for R&D
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Table 13 Market value OLS, NLLS and FE regressions with dependent variable = log(q) controlling for R&D and intangible assets
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Appendix 3: Robustness check, TFP series constructed using sectoral elasticities

See Tables 14, 15, and 16.

Table 14 Basic market value OLS, NLLS and FE regressions with dependent variable = log(q)
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Table 15 Market value OLS, NLLS and FE regressions with dependent variable = log(q) controlling for R&D
Full size table
Table 16 Market value OLS, NLLS and FE regressions with dependent variable = log(q) controlling for R&D and intangible assets
Full size table

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Antonelli, C., Colombelli, A. The generation and exploitation of technological change: market value and total factor productivity. J Technol Transf 36, 353–382 (2011). https://doi.org/10.1007/s10961-010-9198-z

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