Abstract
This paper provides evidence on the mechanisms influencing the patent output of a sample of small and large, entrepreneurial and established biotechnology firms from the input of indirect knowledge acquired from capital expenditures and direct knowledge from in-house R&D. Statistical models of counts are used to analyse the relationship between patent applications and R&D investment and capital expenditures. It focuses on biotechnology in the period 2002–2007 and is based on a unique data set drawn from various sources including the EU Industrial R&D Investment Scoreboard, the European Patent Office (EPO), the US Patent and Trademark Office, and the World Intellectual Property Organisation. The statistical models employed in the paper are Poisson distribution generalisations with the actual distribution of patent counts fitting the negative binomial distribution and gamma distribution very well. Findings support the idea that capital expenditures—taken as equivalent to technical change embodied in new machinery and capital equipment—may also play a crucial role in the development of new patentable items from scientific companies. For EPO patents, this role appears even more important than that played by R&D investment. The overall picture emerging from our analysis of the determinants of patenting in biotechnology is that the innovation process involves a well balanced combination of inputs from both R&D and new machinery and capital equipment.
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Notes
The first two releases of the Scoreboard, in 2004 and 2005 dealt with the top 500 and top 700 EU and the top 500 and top 700 non-EU companies, respectively.
Matching patent datasets with a list of company names, or playing the “names game” as aptly called by Melamed (2006), is a preliminary and controversial step in the assessment of organisations’ patent portfolios. Nevertheless, given the relatively small number of cases, we maintain that the potential problem of reliability has been substantially alleviated by our manual procedure. Since addressing this issue is beyond the scope of our paper, we refer to Melamed (2006), Thoma (2007), Raffo and Lhuillery (2009), and Thursby et al. (2009) among others for more in-depth discussion and proposals for alternative procedures.
Being aware of the advantages and disadvantages of the use of patent-based innovation output indicators (Santarelli and Piergiovanni 1996), in this study we rely upon the assumption of homogeneity of technological content and economic significance of patents within the same technological field. Heterogeneity is conversely assumed to arise from the choice of one or multiple patent institutions.
It has to be considered that both the EPO and USPTO publish all patent applications 18 months after their filing date. However, the USPTO does not publish applications which have been withdrawn or filed with a non-publication request, stating that the application is US only.
Because the US is the world's leading country for the commercial development of biotechnology, companies have a strong incentive to apply for patent protection with the USPTO.
Another important source, the Japanese Patent Office (JPO), has also been excluded. With the JPO, each claim beyond the first requires additional official fees for substantive examination and maintenance. As a consequence of the additional fees, Japanese patents tend to average fewer claims than EPO and USPTO patents. As a result, this system has been seen to encourage numerous filings of narrow claims that build incrementally on fundamental technologies developed by domestic and foreign inventors (Maskus and McDaniel 1999). Thus, for the sake of procedural homogeneity, we decided not to use patent applications with JPO.
According to a detailed report on biotechnology in eighteen European countries and the US (Critical I, 2006), at the end of 2004 the total number of companies in business was 4,154. Thus, the representativeness of our sample with respect to the population of biotech firms in such countries is below 3.0%.
According to OECD (2009), DBFs are firms whose predominant activity involves the application of biotechnology techniques to produce goods or services and/or to perform biotechnology R&D.
In such a case, it is of course unlikely that DBFs invest heavily in new machinery and capital equipment.
This explains the minimum values of zero found for capital expenditure between 2002 and 2006.
Nevertheless, it has to be noticed that the number of patent applications is increasing over time. These figures may also reflect the fact that the monetary figures provided by the Scoreboard are not deflated, but simply converted in €.
It is worth recalling that the regression cannot be linear with count variables. The problem of nonlinearity is handled through nonlinear functions that transform the expected value of the count variable into a linear function, of the explanatory variables. Such transformations are referred to as link functions.
For the purposes of the Scoreboard, companies are allocated to the country of their registered office, which sometimes can be different from their operational or R&D headquarters. The main implication is that company location is independent of the actual location of its R&D activity. Use of this dummy variable is particularly important to take into account the fact that, in the US and other countries, it is common practice to include engineering costs relating to product innovation in R&D expenditures. These engineering costs have been excluded from the Scoreboard only if they have been disclosed separately. Accordingly, an overstatement of some overseas R&D investment figures in comparison with the EU is possible.
Defined, consistent with the OECD “Frascati” Manual (“Guidelines for the collection of R&D data”), as the cash investment funded by the companies themselves.
Defined as “expenditure used by a company to acquire or upgrade physical assets such as equipment, property, industrial buildings.”.
Defined as the total number of consolidated average employees, or year-end employees if average not stated.
As in fact is the case with each of the three patent counts.
For the population of Italian DBFs, Santarelli and Lotti (2008) found a strong positive and statistically significant relationship between patents with EPO and profitability.
Even though they use a stock measure such as capital intensity, i.e. the capital-labour ratio.
Possibly trying to avoid the drawbacks consequent upon the creation of CAFC in the United States. See Section Data and summary statistics above.
References
Acs, Z. J., Braunerhjelm, P., Audretsch, D. B., & Carlsson, B. (2009). The knowledge spillover theory of entrepreneurship. Small Business Economics, 32, 15–30.
Agresti, A. (2002). Categorical data analysis. New York: Wiley.
Archibugi, D., Evangelista, R., & Simonetti, R. (1994). On the definition and measurement of product and process innovations. In Y. Shionoya & M. Perlman (Eds.), Innovation in technology, industries, and institutions (pp. 7–24). Ann Arbor, MI: The University of Michigan Press.
Arora, A., & Gambardella, A. (1990). Complementarity and external linkages: The strategies of the large firms in biotechnology. Journal of Industrial Economics, 38, 361–379.
Audretsch, D. B. (1995). Innovation and industry evolution. Cambridge: MIT Press.
Baum, C. F., Caglayan, M., & Talavera, O. (2012). The effects of future capital investment and R&D expenditures on firms’ liquidity. Boston College Working Papers in Economics #712, Boston College Department of Economics.
Blundell, R., Griffith, R., & Windmeijer, F. (2002). Individual effects and dynamics in count data models. Journal of Econometrics, 108, 113–131.
Brouwer, E., & Kleinknecht, A. (1999). Keynes-plus? Effective demand and changes in firm-level R&D: An empirical note. Cambridge Journal of Economics, 23, 385–391.
Cameron, A. C., & Trivedi, P. K. (1998). Regression analysis of count data. Econometric society monographs n. 30. New York: Cambridge University Press.
Carree, M.A., Della Malva, A., & Santarelli, E. (2012). The contribution of universities to growth: Empirical evidence for Italy. Journal of Technology Transfer (in press).
Cincera, M. (1997). Patents, R&D and technological spillovers at the firm level: Some evidence from econometric count models for patent data. Journal of Applied Econometrics, 12, 265–280.
Cohen, W. M., & Klepper, S. (1996). A reprise of size and R&D. Economic Journal, 106, 925–951.
Comin, D., & Hobijn, B. (2010). An exploration of technology diffusion. American Economic Review, 100, 2031–2059.
Crepon, B., & Duguet, E. (1997). Estimating the innovation function from patent numbers: GMM on count panel data. Journal of Applied Econometrics, 12, 243–263.
Critical, I. (2006). Biotechnology in Europe: 2006 comparative study. Brussels: EuropaBio.
David, P. (1990). The dynamo and the computer: An historical perspective on the modern productivity paradox. American Economic Review, 80, 355–361.
de Rassenfosse, G., van Pottelsberghe, B., & de la Potterie, (2009). A policy insight into the R&D-patent relationship. Research Policy, 38, 779–792.
Deng, Y. (2007). The effects of patent regime changes: A case study of the European Patent Office. International Journal of Industrial Organization, 25, 121–138.
Dosi, G. (1982). Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 3, 147–162.
European Commission. (2009a). The Financing of Biopharmaceutical Product Development in Europe. Brussels: Report prepared by Danish Technological Institute for the DG Enterprise and Industry.
European Commission. (2009b). Recommendation from the Commission to the Council. Brussels, 20 March, SEC 330 final.
Fazeli, S. (2005). The European biotech sector: Could it achieve more? Journal of Commercial Biotechnology, 12, 10–19.
Gambardella, A. (1995). Science and Innovation—The US pharmaceutical industry during the 1980s. Cambridge: Cambridge University Press.
Greenwood, J., & Yorukoglu, M. (1997). 1974. Carnegie-Rochester Conference Series on Public Policy, 46, 49–95.
Greenwood, M., & Yule, G. U. (1920). An inquiry into nature of frequency distribution of multiple happenings. Journal of the Royal Statistical Society, 83, 255–279.
Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. Bell Journal of Economics, 10, 92–116.
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28, 1661–1707.
Hagedoorn, J. (2002). Inter-firm R&D partnerships: An overview of major trends and patterns since 1960. Research Policy, 31, 477–492.
Hall, B. H., & Mairesse, J. (1995). Exploring the relationship between R&D and productivity in French manufacturing firms. Journal of Econometrics, 65, 263–293.
Hall, B. H., & Ziedonis, R. H. (2001). The patent paradox revisited: An empirical study of patenting in the U.S. semiconductor industry, 1979–1995. RAND Journal of Economics, 32, 101–128.
Hausman, J., Hall, B. H., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52, 903–938.
Heinzl, H., & Mittlböck, M. (2003). Pseudo R-squared measures for Poisson regression models with over- or underdispersion. Computational Statistics and Data Analysis, 44, 253–271.
Hercowitz, Z. (1998). The ‘embodiment’ controversy: A review essay. Journal of Monetary Economics, 41(217), 224.
Hopkins, M. H., Martin, P. A., Nightingale, P., Kraft, A., & Mahdi, S. (2007). The myth of the biotech revolution: An assessment of technological, clinical and organizational change. Research Policy, 36, 566–589.
Hulten, C. R. (1992). Growth accounting when technical change is embodied in capital. American Economic Review, 82, 964–980.
Jaffe, A. B., & Lerner, J. (2004). Innovation and its discontents. How our broken patent system is endangering innovation and progress, and what to do about it. Oxford: Princeton University Press.
Kleinknecht, A. (1987). Measuring R&D in small firms: How much we are missing? Journal of Industrial Economics, 36, 253–256.
Lach, S., & Schankerman, M. (1987). The interaction between capital investment and R&D in science-based firms. NBER Working Papers #2377, National Bureau of Economic Research Inc.
Lanjouw, J. O., & Schankerman, M. (2004). Patent quality and research productivity: Measuring innovation with multiple indicators. Economic Journal, 114, 441–465.
Long, S. L. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.
Maddala, G. S. (1983). Limited-dependent and qualitative variables in economics. New York: Cambridge University Press.
Maskus, K. E., & McDaniel, C. (1999). Impacts of the Japanese patent system on productivity growth. Japan and the World Economy, 11, 557–574.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman and Hall.
McKelvey, M., Rickne, A., & Laage-Hellman, J. (2005). The economic dynamics of modern biotechnology. Cheltenham: Edward Elgar.
Melamed, R., Shiff, G. & Trajtenberg, M. (2006). The ‘Names Game’: Harnessing Inventors Patent Data for Economic Research. CEPR Discussion Paper #5833.
Nesta, L., & Saviotti, P–. P. (2006). Firm knowledge and market value in biotechnology. Industrial and Corporate Change, 15, 625–652.
Nightingale, P., & Martin, M. (2004). The myth of the biotechnology revolution. Trends in Biotechnology, 22, 564–569.
OECD. (2009). Biotechnology statistics 2009. Paris: OECD.
Orsenigo, L. (1989). The emergence of biotechnology. London: Frances Pinter.
Pakes, A., & Griliches, Z. (1980). Patents and R&D at the firm level: A first report. Economics Letters, 5, 377–381.
Patel, P., Arundel, A., & Hopkins, M. (2008). Sectoral innovation systems in Europe: Monitoring, analysing trends and identifying challenges in biotechnology. Brussels: Europe Innova, Sector Report.
Patzelt, H., & Brenner, T. (Eds.). (2008). Handbook of bioentrepreneurship. New York: Springer.
Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy, 13, 343–373.
Piva, M., & Vivarelli, M. (2007). Is demand-pulled innovation equally important in different groups of firms? Cambridge Journal of Economics, 31, 691–710.
Raffo, J., & Lhuillery, S. (2009). Patent retrieval comparing different heuristics. Research Policy, 38, 1617–1627.
Roijakkers, N., & Hagedoorn, J. (2003). Inter-firm R&D partnering in high technology industries. In J. H. Dunning & G. Boyd (Eds.), Alliance capitalism and corporate management (pp. 63–90). Northampton: Edward Elgar.
Roijakkers, N., & Hagedoorn, J. (2006). Inter-firm R&D partnering in pharmaceutical biotechnology since 1975: Trends, patterns, and networks. Research Policy, 35, 431–446.
Santarelli, E. (1995). Finance and technological change. London: Macmillan and St. Martin’s Press.
Santarelli, E., & Lotti, F. (2008). Innovative output, productivity, and profitability. A test comparing USPTO and EPO data. Industry and Innovation, 15, 492–509.
Santarelli, E., & Piergiovanni, R. (1996). Analyzing literature-based innovation output indicators: The Italian experience. Research Policy, 25, 689–711.
Santarelli, E., & Sterlacchini, A. (1990). Innovation, formal vs. informal R&D, and firm size: Some evidence from Italian manufacturing. Small Business Economics, 2, 223–228.
Scherer, F. M. (1982). Demand-pull and technological invention: Schmookler revisited. Journal of Industrial Economics, 30, 225–237.
Schmookler, J. (1966). Invention and economic growth. Boston: Harvard University Press.
Tassey, G. (2010). Rationales and mechanisms for revitalizing US manufacturing R&D strategies. Journal of Technology Transfer, 35, 283–333.
Terleckyi, N. (1974). Effects of R&D on the productivity growth of industries. An exploratory study. New York: National Planning Association.
Thoma, G., & Torrisi, S. (2007). Creating powerful indicators for innovation studies with approximate matching algorithms. A test based on PATSTAT and Amadeus databases. CESPRI Working Papers #211, Milano: Università Bocconi.
Thursby, J., Fuller, A. W., & Thursby, M. (2009). US faculty patenting: Inside and outside the university. Research Policy, 38, 14–25.
Utterback, J., & Abernathy, W. (1975). A dynamic model of process and product innovation. Omega, 3, 639–656.
Van Dijk, B., Hertog, R. D., Menkveld, B., & Thurik, R. (1997). Some new evidence on the determinants of large- and small-firm innovation. Small Business Economics, 9, 335–343.
van Pottelsberghe de la Potterie, B. (2010). Europe should stop taxing innovation. Brussels: Bruegel Policy Brief.
van Pottelsberghe de la Potterie, B., & Guellec, D. (2007). The economics of the European patent system. Oxford: Oxford University Press.
Von Tunzelmann, G. N. (1993). Technological and organisational change in industry during the industrial revolution. In P. K. O’Brien & R. Quinault (Eds.), The industrial revolution and British society (pp. 254–282). Cambridge: Cambridge University Press.
Wonglimpiyarat, J. (2008). Technological change in the pharmaceutical industry: Policies for technology transfer and management for the developing countries. International Journal of Technology, Policy and Management, 8, 194–210.
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Previous versions of this paper have been presented at CONCORD 2010—Conference on Corporate R&D (Seville, 3–4 March 2010), at the Workshop on “The Output of R&D Activities: Harnessing the Power of Patents Data-II” (Seville, 27–28 May 2010), and at the 37th Annual E.A.R.I.E. Conference (Istanbul, 2–4 September 2010). The work has benefited from the comments by Michele Cincera, Vincenzo Spiezia, Alessandro Sterlacchini, Marco Vivarelli, and an anonymous reviewer. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission and of Istat.
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Piergiovanni, R., Santarelli, E. The more you spend, the more you get? The effects of R&D and capital expenditures on the patenting activities of biotechnology firms. Scientometrics 94, 497–521 (2013). https://doi.org/10.1007/s11192-012-0711-z
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DOI: https://doi.org/10.1007/s11192-012-0711-z