Abstract
In this work, we analyze the relationship between the patterns of firm diversification, if any, across product lines and across bodies of innovative knowledge, proxied by the patent classes where the firm is present. Putting it more emphatically, we investigate the relationship between “what a firm does” and “what a firm knows.” Using a newly developed dataset matching information on patents and products at the firm level, we provide evidence concerning firms’ technological and product scope, their relationships, the size-scaling and coherence properties of diversification itself. Our analysis shows that typically firms are much more diversified in terms of products than in terms of technologies, with their main products more related to the exploitation of their innovative knowledge. The scaling properties show that the number of products and technologies increases log-linearly with firm size. And the directions of diversification themselves display coherence between neighboring activities also at relatively high degrees of diversification. These findings are well in tune with a capability-based theory of the firm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
On the limitations, but also the relevance of the use of patents as a proxy for innovation, see the critical review by Griliches (1990).
See, in particular, the evidence presented in Dernis et al. (2015) about the patenting strategies of world corporate top R&D investors across the five top intellectual property offices (IP5) which include, in addition to the USPTO and the EPO, the Japan Patent Office (JPO), the Korean Intellectual Property Office (KIPO) and the State Intellectual Property Office of the People’s Republic of China (SIPO).
Notice that a single patent may be owned by more than one firm. In case of co-patenting, our analysis credits the patent to each co-patentee, as it is usually done in the literature (see, for example, Breschi et al. 2003).
Information on total turnover is available only in 2000 and 2003.
More on the dataset in Grazzi et al. (2013).
Notice that figures relative to more recent years are less reliable due to the time lag between a patent application and its grant.
See in particular the Schmoch et al. (2003) concordance table.
In what follows, we will assume that “products” and “technological fields” map one-to-one into each other (a 4-digit ISIC Rev.3 sector).
For a detailed description of the transaction level trade data and the product classification employed refer to Bernard et al. (2015).
We focus on exports to Extra-EU destinations for several reasons. Most importantly, firm-level exports to the EU are not recorded for all exporters and these criteria have changed over time.
The matrix is calculated taking into account only firms which are active in at least two products (technological fields), and only products (technological fields) in which at least two firms are active. The resulting matrix is 136 × 136 in the case of products (taking into account 2571 firms) and 102 × 102 in the case of technological fields (taking into account 940 firms).
When making inference about skewed distributions, as for example distributions of firms across products or technological fields, P value is to be preferred to statistics-based inference, like the t-statistics used in Teece et al. (1994). The point is discussed at greater length in Bottazzi and Pirino (2010).
References
Acs, Z. J., & Audretsch, D. B. (1987). Innovation, market structure and firm size. The Review of Economics and Statistics, 69, 567–575.
Acs, Z. J., & Audretsch, D. B. (1990). Innovation and small firms. Cambridge, MA: MIT Press.
Acs, Z. J., Audretsch, D. B., & Feldman, M. P. (1994). R&D spillovers and recipient firm size. The Review of Economics and Statistics, 76(2), 336–340.
Audretsch, D. B., & Vivarelli, M. (1996). Firms size and R&D spillovers: Evidence from Italy. Small Business Economics, 8(3), 249–258.
Bernard, A. B., Grazzi, M., & Tomasi, C. (2015). Intermediaries in international trade: Products and destinations. The Review of Economics and Statistics, 97(4), 916–920.
Bernard, A. B., Jensen, J. B., Redding, S. J., & Schott, P. K. (2007). Firms in international trade. Journal of Economic Perspectives, 21(3), 105–130.
Bottazzi, G., Dosi, G., Lippi, M., Pammolli, F., & Riccaboni, M. (2001). Innovation and corporate growth in the evolution of the drug industry. International Journal of Industrial Organization, 19(7), 1161–1187.
Bottazzi, G., & Pirino, D. (2010). Measuring industry relatedness and corporate coherence. In LEM papers series 2010/10, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy.
Bottazzi, G., & Secchi, A. (2006). Gibrat’s law and diversification. Industrial and Corporate Change, 15(5), 847–875.
Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69–87.
Brouwer, E., & Kleinknecht, A. (1999). Innovative output, and a firm’s propensity to patent: An exploration of CIS micro data. Research Policy, 28(6), 615–624.
Brusoni, S., Prencipe, A., & Pavitt, K. (2001). Knowledge specialisation, organizational coupling and the boundaries of the firm: Why firms know more than they make? Administrative Science Quarterly, 46(4), 597–621.
Corradini, C., Demirel, P., & Battisti, G. (2016). Technological diversification within UK’s small serial innovators. Small Business Economics, 47(1), 163–177.
Dernis, H., Dosso, M., Hervas, F., Millot, V., Squicciarini, M., & Vezzani, A. (2015). World Corporate Top R&D investors: Innovation and IP bundles. In JRC-IPTS working papers JRC94932, Institute for Prospective Technological Studies, Joint Research Centre.
Dosi, G. (1988). Sources, procedures, and microeconomic effects of innovation. Journal of Economic Literature, 26(3), 1120–1171.
Dosi, G., Faillo, M., & Marengo, L. (2008). Organizational capabilities, patterns of knowledge accumulation and governance structures in business firms: An introduction. Organization Studies, 29(8–9), 1165–1185.
Dosi, G., Grazzi, M., & Moschella, D. (2015). Technology and costs in international competitiveness: From countries and sectors to firms. Research Policy, 44(10), 1795–1814.
Dosi, G., Marengo, L., & Pasquali, C. (2010). How much should society fuel the greed of innovators? On the relations between appropriability, opportunities and rates of innovation. In R. Viale & H. Etzkowitz (Eds.), The capitalization of knowledge. Cheltenham: Edward Elgar.
Dosi, G., Nelson, R. R., & Winter, S. (2000). Introduction. In G. Dosi, R. R. Nelson, & S. Winter (Eds.), The nature and dynamics of organizational capabilities. Oxford: Oxford University Press.
Goldschlag, N., Lybbert, T. J., & Zolas, N. J. (2016). An algorithmic links with probabilities crosswalk for uspc and cpc patent classifications with an application towards industrial technology composition. In US Census Bureau Center for Economic Studies paper no. CES-WP-16-15.
Grazzi, M., Sanzo, R., Secchi, A., & Zeli, A. (2013). The building process of a new integrated system of business micro-data 1989–2004. Journal of Economic and Social Measurement, 38(4), 291–324.
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.
Hall, B. H., & Mairesse, J. (1995). Exploring the relationship between R&D and productivity in French manufacturing firms. Journal of Econometrics, 65(1), 263–293.
Hirsch, S., & Lev, B. (1971). Sales stabilization through export diversification. The Review of Economics and Statistics, 53(3), 270–277.
Kim, W. C., Hwang, P., & Burgers, W. P. (1993). Multinationals’ diversification and the risk-return trade-off. Strategic Management Journal, 14(4), 275–286.
Lybbert, T. J., & Zolas, N. J. (2014). Getting patents and economic data to speak to each other: An algorithmic links with probabilities approach for joint analyses of patenting and economic activity. Research Policy, 43(3), 530–542.
Miller, D. J. (2006). Technological diversity, related diversification, and firm performance. Strategic Management Journal, 27(7), 601–619.
Montgomery, C. A. (1994). Corporate diversification. The Journal of Economic Perspectives, 8(3), 163–178.
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: The Belknap Press of Harvard University Press.
Nesta, L., & Saviotti, P. P. (2006). Firm knowledge and market value in biotechnology. Industrial and Corporate Change, 15(4), 625–652.
Patel, P., & Pavitt, K. (1997). The technological competencies of the world’s largest firms: Complex and path-dependent, but not much variety. Research Policy, 26(2), 141–156.
Pavitt, K. (1998). Technologies, products and organization in the innovating firm: What Adam Smith tells us and Joseph Schumpeter doesn’t. Industrial and Corporate Change, 7(3), 433–452.
Piscitello, L. (2000). Relatedness and coherence in technological and product diversification of the world’s largest firms. Structural Change and Economic Dynamics, 11(3), 295–315.
Piscitello, L. (2004). Corporate diversification, coherence and economic performance. Industrial and Corporate Change, 13(5), 757–787.
Schmoch, U., Laville, F., Patel, P., & Frietsch, R. (2003). Linking technology areas to industrial sectors. DG research: Final report to the European commission.
Teece, D. J., Rumelt, R., Dosi, G., & Winter, S. (1994). Understanding corporate coherence: Theory and evidence. Journal of Economic Behavior and Organization, 23(1), 1–30.
Valvano, S., & Vannoni, D. (2003). Diversification strategies and corporate coherence evidence from Italian leading firms. Review of Industrial Organization, 23(1), 25–41.
Wakelin, K. (1998). Innovation and export behaviour at the firm level. Research Policy, 26(7), 829–841.
Winter, S. G. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10), 991–995.
Acknowledgments
We thank the participants at the Conference on Entrepreneurship, Innovation and Enterprise Dynamics organized by the OECD Working Party on Industry Analysis (WPIA), Paris, December 2014; the Finkt final conference in Rimini (2015); CAED conference in Istanbul (2015); XXII Organization Science Winter Conference Park City (2016) and participants to faculty seminars in Notre Dame University; and SPRU, University of Sussex. We also thank Giulio Bottazzi and Davide Pirino for having allowed to use the computing routines associated to their coherence measure. Marco Grazzi gratefully acknowledges the Centre for Business Research at the University of Cambridge and in particular Andrea Mina for a very fruitful visiting period and the Fondazione Cassa dei Risparmi di Forlì, Grant ORGANIMPRE, for financial support. Daniele Moschella received financial support by the Italian Ministry of Education and Research under the SIR Programme (Project code RBSI14JAFW). The Project has been partly supported by the European Commission under the H2020 RIA (Grant Agreement 649186). Without the unique backing of the Italian Statistical Office, and in particular Roberto Monducci, this all endeavor would not have been possible. The usual disclaimer applies.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dosi, G., Grazzi, M. & Moschella, D. What do firms know? What do they produce? A new look at the relationship between patenting profiles and patterns of product diversification. Small Bus Econ 48, 413–429 (2017). https://doi.org/10.1007/s11187-016-9783-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11187-016-9783-0