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Innovation, international R&D spillovers and the sectoral heterogeneity of knowledge flows

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Abstract

We analyze the relative effects of national and international, intrasectoral and intersectoral R&D spillovers on innovative activity in six large, industrialized countries over the period 1980–2000. We use patent applications at the European Patent Office to measure innovation and their citations to trace knowledge flows within and across 135 narrowly defined technological fields. Using panel cointegration we show that intersectoral spillovers have a key impact on innovation activities and that domestic R&D has a stronger effect than international R&D. However, within technological fields, estimated international R&D spillovers are 2.4 times the national R&D effects. We find significant differences across chemicals, electronics and machinery industries.

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Notes

  1. We use the word industry to refer to broad aggregates like chemicals, electronics and machinery. Our unit of analysis is much more detailed and very close to product groups. We refer to these groups (listed in Table 9 of the "Appendix") as technological fields or technological sectors. We use the term class to refer to specific classifications (like IPC for patents, SITC for trade data or ISIC).

  2. Many papers show that knowledge spillovers tend to be geographically localized (e.g. Maurseth and Verspagen 2002; Bottazzi and Peri 2003; Peri 2005; Branstetter 2001).

  3. See for example Bottazzi and Peri (2007) and Mancusi (2008). Further evidence on international knowledge flows is in Jaffe et al. (1993) and Bacchiocchi and Montobbio (2010).

  4. For example Coe and Helpman (1995), Eaton and Kortum (1996), Frantzen (2002), Park (2004) and Keller (2010). Some doubts are cast by Kao et al. (1999) and Edmond (2001) that use panel cointegration econometrics and by Luintel and Khan (2004) that suggest that data from different countries cannot be pooled.

  5. This is calculated as \( S_{hjt} = \left( {1 - \delta } \right)S_{hjt - 1} + R_{hjt - 1} \) using a depreciation rate (δ) of 15 % (Hall and Mairesse 1995). The first period stock is thus obtained as \( S_{hj1} = R_{hj1} /(\delta + g_{hj} ) \), where g hj is the growth rate of R&D spending in industry j, country h. This is industry-country specific and calculated as the average growth rate over the available period.

  6. nc hij is equal to the number of citations from patents classified into technological field i to patents classified into technological field j and held by to other national firms (i.e. excluding self citations) divided by the total number of national citations outflowing from field i. Note further that in (8) the product is over j ≠ i because spillovers within the same technological field are already included into the own R&D measure; put it differently, their effect cannot be distinguished from that of own R&D.

  7. See footnote 1 for the use of the terms sectors and fields.

  8. In what follows, whenever we refer to patents, we mean patent applications.

  9. Each patent is assigned to the country of residence of the inventor.

  10. Individual applicants have been identified and excluded in the dataset used in the analysis.

  11. The list of fields is reported in Table 10 of the "Appendix". The distribution of the size of technological fields (i.e. the total number of applications over the whole sample period) is highly skewed, with the very large technological fields belonging to the electronics industry and to either Japan or the US.

  12. We have included in the sample also the citations to EPO patents passing through the World Intellectual Property Organization (WIPO).

  13. The use of patent citations as an index of knowledge flow has been validated by a survey of inventors (Jaffe et al. 2000, for the US Patent and Trademark Office) and by the Community Innovation Survey data for the EPO (Duguet and MacGarvie 2005) and corroborates substantial evidence on the type and nature of knowledge spillovers (e.g. Maurseth and Verspagen 2002; Jaffe et al. 1993, Bacchiocchi and Montobbio 2010).

  14. There are relevant differences between citation practices at the USPTO and EPO. In the US there is the 'duty of candor' rule, which imposes all applicants to disclose all the prior art they are aware of. Therefore many citations at the USPTO come directly from inventors, applicants and attorneys and are subsequently filtered by patent examiners.

  15. The search report at the EPO is a document, published typically 18 months after the application date, that has the main objective to discover the prior art relevant for determining whether the invention meets the novelty and inventive step requirements. It represents what is already known in the technical field of the patent application.

  16. National citations and international citations are citations to patents held by firms resident respectively in the same and in a different country. Self citations are citations to previous patents held by the same applicant firm. Note that in tracing and counting patent applications and citations we took co-patenting into account. Note, however, that co-patenting is not so widespread and quite equally distributed across industries. The countries with the higher incidence of co-patenting are France (10 % of patent applications are co-patents), the UK (9 %) and Japan (7 %). Co-patenting is instead particularly low in the US: only 3 % of patent applications are the result of joint effort by more than one firm.

  17. These are obviously available upon request.

  18. These are: Food, Beverages and Tobacco (31), Textiles, Apparel and Leather (32), Wood Products and Furniture (33), Paper, Paper Products and Printing (34), Chemicals excl. Drugs (351 + 352 − 3522), Drugs and Medicines (3522), Petroleum Refineries and Products (353 + 354), Rubber and Plastic Products (355 + 356), Non-Metallic Mineral Products (36), Iron and Steel (371), Non-Ferrous Metals (372), Metal Products (381), Non-Electrical Machinery (382 − 3825), Office and Computing Machinery (3825), Electric. Machin. excluding Commercial Equipment (3830 − 3832), Radio, TV and Communication Equipment (3832), Shipbuilding and Repairing (3841), Motor vehicles (3843), Aircraft (3845), Other Transport Equipment (3842 + 3844 + 3849), Professional Goods (385), Other Manufacturing (39).

  19. This is available at: http://ec.europa.eu/eurostat/ramon/relations/index.cfm?TargetUrl=LST_REL.

  20. This is available at: http://www.macalester.edu/research/economics/page/haveman/Trade.Resources/tradeconcordances.html.

  21. Implicit deflators are calculated as value added at current prices divided by value added volumes expressed in dollars (OECD 2005a). When we could not calculate the deflators because of missing values, we used data at more aggregated level.

  22. In order to exclude technological fields where innovation is a quite rare phenomenon, in each country, we exclude from the analysis those technological fields with rare patenting. Overall, we exclude 2 fields for all countries (chemical 33—Trash—and Maschinery 25—Packaging machines). We further exclude 13 fields for a single country (7 of these are excluded for the US).

  23. See also Wieser (2005) for a summary of the main estimates of output (sales, value added or TFP) elasticity of R&D at the firm level. He surveys 52 papers and shows that the median value of this elasticity is 0.13.

  24. This is in line with Peri (2005) that shows that in the computer industry knowledge flows substantially farther.

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Acknowledgments

The authors thank Laura Bottazzi, Bronwyn Hall, Giovanni Peri, Jacques Mairesse, the participants of the 3rd European Conference on Corporate R&D, EARIE 2010 and seminar participants at KITeS-Cespri, University of Cagliari and Bruxelles. Financial support from the Italian Ministry for Education, Universities and Research is gratefully acknowledged (FIRB, Project RISC—RBNE039XKA: “Research and entrepreneurship in the knowledge-based economy: the effects on the competitiveness of Italy in the European Union”).

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Authors and Affiliations

  1. Department of Management and Technology, Bocconi University, Milan, Italy

    Franco Malerba

  2. CRIOS, Bocconi University, Milan, Italy

    Franco Malerba, Maria Luisa Mancusi & Fabio Montobbio

  3. Department of Economics and Finance, Università Cattolica del Sacro Cuore, Milan, Italy

    Maria Luisa Mancusi

  4. Department of Economics and Statistics “Cognetti de Martiis”, University of Turin, Turin, Italy

    Fabio Montobbio

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  1. Franco Malerba
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  2. Maria Luisa Mancusi
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Correspondence to Maria Luisa Mancusi.

Appendix

Appendix

Table 9 Correlation matrix of the explanatory variables used in the regressions and descriptive statistics
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Table 10 List of technological fields
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Malerba, F., Mancusi, M.L. & Montobbio, F. Innovation, international R&D spillovers and the sectoral heterogeneity of knowledge flows. Rev World Econ 149, 697–722 (2013). https://doi.org/10.1007/s10290-013-0167-0

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