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Knowledge diffusion from university and public research. A comparison between US, Japan and Europe using patent citations

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Abstract

This paper estimates the process of diffusion and decay of knowledge from university, public laboratories and corporate patents in six countries and tests the differences across countries and across technological fields using data from the European Patent Office. It finds that university and public research patents are more cited relatively to companies’ patents. However these results are mainly driven by the Chemical, Drugs & Medical, and Mechanical fields and US universities. In Europe and Japan, where the great majority of patents from public research come from national agencies, there is no evidence of a superior fertility of university and public laboratory patents vis à vis corporate patents. The distribution of the citation lags shows that knowledge embedded in university and public research patents tends to diffuse more rapidly relative to corporate ones in particular in the US, Germany, France and Japan.

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Notes

  1. It’s worthwhile noting that in 2000 the German law abolished the professor’s privilege (the same occurred in Denmark and Austria) while in Italy in 2001 the professor’s privilege was introduced for the first time (Lissoni et al. 2007).

  2. Patent citations delimit the scope of the property right and, at the EPO, are included in the patent document by the patent examiners that draft their reports trying to include all the technically relevant information within a minimum number of citations (EPO 2005). Recent evidence strongly supports their use to measure the value of innovations and to track knowledge flows from the cited to the citing inventors or applicants (Jaffe and Trajtenberg 2002; Haroff et al. 1999). The differences between citation practices between USPTO and EPO are described in Bacchiocchi and Montobbio (2004), with some implications for their use in economic analysis.

  3. The EP CESPRI database contains the 1,391,350 EPO patent applications from 1978 to 2001 and 1,119,761 citations from (and to) EPO patents from 1978 to 2001. EP-CESPRI Bibliographic data come from the Espace Bulletin CD-R produced by the EPO, patent citations come from the REFI tape. This tape does not contain a field with the type of applicant.

  4. The details on the aggregation between the IPC classes and the six sectors are given in the Appendix.

  5. We have identified universities and publicly owned research centres looking at each one of the 82571 applicants’ names in the database.

  6. Among university and PROs the top patentees at the EPO over the whole period are: in France the Centre National de la Recherche Scientifique, the Commissariat a l’Energie Atomique; in Italy the Comitato nazionale per la ricerca e lo sviluppo dell’energia nucleare, the Consiglio Nazionale delle Ricerche (CNR); in Germany the Max-Planck-Gesellschaft and the Forschungszentrum Julich; in the UK the Secretary of State for Defence, the Medical Research Council and the Atomic Energy Authority; in Japan the Agency of Industrial Science and Technology and the Research Development Corporation of Japan. Conversely in the US we find large universities like the Massachusetts Institute of Technology, the University of California and the University of Texas.

  7. There are different reasons for the plausibility of this specification, rather than count data models or selection models. The simplest one relates to the descriptive plot of the citation frequency as a function of time elapsed from the potentially cited patents. Figure 1 shows the gradual diffusion process during the first few years, followed by the natural process of obsolescence. This is particularly evident for the 1980 and 1985 cohorts. A combination of these two processes, thus, can be very useful in describing the citation patterns. Moreover, the particular specification of the model gives the parameters a structural interpretation as explained below.

  8. With 774,818 potentially cited patents and 838,355 potentially citing patents (Table 1) the expected probability that a single patent will receive a citation by another single patent is very small. This makes very difficult, expecially from the computational point of view, to directly model the propensity of one patent to be cited by another patents using count data models.

  9. As an example, let us consider an estimated coefficient α(k=Universities and PROs) =1.15; this means that patents belonging to the category “University and PROs” have a 15% higher probability (across all lags) to receive a citation vis à vis patents belonging to the base category.

  10. Grouping cited year is a reasonable assumption in that we believe that the fertility of invention does not change substantially over time. Empirical results, available from the authors upon request, confirm such assumption.

  11. The weights are needed in order to deal with heteroskedasticity. Since each observation is obtained dividing the number of citations by the product of the total amount of potentially citing and potentially cited patents corresponding to a given cell, it has been weighted by (n tpic n Tg )1/2, following Jaffe and Trajtenberg (1996) and Hall et al. (2001). When estimating the model without weights, a Breusch–Pagan test strongly reject the null of homoskedasticity by a χ2(9) = 115945.21, with a p-value = 0.000.

  12. One of the limitations of this model is the impossibility to replicate the great number of zeros in the data. A possible alternative could be to use count data models, which takes the (inflated) zeros as possible response. However, we prefer to use the double exponential approach in that it allows to distinguish between the different diffusion and decay behaviors of knowledge from university, public laboratory and corporate patents.

  13. The complete table with all estimated coefficients are available from the authors upon request.

  14. A Wald-type test strongly rejects the null H 0:αUniv = a Companies by a χ2(1)  = 37.55, with a corresponding p-value = 0.0000. The same holds for the obsolescence coefficients, where the null is rejected by a χ2(1)  = 9.23, with a corresponding p-value = 0.0024.

  15. Some authors suggest that patent examiners tend to identify favorite examples of prior art (Cockburn et al. 2002). As a result they could cite a stable subset of previous patents and publications. Cockburn et al. (2002) claim that many of these preferred documents are university or public sector patents, which may also be written less strategically than those for private firms.

  16. Since our data are based on the institutional characteristic of the applicants we are considering only university and PRO owned patents and not all the university and PRO invented patents. In order to single out university invented patents it is required to match databases of university and PRO researchers with the inventors name listed in the patents. Preliminary evidence for Italy, France and Sweden suggests that this paper underestimates the role of universities and PROs in the production of patents in European countries (Balconi et al. 2004; Lissoni et al. 2007).

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Acknowledgments

We would like to thank Pierre Mohnen, Mariana Mazzucato and two anonymous referees for their useful comments and suggestions.

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Authors and Affiliations

  1. University of Milan, Milan, Italy

    E. Bacchiocchi

  2. University of Insubria, Varese, Italy

    F. Montobbio

  3. CESPRI - Bocconi University, Milan, Italy

    F. Montobbio

Authors
  1. E. Bacchiocchi
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  2. F. Montobbio
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Corresponding author

Correspondence to F. Montobbio.

Appendix

Appendix

1.1 Data and classifications

Countries are defined on the basis of the address of the first inventor in the patent application. We have used six countries : 1. Germany, 2. France, 3. Italy, 4. United Kingdom, 5. Japan, 6. United States.

The Technological Fields are the US NBER categories as in Hall et al. (2001). We first used 30 technological classes based on the Annex III-A of OECD (1994). This classification aggregates all (primary) IPC codes (version 7 used at the EPO) into 30 technological classes. A concordance table has been created by the authors that reaggregates the 30 classes into the NBER Fields. Further details are available from the authors upon request.

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Bacchiocchi, E., Montobbio, F. Knowledge diffusion from university and public research. A comparison between US, Japan and Europe using patent citations. J Technol Transf 34, 169–181 (2009). https://doi.org/10.1007/s10961-007-9070-y

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