Academic patenting in Germany

1. Academic patenting in Germany A new comprehensive approach for the identification and analysis of academic patents Friedrich Dornbusch

2. Content and aim of the presentation • Brief introduction of the recently developed approach to indentify academic patents in Germany: • How does the matching algorithm work? • Briefly two descriptive results: What are the main trends in academic patenting since the abolition of the “Hochschullehrerprivileg” in 2002? • First step towards analyzes of the filing behavior of applicants - focusing on the relationship between universities and firms: • How does the regional environment influence the filing and respectively the co-operation behavior (measured by academic patents) between universities and firms?

3. Background • Like many European countries Germany implemented a Bayh-Dole like IPR-regime (Geuna/Rossi 2011) in 2002 (abolishment of „Hochschullehrerprivileg“): Universities gain the right and responsibility to exert IPR on “their” inventions and to exploit it. Emergence of new exploitation infrastructure and public funding programs (e.g. Schmoch 2007; von Ledebur 2008). • But large shares of academic patents are still not filed by universities (university invented vs. university-owned) (e.g. Geuna/Rossi 2011; Thursby et al. 2009; Lissioni et al. 2008; Geuna/Nesta 2006): • And consequences for the co-operation patterns of universities and simple transferability of Bayh-Dole Act to European countries are still in need of clarification (Bruneel et al. 2010; Valentin et al. 2007; Fabrizio 2007). • Having methodological problems with regard to identification of academic patents in mind, we development of a new approach for identification and analysis of academic patenting in Germany (and other European countries). • Detailed description in: Dornbusch, F; Schmoch, U.; Schulze, N.; Bethke, N. (forthcoming) - Identification of university-based patents: A new large scale approach.

4. New approach towards identification of patents with academic background • Previous approaches mainly based on keyword searches (Schmoch 2007; Czarnitzky et al. 2007; 2011; von Ledebur 2009; von Proff et al. 2011) or matching of lists (Thursby et al. 2009; Lissoni et al. 2008; 2009). • In Germany we do not have official lists on academic staff available and the search for the PROF-title is based on estimations: • Making analyzes on institutional level difficult • Basic idea of our approach is to test for identical names of authors of scientific publications with university affiliation and inventors on patent filings. • Data sources: PATSTAT and SCOPUS • Main problem: Large datasets  danger of homonyms  need to use different selection criteria.

5. The matching algorithm Recent improvement: NUTS3 including a distance matrix implemented Detailed description in a forthcoming methodological paper

6. Results for Germany – Totals (standard criterion) • Old method indicates falling numbers of academic patents. • New method indicates recovering numbers. • Sinking tendency of professors to indicate their title? (Anecdotic evidence)

7. Academic patenting in Germany – Shares by different applicant types (standard criterion) • Large firms unaffected • SMEs & Private • Other PROs unaffected • University-owned rising.

8. First steps towards analyzes of filing behavior in academic patenting

9. Influence of the local environment of universities on the filing behavior • Universities as local knowledge hubs (Youtie/Shapira 2008), sources for localized knowledge spillovers and collaborations (e.g. Jaffe 1989; Anselin et al. 1997; Laursen et al. 2011). • Counter question: How does the profile of universities local environment influence their filing and respectively co-operation behavior (measured in academic patents) with firms distinguishing between SMEs and large firms? • Testing for: • Geographical distance: Due to higher resource endowments large firms are more likely to bridge greater distances in order to get access to outstanding university research and smaller firms more likely to depend on local knowledge provided by universities(e.g. Bodas-Freitas et al. 2010; Tödtling 2009; Torre 2008; Asheim/Coenen 2005). • Local knowledge base: The pool and type of local knowledge (embodied in employees of local firms) is likely to influence whether if the university finds cooperation partners with adequate absorptive capacity in the region. In doing so, co-operations with SMEs are expected to underlie stronger influences of the knowledge base than with large firms (e.g. Ostergard 2009; Asheim et al. 2007; Agrawal et al. 2006). • Local technological profile: Besides the knowledge pool test for the local technological profiles influence on the co-operation and filing behavior in academic patenting.

10. Hypotheses and Data • H1: The chance for cooperation with MNEs rises with rising distance. • H2: The larger the knowledge base (in the form of highly qualified personnel) in the region the larger the tendency to cooperate with local firms, especially SMEs. • H3: The type of local technological regime influences the tendency to cooperate with SMEs and MNEs in different ways (exploratory hypothesis). • Dataset on level of single academic patent applications indicating different applicant types (UNI, SME, MNE) • Complemented with: • Official sources: Eurostat, Destatis, Bundesinstitut für Bau-, Stadt- und Raumforschung (BBSR). • EUMIDA-dataset for university characteristics. • Additional patent information from PATSTAT. • Additional bibliometric information on university level (SCOPUS).

11. Variables • dV: uni/sme/mne (categorial) • Independent Variables: • Distance in km (H1) • Number of persons in ht-sectors (H2) • Field specific patent intensity (patents/inhabitants) (H3) • Additionally controlling for: • Agglomeration effects: Dummies for core regions, concentrated regions, peripheral regions, local firm size structure. • University characteristics: Size, scientific regard, third party funds, publication intensity. • Patent characteristics: Non patent literature (proxy for intensity of the science link), patent family size.

12. Dataset cleaning • Selection of basic dataset by high precision criterion in order to maximize the validity. • Excluding Fachhochschulen (polytechnics) - only universities in the dataset. • Priority year 2007 • Drop of Patents appearing more than twice to avoid over overestimation of single patents. • Dataset contains the involved universities and the type of applicant (uni/sme/mne ) (N=1201).

13. Summary statistics Data sources: Eurostat, Destatis, BBSR, EUMIDA Dataset, PATSTAT, SCOPUS. Notes: 1 = Units are indicated in thousands; 2 = Units are indicated in hundreds 3 = SME (employees<499); MNU (employees >500)

14. Preliminary model: Multinominal Logit – dV: uni/sme/mnu • Higher distance in inventor teams is positively associated with filings of MNEs. • Higher numbers of employees in local hi-tech sectors positively influence the filings of firms. • Higher numbers in med- and low-tech show negative effect on SMEs to file an academic patent. • Higher numbers in low-tech show a positive effect on UNIs to file an academic patent. • Different local technology regimes: • Referring to the BASE, SMEs have negative effect in INSTR and MED. • Turns into positive for UNIs in marginal effects. • Scientific excellence negative for firm filings, but publication intensity positive for MNEs. Level of significance: *** = 0.01; ** = 0.05; * = 0.10 Notes: 1 = Units are indicated in thousands; 2 = Units are indicated in hundreds

15. Preliminary results and conclusions • Strengthened position of universities: • Rise of universities largely on the expense of privately and SME-owned patents. • Due to resource constraints: “bargaining positions” / “business cycle effect” … ? • The type of local knowledge pool significantly affects filing of and co-operation with SMEs (controlling for agglomeration effects): • Indication that local networks (communities of practice) are important for universities and SMEs to co-operate  These are most likely to be established in hi-tech sectors, due to absorptive capacity and cognitive/science proximity of firm’s employees to university inventors. • Strong effect for distance in inventor teams of MNE-owned patents and strong effect for publication intensity. Indicating MNEs ability to screen distant universities profile?

16. (Much) work remaining • Theoretical (more fine grained) derivation of hypotheses • Specification of the regression approach: • “Interpretation problem”: dV„university owned“ as non “collaboration” ? • Influence of local factors with more fine-grained classifications of the technological profile and a specialization index. • Perhaps use of local technology cycle times • Testing: “Fit” of universities (science/innovation) and regions profile: Ideas?  • Testing: Interaction effects for local firm size structure and knowledge base. • Deepening interpretation of results and drawing of conclusions. • Just an idea: Conducting two or three case studies at German universities within different regions and with different profiles to verify the results qualitatively.

17. Thank you for your attention!

18. Backup

19. New approach towards identification of patents with academic background • Previous approaches: Keyword searches (Schmoch 2007; Czarnitzky et al. 2007; 2011; von Ledebur 2009; von Proff et al. 2011).; Matching lists (Thursby et al. 2009; Lissoni et al. 2008; 2009). • (Germany: No official lists existing and search for PROF-title based on estimations.) • New approach: • Test for identical names of authors with university affiliation and inventors on patents. • Data sources: PATSTAT and SCOPUS • Main advantages: • Enables semi-automated generation of matching lists. • Not dependent on indication of PROF-title (no estimations needed). • All research relevant staff included (no estimations needed). • Enables analyzes on institutional level. • Can be applied to different countries, enabling systematic analyses and comparisons . • Main problem: Large datasets  danger of homonyms  use of different selection criteria.

20. Impact of different selection criteria

21. Academic patenting in Germany by region – totals (years ’05 till ’07 by standard criterion) =0 =1-10 =11-100 =101+

22. Example for the dataset structure • Complemented with: • Data from offical sources like Eurostat and Destatis. • Data from EUMIDA for university characteristics. • Additional patent information from PATSTAT. • Additional bibliometric information on university level (SCOPUS).

23. Preliminary model: Multinominal Logit – dV: uni/sme/mnu Level of significance: *** = 0.01; ** = 0.05; * = 0.10 Notes: 1 = Units are indicated in thousands; 2 = Units are indicated in hundreds