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© Fraunhofer ISI Seite 1 Academic patenting in Germany A new comprehensive approach for the identification and analysis of academic patents Friedrich Dornbusch

Academic patenting in Germany

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Academic patenting in Germany. A new comprehensive approach for the identification and analysis of academic patents Friedrich Dornbusch. Content and aim of the presentation. Brief introduction of the recently developed approach to indentify academic patents in Germany: - PowerPoint PPT Presentation

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Page 1: Academic patenting in Germany

© Fraunhofer ISISeite 1

Academic patenting in Germany

A new comprehensive approach for the identification and analysis

of academic patents

Friedrich Dornbusch

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1. 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?

2. 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?

Content and aim of the presentation

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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.

Background

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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.

New approach towards identification of patents with academic background

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The matching algorithm

Organization matching

Namematching Time windowmatching

Location matching

Classification matching

PATSTAT X Full strings of last-and first name

Priority year First two digits of the postcode

IPC classification =

WIPO 34SCOPUS Author affiliation

= university

Full strings of last-and first name

Publication year:One year time-lag and time-window

First two digits of the postcode

Scopus classification:fine-/ coarse-grained

x uni-inv = 1 if (a names match + b time match + c location match + d subject match)2) Organization 3) Names 4) Time 5) Location 6) Subject

Selection criteria

Recall Precision F-Scores

R=P (F1) P>R (F0,5)R>P (F2)

1-digit pc 0,76 0,63 0,69 0,65 0,73Standard criterion 2-digit pc 0,71 0,77 0,74 0,76 0,72

F-conc 0,71 0,52 0,60 0,55 0,661-digit pc, F-conc 0,64 0,82 0,72 0,78 0,67

High precision 2-digit pc, F-conc 0,59 0,93 0,72 0,83 0,64High recall 2-digit OR (1-digit pc + F-conc) 0,74 0,72 0,73 0,72 0,74

Recent improvement:

NUTS3 including a distance matrix implemented

Detailed description

in a forthcoming methodologi

cal paper

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Old method indicates falling numbers of academic patents.

New method indicates recovering numbers.

Sinking tendency of professors to indicate their title? (Anecdotic evidence)

Results for Germany – Totals ( s t a n d a r d c r i t e r i o n )

0

500

1000

1500

2000

2500

3000

2001 2002 2003 2004* 2005 2006 2007

Num

ber

DPMA NUTS3_30km Benchmark_PROF EPO NUTS3_30km

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Large firms unaffected

SMEs & Private

Other PROs unaffected

University-owned rising.

Academic patenting in Germany – Shares by different appl icant types ( s t a n d a r d c r i t e r i o n )

0.00

0.10

0.20

0.30

0.40

0.50

0.60

2001 2002 2003 2004 2005 2006 2007

Shar

es

Large firms SME Private University-owned Other PROs

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First steps towards analyzes of fil ing behavior in academic patenting

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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.

Influence of the local environment of universit ies on the fil ing behavior

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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).

Hypotheses and Data

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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.

Variables

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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 thetype of applicant (uni/sme/mne ) (N=1201).

Dataset c leaning

Patent_CNT Freq. Percen

t Cum.

1 921 72.63 72.632 280 22.08 94.723 39 3.08 97.79

4 28 2.21 100.00

Total 1,268

100.00

dV Freq. Percent Cum.

0 = Uni 373 31.06 31.061 = SME 200 16.65 47.71

2 = MNU 628 52.29 100.00

Total 1,201

100.00

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Summary statist ics

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)

Variable Obs Mean Std. Dev. Min Max

AVG_DIST_INVTEAM1 1080 82.94 107.28 0.00 728.87

HITEC_EMP1 1201 28.66 18.24 1.48 60.76

MEDHITEC_EMP1 1201 108.06 70.10 8.95 297.46

MEDLOTEC_EMP1 1201 49.13 30.63 8.95 158.56

LOWTECH_EMP1 1201 54.02 24.10 13.25 98.95PATINT_ELECT_ENG1 1201 0.38 0.25 0.04 0.79PATINT_INSTR1 1201 0.28 0.14 0.05 0.52PATINT_CHEM1 1201 0.40 0.24 0.09 1.24PATINT_MED1 1201 0.53 0.31 0.07 1.27PATINT_OTHER1 1201 0.13 0.06 0.01 0.27AGGLO_DUM 1201 1.50 0.78 1.00 3.00

SME_REG2 1201 14.44 6.07 2.93 29.10

MNE_REG2 1201 0.65 0.37 0.07 1.54SCIREG_UNI 1176 8.96 10.40 -37.07 36.69PUB_INT_UNI 1201 0.02 0.01 0.00 0.04

TH_P_FUNDbySTAFF1 1201 38.82 9.55 11.41 65.43

STAFF_UNI1 1201 2749.61 1276.56 31.00 5349.00PAT_FAM_SIZE 1201 2.54 1.85 1.00 15.00PAT_NPL_DUM 1201 0.44 0.50 0.00 1.00uni_sme_mne 3 1201 1.21 0.89 0.00 2.00

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Pre l im inary mode l : Mu l t inomina l Log i t – dV: un i / sme/mnu

Level of significance: *** = 0.01; ** = 0.05; * = 0.10Notes: 1 = Units are indicated in thousands; 2 = Units are indicated in hundreds

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.

AVG_DIST_INVTEAM 0.465 *** 0.509 *** -0.101 *** 0.018 0.084 ***se 0.154 0.150 0.027 0.016 0.029

HITEC_EMP 0.032 ** 0.047 *** -0.009 *** 0.000 0.009 ***se 0.016 0.014 0.003 0.002 0.003

MEDHITEC_EMP 0.001 0.023 *** -0.004 *** -0.002 ** 0.006 ***se 0.007 0.006 0.001 0.001 0.001

MEDLOTEC_EMP -0.028 * 0.022 * -0.002 -0.006 *** 0.008 ***se 0.015 0.012 0.002 0.002 0.003

LOWTECH_EMP -0.061 *** -0.017 0.005 ** -0.007 ** 0.001 0.023 0.015 0.003 0.003 0.004

PATINT_ELECT_ENG 1.628 5.799 *** -0.985 *** -0.301 1.286 ***se 1.322 1.544 0.258 0.194 0.367

PATINT_INSTR -6.715 *** -8.830 *** 1.696 *** -0.120 -1.576 ***se 2.465 1.983 0.332 0.329 0.455

PATINT_CHEM 1.129 * 1.646 *** -0.311 *** 0.006 0.305 ***se 0.677 0.444 0.079 0.094 0.112

PATINT_MED -2.851 ** -1.133 0.310 ** -0.287 -0.023 se 1.368 0.831 0.154 0.178 0.214

PATINT_OTHER 10.099 ** 10.262 *** -2.078 *** 0.453 1.625 * se 4.762 3.763 0.659 0.668 0.932

DUM_CORE_REGse

DUM_URB_REG -0.909 *** 0.466 * -0.048 -0.125 *** 0.173 ***se 0.279 0.273 0.048 0.022 0.054

DUM_PERI_REG 1.368 *** 0.633 * -0.148 *** 0.152 * -0.004 se 0.477 0.336 0.045 0.091 0.094

SME_REG 0.001 -0.180 *** 0.028 ** 0.016 -0.045 ***se 0.092 0.069 0.013 0.012 0.016

MNU_REG 6.942 *** -1.906 -0.023 1.121 *** -1.098 ** se 2.160 1.847 0.316 0.295 0.464

SCIREG_UNI -0.036 ** -0.024 ** 0.005 *** -0.003 -0.003 se 0.015 0.012 0.002 0.002 0.003

PUB_INT_UNI -0.229 0.457 ** -0.061 -0.073 * 0.134 ** se 0.230 0.233 0.038 0.039 0.061

TH_P_FUNDbySTAFF 0.009 -0.030 ** 0.004 ** 0.004 * -0.008 ***se 0.015 0.012 0.002 0.002 0.003

STAFF_UNI -0.059 0.073 -0.009 -0.015 0.023 se 0.152 0.170 0.029 0.023 0.041

PAT_FAM_SIZE 0.033 0.266 *** -0.043 *** -0.020 ** 0.063 ***se 0.079 0.077 0.014 0.008 0.015

PAT_NPL_DUM -0.466 * -1.015 *** 0.181 *** 0.026 -0.207 ***se 0.280 0.235 0.041 0.039 0.054

FIELD CONTROLSse

YES

REFERENCE REFERENCE

MARGINAL EFFECTSSME (1) MNE (2) UNI (0) SME (1) MNE (2)

YES

COEFFICIENTS (BASE UNI (0))N = 1056 | r²_p = 0.221LR(32): 466.502 | Prob > LR: 0

Regi

onDi

stU

nive

rsity

Pate

nt

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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?

Prel iminary results and conclusions

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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.

(Much) work remaining

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Thank you for your attention!

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Backup

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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.

New approach towards identification of patents with academic background

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Impact of different select ion cr iter ia

0

1000

2000

3000

4000

5000

6000

7000

1996 1997 1998 1999 2000 2001 2002 2003 2004* 2005 2006 2007

Num

ber

C-conc F-conc 1-digit pc

2-digit pc OR (1-digit pc + F-conc) 2-digit pc Benchmark

1-digit pc + F-conc 2-digit pc + F-conc

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Academic patenting in Germany by region – totals ( y e a r s ’ 0 5 t i l l ’ 0 7 b y s t a n d a r d c r i t e r i o n )

=0=1-10=11-100=101+

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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).

Example for the dataset structure

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Pre l im inary mode l : Mu l t inomina l Log i t – dV: un i / sme/mnu

Level of significance: *** = 0.01; ** = 0.05; * = 0.10Notes: 1 = Units are indicated in thousands; 2 = Units are indicated in hundreds

CATEGORIES (BASE UNI (0))

AVG_DIST_INVTEAMse

HITEC_EMP 0.024 0.045 *** -0.008 *** -0.001 0.009 ***se 0.015 0.013 0.002 0.002 0.003

MEDHITEC_EMP 0.004 0.021 *** -0.003 *** -0.001 * 0.005 ***se 0.006 0.006 0.001 0.001 0.001

MEDLOTEC_EMP -0.027 * 0.025 ** -0.002 -0.006 *** 0.009 ***se 0.015 0.012 0.002 0.002 0.003

LOWTECH_EMP -0.053 ** -0.006 0.003 -0.007 ** 0.004 0.024 0.017 0.003 0.004 0.004

PATINT_ELECT_ENG 2.314 * 6.367 *** -1.063 *** -0.284 1.348 ***se 1.369 1.426 0.235 0.210 0.344

PATINT_INSTR -6.278 *** -8.306 *** 1.541 *** -0.100 -1.442 ***se 2.431 1.848 0.320 0.337 0.429

PATINT_CHEM 1.271 * 1.367 *** -0.265 *** 0.051 0.214 ** se 0.740 0.438 0.086 0.099 0.104

PATINT_MED -2.958 ** -0.710 0.246 -0.358 * 0.112 se 1.321 0.859 0.151 0.186 0.224

PATINT_OTHER 8.878 * 7.798 ** -1.588 *** 0.524 1.064 se 4.682 3.594 0.613 0.705 0.921

DUM_CORE_REGse

DUM_CONC_REG -0.770 *** 0.675 ** -0.079 -0.134 *** 0.213 ***se 0.276 0.322 0.050 0.021 0.058

DUM_PERI_REG 1.272 *** 0.333 -0.109 ** 0.182 ** -0.073 se 0.448 0.349 0.049 0.089 0.092

SME_REG 0.037 -0.185 *** 0.026 ** 0.023 ** -0.049 ***se 0.092 0.069 0.013 0.012 0.015

MNU_REG 5.187 *** -2.601 0.145 1.001 *** -1.146 ** se 1.964 1.842 0.296 0.309 0.473

SCIREG_UNI -0.025 * -0.020 0.004 * -0.002 -0.002 se 0.015 0.013 0.002 0.002 0.003

PUB_INT_UNI -0.375 0.355 -0.035 -0.089 * 0.124 * se 0.260 0.255 0.038 0.049 0.071

TH_P_FUNDbySTAFF 0.002 -0.032 *** 0.005 ** 0.003 -0.008 ***se 0.015 0.012 0.002 0.002 0.003

STAFF_UNI -0.100 -0.065 0.014 -0.008 -0.006 se 0.155 0.156 0.026 0.025 0.039

PAT_FAM_SIZE 0.096 0.312 *** -0.051 *** -0.016 * 0.068 ***se 0.080 0.074 0.013 0.009 0.015

PAT_NPL_DUM -0.553 ** -1.041 *** 0.184 *** 0.019 -0.203 ***se 0.271 0.241 0.039 0.041 0.057

FIELD CONTROLS YESse

N = 1176 | r²_p = 0.205LR(32): 481.914 | Prob > LR: 0

MARGINAL EFFECTSSME (1) MNE (2) UNI (0) SME (1) MNE (2)

Dist

Regi

on

REFERENCE

Uni

vers

ityPa

tent

YES

REFERENCE