Universities and industrial innovation empirical evidence
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UNIVERSITIES AND INDUSTRIAL INNOVATION: EMPIRICAL EVIDENCE. Natalia Zinovyeva Foundation for Applied Economic Research - FEDEA. Lisbon strategy: Investing in R&D, Boosting innovation, Better education and skills Close interactions between government, university and industry

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UNIVERSITIES AND INDUSTRIAL INNOVATION: EMPIRICAL EVIDENCE

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Universities and industrial innovation empirical evidence

UNIVERSITIES AND INDUSTRIAL INNOVATION: EMPIRICAL EVIDENCE

Natalia Zinovyeva

Foundation for Applied Economic Research - FEDEA


Motivation

Lisbon strategy: Investing in R&D, Boosting innovation, Better education and skills

Close interactions between government, university and industry

Which form should they take?

Motivation


Theoretical arguments

Theoretical arguments

  • Market failures

    • Suboptimal allocation of resources to knowledge production

    • Causes:

      • Fundamental uncertainty in research outcomes

      • Non-proprietary nature of knowledge

      • Information asymmetries between users and producers

  • System failures

    • Inefficiencies in interaction among agents

    • Suboptimal supportive structures

    • Causes:

      • Lock-ins in existing networks

      • Structural inertia

      • Failures in infrastructural provision


Correction strategies

Correction strategies

  • Government as a risk taker: direct procurement of research

  • Incentives: public procurement, taxation relives, university labs, government funding

  • IPR legislation

  • Enabling collaborative schemes

  • Collaboration between university and industry


Evidence on system failure

Evidence on system failure?

  • Edwin Mansfield (1991, 1998)

  • 2 samples of 76 major American firms for 1975-1985 and 1986-1994


Role of university in knowledge clusters

Silicon Valley: beginning in 1938 from Hewlett-Packard - a spin-off of Stanford University

Route 128 knowledge cluster: since the 1930s MIT has spawned 4,000 companies employing more than a million people

Do these cases represent a rule or an exception?

Role of university in knowledge clusters


Methodologies

Descriptive evidence

Surveys and case studies

Econometric studies

Methodologies


1 descriptive evidence on technology transfer

Claim that university presence is important

Route 128 (Dorfman 1983)

Silicon Valley and Route 128 (Saxenian 1985)

Cambridge, UK (Wicksteed 1985)

Counter-examples

High technology centers in England (Breheny and McQuaid 1987)

Some US centers (Colorado Springs and Portland) (Rogers and Larsen 1984)

John Hopkins University (Feldman 1994)

1. Descriptive evidence on technology transfer


2 survey and case study evidence effect of university on firms location

Several studies find that firms consider university presence as an important factor for firms’ location

Premus 1982: 60% of surveyed US firms

Schmenner 1982: 52%

Other studies on the US

Counter-examples

Howells 1984: only 2.6% of firms in pharmaceuticals in England indicate university as their first reason for choosing location, ¾ that it is not significant

Gripaios et al. 1989: only 9% indicate any university effect in the Plymouth region, England.

2. Survey and case study evidence: Effect of university on firms’ location


2 survey and case study evidence effect on innovation activity

2. Survey and case study evidence:Effect on innovation activity

  • Mansfield (1991, 1998)


3 econometric evidence effect of university in high tech location

Claim that university is important for high technology location

Glasmeier (1991)

247 US metropolitan statistical areas in 1982

Dependent variable: High tech employment

University variable: number of colleges

Controls: climate, housing prices, property tax, wage rate, migration, educational options, freeway density, poverty rate,..

Method: OLS

No effect on high tech location

(Markusen et al, 1986)

264 US metropolitan statistical areas in 1977

Dependent variable: High tech employment

University variable: university R&D

Controls: climate, housing prices, property tax, educational options, freeway density, business services

Method: OLS

3. Econometric evidence: Effect of university in high-tech location


3 factors determining the effect of university on high tech location cont

Sectors:

Some evidence of positive effect in various sectors:

Electrical and Electronic Equipment (Bania et al., 1993)

Biotechnology (Audretsch and Stephan 1996, Zucker et al. 1998)

Ambiguous evidence

Chemicals and instruments

Ownership structure

Headquarters consider important proximity to universities, branch plants – no (Malecki, 1986)

Firm size

Big firms tend to locate close to universities (Rees 1991)

3. Factors determining the effect of university on high-tech location (Cont.)


3 econometric evidence effect of university on private innovation activity

Positive effect of universities in the US:

State level (Jaffe, 1989)

29 states in 8 years

Dependent variable: Number of industrial patents

Independent variables: Academic R&D investment

Private R&D investment

Controls: Population size, year dummies

Method: 3SLS.

Instruments: number of private and public universities (in Academic R&D equation) and manufacturing VA (in Private R&D equation)

Feldman (1994) and Feldman and Florida (1994) confirm the findings of Jaffe using innovation count data

Metropolitan statistical areas (Bania et al. 1992, Varga 1998)

Within a metropolitan area (Sivitanidou and Sivitanides 1995)

3. Econometric evidence: Effect of university on private innovation activity


3 econometric evidence effect of university on private innovation activity cont

3. Econometric evidence: Effect of university on private innovation activity (Cont.)

  • Positive effect of universities in Europe:

    • France

      • Autant-Bernard 2001

    • Austria

      • Fisher and Varga 2002

    • Italy

      • Audretsch and Vivarelly 1994

      • Cowan and Zinovyeva 2007


More on the channels of technology transfer

More on the channels of technology transfer

  • The most important channels through which firms benefit from university research: publications, conferences, informal information channels, and consulting (Cohen et al., 1998).

  • Informal interactions (Bercovitz and Feldman, 2006)

  • Even in pharmaceuticals firms heavily rely on these channels (Gambardella, 1995)


How academic research differentiate from any other r d company

Different researchers: Balconi, Breschi, Lissoni (2004)

Academic inventors are more persistent and more central

Networks hosting scientists are larger and more connected than other research networks

Different research output: Henderson, Jaffe, Trajtenberg (1998)

University patenting between 1965 and 1988

Until mid-1980s university patents were more cited, cited by more technologically diverse patents

Why they are different?

Self-selection

Other selection

Incentives

Tasks

How academic research differentiate from any other R&D company?


Summary

Academic basic knowledge takes long time before being used in innovation activity

Both basic and applied academic research is important for industrial innovation activity (local, regional, national) in the short run

Academic research output has the features of general purpose knowledge/technology

Summary:


Possible risks of increasing university industry collaboration and challenges for future research

Possible risks of increasing university-industry collaboration and challenges for future research

  • Crowding out of basic research

  • Limiting the freedom of academic research

  • Decline in scholar productivity

  • Affecting the culture of university oriented on public good creation

  • Restriction on the dissemination of research results (Example: patents on research tools (genetic materials) in biology)

  • High cost of administrative support and reorganization

  • Science becoming inappropriate for graduate research

  • Decreasing quality of education

    • Public institutional expenditures on instruction declined by 6%, - on research rose 4%

  • Crowding out of private research (professors as cheap labor for industry)


Thank you for your attention

Thank you for your attention!

Contact: Natalia Zinovyeva

[email protected]

Foundation for Applied Economic Research (FEDEA)

c/Jorge Juan, 46

28001 Madrid


Summary of identification problems in the econometric analysis

Summary of identification problems in the econometric analysis

  • University effects  Industrial innovation

    (Universities might be created in response to the needs of regional industry in human capital)

  • Academic research  Industrial innovation

    (Academics might themselves benefit from interaction with innovative industrial sector by getting more and better research ideas and opportunities)

  • Location unobserved heterogeneity

  • Separating the direct effect of academic research from the effect of teaching and ultimately graduates’ human capital


Cz 2007 hypotheses

CZ 2007: Hypotheses

  • New universities have a positive effect on regional innovation in the short-run.

  • Some of this effect corresponds to the spillover effect via traditional channels like academic patenting and publishing activity.

  • New universities might push firms to rely more on collaboration with academics as a source of scientific knowledge rather than on own effort on searching the scientific literature.


Main features of the paper

Main features of the paper

  • The effect of new university departments in sciences, medicine and engineering in Italy during 1985-2000

  • Short-term effect of new university departments: the channel corresponding to graduates’ human capital is excluded

  • According to Italian Ministry of Education the decision about the distribution of university departments across Italian regions was largely independent of any features of regional economy:


Observatory for the evaluation of the university system 1997

Observatory for the evaluation of the university system: 1997

“The rule by which new institutions were created does not seem to have followed the logic of tailoring university development to territorial specificities. It seems not to have made reference to a demand for university education, nor to the demand for graduates or to existing infrastructure. […] So, […] at least to a large extent, the prevalent logic was the one of incremental expansion and distribution "by drops of rain", without giving evaluation opportunity to the suppressed initiatives […]”


Universities and industrial innovation empirical evidence

Number of new departments open between 1984 and 2000 by regional demand for corresponding professions


Number of new departments sciences medicine engineering

Number of new departments: Sciences, Medicine, Engineering


The estimation models

The estimation models

With fixed effects for each university and discipline:


Universities and industrial innovation empirical evidence

Data

  • 20 Italian regions during 1985-2001

  • Number of university departments (Sciences, Chemistry and Pharmacy, Agriculture, Medicine, Veterinary, Engineering, Architecture): Italian National Statistical Bureau

  • Regional economic characteristics: GDP, population, R&D expenditure

  • Innovation activity from KEINS EP-INV database (Lissoni, Sanditov, Tarasconi, 2006):

    • Academic and Industrial Patents

    • Patent citations

    • Non-Patent Literature (NPL) citations

  • Academic publications: ISI Thompson Science Citation Index


Annual change in the number of industrial patents

Annual change in the number of industrial patents


Number of patents conditional negative binomial

Number of patents: conditional negative binomial


Academic publications conditional negative binomial

Academic publications: conditional negative binomial


The channels of the university effect on short term industrial patenting

The channels of the university effect on short-term industrial patenting


Evidence of crowding out non patent literature citation intensity by industrial patents

Evidence of crowding-out? Non-patent literature citation intensity by industrial patents


Summary and conclusions of cz 2007

Summary and conclusions of CZ 2007

  • New universities positively affect regional innovation activity:

    • Industrial Patenting responds within 3-4 years

    • Academic patenting and scientific activity increases already after 1-2 years

  • Part of the increase in industrial patents (around 30 percent) is explained by the corresponding growth of academic research

  • Negative correlation between new universities and NPL citation: potential crowding-out of resources devoted by industry to searching the academic literature. If this is the case, it might suppress firms’ continued development of absorptive capacity.


Descriptive evidence

Descriptive evidence:

  • 1988-2003 academic patents quadruple: 800 to 3200

  • 1992-2003 number of US scientific publications flat, causing US decline in world article output from 38% to 30%

  • 1988-2003: number of US patents referenced in scientific articles increased dramatically


Universities and industrial innovation empirical evidence

Share of industrial R&D expenditures in total university R&D expenditures and the share of expenditures spent on basic research, US 1953-2006

SOURCE:  National Science Foundation/Division of Science Resources Statistics, Survey of Research and Development Expenditures at Universities and Colleges, FY 2006.


Universities and industrial innovation empirical evidence

Zucker, Darby, several co-authors (1998a, 1998b, 2000)

Star scientists collaborating with or employed by firms, or who patent, have significantly higher citation rates than pure academic stars

Thursby and Thursby (2007)

3,241 faculty from six major US universities from 1983 through 1999

probability that a faculty member will disclose an invention increased tenfold, the portion of research that is published in “basic” journals remained constant

Link, Siegel, Bozeman (2006)

Academics who allocate a relatively higher percentage of their time to grants-related research are more likely to engage in informal commercial knowledge transfer

Lowe and Gonzalez-Brambila (forthcoming, 2007)

15 research institutes

Faculty entrepreneurs in general are more productive researchers than control groups in terms of publication rate and the impact of their publications

Productivity does not decrease following the formation of a firm

Individual level evidence: how academic scientific productivity changes after academics’ engagement in collaboration with industry?


The relationship between academic research teaching quality and graduates employment outcomes

Sylos Labini and Zinovyeva (2007)

Several surveys of Italian university graduates in 1995-2001

Rich information on individual quality and socioeconomic background

No negative effect of academic patenting activity at the faculty level on teaching quality and graduates’ employment outcomes

The Relationship between Academic Research, Teaching Quality and Graduates’ Employment Outcomes


Public and private r d complements or substitutes

David, Hall, Tool (2000)

Concern: Main focus in the literature is on publicly funded research performed in academic institutions, and nothing on its comparison with the impacts of publicly sponsored R&D conducted under contract by industrial corporations

Public funding of research might “crowd out” private research via its generic impacts on the price of research and development inputs that are in inelastic supply

Not taking onto account price (researchers’ wage) effect leads to overestimation of positive effects of public R&D expenditures

“Investment displacement”: It is likely to exist the lobby for subsidies for projects with high private marginal rates of return, which would enable firms correspondingly to reduce their own outlays (because R&D activities are heterogeneous rather than homogeneous)

Public and Private R&D: complements or substitutes?


Bibliography

Bibliography

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  • Audretsch D. and Vivarelli M. (1994): Small Firms and R&D Spillovers: Evidence from Italy, Discussion Paper 953, Centre for Economic Policy Research

  • Autant-Bernard C. (2001): Science and Knowledge Flows: Evidence from the French Case, Research Policy 30, 1069-107

  • Balconi, Breschi,Lissoni (2004): Networks of inventors and the role of academia: an exploration of Italian patent data, Research Policy, Elsevier, vol. 33(1), pages 127-145

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  • Bania N., Eberts R. and. Fogarty M (1993): Universities and the Startup of New Companies: Can We Generalize from Route 128 and Silicon Valley? The Review of Economics and Statistics 75, 761-766

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  • David, P, B. Hall, A. Tool (2000). "Is public R&D a complement of substitute for private R&D? A review of the econometric evidence", Research Policy, Elsevier, vol. 29(4-5), pages 497-529

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Bibliography cont

Bibliography (Cont.)

  • Feldman M. and Florida R. (1994): The Geographic Sources of Innovation: Technological Infrastructure and Product Innovation in the United States, Annals of the Association of American Geographers 84, 210-229

  • Fischer M. and Varga A. (2002): Spatial Knowledge Spillovers and University Research: Evidence from Austria, Annals of Regional Science

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  • Henderson, R., A. B. Jaffe and M. Trajtenberg (1998): Universities As A Source Of Commercial Technology: A Detailed Analysis Of University Patenting, 1965-1988, The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 119-127.

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  • Lowe, R. and C. Gonzalez-Brambila (2007): Faculty Entrepreneurs and Research Productivity, The Journal of Technology Transfer, Springer, vol. 32(3), 173-194.

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Bibliography cont1

Bibliography (Cont.)

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  • Sivitanidou R. and Sivitanides P. (1995): The Intrametropolitan Distribution of R&D Activities: Theory and Empirical Evidence, Journal of Regional Science 25, 391-415

  • Sylos Labini, M. and N. Zinovyeva (2007): The Relationship between Academic Research, Teaching Quality and Graduates’ Employment Outcomes, paper for the EALE conference, Oslo, 20 – 22 September.

  • Thursby, J. and M. Thursby (2007): Knowledge Creation and Diffusion of Public Science with Intellectual Property Rights. "Intellectual Property Rights and Technical Change," Frontiers in Economics Series, Vol. 2, Elsevier Ltd.

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