Scholarship and Inventive Activity in the University: Complements of Substitutes? By Brent Goldfarb, Gerald Marschke and Amy Smith

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Scholarship and Inventive Activity in the
University Complements of Substitutes?By Brent
Goldfarb, Gerald Marschke and Amy Smith

• Discussant
• Nicola Lacetera
• Case Western Reserve University
• Department of Economics

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The paper

• Question ?0?
• Data novel panel from Stanfords biochemistry
  and electrical engineering department
• 1990-2000, all tenure track faculty
• Scientific productivity publication count
  impact factor weighted
• Inventive activities Disclosed inventions with
  commercial potential
• Teaching taught credits
• Statistical methods
• Count models (Poisson) OLS
• Endogeneity FE, IV VC disbursed, revenues of
  colleagues. 2sls, GMM
• Findings gt0
  in Biochem, 0 in El. Eng.

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Contribution The question

• Relation b/w scientific and inventive activities
  hot topic in the Economics of Science
• Are commercial activities compatible with the
  production of good science?
• Can universities have multiple missions?
  Research, teaching, commerce?
• Political and managerial relevance
• Hicks-Hamilton (1999), Agrawal-Henderson (2002),
  Geuna-Nesta (2003), Azoulay et al. (2004a,
  2004b), Van Looy et al. (2004), Stephan et al.
  (2005), Markiewicz-DiMinin (2005), Breschi et al
  (2005), Calderini-Franzoni (2005), Calderini et
  al. (2005), Murray-Stern (2005) Henderson et al.
  (1998), Mowery et al. (2003)

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Contribution Limits in current studies

• Data
• Publications, Citations, Impact factor
  Scientific value, truncation, relevant journals,
  reasons for citations
• Patents, citations squeeze existing database,
  but appropriate?
• Most inventions not patented, citations by
  examiners
• How about teaching?
• 444
• Methods and Techniques
• Simultaneity, individual heterogeneity,
  unobservables. Progress, lately
• Theory
• What should we expect? How do the different
  incentives interact?
• How to model multiple missions, peer effects,
  career concerns, etc.?

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Contribution The data

• Tenure track Stanford faculty, 1990-2000, two
  depts.
• Publications, and I.F.-weighted avoid
  truncation, consider quality
• Disclosed inventions, NOT patent data at last!!
  More comprehensive
• Teaching record other major activity to
  consider!
• Small number. 15 scientists in Biochem
• How about post-docs? Big deal in Biochem. and
  Engineering
• Stanford representative of average/median
  university? Can generalize complementarity?
  Faculty quality, resources, TLO/TTO efficiency
• I.F. from ISI keep journals constant?
• Disclosed inventions with commercial potential
  selected sample?

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Contribution Methods and techniques

• Take Endogeneity seriously -- GREAT!
• FE, IV, GMM Wooldrigde, Arellano-Bond.
  State-of-the-art techniques
• Identification
• Social interactions and peer effects tricky
  first stage (Mansky 2002)
• Small sample bias of IV techniques (Hausman-Hahn
  2002). Estimates bounce
• Strength of IV show first stage (R2)? Show
  Hausman (1978) test?
• Orthogonality What if

Scientist ability, arrival of a star, major
finding
VC attracted (Zucker et. al)
VC activity, revenues of colleagues
Technological/ scientific shock, opportunity
Publications
More inventions
Inventions
Buy out teaching
Big grant
More research
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Contribution Theory

• Not much theoretical discussion not the aim of
  this paper, but
• What are the underlying theoretical/behavioral
  assumptions?
• Is science-invention the appropriate tradeoff?
  Why not together (biotech)? How about science
  and innovation, or entrepreneurship?
• Are the results surprising? Expected (especially
  after biotech)?
• Why the difference between departments?

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Contribution Summary

• Relevant improvement in data collection
• Concerns selectivity (Stanford, valuable
  inventions), variable construction (teaching
  variable, I.F.), small sample
• Major advances in identification
• Concerns Identification strategy, small sample
• Intriguing questions raised, e.g. difference
  among depts. and measures
• Major contribution!
• But keep an eye at concerns reinforce your
  results, explain them, and find space in a quite
  crowded research area