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

The paper  Question: ≶ 0?  Data: novel panel from Stanford’s biochemistry and electrical engineering department , 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: >0 in Biochem, =0 in El. Eng.

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)

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.?

Contribution – The data  Tenure track Stanford faculty, , 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?  Teaching credits: lab ≠ classroom? Why dummy in regressions? Variance?

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 (R 2 …)? Show Hausman (1978) test? Orthogonality: What if… Technological/ scientific shock, opportunity VC activity, revenues of colleagues Publications Inventions Scientist ability, arrival of a “star”, major finding VC attracted (Zucker et. al…) More inventions Big grant Buy out teaching More research

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?

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…