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Discussant: Nicola Lacetera Case Western Reserve University Department of Economics

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. Question : ≶0 ?

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Discussant: Nicola Lacetera Case Western Reserve University Department of Economics

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  1. 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

  2. Question: ≶0? Data: novel panel from Stanford’s 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: >0 in Biochem, =0 in El. Eng. The paper

  3. 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)

  4. 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.?

  5. 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? • Teaching credits: lab ≠ classroom? Why dummy in regressions? Variance?

  6. 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

  7. 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?

  8. 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…

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