1. Discussion of: “In or Out: Faculty Research and Consulting” by Richard Jensen, Jerry Thursby, and Marie Thursby Saturday, September 30, 2006 EPFL Lausanne, Switzerland Ajay Agrawal University of Toronto

2. Paper Outline Motivation –¼ of patents with a university professor inventor are assigned to firms, rather than –How does this happen? => consulting Theory –Two-stage game structure 1 st stage: firm and government simultaneously choose funding levels 2 nd stage: firm chooses consulting fee; researcher chooses consulting hours –Predictions 2 nd stage: consulting fee, time consulting = f(researcher quality, industry funding, university funding, R&D spillover, research support from firm’s lab, basicness of firm’s research project) 1 st stage: funding levels: firm and gov = f(R&D spillover, research support from firm’s lab, basicness of firm’s research project) Empirics –Use patent assignment to firms as indicator of consulting –1687 patent-inventor pairs –Supports prediction: increase in faculty quality raises the probability of producing a “university” patent –Ambiguous empirical result: increase in government funding lowers the probability of producing a “university” patent

3. Theory Game structure –Why does the second stage follow from the first; is it not more intuitive that these decisions be made simultaneously with the funding decisions of the first stage OR after the outcome of the research (such that p = 0 or 1); if there is important information in the level of government funding, why doesn’t the firm wait for this information prior to making a funding commitment (such that firm makes funding decision and sets consulting rate in the same period)? –Perhaps an example would help the reader develop some intuition for why this structure makes sense (the authors do note that firms may use level of government funding as a quality signal) Payoffs –Why are university projects assumed to be more challenging than firm projects? While this might be true, on average, for science professors, it’s not obvious for engineering professors, particular those likely to patent. What happens to the NE if you relax the assumption that (x I >x 0 )? –Also, why is it assumed that the cross partial derivatives with respect to x and each of the inputs is negative? This assumes that a more difficult project reduces the marginal effect of quality, funding, etc. on the probability of success (my intuition would be that cross partials would be positive, at least for quality)

4. Data Sample construction – inventors that have not applied for a patent are thrown out –The theoretical model revolves around utility from 1) reputation and 2) income; faculty that focus on publishing are allocating resources towards reputation; why not use data for all inventors (dep var: industry patent = 1, 0 otherwise); or consider a 2-stage estimation? Predict patenting in first stage and consulting in second Sample construction – inventors who do not live close to the university are thrown out –Did you only apply this filter to non-university patents? –I recognize that you are trying to clean the data and false negatives are less costly than false positives; however, non-local professor-industry relations are *very* interesting (sabbaticals etc.); perhaps you can flag these patents track down inventor CVs and include appropriate ones as an additional wrinkle? Exception – a rare anomaly? –D-Wave case (quantum computing): firm negotiated with universities rights to own IP, not license; not necessarily any consulting agreement

5. Alternative Hypothesis Firm assignees are considered evidence of consulting. What fraction of firm patents in your sample are by former graduate students (who list their academic advisor as a co-inventor)? Alternative Hypothesis: –Professors that are more productive are more likely to have more graduate students and produce more publications. More graduate students lead to more cases where professor is inventor on firm-assigned patent; in this case, we would find your empirical result even absent consulting

6. Empirics Descriptive Statistics –Compare university vs firm patents in your sample Number of inventors, fraction of university inventors, number of forward cites, technology class Estimation –1687 pairs include 1527 patents and 600 faculty inventors The mean inventor has 2.5 patents; inventors in the tail of the distribution likely have over 20; perhaps you could check robustness of the standard errors to inventor cluster effects

7. General The topic of university faculty consulting has been identified through various surveys as a very important channel for knowledge transfer, yet remains seriously understudied This paper makes important strides not only studying consulting, but doing so in the more complete context of research funding and patenting activity The main impediment to studying consulting has been the lack of access to data; not only is there no central repository of consulting information (like the USPTO or NBER), but in many cases professors have incentives to under report consulting activity. In this case, the authors have backed out consulting behavior from patenting data! Clever! Bravo!