IMPACT OF UNIVERSITY-INDUSTRY CONTRACTS RESULTING IN PATENTS ON THE QUALITY OF PATENTING IN BIOTECHNOLOGY

Transcription

1 IMPACT OF UNIVERSITY-INDUSTRY CONTRACTS RESULTING IN PATENTS ON THE QUALITY OF PATENTING IN BIOTECHNOLOGY Authors Catherine Beaudry, Polytechnique Montreal, Ramine Kananian, Polytechnique Montreal, Abstract This article aims to identify the factors that influence the quantity and quality of patents to which academic inventors have contributed, and in particular, the effects of funding and of consulting. Using a unique database of academic funding providing detailed accounts of grants and contracts over the years, we build an econometric model of patenting taking into account the obvious problems linked with endogeneity. Our results show that while the average amount of funds raised from contracts has a positive non-linear effect on the number of patent applications, obtaining more public funds has the opposite effect. In contrast, public funds have a positive impact on the citations obtained by these patents, whereas a greater amount of contract funds has a detrimental effect on the number of citations obtained. Key Words: Patents, patent quality, funding, consulting, biotechnology 1

2 INTRODUCTION The entrepreneurial university (Etzkowitz, 2003) generally starts with entrepreneurial academics and academic inventors that generate or contribute to patents. As a consequence of the non negligible pressure on universities to increase their relevance to industry, a vast literature on the role, importance and performance of academic-inventors has been developed. Fuelled by the concern that universities may eventually concentrate more on applied and private research rather than on science as a public good (Dasgupta and David, 1987, 1994), a number of studies have examined the possible trade-off between publishing and patenting (Azoulay et al., 2006; Breschi et al. 2007; Jensen et al., 2003; Murray, 2002; Owen-Smith and Powell, 2003; Stephan et al., 2007; Thursby and Thursby, 2002). As put forward by Heller and Eisenberg (1998) as well as Murray and Stern (2007) in their anti-commons argument, the patenting of academic research may even be detrimental to research in general; good examples being the patenting of the Oncomouse or of gene fragments. Others have aimed to assess the economic impact of public science (Cohen et al. 2002; Foray and Lissoni, 2010; Griliches, 1984), some of the mechanisms being the production of patented inventions (Geuna and Nesta, 2006; Jaffe, 1989), university licensing (Dechenaux et al., 2011; Thursby and Thursby, 2007; Thursby et al. 2007), collaboration between industry and academia (Cohen et al., 1998; Mohnen and Hoareau, 2003; Thursby and Thursby, 2011), the creation of spinoffs (Lockett et al. 2003; Zucker and Darby, 1996; Zucker et al., 1998), to name few. In particular, Geuna and Nesta (2006) suggest that patents and publications tend to go hand in hand (pp. 790) and that this may be the cause of the lack of profitability of university patenting. In addition, a great deal of the literature on academic commercialisation to date focuses on the aftermath of the Bayh-Dole legislation in the US (Henderson et al., 1998; Mowery and Ziedonis, 2002; Mowery et al. 2002, Sampat et al., 2003). In Canada, Schiffauerova and Beaudry (forthcoming) highlight the fact that there is no single uniform IP ownership and revenue distribution policy across Canadian universities. In that respect, the situation is much more similar to that of Europe until recently, i.e. before a number of countries adopted Bayh-Dole Act type of legislations (Czarnitzki et al., 2011, pp. 1406), abandoning the so-called professor privilege in 2001 (Geuna and Rossi, 2011; Lissoni et al., 2008). As a consequence, patents from Canadian academics emanate both from publicly funded research, possibly under university or individual ownership, and from privately funded or university-industry collaborative research, generally under private ownership. Our insight into the influence of industry in these commercial academic endeavours therefore remains limited. In this article we aim to analyze the factors that impact patented innovation in the high technology field of biotechnology. We focus specifically on the innovation resulting from academic research, whether publicly or privately funded. In particular, we study two dimensions of innovation resulting from these scholars contributions, the first dimension being the propensity to patent (measured in terms of the quantity of patent applications) and the second being their quality. Similarly to Czarnitzki et al. (2011), our focus is on academic patents as opposed to university patents because we are interested in the impact that public and private organisations have on patented research output regardless of patent ownership. Focusing solely on university patenting would miss a large part of academic patenting (Geuna and Nesta, 2006; Verspagen, 2006). While we agree that university patents facilitate knowledge transfer from the public to the private sector (Verspagen, 2006, pp. 613), the direct contribution of industry and university-industry collaboration constitutes a more direct route for this knowledge, and to some extent intellectual property (IP), transfer, and therefore should not be neglected. 2

3 We use data from the United States Patent and Trademark Office (USPTO) to identify the biotechnology patents of Canadian inventors and assignees and then consider the patents to which Quebec university professors have contributed using the Quebec Ministry of Education Leisure and Sports (MELS) database of academic funding to identify these individuals and to measure the monetary resources at their disposal. Our results show that while the average amount of funds raised from contracts (generally private funds) has a positive non-linear effect on the number of patent applications, obtaining more public funds has the opposite effect. In contrast, public funds have a positive impact on the citations obtained by these patents, whereas a greater amount of contract funds has a detrimental effect on the number of citations obtained. The remainder of the article is structured as follows. The second section provides the theoretical framework and our hypotheses concerning the effects of funding, university-industry collaboration, publishing, and scientific networking on innovative output. The third section describes our methodology, i.e. the data, variables, and estimation methods employed. In the fourth section, we present and analyze our regression results. The article then concludes with a concise discussion and final remarks. THEORETICAL FRAMEWORK AND HYPOTHESES The innovation process in high technology fields is a demanding one, due mainly to the fact that it requires many resources (Robinson, et al., 2007), but also because of the nature of the interactions necessary to produce such innovation (Van Looy et al., 2004). Indeed, discoveries now involve the implication of many actors, including government, industry and university, each being equally as important as the other (Godin and Gingras, 2000). The interest of focusing on the academic s role stems from the idea that they, being the main producers of basic science, are of paramount importance for innovation considering the connection or co-dependence between basic and applied science in high technology fields (Narin et al., 1997; Robinson et al., 2007). According to many scholars, the number of patents can be used as a measure of innovation as nearly 80% of all technological knowledge is contained in them in some way or another, not to mention that they are representative of a novel, non-trivial and useful invention (Blackman, 1995; Rothaermel and Thursby, 2007; Teichert and Mittermayer, 2002). There are nevertheless some drawbacks in using patents since not all innovations are patented (Pakes and Griliches, 1980), for instance, firms may prefer other means of IP protection. Of more relevance to this paper, some researchers may prefer to put their new innovations in the public domain via publication in applied journal. A first dimension that this paper examines is therefore the number of patent applications filed by individual scientists. The second dimension we investigate is the quality of this patenting. Patent quality has been measured in many ways (Hall et al., 2001; Mowery and Ziedonis, 2002), however due to the scale of the data involved in our analysis we rely on two purely bibliometric measures, the number of claims of a patent and the number of citations received, both of which have proven reliable time and again in their own respects (Bonaccorsi and Thoma, 2007; Lanjouw and Schankerman, 2004; Tong and Frame, 2004; Trajtenberg, 1990). While the number of claims is related to the breadth of application of a patent, the number of citations accounts for the usefulness of a patent as prior art to other patented innovations. These studies have generally investigated the types of indicators that correlate with patent value or that contribute to increasing the value of innovation for the firm or organisation. We extend their concept of patent quality to measure the quality of the innovation produced at the individual level, rather than at the assignee level. Other indicators have been used by other scholars, for example patent 3

4 renewals, triadic patents (filed at the European Patent Office, the USPTO and the Japanese Patent Office) but were not retained for this research as we feared the correlation with our innovation loops (see below) would be too strong. We are thus interested in the number of patent applications filed in a given year and the quality of the resulting patents given that at least one patent has been filed, i.e. those to which academic inventors have contributed. Let us now turn to the factors that may influence the innovation productivity and its quality. Funding is particularly relevant in high-tech fields such as biotechnology because of the high costs of infrastructure (Robinson et al., 2007). And while only certain types of funding had been thought to be beneficial for specific outputs, i.e. contracts for patenting and grants for publishing, it has been shown that in fact this is not necessarily the case (Gulbrandsen and Smeby, 2002; Godin, 1998, Geuna and Nesta, 2006). Raising funds from contracts may indeed have a positive effect on scientific output, and public grants may also influence the propensity to patent. Breschi et al. (2007) suggests that publishing and patenting are positively related (i.e. the complementarity effect mentioned in the introduction) because of the collaborative relationships between university and industry which provides scientists with financial resources, inter alia, a concept to which they refer as the resource effect to try to assess the impact of patenting on scientific production. Their argument also accounts for the fact that patents and articles may simply be proxies for the intrinsic quality of a researcher 1, i.e. very prolific colleagues may have a greater likelihood of coming out with patentable innovations. In some sense, by taking into consideration research inputs in terms of public or private funding, we look into the antecedents of academic patenting (Azoulay et al., 2007) as well as into the pecuniary aspect of the resource effect. This brings us to elaborate our first hypothesis: H1: (a) Public and (b) private funding have a beneficial effect on the quantity and quality of patenting. University-industry interactions are of great interest given the fine line between basic and applied science in high technology fields (Narin et al., 1997). Moreover, not only are these collaborations beneficial for firms (Bonaccorsi and Piccagula; 1994, George et al., 2002; Zucker et al., 1998; Murray, 2004) they have been shown to be just as beneficial for the academics collaborating with them due to the opportunity they provide for these individuals to gain exposure to new ideas and also for the building of their professional network (Mansfield, 1995; Agrawal and Henderson, 2002, Siegel et al., 2003). This echoes the resource effect argument of Breschi et al. (2007) according to which collaborating with industry provides access to expensive scientific instruments, [ ] focussed research questions, data, and technical expertise (cognitive resources) (pp. 104) for the academics that venture onto that route. The contracts that academics obtain from firms in these university-industry interactions may require different degrees of faculty involvement: from consulting and advising on a licensed technology (in the case where the IP belongs to the university or the academic), to the successful development and commercialization (Thursby and Thursby, 2011, pp. 608) of the invention. In the latter, the IP may be licensed by the firm or simply belong to the firm, but was developed jointly with the university. Jensen et al. (2010) propose that firm-assigned patents on which academics are named inventors can be utilised as a measure of faculty consulting. Thursby and Thursby (2011) further add that the main reason for which an academic was listed as an inventor on a patent assigned to a firm was consulting. This would seem to suggest that consulting leads to patenting. In 1 This intrinsic quality of academic inventors is obviously a latent variable in our model as it cannot be directly measured. 4

5 reality, the types and effects of consulting are more complex. Three types of academic consulting are considered by Perkmann and Walsh (2008): opportunity-driven, commercialization-driven and research-driven, in their theoretical framework. They propose that firms engage with university researchers for research purposes to extend their in-house basic research, to gain access to emerging technologies, or to provide a bridge between knowledge creation and its exploitation. This type of consultancy is not likely to lead to patents. Neither is the former type of consultancy, driven by opportunity, where no new knowledge is required. It is the commercially-driven consultancy that will involve the most cutting edge research that will often result in patents. With the exception of these studies, very little has been written on the influence of consulting and contracting of academics, especially from an empirical point of view, as firms are generally not loquacious regarding the individuals with which they collaborate and the amounts of funds involved. We are extremely fortunate to have access to a very detailed database to that effect from the MELS in Quebec. Using this information, we will be able to follow the money from the firm to the university and back to the firm as patented innovation. Our second hypothesis hence reads as follows: H2: Academic inventors consulting with industry contribute to a larger number of patents and on patents of greater quality. Another strand of the literature infers collaborative patterns from co-publication and co-invention data obtained from scientific journal articles and patents. Networks of co-publication and coinvention have been regularly used in the literature to measure the social and epistemic proximity of individuals within teams (Balconi et al., 2004; Breschi and Lissoni, 2001; Newman, 2001; Singh, 2005). Given the importance of certain network effects when considering innovation in biotechnology, it is relevant to include them into our analysis. Murray (2004) for instance mentions the importance of academic social capital for science-based entrepreneurial firms. Considering the fact that patents are often the result of teamwork, the position of academics within their knowledge network, because of the inferred access to knowledge that it entails, should influence the propensity to patent as well as the visibility of the work being done, which should yield more citations for these patents. Balconi et al. (2004) for example discovered that academic inventors that contribute to industrial research networks generally occupy more central positions than non-academic inventors. It would therefore seem that academic inventors exchange knowledge with more individuals and organizations. They also found that academic inventors work in larger teams and collaborate on patents with a larger number of partners. Although network measures have been largely used in the analysis of the performance of firms within networks (see for instance Cantner and Graf, 2006; Gittelman, 2007), they are increasingly used for individual scientists and inventors. Considering a scientist s network position is especially pertinent as there seems to be an overlap between university and industry communities (Breschi and Catalini, 2010). We therefore propose the following hypothesis: H3: A better network position will have a positive effect on the number of patents to which a researcher contributes as well as the overall quality of these patents. Finally, there is cause to believe that there is substitution between patenting and publishing (Azoulay et al., 2006; Jensen et al., 2003; Stephan et al., 2007; Thursby and Thursby, 2002), but there could also be a degree of complementarity (Breschi et al. 2007; Murray, 2002; Owen- Smith and Powell, 2003; Stephan et al., 2007). Breschi et al. (2007) as well as Thursby and Thursby (2002) suggest that the apparent trade-off between patenting and publishing may simply be one of delayed publishing so as not to jeopardize the patent. Hence when looking into 5

6 the effects of publishing on patenting for a given year, it would be more likely that publishing activities in a given year take away from patenting activities (or that less publishing is associated with more patenting applications), while possibly increasing the very quality of these patents because of the increased visibility due to publishing. Breschi et al. (2007) however suggest that when patents are owned by business partners, there is a strong correlation between publishing and patenting. We may also expect that most academics do not put all their eggs in the same basket and continue to publish the results from other projects while the publishing of some patent-sensitive research output is on hold. It may also be the case that some academics patent the potentially very lucrative innovations while publishing others in applied journals hence offering them to the public domain. We can therefore formulate our last hypothesis regarding the possible trade-off between publishing and patenting as follows: H4: Publishing has a negative effect on the number of patents to which a scientist has contributed in a given year. It might however have a positive effect on the number of claims and on the number of citations received. METHODOLOGY Data Data was extracted from three main databases using either IPC codes or keyword search based on the OECD definition of biotechnology 2. The patent data was extracted from the United-States Patent and Tradermark Office (USPTO) using the IPC codes. The first question that generally springs to mind regarding the USPTO for Canadian inventors, is why not use the Canadian Intellectual Property Office (CIPO) data? The main reason is simple: the database does not provide the address of inventors in a consistent manner, which is essential to correctly identify academic inventors. We estimate the ensuing bias to be very small due to the proximity of the very large US market compared to that of Canada, hence the necessity for firms to protect their inventions south of the border. The publishing information was extracted from Elsevier s SCOPUS which contains the abstract and citation information of scientific articles using the biotechnology keywords listed in appendix C. The list of Quebec authors identified from the resulting articles database were then used to extract the funding data from the Système d Information sur la Recherche Universitaire (SIRU) of the Quebec Ministry of Education Leisure and Sports (MELS) which is a system that compiles every instance of funding obtained yearly for all academics employed in Quebec universities. This database contains the yearly inputs to the university accounts of each professor giving details on the source (type and name of provider) and amount of funding received per project. For example, a three-year grant is broken down into the three yearly payments made to the university account. Finally, the datasets were cleaned and merged keeping the scientist as the reference point. Once all necessary information was compiled, we kept only the data relating to active researchers, i.e. those that receive funding and have published more than 5 articles in biotechnology during the course of their career, and the years for which they have applied for a 2 The OECD definition of biotechnology patents covers the following IPC classes: A01H1/00, A01H4/00, A61K38/00, A61K39/00, A61K48/00, C02F3/34, C07G(11/00, 13/00, 15/00), C07K(4/00, 14/00, 16/00, 17/00, 19/00), C12M, C12N, C12P, C12Q, C12S, G01N27/327, G01N33/(53*, 54*, 55*, 57*, 68, 74, 76, 78, 88, 92). The list of keywords used for the extraction of the scientific articles is provided in appendix C. 6

7 patent. Hence, only the 214 Quebec academic inventors remain in the database once all these restrictions are applied. As mentioned in the introduction, we are interested in explaining the factors that influence the propensity to patent a larger number of inventions [nbpat] and the quality of these patents measured by either the number of claims [nbclaims], which reflect the scope of a patent, or the number of citations [nbcitp], which refers to the usefulness of that patent. Two types of funding variables are used to verify hypothesis H1 in our models: grants (a) and contracts (b). First, the average amount of grants raised over a period of three years [AvgGrant3] provides insight into the influence of public research support and the extent to which it contributes to the potential commercialisation of applied research. Second, the average amount of funds raised from contracts over three years [AvgCont3] helps us understand the importance of private support of university research. Because it is important to distinguish the private support of research that yields the patenting of innovation in conjunction with firm assignees, what Thursby and Thursby (2011) refer to as consultancy with a substantial involvement from academics, and pushing the commercialisationdriven consultancy of Perkmann and Walsh (2008) to include firm assignees, we introduce a concept which we name innovation loops (represented by [nbloop]). We have carefully and manually linked the contracts that a researcher has received from an organisation with the patents owned by this organisation and to which the funded researchers have contributed and are listed as inventors on the patent documents using the title of the contracts and patents along with the patent abstract. The resulting variable counts the number of these innovation loops to which a researcher contributes in a given year and should help in validating hypothesis H2. We believe this indicator to be a good measure of the specific successful university-enterprise collaboration linked to commercialisation-related consultancy. We expect that experienced academic inventors that patent are more likely to patent more and to produce higher quality patents. We therefore include an ordered dummy variable that takes the value 0 if an academic inventor has not yet patented (i.e. the current patent is his first), the value 1 if he has patented once before, and the value 2 if he has patented more than once in the past [ocumpat]. Turning now to hypothesis H3 on the influence of a scientist s network, we compile two measures of network position, betweenness centrality [BtwCent], which represents the proportion of geodesics between other pairs of scientists that include this scientist, and cliquishness [Cliqness], i.e. the density of the component surrounding a scientist (De Nooy et al., 2005). The former evaluates the importance of a scientist as an intermediary and should have a positive effect on patenting from the simple fact that he has access to a wider range of collaborators. We anticipate that the latter may have a more subtle impact as too much cliquishness, may in fact imply a very closed community around a researcher, which may lack in fresh and new external knowledge, while too little cliquishness may limit the interactions around a researcher, obliging him to be the sole integrator of knowledge. Our last hypothesis, H4, requires the inclusion of the number of articles published in a given year [nbarticles]. During the course of this research, we have estimated a number of regression models adding the number of articles of an individual scientist in a given year as a dependent exogenous variable. Including this variable along with the average amount of grants raised by an academic gives rise to a serious endogeneity problem caused by the fact that grants are generally awarded on past publication record, while future publication depends on the amount of funds raised by individual scientists. For this reason, articles and grants were examined as potential endogenous variables, in a chicken or the egg analysis to try to assess their impact on 7

8 patenting. We found that the number of articles published in the same year as a patent, or in previous years, have no effect on patenting, whether we instrumented for this variable or not. Although the number of past articles had an effect on the amount of grants raised by a scientist, the latter, as an endogenous variable, did not yield significant results, i.e. we could find no endogeneity related to the average amount of grants in the estimation of our model. As a consequence, we decided to drop the number of articles from our regressions, because of its impact on the average amount of grant money raised by an individual scientist and its lack of influence on academic patenting in general. In addition to these variables, a number of controls were added to the models described in the next subsection. First, we include the type of chair occupied by an academic, from no chair (0) to the prestigious Canada Research Chairs (3), to proxy for the intrinsic quality of an academic. Second, another aspect of the experience mentioned above, is simply the age of a researcher. In lieu of real age, we include the career age from the start of ones career, measured from the first publication, the first grant/contract, or the first patent. Third, researchers that have recently published in applied journals, implying that their research is more applied, are probably more likely to be more inclined to patent than colleagues whose papers are more fundamental. We measure the average degree of application of the scientific articles published by the academic inventor in the three years prior to the patent application [DegApp] using the classification of journals of Hamilton (2003), which ranges from 1 (very applied) to 4 (very basic). Fourth, we account for the proportion of academic inventors among the list of inventors of each patent [propuniv], when an academic inventor has filed more than one patent application in a given year, we take the average value of the variable. Finally, we include university dummy variables to take into consideration any environmental effect that are not otherwise measured, year dummy variables and a variable that takes the value 1 for any biotechnology domain that is not nanobiotechnology. Descriptive statistics for these variables are provided in appendix A. 8

9 Table 1: Description of dependent and of explanatory variables Variable Dependent Exogenous Endogenous Instrumental Description nbpat t x Number of patents to which a researcher has contributed for a given year nbclaims t x Number of claims contained in the researcher s patent(s) for a given year nbcitp5 t x Number of citations received by patents after 5 years ln(avggrant3 t-1) x Average grants received in the 3 years preceding the patent application [ln(avggrant3 t-1 )]² x Square of [ln(avggrant3 t-1 )]² ln(avgcont3 t-1) x x Average contracts received in the 3 years preceding the patent application [ln(avgcont3 t-1 )]² x Square of [ln(avgcont3 t-1 )]² 10 4 xbtwcent3 t-2 x Researcher s betweenness centrality in the co-publication network measured over a 3 year period preceding the patent application ln(10 3 x Cliqness3 t-2) x Researcher s cliquishness measured in the co-publication network over a 3 year period preceding the patent application [ln(10 3 x Cliqness3 t-2 )]² x Square of [ln(10 3 x Cliqness3 t-2 )]² DegApp t x Degree of application of the knowledge associated with the articles published by the researcher over the 3 year period leading to the patent application propuninv t x Portion of inventors that are university researchers ocumpat t-1 x Cumulative number of patents on which researcher is listed as an inventor up to year t-1 nbloop t x Number of instances in which an academic is listed as an inventor on a patent owned by the firm (assignee) that originally funded the academic s research up to year t Age t x Researcher s career age defined the number of years since the first grant/contract, the publication of the academic s first article or patent (Age t) 2 x Square of Age t CodeChair x Ordinal indicator taking the value 0 if a researcher has no chair, 1 if he has an industrial chair, 2 being a chair from two of the Canadian federal granting councils and 3 being a Canada research chair CumLoop t-2 x Cumulative number of past loops within which the researcher has collaborated ln(cumcont10 t-2) x Cumulative amount of contracts received ln(avgcont3u t-1 ) x Average amount of contracts awarded to colleagues in the same field and at the same institution of affiliation of an academic BioExcl x Dummy variable taking the value 1 for a patent that is exclusively in biotechnology duniv x University dummy variables, McGill is the omitted university dummy variable d1997-d2005 x Year dummy variables 9

10 Model Due to the discrete nature of our dependent variables, we use count data regression techniques. Poisson and negative binomial are utilized to estimate the factors that influence patent productivity and quality. A Poisson regression is generally appropriate for this purpose (Hausman et al., 1984): Pr( Y it = y it )= expλ ( x it ) ( ) y it! λ x it (1) The model expresses the probability of the number of occurrences of the observed value y it, i.e. the number of articles, depending on the parameter λ that is a function of the explanatory variables x it. This model implies the strong assumption that the variance of the number of occurrences is equal to the expected number of occurrences: Var[ Y it ]= EY [ it ] (2) This assumption implies that there is no over-dispersion (when the variance exceeds the mean) in the sample. Over-dispersion causes for the standard errors to be underestimated, and hence for the significance of the coefficients to be overestimated. As a solution to this over-dispersion, the negative binomial model is generally employed, in which the parameter λ is expressed as follows: λ = exp( π x it )ε (3) Where the error ε follows a Gamma distribution. And hence, the variance is expressed as: Var[ Y it ]= EY it [ ]( 1+ αe[ Y it ]) (4) Here α is the parameter of the Gamma distribution. Because we found over-dispersion in our analyses, only the negative binomial regression is presented in this paper. The model to be estimated can thus be expressed in reduced form as: 2 ln( AvgGrant3 t 1 ), ln( AvgGrant3 t 1 ),ln ( AvgCont3t 1 ), nbpat t 2 nbclaims ln( AvgCont3 t = f t 1 ),10 4 BtwCent3 t 2,10 3 Cliqness3 t 2, nbcit5 t Cliqness3 t 2, DegAppt 1, nbloop t 1,oCumPat t 1, dbioexcl, duniv, dyear (5) A problem then arises because the probability of being part of an innovation loop is not independent from the likelihood of raising funds from contracts. The two concepts are closely intertwined. This simultaneity problem is a common cause of endogeneity. Other causes of endogeneity are related to omitted variables. Because we cannot truly assess the intrinsic quality of academics, nor whether they are comfortable with working towards the patenting of their innovations under the auspices of industry, endogeneity due to unobserved heterogeneity is also an issue. 10

11 To account for potential endogeneity on the amount of contracts raised by each researcher, we use the two-stage residual inclusion (2SRI) method proposed by Terza et al. (2008). This approach requires the estimation of the endogenous variable (here it is the average amount of contracts raised in the past three years, AvgCont3 t-1 ), using ordinary least squares (OLS) regressions, on a number of instruments. The residue of this regression is then computed and added to the negative binomial regression model of interest (Bíró, 2009). Thus the main assumption of the model is that the conditional mean of the dependent variable can be written as follows (from Terza et al., 2008): E y i x in, x ix, x iu = M( x in β n + x ix β x + x iu β u ) (6) where M () is a non linear function, x in is a vector of endogenous variables (there is only one in our case), x ix is a vector of exogenous variables, and x iu is a vector of unobservable latent variables (omitted variables) that has an effect on the dependent variable and is correlated with the endogenous variable. The corresponding regression model can be written as: y i = M( x in β n + x ix β x + x iu β u )+ u i (7) where u i is the error term. The first regression (called auxiliary regression) hence consists in modelling the relationship between the unobserved latent variables and the endogenous variable using a set of instrumental variables (IV), w i that can be written as follows (in this case, we consider a linear relationship): x in = w i α + x iu (8) where w i = x in w +, and w + is the vector of instrumental variables and α is the vector of parameters. These variable have to satisfy the three following conditions: first, the unobserved variables must not be correlated with the instrumental variables E x u w i = 0 ; second, the instrumental variables must be sufficiently correlated with the endogenous variable, which implies that the instrumental variables would be sufficient to estimate the endogenous variable; and third, the instrumental variables are not correlated with the main variable of interest, nor with the error term. The first stage consists in estimating the auxiliary OLS regression and calculating the predicted values: ˆx in = w i ˆ α (9) where ˆα is the estimate of α for the first equation (auxiliary regression). Subsequently, the residuals of the unobserved variables are calculated by the following equation: x iu = x in ˆx in (10) 11

12 The second stage then consists in estimating the negative binomial regression model by introducing the residues as an explanatory variable. The regressions (first and second stages) to be estimated therefore can be expressed as: ln( AveCont3 it 1 )= f ln( AvgCont3U it 1 ),CumLoop it 2,ln( CumCont it 2 ), 2 ( ), ln( AvgGrant3 it 1 ), ln AvgGrant3 it BtwCent3 it 2,10 3 Cliqness3 it 2, Cliqness3 it 2, DegApp it 1,oCumPat it 1, nbloop it, propuniv it,codechair i, Age it, Age 2 it, dbioexcl i, duniv it, dyear t, 2 ln( AvgGrant3 it 1 ), ln( AvgGrant3 it 1 ),ln ( AvgCont3it 1 ), 2 nbpat it ln( AvgCont3 it 1 ),10 4 BtwCent3 it 2,10 3 Cliqness3 it 2, nbclaims it = f Cliqness3 it 2, DegAppit 1,oCumPat it 1, nbloop it, nbcit5 it propuniv it,codechair i, Age it, Age 2 it, dbioexcl i, duniv it, dyear t, ln( AvgCont3 it 1 ) residual1st (11) (12) When instrumenting on the average amount of funding raised from contracts, we include the cumulative number of innovation loops to which an academic inventor has contributed in the past [CumLoop], the cumulative amount of contracts that a researcher has obtained in the past 10 years [CumCont], as well as the average amount of funding received over three years by colleagues in the same field at the same university [AvgCont3U]. REGRESSION RESULTS Table 2 contains the results of our regressions both considering and neglecting potential endogeneity on the contracts received. When considering endogeneity, the second stages of the 2SRI method are presented in the core of the text while the first stage regressions are provided in the appendix. For each dependent variable, the left-hand column (1, 3, 5) shows the results without considering endogeneity, while the right-hand side column (2, 4, 6) presents the second stage regressions considering endogeneity. The corresponding first stage regressions are provided in the appendix B. Only for the number of claims are we not able to appropriately treat the endogeneity problem. 12

13 Table 2: Second Stage negative binomial regressions on the number of patents, claims and citations nbpat t nbclaims t nbcitp5 t Variable W/O Endo Endo W/O Endo Endo W/O Endo Endo (1) (2) (3) (4) (5) (6) ln(avgcont3 t 1 ) ** * *** (0.0316) (0.0378) (0.0371) (0.0786) (0.0723) (0.1598) [ln(avgcont3 t 1 )]² ** * ** * (0.0025) (0.0021) (0.0030) (0.0036) (0.0059) (0.0068) ln(avggrant3 t 1 ) ** ** (0.0706) (0.0806) (0.0675) (0.0847) (0.1192) (0.1373) [ln(avggrant3 t 1 )]² *** ** (0.0048) (0.0062) (0.0047) (0.0076) (0.0078) (0.0132) 10 4 xbtwcent3 t *** *** (0.0415) (0.0412) (0.0625) (0.0628) (0.0933) (0.0924) ln(10 3 x Cliqness3 t 2 ) ** ** ** (0.1911) (0.1932) (0.0097) (0.0114) (0.0235) (0.0275) [ln(10 3 x Cliqness3 t 2 )]² ** ** (0.0074) (0.0075) DegApp t * ** (0.0518) (0.0507) (0.0899) (0.0907) (0.1529) (0.1526) ocumpat t *** *** *** *** *** * (0.0664) (0.0692) (0.0750) (0.0750) (0.1250) (0.1268) nbloop t *** *** *** *** *** *** (0.0123) (0.0114) (0.0262) (0.0275) (0.0307) (0.0414) propuninv t * ** ** (0.2402) (0.2440) (0.2490) (0.2540) (0.3960) (0.3880) BioExcl ** ** (0.0750) (0.0796) (0.1188) (0.1190) (0.3801) (0.3801) CodeChair (0.0547) (0.0539) (0.0651) (0.0672) (0.1244) (0.1246) Age t (0.0938) (0.0930) (0.0792) (0.0803) (0.2572) (0.2508) (Age t ) (0.0032) (0.0031) (0.0030) (0.0030) (0.0101) (0.0098) d01laval ** (0.1257) (0.1290) (0.1687) (0.1767) (0.2529) (0.3018) d04mtlg *** *** (0.1529) (0.1475) (0.1669) (0.1691) (0.2487) (0.2937) d07concordia * *** *** ** (0.1263) (0.1301) (0.1868) (0.2183) (0.3276) (0.3493) d09uqamg *** *** *** (0.1286) (0.1649) (0.2063) (0.2906) (0.3001) (0.5721) d08sherbrooke (0.1655) (0.1662) (0.2449) (0.2437) (0.6910) (0.6939) Year dummies yes yes yes yes yes yes ln(avgcont3 t 1 ) Res *** *** (0.0256) (0.0497) (0.1147) _cons * *** *** ** *** (1.0417) (1.0553) (0.6784) (0.6702) (2.0339) (1.9121) ln(α) ** ** *** *** *** *** (1.5233) (1.8822) (0.0721) (0.0721) (0.1471) (0.1449) Statistics N N groups Group average Log Likelihood

14 The first two regressions, (1) and (2) estimate the factors that influence the number of patent applications of an academic inventor in a given year. While we generally find that obtaining a greater amount of contract funds has a positive effect on the number of patents filed by an academic-inventor. In the interval covered by our data, the quadratic effect is almost linear and increasingly positive (see Figure 1a). In contrast, the number of patent applications decreases with the increasing amount of funding raised from grants. The interval of the quadratic curve covered by our data is concave and decreasing (see Figure 1b). (a) (b) Figure 1: Quadratic effects of contracts (a) and grants (b) on the number of patent applications Similar results are obtained for the number of claims with the difference that the average amount raised in grants is not significant, and the fact that no endogeneity is found in this model. In contrast, the number of citations are negatively affected by contracts but positively influenced by the amount of public grants, as illustrated by Figure 2 (a and b). Other factors that have a positive effect on the number of patents include a researcher s cliquishness in the scientific network. Although the quadratic effect of the variable implies an inverted-u relationship with the number of patents, the portion of the curve of interest is increasing. We have not been able to find this non-linear effect of cliquishness in the other two models for the quality of patents. The negative effect only occurs for the number of citations in regression (6). In addition, the fact that a researcher has contributed to previous patents and that his patents are the fruit of collaboration with industry, i.e. within our innovation loops has a strong positive effect in all our regressions. Consulting in view of contributing to the commercialisation of an innovation yields more patents of higher quality. One element that has a negative effect (albeit a weak one) on the number of patents is the proportion of inventors that are academics. This variable has a much stronger negative effect on the number of claims. This is basically illustrative of a number of facts. First, firms generally patent more than universities and individual scientists. Second, when firms are involved in patents, academics may not represent the majority of inventors. Third, patents owned by firms generally have more claims than, say, university patents. Fourth, university-patents are often the results of university laboratory-based research for which the inventors are almost all academics. 14

15 (a) (b) Figure 2: Quadratic effects of contracts (a) and grants (b) on the number of citations received during the five years following the granting of the patent Moving on specifically to the quality of the patents to which an academic contributes, we find that even though certain factors have similar effects on both the number of claims and the number of citations (our two measures of patent quality), i.e. contracts, a history of patenting and collaboration with industry, it is not the case for many others. In particular, it seems that while grants (U-shaped relationship) and the degree of application of knowledge associated to the patents, have a negative effect on the number of citations received, the same cannot be said for the number of claims. Conversely while having a better betweenness centrality and a lower proportion of university researchers in the inventor s list have a positive effect on the number of claims, these factors have no effect on the number of citations received. These two measures of quality are thus influenced by different factors and should not therefore be treated as equivalent measure of the quality of a patent. In regards to the degree of application of an academic s published research in the few years leading to the application of a patent, we find that the more applied the published research results are, the more citations are obtained for the patents. This augmented visibility from academic journals hence contributes to increasing the number of citations obtained by a patent. DISCUSSION AND CONCLUSION What does this all mean for the propensity to patent and for the quality of the patents generated? At the start of this paper, we set out to validate four hypotheses. Let us now examine each one in turn. The first hypothesis related to funding, H1, is partially validated as contracts have a positive effect on the number and quality of patents, but grants have a generally poor or no effect on claims and citations. Only the number of citations seems to benefit from a greater amount of public research investment. We expected that greater amounts of contracts would have a positive effect on the propensity to patent, as these are traditionally the means by which industry collaborates with university researchers for specific aspects of applied research. Thursby and Thursby (2011) indeed suggest that academics that patent in biotechnology are more likely to have research support from industry, among other things, than 15

16 faculty in other fields. The fact that these contracts also have a positive effect on the quality of these patents reflects the fact that patents whose assignees are firms generally are of greater quality. That is not to say that all the contracts of a researcher lead to patenting. In some way, our results nevertheless support the resource effect of Breschi et al. (2007), but not the publication delay argument. For the particular case of patents produced within innovation loops, we have to turn to our second hypothesis, which aims at following the money from the firm, to the university and back to the firm as patented intellectual property. Our results fully validate H2 since we find that collaboration with industry is associated with a higher patent, claim and citation count. Hence, if a researcher participates in a greater number of innovation loops, i.e. he has received more contracts from organisations that are also the assignees of the patents on which he is a named inventor, he is more likely to generate more patents, and of higher quality. The collaboration with firms therefore ensures that the patents are possibly of greater relevance to industry. A combination of supply-push from academics and of demand-pull from industry is therefore more beneficial to industry than say the sole supply-push of academics via university licenses. As for the importance of the scientific network surrounding academics, our third hypothesis (H3) is only partially validated. We find that betweenness centrality, i.e. the importance of a researcher as an intermediary within the network, only has an impact on the number of claims of a patent and has no impact on the number of patents or on the number of citations obtained. These central individuals potentially have access to more diversified knowledge, which then impacts on the quality of innovation. As betweenness centrality and cliquishness generally move in opposite directions, it is therefore not surprising to find a negative effect of the latter on the number of citations. As a possible explanation, a more integrated or closely intertwined research clique surrounding a researcher reduces his chances of reaching a more diverse community of scientists that could yield more citations. In contrast, although the quadratic effect is negative, implying an inverted-u shaped curve for the impact of this variable, in the part of the curve that corresponds to our data, the relationship between cliquishness and the number of patents is almost linear and increasing. A higher cliquishness, and thus a more integrated research network, contributes to an increased production of patented innovations. Finally, although the results are not presented in the paper, we find that publishing has no effect on the number of patents, claims and citations. The number of articles, or the fact of publishing has never yielded a significant coefficient in any of our tests and was therefore eliminated from the analysis. This allows us to reject H4. In summary, we have analyzed here the factors that impact university researchers patenting abilities in the high technology field of biotechnology and we have found some interesting and unexpected results concerning the effects of funding, collaboration, networking among others. We plan on exploring these elements further in future work. There are a number of limitations to this study, the obvious one relating to the evaluation of academic patents that have benefited from industrial funding (generally when the assignee is the firm), because we do not have access to the amount of R&D spent by the firm on its own research. This limitation is however lessened by the fact that university patents that involve collaboration outside the province of Quebec suffer from the same lack of data regarding the out-of-province funding. A second limitation relates to the lack of information that we have on the aims of each contract. If we did, we could accurately investigate the role and importance of private research support on the production of innovation. 16

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