Academic Incentives and Research
Organization for Patenting at a
Large French University
Nicolas Carayol
ϕ∗
ϕ
BETA (UMR CNRS 7522), Université Louis Pasteur,
61, avenue de la Forêt Noire, F-67085, Strasbourg Cedex,
tel : +33(0)390242104 ; fax : +33(0)390242071,
email : <[email protected]>
February 2004
Abstract : This article provides an empirical study of the determinants of academic patenting. The data concern more than nine
hundred scholars at Université Louis Pasteur in Strasbourg. Our
main results show that academic incentives and career issues do
not stimulate patenting. Research organization plays a major role
for patent production. We find positive effects of laboratory size,
of interdisciplinary research, and of colleagues ages. These effects
differ from the ones that affect publication performance. Lastly,
public contractual funding stimulates patenting while recurrent
public and contractual private funding are not significant.
Key words : Economics of science, Academic Patenting, Laboratory, University.
JEL classification : L31, 031, 032, 034, 038.
∗
This work is part of a larger project on knowledge production at ULP. We
are grateful to P. Llerena, M. Matt, J. Azagra, S. Wolff and all other members
of the team. Acknowledgements extend to the administrative departments, to the
Technology Transfer Office at ULP, and to the CNRS Industrial Liaison Office.
1
1 Introduction
A major and recent event in the intellectual property rights broad concern
resides in the dramatic increase in university patenting (Henderson et al.,
1998 ; Mowery and Ziedonis, 2002). Henderson et al. (1998) underlined a series of factors which may have stimulated this phenomenon ranging from the
evolution of the regulatory environment (Bayh-Dole Act which allowed universities to retain the property rights derived from federally funded research)
to more endogenous phenomena such as the growing industrial funding of
university research and the increase of technology transfer facilities at US
universities. The patenting patterns of academic scientists now become a rising issue for both policy makers and economists in the US and more recently
in Europe (Geuna and Nesta, 2003).
While most empirical studies of academic patenting focused on the university level of analysis (Foltz et al., 2000, 2001 ; Carlsson and Fridh, 2002 ;
Coupé, 2003 ; Payne and Siow, 2003), to the best of our knowledge there was
no previous econometric analysis of individual academic patenting behaviors
with the exception of Agrawal and Henderson (2002) who exclusively focused
on the relation between publications and patents. Even if it did not provide
any econometric evidence, we should mention the study of Wallmark (1998)
dedicated to inventors’ profiles at Chalmers University.
The present paper studies the determinants of patenting behaviors of
more than nine hundred academic scientists over the period 1995-2000 during
which they were employed by a large French University which is ranked first
among French universities (in terms of impact) by the European Report
on Science and Technology (2003), namely the Université Louis Pasteur in
Strasbourg (France). Thus the main originality of our work resides in that it
goes down to the individual academic researcher.
Moreover, our detailed data allow us to specifically investigate two types
of effects for explaining academic patenting. The first one is broadly speaking
connected to how the regime of academic incentives (see for example Dasgupta and David, 1994 ; Stephan, 1996 ; Diamond, 1996) affects patenting
behaviors. We will study whether the traditional effects recorded for publication behavior are also those one observes for patenting behaviors. Moreover is
patenting stimulated or reduced by publications performance ? Finally, how
do academics’ research and publication strategies influence their patenting
behaviors ?
2
The second one is connected to the collective organization of academic
research and its effects on patent production. Mairesse and Turner (2003)
and Carayol and Matt (2004, 2003) clearly showed the importance of lab
determinants on academic publication behaviors. One may thus expect that
research organization also influences individuals’ patenting behaviors. Our
dataset allows us to tackle issues such as : Are larger labs intrinsically more
or less productive ? What are the most favorable close research environment
for patent production ? How does funding structure affect patent production ?
The paper is organized as follows. The next section investigates the expected response of individual researchers to academic career related incentives.
The third section analyzes the potential effects of research organization on
patenting behaviors. The dataset on which this model is estimated is briefly
presented in the fourth section. The fifth introduces the model of academic patenting behavior that we will consider. The sixth section presents the
results which are further discussed in the concluding section.
2 Academic incentives for patenting
The incentive regime in the academic sphere is generated by a specific
competition between individual scholars for monetary and non-monetary rewards (Dasgupta and David, 1994). Scientific achievement, often measured
through publication (and citation) counts, constitutes the basis of academic
“credit” (Merton, 1957), which is in turn associated with (often delayed)
satisfactions (wage increase, recognition by peers, positions in better locations...). This reputation based reward system constitutes an incentive system for publication. Many empirical economists have inferred from it and
tested several predictions1 .
Now if we consider patents as another outcome of academic research,
the question is whether their production respond to the same incentives as
publications. If so, one would expect to find quite similar effects. Nevertheless
many arguments come up in favor of a slightly different incentive regime.
These are connected to the following two main reasons.
i) The research which leads to a high publication performance is not
always the one that leads to patents : There might be some kind of precommitment in both agenda selection and collaborative network formation
for better generating one or the other of the two outcomes.
1
See Stephan (1996) and Diamond (1996) for a survey of empirical findings and Carayol
(2003b) for a model of academic competition.
3
ii) The rewards induced by patents are not necessarily the same as the
one induced by publications. The reputational reward of patents may be
slightly different since it may not be evaluated the same way in the scientific
community and thus it might does not produce the same consequences for
academic careers2 .
Let us consider these two dimensions of academic incentives in a more
concrete manner, in order to discuss the potential effects of status, age, publications, research agendas and collaboration networks on patent production.
In France, permanent researchers may occupy two types of positions :
Either a university professor type position (implying both teaching and research duties) or full-time research positions. The scholars occupying the
former type of positions are employed by universities while the latter are employed by the large national public research organizations such as the CNRS3
or INSERM4 . Nevertheless both categories work together in university labs.
On the one hand, we could expect that patent production would be enhanced
if scientists occupy full-time research positions just because these positions
offer more time to be dedicated to research. On the other hand, full-time
scientists may be more straightly evaluated on very fundamental research
and article counts in the best reviews. Thus, they might be relunctant to
dedicate time to patent production which may produce a negative signal of
strong commitment to fundamental research. Moreover, the faculty members
who have also teaching duties may be more inclined to have connections
with industry for enhancing the job opportunities of their students (Stephan
2001). Thus, they may be more likely to perform applied research and to
produce patents.
In addition, for both types of positions, there is a clear promotion (from
Assistant Professor to Full Professor ; or from Researchers to Director of Research) around mid-career which is usually obtained on the basis of scientific
accomplishments through a peer review process. Such a promotion is not
linked to tenure since, in France, Assistant Professors and Researchers are
tenured from the very beginning of their careers. Nevertheless it implies a
significant increase in wages and social status within the academic sphere.
2
We shall here focus on academic incentives but direct monetary rewards in terms of
shares of royalties might also be considered.
3
“Centre National de la Recherche Scientifique” (National Center for Scientific Research).
4
“Institut de la Sante et de la Recherche Médicale” (National Institute of Health and
Medical Research).
4
The expected effects of promotion are less ambiguous. Since promotion implies increases in wage and social status, there are important incentives for
concentrating efforts before promotion on very academic purposes (publishing) while such incentives may be relaxed once the promotion is awarded.
Moreover, the promotion may act as a signal for potential industrial partners
of the researchers’ abilities, thus increasing their chances to collect industrial
support and to perform research that may lead to inventions and patents.
Concerning the effects of age, Wallmark (1997) observed that there is a
peak around 30-35 years of age for Chalmers University researchers. Thus,
it seems that the patent production curve over the life-cycle may be of the
same shape as the one obtained for article production. Several empirical studies using panel data of publication profiles showed that publication profiles
exhibit an inversed-U shape over the life-cycle (Diamond, 1986 ; Weiss and
Lillard, 1982 ; Levin and Stephan, 1991). Moreover, the reputationally based
reward system in science tends to produce increasing returns that affect academic competition (Cole and Cole, 1970). Consequently, young researchers
have very strong incentives to make efforts in the early stages of their career.
Therefore, if patents are not by-products of articles production and if patent
invention is not viewed by the community as a signal of research excellence,
young researchers may not consider patenting as a relevant objective and
may rather concentrate on publication. On the contrary, older researchers
may have a higher propensity to patent because they may value more social wealth (thus responding more to intrinsic motives rather than academic
incentives) and benefit from a greater experience.
This raises the issue of how publication affects patenting. Stephan et al.
(2002) argued that it is possible to “have the cake and eat it too”. Agrawal
and Henderson (2002) studied the research production of a population of
236 professors who were employed by two departments of MIT in the year
2000 and who generated at least one paper or patent during the period 19831997. They specifically studied the determinants of patenting, and concluded
that “patenting activity does not appear to be significantly depending on
publishing activity” (p. 57). Nevertheless, some arguments still speak for the
non neutrality of that variable. On the one hand, one may expect a positive
effect of publication since the most inventive researchers may exhibit the
best publication and patenting profiles. On the other hand, there may be
a specialization process : Some researchers concentrate on research agendas
that may lead to high publication performances while others focus in research
5
which is likely to lead to patents. In order to distinguish both effects, it might
be useful to measure both publication counts and their average Impact Factor
separately.
There arises the issue of the fit between research strategy and patenting.
Researchers may develop research projects that involve partnerships with researchers in industry. Such a behavior may correspond to a patent production
strategy since industrial partners may bring in concrete needs and, if a promising discovery is made, they may spend money taking ownership over that
invention (or shared property or even engage in license acquisition)5 . The
nature of research has a strong influence on patents production : Wallmark
(1997) insisted on the differences between disciplines. He found that patents
were unevenly distributed among Chalmers’ schools : Chemical Engineering
is around one patent per professor, Physics Engineering is 0.14 and several
schools have none. It seems that research performed in different research domains has very heterogeneous propensity to lead to inventions and to have
inventions being fruitfully patentable. Moreover, the distribution of researchers’ interests over various research domains may be non neutral on patent
production. Combining various interests might reflect “problem-solving” research which Foray and Gibbons (1996) emphasized as being more likely to
generate inventions.
3 Academic research organization and invention
This section is dedicated to studying the expected effects of the collective
organization of research on patenting performance. Innovation is often described in the literature as a collective process. Academic researchers are likely
to benefit from the use of various resources that they may find in their close
institutional and organizational environment so that organizational factors
may affect individual patenting performance.
The first question is related to potential scale effects. Coupé (2003) found
decreasing returns to scale at the university level of analysis. Given the greater measurement errors at a lower level of aggregation, it becomes difficult
5
It should be noticed that in France Universities and National Research institutions
have the full rights over the results of publicly funded research. Unlike in the US regulation,
they can retain full property, share it, sell it, or even contractually let partners (usually
private companies) get these rights.
6
to compute returns to scale at the laboratory level. Usually results are much
more basic and lab size is approximated by the number of permanent researchers (Bonaccorsi and Daraio, 2003). For instance, a negative impact of size
on per capita publication performance was observed by Carayol and Matt
(2004). The same question may be addressed for patent performance.
The literature exhibits contradictory statements concerning the effects of
funding on university patenting. Payne and Siow (2003) show that federal
funding has a significant positive impact. Foltz et al. (2000) find the effect
of federal (plus state) funding to be positive and significant, but industrial
and internal funding are not significant. In the particular case of agricultural biotechnology patents, the same authors find that only internal funding
matters, while neither federal nor industrial funding do. Foltz et al. (2001)
provide a dynamic model while restricting themselves to agricultural biotechnology patents. They find that patenting experience produces more patents
and that internal funding and State funding have a positive significant impact on patent production, whereas industry and federal support have not.
One may argue that such confusing outcomes are essentially due to the too
aggregated level of analysis retained (the university). As Crow and Bozeman
(1987) underline, the nature of the research outcomes is strongly influenced
by the funding structure of the laboratory. One explanation is provided by
Carayol (2003a) : from the detailed analysis of a set of collaborations which
occured between academic laboratories and firms in Europe and the US, it
is shown that reputation and internal organization of the lab are strongly
connected to the nature of contractual funding provided by firms. One may
thus expect that industrial support of academic laboratories favors their researchers’ patenting performance.
One may also wonder whether, at the laboratory level, the presence of
different generations of scientists induces some collective effects. Here, the
issue is not any more related to the productivity trajectory over the lifecycle (impact of scholars’ ages on their own productivity) but concerns the
complementaries between researchers of different ages. Similarly, the types
of positions occupied by close colleagues within labs may also influence individual productivity. One question might be whether the share of full-time
researchers (vs. university professors), or the share of promoted colleagues
improves individual patenting just because they provide better advice or
contacts.
7
The importance of non-permanent researchers on the productivity of permanent ones is mostly ignored by the specialized literature. Carayol and
Matt (2004) emphasized this point using the same sample as in this paper.
Their conclusions strongly highlight the importance of such factors : Nonpermanent researchers tend to significantly affect the amount of permanent
researchers’ outputs. This raises new questions in terms of access to nonpermanent researchers (PhDs and post-docs) and their impact on permanent
researchers’ patenting productivity. Are the “research intensive” post-docs or
the PhD students sustaining patenting performance ?
We may also address the question of the effects of the scientific activity
and quality of colleagues on individual patenting. Carayol and Matt (2004)
and Mairesse and Turner (2002) studying the publishing activity of academic
researchers both found that the productivity of colleagues influences the researchers’ own productions while their quality matters for the average quality
of their own papers. If one assumes that the average impact of colleagues’
publications accounts for their quality, one may expect a positive effect on
individual patenting performance. In addition, it might be of some interest
to compare significance and coefficients with the ones obtained for the individual publication productivity : is it important to have colleagues or to have
personally a high publication profile to enhance patent performance ?
One important issue is related to the nature of the research performed in
the laboratory. It is no more the nature of the individual research profile that
makes the difference for invention, but the collection of various specific skills
(Llerena and Meyer-Krahmer, 2003). One may thus expect that laboratories
which group together researchers of diverse disciplinary profiles are more
fertile environments for patent production.
4 The data
The data concern the research activity of a single university : Louis Pasteur University (ULP) of Strasbourg (France). ULP has an old tradition of
fundamental research and a long-term standing of scientific excellence. Its researchers have received numerous national and international scientific prizes,
including Nobel Prizes. Overall, ULP is one of the largest French universities
in terms of research. The Third European Report on Science & Technology
Indicators 2003 ranks ULP first among French universities in terms of impact
and 11th among European universities. Active researchers count one Nobel
8
laureate, eleven members of the Institut Universitaire de France and eleven
members of the French National Academy of Science. The university research
capacities are reinforced by a close-knit with the major national research bodies such as the CNRS and INSERM. Research and teaching activities cover
a wide range of subjects : Medical Sciences, Mathematics, Computer Science,
Physics, Chemistry, Life Sciences, Geology, Geophysics, Astronomy, Engineering Sciences and Social Sciences.
We collected the variables from administrative reports completed for the
1996 contractual affiliation round6 . 1,460 permanent researchers were reported in these documents : They were all present in 19957 . Similar documents
were collected for the 2001-2004 period. Thus, we had information about
which permanent researchers were still present in the university in 2000. We
excluded all permanent ones that were not on that list in order to be sure
that they did not move to another university or had retired. At the end of
the process, 1,134 permanent researchers remained among whom 908 were
fully informed on all variables of interest.
The patent data come from the French Institute of Intellectual Property
(INPI). We matched our list of permanent researchers with all inventors
appearing in French, European and PCT patent applications. We found 841
patent applications for our whole population from 1980 to 2001. 211 patents
were invented by at least one of our 908 inventors over the 1995-2000 window
retained. These patents correspond to 507 occurrences of ULP inventions since co-invention between researchers is frequent - involving 111 distinct
permanent researchers.
The published articles of each permanent researcher in our database were
also collected using SCI, SSCI and Arts and Humanities ISI databases. More
than 26,000 publication occurrences were recorded over the 1993-2000 period.
We matched this table with our restricted list of permanent researchers and
6
Such a round occurs every four years. All laboratories (and also Faculties and Institutes) have to produce a standardized document, which is usually divided into two distinct
parts : A précis of the past four years and a project for the next four ones. The data cover
the period from 1993 to 2000, which may be separated into two four-year sub-periods :
1993-1996 and 1997-2000, which represent respectively what was achieved during the fouryear periods before and after 1996 (i.e. the new affiliation contract).
7
These contracts were signed in 1996 but prepared long before in order to be evaluated
through peer review procedures conducted by both the Ministry of Research and Education
and funding agencies such as the CNRS and INSERM whose support is expected. This is
why data are assumed to be valid also for the year 1995.
9
kept only the occurrences that were published over the period 1995-2000
for which we are nearly sure they were employed by the university. This
amount includes some double counting as some ULP researchers have coauthored papers. By dividing each occurrence by the number of co-authors
we obtain the effective (normalized) scientific contribution of each author
considered (an author is necessarily a permanent researcher). In addition
each publication item was associated with the impact factor of the review
in which it was published (given in ISI-JCI). That information gives the
opportunity to correct publication performance for impact.
Moreover we have information of all the laboratories to which these permanent researchers were affiliated. We recorded 79 distinct laboratories in
1996 for which we have complete and reliable information. We are thus able
to attach to each individual scientist the variables characterizing their laboratories or colleagues (other permanent researchers) in labs. The number of
permanent researchers in the lab is the single variable which accounts for the
size of the lab. All other variables are proportions. When the variable characterizing the labs were computed from information on permanent researchers,
we always excluded the researcher who was analyzed (e.g. the average age
of colleagues, their positions, their scientific productivity). Some more information on the labs were collected in the 1996 research reports. We were thus
also able to introduce more variables on labs : We included data on types
of personnel which are often not taken into account in empirical analyses. It
would have weakened the analysis not to record the presence of some 1,230
PhD students, 710 post-docs and 1,120 non-researchers (administrative staff
and technicians) that were reported in the year 1996. Lastly we were able to
collect precise information about the funding of the laboratories (excluding
wages). We have data on regular public funding for the period 1996-2000.
We collected information from the Technology Transfer Offices about the
contractual funding over the whole 1993-2000 period. The latter was decomposed according to the sources of funding : distinguishing between public and
private ones.
The variables are fully described in the Appendix. Descriptive statistics
on these variable are to be found in Table 1.
10
5 The model
In this section we present our (simple) model of patent production that
will be estimated. Let the number of patents that agent i invented be given
by the positive integer random variable yi . We assume that patent production
is the result of two superposed processes, such that yi is given by the product
of two other random variables as follows :
yi = zi × yi∗
(1)
The unobserved random variable zi indicates whether i’s research may lead to
a patentable discovery or not. It is a dichotomous variable defined as follows :
½
1 if i’s research may lead to a patent
(2)
zi =
0 otherwise
This variable is assumed to be determined by the vector of covariates wi
according to a given distribution function F (·) as follows :
Pr(zi = 0 |wi ) = F (γ 0 wi )
(3)
E [yi∗ ] = β 0 xi + εi = ln λi + ln ui
(4)
We shall further assume that F (·) is the Logit distribution : F (γ 0 wi ) =
exp (γ 0 wi ) / (1 + exp (γ 0 wi )) .
The unobserved random variable yi∗ accounts for the number of patents
issued from patentable research. Let us consider that the arrival of patents
is govern by Poisson process which has been generalized in order to take
into account the unobserved individual effects. Thus the expected number of
patents for each agent i performing patentable research is given by :
with xi the vector of independent variables and β the vector of its associated
coefficients. The term εi = ln ui stands for the unobserved individual effects.
The distribution of yi∗ is given by the following density function :
f
(yi∗
|xi ) =
Z∞
f
(yi∗
|ui ) g(ui )dui =
0
Z∞
0
∗
e−λi (λi ui )yi
g(ui )dui
yi∗ !
(5)
with f (yi∗ |ui ) the distribution of yi∗ conditioned on xi and ui (which is standard Poisson) and, with g(·) the density function of ui which is usually assumed to be Gamma (ui = exp (εi ) ∼ G (θ)) and normalized in order to have
11
a mean equal to one (E [ui ] = 1) giving us : g(ui ) =
obtain the following expression :
f (yi∗ |xi ) =
θθ −θui θ−1
e
ui .
Γ(θ)
We then
Γ(θ + yi∗ ) yi∗
ri (1 − ri )θ
∗
Γ(yi + 1)Γ(θ)
(6)
i
where ri = λiλ+θ
, which is the form of the Negative Binomial distribution. This
distribution departs from the Poisson one for which the variance is equal to
the mean (λi ). Instead, we now have : E [yi∗ ] = λi and var [yi∗ ] /E [yi∗ ] =
1 + 1θ E [yi∗ ] .
Now back to the observed dependent variable yi , the non conditional
probability of the observed number of patents is thus given by :
Pr (yi = j |xi , wi ) = Pr (zi = 0 |wi ) + Pr (zi = 1 |wi ) × Pr (yi = j |xi , zi = 1)
Knowing that the probability of the observed number of patents conditioned on a zi equal to unity, is equal to the unconditioned probability of the
unobserved random variable yi∗ : Pr (yi = j |xi , zi = 1) = Pr (yi∗ = j |xi ) =
f (j |xi ) , and after trivial recombinations, we can write :
Pr (yi = j |xi , wi ) = F (γ 0 wi ) (1 − f (j |xi )) + f (j |xi )
(7)
This equation is the basic equation of the model that we will estimate. This
model is better known in the literature under the label of Zero Inflated Negative Binomial. It is mentioned in Greene (1994, 1997) and developed in
Cameron and Trivedi (1997).
The log-likelihood which will be maximized is the following :
µ
¶−θ #
1
ln F (γ 0 wi ) + (1 − F (γ 0 wi )) 1 − exp (β0 xi + εi )
L =
θ
i∈S
X
+
[ln (1 − F (γ 0 wi )) + Γ (θ + yi ) − Γ (yi + 1) − ln Γ (θ)
X
"
i∈S
/
(8)
Ã
µ
¶
µ
¶−1 !#
1
1
−θ ln 1 − exp (β0 xi + εi ) + yi ln 1 − 1 − exp (β0 xi + εi )
θ
θ
with S the set of individuals i which have a non null dependent (yi > 0).
12
6 Results
The structure of our data led us to use a count data model. Moreover, a
brief look at the data informed us of some evidence of overdispersion, indicating that a simple Poisson model would not be appropriate (see in Table 1
that we indeed have V ar [yi ] >> E [yi ]). In addition, Figure 1 clearly shows
that the distribution of patents is very skew with a very high proportion of
zeros (even as compared with the distribution of publication performance).
Such a phenomenon may be due to two non exclusive phenomena : unobserved individual heterogeneity or zero inflation. In fact, altogether the Zero
Inflated Poisson model, the Negative Binomial and the Zero Inflated Negative Binomial are natural candidates for us. The latter model appeared to
be preferred to the Zero Inflated Poisson model which is nested in it : The
likelihood test of 1/θ = 0 (εi = 0) being equal to 19.81, the null hypothesis of
no unobserved individual effect has been rejected. In order to select between
the Negative Binomial and the Zero Inflated Negative Binomial let us write
h(·) the density function of the Zero Inflated Negative Binomial, set xi = wi 8
i |xi )
and define mi = ln fh(y
(recalling that f (·) is the density function of the
(yi |xi )
Negative Binomial model as stated in equation 6). We are
able to comq thus
P
P
pute the Vuong statistics as follows : v = √1n ni=1 mi / n1 ni=1 (mi − m̄)2 .
Computed on our data, v is equal to 5.32 which clearly stresses that the Zero
Inflated Negative Binomial model fits better than the Negative Binomial.
This legitimates, on our data, the full model given in (7).
Once the tests had been performed, the two variables of Age i and Discipline i were reintroduced in the inflation equation (among the wi ) while
cohorts of age were removed from it. This is done to account for the idea
that disciplines are largely connected to the fact that some research may or
may not lead to patentable inventions. The estimation results are presented
in Table 2.
Our first result is that age tends to favor patenting : Age4 and to a lesser
extent Age3 are positive and significant. The oldest tend to patent more.
This may indicate that a longer experience (proxied by age) is required for
patents. It may also indicate that older researchers tend to better value inventions as compared to scientific accomplishment. High patenting performance
are usually obtained between 50 and 60 years of age. Moreover there is an
8
The tests were realized with xi = wi gouping all the independent variables exposed
in the preceding section but Age i and Disciplinei .
13
increasing diversity in patenting performance through age (cf. Figure 2, a
similar statement could be introduced for publication performance). Nevertheless, and contrary to the publication performance this diversity is not
captured by the position related variables which are Junior and Fulltime.
This supports the idea that patent production and career profiles are not
much related. Patent production seems to imply a dedicated research strategy : The intensity of co-authoring with industry plays a significant role in
patenting (Indus has a significant and positive coefficient).
In their early careers, researchers seem to concentrate on research purposes that serve publication strategies. Nevertheless, that does not mean we
find a negative relation between publication and patenting (see Figure 3 for
box plots of publication measures and patents). This idea is reinforced by
the negative coefficient of publications (both Perf and Imp) in the inflation
equation (column Logit Selection in Table 2). Since a positive coefficient there
means a higher chance to have zero patents, it indicates that the less active
researchers in terms of publications have a higher probability to invent no
patent at all.
Turning toward the impact of research organization, we find that the average impact factor of colleagues publications (Lab.imp) plays positively on
patent performance. If we assume that this variable may proxy the quality
of interactions within labs, we can conclude that interactions in the immediate environment has a strong impact on patenting behaviors. Nevertheless,
publication performance (quantity) plays negatively. This tends to indicate
that a dedicated patenting strategy should be conceived at the laboratory
level. Such a statement is also strongly supported by the following : The diversity of researchers’ affiliations to various fields of research in laboratories
enhances significantly the patenting performance of their permanent researchers. At the same time, the diversity of individual publication outputs on
JCR domains is not significant (the two not being related as shown in Figure
4). This clearly evidences the importance of interdisciplinary research at the
collective level for patent generation.
Moreover, the characteristics of lab design that are appropriate for patenting seem to be significantly different from the ones that support publication
performance. It should be noticed that the size of labs plays positively while
it usually plays negatively for publications (Bonaccorsi and Daraio, 2003,
Carayol and Matt, 2003). Even if the significance of Lab.perm remains low,
this tends to indicate that larger labs are needed for patent production than
14
for publication performance. Moreover, the features of the labs that sustain
publication performance are the ones that express a strong collective commitment to research : A high share of full-time researchers and a strong
policy for hosting Foreign post-docs (cf. Carayol and Matt, 2004). Here, it is
shown that the labs which host many Foreign post-docs do not patent much
(Lab.postdocF is significant and its coefficient is negative) while older and
un-promoted colleagues favor patenting. Moreover, the enrollment of PhD
students sustains patenting. This suggests that scholars may be more aware
of industrial needs when they also have PhD students around9 , as they may
intend to develop connections with industry and applied research topics to
improve the job perspectives of their students.
Lastly, our results related to the lab funding variables are similar to
the ones obtained for publication performance (Carayol and Matt, 2004) :
Contractual public funding is the only significant funding variable. Thus,
contractual public funding supports patenting while neither the recurrent
public funding nor - and perhaps more surprisingly - the private contractual
funding are significant.
7 Conclusion
In this paper, we have presented some evidence on the determinants of
patent invention by academic researchers employed by the Université Louis
Pasteur in Strasbourg. Both individual characteristics and variables related
to their research laboratory are considered. Such variables allow us to stress
how the academic incentives and the research organization within labs affect
patents.
Our results tend to support the idea that the usual academic reward system does not provide significant incentives for patenting. The researchers tend
to concentrate on publication in their early careers. The reputational reward
of patents within the academic community seems to be low : Career profiles
and patent production are not related. While young researchers tend to focus
on publication performance, older researchers seem to be more sensitive to
intrinsic motives and tend to value more applications of their discoveries (or
eventually to direct payoffs which may not be delayed as academic career
9
See Stephan, 2001 for a discussion on the educational implications of connections with
industry.
15
ones are). Patenting seems to be the result of a dedicated strategy, which
implies contacts with researchers employed by firms.
Moreover, the research organization that sustains patenting does not have
the same design as the one that relates to publications. We find that bigger
size, high quality but less promoted colleagues, PhD supervision and diversity
of disciplinary affiliations enhance patenting. Nevertheless, contractual public
funding is the only funding which influences patenting positively, while even
private funding has no impact. This may be of some interest for science
policy since this effect is identical to the one obtained in a previous work for
publication performance.
8 References
Agrawal, A., and R. Henderson, 2002, ‘Putting Patents in Context : Exploring Knowledge Transfer from MIT,’ Management Science, 48 (1), 44-60.
Bonaccorsi, A., Daraio, C., 2003. Age effects in scientific productivity.
The case of the Italian National Research Council (CNR). Scientometrics 58
(1), 35-48.
Boumahdi, R., Carayol, N., Llerena P., 2003. The private contractual
funding of academic laboratories : A panel data analysis. Mimeo BETAUniversity Louis Pasteur.
Cameron, C., and Trivedi, P., 1997, Models for Count Data. Oxford University Press, Oxford.
Carayol, N., 2003a. Objectives, agreements and matching in science industry collaborations : Reassembling the pieces of the puzzle. Research Policy
32, 887-908.
Carayol, N., 2003b, “Optimal academic competition : Dynamic incentives
and cumulative advantages”, Mimeo BETA-University Louis Pasteur.
Carayol, N., Matt, M., 2004, Individual and collective determinants of
academic scientists’ productivity. Mimeo. BETA.
Carayol, N., Matt, M., 2003. Does research organization influence academic production ? Laboratory level evidence from a large European university,
Mimeo BETA-University Louis Pasteur.
Cole, S., Cole, J., 1973. Social Stratification in Science. University of
Chicago Press, Chicago.
Coupé, T., 2003. Science is Golden : Academic R&D and University Patents. Journal of Technology Transfer 28, 31-46.
16
Crow, M., Bozeman, B., 1987. R&D laboratory classification and public
policy : The effects of environmental context on laboratory behavior. Research policy 16, 229-258.
Dasgupta, P., David, P.A., 1994. Toward a new economics of science.
Research Policy 23, 487-521.
Diamond, A.M., 1996, The Economics of Science, Knowledge and Policy
9 : 6-49.
Diamond, A.M., 1986. The life-cycle research productivity of mathematicians and scientists. The Journal of Gerontology 41, 520-525.
Foltz, J., K. Kim, K. and B. Barham, 2001, ‘A Dynamic Count Data
Analysis of University Ag-Biotech Patents,’ Food Marketing Policy Center
Research Report 56.
Foltz, J., B. Barham, B. and K. Kim, 2000, ‘Universities and Agricultural
Biotechnology Patent Production,’ Agribusiness 16 (1), 82-95.
Foray D., Gibbons M., 1996. Discovery in the context of application.
Technological Forecasting and Social Change : 53, 263-277.
Geuna A. and L. Nesta, 2003, University Patenting and its Effects on
Academic Research. SEWPS No. 99, SPRU-University of Sussex.
Greene, W., 1997, Econometric Analysis, Prentice Hall, Englewood Cliffs.
Greene, W., 1994, Accounting for Excess Zeros and Sample Selection
in Poisson and Negative Binomial Regression Models, Working Paper No.
EC-94-10, Department of Economics, Stern School of Business, New York
University.
Henderson, R., Jaffe, A.B., Trajtenberg, M., 1998. Universities as a source
of commercial technology : A detailed analysis of university patenting, 19651988. Review of Economics and Statistics 80, 119-127.
Levin, S.G., Stephan, P.E., 1991. Research productivity over the life
cycle : Evidence for academic scientists. American Economic Review 81, 114132.
Llerena P., and F. Meyer-Krahmer, 2003, Interdisciplinary research and
the organization of the university : General challenges and a case studt, in :
A. Geuna, A. Salter and E. Steinmueller (eds.), Science and Innovation : Rethinking the Rationales for Funding and Governance, Cheltenham : Edward
Elgar, 69-88.
Knorr K.D., Mittermeier R., Aichholzer G., Waller G., 1979, Individual
publication productivity as a social position effect in academic and industrial
research units. In : F. Andrews (ed.), The Effectiveness of Research Groups
in Six Countries, Cambridge, Cambridge University Press, 55-94.
17
Mairesse, J., Turner, L., 2002. A look at individual differences in scientific research productivity : An econometric analysis of the publications of the
French CNRS physicists in condensed matter (1980-1997). Conference ‘Rethinking science policy : Analytical frameworks for evidence-based policy’,
SPRU, Brighton, March 21-23.
Merton R.K., 1957. Priorities in scientific discovery, American Sociological
Review 22, 635-659.
Mowery, D. C. and A. A. Ziedonis, 2002, ‘Academic Patent Quality Before
and After the Bayh-Dole Act in the United States,’ Research Policy 31, 399418.
Stephan P.E. 2001. Educational implications of university-industry technology transfer. Journal of Technology Transfer. 26 : 199-205.
Stephan, P.E., 1996. The economics of science. Journal of Economic Literature 34, 1199-1235.
Stephan, P.E., Gurmu, S., Sumell, A.J., Black, G., 2002. Individual patenting and publication activity : Having one’s cake and eating it too. Conference ‘Rethinking science policy : Analytical frameworks for evidence-based
policy’, SPRU, Brighton, March 21-23.
Vuong, Q., 1989, Likelihood Ratio Tests for Model Selection and NonNested Hypotheses, Econometrica : 57, 307-334.
Wallmark, J.T., 1997. Innovations and patents at universities : The case
of Chalmers University of Technology. Technovation 17, 127-139.
Weiss, Y., Lillard, L.A., 1982. Output variability, academic labor contracts,
and waiting times for promotion. Research in Labor Economics 5, 157-188.
Appendix : The variables
· Patent i : number of patents that i invented (or co-invented) during
period 1995-2000 (French, EU, US patent applications). It is our dependent
variable yi .
· Age i : i’s age in year 1996.
· Age1 i : dummy variable equal to one if i belongs to the first cohort of age
(Agei ≤ 35) and zero othewise. Similarly, Age2 i = 1 if 35 < Agei ≤ 45, Age3 i
is equal to one if 45 < Agei ≤ 55, and Age4 i is equal to one if Agei > 55.
· Fulltime i : dummy variable equal to one if the permanent researcher has
a full-time research position in year 1996 and zero if he occupies a teach-&research position.
18
· Junior i : dummy variable equal to one if the permanent researchers
remained ‘un-promoted’ (as Assistant Professors or Researchers) in year 1996
and zero otherwise.
· Discipline1 i : dummy variable equal to one if the scientific discipline to
which the agent is affiliated is Mathematics, Discipline2 i stands for Physics,
Discipline3 i stands for Chemistry, Discipline4 i is for Earth Sciences, Discipline5 i is for Engineering Sciences, Discipline6 i is for Biology, Discipline7 i
is for Medicine and Discipline8 i stands for Social Sciences.
· Perf i : publication performance of i over the 1995-2000 window corrected
for co-authorship (strict proportionality).
· Imp i : average impact of i’s publication occurrences over 1995-2000
window (Impact Factor of the journals).
· Indus i : share of i’s publications that were co-authored with at least one
author who mentioned a company as his or her professional affiliation.
· Inter i : share of i’s publications that were co-authored with at least one
author which mentioned an address outside France.
· Interdis i : the diversity of i’s publication occurrences over the scientific
domains of theX
JCR. It is computed as follows (standard entropy measure) :
¡ ¢
φij ln φij with φij the share i’s publications that occured
Interdis i = −
i∈Pi
in domain j.
· Lab.perm i : the number of permanent researchers of the laboratory to
which i belongs.
· Lab.age i : the average age of colleagues in the lab (all other permanent
researchers).
· Lab.junior i : share of un-promoted among colleagues in the lab.
· Lab.fulltime i : share of full-time researchers among colleagues.
· Lab.Pub i : productivity of colleagues over the larger 1993-2000 period,
which corresponds to the average publication performance of colleagues, corrected for co-authorship.
· Lab.Imp i : average impact factor of colleagues’ publication.
· Lab.phd i : average number of PhD students per permanent of the lab.
· Lab.postdocN i : is the average number of National post-docs per permanent of the lab.
· Lab.postdocF i : is the average number of Foreign post-docs per permanent of the lab.
· Lab.nonres i : stands for the number non-researchers per permanent researchers in the lab.
19
· Lab.Interdis i : is the entropy of the distribution of permanent researchers
over the set of subdisciplines (lowest aggregation level obtained from the OST
classification³applicable
in France). It is computed as follows : Lab.Interdis i =
´
X n
nij
ij
−
ln #Li with Li the set of permanent researchers in i’s laboratory,
#Li
i∈Li
#Li it’s cardinal, and nij the number of Li ’s researchers that are affiliated
to domain j.
· Lab.funding i : amount (in thousand Euros) of public recurrent funding
per permanent researcher per year over the period 1996-2000.
· Lab.contractualPub i : amount (in thousand Euros) of contractual public
support per permanent over the largest period 1993-2000.
· Lab.contractualPriv i : amount (in thousand Euros) of contractual received from private sources per permanent over the largest period 1993-2000.
20
Table 1. Descriptive statistics on the variables
Patent
Age
Age1
Age2
Age3
Age4
Fulltime
Junior
Perf
Imp
Interdisc
Indus
Inter
Lab.perm
Lab.pub
Lab.imp
Lab.interdisc
Lab.age
Lab.fulltime
Lab.junior
Lab.phd
Lab.postdocN
Lab.postdocF
Lab.nonres
Lab.funding
Lab.contractualPub
Lab.contractualPriv
Discipline1
Discipline2
Discipline3
Discipline4
Discipline5
Discipline6
Discipline7
Discipline8
Mean
0.447
44.872
0.190
0.305
0.363
0.141
0.504
0.562
3.607
3.204
1.038
0.0448
0.303
36.454
3.186
3.223
0.806
51.794
0.468
0.576
0.841
0.108
0.400
0.797
59.170
441.505
398.002
0.056
0.120
0.154
0.072
0.067
0.361
0.091
0.078
Std. Err.
1.858
9.102
4.754
2.349
0.605
0.115
0.303
26.433
1.834
2.072
0.404
3.481
0.258
0.135
0.470
0.192
0.632
0.839
41.331
425.699
599.401
21
Min
0
26
0
0
0
0
0
0
0
0
0
0
0
2
0
0.363
0
41.625
0
0
0.118
0
0
0
5.417
0
0
0
0
0
0
0
0
0
0
Max.
20
74
1
1
1
1
1
1
42.154
16.016
2.425
1
1
79
9.328
12.718
1.845
66
0.937
1
3
1
5.125
6.35
189.375
5265.644
2267.652
1
1
1
1
1
1
1
1
Table 2. Estimations for model (7) : Zero Inflated Negative Binomial on
Patent, which exhibit 797 zero observations and 111 non-zero observations.
Dep var: Patent
Age
Age2
Age3
Age4
Fulltime
Junior
Perf
Imp
Interdisc
Indus
Inter
Lab.perm
Lab.pub
Lab.imp
Lab.interdisc
Lab.age
Lab.fulltime
Lab.junior
Lab.phd
Lab.postdocN
Lab.postdocF
Lab.nonres
Lab.funding
Lab.contractualPub
Lab.contractualPriv
Discipline2
Discipline3
Discipline4
Discipline5
Discipline6
Discipline7
Discipline8
_cons
1/θ
Negative Binomial
Coef.
Std. Err.
0.459
0.783*
1.277***
-0.093
-0.342
0.003
-0.022
-0.158
3.398***
-0.710
0.013*
-0.351***
0.340***
2.025***
0.219***
0.420
2.574**
1.144***
0.062
-0.623**
-0.892***
0.011
0.001***
0.000
0.397
0.416
0.470
0.255
0.262
0.014
0.069
0.249
1.148
0.631
0.007
0.101
0.122
0.522
0.060
0.897
1.184
0.395
0.812
0.257
0.252
0.010
0.000
0.000
-16.195***
3.951
Logit Selection
Coef.
Std. Err.
-0.016
0.020
-0.406
-0.2528
-0.219***
-0.309**
-0.366
0.048
1.006
0.000
-0.007
0.189
1.987*
0.221**
-0.422
3.500*
0.453
-1.978*
0.053
0.002
0.001
0.001
-0.000
-2.211
-3.022*
-3.520*
38.798
-2.552
-1.217
-2.172
-8.834
0.354
0.373
0.371
0.057
0.127
0.342
1.348
0.761
0.010
0.165
0.223
1.095
0.093
0.154
1.816
0.624
1.053
0.489
0.430
0.013
0.001
0.001
2.000
1.797
1.916
4.94e8
1.814
1.858
2.050
6.404
0.162
***, ** and * indicate that coefficients are statistically significant at the 0.01,
0.05 and 0.10 levels respectively. Concerning Age cohorts and Disciplines variables,
coefficient should be understood as compared with the first modalities which are
taken into reference (Discipline1 and Age1 ).
22
Figure 1. Distributions of patents (Patent) and publication performance
(Perf ) among researchers.
1.5
1
Density
.5
0
0
5
10
Patent
15
20
.3
.2
Density
.1
0
0
10
20
Perf
23
30
40
Figure 2. Two way plots of Age×Patent and Age×Perf (publication performance)
20
Patents
15
10
5
0
20
40
Age
60
80
60
80
40
Perf
30
20
10
0
20
40
Age
24
0
10
Perf
20
30
40
Figure 3. Two way plots of Patent×Perf (# of patents and publication
performance) and Patent×Imp (# of patents and average impact factor)
5
10
Patent
15
20
0
5
10
Patent
15
20
0
5
Imp
10
15
0
25
0
.5
Lab.interdis
1
1.5
2
Figure 4. Two way plots of Interdis×Lab.Interdis (diversity of individual
publication outputs on JCR domains and diversity of researchers affiliation
in the laboratories)
0
.5
1
Interdiscis
26
1.5
2
2.5