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Research Policy 38 (2009) 281–292

Contents lists available at ScienceDirect

Research Policy

journa l homepage: www.e lsev ier .com/ locate / respol

s commercialization good or bad for science? Individual-levelvidence from the Max Planck Society

uido Buenstorf ∗

ax Planck Institute of Economics, Evolutionary Economics Group, Kahlaische Strasse 10, 07745 Jena, Germany

r t i c l e i n f o

rticle history:eceived 2 February 2007eceived in revised form 27 August 2008ccepted 10 November 2008vailable online 4 January 2009

EL classification:2326

a b s t r a c t

Based on new data, this paper studies the invention disclosure, licensing, and spin-off activities of MaxPlanck Institute directors over the time period 1985–2004, analyzing their effects on the scientists’ sub-sequent publication and citation records. Consistent with prior findings, inventing does not adverselyaffect research output. Mixed results are obtained with regard to commercialization activities. The analysissuggests qualifications to earlier explanations of positive relationships between inventing and publishing.

© 2008 Elsevier B.V. All rights reserved.

31

eywords:ublic researchommercializationechnology transfer

icensingpin-off entrepreneurship

. Introduction

Over the past decades, Western societies have increasingly comeo expect public research to generate “useful” results that can be puto practical applications in the private sector. In particular, the rolef intellectual property rights (IPRs) in the knowledge transfer fromublic research has been focused upon. This has led to policy initia-ives such as the Bayh-Dole Act in the United States and analogous

easures in other countries (cf., e.g., Schmoch, 2000; Mowery et al.,001; OECD, 2003; Lissoni et al., 2007). Increasing policy supportas also been given to university spin-offs (Egeln et al., 2002). These

nitiatives were motivated by the intention to make best societal usef the money spent on public research. The commercialization ofesults moreover provides a potential source of income for univer-ities and other public research organizations, thus promising toeduce their dependency on public funds.

Given the incentives faced by universities and other pub-

ic research organizations, in many countries these institutionsncourage their scientist employees to make and disclose inven-ions, which can then be patented and licensed to commercial firms,nd/or to organize spin-off firms. Most institutions have set up tech-

∗ Tel.: +49 3641 68 68 21; fax: +49 3641 68 68 68.E-mail address: buenstorf@econ.mpg.de.

048-7333/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.respol.2008.11.006

nology transfer offices to support the scientist in these activities,but the scientist’s active involvement is nonetheless required in thedisclosure, patenting and commercialization of inventions (Thursbyand Thursby, 2004; Agrawal, 2006). As a consequence, the scientist-inventor is induced to also become an innovator and/or spin-offfounder. Potential repercussions of the ensuing activities for com-mercializing academic inventions through licensing and spin-offformation are in the focus of the present study.

It is important to study commercialization activities becausedetrimental side effects on the scientists’ ability and willingness tofulfill their traditional research and teaching tasks cannot be ruledout on a priori grounds. Indeed, while policy makers and much ofthe broader public have hailed the policy initiatives targeting tech-nology transfer, skeptics have pointed out a number of potentialhazards to the scientists’ academic performance, including compet-ing demands on their time, increased secrecy, and shifts in researchinterests. Further potential for negative repercussions is seen at theinstitutional level (cf. Geuna and Nesta, 2006).

A substantial empirical literature has emerged in the past yearsin which the relevance of these concerns is assessed by study-

ing the relationship between technology transfer and researchoutput at the level of individual researchers. Given limited dataavailability, most prior studies have used patent data to iden-tify technology transfer activities. Accordingly, the focus of thesestudies is on the academic inventions themselves rather than on

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heir commercialization, even though the latter is more relevantrom a societal perspective. An important exception is Lowe andonzalez-Brambila (2007) who analyze the research output of spin-ff founders. The present paper goes one step further and utilizesbroader set of indicators for technology transfer activities: dis-

losed inventions, inventions that are licensed, license revenues, asell as spin-off entrepreneurship with different forms of scientist

nvolvement in the new venture. It then analyzes the relationshipetween these indicators on the one hand and publication/citationeasures on the other, finding substantial differences between the

arious activities. To the author’s knowledge, no comparably broadnvestigation into the individual-level effects of commercializationctivities has been conducted before. By providing evidence onechnology transfer in a non-university public research organiza-ion, and on licensing activities from European public research, thetudy moreover helps to close gaps in the existing empirical recordcf. Geuna and Nesta, 2006).

The analysis is based on longitudinal data for the top echelonf researchers at the Max Planck Society, Germany’s largest non-niversity public research organization dedicated to basic research.or a variety of reasons the Max Planck Society is well suited totudy the relationship between commercialization activities andesearch performance. First, it is large, covers a broad spectrum ofisciplines, and follows a consistent strategy of performing basicesearch. Second, the Max Planck Society has been subject to theame intellectual property rights (IPR) regime since the 1970s; aegime that anticipated the emerging global model of organizingroperty in university inventions. Third, having established a tech-ology transfer subsidiary in 1970, the Society has a long traditionf organized technology transfer, and consistent time series data onnventions and licenses exist. Systematic support of spin-offs wasaken up in the early 1990s.

The remainder of the paper is structured as follows. In Section, theoretical considerations concerning the relationship betweenhe research and commercialization activities of scientists in publicesearch are outlined. Section 3 reviews the existing empirical liter-ture. Section 4 presents the institutional setup of the Max Planckociety and its intellectual property rights (IPR) regime. In Section, the data underlying the econometric analysis are described. Sec-ion 6 presents the methods and results of the analysis, which isiscussed in Section 7. Concluding remarks are made in Section.

. Research and commercialization: competing oromplementary?

Over the past years, various conflicting conjectures have beenut forward regarding the research performance of academic

nventors. The discussion has mostly concentrated on univer-ity patenting. However, the theoretical arguments can generallye extended to commercialization activities, and the conjecturedffects can even be expected to be more pronounced for academicnnovators and spin-off founders than for “mere” inventors.

.1. Negative effects

The skeptical view on academic inventions suggests thatesearch and inventive activities are competing for the scientist’simited time, which implies that more inventing comes with lessesearch output (cf., e.g., Fabrizio and DiMinin, 2008, for a discus-ion). This substitutive relationship would not be present only if

nventions were purely joint products of research activities (e.g.,nstruments originally designed for the researcher’s own purposes)r if the same results could both be published and be used as theasis of commercial products (as is the case, e.g., in some fieldsf the life sciences where “patent-paper pairs” are commonplace;

y 38 (2009) 281–292

cf. Murray and Stern, 2007). Even in these cases, however, timeand effort are required to obtain patent protection for the inven-tion.

Moreover, academic inventions tend to be far from being mar-ketable, and substantial further innovative effort is needed to turnthem into commercial products (Jensen and Thursby, 2001). Theidentification of potential licensees and the successful commercial-ization of inventions frequently hinge on the personal involvementof the inventor who possesses in-depth and largely uncodifiedknowledge about the technology (Thursby and Thursby, 2004;Agrawal, 2006). Scientist-inventors involved in spin-off forma-tion face additional organizational and management challenges inthe new firm. According to this line of reasoning, inventions thatare being commercialized should have more pronounced adverseeffects on individual research activities than those for which nocommercialization activities are initiated, with the strongest neg-ative effects being associated with commercialization throughspin-off activities.

Two further potential hazards of academic inventing are closelyrelated to the above argument: delays in publication and shiftsin interests from basic to more applied work (cf. Calderini andFranzoni, 2004; Geuna and Nesta, 2006). Publication delays may becaused by the legal requirements of the patent application process(Stephan et al., 2007). For scientists pursuing the commercializationof their inventions, additional incentives for postponing publica-tions derive from the need to safeguard first-mover advantagesvis-à-vis potential competitors (of licensees or spin-offs). To theextent that applied work is more easily turned into commercialproducts, inventing researchers may be induced to focus on thiskind of research. This shift in interests is problematic if it compro-mises the generality and relevance of their work. Its effect shouldagain be stronger for scientist-inventors intent on commercializingtheir technologies, and yet stronger for spin-off founders who willaim at broadening the technology base of their fledgling firm byfurther inventions.

The above considerations imply adverse effects of commercial-ization activities on individual research productivity. They can besummed up in the following hypothesis:

Hypothesis 1 (crowding-out). Commercialization activities haveadverse effects on scientists’ research output. These effects arestronger than those of inventive activities for which no commer-cialization efforts are documented; they are stronger for scientistsinvolved in spin-off activities than for those whose inventions arelicensed to pre-existing firms.

2.2. Positive effects

Even though research and commercialization may be compet-ing for a scientist’s limited time, the relationship between theseactivities need not be negative if they have mutually beneficial sideeffects. Several favorable effects of interaction between academicinventors and private-sector firms have been suggested before (cf.,e.g., Stephan et al., 2007). They constitute potentially powerfulsources of benefits for research activities. Firm contacts providelearning opportunities, helping researchers to identify relevantissues for their research as well as approaches to tackle them. Inaddition, the skills and equipment available in firms are often com-plementary to those found in public research labs. Finally, firmsmay have access to networks that the researcher herself is not partof.

Positive effects of firm interaction are more likely to emerge fromcommercialization activities than from academic invention alone,which can in principle proceed in isolation. Inventor involvementin the commercialization process necessitates ongoing interactionwith the private sector. This interaction should be particularly

G. Buenstorf / Research Polic

Table 1Expected effects of invention and commercialization.

Hypothesis/activity Effect on research output

Hypothesis 1 (crowding-out)Invention −Licensed invention − −Spin-off formation − − −

Hypothesis 2 (learning)Invention +Licensed invention ++Spin-off formation +++

Hypothesis 3 (funds)Income +Invention o (controlled for income)

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Licensed invention o (controlled for income)Spin-off formation o (controlled for income)

ntensive for researchers involved in spin-off formation. We accord-ngly expect to find stronger effects of learning for inventors whoctually engage in the commercialization of their technologies,otivating the following hypothesis:

ypothesis 2 (learning). Interaction with private-sector firms pro-ides scientists with learning opportunities that enable them toncrease their research output. The learning effects are strongestor researchers involved in spin-off activities, and they are strongeror inventors of licensed technologies than for inventors of tech-ologies for which no commercialization efforts are documented.

In addition to the learning opportunities generated by private-ector contacts, scientists may also benefit directly from thenancial pay-offs of successful commercialization. Commercialized

nventions often generate flows of income not only for the scientistersonally, but also for her laboratory, which provides additionalesources to step up future research activities.1 License revenuesnd patent sales are direct sources of income. Spin-offs likewise aresource of funding for future research, at least those spin-offs that

icense technologies from the scientist’s employer institution. Addi-ional income may be generated through direct funding of researchctivities based on contract research or research collaborations withrivate-sector partners. Finally, benefits may derive from in-kindesource transfers, for example when firms pay for specific items ofaboratory equipment.

Including data on income flows from successful commercial-zation in the empirical analysis allows for distinguishing theseesource effects from the learning effects suggested by Hypothesis. If academic inventors only benefit from the financial returns ofheir activities, then controlling for the financial pay-off shouldliminate any positive relationship between inventions and com-ercialization on the one hand, and research output on the other.

his is expressed in the following hypothesis:

ypothesis 3 (funds). Income flows from the commercializationctivities of academic researchers through licensing and spin-offormation have a positive effect on the future research output ofhese scientists. This effect is only dependent on the amount ofncome generated.

Hypotheses 1–3 predict different effects of academic inventions

nd commercialization activities on individual research output.hese predictions are summarized in Table 1.

1 As will be detailed in the next section, in the Max Planck Society one-third of allicensing income goes to the inventor’s institute, where it is used to fund researchctivities.

y 38 (2009) 281–292 283

2.3. Individual heterogeneity

Positive correlations between research output and com-mercialization activities need not be caused by systematiccomplementarities, as implied by Hypotheses 2 and 3. Instead, theymay simply reflect uncontrolled heterogeneity among researchers.It has been suggested that differences in inventive activities andresearch performance may both be caused by the same variationsin individual skills, effort, and serendipity – i.e., by having “the rightstuff” for being a successful scientist, as Stephan et al. (2007) putit. If this conjecture holds, scientists coming up with more inven-tions are also likely to be superior researchers, as similar traits arerequired for both activities. Again, an analogous reasoning holds forcommercialization activities: some researchers could be more suc-cessful in licensing and spin-off formation because they are moretalented or more ambitious than others.

Some of the factors underlying such spurious correlations canbe derived from the economics of science (cf. Stephan, 1996). Forexample, lifecycle effects have been found to systematically affectindividual research productivity (Levin and Stephan, 1991). Not allheterogeneity among researchers will be reflected in observables,though, because some explanatory variables remain unobservable,and a degree of luck is also likely to be involved in making importantdiscoveries and inventions. In addition, differences in individualspecializations by discipline and field of research can be expectedto translate into differences in both inventions and publications. Iffields with higher average propensities of publication were at thesame time fields where scientists are more likely to make inven-tions and commercialize them, then a positive relationship at theindividual level might just reflect these differences at the fieldlevel.

The conjecture of individual heterogeneity among scientists willnot be tested directly below. Instead, the econometric analysisincludes observable factors likely to underlie the heterogeneity,and model specifications are adopted that allow for controlling theremaining unobserved heterogeneity. More importantly, the thrustof the analysis focuses on intertemporal variations in the researchoutput of individual researchers rather than on cross-sectional dif-ferences between individuals. In this way, the influence of field-anddiscipline-specific factors is minimized.

3. Prior empirical findings on academic inventors andentrepreneurs

A number of empirical studies have helped to clarify the rela-tionship between academic inventions and individual researchoutput. Most existing work is based on U.S. data, in particularuniversity patents, but new studies using European data havebeen forthcoming lately. The existing empirical evidence stronglysuggests complementarities between academic inventions andindividual research productivity. In contrast, little prior work existsthat investigates the effects of commercialization activities.

The pioneering study by Agrawal and Henderson (2002) ana-lyzes technology transfer from engineering and computer sciencedepartments at MIT. It finds that the number of patents held byindividual faculty members is uncorrelated with their numbers ofpublications, but positively correlated with the citation rates oftheir papers. These results indicate that academic inventors do notpublish more, but more relevant, work than their non-inventingpeers. A broad cross-sectional sample of U.S. university facultybased on the Survey of Doctorate Recipients is utilized by Stephan et

al. (2007). Their study finds that on average, patenting researchersare more prolific authors than the members of the non-patentingcontrol group. Fabrizio and DiMinin (2008) analyze a longitudinalpanel containing annual publication and citation rates of 150 fac-ulty inventors as well as a matched control group of non-patenting

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cientists. In line with the results by Stephan et al. (2007), inventorsre found to publish more and to have more citations to their work.ublication rates moreover go up significantly after a researcher’srst patent. Azoulay et al. (2006) similarly identify a positive rela-ionship between individual patenting and publication activities.heir study is noteworthy both for the size of the sample, whichncompasses 841 academic inventors from the life sciences, and forhe elaborate efforts taken to control for unobserved heterogene-ty. Finally, Thursby and Thursby (forthcoming) differ from the othertudies of academic inventors in that their analysis is not based onatent data, but on inventions disclosed by U.S. university scientistso their employers. For this alternative, more encompassing mea-ure of academic inventions, the finding of a positive relationshipo publication output is reproduced.

Studies of European academic inventors come to similar con-lusions. Van Looy et al. (2006) and Carayol (2007) identifyositive relationships between patenting and publication countsor samples of scientists working at individual Belgian (KU Leu-en) and French (ULP Strasbourg) universities, respectively. Theesults obtained by Van Looy et al. (2006) moreover indicate that thenterests of patenting researchers do not shift toward more applied

ork, and Carayol (2007) finds that scientists publishing in jour-als with higher impact factors are more likely to patent. Otherork has analyzed samples of individuals from specific fields of

esearch. Calderini and Franzoni (2004) investigate the activitiesf Italian scientists and engineers active in the field of materi-ls science. They identify complementary relationships betweenatenting and the quantity and quality of research output. Dis-inguishing between disciplines, positive effects of patenting onublication output are limited to the engineering faculty, whilecientists are not benefiting (Franzoni et al., 2007). This results attributed to the more applied character of engineering. Aample of British, German and Belgian nano-scientists is stud-ed by Meyer (2006). He finds that while only a small fractionf publishing researchers also engage in patenting, these are fre-uently among the most prolific authors. Breschi et al. (2006)tudy the patenting and publication records of 296 Italian uni-ersity faculty from various patent-intensive fields of research,nd a non-patenting control group of equal size, over a 22-yeareriod. Patenting is found to have positive effects on the subse-uent publication output (as well as the citation record) of theseesearchers.

As opposed to the U.S., European universities own only a minor-ty of the patents on inventions made by their employees (Crespi etl., 2006; Lissoni et al., 2007). This implies that patent ownership isweak measure of academic inventing, necessitating the identifica-

ion of academic inventors in patent data. The work of Breschi et al.2006) is based on matching governmental listings of researcherst public universities with inventor names in patent databases, aethodology that has recently been expanded to other European

ountries (Lissoni et al., 2007). Lacking access to such employee list-ngs, existing empirical studies of German academic inventors havenstead identified professors by their title in the inventor fields ofatent databases (Becher et al., 1996; Schmoch, 2007). While this

dentification strategy is not without limitations,2 it enables the

onstruction of comparatively large samples. This is exploited byzarnitzki et al. (2007) who study the effect of patenting on pub-

ication quantity and quality for 3135 German professors over theeriod from 1998 to 2002. Consistent with the results from other

2 Numerous private-sector researchers hold honorary professorships at Germanniversities and thus show up as professors in the databases. At the same time,rofessors may not consistently use their title in patent applications, which after002 may reflect illegitimate circumvention of their employer’s technology transferffice (Becher et al., 1996; Schmoch, 2007).

y 38 (2009) 281–292

studies, patent applications are positively correlated with publica-tion quantity and quality.

To the author’s knowledge, only a single paper has specificallyinvestigated the effects of academic entrepreneurship on individualresearch productivity. Lowe and Gonzalez-Brambila (2007) studya sample of 141 U.S. faculty entrepreneurs. These are defined asuniversity researchers founding spin-off firms to commercializeinventions they previously disclosed to their employers. The studyfinds that faculty entrepreneurs are generally more prolific authorsthan their graduate school peers or co-authors. In addition, orga-nizing a spin-off has positive effects on the researchers’ subsequentpublication and citation records. If spin-off founders are further dis-tinguished by discipline, these effects turn insignificant for thosedisciplines (biomedical and chemical research) where founderstend to be more senior scientists (as compared to engineering).

4. The Max Planck Society and its intellectual propertyrights regime

Unlike the U.S., but similar to other continental European coun-tries such as France and Italy, universities do not fully dominatepublic research in Germany. The Max Planck Society, whose roots goback to the early 20th century, is Germany’s largest non-universitypublic research organization dedicated to basic research. Organizedas a private non-profit association, the Society employs some 4700researchers in 80 institutes located throughout the country.3 It getsalmost 80% of its budget from public, institutional funding (MaxPlanck Society, 2008).

The Max Planck Society’s mission is to take up large-scale, inter-disciplinary, or particularly innovative activities that are beyondthe reach of individual German universities. Its research activitiesencompass the whole spectrum of the sciences and the humani-ties, with the individual institutes organized into three sections: thebiomedical section, the chemistry, physics and technology section,as well as the humanities and social sciences section. The key orga-nizing principle of the Max Planck Society – known as the HarnackPrinciple – is to put the “directors” of Max Planck Institutes, i.e. thehighest-level researchers, into a highly autonomous and powerfulposition. Depending on their size, individual Max Planck Instituteshave from 2 to about 10 directors, who are recruited among success-ful researchers of both German and foreign universities. Currently,there are roughly 270 active directors. Max Planck directors eachlead their own research group and jointly manage the respec-tive institute. Their mission is research-oriented, with substantialinstitutional funding and relatively small teaching loads and admin-istrative burdens. As scientific members of the Max Planck Society,directors are also involved in its strategic decision making.

Academic inventions made within the Max Planck Society havehistorically been treated differently from those of German uni-versity researchers. All employees of German firms are subject tothe Arbeitnehmererfindungsgesetz, which mandates that employeesmust disclose inventions to their employer, and grants the employerthe ownership in employee inventions. University researchers usedto be exempt from this law; they retained the intellectual propertyin whatever inventions emerged from their research (the so-calledHochschullehrerprivileg or “professors’ privilege”). In 2002, the priv-ilege was abolished, and German universities became the legal

owners of the inventions made by their researchers. Universities arenow responsible for the patenting and licensing of these inventions;in particular they have to cover the costs of the patent application.The inventing researcher is entitled to 30% of the gross licensing

3 In addition, in the time period under investigation there were three instituteslocated outside Germany. They all belong to the humanities and social sciencessection and are disregarded in what follows.

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evenues from her technology (Bielig and Haase, 2004; Schmoch,007).

The new IPR regime for inventions by German universityesearchers replicates the rules that Max Planck researchers havelways been subject to. They are required to disclose all their inven-ions to the Max Planck Society, which can then claim ownership inhe technology. In this case, the Society organizes the patent appli-ation (if possible and deemed adequate) as well as the sale anddministration of licenses. The inventing researcher receives 30%f all revenues from licenses and patent sales, and the Max Plancknstitute employing the researcher gets an additional third of allncome.

In 1970 the Max Planck Society established a legally independentechnology transfer subsidiary named Garching Innovation GmbH,hich was renamed Max Planck Innovation GmbH recently. In its

arly years, Garching Innovation engaged in the construction andale of prototypes based on Max Planck inventions. As these activ-ties were of little success, for the past three decades the firm hasonsistently concentrated on patenting and licensing. Max Plancknnovation staff members regularly visit the individual institutes tonform scientists about its technology transfer activities and solicithe disclosure of new inventions. Working with external patentttorneys, Max Planck Innovation administers the application foratents on promising technologies. Technologies are subsequentlyarketed to domestic and foreign firms. The active support of spin-

ffs was taken up in the early 1990s.Since 1979, Max Planck Innovation has closed more than 1500

icense agreements (Max Planck Innovation, 2007). Total returnsrom technology transfer activities exceed D 200 million, with theulk of income resulting from a small number of highly successfulblockbuster” inventions. The revenues of Max Planck Innovationmount to about 1% of the Max Planck Society’s overall budget (Maxlanck Society, 2008).

. Data

Max Planck Innovation’s databases on inventions, licenses, andpin-offs provide the main sources of data for the present study. Dis-losed inventions are taken as the basic units of observation. Theatabases document all disclosed inventions, and the underlying

nventions can be identified for both patents and license agree-ents. Adopting disclosed inventions as the units of analysis helps

o avoid double counting, as frequently multiple patents and licensegreements pertain to the same invention. It also allows for includ-ng non-patented inventions that can nonetheless be licensed andommercialized (e.g., software). In total, the inventions databaseontains some 3000 inventions disclosed since 1970. Disclosureoes not imply that patents ensuing from the inventions are neces-arily assigned to the Max Planck Society. Therefore, an additionaldvantage of using disclosures as the basic units of observation ishat problems of identifying inventions of Max Planck employeesn patent databases are avoided.

Additional information from the license and spin-off databasesas utilized to identify commercialization activities. For all inven-

ions it was recorded whether a license contract or similar formf agreement with a private-sector firm (such as an option on aicense, often based on prior research collaboration) was concluded.icensee names were matched with a list of Max Planck spin-offs to

dentify inventions licensed to spin-offs. In addition, all paymentsesulting from the contracts were deflated to year 2000 Deutscheark equivalents and aggregated into an annual measure of current

ncome flows.4

4 The database does not contain information about direct payments from privaterms to individual institutes that are not administered by Max Planck Innovation.

y 38 (2009) 281–292 285

Spin-off activities are identified in the data in two different ways.First, Max Planck researchers who want to commercialize theirinventions through spin-offs are nonetheless required to disclosethese inventions, and the spin-off has to license the technology fromthe Max Planck Society. The license database therefore containsinformation on inventions licensed to spin-offs. Second, spin-offfounders, operative managers, board members, and consultantsfrom the Max Planck Society are all identified in Max Planck Inno-vation’s spin-off database. These data provide useful informationon what organizing a spin-off does (and does not) imply for thesenior scientists considered in the empirical analysis. None of thedirectors listed as spin-off founders actually joined the operativemanagement of the new venture, but most of them are listed asboard members and/or advisors.

In the present study only those inventions are considered thathave at least one Max Planck director among their inventors. Tothis purpose, the list of inventor names on all inventions disclosedto Max Planck Innovation was matched with a list of Max Planckdirectors, which is based on official publications (Henning andUllmann, 1998; Max Planck Society, 2000) and was updated to2004. By restricting attention to the group of Max Planck directors,non-inventing directors provide a straightforward control group forevaluating the impact of inventions and commercialization activi-ties. Using non-inventing directors as the control group is justifiedbecause the group of directors is homogeneous in various aspects.All Max Planck directors are experienced scientists recruited on thebasis of proven prior achievements. They face similar working con-ditions and are evaluated against the same criteria of success, whichemphasize the individual contributions to the respective fields ofresearch. In addition, the appointment process for new directors isidentical throughout the Max Planck Society, and current directorsare involved in the selection of their future peers. Given the char-acteristics of Max Planck directors, the study analyzes a selectivesubset of experienced first-tier scientists in Germany. The sampleincludes 11 Nobel laureates.

In the subsequent analysis, publication and citation records ofall Max Planck directors in the biomedical section and the chem-istry, physics and technology section are studied, beginning in 1985and excluding directors with less than 5 years in office between1985 and 2004. The humanities section is not taken into considera-tion because the technology transfer activities of its researchers arenegligible. The resulting number of researchers is 314. 140 of them(44.6%) have disclosed at least one invention.

The empirical analysis encompasses a total of 860director–invention pairs disclosed since 1985. There are a smallnumber of joint inventions co-disclosed by two directors, whichare treated as individual inventions for both directors. Countsof inventions by each Max Planck director are recorded by year.Their number is highly skewed, with 87.8% of the observationsequal to zero and the maximum being 11 disclosures in a sin-gle year. 249 inventions were licensed to firms (this numberincludes options and patent sales). 155 inventions were licensedto spin-offs. There are 34 instances of Max Planck directors beinglisted as spin-off founders, and 46 listings as organ members orconsultants (these numbers include a small number of cases ofrepeated spin-off involvement by individual scientists). Typically,researchers become listed as spin-off founders several years afterdisclosing their first invention that is subsequently licensed by the

spin-off. This reflects the time lag between the invention and theestablishment of the spin-off. Since spin-offs frequently enter intomultiple license agreements with the Max Planck Society, some ofthese time lags are substantial.

In cases of license agreements and payments pertaining to multiple inventions, thepayments were equally divided among the inventions.

286 G. Buenstorf / Research Policy 38 (2009) 281–292

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Fig. 1. Histogram of fractional publication counts, 1985–2004.

Annual publication and citation counts are utilized to measurendividual research output. Both measures were obtained from theSI Science Citation Index through an author search in the Web ofnowledge online edition, counting all publications that name theespective researcher as an author. To minimize errors from includ-ng non-Max Planck employees with identical names, the searchas limited to articles containing the respective Max Planck Insti-

ute, or the Max Planck Society more generally, as the author’sffiliation. A total of 33,277 publications were identified in this way.he empirical analysis employs fractional publication and citationeasures (i.e., the weight of each article is given by one over the

umber of co-authors).5 Citations were attributed to the publica-ion year of the cited work, irrespective of the year of the actualitation. A 3-year citation window was taken into consideration, sohat more recent publications have the same chance of being citeds earlier ones. To reflect time lags in publication, the study ana-yzes effects of inventions and commercialization activities on theutput in the following year.6

Both publication counts and citation counts are highly skewedcf. Figs. 1 and 2). For 9.7% of all director-years, no publicationsre listed in the Science Citation Index (citations: 12.5%).7 Theaximum number of non-weighted annual publications (3-year

itations) recorded for a single researcher is 88 (3384). For pub-ications, the mean is 8.9 and the standard deviation is 9.1; foritations, the mean is 88.7 and the standard deviation is 134.7. Forhe fractional publication (citation) measures, the maximum is 20.8475.3), the mean is 2.5 (21.1), and the standard deviation is 2.531.4).

To check the robustness of our results, a wide variety oflternative output measures were experimented with, includ-ng non-weighted publications and citations (analyzed with fixedffects conditional negative binomial models), as well as citations

5 The number of co-authors per article varies greatly in the data; it has a meanf 7.6 and a median of 4. In about 2% of all cases, it exceeded 100. These articlesre assigned a weight of 0.01 in our output measures. Since the empirical anal-sis focuses on intertemporal variation in the publication outputs of individualesearchers rather than cross-sectional comparisons, the use of fractional outputeasures is primarily needed to correct for possible changes in the average num-

er of co-authors brought about by increasing seniority. Note also that due to theeighting, our output measures are no longer integers, thus the use of count dataodels is excluded.6 Publication lags differ between fields and disciplines. Accordingly, assuming a

ublication lag of 1 year is a compromise. Our data suggest, however, that publica-ions in the first year of tenure as a Max Planck director are not reliably attributed tohe May Planck affiliation. This indicates that publication lags may be substantial.

7 This is a very low share compared to other studies using publications and cita-ions as dependent variables. For example, in the study of Czarnitzki et al. (2007),

ore than half of all annual observations are zero.

Fig. 2. Histogram of fractional citation counts, 1985–2004.

accumulated over a 5-year period or an unlimited time span. Thealternative output measures are highly correlated (cf. Table 2) andyielded very similar results. The same holds for model specificationsusing same-year rather than next-year output measures.

Fixed effect specifications are used below to control forcross-sectional heterogeneity across researchers. Accordingly, notime-invariant control variables are included in the analysis. Toaccount for the well-known life cycle effects in research output(Levin and Stephan, 1991), the year of PhD or MD completion wasrecorded for all individuals, and the time span between PhD yearand year of observation is included as an explanatory variable (inlinear and quadratic terms). In addition, a set of 10 dummy variablesis included that denote individual years of tenure as Max Planckdirector. They control for the buildup of resources and reputationonce a scientist assumes a directorship position at the Max PlanckSociety. (The individual variables indicate, respectively, the secondthrough tenth years of tenure, as well as all further years.)

Some model specifications use weighted regression models (seeSection 6 for a detailed description). To construct the weights forthese estimations, institute-level aggregates of cumulated inven-tions and commercialization activities are used. Specifically, thenumbers of inventions, licensing agreements, as well as spin-offs byall employees of the respective institutes were retrieved from theMax Planck Innovation database, beginning in 1970 or the (later)year that the institute was founded. In addition, a dummy variablewas constructed that indicates directors of institutes in the biomed-ical section, as well as three cohort dummies denoting directorsnominated before 1981, between 1981 and 1990, and after 1990,respectively.

In a cross-sectional perspective, the data suggest that bothinventions and commercialization activities are associated withabove-average research performance. As can be seen in Table 3,directors who disclose at least one invention during their tenurehave on average about 50% more publications and 80% more cita-tions per year, and these differences are robust to weighting outputmeasures by the number of co-authors. Very similar patterns areobtained when directors who disclose at least one licensed inven-tion are compared to those that do not. The average annual numberof citations of spin-off founders is even more than twice as large asthat of non-founders.

As these cross-sectional comparisons do not control for differ-ences in the publication and citation cultures of scientific fields and

disciplines, they have to be treated with caution. In the next section,intertemporal differences in the publication output of individualresearchers will be related to invention and commercializationactivities. In this way, a clearer picture of the effects of technologytransfer on research productivity can be obtained.

G. Buenstorf / Research Policy 38 (2009) 281–292 287

Table 2Correlations between alternative output measures.

Publicationcounts

Fractionalpublicationcounts

Citation counts(3 years)

Fractionalcitation counts(3 years)

Citation counts(5 years)

Fractionalcitation counts(5 years)

Citationcounts (all)

Fractionalcitation counts(all)

Publication counts 1.00Fractional publication counts .90 1.00Citation counts (3 years) 1.00Fractional cit. counts (3 years) .79 1.00Citation counts (5 years) .97 .81 1.00Fractional cit. counts (5 years) .74 .97 .81 1.00Citation counts (all) .85 .80 .92 .83 1.00Fractional citation counts (all) .61 .85 .70 .91 .85 1.00

Table 3Cross-sectional comparisons between directors with and without inventions/commercialization activities.

Output measure

Mean publication counts Mean fractional publication counts Mean citationcounts (3 years)

Mean fractionalcitation counts(3 years)

InventorYes 11.2 3.0 118.0 27.8No 7.1 2.0 65.0 15.6

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. Econometric analysis

.1. Methods

The general approach of the present study is to model aesearcher’s output yit+1 (proxied by logarithms of fractional annualublications or citations)8 as a function of her invention orommercialization activities (status) in the previous period x1it,time-varying) personal characteristics x2it. . .xnit, as well as indi-idual fixed effects ci

it+1 = ˇ0 + ˇ1x1it + ˇ2x2it + . . . + ˇnxnit + ci + uit

Including the fixed effects implies that estimation resultsre driven by the within-individual time series variation, thusontrolling for time-invariant unobserved heterogeneity acrossesearchers. This specification is well suited to deal with the chal-enges arising from factors such as differences in talent or in theublication cultures of different fields and disciplines.9

A sequence of models is estimated utilizing alternative proxyariables x1it of inventions/commercialization activities: disclo-ure of inventions, disclosure of inventions that find private-sectoricensees, as well as spin-off activities. In the primary modelpecifications, these activities are interpreted as treatments thatermanently affect the researcher’s future output. In the period

f a researcher’s first invention/commercialization activity and allollowing periods, x1it is assigned the value one, and the furtherevelopment of research output is compared between individualsho are or are not subject to the treatment. Alternatively, a set ofodels is estimated in which all effects are assumed to be limited

8 Specifically, yit+1 = ln(Yit+1 + 1).9 Alternatively, random effects tobit regressions were estimated to account for

he fact that about 10% of all annual publication outputs (13% of all citation outputs)ere zero. The fixed effects regressions are reported below because they yielded

he more conservative results, with Hausman tests indicating that the independencessumption underlying the random effects model is violated (cf. Greene, 2003, ch.3). Qualitatively, the results obtained with the alternative techniques were veryimilar.

73.3 17.5

187.2 40.477.3 18.8

to the next period. These models use counts of inventions (licensedinventions, spin-off involvement) as measures of the respectiveactivities. In contrast to the treatment models, they thus reflect dif-ferences among individual researchers in the extent of inventionand commercialization activities.

Fixed effects models can only account for time-invariantindividual heterogeneity. It has been suggested, however, that het-erogeneity may be time-variant and possibly endogenous in thepresent context (Azoulay et al., 2006). Researchers who madeimportant discoveries may become more successful publishers, butalso are more likely to disclose inventions or engage in commercial-ization activities.10 In this case, selection into treatment reflectspast success and can no longer be assumed to be random.

Following Azoulay et al. (2006), the methodology of inverse-probability-of-treatment-weighted (IPTW) estimation is adoptedto account for this endogeneity. IPTW estimation was originallydeveloped in biostatistics (Robins et al., 2000). It is based on assign-ing to each subject i and period t a weight equal to the inverse ofthe conditional probability that i received her own treatment his-tory up to t (Fewell et al., 2004). Thus, individuals with “unlikely”treatment histories are assigned bigger weights, which serves tocounteract the effect of past output on the selection into treat-ment. Effectively, a pseudo-population of subjects is constructedfor which the observed characteristics no longer predict treatment.These weights are then used in weighted fixed effect regressions of(logarithms of) fractional annual publication or citation counts.

The weights are derived as follows (cf. Fewell et al., 2004): Usinglogistic regression, the researcher’s probability of treatment at timet (denoted as pt) is estimated as a function of the time-varyingconfounder as well as other explanatory variables. In line with

earlier work on academic inventors (Azoulay et al., 2006; Breschiet al., 2006), the logistic regression includes as explanatory vari-ables the researcher’s weighted publication (citation) output int − 1, section, cohort and tenure period dummies, as well as a count

10 This form of heterogeneity is also compatible with the “Matthew effect” sug-gesting that the publications of successful scientists get more attention (Merton,1968).

2 h Policy 38 (2009) 281–292

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ariable measuring the stock of inventions (licenses, spin-off activ-ties) accumulated at the respective researcher’s institute. If theesearcher was indeed subject to treatment in period t, pt directlynters the calculation of the weights; otherwise the probability ofon-treatment (1 − pt) is utilized. The product of these probabili-ies, multiplied over all prior periods in which the researcher wasnder observation, is then used as the denominator of the weight.inally, to prevent extreme weights for individual observations, theumerator of the weight is calculated analogously to the denomi-ator, except that the measure of publications (citations) in t − 1 isot included in the logistic regression.

IPTW estimation is unbiased provided there are no unmeasuredime-varying confounders (Robins et al., 2000). This is a strong andntestable assumption. However, Azoulay et al. (2006) suggest thathe method performs well if the estimated probability of treatments based on a large set of explanatory variables, subjects are drawnrom similar labor markets, and outcomes are measured similarlyor treatment and control groups. These conditions are fulfilled inhe present context.

.2. Results: treatment models

Fixed effects regressions assuming permanent treatment arestimated first. The initial model (Model 1 in Table 4) utilizes anndicator variable to characterize the “inventor” state of an indi-idual researcher. All researchers are in the “non-inventor” stateefore disclosing their first invention. If and when they disclosen invention, they switch to the “inventor” state and remain in itntil leaving the sample. This specification is estimated both withublications (Model 1a) and citations (Model 1b) as the depen-ent variable. Accordingly, the model measures whether inventingesearchers publish more or less frequently, and more or less well,fter their first disclosure. The dynamics in the output of non-nventing researchers are adopted as the benchmark. To controlor life cycle effects, linear and quadratic terms are included forhe number of years elapsed since PhD completion. The full set ofenure year controls is also included.11

The estimations show that numbers of publications are highern post-invention years, as the coefficient estimate of the indica-or variable characterizing the researchers in the “inventor” states positive and significant (at the .05 level). In contrast, a positiveut insignificant estimate is obtained when citations are used ashe dependent variable. Only weak life cycle effects are identifiedn the model (the linear term is insignificant), but this is due to thenclusion of tenure year controls in the model, which indicate thatutput is increasing over the first ten years of being a Max Planckirector.12 To control for the possible effects of time-varying con-ounders, Models 1a–b are re-estimated using IPTW-weighted fixedffects regression (Models 1c–d in Table 5). This slightly reduces theoefficient estimates of the “inventor” variable, but does not affectts significance in the model for publications.

Next, a series of analogous models is estimated where the indica-or variable denoting individuals in the “inventor” state is replacedy alternative indicator variables denoting the various forms of

ommercialization activities. Model 2 studies the effect of first dis-losing an invention that is subsequently licensed to any kind ofrm (i.e., entering the “innovator” state). In Model 3, researchers

nvolved in entrepreneurial activities are identified through their

11 In the estimations analyzing publication output, one subject is disregardedecause his publication count is zero in all years of observation. Two subjects areropped from the analysis of citations because their work is never cited.12 Alternative specifications without the tenure year dummies found significantife cycle effects, with the coefficient estimates for the linear and quadratic termsmplying that publications peaked 26 years after PhD completion (citations: 27ears). Ta

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y 38 (2009) 281–292 289

first invention of a technology that is subsequently licensed to aspin-off. In Model 4, researchers assume the “entrepreneur” state bybeing first listed as a spin-off founder in the Max Planck Innovationspin-off database. All models are estimated for publication and cita-tion measures as alternative dependent variables, and using eitherregular or IPTW-weighted fixed effects regressions. Both methodsyield qualitatively the same results.

Compared to Model 1, restricting the commercialization mea-sure to individuals of licensed technologies increases the magnitudeof the estimated effects on publications and citations (Models2a–d). The coefficient estimates of the indicator variable denotingcommercializing researchers are positive and significant at the .01level for both output measures. Further limiting the commercializa-tion measure to include only inventors of technologies licensed tospin-offs (Model 3a–d) yields very similar results to Models 1a–d.Again, there seem to be systematic effects on publications (signif-icant at the .05 respectively .10 level) but not on citations. Finally,significantly lower levels of publications and citations are foundafter researchers are first listed as spin-off founders (Models 4a–d).

Taken together, these models provide mixed evidence on theeffects of commercialization activities. There seem to be posi-tive effects of inventing commercially useful technologies that aresubsequently licensed. In contrast, actually becoming a spin-offfounder appears to come at a cost in terms of both publicationquantity and quality.

A set of fixed effects regressions with multiple invention andcommercialization measures is used to further probe into theseresults (Models 5–7 in Table 6). In Model 5, both indicator variablesfor entering the inventor state and for disclosing the first licensedinvention are included. We find that controlling for being in theinventor state, researchers publish more, and are cited more fre-quently, after having their first licensed invention. In contrast, beinga “mere” inventor is not systematically related to either outputmeasure. In Model 6, the variable characterizing researchers withinventions licensed to spin-offs is added to the specification. Thisvariable has no significant relationship with either publicationsor citations, indicating that licensing to spin-offs does not yieldspecific “extra” benefits to a researcher. Next, in Model 7 the spin-off inventor proxy is replaced by the spin-off founder proxy. Theresults of this specification suggest decreasing quantity and qualityof research output after a spin-off has actually been formed. BothModels 6 and 7 reproduce the result from Model 5 that researchershave more publications and citations after disclosing their firstlicensed invention.

6.3. Results: count variables with next-period effects

Finally, a set of fixed effect regressions is estimated thatemploy annual counts of inventions/commercialization activities asexplanatory variables. Compared to the earlier models, these mod-els reflect differences in the extent of activities, assuming that theseactivities affect research output only in the next period (Models8–12 in Table 7). The variable denoting current resource flows fromlicensing and commercializing inventions is also added to the speci-fication. This allows for a direct test of the resource effect postulatedin Hypothesis 3.

Model 8 includes annual counts of disclosed inventions. Itsresults indicate that inventing researchers have significantly moreand better (i.e., more extensively cited) publications in the yearsfollowing invention disclosures. In Model 9, a separate count vari-able is measuring invention disclosures that resulted in licenses.

Its coefficient estimate is positive and significant (at the .05 level)with publications as the dependent variable, but insignificant forthe analysis of citations. In Models 10–12, three alternative prox-ies of spin-off activities are added to the specification of Model 9.These are measuring inventions that were subsequently licensed

290 G. Buenstorf / Research Policy 38 (2009) 281–292

Table 6Fixed effects regression models – permanent treatment.

Model 5 Model 6 Model 7

(a) Log fractionalpublication counts

(b) Log fractional3-year citation counts

(a) Log fractionalpublication counts

(b) Log fractional3-year citation counts

(a) Log fractionalpublication counts

(b) Log fractional3-year citation counts

Inventor .0345 (.0346) −.0296 (.0783) .0337 (.0347) −.0268 (.0784) .0386 (.0345) −.0202 (.0777)Inventor (licensed) .0906** (.0409) .2491*** (.0916) .0797* (.0437) .2848*** (.0943) .1218*** (.0407) .3219*** (.0891)Spin-off inventor .0363 (.0502) −.1195 (.1149)Spin-off founder −.2382*** (.0448) −.5555*** (.1016)Years since PhD .0156 (.0113) .0191 (.0251) .0150 (.0113) .0209 (.0251) .0206* (.0112) .0309 (.0248)Years since PhD2 −.0005*** (.0002) −.0007* (.0004) −.0005*** (.0002) −.0007* (.0004) −.0006*** (.0002) −.0009** (.0004)Tenure year controls IncludedConstant .5641*** (.1430) 1.4739*** (.3282) .5724*** (.1442) 1.4465*** (.3281) .4791*** (.1435) 1.2754*** (.3253)No. observ. (individuals) 3658 (313) 3641 (312) 3658 (313) 3641 (312) 3658 (313) 3641 (312)R2 within .095 .069 .095 .069 .103 .079P > F .000 .000 .000 .000 .000 .000

N

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ote: robust standard errors in parentheses.* Significant at .10 level.

** Significant at .05 level.*** Significant at .01 level.

o spin-offs (Model 10), new listings as spin-off founders (Model1), or new listings as spin-off board members and advisors (Model2). Neither variable is significantly related with the researcher’sutput in the subsequent period. Accordingly, the negative effectsf becoming a spin-off founder suggested by the treatment modelsre not found in this model setting.13

Throughout Models 9–12, the positive relationship betweennvention disclosures and research output in the following period isreserved. Significantly negative coefficient estimates are obtained

n all models for the measure of current resource flows. Thus, thenarrowly defined) resource effect conjectured in Hypothesis 3 cane rejected: research output is not boosted by the flow of licensing

ncome.

. Discussion

The present study analyzed the relationship between academicnventing and commercialization activities on the one hand andndividual research productivity on the other, adopting a number ofifferent measures and model specifications. The empirical inves-igation was based on rich data for Max Planck directors. Workingn a non-university public research organization that focuses onasic research and strives for excellence at a globally competitive

evel, these researchers constitute a distinctive subset of Germany’sop-tier scientists.

Confirming the thrust of prior results, there is no evidencendicating that inventive activities of academic researchers aressociated with decreases in research output. As regards the quan-ity of publications, we consistently found post-invention increases.dding to the prior literature, there is substantial evidence forositive effects also when the set of inventions is limited tohose that find private-sector licensees. This leads us to reject theresumption of a general “crowding-out” relationship betweenommercialization activities and research output as posited inypothesis 1. There does not seem to be a fundamental incom-

atibility between engaging in technology transfer and being arolific author of relevant scientific work. The model specifica-ions including multiple treatment variables (Models 5–7) evenuggest that inventing commercially valuable technologies comes

13 If analyzed without the measure of invention counts, the spin-off variables yieldositive but generally insignificant estimates. The only significantly positive associ-tion is found between spin-off inventions and publications. Note that no treatmentodel analogous to Model 12 was analyzed above since the treatment variable cor-

esponding to the entrepreneurship measure used in Model 12 is indistinguishablerom the one used in Model 4.

with increases in research output over and above those associatedwith academic inventions more generally.

In contrast, the implications of spin-off involvement are lessstraightforward. There is only weak evidence suggesting positiveeffects of inventions that are licensed to spin-offs (Model 3), andno indication that the benefits of spin-off involvement exceed thoseaccruing to commercializing researchers more generally (Models 6and 10). More importantly, while no significant short-term negativeimpact of spin-off involvement is observed in the models analyzingnext-period effects (Models 11 and 12), the permanent treatmentspecifications suggest that in the long run founding a spin-off maybe detrimental to the quantity and quality of a researcher’s out-put (Models 4 and 7). The latter result is noteworthy because noneof the scientists in the dataset entered into the operative man-agement of a spin-off. It contrasts with earlier findings by Loweand Gonzalez-Brambila (2007) who interpret entrepreneurship asa transitory treatment and find positive (short-term) effects.

The results on spin-off activities are puzzling given the substan-tial post-invention efforts typically required to develop academicinventions into commercially viable technologies. In part, theseefforts would be expected to fall into the time period followingthe invention but preceding the actual spin-off formation, whichfrequently lasts for several years. If they affected research outputnegatively, this should already show up in the aftermath of theinvention, not just after spin-off formation. Possibly, the resultsreflect that, in our sample, spin-offs were often started at a latestage of a scientist’s career. The declining subsequent research out-put may thus be related to adverse life cycle effects that are notfully controlled by the life cycle variables.14 This may be a transitoryeffect due to the changing institutional environment that facilitatedspin-off formation in more recent years. “Belated” entrepreneurialactivities may thus have been induced.

How do the present findings square with theoretical accountsproposed to explain complementarities between academic inven-tions and research output? Hypothesis 2 conjectured that, ifinteractions with private-sector firms provide relevant learningopportunities, then commercializing scientists should benefit more

than “mere” inventors. The findings on researchers with licensedinventions support this conjecture. In contrast, no specific pos-itive impact of spin-off activities on research productivity wassuggested by the empirical analysis. The relationship between

14 Note that Lowe and Gonzalez-Brambila (2007) also do not find significantly posi-tive effects of spin-off entrepreneurship in those disciplines where spin-off foundersare mostly senior (cf. Section 3 above).

G. Buenstorf / Research Polic

Tab

le7

Fixe

def

fect

sre

gres

sion

mod

els–

effe

cts

ofp

re-p

erio

dac

tivi

ties

.

Mod

el8

Mod

el9

Mod

el10

Mod

el11

Mod

el12

(a)

Log

frac

tion

alp

ubl

icat

ion

cou

nts

(b)

Log

frac

tion

al3-

year

cita

tion

cou

nts

(a)

Log

frac

tion

alp

ubl

icat

ion

cou

nts

(b)

Log

frac

tion

al3-

year

cita

tion

cou

nts

(a)

Log

frac

tion

alp

ubl

icat

ion

cou

nts

(b)

Log

frac

tion

al3-

year

cita

tion

cou

nts

(a)

Log

frac

tion

alp

ubl

icat

ion

cou

nts

(b)

Log

frac

tion

al3-

year

cita

tion

cou

nts

(a)

Log

frac

tion

alp

ubl

icat

ion

cou

nts

(b)

Log

frac

tion

al3-

year

cita

tion

cou

nts

Inve

nti

onco

un

ts.0

414**

*(.

009

3).0

643

***

(.01

98)

.027

8***

(.01

08)

.046

0**(.

0213

).0

278**

(.01

11)

.04

96**

(.02

17)

.028

0***

(.01

09)

.046

4**(.

0215

).0

281**

*(.

0108

).0

461**

(.02

13)

Lice

nse

din

v.co

un

ts.0

470**

(.02

12)

.063

4(.

0467

).0

468**

(.02

34)

.083

1(.

0547

).0

476**

(.02

12)

.06

42(.

0469

).0

497

**(.

0215

).0

643

(.04

72)

Spin

-off

inve

nti

ons

.00

04(.

024

9)−.

0416

(.06

09)

Spin

-off

form

atio

n−.

0211

(.07

73)

−.03

30(.1

791)

Spin

-off

invo

lvem

ent

−.04

73(.

0670

)−.

0172

(.145

4)C

urr

ent

reso

urc

efl

ow−.

00

01**

*(.

00

00)

−.0

002

**(.

00

01)

−.0

001

***

(.0

00

0)−.

00

02**

(.0

001

)−.

00

01**

*(.

00

00)

−.0

002

***

(.0

001

)−.

00

01**

*(.

00

00)

−.0

002

**(.

00

01)

−.0

001

***

(.0

00

0)−.

00

02**

(.0

001

)Ye

ars

sin

cePh

D.0

162

(.01

13)

.020

5(.

0251

).0

163

(.01

13)

.020

6(.

0251

).0

163

(.01

13)

.020

4(.

0251

).0

163

(.01

13)

.020

7(.

0252

).0

164

(.01

13)

.020

6(.

0251

)Ye

ars

sin

cePh

D2

−.0

005

***

(.0

002

)−.

00

07*

(.0

004

)−.

00

05**

*(.

00

02)

−.0

007

*(.

00

04)

−.0

005

***

(.0

002

)−.

00

07*

(.0

004

)−.

00

05**

*(.

00

02)

−.0

007

*(.

00

04)

−.0

005

***

(.0

002

)−.

00

07*

(.0

004

)Te

nu

reye

arco

ntr

ols

Incl

ud

edC

onst

ant

.547

7***

(.143

0)1.

442

1***

(.32

79)

.54

43**

*(.1

430)

1.43

75**

*(.

3280

).5

442

***

(.143

0)1.

440

0***

(.32

82)

.543

3***

(.143

1)1.

4360

***

(.32

83)

.542

3***

(.143

1)1.

4367

***

(.32

83)

No.

obse

rv.

(in

div

idu

als)

3658

(313

)36

41(3

12)

3658

(313

)36

41(3

12)

3658

(313

)36

41(3

12)

3658

(313

)36

41(3

12)

3658

(313

)36

41(3

12)

R2

wit

hin

.097

.069

.098

.070

.098

.070

.098

.070

.098

.070

P>

F.0

00

.00

0.0

00

.00

0.0

00

.00

0.0

00

.00

0.0

00

.00

0

Not

e:ro

bust

stan

dar

der

rors

inp

aren

thes

es.

*Si

gnifi

can

tat

.10

leve

l.**

Sign

ifica

nt

at.0

5le

vel.

***

Sign

ifica

nt

at.0

1le

vel.

y 38 (2009) 281–292 291

spin-off formation and subsequent research output even turnednegative in the models assuming permanent treatment. These find-ings suggest that the close private-sector interaction required byentrepreneurial activities does not further benefit research activi-ties, or that these benefits are compensated by other, more adverseconsequences of spin-off involvement.

Finally, there is no evidence that the flow of income fromlicensing and commercialization of inventions is positively asso-ciated with the quantity or quality of publications producedby researchers. Accordingly, the resource effect predicted inHypothesis 3 can be rejected.

8. Concluding remarks

Analyzing a new dataset on top-tier scientists in a German non-university public research organization, this study found empiricalevidence for a positive relationship between the inventive activitiesof scientists on the one hand and their performance as researcherson the other. Increasing numbers of publications and citations werealso observed for inventors whose technologies were subsequentlylicensed to private-sector firms. In contrast, spin-off founders expe-rienced long-run declines in their research output.

The present results suggest that learning effects from private-sector interaction cannot fully account for the research perfor-mance of academic inventors and commercializing researchers. Atleast in the group of senior scientists studied here, those individualswho presumably had the strongest ties to the private sector were onaverage characterized by decreasing numbers of publications andcitations. Furthermore, a resource effect based on licensing incomecould be rejected. Thus, the primary implication of this study maybe that further work is required to understand the determinants ofresearch productivity and the impact of inventions and commer-cialization activities.

It is important to interpret these results in their broader context.Most importantly, the above analysis was restricted to the individ-ual level. It provides no information on the potential effects thatIPR policies and enhanced emphasis on technology transfer haveon the strategies, staffing decisions, and performance of universi-ties and public research organizations (cf. Feller, 1990; Geuna andNesta, 2006, for potential concerns). Likewise, it is conceivable thatIPRs on research-relevant technologies have a negative impact onthe advance of science if they restrict the access to equipment andresearch tools. There is indeed some evidence suggesting (mod-estly) adverse effects of this kind (Murray and Stern, 2007; Sampat,2004). As with institution-level effects, such developments at thesystem level cannot be identified using the kind of individual-levelevidence that this and other related studies are based upon.

More generally, in spite of the recent attention given to univer-sity licensing and spin-off formation out of public research, theseare only two out of a variety of channels of technology transfer, andthey may not even be the most important ones (Cohen et al., 2002;Sampat, 2006). These considerations suggest that in spite of therecent wave of interest in technology transfer from public research,we still have much to learn.

Acknowledgements

Max Planck Innovation GmbH made this study possible by grant-ing me access to their data. For helpful discussions and comments, Iwant to thank Joerg Erselius, Astrid Giegold, Bernhard Hertel, EvelynKaiser, Ulrich Mahr, and Dieter Treichel, all at Max Planck Inno-

vation, as well as Alex Coad, Matthias Geissler, Jens Krueger, RobLowe, Holger Patzelt, Sidonia von Ledebur, Christian Zellner, andan anonymous referee of this journal. Sebastian Müller, MatthiasSchenk, and Wolfhard Kaus provided valuable research assistance.All errors are my own.

2 h Polic

R

A

A

A

B

B

B

C

C

C

C

C

E

F

F

F

F

G

G

92 G. Buenstorf / Researc

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