Industry Funding of University Research and Scientific Productivity

Abstract

Research conducted by university researchers for industry constitutes one of the main channels through which knowledge and technology are transferred from science to the private sector. Since the value of such inputs for the innovation performance of firms has been found to be considerable, it is not surprising that firms increasingly seek direct access to university knowledge. In particular, industry funding for university research has been increasing in most OECD countries.
This development, however, spurred concerns regarding possible long-run effects on scientific output. While some policy makers argue that the potential of universities to foster and accelerate industrial innovations is not yet fully exploited, others are concerned with the distraction of academics from their actual research mission.
Our results show for a sample of professors in science and engineering in Germany that a higher budget share from industry reduces publication output of professors both in terms of quantity and quality in subsequent years. This finding supports the “skewing problem” hypothesis for science and engineering faculty in Germany. If information sharing among scientists via publications is the basis for cumulative knowledge production and thus for scientific progress, industry funding that reduces publications may have detrimental effects on the development of science. On the other hand, we find that industry funding has a positive impact on the quality of applied research if measured by patent citations. Industry funding may thus have beneficial effects by improving impact and quality of more applied research. If industry funded research results in successfully patentable and industrially relevant technologies it may create economic as well as social value.

Full text

Industry Funding of University Research and
Scientific Productivity
Hanna Hottenrott and Susanne Thorwarth*
I. INTRODUCTION
Over the past decades, universities have widened their activities beyond teaching
and academic research. In particular, university research provides knowledge
inputs to private-sector innovation (Salter and Martin 2001 for a review). One of
the main channels through which knowledge and technology are transferred
from science to the private sector is research conducted by university researchers
for industry. The value of such inputs for the innovation performance of firms has
been found to be considerable (Mansfield 1995, 1998; Zucker et al. 2002; Cohen
et al. 2002). It is therefore not surprising that firms increasingly seek direct
access to university knowledge through sponsoring research projects (OECD
2009).
While some policy makers argue that the potential of universities to foster and
accelerate industrial innovations is not yet fully exploited and thus believe that
there is still room for improving the (social) returns from academic research
(European Commission 2003a,b; OECD 2007; Dosi et al. 2006), others are
concerned with the distraction of academics from their actual research mission.
From a private-sector perspective, the benefits of collaborating with academia
are found to be unambiguously positive, whereas the effects on the scientific
sector are not as clear cut. On the one hand, science may benefit from the
* Hanna Hottenrott: K.U.Leuven, Dept. of Managerial Economics, Strategy and Innovation, Naamsestraat
69, 3000 Leuven, Belgium and Centre for European Economic Research (ZEW), L7, 1, 68168 Man-
nheim, Germany; hanna.hottenrott@econ.kuleuven.be; Phone: +32 (0)16 32 57 93; Fax: +32 (0)16 32 67
32 . Susanne Thorwarth: K.U.Leuven, Centre for R&D Monitoring, Waaistraat 6, 3000 Leuven, Belgium
and Centre for European Economic Research (ZEW), L7, 1, 68168 Mannheim, Germany;
susanne.thorwarth@econ.kuleuven.be; Phone: +32 (0)16 32 57 3; Fax: +32 (0)16 32 57 99. We are
grateful to the Centre for European Economic Research (ZEW) for providing the survey data. We also
thank seminar participants at ZEW, K.U.Leuven and the University of Antwerp as well as participants of
the Technology Transfer Society Annual Conference (2010, Washington, DC) and the SEEK conference
(2011, Mannheim, Germany) for valuable comments. We are grateful to Thorsten Doherr for help with
retrieving the patent data. Hanna Hottenrott gratefully acknowledges financial support from the Research
Foundation Flanders (FWO).
KYKLOS, Vol. 64 – November 2011 – No. 4, 534–555
534 © 2011 Blackwell Publishing Ltd., 9600 Garsington Road,
Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA534

initiation of new ideas from industry or the use of industry funds for hiring
additional researchers and investment in lab equipment (Rosenberg 1998; Siegel
et al. 1999). On the other hand, traditional incentives in scientific research
characterized by knowledge sharing and rapid disclosure of research outcomes
may be distorted (Blumenthal et al. 1996a,b; Campbell et al. 2002). Moreover,
commercial interests may induce scientists to select research projects on the
basis of their perceived value in the private sector and not solely on the basis of
scientific progress. Increased funding from industry may thus be accompanied
by a shift in scientists’ research agendas and in the incentives for disclosure that
leads to a lower number of academic publications and to less effort devoted to
basic research.
This study aims to add to previous research by studying the effects of industry
sponsoring on scientific productivity. Our data contains information on labora-
tory and funding characteristics as well as on publication and patent output for
678 science and engineering professors at 46 different universities in Germany.
Germany is particularly interesting for studying the effects of industry funding as
it has a strong tradition of public research funding on the one hand, and on the
other, experienced the most significant increase in industry funded university
research among all OECD countries (OECD 2009). We find that a higher budget
share from industry reduces publication output of professors both in terms of
quantity and quality in subsequent years. In turn, industry funding has a positive
impact on the quality of applied research if measured by patent citations. Indus-
try funding may thus still have beneficial effects by improving impact and quality
of more applied research. These results have important implications for policy-
makers aiming at encouraging technology transfer between science and industry
and for public funding authorities. An increasing reliance on industry funding
may indeed have an impact on the development of science in the long run. On the
other hand, industry funded research results in successfully patentable and indus-
trially relevant technologies that may create economic as well as social value.
The following section gives an overview of insights from the literature on
industry-science links and their impact on academic research and on the role of
industry funding. Section III describes our data set. The set-up of our empirical
study and the results of the econometric analysis are presented in section IV.
Section V concludes.
II. INDUSTRY-SCIENCE LINKS AND ACADEMIC PRODUCTIVITY
Private sector incentives for engaging in relationships with science stem from the
increased speed and scope of technological change and the emergence of
complex and multidisciplinary research fields. “Science-based technologies”
such as biotechnology or nanotechnology have further strengthened the role of
science for technological innovation. Public science provides important knowl-
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edge inputs and organizational pre-conditions for firms to expand in new fields
of technology (e.g. Mowery 1998; Zucker and Darby 1996).
To stimulate incentives for the commercialization of university research in the
scientific sector reforms of the (legal) research environment in the U.S., but also
in Europe, aimed at reducing the (administrative) burden of such activities for
university researchers. The increased involvement of university researchers in
such activities, however, has also generated a considerable controversy about the
potential long-term effects on the future development of science. These concerns
rest on the assumption that there is indeed a trade-off between research that is
being disclosed in publications and more applied work that is of interest for
industry (Rosenberg and Nelson 1994). Rosenberg (1998), however, regards
industry contacts as a source of new research ideas and thus argues that science
can benefit from increased collaboration with industry. Moreover, Azoulay et al.
(2009) suggest that researchers benefit from the realization of complementarities
between basic and applied research that otherwise would remain foreclosed. The
authors point to intra-person economies of scope that emerge when a scientist is
involved in both the development of academic and commercial research out-
comes. Furthermore, it has been argued that crowding-out of traditional research
can be averted if scientists are assisted in their work for industry by their
university’s technology transfer office (TTO) (Hellman 2007). From the scien-
tists’ perspective, industry grants provide an attractive source of funds supple-
menting ‘core funding’ and other public research funding. Such funds can be
used to hire additional scientists who increase the lab’s overall research output
for both applied and basic research.
Despite these arguments in favor of industry funding for university research,
skeptics argue that the traditional incentives in science that were characterized by
knowledge sharing and rapid disclosure of research outcomes may be affected by
industry grants and contracts (David et al. 1992; Dasgupta and David 1994;
Nelson 2001). The critical question is thus to what degree increasing industry
sponsoring induces a “skewing problem”. Does the option to attract industry
change the incentives of scientists to contribute to public (i.e., non-excludable)
advances in the scientific literature? Even though the relative magnitude of
industrial funding is not really high, it may be a critical resource influencing
faculty behavior. Slaughter and Leslie (1997) as well as Benner and Sandström
(2000) argue that funding influences the behavior and outputs of researchers.
Scientists’ incentives to create and immediately publish their research findings
are obvious if their careers depend on their contributions to science in the form
of publications and (graduate) education. The possibility to generate additional
funds from industry may affect these incentives. Industry grants may not only
affect scientists’ willingness, but also their ability to share information with
fellow scientists. Publishing of research results may for instance be hampered if
industry funding has “strings attached” that affect disclosure of research results
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536 © 2011 Blackwell Publishing Ltd.

for free in academic journals. Cohen et al. (1994) report that a significant share
of industry–university research centers in the U.S. allows cooperating firms to
delete information from published reports and the right to delay publication. A
survey described in Thursby and Thursby (2002) also documents that firms
usually require researchers to sign a contract that includes a delay of publication
clause (see also Louis et al. 2001).
As knowledge sharing among scientists is the basis for cumulative knowledge
production and thus for scientific progress (Haeussler et al. 2009), industry
funding that affects the incentives to share knowledge may have detrimental
effects on the development of science. Further, long-run effects from industry-
funded research projects may arise from the intensively and continuous involve-
ment of the professors in the projects. This involvement has been shown to be
necessary for university inventions to be successfully commercialized, but at the
same time may distract researchers from other types of research (Jensen and
Thursby 2001; Toole and Czarnitzki 2010).
Finally, there may be a tradeoff between doing research for industry and
publishing simply because of the time that is consumed by these alternative
activities. It may become more attractive to spend time doing research that is
closer aligned to industry interests than other (basic) research. In other words,
due to time constraints, researchers’ publishing rates may decrease in favor of
industry funded projects.
2.1. Empirical evidence on the effects of industry-sponsored research
Previous research focused to a great extent on the productivity effects of
increased commercialization of university research via academic patenting and
licensing (e.g. Henderson et al. 1998; Thursby and Thursby 2002; Azoulay et al.
2009; Czarnitzki et al. 2009a), academic entrepreneurship (e.g. Ding and Stuart
2006, Toole and Czarnitzki 2010) and the engagement in contract research (e.g.
Lach and Schankerman 2004; Carayol 2007) and collaborative research (e.g.
Zucker et al. 2002). Although consulting and contract research are often the quid
pro quo for industry funds, there is only a handful of empirical evidence on the
effects of industry funding on university research directly.
Blumenthal et al. (1996a,b) and Campbell et al. (2002) report survey-based
evidence on negative effects from industry sponsoring on the publication of
research results, knowledge sharing and the speed of knowledge disclosure.
Blumenthal et al. (1997) find that U.S. academic life scientists had withheld
research results due to intellectual property rights discussions such as patent
applications (see also Louis et al. 2001). Godin and Gingras (2000), on the other
hand, find that Canadian university researchers with funding from industry
produce more scientific publications than their colleagues without such funding.
They argue that this may be due to the fact that there is no trade-off between
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many types of contract research and academic science, and/or that scientific
quality is a prerequisite for attracting such contracts in the first place. Industry
may thus not only look at the researchers’ past patenting profile in order to assess
their skills but also at publications and hence even strengthen the incentives for
publishing by creating a signal of research quality.
Behrens and Gray (2001) study effects of different funding sources (industry,
government and no external sponsor) on a variety of research processes and
outcomes for graduate students at engineering departments in the U.S. of which
almost 50% spent most of their time working on a project which was supported
by industry. The authors argue that most industry support is channeled by
cooperative research centers where it is complemented by government support.
As a consequence, total industry support amounts to approximately 20%-25% in
the disciplines they study. Their findings suggest, however, that although the
source of sponsorship and, to a lesser degree, the form of sponsorship are
associated with a number of differences, these differences tend to be minor and
related to structural aspects of a student’s research involvement and not eventual
research outcomes.
Van Looy et al. (2004) likewise find no evidence of a skewing problem at the
Catholic University of Leuven in Belgium. They find that professors with indus-
try contracts publish more than their colleagues without such contracts.
However, selection effects are not controlled for in the study which makes it
difficult to determine whether industry funding is causal or a reflection of the fact
that industry selects the most productive researchers. Gulbrandsen and Smeby
(2005) find that researchers at Norwegian universities who had grants from
industry also collaborate more extensively with industry than those without
grants or contracts. They also study the relationship between industry funding
and professors’ self-assessment of their research focus, i.e. basic or applied, and
conclude that industrial funding is related to applied research, but not to basic
research or development. Gulbrandsen and Smeby also find a positive correlation
between industry funding and scientific productivity. However, they do neither
have information about the amount of funding nor on the share of that funding of
the entire research budget. Thus, it may be that the information of whether or not
a professor has funding from industry is insufficient, as the number of grants or
the relative share of industry funding compared to core funding may constitute
the critical factor.
Bozeman and Gaughan (2007) focus their study on the impact of research
grants and contracts on interactive activities with industry and find that industry
funding strengthens industry-science collaboration. Yet, they provide no impli-
cations of increased collaboration on scientific productivity. Boardman and Pon-
omariov (2009) study the effects of industry grants on a broad set of indictors.
They conclude that industry grants increase the likelihood of university scientists
co-authoring papers with industrial scientists for academic journals, however,
HANNA HOTTENROTT/SUSANNE THORWARTH
538 © 2011 Blackwell Publishing Ltd.

provide no “before and after” comparison of the university researcher’s publi-
cation behavior.
Banal-Estanol et al. (2010) find for a panel of engineers employed at major
UK universities, that industry-sponsored research is positively related to publi-
cation output, but only for low levels of such funding. It is detrimental at higher
levels. Meissner (2011) adds to these findings by studying patenting activities of
these scientists and concludes that industry-sponsored research increases the
individual scientist’s propensity to patent and also the likelihood to patent jointly
with firms.
In summary, while the role of particular forms of technology transfer channels
appear to be quite well understood, the effects of industry funding are not as
clear. This study therefore aims to shed light on the impact of private sector
research sponsoring on professors’ subsequent scientific achievements both in
terms of scientific publications and patents.
III. DATA
The empirical analysis of this paper is based on a unique dataset that had been
created from different data sources. The core data had been collected by a survey
among research units at German higher education institutions in the fields of
science or engineering.1In 2000 the Centre for European Economic Research
(ZEW, Mannheim) conducted a survey among a random sample of research units
at general universities, technical universities and polytechnic colleges (“univer-
sities of applied sciences”) stratified by regions. The questionnaire addressed
“head of departments” who are in general full professors who have budget and
personnel responsibility.2
The overall response rate to the survey was 24.4%. After the elimination of
incomplete records, the final sample contains 678 professor-research unit obser-
vations from 46 different institutions of which 56% are Universities (Uni), 23%
are Technical Universities (TUs) and 21% are Universities of Applied Sciences
(UaS).3The key variables of interest are obtained directly from the survey. The
professors were asked to indicate the amount and composition of “third-party
funding” that they received during 1999 in addition to their core funding as a
1. These fields include physics, mathematics and computer science, chemistry and pharmaceuticals,
biology and life sciences, electrical and mechanical engineering and other engineering and related fields
such as geosciences.
2. Usually, a chair has only one professor. Larger universities, however, may also have several professors
at one chair. In any case, only one is the head of the department.
3. For each of the 16 German States (Länder) the sample comprises at least one observation.
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share of their total budget.4In the final sample more than 61% of the professors
received funds from industry. The amount of industry funding and its share of the
total budget (INDFUND) at the level of the research unit differ between the types
of institutions (see Table 1). The share of research grants from public sources of
total budget (GOVFUND) is comparable between universities and technical
universities, but is considerably lower at UaS. TUs have the highest share with
10.6% of their total budget which on average amounts to more than 160 thousand
Euros received during the year of the survey. The average number of staff per
research unit (LABSIZE) is about 20 (median 13). The share of team members
with a non-scientific, but technical background (TECHS) is largest at UaS. Also
the share of people in the team with a PhD (POSTDOCS) is largest at UaS. This,
however, is due to the smaller overall team size and the lack of doctoral students.
We know from the survey whether the professor had contact to his institution’s
Technology Transfer Office (TTO). As it is conceivable that such contacts may
4. It should be noted that the sum of INDFUND and GOVFUND is ‘total third-party funding’ and not the
total budget. Adding this to the ‘core’ institutional funding (COREFUND) yields the units’ overall
funding: TOTALFUND =INDFUND +GOVFUND +COREFUND.
Tab l e 1
Summary statistics (variable means by type of institution)
Description Variable Uni TU UaS
Funding:
Amount Ind. Funding (T €) 98.04 168.46 61.74
Share of Ind. Funding in % of Total Budget INDFUND 7.60 10.56 9.29
Amount Gov. Grants (T €) 181.56 192.07 11.53
Share of Gov. Grants in % of Total Budget GOVFUND 26.64 25.04 6.11
Scientific Output 1994–1999:
Publications PUB1994–1999 16.35 6.46 2.28
Citation Count of Publications CITPUB1994–1999 344.77 128.17 22.82
Average Citations per Publication CITperPUB1994–1999 15.44 7.52 4.67
Patents PAT 1994–1999 1.54 1.27 1.20
Citation Count of Patents CITperPAT1994–1999 16.25 35.61 12.77
Average Citations per Patent CITPAT1994–1999 3.81 4.23 3.71
Scientific Output 2000–2007:
Publications PUB 26.24 13.34 2.99
Citation Count of Publications CITPUB 256.73 124.17 15.76
Average Citations per Publication CITperPUB 7.46 3.57 1.85
Patents PAT 1.44 1.20 1.28
Citation Count of Patents CITPAT 1.02 1.17 1.17
Average Citations per Patent CITperPAT 0.23 0.24 0.10
Controls:
Number of people in lab LABSIZE 21.38 24.31 15.73
Number of years since PhD EXPERIENCE 22.57 24.46 16.32
Contact to TTO dummy TTO 0.66 0.79 0.87
% technical employees TECHS 7.01 7.85 19.87
% employees with PhD POSTDOCS 22.54 19.52 25.50
Female Professor dummy GENDER 0.03 0.03 0.04
HANNA HOTTENROTT/SUSANNE THORWARTH
540 © 2011 Blackwell Publishing Ltd.

impact both stronger technology transfer awareness and the time burden of such
activities, it may also have effects on patenting and publishing activities. The
number of female professors is small with only 22 of the 678 professors in our
sample.
3.1. Patent and Publication data
As we are interested in the scientific performance at the level of the head of the
research unit, we supplemented the survey data with patent and publication
information. We use the patent and publication output of the responding profes-
sor as a proxy for the research output of his research unit.5The data base of the
German Patent and Trademark Office (DPMA) contains all patents filed with the
DPMA. Since applicants are obliged by law to disclose the name of the inventor
in the patent application, we searched through this database for all patents which
listed professors from our sample as inventors. One technique for measuring the
quality or impact of patents is patent citation analysis. We focus on “forward
citations” to the patents, defined as the number of citations received by each
patent following its issue. Patent forward citations have been proved to be a
suitable measure for the quality, importance or significance of a patented inven-
tion and have been used in various studies (see e.g. Henderson et al. 1998; Hall
et al. 2001; Czarnitzki et al. 2009b).
The publication histories of the professors were traced via the ISI Web of
Science® database of Thomson-Scientific (Philadelphia, PA, USA). The database
covers all significant document types within these journals. Records contain
information such as the title, authors, keywords, cited references, abstracts and
other document details. We searched for publications (articles, notes, reviews and
letters) of professors in our sample through the ISI Web of Knowledge® platform
by their name and subsequently filtered the results on the basis of affiliations,
addresses and journal fields. In order to assign the publications correctly to the
professors, we also collected information on their career paths that allowed us to
relate publication records to professors even if the affiliation on the publication did
not correspond to the current one. The publication record in the database also
contains the number of citations for each publication. We use the citation counts
as indication of impact or quality of the publication. Despite some limitations (van
Dalen and Klamer 2005) several authors have shown, that citation counts are an
adequate indicator to evaluate research output (Garfield and Welljams-Dorof
5. Even though we do know the number of each chair’s employees and details on their qualification, we do
not have further details (e.g. name) of the individual team members. Thus, we cannot collect publication
and patent information at the team member level. Using the publications of the head of department is
justified in our setting on the basis that in science and engineering at German institutions it is common
practice to include the ‘head’ on every publication co-authored by his department members (usually pre-
and postdocs).
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1992; Baird and Oppenheim 1994).6Since we are interested in the professors’
publication and patent track record and the respective citation counts before the
survey as well as in their performance in the years after, we collect all patents and
publications from the professor’s first entry until the end of 2007. The number of
past publications depends of course on the academic experience or seniority of the
researcher. To control for differences in experience, we therefore gathered infor-
mation from the German National Library on the year in which the professors
received their PhDs.7From this information, we calculate the years of the
professors’ experience (EXPERIENCE) in academia. Although the professors are
all rather senior (and tenured) academic staff heading a research unit, we still want
to control for life cycle effects. As knowledge depreciates over time scientists are
required to continuously update their previously acquired information for instance
by reading scientific articles. The propensity as well as the intensity to do so,
however, may be adversely affected by the person’s position in the professional
life cycle (Van Dalen 1998). This is, moreover, likely to affect publication output
which has also been shown to depend on the life cycle position (Thursby et al.
2007). The average professor had been working for 22 years since receiving his
PhD when filling out the survey in the year 2000 (median is 22, too).8For our main
analysis, we limited the time horizon for publications, patents and citations to the
period from 1994 to 2007.9We thus fixed the “activity window” to six years before
(1994–1999) and the eight years after the survey (2000–2007).10 In the former
period, professors at universities on average published 16 items, professors atTUs
6. The popular impact factor of the journal in which the article was published would have also been
available, but since we study different fields of science, the journal impact factors have been shown to
be inappropriate (see Amin and Mabe 2000).
7. In Germany a dissertation needs to be published in the German National Library (Deutsche National-
bibliothek). This central library collects, permanently archives, documents, records, and makes publicly
available bibliographically all German and German-language publications from 1913 onwards.
8. For a few professors, who according to their CVs either obtained their doctoral degree abroad or do not
have a PhD, we used the year of their first publication as a proxy for the beginning of their academic
career. If professors with very common names like “Müller” or “Fischer” and also common first names
appeared in our dataset, we preferred to drop these observations from our dataset since publication
and/or patent data could not be uniquely identified.
9. We also tested the robustness of the results to a model specification with all publications and patents
from the first publication or patent found in the data base. The main results remained unchanged.
10. It should be noted that the potential impact of a legal reform that abolished a special clause in the law
on employee inventions (Professors’ Privilege) which came into force in February 2002
(Arbeitnehmererfindungs-Gesetz, ArbEG, 2002) should be limited for our study. Prior to this reform,
university researchers were exempted from the general obligation of employees to disclose job-related
inventions to their employers and could thus keep the ownership of their patents. While in the years
after the Bayh-Dole Act U.S. university patent applications escalated, von Ledebur et al. (2009) find no
such evidence for Germany. Thus, the reform basically led to a shift in the ownership of the patents, but
not in its numbers. It should, therefore, not affect our data as we looked up patents based on academic
inventors not applicants. Moreover, a substitution of university ownership for firm ownership of patents
(if the patent was the result of paid contract research and therefore belongs to a firm) should not affect
our results as we use the overall count and not just university owned patents.
HANNA HOTTENROTT/SUSANNE THORWARTH
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about six and UaS professors two. While we find high citations counts for
university publications, the ‘times cited’for the other two categories is much lower
(344 compared to 128 and 23, respectively). This is also reflected in the average
number of citations per publication although the difference between universities
and technical universities is much smaller (see Table 1). For patent applications,
the picture is less diverse across types of institutions. The average number of
patent applications is 1.54 for university patents, 1.27 for patents from technical
universities and 1.20 from UaS. Patents from technical universities are, however,
cited more frequently.Arelatively small number of professors are responsible for
the majority of publications. 14% of the professor published nearly 50% of the
total number of publications. The same is true for citations: there are very few
highly cited professors, 11% with more than 1,000 total citations or more than 40
citations per paper. These patterns are characteristic for publication output (see
e.g. Stern and Jensen 1983). For patent applications and citations, we see a similar
picture. 45% have not applied for a patent at all. From the total of 3,079 patent
applications, 10% of the professors account for a quarter of these patents. The fact
that not all patent applications are successful has to be taken into account when
looking at the mean of patent forward citations which indicates that 67.7% of the
patents received no citation at all. Industry funding is highest in engineering, in
particular for mechanical engineering with more than 240.000€or about 14% of
their total budget. The distribution of industry funds, however, is skewed (the
median for mechanical engineering is about 88.000€and 10% of total budget).
The share of industry funding is lowest in physics and mathematics which is
probably due to the rather theoretical research orientation of many professors in
these fields (Table A.1 in the appendix). Looking at research productivity by fields
illustrates that in chemistry, physics, and biology, professors published most and
also received a larger number of citations per publication compared to mechanical
or electrical engineering. Patenting activity is highest among electrical engineers
and – as expected – lowest among mathematicians and computer scientists both in
terms of patent application as well as in terms of citations that their patents receive
(Table A.2 in the appendix).
IV. EMPIRICAL ANALYSIS
Primarily, our analysis aims to shed light on the effects of industry funding on
scientific productivity. As potential effects are unlikely to show up immediately,
we observe the scientific output up to eight years after the survey. We thus expect
journal publication output and patent applications in the post-survey period
2000–2007 to be a function of the share of industry funding (INDFUND) and the
share of public grants (GOVFUND) received by the research unit and the
“heads’” past publication and patenting efforts (PUB1995–1999,PAT
1995–1999) as past
performance is likely to affect future performance due to a „cumulative advan-
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tage“. Additionally, lab size (LABSIZE), experience (EXPERIENCE), and the
skill composition at the lab in terms of the percentage of technical employees
(TECHS) and post doctoral researchers (POSTDOCS) may affect scientific pro-
ductivity. Finally, we consider attributes such as the research field, the type of
institution and gender as control variables in the econometric models to be
estimated. Figure 1 depicts the development of industry funding for all German
higher education institutions in the period 2000–2007 that is not covered by the
survey. Compared to the year 2000, the amount has increased by more than 40%.
Remarkably, the institutions’core funding has been decreasing since 2002, while
total budgets remained relatively unchanged. Concerns raised by Lee (1996)
regarding the effects of industry involvement in science on long-term, disinter-
ested, fundamental research in the light of ‘declining federal R&D support’ in the
U.S. can thus be raised here as well. Unfortunately, the information on industry
funding in the survey is limited to the year 1999. Data at the institutional level (as
shown in Figure 1) documents an increase at the aggregate level in the post-
survey years. This leads us to regard the survey-numbers for 1999 at the research
unit level as “lower bound” of the industry funding received by the research unit
in subsequent years. Public grants increased likewise which confirms findings by
Auranen and Nieminen (2010), who report a development towards a more
competitive funding structure. GOVFUND is included to control for a profes-
sor’s success in attracting public funds. Additionally, as publication or patent
output may not only be affected in terms of quantity, but also quality, we estimate
Figure 1
University Funding (% changes relative to the year 2000)
Source: DESTATIS, series 11, issue 4.3.2, own calculations.
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
Delta 2000=100
Amount Indu stry Funding Core F unding Amount Public Grants Total Budget
2000 2002 2003 2004 2005 2006 2007
HANNA HOTTENROTT/SUSANNE THORWARTH
544 © 2011 Blackwell Publishing Ltd.

the effects on citation counts (CITPUB, CITPAT) and average citations per
publication and patent (CITperPUB, CITperPAT).
4.1. Econometric set-up
The number of publications and patent applications is restricted to non-negative
integer values and also characterized by many zeros, since not all of the profes-
sors in our sample show a positive number of publications and/or patents. The
same applies for the number of citations for both measures. Hence, in order to
investigate the relationship between funding and research output, we estimate
count data models. This leads to the following estimation equation which is
assumed to be of an exponential functional form:
λαβ
it i i it i i it i
E Y Z X c exp Z X c=
[]
=+
′+
()
−,,
,,
2000 2007 1999 1999 (1)
where Yiis the count variable and stands either for publication counts (PUB),
publication citations (CITPUB), patent applications (PAT), patent citations
(CITPAT) or citations per item (CITperPUB, CITperPAT) by scientist iwithin the
time span 2000 until 2007 which is assumed to be Poisson distributed with
lit >0. Zi,1999 denotes the share of industry funding (INDFUND) in the survey’s
reference year 1999.11 Xit represents the set of controls including the share of
public grants (GOVFUND), aand bare the parameters to be estimated. ciis the
individual specific unobserved effect, such as individual skills of each scientist or
their attitude towards publishing or patenting. Usually, cross-sectional count data
models are estimated by applying Poisson and negative binomial regression
models (negbin). A basic assumption of the Poisson model is equidispersion, i.e.
the equality of the conditional mean and the conditional variance which is
typically violated in applications leading to overdispersion. This led researchers
to the use of the negbin model since it allows for overdispersion. Although the
negbin model relaxes this assumption of equidispersion, it is only consistent (and
efficient) if the functional form and distributional assumption of the variance
term are correctly specified. For the Poisson model, however, it has been shown
that it is consistent solely under the assumption that the mean is correctly
specified even if overdispersion is present (Poisson Pseudo (or Quasi) Maximum
Likelihood). In case the assumption of equidispersion is violated and hence the
obtained standard errors are too small, this can be corrected by using fully robust
standard errors (see Wooldridge 2002); which is what we do.
A major drawback of cross-sectional data sets is that they usually do not allow
to control for unobserved heterogeneity which is most likely to be present in our
11. Note that we estimate Poisson models also for the non-integer dependent variables (citations per
publication / patent). See Wooldridge chapter 19, p. 676.
INDUSTRY FUNDING OF UNIVERSITY RESEARCH AND SCIENTIFIC PRODUCTIVITY
© 2011 Blackwell Publishing Ltd. 545

data. Hence, if unobserved effects like, e.g. specific skills of each scientist, are
positively correlated with the right hand side variables such as industry funding,
the estimated coefficient of the industry funding variable is upwards biased. A
solution is provided by the linear feedback model suggested by Blundell et al.
(1995, 2002) who argue that the main source of unobserved heterogeneity lies in
the different values of the dependent variable Yiwith which observation units
(professors in our case) enter the sample. The model approximates the unob-
served heterogeneity by including the log of the Yifrom a pre-sample period
average into a standard pooled cross-sectional model (ln[PUB_MEAN],
ln[PAT_MEAN] etc.). In case Yiis zero in the pre-sample period, e.g. a professor
had no publications, a dummy is used to capture the “quasi-missing” value in log
Yiof in the pre-sample period (d[PUB_MEAN =0], d[PAT_MEAN =0] etc). We
constructed the pre-sample mean estimator by using six pre-sample observations
values of Yfor the years 1994 to 1999.
4.2. Results
Table 2 presents the results of the Poisson regressions on the publication output
indicators. The effect of INDFUND is significantly negative for both the publi-
cation count and the citations count as well as for citations per publication in the
years after the survey. That is, a higher share of industry funding (in 1999) leads
to a lower publication output in subsequent years (2000–2007) both in terms of
quantity and quality. To be more precise, an additional percentage point of in the
share industry funding of total budget reduces publication output by 0.8%. This
implies an average loss of one publication for a 5.5% increase in industry
funding (that is on average about 6000 €) in the following 8 years. This effect
becomes more pronounced if we look at the indicators referring to publication
quality. The number of citations decreases 1.3% (1.6% in the fixed effects model)
and the number of citations per publication is reduced by 1.3% in both specifi-
cations. The share of public research grants (GOVFUND) on the other hand has
a positive and significant effect on publication output both in terms of publica-
tion count and citations per publication. This effect, however, is not robust to the
fixed effects specification.
Table 3 depicts the results from the patent equation. Interestingly, a higher
share of industry funding has no effect on the number of patents, but does have
apositive impact on patent citations and citations per patent. That is an increase
of 2.6% (2.5% in the model with fixed effects) with each additional percentage
point sponsored by the private sector.
As patents can only receive citations if they were granted, the positive effect
here can also be interpreted as a novelty and quality effect of industry funds on
professors’ patents. Unlike in the publication model, where past publication
record was significant but not past patenting activity, the patent equation shows
HANNA HOTTENROTT/SUSANNE THORWARTH
546 © 2011 Blackwell Publishing Ltd.

Table 2
Estimation results (678 obs.) on publication output
Variable Poisson Model Poisson Model with Fixed Effects
PUB CITPUB CITperPUB PUB CITPUB CITperPUB
INDFUND -0.008** -0.013*** -0.013*** -0.008** -0.016*** -0.012***
(0.004) (0.006) (0.005) (0.003) (0.006) (0.005)
GOVFUND 0.007*** 0.005 0.005** 0.004 0.002 0.003
(0.002) (0.003) (0.002) (0.002) (0.003) (0.002)
PUB1994–1999 0.013***
(0.002)
PAT 1994–1999 0.012
(0.011)
CITPUB䉬1994–1999 0.001*** 0.014***
(0.000) (0.002)
CITPAT䉬1994–1999 -0.000 -0.003**
(0.001) (0.002)
LABSIZE 0.123* 0.366*** 0.103* 0.111** 0.165** -0.042
(0.069) (0.102) (0.057) (0.057) (0.065) (0.052)
LABSIZE2-0.000 -0.000** -0.000 -0.000 -0.000 -0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
EXPERIENCE -0.042 -0.027 0.015 -0.054 -0.038 -0.001
(0.037) (0.034) (0.020) (0.034) (0.028) (0.020)
EXPERIENCE20.000 -0.000 -0.000 0.001 0.000 -0.000
(0.001) (0.001) (0.000) (0.001) (0.001) (0.000)
TTO 0.215* 0.049 0.136 0.130 0.096 0.180**
(0.129) (0.138) (0.089) (0.119) (0.118) (0.091)
TECHS 0.003 0.007 0.000 0.005 0.008 0.004
(0.007) (0.010) (0.004) (0.005) (0.005) (0.004)
POSTDOCS 0.002 -0.004 -0.004 -0.000 -0.002 -0.004
(0.004) (0.005) (0.002) (0.004) (0.003) (0.002)
GENDER 0.017 -0.204 -0.203 0.136 -0.078 -0.220
(0.194) (0.279) (0.193) (0.156) (0.248) (0.208)
ln[PUB_MEAN] 0.601***
(0.053)
ln[PAT_MEAN] 0.057
(0.068)
ln[CITPUB_
MEAN]
0.163***
(0.048)
ln[CITPAT_
MEAN]
0.643***
(0.047)
ln[CITperPUB_
MEAN]
0.277***
(0.033)
ln[CITperPAT_
MEAN]
-0.044
(0.030)
Log-Likelihood -6,379.11 -63,901.38 -2,308.94 -5,348.40 -44,018.36 -2,208.85
Joint sign. inst.
dum. c2(2)
80.53*** 43.86*** 22.71*** 38.26*** 16.05*** 10.99***
Joint sign. field
dum. c2(6)
57.36*** 95.66*** 39.32*** 16.24** 14.15** 8.07
McFadden’s R20.487 0.603 0.337 0.570 0.727 0.366
Notes: Standard errors in parentheses are robust, all models contain a constant, field and institution
type dummies.
䉬CITperPUB and CITperPAT for the model presented in column 3. Pre-sample dummies
d[X_MEAN] for observations with zero means are not presented. *** (**, *) indicate a significance
level of 1% (5%, 10%).
INDUSTRY FUNDING OF UNIVERSITY RESEARCH AND SCIENTIFIC PRODUCTIVITY
© 2011 Blackwell Publishing Ltd. 547

Tab l e 3
Estimation results (678 obs.) on patent output
Variable Poisson Model Poisson Model with Fixed Effects
PAT CITPAT CITperPAT PAT CITPAT CITperPAT
INDFUND 0.003 0.026** 0.028*** -0.002 0.024* 0.028**
(0.005) (0.011) (0.010) (0.006) (0.016) (0.013)
GOVFUND 0.003 -0.003 -0.001 0.003 -0.004 -0.002
(0.004) (0.011) (0.008) (0.004) (0.013) (0.008)
PUB1994–1999 0.009***
(0.003)
PAT 1994–1999 0.099***
(0.012)
CITPUB䉬1994–1999 0.000*** -0.002
(0.000) (0.006)
CITPAT䉬1994–1999 0.000 0.002
(0.000) (0.004)
LABSIZE 0.157 0.540* 0.492** 0.115 0.464* 0.405**
(0.118) (0.317) (0.220) (0.102) (0.325) (0.204)
LABSIZE2-0.000 -0.000 -0.000 -0.000* -0.000 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
EXPERIENCE -0.039 0.097 0.088 -0.049 0.150 0.111
(0.064) (0.104) (0.075) (0.050) (0.111) (0.083)
EXPERIENCE20.000 -0.003 -0.002 0.000 -0.004 -0.002
(0.001) (0.002) (0.002) (0.001) (0.003) (0.002)
TTO 0.269 1.176*** 0.494 0.099 0.937** 0.335
(0.345) (0.364) (0.450) (0.330) (0.394) (0.464)
TECHS 0.001 0.005 0.013 -0.001 0.004 0.008
(0.006) (0.011) (0.011) (0.005) (0.012) (0.010)
POSTDOCS 0.006 -0.005 0.002 0.007 -0.003 0.003
(0.006) (0.013) (0.009) (0.005) (0.015) (0.011)
GENDER 0.179 -2.131*** -2.925*** 0.341 -2.255*** -2.977***
(0.331) (0.826) (0.871) (0.225) (0.636) (0.681)
ln[PUB_MEAN] 0.032
(0.075)
ln[PAT_MEAN] 0.523***
(0.088)
ln[CITPUB_
MEAN]
0.198**
(0.087)
ln[CITPAT_
MEAN]
0.259**
(0.136)
ln[CITperPUB_
MEAN]
0.195*
(0.101)
ln[CITperPAT_
MEAN]
0.090
(0.088)
Log-Likelihood -1,343.47 -1,318.19 -348.20 -1,173.97 -1,190.98 -325.91
Joint sign. inst.
dum. c2(2)
1.27 3.05 4.17 0.78 1.07 2.05
Joint sign. field
dum. c2(6)
19.48*** 24.68*** 20.01*** 11.42* 14.00** 11.64*
McFadden’s R20.250 0.235 0.183 0.345 0.309 0.236
Notes: Standard errors in parentheses are robust, all models contain a constant, field and institution
type dummies.
䉬CITperPUB and CITperPAT for the model presented in column 3. Pre-sample dummies
d[X_MEAN] for observations with zero means are not presented. *** (**, *) indicate a significance
level of 1% (5%, 10%).
HANNA HOTTENROTT/SUSANNE THORWARTH
548 © 2011 Blackwell Publishing Ltd.

that both past publications and past patent applications significantly determine
future patent outcome. Public grants, on the contrary, have no impact on future
patent activity.
To sum up, depending on the expression of Yi, we find that:
1. a<0, hence industry funding decreases output if
䊏Yidenotes publication counts (PUB), the total number of citations to
publications (CITPUB) or the average number of citations per publica-
tion (CITperPUB)
2. a=0, i.e. industry funding has no effect on the output variable if
䊏Yistands for patent applications (PAT)
3. a>0, i.e. industry funding increases scientific output if
䊏Yistands for patent citations (CITPAT) or the average number of citations
per publication (CITperPUB).
The main results are robust to the inclusion of the ‘fixed effect’ in the linear
feedback model. It should be noted that we also tested a non-linear specification,
i.e. we included the squared value of INDFUND to test whether the negative (or
positive effect in the patent citation equations) effect of INDFUND may only
occur up from a certain level of industry funding. The inclusion of INDFUND2,
however, did not affect the significance of INDFUND, but it was never signifi-
cant itself. The institution type (Uni, TU, UaS) dummies are jointly significant in
the publication equations, but not in the patent equations. Generally, publications
were significantly lower at TUs and UaS compared to universities that served as
reference category. The research field dummies are jointly significant in all
models (except in the CITperPUB fixed effect specification) capturing differ-
ences in publication patterns across research fields. The contact to a TTO has a
positive impact on patent citations. In most specifications we find a linear effect
of lab size on the output measures, i.e. larger labs produce more publications for
the head of the department. This supports our assumption underlying the use of
the research unit’s head’s performance as proxy for the performance of his lab.
We do not observe any experience-related effects. This is probably not surprising
since the professors in our sample are quite homogenous in their level of
experience.
V. CONCLUSION AND DISCUSSION
While from a private-sector perspective, the benefits from collaborating with
academia are found to be unambiguously positive, the effects on the scientific
sector were not as clear. This study aimed at filling a gap in the literature by
providing insights on the effects of industry funding on scientific productivity.
The results show that the share of industry funding of the scientists total budgets
INDUSTRY FUNDING OF UNIVERSITY RESEARCH AND SCIENTIFIC PRODUCTIVITY
© 2011 Blackwell Publishing Ltd. 549

has reached a point (already in 1999 and shares have been increasing ever since)
that is sufficiently high to negatively affect publication output. In other words,
professors in our sample publish less in subsequent years the higher the share of
industry funds relative to their total budget. This finding supports the “skewing
problem” hypothesis for science and engineering faculty in Germany. If infor-
mation sharing among scientists via publications is the basis for cumulative
knowledge production and thus for scientific progress, industry funding that
reduces publications may have detrimental effects on the development of
science. Cohen et al. (2002) find the most important channel for knowledge
transfer from science to industry to be the publication of research results. Thus,
if industry funding reduces publications, not only the development of science
could be impeded, but also technology transfer. Transfer may be strengthened
between the university and the firms providing funds, but may be reduced for all
the others. On the other hand, we find that a higher share of industry funding does
not impact the number of patent applications on which the respective professor
is listed as inventor. We do, however, observe a significant positive effect on their
impact in terms of forward citations to those patents. This effect can also be
interpreted as a quality indicator as naturally only granted patents can receive
citations. Thus, patents of professors whose research is supported by industry
may not only be more successful in the granting process, but also more visible
and relevant for further applications in industry and hence receive more forward
citations.
We believe the results from this study are provocative for policy analysis and
public funding authorities. An increasing reliance on industry funding compared
to stagnating core funding may indeed affect the development of science in the
long run if publication output is reduced. On the other hand, industry funding
may be very valuable for professors’ applied research and the success of their
patenting activities.
Despite all efforts, our study is not without limitations and the results pre-
sented ought to be interpreted with those caveats in mind. It could be argued that
there is a bias in direction of above-average performers as our sample comprises
information on “heads of research units” only. These academics must have
performed well in their past carrier in order to hold such a position. Researchers
at earlier stages of their career may be led by other incentives that for instance
increase their paper output despite of industry funding. Further, we do not know
from how many different firms funding had been obtained and we cannot make
any judgment on the effects on research content. Future research could assess the
effects on the scientists’ research content measured by changes in journal types
and patent classifications.
Finally, it should be kept in mind that the results may depend on the institu-
tional setting in Germany where university research traditionally has been pre-
dominantly financed by public sources and where the increase in industry
HANNA HOTTENROTT/SUSANNE THORWARTH
550 © 2011 Blackwell Publishing Ltd.

sponsorship had been most significant. From 1997 to 2007, industry funding for
public R&D in Germany doubled from 6.2% to 12.5% of R&D expenditure in
higher education. It would therefore be highly desirable to study the relationship
between industry funding and scientific productivity in settings that are compa-
rable to those of Germany, for instance Austria where the share also had more
than doubled from 2% in 1998 to 4.5% in 2007 (OECD 2009). While this recent
OECD data shows a rise in industry funding for public sector R&D in more than
half of all OECD countries, industry sponsorship accounts on average for a much
lower share of university research funding in the U.S and the U.K where it had
been declining in the period 1997 to 2007. Thus, studying very different settings
like in the U.S. or U.K. could likewise provide interesting insights. Moreover,
while in Germany industry funds for science seem to be rather equally distrib-
uted, in other countries sponsoring firms seem to focus on specific institutions.
Geuna (1997) finds that in the U.K. industrial funding that is long-term and/or
has “no strings attached” is focused on a few universities, while a larger number
of technology oriented institutions receive the shorter-term and less basic con-
tracts. A similar argument can probably be raised with respect to US universities
where the universities that depend most heavily on industry tend to be smaller
institutions with a single R&D specialty related to local industry (Mansfield and
Lee 1996). Further research on the structural differences in the distribution of
industry funding may thus help to explain the divergence between the results
from this study and the research performance of scientists at top institutions like,
for instance, the Massachusetts Institute of Technology (MIT) which is to a high
degree funded by the private sector.
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APPENDIX
Table A.1
Funding by Research Field
Field Freq. % Amount of
Industry Funding
(T €)
% Ind.
Funding of
Total Budget
Physics 104 15.34 47.52 4.32
Mathematics and Computer Science 107 15.78 39.09 5.95
Chemistry 95 14.01 68.05 6.06
Biology 58 8.55 28.70 7.46
Electrical Engineering 101 14.90 130.75 11.54
Mechanical Engineering 110 16.22 241.43 14.13
Other Engineering 103 15.19 150.48 10.13
678 100.00
HANNA HOTTENROTT/SUSANNE THORWARTH
554 © 2011 Blackwell Publishing Ltd.

SUMMARY
Research conducted by university researchers for industry constitutes one of the main channels through
which knowledge and technology are transferred from science to the private sector. Since the value of such
inputs for the innovation performance of firms has been found to be considerable, it is not surprising that
firms increasingly seek direct access to university knowledge. In particular, industry funding for university
research has been increasing in most OECD countries.
This development, however, spurred concerns regarding possible long-run effects on scientific output.
While some policy makers argue that the potential of universities to foster and accelerate industrial inno-
vations is not yet fully exploited, others are concerned with the distraction of academics from their actual
research mission.
Our results show for a sample of professors in science and engineering in Germany that a higher budget
share from industry reduces publication output of professors both in terms of quantity and quality in
subsequent years. This finding supports the “skewing problem” hypothesis for science and engineering
faculty in Germany. If information sharing among scientists via publications is the basis for cumulative
knowledge production and thus for scientific progress, industry funding that reduces publications may have
detrimental effects on the development of science. On the other hand, we find that industry funding has a
positive impact on the quality of applied research if measured by patent citations. Industry funding may thus
have beneficial effects by improving impact and quality of more applied research. If industry funded research
results in successfully patentable and industrially relevant technologies it may create economic as well as
social value.
Table A.2
Scientific Productivity by Research Field
PUB CITPUB CITperPUB PAT CITPAT CITperPAT
Field Publications 1994–1999 Patents 1994–1999
Physics 22.47 612.89 21.74 1.11 17.11 2.97
Mathematics and Computer Science 3.97 44.49 6.57 0.21 0.84 0.56
Chemistry 27.53 513.24 16.07 1.80 23.24 5.47
Biology 11.52 320.59 21.83 0.91 7.60 3.67
Electrical Engineering 3.93 53.88 5.62 2.27 33.74 7.28
Mechanical Engineering 3.46 28.12 4.99 1.84 39.69 5.65
Other Engineering 6.94 93.62 7.97 1.57 12.33 1.70
Publications 2000–2007 Patents 2000–2007
Physics 33.29 419.68 9.45 0.91 1.06 0.20
Mathematics and Computer Science 6.50 39.54 3.61 0.25 0.08 0.02
Chemistry 39.06 376.64 8.40 1.52 0.67 0.13
Biology 19.45 247.71 9.26 1.14 0.76 0.15
Electrical Engineering 11.58 84.04 3.00 1.90 2.11 0.45
Mechanical Engineering 6.54 24.91 2.31 1.91 0.91 0.26
Other Engineering 15.33 94.94 3.78 1.79 0.84 0.20
INDUSTRY FUNDING OF UNIVERSITY RESEARCH AND SCIENTIFIC PRODUCTIVITY
© 2011 Blackwell Publishing Ltd. 555