Major legislative, legal, and technological changes paved the way
for a period of remarkable growth in the patenting of life science
research by U.S. universities in the 1980s and 1990s. (1) During this
time, the relative importance of life science patents granted to U.S.
universities grew from 10% of all university patents in 1980 to almost
25% in 1999. (2) This dramatic expansion in the role of life sciences
research occurred in a period when the annual number of patents granted
to U.S. universities grew almost tenfold from 340 patents granted in
1980 to 3,274 in 1999. Similarly, funding to support research and
education activities in the life sciences at major research universities
nearly doubled in constant dollar terms, with an especially rapid
expansion occurring in the 1990s (National Science Foundation 2000). As
a leading edge technology for U.S. universities, life science patenting
has clearly become a major research priority over the past two decades,
but many would ask, at what cost? This article analyzes a particular
aspect of this question by identifying the impact of life science
patents on other university life science research outputs, namely,
published articles and doctorates.
Researchers concerned about university-level trade-offs associated
with the expansion of patenting tend to focus on three potential
negative outcomes: (a) universities moving away from basic research to
pursue commercial patents (Kennedy 2000; Dasgupta and Ray 1994;
Blumenthal et al. 1996); (b) universities placing priority on the
establishment of intellectual property rights instead of on knowledge
generation and idea sharing and those intellectual property rights
making public research more difficult (Rai and Eisenberg 2003; Campbell
et al. 2002; Blumenthal et al. 1996); and (c) university research
quality declining as patent activity increases (Henderson, Jaffe, and
Trajtenberg 1998; Sampat, Mowery, and Ziedonis 2003). All three of these
trade-offs can be translated into university research production
outcomes, the first two into fewer and lower-quality journal articles
and potentially fewer doctorates, and the third into lower-quality
patents or articles (i.e., ones with fewer citations). Concerns about
these tradeoffs are especially heightened in discussions of patenting
trends in public, land grant universities, which historically have been
viewed as institutions dedicated to creating public goods in their
research and teaching enterprises (Atkinson et al. 2003).
Most quantitative research on the impacts of academic patenting has
focused on effects outside the university. Some important examples are
the Jensen and Thursby's (2001) examination of the private
investment incentives associated with universities having the right to
offer exclusive licensing of their patents, and the Zucker, Darby, and
Brewer (1998) exploration of the synergy between top scientists and
biotech firms where universities and companies are proximately located.
(3) However, only with respect to the evolution of patent quality has
there been any systematic empirical analysis done on the effects of
increased patenting on university research performance, with the most
recent evidence on patent citations suggesting no significant changes
(Sampat, Mowery, and Ziedonis 2003). One case study at MIT has shown
complementarities between patents and other research outputs for
university scientists (Agrawal and Henderson 2002), but its results are
limited to two departments at the top patenting university in the
country. In the sociological literature, Owen-Smith and Powell (2003)
suggest that high-quality research generates both highly cited articles
and a rich pool of potential patent opportunities for enterprising
technology transfer offices to exploit. This positive outcome is the
main "synergy" of interest in this article; that is, the
degree to which there are scope economies associated with high-quality
research generating both patents and traditional research outputs
(articles and trained students) in a more cost effective manner than if
those research outputs were produced separately.
Using panel data for U.S. universities, this article explores the
evidence for two types of increasing returns (scale and scope economies)
in the production of three major life science research outputs: patents,
articles, and doctorates. These measures are important indicators of
potential synergies associated with the portfolios of university
research outputs. While they are not, in and of themselves, direct
welfare measures, they can help to identify whether more production of
these university research outputs, separately or jointly, is more cost
efficient. The methods used below allow the construction of both
"overall" scope and scale estimates as well as a distribution
across university sizes and type to investigate whether scale and scope
outcomes are more prevalent in life sciences research.
The methodological approach builds on Baumol, Panzar, and
Willig's (1988) framework by constructing a university
multiple-output cost function. We present panel data estimates of the
multiple-output cost function from fixed-effects and random-effects
models for both quantity and quality-adjusted outputs.
The panel data econometrics advance previous cost-function
estimations aimed at identifying the underlying properties of university
production processes, as do the quality adjustments made on output
quantities. These empirical innovations are made possible by a data set
that combines annual data from 1981 to 1998 for ninety-six U.S.
universities on life science research expenditures, patents, journal
articles, and doctorates, including citation data for the patents and
journal articles that can be used to construct quality-adjusted output
measures. The analysis starts with nonparametrically smoothed costs
surfaces that provide visual evidence of scope and scale economies in
the production of patents and articles, especially among universities
with medium to large production levels. Econometric estimates from a
series of panel data models provide the basis for testing systematically
for the presence of economies of scale and scope. The estimates reveal
economies of scale in both the quantity and quality-adjusted data, and
economies of scope in only the quality-adjusted data. Comparisons across
university types reveal the strongest scale and scope economies in land
grant universities.
The organization of the article is as follows. In the next section,
an explanation is given for how scale and scope are measured in a
multiproduct cost function. The estimation strategy is presented in the
third section followed by a section introducing the panel data set on
U.S. university life science research, and explaining how the
quality-adjusted research outputs are constructed. In the fifth section,
we provide the results of the empirical analysis, which is followed by a
conclusion.
Measuring Scale and Scope in a Multiproduct Cost Function
Standard analyses of patent production both in industry and at
universities, (Hausman, Hall, and Griliches 1984) have used a production
function approach to estimate the determinants of patent production.
Building on Arora (1995), a recent piece by Graff, Rausser, and Small
(2003) tests for complementarities in reduced form production function
models among private firms. These techniques rest heavily on key
assumptions regarding the nature of complementarities and the validity
of some exclusion restrictions, which are unlikely to be satisfied in
the typical university setting where output prices are difficult to
measure. (4)
A more promising line of inquiry for identifying synergies or
trade-offs among multiple outputs involves using the dual, i.e., a
cost-minimization framework as set forth by Baumol, Panzar, and Willig
(1988). Since their work on scale and scope economies first appeared,
this cost function approach has been applied extensively to many sectors
including universities (de Groot, McMahon, and Volkvein 1991; Cohn,
Rhine, and Santos 1989). Previous university applications of the Baumol,
Panzar, and Willig (1988) framework involve either cross-sectional
analyses, or pooled versions of panel data.
Typical multiproduct cost function estimations are based on a
version of the following equation,
(1)
C(Y, w) = [a.sub.o] + [summation over (j)] [b.sub.j][Y.sub.j] + 1/2
[summation over (j)] [summation over (k)] [C.sub.jk][Y.sub.j][Y.sub.k]
+ [summation over (l)] [d.sub.l][w.sub.l] + 1/2 [summation over
(l)] [summation over (m)] [d.sub.lm][w.sub.l][w.sub.m],
where C(Y, w) is the total cost of producing a vector of outputs Y
with a vector of input prices w, and [a.sub.o], [b.sub.j], [c.sub.jk],
[d.sub.l], [d.sub.lm] are scalars. (5) The coefficient estimates,
[b.sub.j] and [C.sub.jk], are then used as evidence for synergies and
trade-offs and as arguments in the construction of estimates for ray
economies of scale and economies of scope using formulas presented
below. In order for the cost function to be valid it must satisfy
homogeneity of degree 1 in input prices. The procedure for ensuring this
is described below in the empirical implementation section.
Ray Economies of Scale and Scope
Following the standard formulas in Baumol, Panzar, and Willig
(1988), we calculate ray economies of scale and scope from the cost
function parameters. They are calculated as follows:
1. Ray economies of scale: The ray economies of scale for the joint
production process are defined by:
(2) [S.sub.n](Y) = C(Y)/[[summation].sub.j] [Y.sub.j][partial
derivative]C(Y)'/[partial derivative][Y.sub.j]
where ray economies of scale exist if [S.sub.n] (Y) is greater than
1.
2. Economies of scope: The economies of scope for a product set t
relative to the product set of all other n products not including t: (n
- t), can be computed from following function:
(3) [SC.sub.t](Y) = [C([Y.sub.t]) + C([Y.sub.n-t])
- C(Y)]/C(Y),
where C([Y.sub.t]) is the cost of producing only the product set t
and C([Y.sub.n-t]) is the cost of producing the other n products except
those in set t. Economies of scope exist when [SC.sub.t](Y) > 0.
In this application, we analyze economies of scope that compare
producing patents as a separate operation from articles and doctorates
and producing all three together as a single operation.
Econometric Specification
In the case of university research output in the life sciences, the
vector of outputs Y is measured by journal publications, patents, and
doctorates, while the costs are measured by the total expenditures on
life sciences research in a given year. To control for the presence of
university-specific effects in the error structure, panel data are used
to estimate a panel data model, such as that presented in equation (4).
(4) [C.sub.it] = [alpha] + [x.sub.it][beta] + [u.sub.it], where
[u.sub.it] = [v.sub.i] + [[epsilon].sub.it],
where [C.sub.it] are costs, [x.sub.it] represent the independent
variables (Y, w), [beta] is a vector of parameters to be estimated,
[v.sub.i] is a university-specific residual estimated as either a fixed
or random effect, while [[epsilon].sub.it] is the "usual"
residual which contains both a time-specific element and a standard
equation residual (Wooldbridge 2002).
We estimate two versions of the econometric specification under
different assumptions on the two error terms. A fixed effects model,
which estimates [v.sub.i] separately for each university, is presented
first. A random effects specification is then estimated to accommodate a
number of regressors that change infrequently and to include a number of
indicator variables that parameterize the differences between
universities in ways that we predict will affect the cost of research
production, e.g., presence of a longstanding tech transfer office and
medical school, and land grant status. The random effects estimator
imposes the assumption that [x.sub.it] and the random effects,
[v.sub.i], are uncorrelated. The results of a Hausman test of this
assumption along with tests for significance of the random effects and
for heteroskedasticity are described in the results section.
In terms of the functional form of the multiple-output cost
framework, the literature presents a number of variants, including the
generalized quadratic and the translog forms. Since a key independent
variable, patents, is zero for nearly a quarter of the university-year
combinations the translog formulation would be undefined for a large
part of our sample unless we added an ad hoc small number to each of
these data points. Given that the literature provides no specific
guidance on the optimality of one functional form over another; we
therefore have chosen the generalized quadratic because it minimizes the
number of ad hoc assumptions necessary for implementation. None of the
key results about economies of scale or scope presented in this article
are sensitive to our choice of functional form. (6)
In order to meet the theoretical requirements of a cost function,
we impose homogeneity in input prices in the manner suggested by
Chambers (1988):
(5) C = [w.sub.l] f (y, [w.sub.m]/w.sub.l]),
where [w.sub.l] and [w.sub.m] are two different input prices. The
interpretation and estimation are facilitated if one divides through by
[w.sub.l] to get the equation to be estimated as:
(6) C/[w.sub.l] = f (y, [w.sub.m]/[w.sub.l]).
The choice of normalizing input price is discussed below in the
data section.
Finally, the econometric models estimated below use both strict
quantity measures for research output and quality measures in which
citations of articles and patents are used to control for quality of
those two research outputs. The specifics of this citation adjustment
are discussed next in the data section.
Data on University Life Science Research, 1981-1998
The data set combines information on life science research inputs
and outputs for ninety-six U.S. universities over an eighteen-year
period, spanning an era of remarkable growth in the role of life
sciences in universities and the global economy. We focus on the segment
of life sciences--biological and agricultural sciences--that has been
most affected by recent court rulings in the United States that allow
patenting of life forms. Following the National Science
Foundation's (NSF) definition of "life sciences," these
categories include departments that produce most biotechnologies and
agricultural science research, but exclude departments that are
primarily engaged in clinical medicine (see Appendix for a complete
listing). This choice is consistent with a historical division within
most universities, where biological and agricultural life sciences are
contained in distinct administrative units from medical and
pharmaceutical schools. The ninety-six U.S. universities roughly
correspond to the Carnegie classification of "Research I"
universities, and they are responsible for the vast majority of U.S.
university production of articles and patents in life sciences. (7) The
exact choice was driven in large part by the availability of accurate
article and cost data.
For the dependent variable in our estimation, [C.sub.it], we use
university life science research costs as measured by the NSE The
university's outputs, the elements of the vector [Y.sub.it], are
measured as life sciences patents, articles, and doctorates. (8) Life
science patent assignee and citation information were extracted from the
NBER patent database (Hall, Jaffe, and Trajtenberg 2003), while the
Science Citation Index (ISI Web of Science 2002) provided the life
science article and citation counts by year for each university. Patents
are credited by application year rather than by grant date in order to
measure them as close as possible to the date research costs were
involved. In addition, although our cost measure does not include
teaching costs, we include the university's undergraduate student
to faculty ratio as a method of controlling for differences in teaching
loads that might influence the costs of research.
We use three input costs, [w.sub.it], in the cost function
estimation: average faculty salary, average wage rate in the
university's town as a measure of the cost of support personnel,
and an index of overall costs in agricultural research compiled by
Huffman and Evenson (2005). In order to preserve homogeneity of degree 1
in prices we divide all input prices and our dependent variable,
research costs, by the research cost index. Since this also has the
effect of deflating our cost variables, we otherwise use nominal values
of the variables.
Also to capture the university's level of technology transfer
infrastructure we include two indicator variables: one captures whether
the university has a technology transfer office, while the other
captures whether a university had a technology transfer office before
the promulgation of the Bayh-Dole act in 1980. Finally, in the random
effects models, we include three variables to control for missions of
universities that may be poorly measured in the outputs variables we
use. They are (a) LGU an indicator variable for whether a university has
land grant status, (b) "Extension FTE" a measure of the number
of extension personnel (measured in FTE) in the state (Ahearn, Lee, and
Bottom 2002), and (c) "Med School" for whether a university
has a medical school. We expect that land grant universities will have
higher base costs (positive coefficient) because of their multiple
outreach missions of providing public goods to the state. These outreach
missions may be poorly measured by the research outputs we are
including. We therefore include the extension FTE measure, which we
expect to be positive since it represents a major component of the
outreach mission and we expect that servicing a larger set of extension
personnel could raise the costs of research. (9) The medical school
dummy variable should be negative if we have counted outputs from
medical programs without accounting for their costs, or insignificant
otherwise.
We estimate two sets of regressions, one using quantity output
measures and another adjusting the quantity of articles and patents by
their citation counts as a measure of quality. (10) Citation adjustments
were sought because in the case of research output, quality is likely to
matter significantly to the implicit value of the research and also to
the potential synergies between patents and articles. In the first case,
highly cited articles and patents are likely to generate flows of
additional research or licensing funds to the author or assignee, while
in the latter research that gives rise, for example, to an article that
is highly cited may also be more likely to generate a patent than would
a larger number of uncited articles. Empirically, studies of patent
citations have shown that they provide a proxy for both the quality of a
patent and knowledge spillovers from patents, because each time a new
patent uses a piece of research from another patent it is obligated to
cite the previous patent (Henderson, Jaffe, and Trajtenberg 1998).
Article citations are also commonly used as measures of quality in
studies of departmental or university quality, e.g., Adams (1998).
Using citations requires attending to the time and subject
dependency of the counts, namely, the truncation problem associated with
more recent articles or patents that may not have had time to generate
many citations as well as different citation rates across disciplines
(Sampat, Mowery, and Ziedonis 2003). The citation adjustment measure
constructed here for each life science article/patent is the deviation
from the average citation rate of an article/patent in the same broad
patent class or disciplinary category published in the same year. For
example, a 1995 biochemistry article with ten citations is compared to
the average level of citations of all biochemistry articles produced in
1995. For a given year, the average article within a disciplinary
category has a citation rate of 1, with higher-quality articles then
having a measure greater than 1 and lower-quality articles receiving a
measure between zero and 1. This relative citation approach minimizes a
truncation bias that would be introduced using an absolute citation
count. Further details on the citation measure are in the appendix.
Empirical Results
Descriptive Statistics
A useful starting point for considering the issue of trade-offs or
synergies between university life science article, patent, and doctorate
production is an aggregate view of the recent trends in those outputs.
Table 1 demonstrates the tremendous takeoff in life science patent
production at U.S. universities in the 1990s, with the number of
accepted patents in 1998 at sixteen times the level of 1981. Table 1
also shows the approximately 50% growth in published life sciences
articles from 1981 to 1998 and the 33% growth in life science doctorates
at a time in which life science R&D expenditures grew 88% in real
terms. These growth rates in life science production are quite
remarkable when contrasted with Foltz et al.'s (2005) estimates of
only a 1% yearly rate of technical change in the university production
process. (11)
The growth in life science article production shows steady growth
over the entire period averaging about 2.4% per year, with the most
rapid growth period being between 1984 and 1992. Patents show short
growth spurts in the 1980s and then stable growth until 1995 when three
years of exponential growth occurred. Doctorates, meanwhile, grew most
in the early 1990s. While the boom in life science patenting in the late
1990s may have been fueled by the growth in life science article
production in the earlier period, the leveling off of all three research
outputs at much higher levels at the end of the 1990s suggests that at
least strict tradeoffs among articles, doctorates, and patents during
the boom era of life science patenting did not occur. It is possible,
nonetheless, that the explosion in patent activity in the latter part of
the decade may have dampened the other forms of research production.
Cost Surfaces
While table 1 demonstrates the growth of university life science
outputs, it does not give evidence on potential complementarities
between outputs. Descriptive evidence of economies of scale and scope
can be seen in the realized cost surfaces of university production
choices. A cost surface (or region) with cost complementarities will be
convex with respect to costs across the two outputs, higher along the
edges where more of a single product is produced and lower in the middle
where both products are produced. A cost surface exhibiting returns to
scale in a single product will be concave to the origin along one output
axis.
Descriptive evidence on the shapes of university life sciences
research cost surfaces is presented in figures 1 and 2 using a
nonparametric Lowess smoothing estimation procedure and the pooled data
set. (12) The relationship between articles and patents in quantity
space is shown in figure 1. With its strong concavity along the article
axis, it suggests significant returns to scale in article production,
and with both convex and concave regions in the article-patent plane it
also appears to show some regions of cost complementarities along with
some regions of trade-offs. For example, one major convex region appears
between thirteen and twenty-two patents and 660 to 1,080 articles. Also
noteworthy is the plateau at the upper end of the article distribution,
above 1,700 articles per year, where increases in either articles or
patents appear relatively costless. This provides some suggestion that
returns to scale and economies of scope may exist for the most
productive/largest universities.
[FIGURES 1-2 OMITTED]
The second nonparametric cost surface (figure 2) depicts the
citation-adjusted cost relationship between articles and patents. Along
the article axis, the initial slope of this surface shows much steeper
costs than did the quantity version, suggesting that quality research
articles do not come cheaply. At higher levels of quality-adjusted
article output, however, economies of scale do appear and persist. The
cost surface also shows approximately the same inflection points for the
region of convexity between articles and patents, but overall this
surface is less suggestive of cost complementarities than was the
surface in quantity space.
Econometric Estimates
The nonparametric cost surfaces obviously do not control for other
factors and therefore only provide suggestive evidence about scale and
scope economies. The next step in the analysis is to estimate for both
quantity and citation-adjusted outputs the life science research cost
function using panel data methods. Estimates are presented in tables 2
and 3 using a panel of 1,563 data points from eighty-seven universities
over eighteen years (1981-1998). Each table presents two regression
models: a fixed effects and a random effects regression. The tables also
show the chi-square ([chi square]) statistic from the Hausman test of
random versus fixed effects, the Breusch Pagan test that the random
effects parameter [v.sub.i] is different than zero and two t-tests for
the most likely types of heterskedasticity, that the estimated variance
is different (a) by university and (b) by year.
The dependent variable is university life science research and
development expenditures measured in thousands of dollars. In addition
to the quadratic formulation for the three research outputs and the two
input costs we include a number of regressors to control for possible
unmeasured differences. We include the university-wide undergraduate
student-to-faculty ratio in order to control for potentially higher
research costs for places with higher undergraduate teaching
responsibilities. The regression also includes an indicator variable for
whether the university is a land grant (LGU) institution, a medical
school dummy variable, and the two technology transfer variables
described above.
Table 2 presents fixed effects and random effects parameter
estimates for the quantity model, while table 3 presents the
citation-adjusted parameters. The tables also show the results of the
tests of the error terms, while table 4 presents the estimates of scale
and scope that are derived from inserting the parameters into equations
(2) and (3) from the regression estimates. In terms of regression
diagnostics, all equations have reasonably high [R.sup.2]'s. For
the random effects models, we cannot reject the null hypothesis of
homoskedasticity, while the Breusch Pagan test shows that we can reject
the null hypothesis that [v.sub.i] = 0. For the quantity regressions,
the insignificant Hausman test implies that we cannot reject the
random-effects model as the correct model, while we can reject the
random-effects model specification for the citation-adjusted results.
All of our model specifications provide similar and highly
significant results for most of the regressors. The coefficient
estimates for articles and patents in all regressions show that their
production increases costs, but at a decreasing rate. These significant
estimates provide supportive evidence of the necessary conditions for
scale economies in those outputs. The parameter estimates on graduate
student production have more ambiguous but statistically insignificant
effects. The interaction terms between outputs show significant
trade-offs between PhD's and both articles and patents, although in
the citation-adjusted regressions the significance of the PhD/article
trade-off disappears. The negative, though insignificant, coefficient on
the article/patent interaction term suggests some possibility of
synergies between these outputs.
As anticipated, both the faculty salary and LGU variables
positively and significantly increase research costs, while staff wage
is positive but not significant. The insignificant parameters on the
undergraduate-to-faculty ratio suggest that, at an aggregate level,
undergraduate teaching responsibilities do not spill over to a great
degree onto research costs. Schools with medical schools did not have
significantly different costs than those without, which supports the
division we have imposed between the life sciences and other related
parts of the university We find no significant effect of extension
personnel on overall research costs, suggesting that the higher base
costs at land grant universities in life sciences research come from
sources other than the extension mission. The technology transfer office
variables provide some surprising results, with the existence of a
technology transfer office causing an increase in overall research
costs. This effect is partially muted by the negative estimated
parameter for those with technology transfer offices in existence before
1980, but that estimated parameter is not significant. Overall, this
result is suggestive of trade-offs between increased technology transfer
activities and overall research costs. (13)
While the regression coefficients provide suggestive evidence of
scale and scope by output, estimates of ray economies of scale and scope
derived using equations (2) and (3) provide the global measures of
interest. These are estimated using the regression coefficient estimates
and values of the independent variables in the formulas for ray
economies of scale and scope. These are presented two ways: in table 4
using the mean of the independent variables and the regression estimates
from tables 2 and 3, while table 5 presents median scope estimates for
different types of universities (public/private and large/small) using
the random effects parameters and independent variables for each of the
universities to generate a distribution of scope and scale estimates. In
table 4, the mean scale and scope estimates are tested using nonlinear
Wald tests, which takes into account the variance of the estimated
parameters and tests whether scale = 1 or scope = 0. Significance tests
in table 4 are denoted by asterisks on the coefficients.
Table 4 shows significant estimates of increasing returns to scale,
with the citation-adjusted regressions exhibiting larger scale
economies. Table 5 demonstrates that these returns to scale are greatest
at land grant universities, while nonland grant universities show
lower-scale economies that approach constant returns to scale for the
quantity regressions.
The scope estimates have more varied patterns in tables 4 and 5.
The mean estimates from the quantity regressions show no significant
evidence of economies of scope, while the citation adjusted models do
exhibit significant economies of scope. This suggests that synergies
between patents and other research outputs are most pronounced in the
production of high-quality outputs. In the median estimates presented in
table 5, the estimates of economies of scope are larger, especially for
land grant universities, and are greatest for the small land grant
universities.
Overall, the results for the median university in table 5 suggest
that economies of scale and scope are the strongest for land grant
institutions. Moreover, the finding in table 5 that these economies are
even stronger in the citation-adjusted measures suggests that quantity
and quality of articles and patents go hand in hand. The cost advantages
that these increasing returns may provide the leading universities could
cause divergence in productivity and overall performance even among
Research I universities.
Conclusions
This work has estimated cost functions for university life science
research using panel data methods in order to investigate economies of
scale and scope. In contrast to much of the literature on academic
patenting, the dual formulation used here allows an explicit estimate of
cost complementarities and obviates the need to specify prices for
research outputs. The results demonstrate the benefits of using panel
data to take into account time- and university-specific effects as well
as the importance of taking into account quality in measuring university
outputs.
In contrast to a literature that has worried about both the
declining quality of university patenting and an increased
commercialization of the academic enterprise due to patenting especially
in the life sciences, the results show evidence of economies of scope
between patents and other missions of research universities in the life
sciences. Once one adjusts for the quality of the output, our data
suggest significant synergies between patents and other research
outputs. This implies that rather than declining patent and article
quality due to the increase in university patenting, we find evidence of
lowered costs for producing high-quality outputs simultaneously.
The synergies between patents and traditional research outputs are
especially evident for land grant universities. They exhibit the highest
levels of economies of scale and scope, although they also have higher
base costs as evident in the large and positive coefficient on the LGU
dummy variable. We find that these higher base costs are not directly
related to their extension mission though they may come from the
expanded mandate land grant universities have to provide public goods to
their states. The efficiency in the production process evident in
higher-scale and scope economies for land grant universities may come
from the discipline imposed by two decades of shrinking state budgets
and legislative oversight, or may be due to different internal
organizational structures. Whatever the cause, the strong economies of
scale and scope in life science research among land grant institutions
suggest that these universities have a distinct cost advantage in the
production of high-quality life sciences outputs.
Our results leave some key issues on the effects of patenting on
university life science research open for further research and analysis.
The advent of technology transfer offices appears to increase costs in
the life sciences rather than reduce them. While this effect may be due
to the relative immaturity of the technology transfer process during our
study period, this effect is significant and robust to alternative
specifications. It suggests that there is a long learning curve to the
operation of an effective technology transfer office before it generates
positive synergies to a university.
In addition, the estimations show evidence of trade-offs between
graduate student training and both patent and article production. The
fact that this effect is stronger for patents than for articles suggests
some trade-offs with respect to the long-term effects of the Bayh-Dole
act, if research productivity in articles and patents comes in part at
the expense of training the next generation of scientist. Future
research with university level cost functions, perhaps at the level of
all university outputs, might be able to shed more light on the
potential trade-offs between training graduate students and other
outputs.
While this work has found some synergies at the university level in
the production of life science outputs at the university level, it
leaves open a number of questions on how far reaching these results are.
Do these synergies exist for all scientific outputs? Are they the
product of aggregating to the university level or are they also present
within individual labs or even faculty members? We plan to investigate
these issues in future research.
Data Appendix
Academic Departments
We follow the National Science Foundation's NCES
classification of disciplines for the agricultural and biological
sciences. This definition includes what are generally the life science
departments that do most research, but excludes clinical medical
departments. The following broad department groups are included in the
NSF definition of agricultural and biological sciences:
Agricultural: agricultural chemistry, agronomy, animal science,
fish and wildlife, forestry, horticulture, plant sciences, aquaculture,
soil sciences, landscape architecture, conservation, renewable natural
resources.
Biological: anatomy, cellular, and developmental biology;
biochemistry/chemistry; biostatistics and epidemiology; ecology and
organismal biology; foods and nutrition; general biology/bioscience;
genetics and molecular biology; microbiology and immunology; pathology;
pharmacology and toxicology; physiology and biophysics; veterinary
sciences.
Patents
Patent data were culled from the NBER patent database, where they
were identified as having a university assignee. Patents assigned to the
University of California system were associated with a campus (Berkeley,
Davis, Los Angeles, etc.) by the location of their authors through
searches of campus directories.
Patents were categorized as life sciences based on the categories
and subcategories in Hall, Jaffe, and Trajtenberg (2003, pp. 452-53).
Patents were chosen in the NBER subcategories 33 (biotechnology as part
of the drugs and medical cat