Order Description
ECONOMIC GEOGRAPHY ASSIGNMENT PAPER
Questions:
1. The introduction of your paper should briefly note why, in a class on regional economic growth, we might be interested in the debate around knowledge flows/spillovers.
2. The first of the main sub-sections of your paper should provide a brief review of the main arguments of the Jaffe et al. (1993) paper. Highlight the methodology they use for the analysis. (I am not concerned with statistical analysis, rather the general logic of their argument.)
3. The second of the main sub-sections of your paper should provide a brief review of the main arguments made by Breschi and Lissoni (2005). (You do not need to examine in any detail the statistical analysis in section 4 of this paper.)
4. In your conclusion, you should discuss whether the Jaffe methodology is of any use, whether it can be improved (after Breschi and Lissoni), or whether it should be abandoned.
ECONOMIC GEOGRAPHY ASSIGNMENT PAPER
Before getting started, you were asked to read journal articles by Jaffe et al. (1993) and by Breschi & Lissoni (2005). Jaffe et al. (1993) outline a way of examining the geography of knowledge flows using patent citation data. Breschi & Lissoni (2005) note some difficulties with the methods and data employed by Jaffe et al. (1993). In this paper assignment, I want you to revisit this debate. The specific prompts (questions) for the assignment are indicated below.
You should write your response in the form of a single essay, with an introduction, sub-sections, and a conclusion. Your written paper should not exceed six (6) pages, excluding references.
Questions:
1. The introduction of your paper should briefly note why, in a class on regional economic growth, we might be interested in the debate around knowledge flows/spillovers.
2. The first of the main sub-sections of your paper should provide a brief review of the main arguments of the Jaffe et al. (1993) paper. Highlight the methodology they use for the analysis. (I am not concerned with statistical analysis, rather the general logic of their argument.)
3. The second of the main sub-sections of your paper should provide a brief review of the main arguments made by Breschi and Lissoni (2005). (You do not need to examine in any detail the statistical analysis in section 4 of this paper.)
4. In your conclusion, you should discuss whether the Jaffe methodology is of any use, whether it can be improved (after Breschi and Lissoni), or whether it should be abandoned.
Chapter 28
KNOWLEDGE NETWORKS FROM PATENT
DATA
Methodological Issues and Research Targets
Stefano Breschi1 and Francesco Lissoni2
1 Cespri, Dept. of Economics, Università “L. Bocconi”, Milan, Italy
E-mail: stefano.breschi@uni-bocconi.it.
2 Dept. of Mechanical Engineering, Università di Brescia, Brescia, Italy
Abstract: The economic literature on technical change has increasingly relied upon
patent citation data to measure inter-personal knowledge flows. Many doubts
exist about whether patent citations really reflect the designated inventors’
knowledge of both their technical fields, and of the other inventors and experts
therein: citations, in fact, come mainly from the patent examiners, and
possibly the patent applicant’s lawyers, rather than from inventors themselves.
Unfortunately, most of the papers dedicated to discussing these interpretation
issues deal with USPTO data, whose citation rules are quite exceptional if
compared to those of other patent offices. In addition some confusion exists
between the two issues of awareness (whether citing inventors actually knew
of the cited patents) and existence of a knowledge flow (whether some
information on the contents of the cited patents has however reached the,
possibly unaware, citing inventor). Questionnaires addressed to inventors are
severely affected by this confusion, and can hardly dispel the existing doubts.
We then propose to apply social network analysis to derive maps of social
relationships between inventors, and measures of social proximity between
cited and citing patents. Logit regressions demonstrate that the probability of
observing a citation is positively influenced by such proximity. In order to
perform such regressions, however, a specific sampling scheme has to used,
which we also illustrate and discuss.
613
H.F. Moed et al. (eds.), Handbook of Quantitative Science and Technology Research, 613-643.
© 2004 Kluwer Academic Publishers. Printed in the Netherlands.
Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations
Author(s): Adam B. Jaffe, Manuel Trajtenberg, Rebecca Henderson
Source: The Quarterly Journal of Economics, Vol. 108, No. 3 (Aug., 1993), pp. 577-598
Published by: Oxford University Press
Stable URL: http://www.jstor.org/stable/2118401 .
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GEOGRAPHIC LOCALIZATION OF KNOWLEDGE
SPILLOVERS AS EVIDENCED BY PATENT CITATIONS*
ADAM B. JAFFE
MANUEL TRAJTENBERG
REBECCA HENDERSON
We compare the geographic location of patent citations with that of the cited
patents, as evidence of the extent to which knowledge spillovers are geographically
localized. We find that citations to domestic patents are more likely to be domestic,
and more likely to come from the same state and SMSA as the cited patents,
compared with a “control frequency” reflecting the pre-existing concentration of
related research activity. These effects are particularly significant at the local
(SMSA) level. Localization fades over time, but only very slowly. There is no
evidence that more “basic” inventions diffuse more rapidly than others.
The last decade has seen the development of a significant body
of empirical research on R&D spillovers.1 Generally speaking, this
research has shown that the productivity of firms or industries is
related to their R&D spending, and also to the R&D spending of
other firms or other industries. In parallel, economic growth
theorists have focused new attention on the role of knowledge
capital in aggregate economic growth, with a prominent modeling
role for knowledge spillovers (e.g., Romer [1986, 1990] and Grossman
and Helpman [1991]).
We know very little, however, about where spillovers go. Is
there any advantage to nearby firms, or even firms in the same
country, or do spillovers waft into the ether, available for anyone
around the globe to grab? The presumption that U. S. international
competitiveness is affected by what goes on at federal laboratories
and U. S. universities, and the belief that universities and other
research centers can stimulate regional economic growth2 are
predicated on the existence of a geographic component to the
*We gratefully acknowledge support from the Ameritech Foundation, via the
Ameritech Fellows program of the Center for Regional Economic Issues at Case
Western Reserve University, and from the National Science Foundation through
grant SES91-10516. We thank Neil Bania, Ricardo Caballero, Michael Fogarty, Zvi
Griliches, Frank Lichtenberg, Francis Narin, seminar participants at NBER and
Case Western Reserve University, and two anonymous referees for helpful comments.
Any errors are the responsibility of the authors.
1. E.g., Jaffe [1986] and Bernstein and Nadiri [1988, 1989]. For a recent survey
and evaluation of this literature, see Griliches [1991].
2. See, e.g., Minnesota Department of Trade and Economic Development
[1988]; Dorfman [1988]; Feller [1989]; and Smilor, Kozmetsky, and Gibson [1988].
? 1993 by the President and Fellows of Harvard College and the Massachusetts Institute of
Technology.
The Quarterly Journal of Economics, August 1993
578 QUARTERLY JOURNAL OF ECONOMICS
spillover mechanism. The existing spillover literature, however, is
virtually silent on this point.3
In the growth literature it is typically assumed that knowledge
spills over to other agents within the country, but not to other
countries.4 This implicit assumption begs the question of whether
and to what extent knowledge externalities are localized. As
emphasized recently by Krugman [1991], acknowledging the importance
of spillovers and increasing returns requires renewed attention
by economists to issues of economic geography. Krugman
revives and explores the explanations given by Marshall [1920] as
to why industries are concentrated in cities. Marshall identified
three factors favoring geographic concentration of industries: (1)
the pooling of demands for specialized labor; (2) the development of
specialized intermediate goods industries; and (3) knowledge spillovers
among the firms in an industry. Krugman believes that
economists should focus on the first two of these, partially because
he perceives that “[k~nowledge flows, by contrast, are invisible;
they leave no paper trail by which they may be measured and
tracked, and there is nothing to prevent the theorist from assuming
anything about them that she likes” [Krugman, p. 53].
But knowledge flows do sometimes leave a paper trail, in the
form of citations in patents. Because patents contain detailed
geographic information about their inventors, we can examine
where these trails actually lead. Subject to caveats discussed below
relative to the relationship between citations and spillovers, this
allows us to use citation patterns to test the extent of spillover
localization. We examine citations received by patents assigned to
universities, and also the citations received by a sample of domestic
corporate patents. If knowledge spillovers are localized within
countries, then citations of patents generated within the United
States should come disproportionately from within the United
States. To the extent that regional localization of spillovers is
3. Jaffe [1989] provides evidence that corporate patenting at the state level
depends on university research spending, after controlling for corporate R&D.
Mansfield [1991] surveyed industrial R&D employees about university research
from which they benefited. He found that they most often identified major research
universities, but that there was some tendency to cite local universities even if they
were not the best in their field.
4. The existence of this implicit assumption was noted by Glaeser, Kallal,
Scheinkman, and Shleifer [1991]: “After all, intellectual breakthroughs must cross
hallways and streets more easily than oceans and continents.” Grossman and
Helpman [1991] consider international knowledge spillovers explicitly.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 579
important, citations should come disproportionately from the same
state or metropolitan area as the originating patent.5
The most difficult problem confronted by the effort to test for
spillover-localization is the difficulty of separating spillovers from
correlations that may be due to a pre-existing pattern of geographic
concentration of technologically related activities. That is, if a large
fraction of citations to Stanford patents comes from the Silicon
valley, we would like to attribute this to localization of spillovers. A
slightly different interpretation is that a lot of Stanford patents
relate to semiconductors, and a disproportionate fraction of the
people interested in semiconductors happen to be in the Silicon
valley, suggesting that we would observe localization of citations
even if proximity offers no advantage in receiving spillovers. Of
course, the ability to receive spillovers is probably one reason for
this pre-existing concentration of activity. If it were the only
possible reason, then, under the null hypothesis of no spillover
localization we should still see no localization of citations. As
discussed above, however, there are other sources of agglomeration
effects that could explain the geographic concentration of technologically
related activities without resort to localization of knowledge
spillovers. For this reason, we construct “control” samples of
patents that are not citations but have the same temporal and
technological distribution as the citations. We then calculate the
geographic matching frequencies between the citations and originating
patents, and between the controls and originating patents.
Our test of localization is whether the citation matching frequency
is significantly greater than the control matching frequency. Since
the “control” matching frequency is, itself, likely to be partly the
result of spillover-localization, we believe this to be a conservative
test for the existence of localization.
The first section of the paper describes patents and patent
citations, explains the construction of the control samples, and
5. Glaeser, Kallal, Scheinkman, andShleifer [1991] characterizethe “Marshall-
Arrow-Romer” models as focusing on knowledge spillovers within the firms in a
given industry. They examine the growth rate of industries in cities as a function of
the concentration of industrial activity across cities, within-city industrial diversity,
and within-city competition. They find that within-city diversity is positively
associated with growth of industries in that city, while concentration of an industry
within a city does not foster its growth. They interpret this contrast to mean that
spillovers across industries are more important than spillovers within industries. As
is discussed below, there is evidence from the R&D spillover literature to suggest
that across-industry knowledge spillovers are, indeed, important. In this study, we
do not consider the industrial identity of either generators or receivers of spillovers,
though we do have some information on their technological similarity.
580 QUARTERLY JOURNAL OF ECONOMICS
considers more carefully how citations might be used to infer
spillovers. The second section presents the results of the tests of
geographic localization. The following section examines whether
the probability of geographic localization of any given citation can
be explained by attributes of the originating or citing patents, or of
relationships between them. A concluding section follows.
I. EXPERIMENTALD ESIGN
A. Patents and Patent Citations6
A patent is a property right in the commercial use of a device.7
For a patent to be granted, the invention must be nontrivial,
meaning that it would not appear obvious to a skilled practitioner
of the relevant technology, and it must be useful, meaning that it
has potential commercial value. If a patent is granted,8 a public
document is created containing extensive information about the
inventor, her employer, and the technological antecedents of the
invention, all of which can be accessed in computerized form.
Among this information are “references” or “citations.” It is the
patent examiner who determines what citations a patent must
include. The citations serve the legal function of delimiting the
scope of the property right conveyed by the patent. The granting of
the patent is a legal statement that the idea embodied in the patent
represents a novel and useful contribution over and above the
previous state of knowledge, as represented by the citations. Thus,
in principle, a citation of Patent X by Patent Y means that X
represents a piece of previously existing knowledge upon which Y
builds.
The examiner has several means of identifying potential
citations. The applicant has a legal duty to disclose any knowledge
of the prior art that she may have. In addition, the examiner is
supposed to be an expert in the technological area and be able to
6. All of the data we use relate to patents granted by the U. S. patent office.
About 40 percent of U. S. patents are currently granted to foreigners. Other
countries, of course, also grant patents, leading to some ambiguity in the meaning of
phrases like “U. S. patent” and “foreign patent.” We shall use the phrase “U. S.
patent” to mean a patent granted by the U. S. patent office, regardless of the
residence of the inventor. We shall use the phrase “domestic patent” to refer to a
patent granted (by the U. S. patent office) to an inventor residing in the United
States. We shall use the phrase “foreign patent” to refer to a patent granted by the
U. S. patent office to non-U. S. residents.
7. Ideas are not patentable; nor are algorithms or computer programs, though
a chip with a particular program coded into it might be. The definition of a device
was recently broadened to include genetically engineered organisms.
8. There is no public record of unsuccessful patent applications.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 581
identify relevant prior art that the applicant misses or conceals.
The framework for the search of the prior art is the patent
classification system. Every patent is assigned to a nine-digit
patent class (of which there are about 100,000) as well as an
unlimited number of additional or “cross-referenced” classes. An
examiner will typically begin the search of prior art using her
knowledge of the relevant classes. For the purpose of identifying
distinct technical areas, we utilize aggregations of subclasses to a
three-digit level; at this level there are currently about 400
technical classes.9
For this study, we begin with two cohorts of “originating”
patents, one consisting of 1975 patent applications and the other of
1980 applications. In each cohort we include all patents granted to
U. S. universities and two samples of U. S. corporate patents10
chosen to match the university patents by grant date and technological
distribution. These sets of originating patents were chosen
because we conjectured that the extent of geographic localization
might differ depending on the nature of the originating institution.
As discussed below, such differences turn out to be minor. The
1975 originating cohort contains about 950 patents that had
received a total of about 4750 citations by the end of 1989. The
1980 originating cohort contains about 1450 that had received
about 5200 citations by the same time.
B. Construction of “Control” Samples
The main idea of this paper is to compare the geographic
location of the citations with the originating patent that they cite.
But to make such a comparison meaningful, we have to consider
how often we would expect them to match under some “null”
hypothesis. That is, we need to compare the probability of a patent
matching the originating patent by geographic area, conditional on
its citing the originating patent, with the probability of a match not
conditioned on the existence of a citation link. This noncitationconditioned
probability gives us a baseline or reference value
against which to compare the proportions of citations that match.
9. Examples of three-digit patent classes are “Batteries, Thermoelectric and
Photoelectric”; “Distillation: Apparatus”; “Robots”; seventeen distinct classes of
“Organic Compounds”; and the ever-popular “Whips and Whip Apparatus.”
10. The “top corporate” sample consists of patents granted to the 200
top-R&D-performing firms in the United States, as reported in S.E.C. 10-k forms
and compiled by Compustat. The “other corporate” sample contains patents
assigned to U. S. corporations that are not universities and not, in the “top
corporate” sample.
582 QUARTERLY JOURNAL OF ECONOMICS
We call this baseline or reference probability the “control
frequency.”
Two considerations drove our choice for constructing the
control frequency. First, the fraction of U. S. patents granted to
foreigners has been climbing steadily during the period under
study here. We do not want to conclude that citations are initially
localized, but that this localization fades over time, simply because
of this aggregate trend. Second, countries (and cities and states)
differ in their areas of technological focus. Although such technological
specialization is probably due, in part, to geographic localization
of spillovers, we want to be conservative and test whether spillovers
are localized relative to what would be expected given the
existing distribution of technological activity.
To derive a control frequency that would be immune to
contamination from either aggregate movements over time or
localization based on the pre-existing concentration of technological
activity, we went back to the patent data base and found a
“control patent” to correspond to each of the citing patents. For
each citing patent, we identified all patents in the same patent class
with the same application year (excluding any other patents that
cited the same originating patent). We then chose from that set a
control patent whose grant date was as close as possible to that of
the citing patent. This process yielded, for each set of citing
patents, a corresponding control sample of equal size, whose
distribution across time and technological areas is essentially
identical to that of the citation data set. Each control patent is
paired with a particular citing patent, allowing us to compare the
geographic location of the control patent with that of the originating
patent cited by its counterpart in the citing dataset. The
frequency with which these control patents match geographically
with the originating patent is an estimate of the frequency with
which a randomly drawn patent that is not a citation, but has the
same technological and temporal profile as the citation, matches
geographically.
To put it slightly differently, when we calculate the frequency
with which the citations match the geographic location of the cited
patents, we are estimating the probability of geographic match for
two patents, conditional on there being a citation link and also
conditional on the technological nature and timing of the citation.
When we calculate the frequency with which the “control” patents
match geographically with the cited patents, we are estimating the
probability of geographic match for two patents, conditional only
LOCALIZATION OF KNOWLEDGE SPILLOVERS 583
on the technological nature and timing of the citation. If the
citation match frequency is significantly higher, then that implies
that citations are localized even after controlling for timing and
technology.
C. Issues Relating to the Use of Citations to Infer Spillovers
With the construction of the control samples, we believe that
we have designed a very clean test of the extent to which patent
citations are geographically localized. Before going on, we must
address the validity of drawing inferences about knowledge spillovers
from patent citations. For discussion purposes, we can
classify the links that might exist between two inventions into one
of three groups: spillovers accompanied by citations, citations that
occur where there was no spillover, and spillovers that occur
without generating a citation. Our experiment uses the first set,
but clearly the other two are non-empty. The key question is
whether and to what extent we expect that either of the latter two
groups would be systematically more or less localized than the
group we examine. Though there are a number of considerations,
all difficult to quantify, we believe that on balance it is reasonable
to draw inferences about spillovers from citations.
As a general consideration, it is important to keep in mind that
any analogy between patent citations and academic article citations
cannot be taken too far. Academics may cite a friend (or neighbor)
just to be nice, since the price of doing so is infinitesimal, or even
negative if a longer list of references is perceived as making the
research look more thorough. An inventor who did the same in a
patent application is, in effect, leaving money lying on the table: if
those citations are included in the final patent the inventor has
reduced the scope of her monopoly. Further, the patent examiner
should not include such citations in the patent even if the inventor
did put them there. Thus, it does not seem that “gratuitous”
citations are a serious concern.
A deeper problem is created by “real” citations that are not
spillovers. For example, suppose that a firm gets a patent on an
invention and then contracts with another firm to make some part
of it, or a machine necessary to make it, or any other aspect of the
downstream development. It is possible that such a contractor
might later get a patent on a related technology. To the extent that
the flow of rents between these parties is governed by a complete
contract, there could conceivably be no externality running from
the original inventor to the contractor. If we now add to this
584 QUARTERLY JOURNAL OF ECONOMICS
hypothetical contract the assumption that such contracted development
is relatively likely to be localized, we have the potential for
the observed localization of citations to be greater than the true
localization of knowledge spillovers.1I
Although such “internalized spillovers” surely exist, it is likely
that most citations that are not spillovers are of a different sort:
citations (added by the examiner) to previous patents of which the
citing inventor was unaware. Clearly, no spillover occurs in this
case. Further, it seems likely that citations of this sort should not
be any more geographically localized than the control patents. If
many citations are in this category, it introduces “noise” into the
citations as a measure of spillovers, and biases the results away
from finding significant localization. Our a priori belief is that this
category is much larger than the previous one, suggesting that
spillovers are, on balance, probably more localized than citations,
but readers with different beliefs should interpret our results
accordingly.
Finally, there are an enormous number of spillovers with no
citations, since only a small fraction of research output is ever
patented. In particular, much of the results of very basic research
cannot be patented. It is plausible that basic research generates the
largest spillovers,12 and also that basic research is communicated
via mechanisms that are less likely to be localized, such as
international journals. For this reason, it is probably appropriate
to view our results as related to applied research, and to exercise
care in extrapolating to the localization of spillovers from extremely
basic research.
D. Geographic Assignment of Patents
The preceding discussion has presumed that the “location” of
a patent is an unambiguous construct. The patent data contain the
country of residence of each inventor, and the city and state of
11. As discussed below, we focus on tests of localization that exclude citing
patents that are owned by the same organization as the originating patent, precisely
because such “self-citations” do not represent an externality. Citations by other
organizations that have an economic relationship with the original inventor could
be viewed as similar to self-citations. There is, however, a significant difference: we
expect that, in general, the contract between the two parties will be quite
incomplete, making it more likely than not that the citing organization could
capture some rents from the original invention and hence benefit from at least a
partial spillover.
12. This question is analyzed in detail in Trajtenberg, Henderson, and Jaffe
[1992].
LOCALIZATION OF KNOWLEDGE SPILLOVERS 585
residence for U. S. inventors.13 Use of this information is complicated
by the fact that patents can have multiple inventors who can
live in different places. The following procedure was followed:
1. For U. S. inventors, city/state combinations were placed in counties using a
commercially available city directory; each U. S. inventor was then assigned to an
SMSA14 based on state and county. For this purpose an additional “phantom” SMSA
was created in each state, encompassing all counties in the state outside of defined
SMSAs. Approximately 98 percent of inventors were successfully assigned to SMSAs.
2. Assignments of each patent to a country, a state, and an SMSA were then
made based on pluralities of inventors. So, for example, a patent with one inventor
living in Bethesda, MD, one in Alexandria, VA, and one in rural Virginia would be
assigned VA for its state and Washington, DC, for its SMSA. Ties were assigned
arbitrarily, except that ties between true SMSAs and phantom SMSAs were
resolved for the true one and ties between United States and foreign were resolved
in favor of foreign.15
II. RESULTS ON EXTENT OF LOCALIZATION
As a prelude to the geographic analysis, Table I and Figure I
present some descriptive data about the citations and their relationship
to the originating patents. Table I shows that about 80-90
percent of the 1975 patents and 70-80 percent of the 1980 patents
had received at least one citation by the end of 1989, with the
higher proportion in each case applying to the university patents.
Mean citations received (including zeros) were four-six for 1975
and three-four for 1980, again with the higher numbers corresponding
to the university patents.16 The average lag between the
originating application year and the application year of the citing
13. Published data on the geographic distribution of U. S. patents (including
those cited in the popular press) are based on the location of the organization or
individual to which the patent is “assigned” by the inventor (usually her employer).
For analysis of localization, the location of the assignee is not a desirable datum.
There is an ambiguity in such data relating to the way employees of multinational
corporations make their assignments. An employee of “Honda” in the United States
could assign her patent to “Honda U.S.A., Inc.” or she could assign it to the parent
company in Japan. In the former case, the patent office would call it a domestic
patent, and in the latter case a foreign patent; similarly for IBM Switzerland.
14. These assignments were made based on the 1981 SMSA definitions. In
areas where Consolidated Metropolitan Statistical Areas were defined in 1981, these
were used; elsewhere Metropolitan Statistical Areas were used. Hence we use the
generic term “SMSA.”
15. At the country level, 98 percent of patents were assigned unanimously. At
the state level, 90 percent were assigned unanimously; an additional 4 percent had
more than half of inventors in a single state. At the SMSA level, 86 percent were
assigned unanimously, and an additional 6 percent had a clear majority. Overall, 4.5
percent of patents were assigned to “phantom” SMSAs.
16. Our companion paper [Trajtenberg, Henderson, and Jaffe, 1992] explores
in detail the use of citation intensity and related measures for measuring the
basicness and appropriability of inventions.
586 QUARTERLY JOURNAL OF ECONOMICS
TABLE I
DESCRIPTIVE STATISTICS
Percent Total no. Mean Average Percent Percent same
Originating receiving of citations citation self- patent
dataset citations citations received laga b citationsb classbc
1975
University 88.6 1933 6.12 6.53 5.6 54.3
Top corporate 84.2 1476 4.70 7.17 18.6 55.7
Other corporate 82.3 1341 4.22 7.82 9.1 57.5
1980
University 79.9 2093 4.34 4.36 8.9 56.3
Top corporate 79.9 1701 3.54 4.41 24.6 58.3
Other corporate 74.1 1424 2.95 4.46 12.6 57.2
a. Application year of citing patent minus application year of originating patent.
b. For those patents receiving any citations.
c. Comparison is at the three-digit level (see text).
patent is 6.5 to 8 years for the 1975 cohort, and a little over 4 years
for the 1980 cohort.
The inference that a citation indicates a possible knowledge
spillover is much less clear in the case where the citing patent is
owned by the same organization as the originating patent. For this
reason, we distinguish what we call “self-citations.” A self-citation
is defined as a citing patent assigned by its inventors to the same
party as the originating patent, which is, by construction, either a
university or a domestic corporation. Not surprisingly, the selfcitation
rate differs for the different sources of originating patents,
with universities having the lowest and top corporations the
highest rates.17 Finally, Table I shows that 55 to 60 percent of citations
have a primary patent class that is the same as the primary
patent class of the originating patent, indicating that the originating
and citing patents are technologically close to one another.
Figure I provides additional detail on the distribution of lags
between originating and citing patents, again defined as the
difference in application years. The figure shows that citations are
few in the early years,18 and reach a plateau after about three years.
17. The apparent increase in self-citation rates between 1975 and 1980 is
probably spurious; self-citations tend to come earlier than other citations. See
Trajtenberg, Henderson, and Jaffe [1992] for more on this issue.
18. Patents are typically granted one to three years after application; thus, a
citation lag of zero or one implies that the citing patent may well have been applied
for before the originating patent was actually granted. Pending applications are not
public, so in this case the citation would almost surely have been identified by the
examiner.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 587
1975 Cohort
Number of Citations
250
20 0 . .. …….. — – .. …..
5 0 – – – – – – – – – – – – – – – – – – – – – – – – – –
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Citation Lag (difference between application years)
1980 Cohort
Number of Citations
500
3 0 0 — – — – — – – . ….. ……….. ——…-..-.—–…
300 …. . . ….. .. /. . ..\…. .. ….. …. . . … .
0 1 2 3 4 5 6 7 8 9
Citation Lag (difference between application years)
-University Citations ? Top Corporate Cit. * Other Corporate Cit.
FIGURE I
588 QUARTERLY JOURNAL OF ECONOMICS
TABLE II
SMSA DISTRIBUTIONS FOR SOME DATASETS
All
Controls citations
1975 Citations for to patents
1975 Top Citations to 1975 citations from in
University corporate to 1975 top to 1975 NY
Location originating originating university corporate university SMSA
Foreign – – 31.8 31.4 35.8 31.2
Boston 15.0 3.1 7.5 4.6 5.1 4.0
Los Angeles/
Anaheim 7.0 4.8 9.0 5.7 6.1 3.9
San Francisco/
Oakland 5.1 1.4 3.8 3.7 6.1 3.5
Madison, WI 4.2 – 1.6 0.5 0.6
Philadelphia/
Wilmington 4.2 9.3 5.4 8.2 4.5 9.1
Rural Iowa 3.8 – 1.6 0.6 0.2
San Jose 3.5 2.8 4.0 3.4 – 1.9
New York/
NJ/CT 3.2 13.5 9.7 11.7 13.7 28.5
Salt Lake City 3.2 – 2.1 0.5 0.4
Detroit/
Ann Arbor 2.6 2.4 2.6 1.7 1.7 1.2
Minneapolis/
St. Paul 1.3 5.2 2.8 2.9 1.9 2.1
Chicago 1.9 4.2 3.9 5.7 5.6 4.2
Albany 0.6 3.1 1.9 2.1 1.3 0.8
All figures are percentages. SMSA percentages for citations and controls are relative to domestic total.
It is not possible to tell for sure from these data when (if ever) that
plateau tails off; the apparent tail-off in both panels of the figure is
due at least in part to the 1989 observational cutoff.’9 For 1975 the
higher citation rate for university patents is particularly pronounced
in the early years; this pattern is not apparent in the 1980
cohort.
Before getting to our formal test of localization, an examination
of Table II is useful to get a sense for the extent of geographic
concentration in these data. It shows the fraction of patents
coming from abroad and from a selection of major U. S. SMSAs for
19. The dropoff in both panels corresponds approximately to application year
1987 (1975 + 12 and 1980 + 7). Typically, a significant fraction of applications have
not been granted within two years, so when we looked in 1989 this fraction of 1987
applications were not yet granted.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 589
several of the datasets. Not surprisingly, a measurable fraction of
university patents comes from Madison, WI; this is not true for
corporate patents. A measurable (though smaller) fraction of the
citations of university patents comes from Madison, and this
fraction is larger than that for the controls. Indeed, the controls for
the university citations look generally “more like” corporate
patents than do the citations, suggesting that localization may be
present. Other qualitative evidence of localization is apparent in
the table, including the high percentage of NY SMSA citations that
come from the NY SMSA.
The basic test of localization is presented in Table III. For each
geographic area and each originating dataset, it presents the
proportion of citations that geographically matched the originating
patent. These proportions are shown both with and without selfcitations.
The matching proportions for the control samples are then
shown, as well as a t-statistic testing the equality of the control
proportions and the citation proportions (excluding self-citations).20
We focus first on the 1975 results on the left of the table.
Starting with the country match, we find that citations including
self-citations are domestic about 6 or 7 percent more often than the
controls. Excluding self-citations eliminates this difference for the
top corporate citations and cuts it roughly in half for the others.
The remaining difference between the citations excluding self-cites
and the controls is only marginally significant statistically.
Looking at the 1975 results for states, we find that citations of
university patents come from the same state about 10 percent of
the time; this rises to 15 percent for other corporate and 19 percent
for top corporate. Excluding self-citations, however, makes a big
difference. The university and top corporate proportions are cut to
6-7 percent, and the other corporate to just over 10. For the
university and other corporate cohorts, the matching frequencies
excluding self-citations are significantly greater than the matching
control proportions.
20. Let Pc be the probability that a citation comes from the same geographic
unit as the originating patent; letpo be the corresponding probability for a randomly
drawn patent in the same patent class (control). We test Ho:p, = poversus Ha pc > Po
using the test statistic:
t
~PC -Po
\/f;C( – PC) + 60(1 – Pow]n
wherePc andPo are the sample proportion estimates ofpc andpo. This statistic tests
for the difference between two independently drawn binomial proportions; it is
distributed as t.
590 QUARTERLY JOURNAL OF ECONOMICS
TABLE III
GEOGRAPHIMCA TCHINGFR ACTIONS
1975 Originating cohort 1980 Originating cohort
Top Other Top Other
University corporate corporate University corporate corporate
Number of
citations 1759 1235 1050 2046 1614 1210
Matching by country
Overall citation
matching
percentage 68.3 68.7 71.7 71.4 74.6 73.0
Citations excluding
self-cites 66.5 62.9 69.5 69.3 68.9 70.4
Controls 62.8 63.1 66.3 58.5 60.0 59.6
t-statistic 2.28 -0.1 1.61 7.24 5.31 5.59
Matching by state
Overall citation
matching
percentage 10.4 18.9 15.4 16.3 27.3 18.4
Citations excluding
self-cites 6.0 6.8 10.7 10.5 13.6 11.3
Controls 2.9 6.8 6.4 4.1 7.0 5.2
t-statistic 4.55 0.09 3.50 7.90 6.28 5.51
Matching by SMSA
Overall citation
matching
percentage 8.6 16.9 13.3 12.6 21.9 14.3
Citations excluding
self-cites 4.3 4.5 8.7 6.9 8.8 7.0
Controls 1.0 1.3 1.2 1.1 3.6 2.3
t-statistic 6.43 4.80 8.24 9.57 6.28 5.52
Number of citations is less than in Table I because of missing geographic data for some patents. The
t-statistic tests equality of the citation proportion excluding self-cites and the control proportion. See text for
details.
At the SMSA level, 9 to 17 percent of total citations are
localized. This again drops significantly when self-citations are
excluded, but 4.3 percent of university citations, 4.5 percent of top
corporate citations, and 8.7 percent of other corporate citations are
localized excluding self-cites. This compares with control matching
proportions of about 1 percent, and these differences are highly
significant.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 591
The results for citations of 1980 patents (right side of Table
III) are even stronger and more significant. For every dataset, for
every geographic level, the citations are quantitatively and statistically
significantly more localized than the controls. The general
increase in the proportion of U. S. patents taken by foreigners is
reflected in a decline of 3 to 6 percent in the control percentages
matching by country. The citation matching percentages actually
rise, however, particularly for top corporate citations. It is impossible
to tell from this comparison whether this represents a real
change, or whether it is the result of the 1980 citations having
shorter average citation lags. Since this gets to the issue of
explaining which citations are localized, we postpone discussion
until the next section.
Before moving on, the results on the extent of localization can
be summarized as follows. For citations observed by 1989 of 1980
patents, there is a clear pattern of localization at the country, state,
and SMSA levels. Citations are five to ten times as likely to come
from the same SMSA as control patents; two to six times as likely
excluding self-citations. They are three to four times as likely to
come from the same state as the originating patent; roughly twice
as likely excluding self-cites. Whereas about 60 percent of control
patents are domestic, 70 to 75 percent of citations and 69 to 70
percent of citations excluding self-cites are domestic. Once selfcites
are excluded, universities and firms have about the same
domestic citation fraction; at the state and SMSA level there is
weak evidence that university citations are less localized. For
citations of 1975 patents, the same pattern, but weaker, emerges
for citations of university and other corporate patents. For top
corporate there is no evidence of localization at the state or country
levels, though the SMSA fraction is significantly localized. Thus,
we find significant evidence that citations are even more localized
than one would expect based on the pre-existing concentration of
technological activity, particularly in the early years after the
originating patent.
III. FACTORAS FFECTINGTH EP ROBABILITOYF LOCALIZATION
The contrast between the 1975 and 1980 results suggests that
localization of early citations is more likely than localization of
later ones. This accords with intuition, since whatever advantages
are created by geographic proximity for learning about the work of
others should fade as the work is used and disseminated. Another
592 QUARTERLY JOURNAL OF ECONOMICS
hypothesis that is implicit in the previous discussion is that
citations that represent research that is technologically similar to
the originating research are more likely to be localized, because the
individuals pursuing these related research lines may be localized.
In addition, attributes of the originating invention or the institution
that produced it may affect the probability that its spillovers
are localized.
To explore these issues, we pooled the citations (excluding
self-cites) to university and corporate patents for each cohort, and
ran a probit estimation with geographic match/no match between
the originating and citing patents as the dependent variable. As
independent variables we included the log of the citation lag (set to
zero for lags of zero), dummy variables for top corporate and other
corporate originating patents, interactions of the lag and these
dummies, and a dummy variable equal to unity if the citation has
the same primary class as the originating patent. We also included
a dummy variable that is unity if the control patent corresponding
to this citation matches geographically with the originating patent,
to control for the general increase over time in the fraction of U. S.
patents granted to foreigners.
We also included two variables relating to the originating
patent suggested by our work on basicness and appropriability of
inventions [Trajtenberg, Henderson, and Jaffe, 1992]. The first,
“generality” is one minus the Herfindahl index across patent
classes of the citations received.21 It attempts to capture the extent
to which the technological “children” of an originating patent are
diverse in terms of their own technological location. Thus, an
originating patent with generality approaching one has citations
that are very widely dispersed across patent classes; generality of
zero corresponds to all citations in a single class. We argue
elsewhere that generality is one aspect of the “basicness” of an
invention. One might hypothesize that basic research results are
less likely to be localized, because their spread is more likely to be
through communication mechanisms (e.g., journals) that are not
localized. The other variable characterizing the originating invention
is the fraction of the originating patent’s citations that were
21. Let Cik be the number of citations received by patent i from subsequent
patents whose primary patent class is k, and let Cl be the total number of citations
received by patent i. The measure of generality is then
Gi = 1 –
k
(2C)-
LOCALIZATION OF KNOWLEDGE SPILLOVERS 593
TABLE IV
GEOGRAPHIC PROBIT RESULTS
Country match State match SMSA match
1975 1980 1975 1980 1975 1980
Dummy for control 0.139 0.085 0.396 0.300 * 0.283
Sample match (0.045) (0.041) (0.124) (0.102) (0.172)
Log of citation -0.078 0.094 -0.264 0.198 -0.123 0.037
lag (0.049) (0.056) (0.073) (0.079) (0.057) (0.086)
Dummy for top -0.114 -0.010 -0.383 0.013 -0.234 -0.208
corporate (0.168) (0.127) (0.249) (0.177) (0.288) (0.200)
Dummy for other 0.069 0.053 -0.214 -0.007 0.325 -0.042
corporate (0.209) (0.134) (0.277) (0.189) (0.291) (0.207)
Log-lag 0.046 -0.016 0.226 0.007 0.102 0.156
*top corp. dummy (0.091) (0.086) (0.138) (0.115) (0.156) (0.131)
Log-lag 0.008 -0.026 0.307 0.036 0.037 0.039
*other corp. dummy (0.108) (0.091) (0.147) (0.124) (0.155) (0.138)
Dummy for matching -0.085 0.069 -0.013 0.034 -0.057 -0.016
patent class (0.050) (0.045) (0.073) (0.058) (0.080) (0.068)
Generality of origin 0.092 0.177 0.026 -0.140 0.013 -0.298
patent (0.091) (0.088) (0.136) (0.111) (0.150) (0.130)
Origin fraction -0.813 0.162 0.815 0.883 1.174 0.828
Self-citations (0.180) (0.124) (0.246) (0.134) (0.237) (0.154)
# of observations 3581 4217 3573 4215 3566 3972
# of matches 2363 2925 256 490 197 298
Log likelihood -2269 -2559 -894 -1459 -736 -1022
*The number of observations for which the control patent matched at the SMSA level was so small that this
parameter could not be estimated.
Standard errors are in parentheses. All equations also included five technological field dummies.
self-cites. We take a high proportion of self-cites as evidence of
relatively successful efforts by the original inventor to appropriate
the invention. We expect that the nonself-citations to such a patent
are more likely to be confined to suppliers, customers, or other
firms that the inventing firm has a relationship with, and may
therefore tend to be localized.
Finally, the extent of localization depends fundamentally on
the mechanisms by which information flows, and these mechanisms
may be different in different technical fields. For this reason,
we also included dummy variables for broad technological fields.22
The results are presented in Table IV. Because of the presence
22. (1) Drugs and Medical Technology; (2) Chemicals and Chemical Processes
Excluding Drugs; (3) Electronics, Optics, and Nuclear Technologies; (4) Mechanical
Arts; and (5) All Other.
594 QUARTERLY JOURNAL OF ECONOMICS
of the interaction terms between the lag and the corporate
dummies, the coefficient on the lag itself corresponds to the fading
of localization of citations of university patents. There is evidence
in the 1975 results of such fading. This effect is statistically
significant at the state and SMSA levels; its quantitative significance
is discussed further below. For the citations of corporate
patents, the interaction terms measure the difference between
their fading rates and those of university citations. These terms are
generally not statistically significant. In only one case (other
corporate, 1975) could we reject the hypothesis of equality of fading
rates at traditional confidence levels. There is, however, weak
evidence that the corporate citations do not fade as rapidly as those
of university patents, at least at the state and SMSA levels. The
coefficients on the corporate dummies themselves capture differences
in the predicted probability of localization for citations with
lags of zero or one year. These are all insignificant, and there is no
clear pattern.
The matching-patent-class and generality variables do not
work well. The effects are generally insignificant, and show no
consistent pattern. The effect of the self-citation fraction, however,
is strong and puzzling. At the state and local level, there is a very
significant effect in the predicted direction: citations of patents
with a high self-citation fraction are more likely to be localized.
This is not just saying that self-citations are localized, since they
are excluded; it is the other citations that are more localized. At the
country level, at least in 1975, this effect is reversed and is
significant. Taking all results together, it suggests that for patents
with a lot of self-citations, the nonself-citations are more likely to
be foreign, but those that are domestic are more likely to be in the
same state and SMSA as the originating patent.
The 1980 results are disappointing. The coefficient on the time
lag term switches sign, though it is generally insignificant. One
possibility is that these citations span too short a time period to
capture the lag effect well. To test this possibility, we reran the
estimation in Table IV on the 1975 citations, excluding all that
were granted after 1984. This analysis tells us what we would have
believed about citations of 1975 patents if we had looked for them
only as long as we have looked for the citations of the 1980 patents.
The results (not reported here) looked “more like” the 1980 results
than the original 1975 results did. In particular, the coefficient on
the lag term was insignificant, and was positive at the SMSA level.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 595
TABLE V
PREDICTED LOCALIZATION PERCENTAGES OVER TIME (BASED ON 1975 PROBIT
RESULTS FOR CITATIONS OF UNIVERSITY PATENTS)
Predicted percentage for:
Lag Same country Same state Same SMSA
0orlyear 67.1 9.7 4.8
5 years 65.5 6.5 4.0
10 years 64.6 5.3 3.7
25 years 63.5 4.0 3.3
Thus, it may be that the “perverse” results for the 1980 sample
would go away if we had later citations to include.
A probit coefficient does not have an economically meaningful
magnitude, because of the need to standardize the variance of the
underlying error distribution. However, we can calculate what the
coefficients imply about changes in the predicted probabilities. This
is done in Table V, using the 1975 lag coefficient.23 Table V was
constructed by calculating the predicted localization probability
using the results of Table IV, evaluating the citation lag at different
values, and evaluating the other independent variables at the mean
of the data. It shows that the estimates correspond to a reduction
in the localization fraction after, for example, ten years, from 67.1
percent to 64.6 percent at the country level, 9.7 percent to 5.3
percent at the state level, and 4.8 percent to 3.7 percent at the
SMSA level.
IV. DISCUSSION AND CONCLUSION
Despite the invisibility of knowledge spillovers, they do leave a
paper trail in the form of citations. We find evidence that these
trails, at least, are geographically localized. The results, particularly
for the 1980 cohort, suggest that these effects are quite large
and quite significant statistically. Because of our interest in true
externalities, we have focused on citations excluding self-cites. For
some purposes, however, this is probably overly conservative.
From the point of view of the Regional Development Administra-
23. As discussed above, this is the point estimate of the lag coefficient for
citations of university patents. The point estimates are different for the corporate
originating patents, but we have not performed separate calculations for each
dataset.
596 QUARTERLY JOURNAL OF ECONOMICS
tor, it may not matter whether the subsequent development that
flows from an invention is performed by the inventing firm, as long
as it is performed in her state or city. Our results are also
conservative because we attribute none of the localization present
in the control samples to spillovers, despite the likelihood that
spillovers are, indeed, one of the major reasons for the pre-existing
concentration of research activity.
We also find evidence that geographic localization fades over
time. The 1980 citations, which have shorter average citation lags,
are systematically more localized than the 1975 citations. By using
a probit analysis, we produced estimates of the rate of fading.
These estimates seem to suggest a rate of fading that is both
smaller than one would expect, and smaller than would be necessary
to explain the difference between the 1975 and 1980 overall
matching fractions. One possibility is that the difficulty of measuring
the rate of fading is due to the “contamination” of citations by
the patent examiner. As noted in footnote 18, it is particularly
likely that citations with very short lags were added by the
examiner. If we believe that such citations are less likely to
represent spillovers and less likely to be localized, then this would
tend to bias toward zero our measure of the effect of time on
localization.24
We find less evidence of the effect of technological area on the
localization process. Citations in the same class are no more likely
to be localized. Overall, there is not really any evidence in these
data that the probability of coming from a given geographic
location conditional on patent class is different from the unconditional
probability. This may be due to the arbitrary use of the
“primary” patent class, to the exclusion of the “cross-referenced”
classes. There is no legal difference in significance between the
primary and cross-referenced classes, and in many cases the
examiners do not place any significance on which class is designated
primary. In future work we hope to explore whether using
the full range of information contained in the cross-referenced
classes provides a better technological characterization of the
patents.
In this context it is worth noting that part of what is going on
is probably that knowledge spillovers are not confined to closely
related regions of technology space. Approximately 40 percent of
24. Attempts to test for this possibility by including a dummy variable for age 0
or 1, as well as other explorations of possible nonlinearity in the match-lag
relationship, were inconclusive.
LOCALIZATION OF KNOWLEDGE SPILLOVERS 597
citations do not come from the same primary patent class; even at
the level of the five broad “technological fields” listed in footnote
21, 12 to 25 of percent of citations are across fields. This is
consistent with Jaffe [1986], which found that a significant fraction
of the total “flow” of spillovers affecting firms’ own research
productivity comes from firms outside of the receiving firm’s
immediate technological neighborhood.
We find surprisingly little evidence of differences in localization
between the citations of university and corporate patents. The
largest difference is that corporate patents are more often selfcited,
and self-cites are more often localized. The probit results do
not allow rejection of the hypothesis that the initial localization
rates for nonself-citations are indistinguishable for the different
groups. They do provide some weak evidence that this initial
localization is more likely to fade for the university patents, at least
at the state and local levels.
In order to provide a true foundation for public policy and
economic theorizing, we would ultimately like to be able to say
more about the mechanisms of knowledge transfer, and about
something resembling social rates of return at different levels of
geographic aggregation. The limitations of patent and citation data
make it difficult to go much farther with such questions within this
research approach. Ex post, the vast majority of patents are seen to
generate negligible private (and probably social) returns. In future
work we plan to identify a small number of patents that are
extremely highly cited. It is likely that such patents are both
technologically and economically important [Trajtenberg, 1990].
Case studies of such patents and their citations could prove highly
informative about both the mechanisms of knowledge transfer, and
the extent to which citations do indeed correspond to externalities
in an economic sense.
HARVARD UNIVERSITY
TEL-AVIV UNIVERSITY
MASSACHUSETTSI NSTITUTEO F TECHNOLOGY
REFERENCES
Bernstein, J., and M. Nadiri, “Interindustry R&D Spillovers, Rates of Return, and
Production in High-Tech Industries,” American Economic Review Papers and
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