A new mapping of technological interdependence

Abstract

How does technological interdependence affect innovation? We address this question by examining the influence of neighbors' innovativeness and the structure of the innovators' network on a sector's capacity to develop new technologies. We study these two dimensions of technological interdependence by applying novel methods of text mining and network analysis to the documents of 6.5 million patents granted by the United States Patent and Trademark Office (USPTO) between 1976 and 2021. We find that, in the long run, the influence of network linkages is as important as that of neighbor innovativeness. In the short run, however, positive shocks to neighbor innovativeness yield relatively rapid effects, while the impact of shocks strengthening network linkages manifests with delay, even though lasts longer. Our analysis also highlights that patent text contains a wealth of information often not captured by traditional innovation metrics, such as patent citations.

Full text

Contents lists available at ScienceDirect
Research Policy
journal homepage: www.elsevier.com/locate/respol
A new mapping of technological interdependence
Andrea Fronzetti Colladon a, Barbara Guardabascio a, Francesco Venturini b,c,d,e,∗
aUniversity of Perugia, Italy
bUniversity of Urbino Carlo Bo, Italy
cNational Institute of Economic and Social Research, NIESR, UK
dThe Productivity Institute, TPI, UK
eCentre for Innovation Research - Lund University CIRCLE, Sweden
ARTICLE INFO
Keywords:
Technological interdependence
Neighbor innovativeness
Innovation network structure
Patent text mining
Long-run estimates
Local projections
ABSTRACT
How does technological interdependence affect innovation? We address this question by examining the
influence of neighbors’ innovativeness and the structure of the innovators’ network on a sector’s capacity
to develop new technologies. We study these two dimensions of technological interdependence by applying
novel methods of text mining and network analysis to the documents of 6.5 million patents granted by the
United States Patent and Trademark Office (USPTO) between 1976 and 2021. We find that, in the long
run, the influence of network linkages is as important as that of neighbor innovativeness. In the short run,
however, positive shocks to neighbor innovativeness yield relatively rapid effects, while the impact of shocks
strengthening network linkages manifests with delay, even though lasts longer. Our analysis also highlights
that patent text contains a wealth of information often not captured by traditional innovation metrics, such
as patent citations.
1. Introduction
Technological interdependence has long been recognized as a driver
of innovation and technological change (Rosenberg,1979). The ability
of an innovator to develop new technologies is influenced by the pool
of external knowledge available in the economy, which results from
prior research successfully conducted by other innovators. In this paper,
we look at two sources of technological interdependence (Scherer,
1982a;Archibugi,1988;Liu and Ma,2021). The first source is the
degree of innovativeness and proximity of neighbors. The closer and
more successful neighboring innovators are, the more likely a firm
can leverage their knowledge to develop new products or production
methods. The second source of technological interdependence is de-
termined by the network of relationships that an innovator maintains
within the technology space. A higher number of connections with
other innovating entities, as well as a more central position in the
innovators’ network, increase the likelihood that the innovator can
access, assimilate, and integrate external knowledge. This dimension
of technological interdependence is reflected in the topology of the
innovation network.
The impact of neighbor innovativeness on the success of innovation
and on returns to research is a widely discussed topic in the literature
(Jaffe,1989). Technology transfers are channeled by sales of innovative
inputs and technology licensing (pecuniary spillovers), and by learning
∗Correspondence to: Department of Economics, Society and Politics, University of Urbino Carlo Bo, Via Saffi, 42, 61029 Urbino PU, Italy.
E-mail address: francesco.venturini@uniurb.it (F. Venturini).
and imitation processes (knowledge spillovers). Input–output analysis
has been widely used to gather information on intersectoral technology
exchange. This can be observed, for instance, through inter-industry
transactions of intermediate or capital inputs (embodied technological
change) or through bilateral citation flows among patent documents
(disembodied technological change; Keller,2004). The benefits derived
from these factors are directly related to the absorptive capacity of
recipient firms (Cohen and Levinthal,1989) and to the technological
proximity of these to other innovators (Jaffe,1986).
While information on neighbor innovativeness, as a source of tech-
nological interdependence across firms and sectors, provides valuable
insights into the drivers of innovation, nowadays it may be of limited
guidance as modern industrial systems rely on increasingly deeper
intersectoral connections (Acemoglu et al.,2016a). The structure of
technological linkages, the degree of connectivity among various tech-
nology sectors, the position within a densely populated network of
innovators all play a significant role in determining the success of
innovation and the direction of technological change.
Both the degree of neighbors’ innovativeness and the structure
(topology) of the innovators’ network are recognized as a driver of
technological advancement and economic growth, potentially as im-
portant as the internal sources of innovation (Romer,1990;Coe and
Helpman,1995 and Cao and Li,2019). However, previous research
https://doi.org/10.1016/j.respol.2024.105126
Received 30 July 2023; Received in revised form 21 August 2024; Accepted 11 September 2024
Research Policy 54 (2025) 105126
0048-7333/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).

A. Fronzetti Colladon et al.
on these two dimensions of technological interdependence has shown
minimal overlap. Some studies, for instance, have focused on how
technological interdependence induced by knowledge spillovers influ-
ences the ability to innovate (Acemoglu et al.,2016b). In contrast,
other research has investigated self-generation mechanisms of net-
work linkages and structural interdependence, looking at how existing
connections among innovators lead to the formation of additional
technology linkages (Taalbi,2020).
Notwithstanding a long tradition of studies on technology interde-
pendence, there remain several key issues that have not been fully
explored in the existing literature. First, it is unclear whether the
effect of neighbor innovativeness and that of network structure are
related and self-enforcing or, rather, can be seen as complementary
dimensions of the same phenomenon. Second, the structure of network
linkages that foster technological interdependence among sectors, and
affect their ability to innovate, has been scarcely examined. Third,
how technology shocks, which alter one of the two dimensions of
technological interdependence, propagate from one sector to another
and impact their innovation performance remains uncertain.
There are two main motivations behind these gaps in the literature.
The first explanation relates to the sources of information used to
measure innovation, to capture the characteristics of new technologies,
and track linkages among technology sectors. Standard sources include
expert surveys, statistics on technology licenses, capital goods pur-
chases, international trade of goods and services, and information from
patent documents. Patents are regarded as a highly reliable indicator
of new technological ideas introduced to the market (Griliches,1990).
Patents have become increasingly prominent as they provide highly
standardized and easily accessible information, which is available on
a large scale and covers several aspects of innovation (Hall et al.,
2001). The number of patents is often used to quantify the amount of
innovative output, whereas prior art claims approximate the breadth
of patented innovation. Backward citations accurately describe the
derivative nature of innovations, while forward citations capture how
current innovations impact the development of future technologies.
There are, though, some known pitfalls in using citations (Jaffe and
Rassenfosse,2017). First, there is a marked upward trend in citing, with
wide and persistent differences across fields. This raises concerns about
the comparability of citations over time and across various technolog-
ical domains. Second, in several areas, citations are used strategically.
Applicants may not disclose prior arts, thus affecting the flow of sub-
sequent citations or, alternatively, may disclose prior arts only where
patents are relevant to appropriate returns on their own innovation
(e.g., chemicals and drugs) or to block innovation by competitors
(e.g., computers and electronics). Third, citation flows are influenced
by patent laws and examination procedures. Even within the same
jurisdiction, the outcome and timing of patent assessments can vary
significantly depending on the examiner (Criscuolo and Verspagen,
2008).
The second (but not less important) explanation of the above-
described gaps in the literature relates to how the structure of network
linkages is modeled. As earlier mentioned, the most popular approach
is to look at direct connections between sourcing (the ‘innovator’) and
absorbing entities (the ‘imitator’), assuming that the strength of the
linkage is related to their technological proximity (Jaffe,1986). Exten-
sions of this approach include country-level analyses based on measures
of trade and geographical distance (Coe and Helpman,1995;Madsen,
2007) and sectoral-level analyses based on within- and cross-country
measures of intermediate input transactions (Scherer,1982b;Verspa-
gen,1997b;Pieri et al.,2018). Only a limited number of studies have
looked at how indirect linkages model technology interdependence
across sectors. Leoncini et al. (1996) use network analysis on input–
output relations to track international differences in the technological
system: for example, Germany exhibits dense and evenly distributed
intersectoral innovation linkages, whereas Italy has a limited number
of high-tech sectors coexisting with a large pool of traditional sec-
tors. Acemoglu et al. (2016b) use citation networks to map the linkages
across US technology fields and predict their innovation capacity. Cao
and Li (2019) measure technology applicability using citation net-
works and predict the contribution of each sector (node) to knowledge
development within the entire technology space (network).1Taalbi
(2020) examines which factors affect the creation of new inter-industry
technology linkages, proxied by innovation commercialization between
sectors, finding that direct connections have a greater impact than
indirect ties.
Drawing upon the foundations of existing literature, this study
examines how interdependence in the technology space influences
the creation of new knowledge. Specifically, we explore how the in-
novativeness of neighboring entities and the structure (topology) of
network linkages, which reflects the position of the sector within the
technology space, collectively contribute to the success of innovation
activities. Through text mining techniques, we analyze the abstract of
6.5 million patents granted by the United States Patent and Trademark
Office (USPTO) between 1976 and 2021 and apply network analysis
to uncover the strength of technological interdependence among dif-
ferent sectors (classes). We design a knowledge production function in
which innovation output depends, along with standard determinants,
on the degree of neighbor innovativeness and the strength of structural
linkages within the innovation network. Using data related to 128
technology sectors, we first estimate our empirical model with panel
dynamic regression methods and then assess the response of innovation
output to technology shocks affecting both dimensions of technological
interdependence.
Our first key finding is that both neighbor innovativeness and the
network structure play a crucial role in shaping sector innovation
performance. This finding is significant as it bridges two streams of
empirical evidence that have previously developed with minimal over-
lap. The effect of these two factors is quantitatively comparable in
the long run. As a second key piece of evidence, our research demon-
strates that a shock increasing the degree of neighbor innovativeness
leads to a higher level of sector innovation within a relatively short
time frame (approximately in less than five years) but then vanishes
out. Conversely, the effect of positive shocks affecting the network
of structural linkages takes longer to become statistically significant
and economically impactful (more than five years); however, the latter
effect is more long-lasting. In general, the responsiveness of sector
innovation to unanticipated changes in both dimensions of techno-
logical interdependence has arisen only in the most recent decades.
Our third main result highlights the significant impact of structural
linkages through various measures of network centrality, including
degree centrality, betweenness, closeness, and distinctiveness (Freeman
et al.,2002;Fronzetti Colladon and Naldi,2020), as well as the
Katz metric of centrality (Katz,1953). The paper exploits information
conveyed by the full set of network centrality measures to construct
a multi-dimensional (latent) factor that summarizes variation in inno-
vation network linkages that is relevant to predict sectoral innovation
(see Lanjouw and Schankerman,2004). We demonstrate that the latent
factor possesses a greater explanatory power for assessing the impact
of structural linkages compared to any individual measure of network
centrality from which it is derived. Finally, as a further novel piece
of evidence we show that, when using patent text information, the
impact of technological interdependence (broadly intended) is larger
than when using traditional (citation-based) measures. Patent texts may
1Hotte (2023) uses a two-layer, two-way related network to study the
impact of inter-industry interactions on various dimensions of US industry
performance. The upper layer is based on input–output (trade) relations, and
the bottom layer on inter-industry technology (citation) relations. The latter
linkages appear to dominate as showing positive effects both horizontally and
vertically.
Research Policy 54 (2025) 105126
2

A. Fronzetti Colladon et al.
indeed capture intersectoral linkages over a broader set of technical
features, maybe induced by incremental innovations, whereas patent
citations are likely to trace intersectoral linkages related to (parts of)
leading technologies. We document the robustness of all these results
in many dimensions, namely (i) the measure of patent output (simple
vs. quality-adjusted patent counts), (ii) the proxy for technological
distance (random vs. text similarity), (iii) the modeling of unobservable
factors (time dummies vs. common correlated effects), (iv) cross-sector
differences (slope homogeneity vs. heterogeneity), and finally, (v) the
estimation procedure (log-linear vs. count data regression).
This paper addresses three main strands of the literature. First, we
contribute to a deeper understanding of the inter-sectoral influences
in innovation processes (Castellacci,2008), sectoral patterns of inno-
vation and technological specialization (Pavitt,1984;Malerba,2002;
Archibugi et al.,2023), as well as the trajectories of technological
development (Dosi,1982). In this regard, we illustrate the growing
importance of technological interdependence for innovation develop-
ment and the increasing complexity of this interdependence over time.
Second, our research contributes to the debate surrounding the puzzling
decline in research productivity and its correlation with the (potential)
decrease in knowledge spillovers. This phenomenon may be prompting
companies to intensify their own R&D efforts in order to maintain con-
sistent rates of innovation (Venturini,2012;Bloom et al.,2020). Our
research indicates that technological interdependence has significantly
grown in recent years. This would exclude any causal nexus between
changes in the effects of knowledge spillovers and structural linkages
(which are increasing) and changes in returns to research (which are
decreasing). Third, we contribute to improving the measurement of
technological change, showing that new data methods are a powerful
tool to study innovation at a granular level and, in the meantime,
identify aggregate technological trends (Scherer,1983;Kelly et al.,
2021). More in detail, we show that technological interdependence
can be well gauged through textual analysis of patent documents, in
addition to more traditional measurement approaches.
The remainder of the article is organized as follows. Section 2briefly
reviews the literature. Section 3presents the empirical model. Section 4
describes data sources and provides summary statistics. Econometric re-
sults are reported in Section 5, while Section 6outlines the conclusions
of our study.
2. Literature review
Our research converges at the nexus of various strands of the lit-
erature. These include studies on knowledge spillovers and technology
complementarities, the evolution of technological systems, and, not less
importantly, new data methods for innovation measurement.
2.1. Innovation and technological interdependence
Innovation is an original piece of knowledge that expands the exist-
ing state of technological knowledge. Innovation is created in response
to changes in firms’ demand conditions, in the opportunities offered by
technology pushes (Mowery and Rosenberg,1979), and is developed
by exploiting both internal or external knowledge sources (Pavitt,
1984), following sectoral technology patterns (Malerba and Orsenigo,
1997). Among external sources of innovation, a major attention of the
literature has been paid to knowledge spillovers and the conduits of
technology flows between source and recipient entities of technological
knowledge (Schmookler,1966;Scherer,1982b,a;Verspagen,1997a).
Most works build inter-industry matrices of technology transfer by ex-
trapolating information from thematic surveys, technology licenses, or
looking at patent citation flows (Archibugi and Pianta,1996). All these
measures capture transfers of disembodied technological knowledge,
meaning the knowledge is not incorporated in any input exchanged
between firms or sectors. Other important pieces of knowledge are
embodied, and spread through the exchange of intermediate inputs
and high-tech investment goods, as well as through the movement of
workers between different jobs (Keller,2004;Mendi,2007;Venturini,
2015).2However, knowledge spillovers are limited in their geographi-
cal scope and tend to be spatially concentrated. Peri (2005) documents
for the US that ‘‘only’’ one-fifth of knowledge is exploited outside
the geographical area of creation. Bottazzi and Peri (2003) find that
knowledge spillovers in Europe remain confined within a 300 km radius
from the place of innovation.
A related line of studies examines the vertical transmission of in-
novation shocks, i.e., how innovation in upstream technology sectors
transmits downstream, favoring innovation of technology users. Track-
ing vertical linkages through the citation network, Acemoglu et al.
(2016b) document that upstream innovation explains 14% of panel
variation in innovation achievements of downstream technology sec-
tors. Funk and Owen-Smith (2017) gauge how new technologies are
used by later technologies or alter the use of earlier technologies
through network-based metrics, categorizing innovations either as con-
solidating or destabilizing. A common finding in this literature is that
the structure of linkages, sometimes modeled as networks, have a stable
architecture made by key hubs linked to numerous downstream users,
and that these connections affect the creation of new ties and the
development of further innovation.
Evolutionary studies of innovation examine the systemic mech-
anisms that lead to novelty and the development of breakthrough
technologies. Innovation is seen as an original recombination of ex-
isting pieces of knowledge developed in different branches of the
economy (Weitzman,1998). A specialized knowledge base is consid-
ered a key requisite to innovate. Nonetheless, knowledge diversification
offers relevant gains in the development of new technologies, due to
knowledge cross-fertilization and recombination and the diversification
of innovation risk (Garcia-Vega,2006). The degree of knowledge relat-
edness determines whether knowledge created in one sector is easily
exploitable in other sectors or geographical areas (Frenken et al.,2007;
Castaldi et al.,2015). Related knowledge variety is, on average, posi-
tively correlated with the rate of innovation. Conversely, breakthrough
innovations originate from combining unrelated variety knowledge,
open up new domains for technological advancement and pave the
way for additional incremental innovations by recombining related
knowledge varieties (Schoenmakers and Duysters,2010).
Innovation can also be viewed as a development of ‘‘adjacent possi-
bles’’ (Kauffman,2000). Novelties emerge from the interaction among
different forces in complex systems (natural, socio-economic, techno-
logical), result from the combination of past discoveries, and develop
in areas once thought difficult to reach. The emergence and distribution
of novelties follow well-defined statistical laws, such as the Heaps’ law
and Zipf’s law, respectively (Tria et al.,2014). Novelties emerge in
every field of human activity. Initially, they coexist and compete with
older and concurrent ideas. Over time, they rapidly gain popularity
through a self-reinforcing mechanism, often described as ‘‘the rich get
richer’’ mechanism, ultimately prevailing over other ideas (Monechi
et al.,2017). In this context, Taalbi (2023) shows that the structure of
the product (or technology) space is a good predictor for the new areas
in which firms will innovate: the firm development of new product
types (commercialized innovations) depends on search scope (the firm’s
share of cited patent classes) and search depth (the firm’s proportion
of cited patent classes relative to the recent past). In a related pa-
per, Taalbi (2020) studies the evolution of the technological system in
Sweden, using innovations commercialized to other sectors as a proxy
for new technological ties: 30% of variation in these new connections
can be attributed to pre-existing network linkages. Similarly, Kim and
2Hanley (2017) studies within-industry dependence in innovation processes
(so-called innovation sequentiality) by looking at patent transfers between
companies active in the same technological field: industries with greater
sequentiality are found with higher rates of innovation and profitability.
Research Policy 54 (2025) 105126
3

A. Fronzetti Colladon et al.
Magee (2017) use patent citation flows to predict changes in the
topology of innovation networks across US technology sectors.
One topic that has been extensively explored in the literature is
the diversification of a firm’s patent portfolio. These strategies follow
trajectories reflecting the ties and distance among the technological
fields in which innovating companies are active (Breschi et al.,2003).
Companies often engage in technological diversification before ex-
panding their offerings, as the development of new products involves
leveraging a diverse range of technologies (Pavitt,1998). Historically,
there has been a notable alignment between technological and pro-
ductive activities in which firms engage (Teece et al.,1994). Market
and technological diversification are driven by knowledge coherence,
as firms gradually shift towards areas of the product and the technology
space where the knowledge required is close to their competencies.
However, according to recent evidence, product diversification would
anticipate technological diversification (Piscitello,2000). Furthermore,
for the majority of firms, the extent of product diversification would be
greater than that of technological diversification (Dosi et al.,2017).
2.2. New data methods for innovation measurement
In the literature on innovation and technological change, patent
documents have long been used as a primary source of information. At
the firm level, patents are found to be significantly related to various
dimensions of performance, such as productivity or market value (Hall
et al.,2005). At the aggregate level, the nexus between patenting
and productivity performance has been less clear, probably due to
mismeasurement issues, and the effect of confounding factors such as
institutions, etc. (see Nagaoka et al.,2010). However, Madsen (2008)
and Kogan et al. (2017) have recently shown that the rate of patenting
and breakthrough innovations are positively related to the growth rate
of GDP per capita (or per worker) in the very long run.
In recent years, significant advancements have been made in col-
lecting information from patent documents, largely due to the imple-
mentation of advanced data analysis techniques (Arts et al.,2021).
Machine learning-based textual analysis has transformed research in
this field by overcoming numerous limitations of traditional measures
of patented innovations. Patent text is characterized by precise tech-
nical language and high word standardization, allowing for a highly
accurate assessment of innovation. Semantic patent text search enables
a more seamless analysis of innovation with respect to the use of patent
metadata, which are crystallized along well-defined (pre-packaged)
criteria (citations, claims, etc.). Text extraction from patent documents
is fruitful for inferring the technological proximity between innovating
firms and the level of technological interdependence existing across
sectors, while business documents provide processable information on
companies’ innovation strategy (Fattori et al.,2003).3
Bergeaud et al. (2017) is one of the first works using patent text
information to study technological development. These authors devise
a categorization based on the content of the USPTO patent abstracts and
compare these groupings with technological (IPC) classes along var-
ious dimensions (diversity, originality, generality, etc.). For instance,
the citation rate of patents belonging to the same semantic class is
significantly higher than that of patents within the same technological
class. This finding suggests that traditional systems of classification
could produce imperfect categorization of innovations (see Moed et al.,
2006;Lafond and Kim,2019).
Arts et al. (2018) build text-based similarity indicators for the entire
corpus of USPTO patents. These measures are able to reproduce earlier
findings of the literature on localized knowledge spillovers based on
3See Nathan and Rosso (2022,2015) for a study on the mapping of digital
firms and their innovation performance based on text mining of data collected
through the scrapping of company websites.
standard citation metrics but present a much higher statistical relia-
bility. Measures of patent text similarity (cosine proximity) reveal, for
the US, a local concentration of knowledge spillovers weaker than that
emerging from citation flows (Feng,2020). As discussed above, this
may reflect the firm’s strategic use of patents, the fact that citation
paths are influenced by the background of examiners or attorneys and,
not less important, that patent documents convey a larger body of
information about the innovation than citation streams.
Gerken and Moehrle (2012) develop an index of innovation novelty
constructed by comparing the semantic structure of patent documents
filed at distant points in time. Kelly et al. (2021) gauge innovation
radicalness (significance) with the ratio of patent text measures of
forward and backward similarities, finding that groundbreaking inno-
vations drive long-term economic growth in the US. Carvalho et al.
(2021) investigate innovation strategies of US firms active in new
technological areas by mining their patent documents, detecting a
positive association between the strategy of innovation exploitation
and sales growth. Mann and Püttmann (2023) use keyword search
analysis on US patent documents to measure automation innovations,
mapping sectors in which these technologies are developed and sectors
in which they are used. Park et al. (2023) use network analysis to
build similarity measures for a collection of patents and scientific
publications using information on citations and document texts. The
results of their research suggest that new technologies are currently
less disruptive than in previous eras, indicating a potential slowdown
in research productivity and in the rate of technological progress.
3. Empirical model
Our analysis provides new insights into how technological interde-
pendence impacts sectoral innovation, considering the role of neighbor
innovativeness and related knowledge spillovers, as well as the struc-
ture (topology) of the innovation network and the position that each
sector holds within the technology space.
We assess how technological interdependence affects innovation by
estimating a knowledge production function at the level of techno-
logical sectors (patent classes). We assume that new knowledge ()
is created thanks to the absorption of knowledge developed by other
sectors (neighbor innovativeness) and the linkages that each sector
maintains with other innovative entities (network structure). Formally,
 is assumed to depend on , which is our proxy for technological
interdependence (broadly intended) among technology areas, while 
identifies the effect of this force on the capacity of each sector to create
new knowledge (innovation):
 =() = .(1)
Eq. (1) can be extended to include other standard drivers of knowledge
generation, such as the cumulative value (stock) of knowledge devel-
oped within each technology area, .reveals whether the state of
technological knowledge generated in the past affects the output of
current innovation processes, by favoring the intertemporal (within-
industry) transmission of knowledge (standing-on-the-giants’-shoulders vs
fishing-out effects; Caballero and Jaffe,1993). Following Ha and Howitt
(2007), we extend Eq. (1) in two further respects. First, we account
for the effect of the current innovation effort, (R&D input), that
could reinforce the intertemporal transmission of knowledge. Second,
we consider the degree of product diversification of the sector, , that
may (fully or partially) outweigh the stimulating effect of the other two
internal drivers of innovation (and ):
 =(, , ,  ) = ⋅⋅⋅.(2)
We estimate the stochastic, log-linear version of Eq. (2) that con-
siders two sources of intersectoral technology dependence, namely
the degree of neighbor innovativeness and the intensity of the struc-
tural linkages within the innovation network, respectively denoted as
Research Policy 54 (2025) 105126
4

A. Fronzetti Colladon et al.
 and   . Below, Section 4describes how these two sources of
technological interdependence are measured.
ln  =+ ln  


  
+ ln  


  
+ln  +ln  +ln  + (3)
In Eq. (3), is defined as the flow of new patents granted to each
sector at any point in time . The impact of (with = or
 ) may be positive when the success of innovation activities is self-
sustaining across sectors due to knowledge spillovers, technological
complementarities, etc. (>0), or negative because of research
cost inflation (resource extraction), innovation duplication or lock-in
effects (<0). should capture dynamic (intertemporal) returns
to innovation ( > 0): these could be highly persistent because of the
cumulative effects of knowledge creation over time (∼ 1) or diminish
due to the exhaustion of technological opportunities ( < 1). reflects
the size of purposeful innovation (R&D) effort, which is undertaken
to expand the state of technological knowledge ( > 0). is usually
defined in terms of human resources allocated to innovation processes.
reflects the number of companies (applicants) engaged in innovation
activities in each sector (class). A negative value for the coefficient of
this variable would indicate that innovation effort increases less than
proportionally with the number of innovators, thus reducing aggregate
returns to R&D ( < 0). By contrast, a positive value would indicate
that companies have the opportunity to leverage economies of scope
and achieve greater returns in sectors with a higher concentration of
innovative firms ( > 0). The effect of the systematic (time-invariant)
differences existing across sectors in the patent propensity, to engage
in innovation networks, etc., is captured by sector-specific fixed effects
().
In our regression model, the effect of common exogenous shocks
is primarily accounted for by expressing all variables in deviation from
the yearly (cross-sectional) average. This is equivalent to using common
time dummies and is helpful to neutralize the bias associated with weak
levels of residuals’ cross-sectional correlation in innovation processes
(Cross-Sectional Dependence, CSD). However, in robustness checks, we
control for strong cross-sectional dependence by including common
correlate effects (CCE) in the specification. These terms are calculated
as cross-sectional averages of all (not demeaned) variables in the model,
and serve to mitigate the bias associated with co-movements induced
by ‘‘third factors’’ such as technology, trade or demand shocks, having a
differentiated impact withinin the technology space (i.e. across sectors).
Eberhardt et al. (2013) emphasize the importance of properly control-
ling for the impact of unobservable common factors. Failure to account
for these effects can lead to misinterpret them as evidence of knowledge
spillovers, as the latter are typically measured using the proximity-
weighted average of innovation efforts (or outcomes) of neighboring
entities, such as firms, industries, regions, or countries.
It should be stressed that Eq. (3) models the long-run (equilibrium)
relation of technological interdependence existing among sectors. How-
ever, in light of the long-time dimension of our data (see below for
details), the regression is estimated with a dynamic specification, i.e., as
an autoregressive distributed lag model (ARDL). This procedure ensures
consistency of long-run estimates irrespective of the integration order
of the variables, and is robust to reverse causality when the lag struc-
ture is optimally specified. Below, we report the long-run parameters
estimated for Eq. (3). These can be interpreted as elasticities.4
4Considering a general long-term relation of the following type,  =
+ +, the corresponding dynamic specification with one-year lag of
the variables is formulated as  =+1−1 +2 +3−1 +. From the
latter, one can then recover the long-term effect of the explanatory variable
as = (2+3)∕(1 − 1).
4. Data sources, methods and variable description
4.1. Patent data and text mining
We perform our analysis on the universe of utility patents granted
by the US Patent and Trademark Office (USPTO) between 1976 and
2021. The USPTO patent data is a valuable source as it offers a
comprehensive overview of the most important world’s technology
market, providing highly reliable information on several characteristics
of patented inventions. This data has been utilized to gain insights
into technology trends, analyze firm performance, and inform strategic
decision-making in various industries (Griliches,1990;Hall et al.,2001;
Hall and Harhoff,2012). The majority of papers in the literature
have used coded information on names and locations of applicants (or
inventors), and on the characteristics of their inventions (such as cites
made and received, technological classes, and co-patenting processes).
However, the USPTO now provides the entire bulk of patent documents
in a machine-readable format, enabling the mining of these texts and
the creation of more sophisticated measures of innovation content.5
We analyze the abstracts of 6,497,894 patents for which we have
relevant information on application date, technological class, etc. This
approach offers the benefit of concentrating on concise texts that have
remained largely consistent over a period of half a century. Patent
abstracts have not been significantly affected by changes in patent laws
that modified the requisites of patentability, the examination process
and, as a consequence, the timing and quality of these procedures.
van Pottelsberghe de la Potterie (2011) outlines the evolution of the
US patent jurisdiction since 1980, highlighting the impact of various
Supreme Court decisions. These have gradually expanded the scope
of patentable subjects (including genetically engineered bacteria, soft-
ware, business methods, financial service products, and more), while
also relaxing the novelty requirements for obtaining a patent. All this
significantly increased application workload, which lowered the quality
of the examination process and, in turn, stimulated the demand for
patent protection for low quality innovations (see also Jaffe,2000).
We implement our study by assigning patents to 3-digit techno-
logical categories resulting from the Cooperative Patent Classifica-
tion (CPC) and classifying them according to their application date.
We examine patent abstracts using the SBS BI software, which al-
lows to conduct advanced textual analyses and create semantic net-
works (Fronzetti Colladon and Grippa,2020). The procedure has been
implemented with the following steps. First, we preliminary remove
punctuation, stop-words, and special characters (Perkins,2014) then,
after lowercasing the text, we extract the stems through the Porter
algorithm (Willett,2006).6Second, we assemble the vectors of ab-
stracts into a corpus-term matrix with sector/year by rows and word
occurrences by column. Cells in the matrix assume the value of 1 if
the column term appears at least once in a specific set of abstracts
(row), and 0 otherwise.7Third, we exclude highly common terms that
appear in more than 75% of abstracts, and rare terms that appear in less
than 0.1% of these documents. The analysis is conducted on the most
recurrent terms in the resulting vocabulary (up to a maximum of 15,000
words). Subsequently, we apply the well-known Term Frequency In-
verse Document Frequency (TFIDF) transformation to the corpus-term
matrix (Roelleke and Wang,2008). This transformation assigns greater
5Patent text data are retrieved from the following link:
https:/patentsview.org/download/detail_desc_text.
6For example, the terms ‘‘beauty’’ and ‘‘beauties’’ would both be
transformed into the word ‘‘beauti’’.
7As discussed later, we also exploit an alternative approach by populating
the matrix cells with word frequencies found within patent abstracts. However,
this method does not yield significantly different results. This suggests that
assessing the presence of a term within a set of patents for one technology
sector in one year may be sufficient to determine its similarity to the other
sectors.
Research Policy 54 (2025) 105126
5

A. Fronzetti Colladon et al.
importance to the most recurrent terms in patent documents but that,
at the same time, are not common across all technology sectors. Lastly,
we use the L2 normalization to account for differences in the number
and length of abstracts across technology sectors. In practice, we re-
scale the row vectors so that the square of their cells sums up to one,
 =   ∕   (see Kelly et al.,2021). This matrix serves
for the construction of the similarity network which, as described in
the next section, exploits information on cosine similarity between the
cells of the matrix rows.
To illustrate the outcome of our text mining process, we consider the
abstract of three hypothetical patents as an example. Abstract 1: This
invention discloses a machine-learning model for predicting the maintenance
needs of industrial machinery. The model utilizes sensor data and historical
maintenance records to identify patterns and predict potential failures before
they occur. Abstract 2: This patent concerns an AI-powered system for
proactive maintenance of industrial equipment. The system leverages sensor
data analytics and machine learning algorithms to anticipate equipment
failures and optimize maintenance schedules. Abstract 3: This invention
concerns a chemical composition for improving the adhesive properties of a
bonding agent. The composition comprises a unique blend of polymers and
additives that enhance the strength and durability of the bond. The first two
patents are clearly more similar to each other than the third one, as
both refer to ‘‘machine learning’’ and ‘‘maintenance’’, which are terms
that appear in their abstracts but are less frequent in the overall corpus
of patent documents (common words such as ‘‘the’’, ‘‘an’’, and ‘‘of’’ are
filtered out during the pre-processing).
4.2. Measuring technological interdependence
We measure technological interdependence by constructing proxies
for the degree of knowledge spillovers (neighbor innovativeness), and
for the topology of structural linkages and the sector position within
the technology space (network structure). To construct these measures,
we primarily exploit information extracted from the text of patent
abstracts. However, to gain insights into the informative advantage
offered by this methodology, we also construct measures of sectoral
interdependence based on bilateral citation flows, following the main
practice in the literature. When analyzing network structure (topology),
we take into account the influence of both direct linkages and indirect
connections by employing several measures of sector (node) centrality
within the technology space (network).
Neighbor innovativeness
Technological interdependence between sectors and , fueled by
neighbor innovativeness, is measured with the proximity-weighted
mean of their innovation (patenting) capacity (that is, our outcome
variable ), for any year between 1976 and 2021:

=


=1
   = 0   =(4)
The subscript is omitted wherever possible for sake of brevity. Prox-
imity weights derived from patent texts are computed using a cosine
similarity index, defined as  = =⋅with  ∈ [0,1], where
is the corpus-term matrix described in the previous subsection. By
contrast, proximity weights based on patent cites are computed using
the relative flows of bilateral citations, defined as  = ∕
where
 identifies the number of patent citations made by sector to patents
of sector over the total number of citations made by the former sector.
Below, we denote as 𝐖the matrix of weights, with -element defined
by  .
Our metric of neighbor innovativeness builds upon Acemoglu et al.
(2016b). One key difference between these two measures lies in the fact
that cosine similarity does not reveal the underlying origin of the ideas
that are underneath innovations. Instead, it measures the proximity
between earlier and subsequent innovations based on their descriptions
in the patent abstract. In contrast, patent citations trace the vertical
(unidirectional) transmission of technological knowledge between cited
and citing innovations.
Network structure
The structure (topology) of connections among innovators is an-
other important factor driving the success of the sector’s innovation
activities. Differently from the other dimension of technological in-
terdependence, which relies upon innovation capacity and proximity
of linked sectors, the structure of the innovation network reflects the
position of each sector in the technology space and the intensity of
inter-sectoral linkages. In the network, each node corresponds to a tech-
nology (innovating) sector, and the arcs connecting the nodes reflect
the relationships existing among sectors (innovators). The intensity of
the linkages is gauged by weighting the arcs between nodes with the
pairwise similarity scores (constructed as detailed above).
Our metrics of structural linkages come from social network analysis
(Wasserman and Faust,1994). We measure the network centrality of
each technology sector using well-known metrics earlier used in the
analysis of patent citations, such as degree, betweenness and close-
ness centrality (Katz,1953;Hung and Wang,2010;Liu et al.,2021;
Sternitzke et al.,2008). Furthermore, we also take into account the
newly developed metric of distinctiveness centrality, which offers the
advantage of capturing exclusive connections between technology sec-
tors (Fronzetti Colladon and Naldi,2020). We produce a similarity
network for each year of the time interval of our study. By construction,
these networks are complete and symmetrical. However, in order to
streamline their structure, we eliminate arcs with minimal similarity
scores, specifically those that fall within the lowest quartile of the
similarity distribution. The set of centrality measures adopted allow to
consider both direct and indirect linkages within the network.
The first metric utilized is the centrality index developed by Katz
(1953). This measure sums up the number of arcs (linkages) existing
between nodes, and weighs these connections by a decay (penalty)
parameter (with ∈ (0,1)) that penalizes paths in relation to their
length: when is low (high) a greater (lower) weight is attached to the
shortest path length (Liben-Nowell and Kleinberg,2003;Taalbi,2020):
 ,
=
∞

=1


=1

 = 1 + 


=1
 + (


=1
 )2+.. + (


=1
 )∞(5)
#»
 , = ((𝐈−𝐖)−1 −𝐈).
The Katz index, #»
, is defined as a vector (denoted by →) in which each
element is taken in absolute terms. 𝐖is the similarity matrix based on
patent text (or bilateral citations described above), and the subscript 
denotes its transpose. 𝐈is the identity matrix, whilst is a vector of size
consisting of ones. In the first formulation of Eq. (5), the convergence
of summation is ensured if 1∕is larger than the greatest eigenvalue of
𝐖. Since each cell in 𝐖is a measure of the direct linkage between each
pair of sectors, a valuable property of the Katz measure of centrality is
that the overall (structural) linkages can be decomposed into the sum
of direct (first-order) linkages (,  ) and indirect (higher-order)
linkages (,  ):
#»
,  =𝐖
#»
,  =
#»
 , −(𝐖).(6)
In the two equations above, the superscript  stands for Network
Structure,  for Katz, whilst the subscripts and for Direct and
Indirect linkages.
Another key measure that we use to capture direct linkages in
the technology space is the index of Degree centrality (Wasserman and
Faust,1994). This measure reflects the number of connections that
a node has within a network. In networks where connections have a
direction, it is possible to differentiate between incoming and outgoing
arcs. The total number of incoming arcs is referred to as in-degree,
while the number of outgoing arcs is known as out-degree. For each
node (sector) , the Degree centrality formula used is:
,
=() =


=1,≠
( >0) (7)
where ( >0) is a function that assumes the value of one if there
is an arc connecting nodes and with a positive weight, and zero
Research Policy 54 (2025) 105126
6

A. Fronzetti Colladon et al.
otherwise. In order to ensure comparability across networks of varying
sizes, we standardize degree centrality by dividing the index by (− 1).
In the weighted version, degree centrality is calculated by adding up
the weights of the arcs connected to a node. For instance, if patents
from sector A are cited 100 times in total by three other sectors, the in-
degree of A would be 3, as the sector has three incoming connections;
however, the weighted in-degree would be 100, considering the total
weight of incoming arcs. It is also worth noting that, for the purpose of
our analyses, we exclude self-loops. In Eq. (7), the superscript ,  
stands for Network Structure measure of Direct linkages based on
DeGree centrality.
To gauge the effect of indirect connections, we consider an ad-
ditional set of network centrality measures, namely the indexes of
(i) betweenness, (ii) closeness, and (iii) distinctiveness centrality. Be-
tweenness centrality quantifies how often a node lies in the shortest
path connecting each pair of other nodes, reflecting thus its brokerage
power (Wasserman and Faust,1994). The weighted version of this
index, which is useful in our case as similarity and citation networks are
particularly dense, is obtained considering the inverse of arc weights to
calculate network distances (Opsahl et al.,2010). This means that arcs
with a higher number of citations, or a greater text similarity, will be
considered closer when calculating the shortest paths. For node , the
Betweenness index is calculated as:
, 
=() = 
<
 ()

(8)
where is distinct from nodes and . is the total number of the
shortest paths connecting nodes and , and  ()is the number of
paths that include node . The index is divided by (− 1)(− 2)∕2
for undirected graphs, and by (− 1)(− 2) for directed graphs, to
make it comparable across networks of different sizes. In Eq. (8), the
superscript ,   indicates Network Structure measure of Indirect
linkages based on BEtweenness centrality.
Closeness centrality determines the proximity of a node to all other
nodes in a network. It is calculated as the inverse of the sum of the
shortest path lengths from that node to all the others (Wasserman and
Faust,1994). For node , the Closeness index is given by
, 
=() = − 1
(, )(9)
where is the total number of nodes, (, )represents the shortest
distance between nodes and , while the term −1 is used to normalize
the closeness value and make it comparable across networks of different
sizes. In Eq. (9),,   indicates Network Structure measure of
Indirect linkages based on CLoseness centrality.
Distinctiveness centrality builds upon degree centrality but considers
the characteristics of the nodes connected to node . Unlike degree
centrality, which assigns equal importance to all connections, distinc-
tiveness centrality emphasizes distinctive connections between nodes
by penalizing the links to nodes that have a high degree (Fronzetti Col-
ladon and Naldi,2020). From this perspective, it would be more
beneficial for a technology sector to be connected to sectors with fewer
connections, than to those with numerous links in the network, as it
would allow to benefit exclusive technology transfers. For node , the
Distinctiveness index is computed as:
,
=() =


=1,≠
log10
− 1

( >0) (10)
where is the total number of nodes, is the degree of node and
( >0) is a function that assumes the value of one if there is an
arc connecting nodes and with a weight greater than zero. The
Distinctiveness index can be normalized by scaling it on its theoret-
ical upper bound, that is (− 1) log10 (− 1). In Eq. (10),, 
stands for Network Structure measure of Indirect linkages based on
DIstinctiveness centrality.8
8The Distinctiveness formula can also be generalized for directed networks
to calculate in- and out-distinctiveness (Fronzetti Colladon and Naldi,2020).
We condense the information conveyed by the above-mentioned
centrality metrics by extracting a common latent factor from all these
indicators through Principal Component Analysis (PCA),  , (Lan-
jouw and Schankerman,2004). By exploiting information on mul-
tiple characteristics of the network, the composite indicator is able
to capture common variation across the different centrality measures
and leave out idiosyncratic measurement errors, better measuring the
intensity of structural linkages within the innovation network. The re-
gression results yielded using the composite factor should be compared
with those obtained with the Katz metric built for the overall set of
inter-sectoral linkages (namely, direct linkages plus indirect linkages).
Computationally, for each year of our timeframe, we build a latent
factor based on the first principal component extracted. The factor
exploiting information on text similarity explains between 90 and 98%
of the variability of centrality metrics. The information content of this
index has significantly increased over time. The latent factor built on
citation networks is able to explain even a higher portion of variation
and possesses an information content which is quite stable over time.
In text similarity networks, distinctiveness contributes to the latent
common factor more than any other centrality measure, accounting for
between 40% and 50% of its variation over time. On the other hand,
degree centrality emerges as the primary contributor to the latent factor
derived from citation networks, making up approximately 50% of the
total variance.
4.3. Variable description
We conduct our analysis considering a panel sample of 128 technol-
ogy sectors identified at the 3-digit level of the CPC classification. The
work covers the period from 1976 to 2021. Using various information
included in the USPTO dataset, we are able to build three different
groups of variables: innovation outcome,technological interdependence
(neighbor innovativeness and network structure) and control variables.
As a baseline measure of innovation outcome (), we utilize the
simple count of patent applications. However, to account for hetero-
geneity in the quality of innovations, we weigh patent counts with the
number of citations received (forward citations). Primarily, we adopt
an univocal assignment approach and attribute each patent to only
one technology sector, identified as the first CPC class listed in the
patent document. This means that each patent is associated with only
one primary class, even though it may be considered as a realization
of different technology areas in light of the full list of CPC codes
reported in the document. In our robustness checks, we also explore
amultiple assignment approach. Each patent is hence considered as
having multiple realizations and is evenly assigned to all its CPC classes,
including primary and secondary classes. When conducting these ro-
bustness regressions, we maintain the structure of linkages derived
using the univocal approach and exclude any connections between
a patent’s primary and secondary classes. This helps avoid spurious
interdependence among technology sectors.
Technological interdependence is sourced by the pool of knowledge
directly related to the degree of innovativeness of neighborings, and to
the structure of connections among sectors in the innovation network.
Neighbor innovativeness is measured with several proximity-weighted
averages of innovations developed by other sectors. As discussed above,
innovation output is measured both in terms of simple patent counts
and forward cites-adjusted number of patent applications. To gauge
inter-sectoral knowledge transmission, we evaluate innovation from
other sectors with weights extracted from our novel matrix of text
(cosine) similarity, as well as from a more traditional matrix reflecting
bilateral citations flows. The latter expresses the number of citations
made by sector to sector as a portion of the total number of citations
made by the citing sector. On this basis, our proxies for neighbor
innovativeness include the following variables: bilateral Cites-weighted
Counts (CWC), bilateral Cites-Weighted Forward cites (CWF), Text
similarity-Weighted Forward cites (TWF). The influence of Network
Research Policy 54 (2025) 105126
7

A. Fronzetti Colladon et al.
Table 1
List of the variables.
Label Description Formula
Innovation outcome
Number of raw or quality-adjusted patent counts
Internal innovation factors
Cumulative number of raw or quality-adjusted patent counts  = + (1 − 0.15) × −1
Number of inventors per firm’s patents
Number of applicants per sector
Technological interdependence (time subscript omitted)
Neighbor Innovativeness ( )
 Bilateral Cites-weighted Counts (CWC) 
=
=1   =
 = 0
Bilateral Cites-Weighted Forward cites (CWF)
Text similarity-Weighted Forward cites (TWF) 
=
=1   = 0  =
Network Structure ( )
,  Katz (direct) , 
=
=1 
,  Katz (indirect) , 
=, 
−(
=1  )
,  Katz (total) , 
=∞
=1 
=1 

, Degree (direct) ,
=
=1,≠( >0)
, Betweenness (indirect) ,
=<
()

,  Closeness (indirect) , 
 =−1
(,)
, Distinctiveness (indirect) ,
 =
=1,≠log10
−1

( >0)
, Latent Factor (total)
structure, which depends on the position of the sector within the tech-
nology space, is evaluated through the Katz metrics (for overall, direct
and indirect linkages), the other four metrics of network centrality,
i.e., Degree, Betweenness, Closeness, and Distinctiveness, as well as the
Latent factor derived from the last group of indicators. All measures of
technological interdependence are built by exploiting information both
on patent text similarity and bilateral citation flows.
Finally, we consider several control variables, proven to affect the
outcome of innovation activities in the earlier literature. One of such
variables is the cumulative value of internal knowledge, defined as
the sector’s stock of patents, . The patent stock is built from the
annual flow of patent applications with the perpetual inventory method
using a depreciation rate of 15%. The same procedure is used when
we use quality-adjusted (citation-weighted) measures of patent counts.
As a measure of innovation effort, we look at the average number of
inventors involved in patenting, computed at the level of the individual
firm (applicant) active in each sector. This can be considered as a proxy
for the amount of human resources allocated to innovation processes.
Lastly, we measure the effect of product diversification with the number
of applicants active in each sector. The full list of the variables used in
the regression analysis and the methods adopted in their construction
are illustrated in Table 1.
As discussed above, we estimate the regression mainly as a log-
linear model. To this aim, we handle zeros of the dependent variable
using the following transformation, ln(1 + ), and then, in robustness
checks, assess how this assumption influences the regression results.
4.4. Descriptive analysis
The main summary statistics for the variables used in our work are
displayed in Table 2, while the matrix of their correlation coefficients
can be found in Table A.1 of Appendix. For the sake of brevity,
we report means and standard deviations only for the measures of
technological interdependence based on text similarity.
On average, over 1100 patents are applied annually by each of
the 128 technology sectors, based on univocal patent assignment to
primary technology classes. As known, the distribution of patent re-
alizations is very skewed, and the standard deviation of this variable
is much larger than its mean (3.5 thousand). The number of forward
citations per sector is 796, implying that each application has less
than one citation, on average. There is significant variation in the
distribution of citations, with some patents receiving a high number
of citations and most of them only a few. The standard deviation of
forward citations is 2215 per year.
The measures of neighbor innovativeness reveal that the pool of
external knowledge potentially available to each sector for implement-
ing its innovation activities is much higher when using a weight-
ing scheme based on patent citation flows (CWC and CWF). How-
ever, it can be seen that variation in neighbor innovativeness is much
smaller compared to the mean when we use cosine similarity to weigh
quality-adjusted patents (TWF). This discrepancy can be attributed to
the higher skewness of the citation distribution as opposed to text
similarity.
The intensity of structural linkages featuring the innovation network
denotes a less uneven distribution compared to neighbor innovative-
ness. Notably, the standard deviation of all the network centrality
measures is smaller than their mean, except for Betweenness, which is
highly skewed, and the latent factor which has a standard normal dis-
tribution by construction. The mean of the Katz index is 0.02 for direct
connections and 23.21 for indirect ones. When considering the other
indexes reflecting structural linkages, that are built using information
on text similarity, one has to bear in mind that all these measures are
normalized (i.e., ranging between 0 and 1), and that links in the lowest
quartile of the similarity distribution have been removed. Overall,
text similarity networks are quite dense (Mean, M, 0.749, Standard
Deviation, SD, 0.048), with a high clustering coefficient (M 0.893, SD
0.021) and a low (unweighted) average shortest path length (M 1.217,
SD 0.046). The average normalized degree is 0.746 (SD 0.241), while
Research Policy 54 (2025) 105126
8

A. Fronzetti Colladon et al.
Table 2
Descriptive Statistics.
Variable Mean SD
Innovation outcome
Patent counts (univocal) 1,103.6 3,547.4
Forward cites (univocal) 796.23 2,215.8
Internal innovation factors
Cumulative knowledge (patent stock) 5,929.8 17,532.3
Innovation effort (# of inventors per firm) 2.288 0.535
Product proliferation/diversification (# of classes per firm) 203.9 490.4
Technological interdependence
Neighbor Innovativeness ( )
Bilateral Cities-Weighted Counts (CWC) (in 1,000) 588.7 4,838.3
Bilateral Cites-Weighted Forward cites (CWF) (in 1,000) 240.3 1,376.2
Text similarity-weighted forward cites (TWF) (in 1,000) 22.67 14.62
Network Structure ( )
Katz (direct) 0.023 0.007
Katz (indirect) 23.21 7.002
Katz (total) 22.98 7.072
Degree 0.746 0.241
Betweeness 0.001 0.004
Closeness 0.171 0.047
Distinctiveness 0.032 0.014
Latent factor 0.001 0.970
Notes: Statistics are computed over sectors and years. All measures of technological interdependence use information on text
similarity.
the average total similarity score of each sector (average weighted
degree) is 20.809 (SD 8.616). This information on the network structure
is not reported in Table 2 for brevity.
In Figs. 1 and 2, we show the heatmaps of the pairwise correlation
across sectors in terms of citation flows and text similarity, obtained
considering the entire time span between 1976 and 2021. The full
list of 128 technology classes (3-digit level) is reported on the bottom
horizontal and the right-hand vertical axes. The corresponding 2-digit
classes (30 sectors) are listed on the top horizontal and the left-hand
vertical axes to facilitate the comparison with earlier studies using data
at a lower level of disaggregation. Note that we disregard self-citations
and self-similarity by setting the value of the cells on the principal
diagonal of the matrices to zero. Similarly to Acemoglu et al. (2016b),
we normalize the cells of the two matrices on the total sum of each row.
In this way, we have row percentages that are fully comparable to the
two-digit citation representation provided by Acemoglu et al. (2016b).
A few key points are in order. First (and reassuringly), looking at the
citation heatmap, there emerges a strong correspondence between our
matrix and that reported in the above-cited paper, as denser regions
emerge in similar areas of the technology space. Second, comparing
our heatmaps, it emerges that the areas with a stronger tone fall in
the same technology classes, namely, along the principal diagonal and
on the bottom-right cells of the matrix. However, as known, citations
concentrate in a few key areas (cells). Conversely, text similarity values
are more sparse and homogeneous. This indicates that although our
measures of citation and text similarity are likely to capture the same
key technological trends, the latter measures are also able to collect
information on a broader set of technical characteristics that are more
pervasive and, possibly, less technically complex.
5. Regression results
5.1. Influence of neighbor innovativeness
We start the analysis by considering the impact of neighbor innova-
tiveness on sectoral innovation capacity (Table 3). As discussed above,
we relate to the literature on knowledge spillovers where these transfers
are measured in terms of the innovation output of neighboring sectors,
weighted by a proximity measure between the sourcing and recipient
entities. Our primary interest is to evaluate whether the effect of this
source of technological interdependence changes with the nature of
the patent variable used to quantify innovation output (namely, patent
counts vs forward cites-weighted patents) and of the information used
to track intersectoral linkages (namely, bilateral citation flow vs patent
text). In this section, we will illustrate how far our estimates fall from
the major results of this literature.
In our starting regression (column (1)), we measure innovation out-
put in terms of patent counts per univocal technology sector (i.e., each
patent is assigned to its primary class), and neighbor innovativeness
in terms of the citation-weighted mean of innovations patented by the
other sectors of the economy (CWC). This regression shows that sector
interdependence, induced by neighbor innovativeness, is positively and
significantly related to the innovation output of the other technology
areas.9Quantitatively, a one-percent increase in the innovation output
of linked (sourcing) sectors is associated with a 0.122 percent increase
in the patenting performance of recipient sectors. Comparable evidence
for the US can be found, among others, in Jaffe (1986), while more
recent estimates provided by Bloom et al. (2013) lie at an upper bound
(around 0.4). As known, the count of patent applications as a measure
of innovation output is not informative about the quality of new tech-
nologies. Hence, in column (2), we do weigh each realized innovation
(patent count) with the number of citations received (CWF), finding a
larger effect for our proxy for neighbor innovativeness (0.151).
In column (3), we run the previous regression adding as a further
explanatory variable the pool of external knowledge made available
by neighboring innovators measured using the proximity matrix based
on patent text similarity (TWC). This regression highlights that both
proxies for neighbor innovativeness are statistically significant and
quantitatively important, suggesting that they may capture two distinct
dimensions of knowledge transfers. While citations may trace intersec-
toral links around (parts of) key technologies, patent texts could capture
connections across a wider range of technical features. The second
dimension of knowledge transfers, measured exploiting information
9In this (and following) regression(s), we observe that the adjustment pa-
rameter is always negative and statistically significant, indicating the existence
of a stable (equilibrium) relationship between dependent and explanatory
variables in the long run.
Research Policy 54 (2025) 105126
9

A. Fronzetti Colladon et al.
Fig. 1. Heatmap of citations.
Notes: CPC 1 - and 2 digit categories. A-Human Necessities A0-Agriculture. A2-Foodstuffs; Tobacco. A4-Personal or Domestic Articles. A6-Health; Life-Saving; Amusement. A9-
Miscellaneous, of Human Necessities. B-Performing Operations; Transporting. B0-Separating; Mixing. B2-Shaping. B3-Shaping. B4-Printing. B5-Transporting. B6-Microstructural
Technology; Nanotechnology. B9-Miscellaneous, Of Performing Operations; Transporting. C-Chemistry; Metallurgy. C0Chemistry. C2-Metallurgy. C3-Metallurgy. C4-Combinatorial
Technology. C9-Miscellaneous, of Chemistry; Metallurgy. D-Textiles; Paper. D0-Textiles or Flexible Materials not Otherwise Provided for. D2-Paper. D9-Miscellaneous, of Textiles;
Paper. E-Fixed Constructions. E0-Building. E2-Earth or Rock Drilling; Mining. E9-Miscellaneous, Of Fixed Constructions. F-Mechanical Engineering; Lighting; Heating; Weapons;
Blasting. F0-Engines or Pumps. F1-Engineering in General. F2-Lighting; Heating. F4-Weapons; Blasting. F9-Miscellaneous, of Mechanical Engineering; etc. G-Physics. G0-Measuring;
Optics; Horology; Controlling; Computing; Signaling. G1-Acoustics; Information Storage; Instruments; ICT Adapted to Applications. G2-Nuclear Physics; Nuclear Engineering.
G9-Miscellaneous, of Physics.
Each value in the cells is row normalized.
Fig. 2. Heatmap of text similarity.
Notes: CPC 1- and 2 digit categories. A-Human Necessities A0-Agriculture. A2-Foodstuffs; Tobacco. A4-Personal or Domestic Articles. A6-Health; Life-Saving; Amusement. A9-
Miscellaneous, of Human Necessities. B-Performing Operations; Transporting. B0-Separating; Mixing. B2-Shaping. B3-Shaping. B4-Printing. B5-Transporting. B6-Microstructural
Technology; Nanotechnology. B9-Miscellaneous, Of Performing Operations; Transporting. C-Chemistry; Metallurgy. C0Chemistry. C2-Metallurgy. C3-Metallurgy. C4-Combinatorial
Technology. C9-Miscellaneous, of Chemistry; Metallurgy. D-Textiles; Paper. D0-Textiles or Flexible Materials not Otherwise Provided for. D2-Paper. D9-Miscellaneous, of Textiles;
Paper. E-Fixed Constructions. E0-Building. E2-Earth or Rock Drilling; Mining. E9-Miscellaneous, Of Fixed Constructions. F-Mechanical Engineering; Lighting; Heating; Weapons;
Blasting. F0-Engines or Pumps. F1-Engineering in General. F2-Lighting; Heating. F4-Weapons; Blasting. F9-Miscellaneous, of Mechanical Engineering; etc. G-Physics. G0-Measuring;
Optics; Horology; Controlling; Computing; Signaling. G1-Acoustics; Information Storage; Instruments; ICT Adapted to Applications. G2-Nuclear Physics; Nuclear Engineering.
G9-Miscellaneous, of Physics.
Each value in the cells is row normalized.
extracted from patent documents, has been broadly neglected in the
literature but, quantitatively, it looks as important as that identified by
patent citations, as discussed in Feng (2020).
It should be noted that, thus far, we have adopted a parsimonious
algorithm that disregards word frequency in constructing the measure
of text similarity (see Section 4for details). This, however, could
artificially increase the similarity between the abstracts. For this reason,
we replicate our benchmark regression in column (3) using a text
similarity matrix derived by considering all word occurrences, finding
similar results. This and all the other results which we cite hereinafter,
but that are unreported for the sake of brevity, are available upon
request.
Research Policy 54 (2025) 105126
10

A. Fronzetti Colladon et al.
Table 3
Long-run estimates for the effect of Neighbor Innovativeness.
(1) (2) (3) (4) (5) (6)
Neighbor Innovativeness
Citation flows 0.122*** 0.151*** 0.107*** 0.098*** 0.089*** 0.009***
(0.002) (0.001) (0.002) (0.010) (0.004) (0.001)
Text similarity 0.541*** 0.519*** 0.661*** 0.117***
(0.0113) (0.010) (0.021) (0.023)
Controls
Cumulative internal knowledge 0.882***
(0.051)
Innovation effort 0.031
(0.052)
Technology diversification 0.169***
(0.014)
Adjustment parameter −0.081*** −0.069*** −0.101*** −0.101*** −0.101*** −0.359***
(0.009) (0.008) (0.010) (0.010) (0.010) (0.055)
Patent variable Patent counts Forward cites Forward cites Forward cites Forward cites Forward cites
Matrix weights Cites Cites Text Text Text Text
(CWC) (CWF) (TWF) (TWF) (TWF) (TWF)
Patent assignment Univocal Univocal Univocal Multiple Univocal Univocal
Sector aggregation (CPC) 3 digit 3 digit 3 digit 3 digit 2 digit 3 digit
Observations 5,632 5,632 5,632 5,632 1,320 5,390
R-squared 0.145 0.146 0.141 0.141 0.090 0.041
Number of sectors 128 128 128 128 30 128
Notes: Long-run estimates (elasticities) derived from an ARDL(2,1). All variables are expressed in logs. Heteroskedasticity-Autocorrelation Consistent (HAC) standard errors are in
parentheses. All regressions use sector-specific fixed effects and account for the effect of common time shocks (time dummies) using variables expressed in deviation from their
yearly means. Innovation is measured by the raw number of patent counts (column (1)) and by the forward cites-adjusted number of patent counts (columns (2)–(6)). The matrix
of technological proximity is based on bilateral citation flows in columns (1)–(2), and on pairwise cosine similarity of patent texts in columns (3)–(6). Each patent is univocally
assigned to one technology sector (class) in columns (1)–(3) and (5)–(6), and to multiple sectors based on the full list of technology classes listed in the patent document in column
(4). Technology classes (sectors) are based on the Cooperative Patent Classification (CPC). Columns (1)–(4) and (6) use data at the 3-digit level of technology classes (128 sectors);
column (5) uses data at the 2-digit level (30 sectors). *** , ** , * denotes statistical significance at the 1, 5 and 10% level, respectively.
One issue in the estimates, that we have discussed above, is that
each patent is associated with only one (primary) class when it could
be seen as the realization of an innovation in different technology
areas (sectors), as resulting from the full list of technology classes
reported in the patent document. When considering patents assigned
univocally to one technology class, we may downstate the technological
capabilities of the firm and, in turn, overstate the impact of neighbor
innovativeness, as we do not discern the firm capacity to develop
technologies in contiguous technology areas. This implies that, in the
benchmark regression, our proxy for neighbor innovativeness might
capture the effect of horizontal relatedness, rather than that of genuine
knowledge transfers. However, one can broadly infer the technologi-
cal capabilities of the companies by examining all technology classes
outlined in the patent documents. For this reason in column (4), we
run our regression with a multiple class assignment for each patent,
but preserve the structure of technological linkages used previously
(i.e., the same citation-based and text-based distance matrices), so to
avoid spurious interdependency between sectors. In this regression, the
parameter size of both explanatory variables falls only marginally with
respect to the benchmark regression.10
Another potential concern regarding our estimates is that the effect
of neighbors’ innovation capacity may be oversized as patent classifi-
cations are imperfect and fuzzy demarcations of the actual structure of
the knowledge economy. In order to validate the accuracy of our main
results, we conduct a regression analysis using data at a less detailed
level of disaggregation. Namely, we consider data for 30 sectors at the
10 Note that if we assign each application fractionally to all classes reported
in the patent document, the fall in the parameter size of neighbor innova-
tiveness is greater than in column (4). This finding should be taken with
caution due to the impossibility of assigning patent documents proportionally
to various technology classes (sectors). This is likely to generate a classical
measurement error that downward biases the parameter of our proxies for
neighbor innovativeness.
two-digit level of the CPC categorization (column (5)). Although it is
difficult to predict the direction of the bias associated with the mea-
surement errors caused by imperfect patent classification, which would
affect both dependent and explanatory variables, the results in column
(5) unequivocally confirm the effect of neighbor innovativeness.11
Finally, in the last regression of Table 3, we assess our estimates
to omitted variables’ bias and control for the effect of internal sources
of innovative success. Specifically, we include into the regression the
cumulative value of innovations developed in the past by the sector (the
patent stock), the average amount of innovation resources currently
used by the firms (number of inventors per patent), and the degree of
firm product diversification/proliferation (number of applicants active
in each sector). As column (6) shows, there is a systematic fall in the
influence of the neighbors’ innovation when including our set of control
variables. As discussed above, it is likely to reflect the strong persistence
over time in the effects of knowledge spillovers, technology transfers,
etc. There is a long-lasting comovement in innovation activities across
sectors, implying a strong correlation between the cumulative value of
a sector’s patents and our measure of neighboring innovativeness. Inter-
temporal knowledge returns (or dynamic returns) are a typical driver
of innovation outcomes (Caballero and Jaffe,1993): firms with a larger
technological knowledge, developed over time through successful in-
novation, have an advantage in generating new knowledge compared
to innovators with a smaller past engagement in R&D. The parameter
size of the patent stock (0.882) signals that inter-temporal (within-
industry) spillovers are positive but slightly decreasing over time. This
may reflect the increasing difficulty of doing R&D caused by dimin-
ishing technological opportunities or the fall in the cost-effectiveness
of R&D (Bloom et al.,2020). This finding departs from major results
of the earlier literature based on cross-country data (from Madsen,
11 See Lafond and Kim (2019) for a pioneering application of endogenous
clustering to the USPTO data.
Research Policy 54 (2025) 105126
11

A. Fronzetti Colladon et al.
Table 4
Long-run estimates for the effect of Network Structure: Katz centrality metrics.
(1) (2) (3) (4) (5) (6) (7) (8)
Neighbor Innovativeness
Text similarity 0.117*** 0.321*** 0.112*** 0.283*** 0.120*** 0.233***
(0.023) (0.061) (0.022) (0.051) (0.022) (0.056)
Citation flows 0.009*** 0.008*** 0.007*** 0.008*** 0.008*** 0.007***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Network Structure
Katz (total)
Text similarity 0.074*** −0.197***
(0.020) (0.053)
Citation flows 0.008*** 0.006***
(0.001) (0.001)
Katz (direct)
Text similarity −0.929*** −0.773***
(0.142) (0.161)
Citation flows 0.014*** 0.012***
(0.005) (0.005)
Katz (indirect)
Text similarity 0.799*** 0.713***
(0.123) (0.129)
Citation flows 0.003*** 0.003***
(0.001) (0.001)
Controls Yes Yes Yes Yes Yes Yes Yes Yes
Adjustment par. −0.359*** −0.355*** −0.376*** −0.361*** −0.362*** −0.361*** −0.363*** −0.365***
(0.055) (0.056) (0.059) (0.055) (0.055) (0.058) (0.055) (0.058)
Obs. 5,390 5,390 5,390 5,390 5,390 5,390 5,390 5,390
R-squared 0.041 0.042 0.043 0.041 0.041 0.041 0.041 0.041
Notes: Long-run estimates (elasticities) derived from an ARDL(2,1). All variables are expressed in logs. Heteroskedasticity-Autocorrelation Consistent (HAC) standard errors are
in parentheses. All regressions account for the effect of common time shocks (time dummies) using variables expressed in deviation from their yearly means. Controls included:
Cumulative internal knowledge; Innovation effort; Technology diversification. Innovation output is measured by the forward cites-adjusted number of patent counts. The matrix of
technological proximity is based on bilateral citation flows and on pairwise cosine similarity of patent texts. Each patent is univocally assigned to one technology sector. *** , **
, * denotes statistical significance at the 1, 5 and 10% level, respectively.
2008 onwards), which points to highly persistent returns of past knowl-
edge on the creation of innovations (constant intertemporal spillovers).
However, our evidence of decreasing returns to scale of R&D aligns to
previous studies conducted on the US and European industries (Ven-
turini,2012;Mason et al.,2020). The impact of the current R&D effort,
here approximated by the number of inventors involved in innovative
processes, seems to overlap with that of the knowledge (patent) stock,
as the former explanatory variable, albeit positively signed, is never
significant. While expanding product varieties should depress the net
returns to innovation effort according to the Schumpeterian growth
theory (Ha and Howitt,2007), we find a positive effect for our proxy for
product proliferation/diversification, namely the number of applicants
active in each sector. This would signal that companies active in the
same sector are likely to exploit technological complementarities or
economics of scope in their innovation processes.
In the Appendix, we conduct a set of econometric checks on our
baseline regression (Table A.2). Specifically, we alternatively use: (i) a
richer dynamic adjument to neutralize the effect of reverse casuality;
(ii) a linear dynamic regression robust to misspecified dynamics and
error serial correlation (the Cross-Sectional augmented Distributed-
Lag, CS-DL, in place of the ARDL regression); (iii) robust controls to
common un-observable factors (Common Correlated Effects, CCE, in
place of time dummies); (iv) counterfactual (random) distance weights;
(v) count data regression (negative binomial); and finally (vi) inverse
hyperbolic sine transformation to account for missing values. In all
these cases, our main results of Table 3 are largely confirmed.
5.2. Influence of the network structure
Next, we account for the effect of structural linkages by utilizing
a comprehensive range of network centrality measures that have been
previously introduced. We present the results of this analysis in two
parts. Table 4 reports estimates obtained adopting the Katz metric,
which is also decomposed to assess the influence on innovation per-
formance exerted by the direct and indirect connections existing across
technology sectors. Table 5 illustrates the results obtained using the
index of degree centrality for measuring direct linkages, and separately
the indicators of betweenness, closeness, and distinctiveness for gaug-
ing indirect linkages. The latter table also presents the results obtained
using the latent factor, built to capture the full spectrum of effects
produced by structural linkages, as measured by the second group
of network centrality indicators. Again, in both regression tables we
include measures of technological interdependence constructed using
either bilateral citations or text similarity.
In columns (2) and (3) of Table 4, we include the Katz centrality
index measuring the overall network of structural linkages and observe
that it is positively and significantly related to sectoral innovation
output. In line with our earlier estimates, the coefficient size of the
variable based on text similarity is much larger than the one obtained
using citation flows. However, the coefficient of the Katz index based on
text similarity turns negative when we include this variable in the same
specification with our measure of neighbor innovativeness (column
(4)). This is as the latter regressor is built using (bilateral) direct link-
ages to weigh the innovation output of sourcing sectors. Consistently,
Research Policy 54 (2025) 105126
12

A. Fronzetti Colladon et al.
Table 5
Long-run estimates for the effect of Network Structure: Network centrality metrics.
(1) (2) (3) (4) (5) (6)
Neighbor Innovativeness
Text similarity 0.117*** 0.040* 0.205*** 0.074*** 0.087*** 0.067***
(0.023) (0.021) (0.024) (0.021) (0.022) (0.020)
Citation flows 0.009*** 0.008*** 0.009*** 0.008*** 0.008*** 0.008***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Network Structure
Degree
Text similarity 0.001***
(0.001)
Citation flows 0.012***
(0.001)
Betweeness
Text similarity 0.004
(0.003)
Citation flows 0.011***
(0.001)
Closeness
Text similarity 0.009**
(0.004)
Citation flows 0.006***
(0.001)
Distinctiveness
Text similarity 0.015***
(0.004)
Citation flows 0.006**
(0.003)
Latent factor
Text similarity 0.041***
(0.009)
Citation flows 0.035***
(0.006)
Controls Yes Yes Yes Yes Yes Yes
Adjustment par. −0.359*** −0.319*** −0.314*** −0.376*** −0.369*** −0.631***
(0.055) (0.052) (0.050) (0.055) (0.050) (0.035)
Obs. 5,390 5,390 5,390 5,390 5,390 5,390
R-squared 0.041 0.041 0.041 0.041 0.041 0.041
Notes: Long-run estimates (elasticities) derived from an ARDL(2,1). All variables are expressed in logs, except Degree, Betweenness, Closeness, Distinctiveness, and the latent factor
which enter the regression multiplied by 100 so that their parameters can be treated as elasticities. Heteroskedasticity-Autocorrelation Consistent (HAC) standard errors are in
parentheses. Controls included: Cumulative internal knowledge; Innovation effort; Technology diversification. Innovation output is measured by the forward cites-adjusted number
of patent counts. The matrix of technological proximity is based on bilateral citation flows and on pairwise cosine similarity of patent texts. Each patent is univocally assigned to
one technology sector. *** , ** , * denotes statistical significance at the 1, 5 and 10% level, respectively.
when we decompose the Katz metric into the effects associated with
direct and indirect linkages, the former are found to be negatively
related to innovation output, while the latter variable has a positive
coefficient (column (6)). This finding clearly points to the overlapping
between the text-based measure of neighbor innovativeness and the
text-based measure of direct linkages. It is important to emphasize
that a different pattern of results is found when the Katz measures are
derived from the networks of citation flows (columns (7) and (8)): these
variables are always positively and significantly associated with sector
innovation and, albeit small in size, present quite stable coefficients
across regressions (see Taalbi,2020 for consistent results).
In Table 5, we explore the influence of the network structure
using our second group of centrality indicators. First, we extend the
benchmark regression (with controls) with a measure of direct linkages
captured by degree centrality (column (2)).12 The effect of this explana-
tory variable is positive and significant both when it is based on text
similarity and on citation flows. However, the impact of the former
version of the variable largely overlaps with the effect of neighbor
12 In Table 5, all variables are expressed in logs, except Degree, Betweenness,
Closeness, Distinctiveness, and the latent factor. These variables enter the
regression multiplied by 100 so that their parameters can be treated as
elasticities and are comparable to the coefficient of the other regressors.
innovativeness, which uses direct linkages to weigh external innova-
tion. Consequently, the coefficient of the variable capturing knowledge
spillovers falls at the limit of the significance region in column (2).
At the same time, the impact associated with the direct connections
within the network, as measured by degree centrality based on bilateral
citations, is statistically significant and economically substantial.
As a second step, we quantify the impact of the overall linkages
channeled by the complex structure of intersectoral relations (columns
(3)–(6)). We find strong indication that the network structure positively
affects innovation performance with all indicators adopted, expect
when using the index of closeness centrality calculated on text simi-
larity. It is important to highlight that the long-run elasticity identified
for the latent factor (column (6)) largely exceeds the impact estimated
for each individual measure of centrality from which it is derived
(columns (2)–(5)). This suggests that the latent factor has the ability to
capture variation in structural linkages within the network that would
otherwise go unnoticed when utilizing individual measures of network
centrality one at a time.
Summing up, the results of this section reinforce the idea that
the network structure (topology) of sectoral linkages is both complex
and relevant, and that social network analysis methods can effectively
help leverage its informative content. More importantly, evidence in
Tables 4 and 5suggests that the effect of network linkages on the
success of sectoral innovation activities is comparable in size to that
of neighbor innovativeness.
Research Policy 54 (2025) 105126
13

A. Fronzetti Colladon et al.
Fig. 3. Innovation response to technological shocks: Neighbor innovativeness vs network structure.
Notes: The graph reports the forward effect coefficients associated with the local projection estimation in Eq. (11). All estimates use fixed effects, time dummies, and standard
errors robust to heteroskedasticity. The graphs report the confidence bands at 90 and 95%. The event is identified with the year of peak increase in the variable under assessment.
The graph on the left-hand (right-hand) side considers a shock to neighbor innovativeness (network position) while keeping the other measure of technological interdependence
as control. The degree of neighbor innovativeness is measured with the proximity-weighted number of quality-adjusted patents of the other sectors. The structure of the network
linkages is summarized by the latent factor extracted from the indicators of network degree, betweenness, closeness and distinctiveness centrality.
5.3. Innovation response to technological shocks in linked sectors
We conclude the analysis by assessing how shocks, that affect tech-
nological interdependence, spread through the technology space and
affect sector innovation. Specifically, we implement an event analysis
and simulate the change in innovation output (response) after an
innovation shock in technologically related sectors (impulse). We use
our dynamic regression model to perform a local projection analysis
as originally devised by Jordà (2005).13 For each sector, we consider
as an event the year with the peak increase in innovation outcome of
13 See Akcigit et al. (2022) and Madsen et al. (2024) for recent applications
in macro-economic analysis of innovation.
Research Policy 54 (2025) 105126
14

A. Fronzetti Colladon et al.
connected sectors, (=,), and assess the associated change
in  within the horizon of ten years after the shock. In essence, we
run a set of forward-effect regressions in which the event variable is
a dummy of value one in the peak year, and zero otherwise. For all
regressors, we consider a set of lags, , to filter out the effect of their
adjustment dynamics and a set of leads to exclude bias induced by
anticipation effects (= 2). The specification used in the event analysis
is shaped as follows:
,+=+
3

=1
1,−+
2

=0
2,−+
2

=0
3,−
+


=0
(4++5,+) +  (11)
where identifies the event dummy, are the control variables, and
 the error terms. = 1,…,10 is the time horizon of the estimated
forward effect. All estimations use fixed effects, time dummies, and
standard errors robust to heteroskedasticity. The graphs report the
confidence bands at 90 and 95%.
We consider two types of shocks separately, one on the variable
measuring the degree of neighbors’ innovativeness, and one on the
variable reflecting the intensity the structural linkages, namely the
latent factor. We treat as primary proxy for technological interdepen-
dence the measure constructed on text similarity and use the other
variable, based on bilateral citations, as a control. However, as a
counterfactual exercise, we look at the peak increase (shock) in the
citation-based measure of technological interdependence and employ
the other measure based on text similarity as a control. Since we are
interested in understanding whether the propagation of technological
shocks has changed recently, we distinguish the responses by the time
interval of the data. Accordingly, we first consider the entire period
between 1976 and 2021 and then look at the interval since 1995.14
The results of the event analysis offer particularly valuable insights
(see Fig. 3). Considering the overall time span, there is no systematic
response of sector innovation to positive shocks affecting both dimen-
sions of technological interdependence (neighbor innovativeness and
network structure). However, the pattern of results changes remark-
ably from 1995 onward, when the effect of an unanticipated change
in neighbor innovativeness turns out to be significant and manifests
sooner than shocks affecting the network structure. The effect of the
former shocks emerges in (and remains significant for) less than five
years, while the impact of an unexpected change in network link-
ages becomes significant after half decade and grows smoothly over
time. Our simulation results suggest that the influence of network
linkages may even prevail for magnitude over the impact of neighbor
innovativeness.
Lastly, in order to ascertain whether patent documents are a valid
source of information on technological shocks affecting sector innova-
tion, we perturb the citation-based measures of technological interde-
pendence, considering as an event the year with a peak increase in this
group of variables. In such a regression, we do not find any appreciable
response in patenting activities of the sectors. This finding confirms that
patent text conveys valuable information on the development of new
technologies and on the direction of technological change.
14 As argued above, we account for the effect of time shocks, having a
homogeneous effects across sectors, using time dummies. This type of regres-
sions do not support the control for the impact of cross-sector heterogeneous
effects through CCEs. However, the risk that unobservable (‘‘third’’) factors are
driving the results of the event analysis is excluded by the fact that only 3% of
(4 out 128) sectors has the same event year (i.e., the peak year in the impulse
variable).
6. Conclusions
This paper has analyzed how technological interdependence affects
the sectors’ ability to innovate, looking at the effects of neighbor
innovativeness and at the structure (topology) of technological link-
ages within the innovation network. By examining the abstract of 6.5
million patents, granted by the United States Patent and Trademark
Office (USPTO) between 1976 and 2021, we have first shown that
both dimensions of technological interdependence matter for sectoral
innovation. In the long run, the impact of network linkages turns
out to be as important as that of neighbor innovativeness. In the
relatively short run, positive shocks to neighbor innovativeness produce
relatively faster effects, while the impact of the shocks strengthening
network linkages lasts longer and is apparently quantitatively larger.
We have then demonstrated that patent texts represent a rich source
of information on innovations’ content. On average, text-based data
exhibit a greater level of consistency compared to cite-based data and
are suitable to identify both the effect of neighbor innovativeness and
network structure.
All these findings have important implications for the evolution of
research on innovation and technological change, knowledge spillovers
and structural (network) linkages. Our study has highlighted that the
natural language processing of patent documents offers a unique oppor-
tunity to move beyond traditional methods of innovation classification
and analysis, such as the CPC categorization. By employing text mining
techniques, researchers can endogenously determine clusters of similar
technologies, can study how these evolve over time and uncover factors
behind their development. Another area deserving exploration is the
analysis of patent text (dis)similarity for the purpose of technological
forecasting, i.e., to predict which areas of the technology space are
likely to expand in the near future. A newer generation of textual
analysis techniques, for example based on transformers or large lan-
guage models (ChatGPT, etc.), could be used to this purpose in light of
their high potential. There has been increasing effort in social sciences,
especially among sociologists, in learning human behavior through dig-
ital tracks collected via ‘‘cognitive artifacts’’ (e.g., smartphones). Large
language models such as those based on transformers are increasingly
important for the develop the new lines of research, for instance as
those focused on the interplay between humans and ambient intelli-
gence (the Metaverse). This could help understand the latest evolution
of the technology space towards new digital fields (Godwin-Jones,2021
and 2023). These topics are left as subjects for future research.
CRediT authorship contribution statement
Andrea Fronzetti Colladon: Writing – review & editing, Writing –
original draft, Visualization, Software, Methodology, Formal analysis,
Conceptualization. Barbara Guardabascio: Writing – review & editing,
Writing – original draft, Methodology, Visualization, Formal analy-
sis, Data curation. Francesco Venturini: Writing – review & editing,
Writing – original draft, Methodology, Formal analysis, Data curation,
Conceptualization.
Data availability
Data will be made available on request.
Acknowledgment
The authors gratefully acknowledge the Editor, Prof. Adam Jaffe,
and three anonymous referees for helpful comments.
Research Policy 54 (2025) 105126
15

A. Fronzetti Colladon et al.
Table A.1
Correlation structure.
Label Counts FWC CWC CWF TWF Katz direct Katz indirect
Counts 1.000
FWC 0.8991∗1.000
CWC 0.7034∗0.3727∗1.000
CWF 0.8616∗0.6706∗0.8106∗1.000
TWF 0.4103∗0.4721∗0.1356∗0.3218∗1.000
Katz direct 0.2566∗0.2727∗0.1223∗0.1605∗0.6210∗1.000
Katz indirect 0.2765∗0.2945∗0.1325∗0.1751∗0.6290∗0.9993∗1.000
Katz total 0.2765∗0.2945∗0.1325∗0.1751∗0.6290∗0.9993∗1.000∗
Degree 0.1111∗0.1232∗0.0484∗0.0624∗0.4648∗0.8899∗0.8797∗
Betweeness 0.0390∗0.0684∗−0.0210 −0.0219 0.1809∗0.3615∗0.3580∗
Closeness 0.1939∗0.2056∗0.0906∗0.1157∗0.5422∗0.9760∗0.9715∗
Distinctiveness −0.0169 0.0370∗−0.0751∗−0.0769∗0.2650∗0.7136∗0.7052∗
Latent Factor 0.1203∗0.1437∗0.0372∗0.0522∗0.4784∗0.9380∗0.9296∗
Label Katz total Degree Betweeness Closeness Distinctiveness Latent
Factor
Katz total 1.000
Degree 0.8797∗1.000
Betweeness 0.3580∗0.2566∗1.000
Closeness 0.9715∗0.8996∗0.3498∗1.000
Distinctiveness 0.7052∗0.7748∗0.4403∗0.7254∗1.000
Latent Factor 0.9296∗0.9649∗0.4211∗0.9524∗0.8619∗1.000
FWC: Forward cites (univocal). CWC: Bilateral cities-weighted patent counts. CWF: Bilateral weighted Forward cites. TWF: Text
similarity-weighted forward cites. All centrality measures are based on text similarity. * significant at 5%
Appendix
A.1. Econometric checks
In Table A.2, we display the results of some econometric checks
conducted on our baseline regression illustrated in Table 3, which is
reported here in column (1) as a reference. As a first check, we estimate
our specification using a richer dynamic adjustment. It is known that
the ARDL specification yields consistent estimates, which are robust to
reverse causality when the lag structure is sufficiently rich, but such
estimates may be inefficient if too many lags are used in the regression.
For this reason, in columns (2) and (3), we extend the lag order of the
variables to further two and four lags, finding an effect for neighbor
innovativeness (and related knowledge spillovers) consistent with our
earlier regressions.
To further address the sensitivity of our results to the modeling
of the dynamic adjustment, we run the Cross-Sectional augmented
Distributed-Lag (CS-DL) regression (Chudik et al.,2016). With respect
to the ARDL model, this procedure has a superior performance when
the dynamic of the variables is misspecified and there is error serial
correlation, especially with moderately long data like ours. CS-DL
estimates in Column (4) reveal a parameter size for neighbor inno-
vativeness only slightly smaller than ARDL estimates, suggesting that
our benchmark estimates in column (1) can be considered highly
plausible.15
In column (5), we run the model on original data (i.e., all series
are not expressed as time-de-meaned variables) but include the yearly
averages across the panel units of the dependent and explanatory vari-
ables. These average terms are known as Common Correlated Effects
(, so-called CCEs) and are used to purge estimates from the impact
of strong cross-sectional dependence, induced by common unobserv-
able shocks that have sector-specific (local) effects (⋅). These
terms, in particular, would remove the effect of ‘‘third (unknown)
15 The CS-DL specification used is shaped as  =0+1+
=0 −+,
where  is the first difference of the explanatory variable, and is its lag
order. The auxiliary variable is used to purge out the regression from the effect
of dynamic adjustment, error serial correlation, etc. In Table A.2,is set to
zero, but similar results emerge using a different lag order.
factors’’ that would cause spurious correlation between dependent and
explanatory variables. This could occur especially in the presence of
commonalities, i.e., when the sectors have similar knowledge bases but
innovate independently of each other.16 In estimation in column (5),
the effect of neighbor innovativeness is still significant, hence excluding
the risk that our proxies for knowledge spillovers capture the impact
of unmeasurable common factors (see Eberhardt et al.,2013). Note,
however, that the elasticity of innovation output to both measures of
neighbor innovativeness is smaller, signaling an important source of
variation in sector innovation, induced by omitted factors, that we are
unable to account for in our benchmark regression.
To corroborate the view that patent texts are a meaningful source
to represent the technology content of innovations, we estimate a
counter-factual regression in which we assign random values of co-
sine similarity to each pair of technological classes, while preserving
bilateral citation flows as weights in the other measure of neighbor
innovativeness (column (6)). Not surprisingly, the impact estimated
for the text similarity-based measure of neighbor innovativeness is
at odds with all our previous regressions, while the coefficient of
the citations-based measure of neighbor innovativeness is moderately
larger.
In columns (7) and (8), we address another potential source of con-
cern for our estimates, i.e., that they are influenced by the assumption
of (slope) homogeneity in the effect of knowledge spillovers while, thus
far, we have confined heterogeneity to sector fixed effects. However,
as long as sectors structurally differ in gaining from the innovation
of linked units, the effect estimated for neighbor innovativeness may
be biased. To exclude this risk, we estimate the model with the mean
group estimator, i.e., we run sector-by-sector regressions and take the
mean robust to outliers of these coefficients (Bond et al.,2010). This
procedure has been applied both to the specification that is equiva-
lent to using time dummies (weak cross-sectional dependence; column
(7)) and to the specification using CCE terms (strong cross-sectional
dependence; column (8)). In either case, the coefficient of the text
similarity-based measure of neighbor innovativeness is much larger
than in our benchmark regression (column (1)), while the citation-
based measure has a much smaller elasticity. All this reveals that there
16 The estimator used in column (5) corresponds to the cross-sectionally
augmented version of the ARDL regression developed by Chudik and Pesaran
(2015) with homogeneous (slope) coefficients.
Research Policy 54 (2025) 105126
16

A. Fronzetti Colladon et al.
Table A.2
Long-run estimates for the effect of neighbor innovativeness: Econometric checks.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Neighbor innovativeness
Citation flows 0.107*** 0.128*** 0.122*** 0.074*** 0.0262*** 0.165*** 0.028*** 0.012*** 0.075*** 0.043***
(0.001) (0.002) (0.003) (0.005) (0.002) (0.002) (0.006) (0.002) (0.002) (0.003)
Text similarity 0.541*** 0.576*** 0.518*** 0.449*** 0.218*** −0.115*** 1.687*** 1.631*** 0.254*** 0.415***
(0.011) (0.012) (0.013) (0.034) (0.023) (0.038) (0.141) (0.102) (0.021) (0.031)
Adjustment parameter
−0.101*** −0.092*** −0.093*** −0.386*** −0.063*** −0.266*** −0.540***
(0.010) (0.011) (0.012) (0.022) (0.009) (0.024) (0.028)
Cross-Sectional Dependence (CSD) Weak Weak Weak Weak Strong Weak Weak Strong Weak Weak
Time dummies Time dummies Time dummies Time dummies CCE Time dummies Time dummies CCE Time dummies Time dummies
Patent variable Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites Forw. Cites
Matrix weights:
variable Text Text Text Text Text Random Text Text Text Text
scheme Direct Direct Direct Direct Direct Direct Direct Direct Direct Direct
Model ARDL (2, 1) ARDL (4, 3) ARDL (6, 5) CS-DL ARDL (2, 1) ARDL (2, 1) ARDL (2, 1) ARDL (2, 1) Negative binomial Inverse hyperbolic sine
Parameter Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Heterogeneous Heterogeneous Homogeneous Homogeneous
Obs. 5,632 5,376 5,120 5,760 5,248 5,504 5,632 5,376 4,608 5,888
R-squared 0.141 0.146 0.152 0.612 0.663 0.147 0.089 0.5402
RMSE 0.15
Log-likelihood −28,794
Alpha 0.172
Notes: Long-run (dynamic) estimates derived from an ARDL regression are reported in columns (1)-(3) and (5)–(8). These use a different lag structure, as indicated in the table. Long-run (dynamic) estimates derived from a CS-DL regression
are reported in column (4). Static estimates derived from a negative binomial and the inverse hyperbolic sine transformation are shown in columns (9) and (10). In all regressions excluding columns (9) and (10), variables are expressed
in logs. Heteroskedasticity-Autocorrelation Consistent (HAC) standard errors are in parentheses. All regressions use sector-specific fixed effects. Expect that in columns (5) and (8), all regressions allow for the effect of common time shocks
(time dummies) using variables expressed in deviation from their yearly means; this accommodates the effect of weak cross-sectional dependence (CSD). Common Correlated Effects (CCE), computed as cross-sectional means of all variables
of the model, are used in columns (5) and (8); this accommodates the effect of strong cross-sectional dependence (CSD). All regressions consider homogeneous slope coefficients excluding estimates in columns (7) and (8), which assume
heterogeneous slope parameters. Innovation is measured by the forward cites-adjusted number of patent counts in all regressions. Patents are univocally assigned to one primary CPC class. The matrix of technological proximity is always
based on the pairwise cosine similarity of patent texts, with the exception of column (6), where the proximity values are assigned randomly. All estimates use data at the 3-digit level of technology classes (128 sectors). *** , ** , * denotes
statistical significance at the 1, 5 and 10% level, respectively.
are large disparities across sectors in exploiting external knowledge
sources.
Next, we take into more serious consideration the count data nature
of the dependent variable and check whether our estimates reflect the
log-linearization of Eq. (2). To address this issue, we run our knowledge
production function as a negative binomial regression, using forward
cites-adjusted patents as the left-hand side variable and, as regressors,
our proxies for neighbor innovativeness, expressed in logs. In this way,
the estimated parameters can be interpreted as elasticities and are
comparable to our previous results. The negative binomial is estimated
as a static regression pooling time-demeaned data among cross sections,
where the fixed effect of each sector is approximated by the pre-sample
mean values of innovation outcome (Bloom et al.,2013;Igna and
Venturini,2023). Pre-sample fixed effects are computed as the average
of the dependent variable over a time window preceding the interval
of the regression; they should capture systematic differences existing
across sectors in the patent propensity, in connecting to the other
innovating units and to their ability to gain from external knowledge.
Our count data regression yields lower elasticities for both measures
of knowledge spillovers (0.075 and 0.254 respectively, column (9))
that, however, remain overall consistent with the results of our linear
regression (column (1)).17
Lastly, we account for the bias that may be produced by the linear
transformation, ln(1 + ), used to handle observations with zeros.
Accordingly, we run our benchmark regression using the inverse hyper-
bolic sine transformation of original variables (not logged) as proposed
by Bellemare and Wichman (2020). This is run as a static regression
with sector and year fixed effects, obtaining an elasticity of 0.043 for
the citation-based measure of neighbor innovativeness and 0.415 for
the one based on text similarity (column (10)). This indicates that our
results in column (1) are not plagued by log-linearization.
17 We compute the pre-sample mean of the dependent variable over the
period 1976 and 1985 and use the interval from 1986 to 2021 as the regression
period.
References
Acemoglu, D., Akcigit, U., Kerr, W., 2016a. Networks and the macroeconomy: An
empirical exploration. NBER Macroecon. Annu. 30 (1), 273–335.
Acemoglu, D., Akcigit, U., Kerr, W.R., 2016b. Innovation network. Proc. Natl. Acad.
Sci. 113 (41), 11483–11488.
Akcigit, U., Grigsby, J., Nicholas, T., Stantcheva, S., 2022. Taxation and innovation in
the twentieth century. Q. J. Econ. 137 (1), 329–385.
Archibugi, D., 1988. In search of a useful measure of technological innovation (to
make economists happy without discontenting technologists). Technol. Forecast.
Soc. Change 34 (3), 253–277.
Archibugi, D., Mariella, V., Vezzani, A., 2023. What Next? Nations in the Technological
Race Through the 2030. Technical Report 2023, CNR.
Archibugi, D., Pianta, M., 1996. Measuring technological change through patents and
innovation surveys. Technovation 16 (9), 451–519.
Arts, S., Cassiman, B., Gomez, J.C., 2018. Text matching to measure patent similarity.
Strateg. Manag. J. 39 (1), 62–84.
Arts, S., Hou, J., Gomez, J.C., 2021. Natural language processing to identify the creation
and impact of new technologies in patent text: Code, data, and new measure. Res.
Policy 50 (2), 104144.
Bellemare, M.F., Wichman, C.J., 2020. Elasticities and the inverse hyperbolic sine
transformation. Oxf. Bull. Econom. Stat. 82 (1), 50–61.
Bergeaud, A., Potiron, Y., Raimbault, J., 2017. Classifying patents based on their
semantic content. PLoS One 12 (4), 1–22.
Bloom, N., Jones, C.I., Reenen, J.van., Webb, M., 2020. Are ideas getting harder to
find? Amer. Econ. Rev. 110 (4), 1104–1144.
Bloom, N., Schankerman, M., Reenen, J.van., 2013. Identifying technology spillovers
and product market rivalry. Econometrica 81 (4), 1347–1393.
Bond, S., Leblebicioglu, A., Schiantarelli, F., 2010. Capital accumulation and growth:
A new look at the empirical evidence. J. Appl. Econometrics 25 (7), 1073–1099.
Bottazzi, L., Peri, G., 2003. Innovation and spillovers in regions: Evidence from
European patent data. Eur. Econ. Rev. 47 (4), 687–710.
Breschi, S., Lissoni, F., Malerba, F., 2003. Knowledge-relatedness in firm technological
diversification. Res. Policy 32 (1), 69–87.
Caballero, R.J., Jaffe, A.B., 1993. How high are the giants’ shoulders: An empirical
assessment of knowledge spillovers and creative destruction in a model of economic
growth. NBER Macroecon. Annu. 8, 15–86.
Cao, J., Li, N., 2019. Growth through inter-sectoral knowledge linkages. Rev. Econ.
Stud. 86 (5 (310)), 1827–1866.
Carvalho, V.M., Draca, M., Kuhlen, N., 2021. Exploration and exploitation in US
technological change. In: CAGE Online Working Paper Series 575, Competitive
Advantage in the Global Economy. CAGE.
Castaldi, C., Frenken, K., Los, B., 2015. Related variety, unrelated variety and techno-
logical breakthroughs: An analysis of US state-level patenting. Reg. Stud. 49 (5),
767–781.
Research Policy 54 (2025) 105126
17

A. Fronzetti Colladon et al.
Castellacci, F., 2008. Technological paradigms, regimes and trajectories: Manufacturing
and service industries in a new taxonomy of sectoral patterns of innovation. Res.
Policy 37 (6), 978–994.
Chudik, A., Mohaddes, K., Pesaran, M.H., Raissi, M., 2016. Long-Run Effects in Large
Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors. Emerald
Group Publishing Limited, pp. 85–135.
Chudik, A., Pesaran, M.H., 2015. Common correlated effects estimation of het-
erogeneous dynamic panel data models with weakly exogenous regressors. J.
Econometrics 188 (2), 393–420.
Coe, D.T., Helpman, E., 1995. International R & D spillovers. Eur. Econ. Rev. 39 (5),
859–887.
Cohen, W.M., Levinthal, D.A., 1989. Innovation and learning: The two faces of R & D.
Econ. J. 99 (397), 569–596.
Criscuolo, P., Verspagen, B., 2008. Does it matter where patent citations come from?
Inventor vs examiner citations in European patents. Res. Policy 37 (10), 1892–1908.
Dosi, G., 1982. Technological paradigms and technological trajectories: A suggested
interpretation of the determinants and directions of technical change. Res. Policy
11 (3), 147–162.
Dosi, G., Grazzi, M., Moschella, D., 2017. What do firms know? What do they produce?
A new look at the relationship between patenting profiles and patterns of product
diversification. Small Bus. Econom. 48 (2), 413–429.
Eberhardt, M., Helmers, C., Strauss, H., 2013. Do spillovers matter when estimating
private returns to R & D? Rev. Econ. Stat. 95 (2), 436–448.
Fattori, M., Pedrazzi, G., Turra, R., 2003. Text mining applied to patent mapping: A
practical business case. World Pat. Inf. 25 (4), 335–342.
Feng, S., 2020. The proximity of ideas: An analysis of patent text using machine
learning. PLoS One 15 (7), 1–19.
Freeman, L.C., et al., 2002. Centrality in Social Networks: Conceptual Clarification.
Social Network: Critical Concepts in Sociology. vol. 1, Routledge, Londres, pp.
238–263.
Frenken, K., Oort, F.V., Verburg, T., 2007. Related variety, unrelated variety and
regional economic growth. Reg. Stud. 41 (5), 685–697.
Fronzetti Colladon, A., Grippa, F., 2020. Brand Intelligence Analytics. in Digital Trans-
formation of Collaboration: Proceedings of the 9th International COINs Conference.
Springer, pp. 125–141.
Fronzetti Colladon, A., Naldi, M., 2020. Distinctiveness centrality in social networks.
PLoS One 15 (5), e0233276.
Funk, R.J., Owen-Smith, J., 2017. A dynamic network measure of technological change.
Manage. Sci. 63 (3), 791–817.
Garcia-Vega, M., 2006. Does technological diversification promote innovation?: An
empirical analysis for European firms. Res. Policy 35 (2), 230–246.
Gerken, J.M., Moehrle, M.G., 2012. A new instrument for technology monitoring:
Novelty in patents measured by semantic patent analysis. Scientometrics 91 (3),
645–670.
Godwin-Jones, R., 2021. Big data and language learning: Opportunities and challenges.
Lang. Learn. Technol. 25 (1), 4–19.
Godwin-Jones, R., 2023. Emerging spaces for language learning: AI bots, ambient
intelligence, and the metaverse. Lang. Learn. Technol. 27 (2), 6–27.
Griliches, Z., 1990. Patent statistics as economic indicators: A survey. J. Econ. Lit. 28
(4), 1661–1707.
Ha, J., Howitt, P., 2007. Accounting for trends in productivity and R & D: A
schumpeterian critique of semi-endogenous growth theory. J. Money Credit Bank.
39 (4), 733–774.
Hall, B.H., Harhoff, D., 2012. Recent research on the economics of patents. Annu. Rev.
Econ. 4 (1), 541–565.
Hall, B.H., Jaffe, A.B., Trajtenberg, M., 2001. The NBER patent citation data file:
Lessons, insights and methodological tools. Working Paper 8498, National Bureau
of Economic Research.
Hall, B.H., Jaffe, A., Trajtenberg, M., 2005. Market value and patent citations. Rand J.
Econ. 36 (1), 16–38.
Hanley, D., 2017. Innovation and Patent Policy with Interdependent Technology.
Technical report, University of Pittsburgh.
Hotte, K., 2023. Demand-pull, technology-push, and the direction of technological
change. Res. Policy 52 (5), 104740.
Hung, S.-W., Wang, A.-P., 2010. Examining the small world phenomenon in the patent
citation network: A case study of the radio frequency identification (RFID) network.
Scientometrics 82 (1), 121–134.
Igna, I., Venturini, F., 2023. The determinants of AI innovation across European firms.
Res. Policy 52 (2), 104661.
Jaffe, A.B., 1986. Technological opportunity and spillovers of R & D: Evidence from
firms’ patents, profits, and market value. Am. Econom. Rev. 76 (5), 984–1001.
Jaffe, A.B., 1989. Characterizing the technological position of firms, with application to
quantifying technological opportunity and research spillovers. Res. Policy 18 (2),
87–97.
Jaffe, A.B., 2000. The US patent system in transition: Policy innovation and the
innovation process. Res. Policy 29 (4), 531–557.
Jaffe, A.B., Rassenfosse, G.de., 2017. Patent citation data in social science research:
Overview and best practices. J. Assoc. Inform. Sci. Technol. 68 (6), 1360–1374.
Jordà, O., 2005. Estimation and inference of impulse responses by local projections.
Amer. Econ. Rev. 95 (1), 161–182.
Katz, L., 1953. A new status index derived from sociometric analysis. Psychometrika
18 (1), 39–43.
Kauffman, S.A., 2000. Investigations. Oxford University Press.
Keller, W., 2004. International technology diffusion. J. Econ. Lit. 42 (3), 752–782.
Kelly, B., Papanikolaou, D., Seru, A., Taddy, M., 2021. Measuring technological
innovation over the long run. Am. Econom. Rev. Insights 3 (3), 303–320.
Kim, J., Magee, C.L., 2017. Dynamic patterns of knowledge flows across technological
domains: empirical results and link prediction. arXiv preprint arXiv:1706.07140.
Kogan, L., Papanikolaou, D., Seru, A., Stoffman, N., 2017. Technological innovation,
resource allocation, and growth. Q. J. Econ. 132 (2), 665–712.
Lafond, F., Kim, D., 2019. Long-run dynamics of the US patent classification system.
J. Evol. Econom. 29 (2), 631–664.
Lanjouw, J.O., Schankerman, M., 2004. Patent quality and research oroductivity:
Measuring innovation with multiple indicators. Econom. J. 114 (495), 441–465.
Leoncini, R., Maggioni, M.A., Montresor, S., 1996. Intersectoral innovation flows and
national technological systems: Network analysis for comparing italy and germany.
Res. Policy 25 (3), 415–430.
Liben-Nowell, D., Kleinberg, J., 2003. The link prediction problem for social networks.
In: Proceedings of the Twelfth International Conference on Information and
Knowledge Management. pp. 556–559.
Liu, E., Ma, S., 2021. Innovation networks and R & D allocation. NBER Working Papers
29607, National Bureau of Economic Research, Inc.
Liu, W., Song, Y., Bi, K., 2021. Exploring the patent collaboration network of China’s
wind energy industry: A study based on patent data from CNIPA. Renew. Sustain.
Energy Rev. 144, 110989.
Madsen, J.B., 2007. Technology spillover through trade and tfp convergence: 135 years
of evidence for the OECD countries. J. Int. Econ. 72 (2), 464–480.
Madsen, J., 2008. Semi-endogenous versus schumpeterian growth models: Testing the
knowledge production function using international data. J. Econ. Growth 13 (1),
1–26.
Madsen, J., Minniti, A., Venturini, F., 2024. Declining research productivity and income
inequality: A centenary perspective. J. Econom. Dynam. Control 167, 104924.
Malerba, F., 2002. Sectoral systems of innovation and production. Res. Policy 31 (2),
247–264.
Malerba, F., Orsenigo, L., 1997. Technological regimes and sectoral patterns of
innovative activities. Ind. Corp. Chang 6 (1), 83–117.
Mann, K., Püttmann, L., 2023. Benign effects of automation: New evidence from patent
texts. Rev. Econ. Stat. 105 (3), 562–579.
Mason, G., Rincon-Aznar, A., Venturini, F., 2020. Which skills contribute most to
absorptive capacity, innovation and productivity performance? Evidence from the
US and western europe. Econom. Innov. New Technol. 29 (3), 223–241.
Mendi, P., 2007. Trade in disembodied technology and total factor productivity in
OECD countries. Res. Policy 36 (1), 121–133.
Moed, H., Glanzel, W., Schmoch, U., Thelwall, M., 2006. Handbook of Quantitative
Science and Technology Research. Springer International Publishing, New York,
(Editors).
Monechi, B., Ruiz-Serrano, A., Tria, F., Loreto, V., 2017. Waves of novelties in the
expansion into the adjacent possibles. PLoS One 12 (6), e0179303.
Mowery, D., Rosenberg, N., 1979. The influence of market demand upon innovation:
A critical review of some recent empirical studies. Res. Policy 8 (2), 102–153.
Nagaoka, S., Motohashi, K., Goto, A., 2010. Patent statistics as an innovation indicator.
In: Hall, B.H., Rosenberg, N. (Eds.), Handbook of the Economics of Innovation.
In: Handbook of the Economics of Innovation, vol. 2, (25), North-Holland, pp.
1083–1127.
Nathan, M., Rosso, A., 2015. Mapping digital businesses with big data: Some early
findings from the UK. Res. Policy 44 (9), 1714–1733, The New Data Frontier.
Nathan, M., Rosso, A., 2022. Innovative events: Product launches, innovation and firm
performance. Res. Policy 51 (1), 104373.
Opsahl, T., Agneessens, F., Skvoretz, J., 2010. Node centrality in weighted networks:
Generalizing degree and shortest paths. Soc. Netw 32 (3), 245–251.
Park, M., Leahey, E., Funk, R.J., 2023. Papers and patents are becoming less disruptive
over time. Nature 613, 138–144.
Pavitt, K., 1984. Sectoral patterns of technical change: Towards a taxonomy and a
theory. Res. Policy 13 (6), 343–373.
Pavitt, K., 1998. Technologies, products and organization in the innovating firm: What
adam smith tells us and joseph schumpeter doesn’t. Ind. Corp. Chang 7 (3),
433–452.
Peri, G., 2005. Determinants of knowledge flows and their effect on innovation. Rev.
Econ. Stat. 87 (2), 308–322.
Perkins, J., 2014. Python 3 Text Processing with NLTK 3 Cookbook. Packt Publishing
Ltd.
Pieri, F., Vecchi, M., Venturini, F., 2018. Modelling the joint impact of R & D and ICT
on productivity: A frontier analysis approach. Res. Policy 47 (9), 1842–1852.
Piscitello, L., 2000. Relatedness and coherence in technological and product di-
versification of the world’s largest firms. Struct. Change Econ. Dyn. 11 (3),
295–315.
van Pottelsberghe de la Potterie, B., 2011. The quality factor in patent systems. Ind.
Corp. Chang 20 (6), 1755–1793.
Roelleke, T., Wang, J., 2008. TF-IDF uncovered: A study of theories and probabilities.
In: Proceedings of the 31st Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval. pp. 435–442.
Research Policy 54 (2025) 105126
18

A. Fronzetti Colladon et al.
Romer, P.M., 1990. Endogenous technological change. J. Polit. Econ. 98 (5), 71–102.
Rosenberg, N., 1979. Technological interdependence in the american economy. Technol.
Cult. 20 (1), 25–50.
Scherer, F., 1982a. Inter-industry technology flows in the United States. Res. Policy 11
(4), 227–245.
Scherer, F.M., 1982b. Inter-industry technology glows and productivity growth. Rev.
Econ. Stat. 64 (4), 627–634.
Scherer, F.M., 1983. The propensity to patent. Int. J. Ind. Organ. 1 (1), 107–128.
Schmookler, J., 1966. Invention and Economic Growth. Harvard University Press.
Schoenmakers, W., Duysters, G., 2010. The technological origins of radical inventions.
Res. Policy 39 (8), 1051–1059.
Sternitzke, C., Bartkowski, A., Schramm, R., 2008. Visualizing patent statistics by means
of social network analysis tools. World Pat. Inf. 30 (2), 115–131.
Taalbi, J., 2020. Evolution and structure of technological systems - an innovation output
network. Res. Policy 49 (8), 104010.
Taalbi, J., 2023. Long-run patterns in the discovery of the adjacent possible. arXiv
preprint arXiv:2208.00907.
Teece, D.J., Rumelt, R., Dosi, G., Winter, S., 1994. Understanding corporate coherence:
Theory and evidence. J. Econ. Behav. Organ. 23 (1), 1–30.
Tria, F., Loreto, V., Servadio, V.D.P., Strogatz, S.H., 2014. The Dynamics of Correlated
Novelties. Scientific Reports, vol. 4, pp. 1–8, Article 5890.
Venturini, F., 2012. Looking into the black box of schumpeterian growth theories: An
empirical assessment of R & D races. Eur. Econ. Rev. 56 (8), 1530–1545.
Venturini, F., 2015. The modern drivers of productivity. Res. Policy 44 (2), 357–369.
Verspagen, B., 1997a. Estimating international technology spillovers using technology
flow matrices. Rev. World Econom. (Weltwirtschaftliches Archiv) 133 (2), 226–248.
Verspagen, B., 1997b. Measuring intersectoral technology spillovers: Estimates from the
European and US patent office databases. Econom. Syst. Res. 9 (1), 47–65.
Wasserman, S., Faust, K., 1994. Social network analysis: Methods and applications. In:
Structural Analysis in the Social Sciences. Cambridge University Press.
Weitzman, M.L., 1998. Recombinant growth. Q. J. Econ. 113 (2), 331–360.
Willett, P., 2006. The porter stemming algorithm: Then and now. Program 40 (3),
219–223.
Research Policy 54 (2025) 105126
19