climbing the ladder of technological development...will diversify into new innovative activities is...
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Paper to be presented at the DRUID Academy conference in Rebild, Aalborg, Denmark on January
21-23, 2015
Climbing the Ladder of Technological DevelopmentSergio Petralia
Utrecht UniversityURU Research Centre
Pierre-Alexandre BallandUtrecht University
Economic [email protected]
andrea MorrisonUtrecht university
economic [email protected]
AbstractClimbing the Ladder of Technological DevelopmentSergio Petralia (Utrecht University, The Netherlands)
Ph.D Extent: From 1st September 2013 to 31st December 2016Email: [email protected]
ABSTRACT:
The study of the role of structural transformation in industrial development can be traced back to the work of Prebisch(1950), Singer (1950), Lewis (1954), and Hirschman (1958). Their main concern related to the adverse effects of theconcentration of exports on primary products and the deteriorating terms of trade. Within this literature, a successfulstrategy for development required diversification from traditional activities, mainly in primary sectors, to modern andcomplex activities which were more dynamic and offered higher potential for growth.
In recent years this discussion has seen a renewed interest for both, scholars and policy makers, and a new consensusseem to have arisen: Economic development requires diversification, not specialization (Rodrik 2003, Bell 2007 and
2009). This consensus builds on recent empirical evidence by Imbs and Wacziarg (2003), which documented a clearU-shaped relationship between GDP per capita and sectoral concentration of production capacity, meaning that asincome per capita increases economies become less concentrated, until this pattern reaches a turning point andcountries start specializing again. A substantial number of studies have gone further investigating whether this empiricalregularity would hold for exports as well (Klinger and Lederman 2004 and 2006, Aditya and Acharya 2011, and Cadot etal. 2011).
This paper investigates countriesâ?? patterns of technological diversification, as reflected by their patenting activity onthe United States Patent and Trademark Office (USPTO). First, we look at countriesâ?? patterns of concentration ofpatenting activity and confirm the existence of a remarkably similar, simultaneous process of diversification andspecialization to that documented for productive and exporting capacity. Second, we use disaggregated data onpatenting activity by type of technology to understand in deeper detail how this process took place. Our primary goal isto provide further understanding on how characteristics of technologies, relatedness among technologies, andcountriesâ?? profiles of competences drive/constrain possible paths of technological development. In particular, wepropose a two-step econometric model which: a) takes into account that factors influencing the decision to participate ina certain type of technology may be different than those affecting the decision to specialize on it; and b) deals with theproblem of self selection in this sample.
We show that the likelihood a country will diversify into a new innovative activity is higher for those activities that arerelated to its existing profile of competences, and additionally, that this effect is stronger at early stages of development,meaning that the likelihood a country will diversify into new innovative activities is heavily constrained by its existingprofile of competences. This likelihood also decreases as technologies become more complex, however, oncediversification has taken place, countriesâ?? tend to specialize in more complex and profitable technologies.
Most of the policy recommendations regarding technological catch up have been done focusing on countriesâ?? patternof technological specialization, and pointing towards the need to engage in the production of more sophisticated andcomplex technologies. Our sharper and deeper characterization of the process of technological development, whichstresses the differences between diversifying and specializing, suggests that any policy strategy aiming at diversifyingcountriesâ?? technological capabilities shouldnâ??t be conceived disregarding the role of indigenous capabilities andthe initial pattern of comparative advantages. While our results confirm that indeed, countriesâ?? tend to specialize inmore complex and profitable technologies, this specialization process occur among the menu of technologies countriesalready have acquired the capabilities to produce. When it comes to diversify into new activities, the likelihood a countrywill diversify into new innovative activities is heavily constrained by its existing profile of competences, especially at earlystages of the development process.
Jelcodes:O31,O14
Climbing the Ladder of Technological Development
Sergio Petraliaa,*
Pierre-Alexandre Ballanda,c
Andrea Morrisona,b
ABSTRACT:
VERY PRELIMINARY DRAFT
DO NOT DISTRIBUTE OR QUOTE
a) Utrecht University, URU Research Centre; b) Bocconi University, CRIOS; c) Lund University, Circle. * Corresponding author: Heidelberglaan 2, 3584 CS Utrecht | Utrecht University | Room 6.19 | T. +31 (0)30 253 1368 | F. +31 (0)30 2532037 | [email protected]
1. Introduction
The study of the role of structural transformation in industrial development can be traced back
to the work of Prebisch (1950), Singer (1950), Lewis (1954), and Hirschman (1958). Their main
concern was related to the adverse effects of the concentration of exports on primary products
and the deteriorating terms of trade. As a result, they considered that a successful strategy for
development required diversification from traditional activities, mainly in primary sectors, to
“modern” and “complex” activities, which were more dynamic and offered higher potential for
growth.
In the recent years this discussion has seen a renewed interest from both, scholars and policy
makers, and a ‘new’ consensus seem to have arisen: ‘Economic development requires
diversification, not specialization’ (see Rodrik 2006 and Bell 2009). This ‘consensus’ builds on
recent empirical evidence, which documented a clear and sustained pattern of diversification of
countries’ productive structure during most of their development path (Imbs and Wacziarg
2003, Klinger and Lederman 2004 and 2006, Aditya and Acharya 2013, and Cadot et al 2011).
This paper investigates countries’ patterns of accumulation and diversification of innovative
capabilities, as reflected by their patenting activity on the United States Patent and Trademark
Office (USPTO). First, we look at countries’ concentration patterns in patenting activity and
show there exists a process of diversification/specialization of countries’ innovative
capabilities, with a remarkable similar pattern to that found for domestic production and
exporting capacity. We found an U-shaped relationship between GDP per capita and
concentration of patenting activity.
Second, we use disaggregated data on patenting activity by type of technology to understand in
deeper detail how this process took place. Our primary goal is to provide further
understanding on how countries’ indigenous competences and characteristics of technologies,
such as their ‘complexity’ and ‘relatedness’ among them, may drive or constraint possible paths
of technological development. In particular, we propose a two-step econometric model which:
a) takes into account that factors influencing de decision to participate in a certain type of
technology may be different than those affecting the decision to specialize on it; and b) deals
with the problem of self selection in this sample.
We show that the likelihood a country will diversify into new technological activities depends
significantly on countries’ existing competences as well as characteristics of the technologies in
question. In particular, we show that the likelihood a country will diversify into a new
innovative activity is higher for those activities that are ‘related’ or ‘close’ to its existing profile
of competences. All these effects are stronger at early stages of development, meaning that the
likelihood a country will diversify into new innovative activities is heavily constrained by its
indigenous capabilities, where path dependency is a key element. Regarding countries’ patterns
of specialization, we found that as countries move along their development path, they tend to
specialize in more ‘complex’ and ‘profitable’ technologies.
The implications of these results are twofold; on the one hand, our first finding brings to the
front the importance of technological diversification in the development process. On the other
hand, our sharper and deeper characterization of the process of technological development,
suggests that any policy strategy aiming at diversifying countries’ technological capabilities
shouldn’t be conceived disregarding the role of indigenous capabilities and the initial pattern of
comparative advantages.
This paper is structured as follows: The next section reviews the literature studying countries’
patterns of diversification and specialization, to later focus on mechanisms through which
‘relatedness’ among technologies may affect these processes. In section number three we
present the data and describe the methodology, while section four discusses the results. The
last section concludes.
2. Framework !
The importance of structural transformation in industrial development has received renewed
attention from both, scholars and policy makers; and a ‘new’ consensus seems to have arisen:
‘Economic development requires diversification, not specialization’. In fact, this quote has
been taken directly from Rodrik (2006), which discusses stylized facts in industrial
development. In the same line, but within a context of policy recommendations for capability
building, Bell (2009) states: ‘entering into new lines of economic activity (starting new
industries) is just as important as becoming increasingly efficient in existing ones, and over the
longer term it is more important’. This ‘consensus’ builds on recent empirical evidence, which
documented a clear and sustained pattern of diversification of countries’ productive structure
during most of their development path (see Imbs and Wacziarg 2003, Klinger and Lederman
2004 and 2006, Aditya and Acharya 2013, and Cadot et al. 2011). This paper aims at providing
a better characterization of countries’ patterns of accumulation and diversification of
innovative capabilities, as reflected by their patenting activity at the USPTO.
At country level, innovation studies focusing on patterns of technological specialization and
diversification are very limited and restricted only to advanced economies, mostly due to data
limitations. Archibugi and Pianta (1991) found an inverse relationship between countries’
technological size (measured by cumulative R&D expenditure) and the degree of sectoral
concentration of technological activities. They covered the period 1975-1988 and used patent
information for around a dozen of countries, mostly OECD members. Cantwell and Vertova
(2004) looked at the evolution of technological specialization in seven developed economies
between 1890 and 1990, finding also a similar patterns regarding the relationship between
countries’ technological size and the degree of sectoral concentration. More recently, and
within the development and trade literature, Imbs and Wacziarg (2003), Klinger and Lederman
(2004 and 2006), Aditya and Acharya (2013), and Cadot et al (2011) documented a clear and
sustained pattern of diversification in countries’ productive and exporting structure, which
persists along most of the development process.
These studies agree, from different perspectives, on the importance of diversification for
development and leave the door open for questions regarding the characterization of these
diversification and specialization processes (i.e. of the productive, exporting, and innovative
capacity). In that direction, Vertova (2001) analyzed patent activity for a handful of developed
economies and covering a period of 100 years (1890-1990), finding that only few countries
were able to specialize in fast-growing technological fields. Lall (2000) explored export patterns
of developing countries finding evidence that countries with an export portfolio more oriented
towards technology-intensive products tend to grow faster in the world trade. Also, Haussman
et al. (2006) developed an index measuring the "quality" of countries’ export baskets and
showed that countries specializing in products which lay higher on this quality spectrum tend
to perform better. Additionally, Hidalgo et al. (2007) and Hidalgo and Haussman (2009 and
2011) found evidence that countries’ export patterns become more ‘sophisticated’ and
‘complex’ as they develop. Therefore, the evidence seems to point towards two preliminary
conclusions: First, that the diversification of countries’ productive and innovative capabilities is
an important part of the development process and second, that well performing countries
posses a distribution of their production and exporting capacity oriented towards the
production of more ‘sophisticated’ goods.
An additional factor, which has gained considerable attention, has to do with the role of
‘relatedness’ among products and knowledge, and its effect on the diversification process. At
the country-level, the role of ‘relatedness’ on diversification has been studied by Hidalgo et al.
(2007) and Hidalgo and Hausmann (2009 and 2011). These authors developed a framework
that incorporates ‘relatedness’ among products as a key element, the so called ‘product space’
(PS). The PS is a network-based representation of the economy where the nodes define
product categories and the ties among them indicate their degree of relatedness. Using trade
data at a very disaggregated product-country level they studied how the structure of the PS
affects countries’ patterns of diversification and specialization, in particular, they found that
product diversification depends considerably on the capabilities already present in that country,
meaning that countries move through the PS by developing goods close to those they currently
produce.
At the firm level, results show that firms tend to follow coherent patterns of diversification;
within the innovation literature, Jaffe (1986) and Breschi et al. (2003) found that firms’ tend to
diversify into groups of activities that share a common or complementary knowledge base. Yip
(1982) studied firms’ choices between internal development and acquisition and found that the
likelihood of entry into new markets increases as those markets are more related to firms’ own
characteristics. MacDonald (1985) analyzed patterns of diversification within U.S.
manufacturing firms, finding they were more likely to enter rapidly growing industries, and
industries that were related to their primary activities through supply relationships or marketing
similarities. Additionally, Teece et al. (1994) showed U.S. manufacturing firms maintain certain
level of coherence while diversifying to neighboring activities1.
Most of the literature studying processes of diversification at firm level has concentrated in
product diversification; however, the relationship between product diversification and firm
technological diversification is not an unambiguous one. It could be possible for a firm to
diversify its productive activity by taking advantage of its own technological competences,
maybe at the expense of needing additional investments in their marketing or organizational
structure, but without incurring in a process of technological learning. The reverse argument is
also valid. This study is concerned with the process of technological diversification; the
interaction between product and technological diversification falls outside the scope of this
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!
1 Studies focusing on industrial evolution of regions have found that they tend to diversify into
!
paper and will not be discussed here. In what follows, we revise the innovation literature
regarding firms’ incentives for technological diversification and incorporate the effect of
‘relatedness’ in this process.
The innovation literature has identified several factors affecting firms’ incentives to diversify
technologically:
a) Inter-industry differences in Technological Opportunities (TOs), i.e.
industry differences in “the productivity of R&D” (Jaffe 1986, Klevorick et
al. 1995, Laursen 1999, and Malerba 2002 and 2004)
b) Dynamism of demand conditions (Schmookler 1966)
c) The effect of risk and volatility (Koren and Tenreyro 2007 and 2013)
d) Entry Barriers, as described in Perez and Soete (1988).
The categories outlined above group different arguments related to firms’ incentives for
technological diversification. They include cases where diversification may be triggered by
firms’ attempts to move to more ‘profitable’ positions, either motivated by differentials in the
rates of return to R&D investment, or by growing demand conditions. Additional factors
include efforts to mitigate the impact of shocks affecting productivity, and easiness of access
due to lower requirements of initial investments, and technological or scientific knowledge. All
these incentives affecting firms’ decision to diversify can, in principle, be studied without
referring to the role of ‘relatedness’ among technologies. However, and unlike other types of
diversification, which can be accomplished without engaging into an active process of learning
and capability accumulation 2 , firm’s possibilities for technological diversification depend
heavily on their capabilities to acquire, accumulate, and process the knowledge required to do
so. This characteristic of the process of technological diversification that mainly motivates our
arguments regarding the importance of the role of ‘relatedness’. We identify two channels
through which ‘relatedness’ may affects/constraints firms’ possibilities for technological
diversification:
a) Economies of scope in the use of knowledge
b) Firms’ absorptive capacity.
The first argument relates to the fact that firms have economies of scope in the ‘use of one
piece of knowledge’ (Penrose 1959, Teece 1982), meaning that the same type of knowledge
could be used as an input in multiple technological fields. Therefore, the more related two
technological fields, the bigger the share of common heuristics and scientific principles they
rely on, and consequently, the bigger the possibilities to take advantage of the already acquired
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!
2 For instance, a firm that is not exporting a product it already produces can start doing it if macroeconomic conditions change favorably. This firm may therefore diversify its exporting capacity without any need to incorporate scientific knowledge.
knowledge. Going back to our previous characterization, economies of scope will affect
barriers to entry by reducing investment costs, and the costs of acquiring the scientific and
technological knowledge required to assimilate and carry out the innovation.
The second channel we identified as a factor explaining the importance of ‘relatedness’ for
technological diversification has to do with firms’ absorptive capacity (Cohen and Levinthal
1989). According to this view, prior knowledge confers the ability to recognize the value of
new information, assimilate it, and exploit it to commercial ends. This implies that firms’
absorptive capacity facilitates the incorporation of new knowledge and the possibility for its
commercial exploitation, it confers the firm with the ability to better understand the challenges
and opportunities lying before it, to form more accurate expectations, and to evaluate benefits
and costs associated with its introduction. Therefore, firms’ absorptive capacity directly affects
its perception of the technological opportunities a given technology may offer, it may have also
an impact on firms’ ability to form accurate expectations about its demand, and the risks
associated with an attempt to diversify into it. Therefore, the more related a technology is with
firms’ absorptive capacity, the more likely it will accurately assess the benefits and costs
associated with its introduction.
The innovative process is not the outcome of an individual process of learning and capability
accumulation, it is placed and determined within a larger system that supports and benefits
from it. It is not completely understood, however, how firms’ possibilities for technological
diversification may be affected in situations where the environment they operate in does not
provide the necessary support (i.e. the basic knowledge base that may enable participation in
key industries, stable macroeconomic conditions required for long run planification, and where
the networks of actors mediating knowledge flows is not well connected). It is reasonable to
think that the effect of ‘relatedness’ may be even more important within scattered, not well-
developed systems; implying that the likelihood a firm, foreign or local, will successfully
diversify into a particular technological activity is highly influenced by the national context.
This paper aims at investigating countries’ patterns of accumulation and diversification of
innovative capabilities. We complement previous innovation studies of this sort, Archibugi and
Pianta (1991), Cantwell and Vertova (2004), and Vertova (2001), by providing a broader and
more comprehensive approach; which documents concentration patterns in patenting activity
for 175 countries and covering a period of 30 years (1978-2007). These results are obtained
using the same methodology as in Ims & Wacziarg (2003) and therefore are directly
comparable to the patterns found for productive and exporting capacity. Additionally, we
analyze countries’ patterns of technological specialization complementing the evidence found
by Vertova (2001), Lall (2000), Haussman et al. (2006), Hidalgo et al. (2007) and Hidalgo and
Haussman (2009 and 2011) regarding countries’ patenting and exporting specialization
patterns. We do this using disaggregated data on patenting activity by type of technology, in
order to test how countries’ indigenous competences and characteristics of technologies, such
as their ‘complexity’ and ‘relatedness’ among them, drive or constraint possible paths of
technological development. We propose an econometric model which stresses the differences
between diversifying and specializing, in order to reflect the idea that whereas in developed
economies the process of technological upgrading looks more like maintaining or improving
an already established level of competitiveness and growth, in developing countries the main
concern relates to the ability to expand its competences and “catching-up”; where ‘relatedness’
among technologies plays a fundamental role determining the feasibility of this process.
In the next section we describe the data used in the analysis, define the variables of interest,
and develop the econometric model. We pay particular attention to the fact that factors
influencing the decision to participate in a certain type of technology may be different than
those affecting the decision to specialize on it; we also deal with issues related to self-selection
in our sample.
!
3. Data and Methods
We start this section by describing the data sources and variables we will use through the
analysis. Later, we explain our methodological approach, aimed at characterizing countries’
patterns of specialization and diversification.
3.1 Data Sources and Variables
We rely on multiple sources of information. At the country level, we use the World Bank’s
World Development Indicators (WDI) database; which provides a diverse collection of
development indicators compiled from officially-recognized international sources.
Additionally, data on bilateral trade flows and distances were obtained from the BACI database
developed by the CEPII, while levels of IPR protection were taken from Park (2008). At the
technology level we use information from two sources: The USPTO for patenting activity;
and the Annual Survey of Manufactures (ASM) from the US census bureau for industry data.
We start this subsection by describing in detail how the variables were constructed, and finish
with a statistical description of them.
Evaluating countries’ technological trajectories requires being able to track and quantify
countries’ technological capabilities and their changes over time. We measure patterns of
specialization by computing countries’ Revealed Technological Advantages (RTA) for each
technological class (see Soete 1980, 1987). In particular,
!!"# ! !"#!"# !!"#$%#&!"# !"#$%#&!"#!
!"#$%#&!"#! !"#$%#&!"#!"
Where c stands for country, j for technological class, and t for the time period (three years
intervals). We assign the nationality of a patent by looking at inventors’ addresses; and consider
a patent to be part of country c portfolio of competences whenever an inventor resides there.
Therefore, this index provides information on countries’ patterns of technological
specialization by comparing the share each technology represents in countries’ own profile of
patenting activity, relative the world average. A value above unity indicates that country c has a
relatively high specialization pattern for a particular technological class when compared to the
world average. Additionally, note that this measure implicitly defines countries’ participation in
the US technological market, as specialization patterns can only be calculated for those
countries participating in that market. We come back to this issue in the next subsection when
we discuss the methodology.
Table 1 below provides a short description of all country level variables. These variables aim at
capturing country specific effects influencing patterns of participation and specialization.
Distance to the US market, as well as language barriers, and differences in purchasing power
directly shift up barriers to entry. Additionally, countries’ outward orientation and the
importance of the US market as a commercial partner may increase the likelihood of
participation in the US ‘technological market’, the former by reflecting the need of
technological upgrading to compete in international markets, while the later to capture the
effect that strong trade relationships have on patenting activity. Also, changes in IPR regimes
may change incentives to patent both locally and abroad. Lastly, population and GDP per
capita aim at capturing size effects, and remaining factors that could be related to countries’
levels of development.
Table 1: Country-level Variables
Variable Name Description Source
Distance Thousands of km to US (using capital cities) CEPII
Language Whether or not English is an official language CEPII
Ex. Rate --------------------------------- WDI
Population Population in millions WDI
GDP per capita GDP per capita in 2005 US dollars WDI
Outward Orientation Share of total exports to GDP WDI
Trade Share of exports oriented to the US market BACI
IPR Index Index of IPR protection Park (2008)
We move now to the construction of the technology-level variables, which requires the
introduction of additional concepts. We start by building the so called Technological Space
(TS), which entails defining ‘relatedness’ among technologies, and later use it to build a set of
variables aimed at characterizing different aspects of technologies. We also propose an
additional set of variables using patenting activity and information of industries technologies
contribute to.
The concept of TS was first addressed empirically by Jaffe (1986, 1989), where he calculated
relatedness among two given technologies by looking at how often they were used in
combination with a third technology. In a similar manner, we construct the TS by following
the “product space” (PS) framework developed by Hidalgo et al. (2007). In this case, the TS
can be seen as a network-based representation of the technological production, where nodes
define technologies and the ties among them indicate their degree of relatedness (see also
Rigby 2012; Boschma et al. 2013; and Boschma et al. 2014). We identify 437 technologies using
the USPTO patent classification and measure relatedness by counting co-occurrences of
technologies (or classes) among patents. In particular, the degree of relatedness between
technology ! and ! is measured as follows:
!!" !!!"
!!!!
Where !!" counts the co-ocurrences of technologies ! and !, and !! and !! !count the number
of ocurrences (size) of technologies. This is often referred to as the cosine similarity measure
and it has been widely applied in recent work (see Eck and Waltman 2009 for a detailed
analysis). Therefore the more often two technological classes appear together within the same
patent, the more related those technologies are, after controlling for the effect of size.
We use then the TS to measure two particular aspects of technologies, their sophistication and
complexity. Using the PS framework, it has been shown that more-sophisticated products are
generally located in the densely connected core of the PS, whereas less sophisticated occupy
the periphery (Hidalgo et al. 2007). We therefore include the closeness centrality measure of
each technology, which is defined as the inverse of the average length of the shortest paths
to/from all the other vertices in the graph (Freeman 1979). Then for technology ! and distance
measured as the shortest path between nodes, the centrality measure can be computed as:
!! ! !!!! !!!
!!
Additionally, Hidalgo and Haussman (2009) showed that it is possible to quantify the
complexity of products by characterizing the structure of the TS. The main idea is that, by
treating the PS as a bipartite network in which countries are connected to the products they
produce; complex economic structures can be characterized as those producing a wider range
of exclusive products (i.e. non ubiquitous, produced by very few countries). A country with a
complex technological structure will not only produce knowledge in many different
technological classes, but they will do so in technologies requiring capabilities found only in a
handful of countries. Therefore, the construction of an Index of Technological Complexity
(ITC) requires combining information on both, the 2-mode degree distribution of a country
(diversity) and the 2-mode degree distribution of the technologies it produces (ubiquity). We
follow their ‘method of reflections’ and iteratively calculate:
!!!! !!
!!!!!!"!!!!!!!
!!!! !!
!!!!!!"!!!!!!!
The matrix !!" takes value 1 if country c is a significant producer of technology j, and zero
otherwise. We consider country c to be a significant producer of technology j if its RTA>1.
!!!! and !!!! measure levels of diversification of a country (the number of technologies
produced by that country), and the ubiquity of a technology (the number of countries
producing that technology). Each additional step incorporates feedback effects and produces
more precise estimations of the knowledge complexity of countries by using information on
the complexity of technologies they produce. By the same token, !!!!!estimates the knowledge
complexity of a technology using information on the complexity of countries that produce this
technology. For a detailed description of the procedure and the properties of the indicator see
Hidalgo and Haussman (2009).
We also include three more variables, the size and the Herfindhal concentration index of each
technological class, and the weighted average of the value added of industries the technological
class contributes to. The first variable aims mainly at controlling for size effects. Even tough
the level of patenting activity has been used to measure technological opportunities, as in
Laursen (1999); here we don’t go beyond any interpretation other than capturing differences in
the propensity of patenting activity among technological classes. As it is customary, we use the
Herfindhal index as an indicator of the amount of competition among countries within a
particular technological domain.
Along the introduction we aimed at disentangling whether profitability of technological
domains affect diversification and specialization patterns. We capture this effect by looking at
the value added of industries technological classes contribute to. Note that the USPTO
provides a concordance linking technological classes to standard industrial classifications;
which can be used to match technological classes with characteristics of the industries. We use
this concordance to generate a weighted average of the value added technologies contribute to
using information on value added by industry according to the ASM, more specifically:
!"! ! !!"
!
!"!
Where i indexes technological classes and s industrial sectors. !!" is a weighting matrix which
assigns a weight proportional to the amount of technological subclasses within class i
contributing to industry s according to the USPTO concordance. Table 2 below summarizes all
the tech-level variables.
Table 2: Technology-level Variables
Variable Name Description Source
Value Added Value added of industries technologies contribute to (in billions of dollars) ASM
Centrality Closeness centrality measure of the technology USPTO
Size Number of patents within the technological class (in logs) USPTO
Herfindhal Index Herfindhal concentration index of patenting activity USPTO
ITC Index of Technological Complexity (as in Hidalgo et al. 2009) USPTO
Table 3 below shows descriptive statistics of the aforementioned variables. After combining all
different sources of information we ended up we a sample of 113 countries covering a 15 years
period, from 1993 to 2007. The main limiting factor was the availability of industry data. Three
points are worth highlighting, first note that from the whole sample of countries we have
information of, 69% of them participated at least once in the US technological market while
only 50% did it recurrently for all periods considered. Additionally, note that for our measure
of specialization the presence of outliers may be an issue, as it has a very uneven distribution
(MENTION DE TRANSFORMATION). Also note that our ITC appears to be highly
correlated with the size of the technological classes. All these issues will be addressed during
the discussion of the results, in fact, we will work with a corrected measure of specialization to
avoid our results be driven by the presence of outliers.
Table 3: Main Descriptive Statistics
Mean SD Min Max Specialization 3.38 27.89 0.01 4494
Country Level Variables
Mean SD Min Max Mean SD Min Max
Distance 8.50 3.52 0.54 16.8 GDP per cap 10286 15148 92.31 84072 Language 0.30 0.46 0 1 Outward O. Ex. Rate Trade 0.17 0.20 0 0.91 Population 48.48 156.5 0.1 1310 IPR Index 3.06 0.94 0.54 4.67
Technology Level Variables
Mean SD Min Max Correlation Table
Value Added 82.36 41.61 5.45 190.9 1.000 Centrality 0 1 -4.6 3.68 -0.141 1.000 Log Size 5.48 1.68 0 9.52 -0.256 0.189 1.000 Herfindahl 0.36 0.13 0.11 0.92 -0.100 -0.083 -0.092 1.000 ITC 0 1 -6.1 2.32 0.349 -0.163 -0.718 -0.092 1.000
Number of Countries in the Sample: 113 Participation Rate: 69% at least once, 50% for all periods Coverage: 1993-2007 (5 intervals of 3 years each)
!
3.2 Methodology
This paper is concerned with the study of technological diversification and specialization
patterns at the country level. In particular, we are interested in understanding the role of
‘relatedness’ in the diversification process, and characterizing specialization patterns along
stages of development by looking at characteristics of technologies. Note, however, that
specialization patterns are only seen for countries that participate in the US technological
market; meaning that countries having capabilities in some particular technological field may
decide not to patent at the USPTO even though they may have the required competences to
do so. This could affect observed specialization patterns by underrepresenting patenting
activity of certain countries; for instance, distant and non-English speaking countries may face
higher patenting costs due to higher translation and monitoring costs. This means that the US
‘technological market’ may not be an unbiased sample of the world production of knowledge
and its distribution over countries. Additionally, the existence of unobserved factors such as
initial investment costs of entering into new technologies may bias the observed patterns of
specialization, even if they are uncorrelated with our technology-level variables; as they are
surely correlated with the likelihood of participation. Therefore, we would like to control for
country and technology specific effects while analyzing patterns of specialization, in order to
correct for potential biases due to factors (observed or not) affecting the participation decision.
Unfortunately, there are no possible assumptions under which one can allow for unobserved
effects in the selection equation as in linear panel data models. Adding the Inverse Mills Ratio
in the selection equation (in this case the specialization equation) and using FE does not
produce consistent estimators. Wooldridge (1995) proposes a sample selection panel data
model where the unobserved individual effect can be arbitrarily correlated with the observed
explanatory variables. Its Mundlak-like version requires assuming that the unobserved effect
has a conditional normal distribution with constant variance and linear expectation that
depends on the averaged values, over time, of the observed variables. Additionally, we are
required to assume normality on the error in the participation equation although it can display
serial correlation and unconditional heteroskedasticity; idiosyncratic errors in the specialization
equation do not require distributional assumptions (see Wooldridge 2002, pages 487, 540 and
582 for further details, also Wooldridge 1995 provides details on how to correctly estimate the
variance). We propose to estimate a sample selection panel data model as in Wooldridge
(1995), consider the following model:
!!!"!! !! ! !!! !!"
!! !!!!"
!
! ! !"#!" ! !!!!!
! ! !!"#!
!!"#!! !! ! !!!"#!! ! !!!!"#!!
!
! ! !!!!!
! ! !!!!!
! ! !!!"! ! !!"#!
!!"#!! !!"# if !!"# ! !
!!"# ! !!!!"#!! !!
Where !!"# and !!"# represent specialization and participation patterns for country c and
technology j at period t, 1[.] denotes the indicator function, !!"! for k:1…5, and !!
! for r:1…8
contain all technology and country-level variables respectively; GDP stands for GDP per
capita, !!"# contains a set of dummy variables indicating the distance to the nearest
competence in the previous period (calculated as the shortest path length), and !!"#!! and !!"#
!
are error terms. In our model, the unobserved country and technology effects are captured by
!! and !! .
In the specialization equation we focus on how characteristics of the technologies, are
associated with specialization patterns along the development process by interacting them with
GDP levels; in this equation we control for country specific effects by including the averaged
values over time of all country level variables. In the participation equation we focus on the
effect of ‘relatedness’ and path dependence by creating a set of dummies identifying the most
proximate technological class in which countries participated in the previous period. We
introduce !!"#!! to capture whether countries already had competences in that technological
domain, while !!"#!!! !!!"#!!
! !!!"#!!! !!!"#!!
!" are dummies taking value one if countries
showed patenting activity in the previous period at a distance of one, two, three path lengths in
the TS, or no participation at all, respectively. Note that these dummies are mutually exclusive
and collectively exhaustive, !!"#!! trivially represents a distance of zero and identifies instances
of no diversification, while all the other remaining dummies capture diversification events. The
base category represents a distance higher than three. Table four below shows their
distribution.
Table 3: Nearest Activity (Distribution)
Participated (t-1) 1 2 3 Rest Didn’t Participate (t-1)
Participated (t) 80.1 11.5 3.6 1.6 3.2 0.5
Didn’t Participate (t) 5.1 9.5 6.4 4.8 24.0 50.2
The inclusion of !!"#!!!raises the issue of how we treat the initial observation. In that respect
we follow Heckman (1981) and assume that !!"! also follows a probit model. This approach
requires to condition on !!"!! as well, see Wooldridge (2002 page 493) for a detailed
description. As stressed above, this model requires assuming several non-trivial distributional
assumptions; not only on the error terms of the participation and specialization equations but
in the way the individual effects are modeled. We test the robustness of our results by
including a Linear Probability Model (LPM) of the participation equation that also includes
country and technology FE.
4. Results
This section documents the results of the paper. First, we look at aggregated patterns of
diversification in patenting activity to later present and discuss the results of our econometric
model.
Figure 1 below shows countries’ concentration of patenting activity at the USPTO. We plot a
non-parametric (lowess) regression of the concentration of patenting activity (using the Gini
coefficient) and GDP per capita, covering a period of 30 years (1978-2007) for total of 175
countries and 437 technological classes.3 We follow Imbs and Wacziarg (2003) methodology,
which entails estimating a local FE linear regression at each interval of the lowess regression.
As previously mentioned, we find an U-shaped relationship between GDP per capita and the
concentration of patenting activity. Even tough the pattern is remarkably similar to that found
for production and exporting capacity, there are some differences worth pointing out: First,
the variance of the estimation seems to be higher, reflecting the more irregular behavior of
patenting activity. Second, the turning point, which was found to be around the income of
Ireland in 1992 in previous estimations, appears at a later stage in the development process.
Note, however, that shifts in the estimated value of the concentration measure come from two
sources, either from changes in the average intercept or the slope of the FE regression at each
interval. As we move to the right and incorporate different countries, country intercepts shift
the estimated value up or down (between variation) while the slope of the coefficient gives us
the direction they are following, on average (within variation). Then, inspection of Figure 1.1,
which captures the compound effect, shows a turning point appearing at a later stage of the
development process. However, this has to do primarily with changes in the average intercept;
figure 1.2 shows that when it comes to measuring the average direction followed by countries,
the re-concentration of patenting activity starts around the income of Ireland in 1992.
Figure 1: Concentration of Patenting Activity and Stages of Development
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!
3 As we are only looking at patenting activity, we can analyze a broader period than described in section three.
Our previous result point towards the importance of technological diversification and the need
for a better understanding of its determinants. In what follows we start by describing how
countries and technology characteristics affect the likelihood of participation, to later focus on
our main variable of interest and discuss the role of relatedness and countries’ existing
competences on the likelihood of diversification. We finish by analyzing patterns of
specialization.
Table four below shows the results of our estimations for the participation equation, including
a robustness check aimed at testing whether our results may have been driven by the
restrictions we imposed with the baseline model (i.e. the impossibility of appropriately control
for country and tech effects and the distributional assumptions). All country-level variables are
highly significant and in line with what we could expect; the likelihood of participation in any
given technology is higher for countries having sounder IPR regimes, stronger currencies, and
an export profile more oriented to the US market. Additionally, this likelihood is higher the
more populated and developed the country. After controlling for the share of exports to the
US market, outward orientation of production has a negative impact. The effect of distance is
positive mainly due to the fact that LACs are closer while European and Asian, which have
higher participation, are far away. Additionally, sharing language impacts negatively given the
substantial proportion of developing countries, in number, having English as an official
language. With respect to technology-level variables, we find it more likely to see countries
participating in technologies with a lower degree of concentration, bigger size, less complexity,
and lower centrality. The value added of the industries technologies contribute to doesn’t have
a significant impact.
Results regarding the role of ‘relatedness’ and countries indigenous competences confirm our
hypothesis. The likelihood a country will diversify into a new technological activity is higher
the closer that technology is with respect to the profile of existing competences in that
country. Figure 2.1 below shows this result for two countries, Netherlands and Brazil and for a
representative technology4, where the effect of ‘relatedness’ is plotted against the likelihood of
diversification. This figure has two purposes: First, to graphically assess the importance of
relatedness in the process of technological diversification; note that the likelihood of
diversification can fall as much as 10% when trying to diversify into a technology that is two
steps away from existing competences. Second, to exemplify how this effect can be even more
restrictive for developing countries. Consider the case of Brazil, which can be seen as one of
the countries with more possibilities for technological diversification among developing
economies; note that even if the distance effect decays stronger for The Netherlands, a
decrease of around 10% in the likelihood of diversification associated with trying to reach a
technology two steps away represents a fall on the likelihood of success of about 20 % (from
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!4 Technology aattributes were set to their average values.
Table 4: Results of the Participation Equation
Baseline Model
(Wooldridge 1995)
Baseline Model
(interacted with GDP)
Robustness Check
(LPM)
Relatedness Measures
Intercept -5.983*** (0.078)
-6.019*** (0.077)
!!"! 0.699*** (0.015)
0.708*** (0.015)
0.183*** (0.0098)
!!"#!! 1.49*** (0.020)
1.42*** (0.020)
0.291*** (0.014)
!!"#!!! 0.723***
(0.019)
!!"#!!! 0.442***
(0.023)
!!"#!!! 0.274***
(0.028)
!!"#!!!" -0.65***
(0.031) -0.621***
(0.031) -0.123***
(0.018)
!!"#!!!!! 0.641***
(0.02) 0.022*** (0.005)
!!"#!!!!!
!* GDP per capita -0.006*** (0.001)
-0.0014* (0.0005)
Tech-Level Variables
Value Added 0.0003* (0.0001)
0.0003* (0.0001)
Centrality -0.040*** (0.0057)
-0.0397*** (0.0057)
Log Size 0.236*** (0.0069)
0.224*** (0.0069)
Herfindahl -0.38*** (0.049)
-0.38*** (0.049)
ITC -0.126*** (0.010)
-0.141*** (0.011)
Country-Level Variables
Distance 0.063*** (0.002)
0.065*** (0.002)
Language -0.113*** (0.013)
-0.113*** (0.013)
Ex. Rate XXX*** (XXX)
XXX*** (XXX)
Population 0.001*** (0.000)
0.001*** (0.000)
GDP per capita 0.009*** (0.0005)
0.013*** (0.0006)
Outward Orientation -0.001*** (0.0001)
-0.001*** (0.0001)
Trade 0.852*** (0.035)
0.876*** (0.035)
IPR Index 0.700*** (0.013)
0.711*** (0.013)
Country Fixed Effects No No Yes Tech Fixed Effects No No Yes Time Fixed Effects Yes Yes Yes Cluster S.E. No No Yes (Country and Tech) AIC 71004
50 to 40 percent), while it represents a fall of almost 50% for Brazil (from around 20 to 10
percent). Additionally, The Netherlands never reaches the starting point of Brazil, meaning
that its likelihood of diversification in distant technologies is higher than the one experienced
for Brazil in related technologies.
Therefore Figure 2.1 shows that even if the rate of decay is constant across countries, the
likelihood of diversification for developing economies may reach values near zero sooner, as
the likelihood of diversification for developing economies is ‘generally’ lower for every value of
distance we consider, due to country specific effects. As we mentioned in the introduction, it is
also possible that the rate of decay is steeper at early stages of development, with ‘relatedness’
having a higher impact at early stages. We test this hypothesis in a new regression by
interacting countries’ GDP per capita with a variable measuring proximity of the new
technology, and leaving other things equal. We divide possible diversification opportunities in
‘related’ and ‘not related’, where we consider a technology being related if
!!"#!!!
!!!"#!!!
! !"!!!"#!!! take value one, and zero otherwise. Note that this regression is then
analogous to our baseline model but with the set of dummies aggregated in order to reduce the
number of interactions. Results are reported in the second column of table four. Table four
above and figure 2.2 below show that the likelihood of diversification is higher for related
technologies but its importance decreases as countries develop, meaning that at later stages of
development the likelihood of diversification do not differ significantly between ‘related’ and
‘unrelated’ technologies.
Figure 2: Role of Relatedness on the Likelihood of Diversification
Therefore we showed that the likelihood a country will diversify into new technological
activities depends significantly on countries’ existing competences as well as characteristics of
the technologies in question. In particular, we showed that the likelihood a country will
diversify into a new innovative activity is higher for those activities that are ‘related’ or ‘close’
to its existing profile of competences. All these effects are stronger at early stages of
development, meaning that the likelihood a country will diversify into new innovative activities
is heavily constrained by its indigenous capabilities and its NSI, where path dependency is a
key element of this process. Column three of table four shows an analogous LPM aimed at
checking the robustness of our results by controlling for country and technology FE, no
significant differences can be found.
Now we turn to the last part of this section, the analysis of countries’ patterns of specialization.
We look at different characteristics of technologies, such as their complexity and their position
in the technological space, in order to evaluate whether different features of the technologies
can be associated with different stages of the development process. This analysis is motivated
by the fact that, in addition to the ‘consensus’ regarding the fundamental role of diversification
for economic development; scholars have also, almost unambiguously, emphasized the need to
engage in the production of more ‘sophisticated’ and ‘complex’ goods and technologies to
foster development and experience higher growth (Rodrik 2006, Lall 2000, Haussman et al.
2006, Cimoli et al. 2007, Hidalgo et al. 2007, Hidalgo and Haussman 2009, and De Felipe
2012). Before, we found that the likelihood of participation is higher for technologies having
lower levels of concentration, bigger size, less complexity, and lower centrality. When it comes
to the evaluation of specialization patterns, we see that countries tend to move towards more
‘profitable’ and ‘dynamic’ technologies. We find, as reported in table 5 and figure 3 below, that
at later stages of the development process countries’ specialize in bigger and more complex
technologies, with higher value added and lower degree of concentration. The position in the
technological space seems not to be correlated with stages of development after other
characteristics have been controlled for, as the coefficient of the centrality measure cannot be
said to be significantly different from zero.
Table 5: Results of the Specialization Equation
Baseline Model
(Wooldridge 1995)
Tech-Level Variables
Intercept 5.906*** (0.1305)
Value Added -0.0005* (0.0003)
Centrality -0.0826*** (0.0097)
Log Size 0.4057*** (0.0130)
Herfindahl -0.5736*** (0.0935)
ITC -0.1952*** (0.0150)
Value Added * GDP pc 0.00004*** (0.00001)
Centrality * GDP pc -0.00023*** (0.00032)
Log Size * GDP pc 0.0060*** (0.0003)
Herfindahl * GDP pc -0.3024*** (0.0029)
ITC * GDP pc 0.0033*** (0.0005)
Country-Level Variables
Distance -0.057*** (0.0019)
Language 0.042*** (0.012)
Ex. Rate XXX*** (XXX)
Population -0.0007*** (0.00002)
GDP per capita -0.035*** (0.0026)
Outward Orientation -0.00016* (0.00008)
Trade -0.59*** (0.033)
IPR Index -0.59*** (0.033)
IMR 0.531***
(0.01) Country Fixed Effects No Tech Fixed Effects No Time Fixed Effects Yes Cluster S.E. No
Figure 3: Countries’ Patterns of Technological Specialization
These results are in line with what has been found and suggested previously within the
innovation, trade, and development literature, i.e. that well performing countries generally
posses a distribution of their production and exporting activities biased toward the production
of more ‘sophisticated’ and ‘complex’ goods and technologies.
!
5. Concluding Remarks
In this paper we investigated countries’ patterns of accumulation and diversification of
innovative capabilities as reflected by their patenting activity at the USPTO. At an aggregated
level of analysis, we looked at concentration patterns in patenting activity for 175 countries and
over a period of 30 years (1978-2007), and showed there exists a process of diversification of
countries’ innovative capabilities with a remarkable similar pattern to that found for domestic
production and exporting capacity. We found an U-shaped relationship between GDP per
capita and the concentration of patenting activity. Additionally, and using disaggregated data
on patenting activity by type of technology, we tested how countries’ indigenous competences
and characteristics of technologies, such as their ‘complexity’ and ‘relatedness’ among them,
drive or constraint possible paths of technological development.
We found that, whereas in developed economies the process of technological upgrading looks
more like maintaining or improving an already established level of competitiveness and
growth, in developing countries the main concern relates to the ability to expand its
competences and ‘catch-up’; where ‘relatedness’ among technologies plays a fundamental role
determining the feasibility of this process. This suggests that any policy strategy aiming at
diversifying countries’ technological capabilities shouldn’t be conceived disregarding the role of
indigenous capabilities and the initial pattern of comparative advantages.
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