On Innovation and Exports
- Evidence Using the Gravity Equation Approach
Author:
Peter Lange
MSc. International Economic Consulting
Academic Supervisor:
Philipp Schröder
Department of Economics
Aarhus School of Business
University of Aarhus
Master’s thesis
Aarhus School of Business, Aarhus University
January
2009
Abstract
The developing countries of the world are increasingly becoming “the world’s factory”,
leading to a movement of low-skilled production from the developed countries to the
developing countries. As a response, the developed countries are increasingly becoming
knowledge-based economies and have directed their focus towards research and
development (R&D) and innovation, in an attempt to maintain a high level of income
and growth. As a result of this, researchers have investigated the link between
innovation and trade and developed two models; the exogenous product-cycle models
and the endogenous product-cycle models. The former being international trade models
with product-cycle features in the production of goods over time predicting that
innovation influences exports through a causal effect, treating innovation as an
exogenous variable. The latter being trade models with product-cycle features, where
the rate of innovation is endogenized, and the models predict dynamic effects of
international trade on innovative activity, a so-called “learning-by-exporting” effect.
This thesis contributes to the literature by proving the exogenous product-cycle model.
This is done by applying an augmented version of the gravity equation, including a
variable for innovation, on a country-level dataset containing more than 17.000
observations on the trade flows between 36 countries, as well as on a sector-level
dataset, containing more than 3.000 observations on the export flows from 12 sectors in
the US towards 35 countries. The approach takes recent econometric estimation issues
into consideration, and controls for the aspect of reverse causality. The results confirm
the predictions by the exogenous product-cycle models; innovation causes exports, but
specifies that it is the innovation-output that is the direct driver of exports.
KEYWORDS: Innovation, R&D, Patents, Exports, Gravity Equation, Product-cycle
models
Table of contents
1. Introduction ............................................................................................................................................ 1
1.1 PROBLEM STATEMENT......................................................................................................................... 2
1.2 METHODOLOGY .................................................................................................................................. 2
1.3 DELIMITATION .................................................................................................................................... 3
1.4 STRUCTURE......................................................................................................................................... 3
2. Innovation ............................................................................................................................................... 5
2.1 THE TERM “INNOVATION” ................................................................................................................... 5
2.2 WHY DO FIRMS INNOVATE?................................................................................................................. 7
3. Models of trade ....................................................................................................................................... 9
3.1 A BRIEF OVERVIEW OF THE DEVELOPMENT OF TRADE MODELS ........................................................... 9
3.2 DIVERGING MODELS OF INNOVATION AND EXPORTS ......................................................................... 13
3.2.1 Product-cycle models................................................................................................................ 13
3.2.2 An exogenous product-cycle model .......................................................................................... 14
3.2.3 An endogenous product-cycle model ........................................................................................ 19
4. Empirical evidence ............................................................................................................................... 28
5. The gravity equation ............................................................................................................................ 35
5.1 THE EVOLUTION AND FOUNDATION OF THE GRAVITY EQUATION ...................................................... 35
5.2 RECENT ESTIMATION ISSUES ............................................................................................................. 36
6. Analysis ................................................................................................................................................. 40
6.1 MODEL SPECIFICATION AND DATA .................................................................................................... 40
6.2 DESCRIPTIVE STATISTICS .................................................................................................................. 44
6.3 RESULTS AT THE COUNTRY-LEVEL .................................................................................................... 48
6.4 ANALYSIS WITH TWO INNOVATION VARIABLES ................................................................................. 53
6.5 SECTOR-LEVEL ANALYSIS ................................................................................................................. 56
6.5.1 Data .......................................................................................................................................... 56
6.5.2 Methodology ............................................................................................................................. 57
6.5.3 Descriptive statistics ................................................................................................................. 57
6.5.4 Results at the sector-level ......................................................................................................... 58
6.6 CONCLUSION ON THE DATA ANALYSES ............................................................................................. 60
7. Conclusion ............................................................................................................................................. 62
List of references ...................................................................................................................................... 64
Appendix ................................................................................................................................................... 73
List of tables
Table 6.1 Summation of the variables of interest ....................................................................................... 45
Table 6.2 OLS estimations ......................................................................................................................... 48
Table 6.3 FE estimations with R&D and EPO scaled patent applications ................................................. 50
Table 6.4 FE estimations with EPO not-scaled patent applications and USPTO patents granted .............. 51
Table 6.5 FE estimations with R&D and EPO scaled patent applications ................................................. 53
Table 6.6 FE estimations with R&D and EPO not-scaled patent applications ........................................... 53
Table 6.7 FE estimations with R&D and USPTO patents granted ............................................................. 54
Table 6.8 Summation of the variables of interest ....................................................................................... 57
Table 6.9 FE estimations using sector-level data ....................................................................................... 59
List of figures
Figure 3.1 Demand for labor in north ......................................................................................................... 15
Figure 3.2 Capital and innovation .............................................................................................................. 18
Figure 3.3 Innovation and imitation in the steady state .............................................................................. 25
Figure 6.1 Scatter plot of R&D and exports ............................................................................................... 46
Figure 6.2 Scatter plot of EU scaled patent applications and exports......................................................... 46
Figure 6.3 Scatter plot of EU not-scaled patent applications and exports .................................................. 47
Figure 6.4 Scatter plot of US patents granted and exports ......................................................................... 47
Figure 6.5 Scatter plot of R&D and exports ............................................................................................... 58
0
1. Introduction
In the developed countries of the world, there is a constant pursuit of a higher standard
of living and increases in the income and wealth of its citizens. One way to increase the
income of a nation is through international trade, a topic that has had immense interest
for economists over time, beginning already in 1776 with the theory of absolute
advantages developed by Adam Smith, and for many years being one of the most
researched areas within economics. Trade is acknowledged to affect a nation’s income
through many channels, e.g. specialization via comparative advantages, exploitation of
increasing returns from larger markets, exchange of ideas through communication and
travel, and the spread of technology through investment and exposure to new goods
(Frankel and Romer, 1999).
Another trend among the developed countries is the movement towards a dependence
on knowledge, information and a higher skill-level of the workforce. Thus, these
countries are turning into knowledge-based economies where knowledge is important
on all levels; individuals with higher levels of education or skill-levels have better and
higher paid jobs, firms with higher levels of knowledge do better than those with lower
levels and countries with a higher knowledge-base perform better (OECD, 1997). This
need for knowledge stems from the increasing competition from the developing
countries within low-skilled production. The developing countries are able to produce at
lower costs than their developed counterparts, with the effect of gradually moving the
low-skilled production of the world to e.g. Asia. This pattern is predicted by the
product-cycle models, of which a first-version can be found in Vernon (1966), and is
empirically proven by e.g. Kumar and Siddarthan (1994). Knowledge can be gained
through the investment in education and research and development (R&D) as well as in
other innovative activities, and the knowledge obtained through this can be in the form
of e.g. new product developments, technological change and information. Innovation is
therefore a source and an integrated part of the knowledge creating process in a country,
of which a high level in turn will lead the country to outperform its competitors (OECD,
2005).
1
Both international trade and innovation activity are therefore important drivers for the
increase in income and competitive advantage of the developed countries. As a result,
the link between innovation and trade has drawn the attention of many economists, and
created many directions of research. One particular area of interest has been to
investigate whether innovation causes exports, or if there is a “learning-by-exporting”effect, implying that it is export that leads to innovation. It is to this literature that this
thesis wishes to contribute.
1.1 Problem statement
The objective of this thesis is to answer the following question:
“Does innovation cause exports?”
As mentioned, this is a question that has been studied by many researchers, mostly
finding evidence confirming it. However, this thesis will attempt to examine this
question by using an approach that, to the author’s knowledge, has not been done
before. Furthermore, the separated effects of innovation-input and innovation-output
will be estimated and it will be discussed whether it is the resources spent on developing
innovations or the finished innovations themselves that have the higher importance.
Moreover, the analysis will be done at two levels; country- and sector-level, and control
for country/sector specific effects and attributes. Finally, the results will be discussed
with the purpose of identifying possible policy recommendations.
1.2 Methodology
The gravity equation is used in this thesis to analyze whether innovation affects trade.
The gravity equation has been used by trade economists for more than 40 years, and
explains trade between countries by regressing a number of explanatory variables on the
trade volume between them. In this thesis, an augmented version of the gravity equation
will be applied, in which innovation will be used as an explanatory variable for the trade
between a group of countries. Furthermore, a number of variations of the model will be
analyzed, by e.g. including more than one innovation variable, by controlling for reverse
causality and by using data on a less aggregated level.
2
1.3 Delimitation
Although this thesis argues that trade affects and is important for growth and thereby
the income of a country, economic growth theory as such will not be discussed. Instead,
the author acknowledges this causal link, and chooses to focus on what affects trade.
More specifically, this is done by investigating the effect of innovation on trade, as
mentioned in the introduction.
Furthermore, due to data limitations, firm-level analysis is not conducted, although it
would have been relevant in such context as it would allow the author to control for
firm heterogeneity, hence providing more accurate results.
1.4 Structure
The rest of this thesis is structured as follows:
Section 2 defines the term “innovation”, and shortly presents a discussion of why
innovations occur. This is done to obtain an understanding of how innovation is
perceived in this thesis, and to lay the foundation of the analysis in which innovation is
the key parameter of interest.
Section 3 consists of two parts. First, a brief overview of the development of trade
models over time is presented. The purpose of this is to determine where in the
literature this thesis contributes. Second, two models of innovation and trade with
diverging views are discussed in detail, one claiming that innovation leads to export,
while the other claims that there is a learning-effect from exporting in turn leading to
more innovation. It is the former which this thesis wishes to test empirically.
Section 4 discusses the existing empirical evidence on the link between innovation and
trade. The results are somewhat mixed with regards to which model is correct,
underlining the importance of new evidence.
Section 5 introduces the gravity equation, which is the model applied in the analyses of
this thesis. It shortly presents the evolution of the model and the typically applied
version. Thereafter, more recent estimation issues are discussed.
3
Section 6 consists of the analyses. First, the model specification used in this thesis is
presented, and the data as well as the variables included are discussed in detail. This is
followed by a presentation of the data where after the results of the various analyses at
the country-level as well as the sector-level are discussed.
Section 7 concludes on the findings of the thesis.
4
2. Innovation
This section presents the evolution of the term “innovation” and a short discussion of
why firms innovate. This is done to reach an understanding of what innovation is, and to
determine how innovation in this thesis is perceived as well as how it is measured in the
analyses.
2.1 The term “innovation”
One of the first to bring about the concept of innovation was the economist Joseph
Schumpeter. In 1934, he proposed a list of various types of innovation (OECD, 1997):

Introduction of a new product or a qualitative change in an existing product;

Process innovation new to an industry;

The opening of a new market;

Development of new sources of supply for raw materials or other inputs;

Changes in industrial organization.
Later, in 1939, he introduced an even wider definition of innovation as being: “Any
doing things different” (Schumpeter, 1939). More recently, the term has evolved and
been changed numerous times, depending on the purpose and/or author of the study. As
mentioned in the introduction, for many years the developed countries have sought to
increase economic growth through the support of innovation activities. This led to the
cooperation of the OECD and the EU in creating the Oslo manual, which is a manual on
the guidelines for collecting and interpreting innovation data. It includes among other
things definitions of the term innovation with the focus on a firm-level, and in the first
and second versions of 1992 and 1997, innovation was defined as a development of
“new and significantly improved technological products (goods and services) and
processes.” (OECD, 1997). In the third version of 2005, based on recent research and
studies, the manual defined four types of innovations within firms (OECD, 2005):
5

Product innovations; defined as the introduction of a good or service that is new
or significantly improved with respect to its characteristics or intended users.
This includes significant improvements in technical specifications, components
and materials, incorporated software, user friendliness or other functional
characteristics.

Process innovations; defined as the implementation of a new or significantly
improved production or delivery method. This includes significant changes in
techniques, equipment and/or software.

Organizational innovations; defined as the implementation of a new
organizational method in the firm’s business practices, workplace organization
or external relations.

Marketing innovations; defined as the implementation of a new marketing
method involving significant changes in product design or packaging, product
placement, product promotion or pricing.
Innovation is in this thesis proxied by R&D expenditure and various patent counts. The
variable for R&D expenditure of the different countries is a measure of innovationinput, and is used in the analysis in sections 6.3-6.4 and covers the official spending on
R&D by firms, as well as by governments and other donors. In the analysis in section
6.5, only the companies’ R&D expenditure is used. R&D spending as a proxy for
innovation will therefore cover product and process innovations, as R&D is expected to
lead to the creation of new products and the improvement of existing, as well as to
process innovation that improves the cost structure of the firms (Wakelin, 1998a). R&D
will to some extent also cover marketing innovations, as a number of new innovating
product designs and packaging can be expected to come from official R&D departments
or divisions. Organizational innovation is most likely not covered with R&D.
The various patent counts used in this thesis as proxies for innovation are, as opposed to
R&D, measures of innovation-output and only used in sections 6.3-6.4. Patent counts
cover product innovations and some process innovations, whereas they will not cover
any marketing or organizational innovations.
6
Overall, the R&D expenditure and the patent count variables are expected to capture
most aspects of innovation and are thus believed to be reliable proxies. A more
thorough discussion of these proxies and their pros and cons can be found in section 6.1.
2.2 Why do firms innovate?
There are many reasons as to why firms innovate and Schumpeter was one of the first to
determine that firms innovate to capture rents (OECD, 1997). If a firm e.g. invents a
new product it will give the firm a monopoly position, thereby gaining a monopoly rent.
Instead, if a firm creates a process innovation that increases productivity, it will be able
to produce at lower costs which in turn will give the firm the possibility to decrease its
price to gain market shares or to sell with a higher mark-up. Other reasons for firms to
innovate could be defensive ones, in which the firm innovates to try and catch-up with
an innovative competitor, or that the firm innovates new standards that it then tries to
enforce on the competitors, gaining a strategic market position (OECD, 1997).
There are different arguments regarding which environment will make firms innovate
the most. Schumpeter argues that more competitive environments will lead to less
innovation and that firms holding monopoly power will tend to innovate more, since
they are better able to take advantage of scale economies (Schumpeter, 1942 in Smith et
al., 2002). Arrow on the other hand argues for the opposite. He shows that firms
operating in competitive environments will have stronger incentives to innovate using
an example of a cost-reducing innovation. Arrow argues that a cost-reducing innovation
will create a monopoly rent for the firm in competitive competition which the
monopolist already has, thus making the incentives to innovate higher for the firm in the
competitive environment (Arrow, 1962).
This thesis does not investigate whether Schumpeter or Arrow is right. However, using
the gravity equation approach on country- as well as sector-level data, while controlling
for country/sector specific factors, the analysis will capture the innovation effect,
regardless of whether the innovation is created in an environment determined by a
monopolist or under more competitive conditions.
7
The subsequent section introduces a brief overview of the development of trade models
and discusses two models of innovation and trade in detail.
8
3. Models of trade
This section first presents a brief “timeline” of the evolution of trade models to
determine where this thesis contributes to the existing literature. Thereafter, it discusses
in detail two models of innovation and trade. These models lay the theoretical
framework for this thesis and represent two diverging views, namely that innovation
causes trade and that trade causes innovation.
3.1 A brief overview of the development of trade models
Economists have for many years developed and discussed theories that aim at
explaining world trade. Adam Smith already formulated his theory back in 1776, where
he argued that countries should trade the commodity with which they have an absolute
advantage, and import the commodities that can be bought cheaper than the country can
produce. David Ricardo took a different view in 1817, and introduced the theory of
comparative advantages. The theory uses differences in technology as the driving force
behind international trade flows, and argues that it is the relative costs that are important
in determining a country’s production advantage (Van Marrewijk, 2007, p. 53).
These classical models of trade did, however, have some difficulties explaining actual
trade. For example, considering only one factor of production is limiting the analysis,
and not in line with real-world production. Second, actual trade flows between
developed and less-developed countries were much smaller than predicted by the
classical trade theories. This led to the development of the neo-classical trade theories
and in 1933, Bertil Ohlin published a book with a new model of trade, developed with
Eli Heckscher. Their contribution, called the Heckscher-Ohlin (HO) model, laid the
foundation of the neo-classical trade theories and was a model between two countries
with two factors of production (capital and labor), two products and identical production
functions. Their model implied that a country will export the product which intensively
uses the abundant factor of production in the country and assumed that developed
countries were capital-abundant and that developing countries were labor-abundant.
Thus the difference from Ricardo’s theory is that the HO-model introduces a second
9
production factor and that it is the difference in factor abundance that determines what a
country will export (Van Marrewijk, 2007, p. 73ff.). From the HO-model, three main
propositions were derived, and along with the HO-model, these are generally accepted
as being the main results of the neo-classical trade theory. The first propostion was the
Stolper-Samuelson proposition, developed by Wolfgang Stolper and Paul Samuelson in
1941. Their proposition argues that an increase in the price of a good will increase the
reward to the factor of production (e.g. wages) used most intensively in producing the
good, and a reduction in the reward to the other factor of production (Feenstra, 2004,
p.13). The second proposition was the factor price equalization proposition, developed
in 1948/9, also by Paul Samuelson. The proposition states that the movement towards
international free trade of goods will lead to an equalization of the rewards to the factors
of production used. This means that, if e.g. a developed country and a developing
country start to trade freely, the higher wages in the developed country will start to fall,
while the lower wages in the developing country will start to rise, over time moving
towards equalization (Van Marrewijk, 2007, p. 73). The last proposition from the neoclassical trade theories is the Rybczynski proposition, developed by Tadeusz
Rybczynski in 1955. He argued that an increase in the supply of a factor of production
will increase the output of the product that uses this factor of production intensively,
and lead to a decline in the output of the other good (Feenstra, 2004, p. 18).
Testing the HO-model in 1953, Wassily Leontief discovered that US export production
was less capital-intensive than the import production, contradicting the model. This
“Leontief-paradox” led to alternative model specifications of the HO-model and
alternative models (Feenstra, 2004, p. 37ff.). One such model was the Linder
Hypothesis, developed in 1961 by Staffan Burenstam Linder, who argued that it is the
structure of demand that determines the volume of trade, meaning that producers in each
country produce to meet the demand of its’ own consumers, and that international trade
is a way to meet the demand of the consumers (Grimwade, 2000, p. 56).
However, the neo-classical theories of trade still did not reflect the reality perfectly, and
a new strand of theories evolved, named the “new” trade theories. These were inspired
by the empirical observation that intra-industry trade existed, meaning that countries
10
trade similar products with each other, which could not be explained by existing
theories. The “new” trade theory models are typically formulated following a
framework of monopolistic competition developed by Avinash Dixit and Joseph Stiglitz
in 1977. This framework revolutionized model building in economics, by allowing for
horizontally differentiated products and assuming a utility function with constant
elasticity of substitution (Van Marrewijk, 1997, p. 207ff.)1. The framework thus made it
possible to build models that consider intra-industry trade, and several of these
appeared. One such model is Paul Krugman’s “love-of-variety” model, in which two
symmetrical countries with respect to technology and demand, but different in size of
the labor force, open for trade with each other. With trade, the price and output stay the
same because they do not depend on market size, but the number of varieties available
in each country goes up. Hence, the only gains are through the increase in varieties
which makes the utilities of the consumers go up as they are able to substitute domestic
low-marginal-utility products with high-marginal-utility products from abroad
(Krugman, 1979a and 1980). Another approach to explain intra-industry trade is the
Lancaster model, where two countries produce the same variants (differing in quality)
and trade increases the total number of varieties but leads to fewer varieties produced in
each country, leading to intra-industry trade. The model therefore has two sources of
welfare increases; price falls, due to increasing competition and economies of scale, and
that the consumers are able to come closer to their ideal variant (Lancaster, 1979). Yet
another approach is the Ethier model, which seeks to explain the large volume of intraindustry trade in intermediate products, concluding that a larger market will lead to an
increase in the number of intermediate goods, in turn leading to efficiency gains for
final goods producers (Ethier, 1982).
The models of the “new” trade theory therefore generally assumed that firms are
identical and are all selling both domestically and in the foreign markets. The
introduction of firm-level data however, improved empirical studies. Bernard and
Jensen e.g. found that, within an industry, not all firms export, and that the exporters are
larger firms which are more productive and pay higher wages (Bernard and Jensen,
1995). This led to the development of the “new new” trade theory, where firms were
1
The reader is referred to Dixit and Stiglitz (1977) for further details.
11
now modeled as being heterogeneous within industries, accepting the empirical
observation that not all firms export. Specifically, the fact that firms face fixed sunk
costs of exporting was now being implemented into the models (Greenaway and
Kneller, 2007). The paper drawing most attention has been Melitz (2003), who build a
model with heterogeneous firms in monopolistic competition, where firms incur fixed
costs of exporting and face an exogenous draw of productivity. It is the combination of
the two that determines who remains in the market, who produces domestically and who
exports. This leads to an increase in productivity in an industry because exports will
lead to an increase in expected profits, in turn leading to more firms entering. Melitz
argues that this leads to an increase in the productivity level needed to survive, resulting
in more firms exiting due to rising productivity demands to stay, finally resulting in a
higher average productivity. Furthermore, in general the possibility of exporting will
allow the most productive firms to expand their operations while less productive firms
will be forced to decrease theirs (Melitz, 2003, in Greenaway and Kneller, 2007). Melitz
(2003) has become the widely accepted model, and is now being extended and
developed by other authors to capture and describe other particularities of intra-industry
trade2.
To summarize, the theories of international trade have developed from a focus on
differences in technology across countries, to a difference in production factor
abundance to most recently focusing on productivity differences within industries.
Some empirical researchers have focused on the causality of productivity and exports,
thus whether already productive firms export as opposed to exporting creating increased
productivity. Most evidence points toward the former, see e.g. Bernard and Wagner
(1997), Bernard and Jensen (1999) among others, and Greenaway and Kneller (2007)
for a substantial literature review on the topic. Accordingly, firms that are able to
increase their productivity stand a better chance of going into exporting. One way to
increase the productivity of a firm is through process and product innovations which as
mentioned in section 2.2 can give firms a competitive advantage. Therefore, the link
between innovation and exports has likewise caught the attention of researchers and two
diverging views of this link will be presented in the following section.
2
The reader is referred to Greenaway and Kneller (2007) for a literature review of this.
12
3.2 Diverging models of innovation and exports
In the theoretical literature, two major trends regarding the relationship between
innovation and exports have been discussed. One in which international trade models
with product-cycle features in the production of goods over time are used to predict that
innovation influences exports through a causal effect, treating innovation as an
exogenous variable. The other trend is the use of endogenous growth product-cycle
models, where the rate of innovation is endogenized, and the models predict dynamic
effects of international trade on innovative activity, a so-called pro-competitive or
“learning-by-exporting” effect. The following will present two such models.
3.2.1 Product-cycle models
The first to discuss the trend of product-cycle models was Raymond Vernon (1966). He
claimed that the US is the most likely place for new products to emerge, as it has the
largest market, the highest income for consumers, and a high cost per unit of labor,
creating incentives to produce labor-saving products3. Vernon goes on by stating that in
the early phases of product development, the entrepreneur will choose the US as the
location to produce his still unstandardized product, due to three points; a greater
flexibility in inputs, a low price elasticity of demand and the need for rapid
communication (Vernon, 1966). The greater flexibility in inputs is preferred since the
product has not yet been standardized and it is therefore important for the producer to be
able to shift between inputs. That the price elasticity of demand is low means that
consumers are not very sensitive towards the price, which again means that the producer
does not need to consider decreasing costs of production as the most important factor in
this early stage. Finally, the need for swift communication with consumers and
suppliers creates an incentive to keep the production facilities in the local market
(Vernon, 1966). As the product starts to mature, product standards and cost
considerations become increasingly important. Furthermore, a demand for the product
starts emerging in other advanced economies, making it likely for entrepreneurs to start
producing in these locations. This decision will of course depend on the marginal cost
of production as well as transportation costs to the foreign market; if these exceed the
expected cost of producing overseas, it is likely that an entrepreneur will consider
Vernon mentions ”the home washing machine” and fork-lift trucks as examples of a consumer good and
a producer good, respectively.
3
13
production abroad. Vernon goes on to argue that the American firm now established
with production facilities in another advanced economy will, if costs allow, begin to
export to third-country markets and even export back to the US (Vernon, 1966). This
will create incentives for competitors of the entrepreneur to likewise invest in new
production facilities abroad, in order not to lose market shares and potential new
markets, expanding their view of the market beyond “just” the US. When the product
later becomes standardized, Vernon states that production facilities will move to thirdworld countries, in order to, once more, save on production costs. He is however, a bit
vague on this last part, as empirics at the time of the article are not sufficient to support
this hypothesis, and, as Vernon points out: “The reason why so few relevant cases come
to mind may be that the process has not yet advanced far enough.” (Vernon, 1966).
Vernon does not draw any conclusions regarding policy recommendations for
governments in response to production facilities moving away from advanced
economies, neither does he focus on innovation as such. He does, however, build a
foundation regarding product-cycle models which Paul Krugman uses in an article from
1979, to develop a model of international trade and innovation (Krugman, 1979b).
3.2.2 An exogenous product-cycle model
Krugman (1979b) constructs a first version of the model including one factor of
production only (labor) and two countries: innovating North and non-innovating South,
where innovation is considered as the introduction of new products. Furthermore,
innovation is treated as exogenous, meaning that it has an effect on the model, but that
the model does not affect it. In the model it is assumed that new products are produced
immediately in North but only after a period of time in South. This implies that there are
only two kinds of products; new, produced in North only and old, produced in South
only. The consumers maximize the following utility function:
𝑛
𝑈 = {∑ 𝑐(𝑖)𝜃 }
1/𝜃
, 𝑤ℎ𝑒𝑟𝑒 0 < 𝜃 < 1
𝑖=1
14
Where c(i) is consumption of the ith good and n is the total number of products. Perfect
competition is furthermore assumed meaning that the price in the respective countries
(Pn and Ps) equals their wage rates (wn and ws). For South to be able to produce, a
technology transfer has to happen in which a new product becomes an old product (this
is similar to a patent that runs out). To see what this means to labor in North, figure 3.1
can be observed.
Figure 3.1 Demand for labor in north
Source: Krugman, 1979b
The horizontal axis represents the demand for northern labor, while the vertical axis
represents the wage differential between the two countries. The wage differential or
relative wages, wn/ws, determines which country produces which products. As
mentioned, North is in the model assumed to be the only country producing new
products, but when the wage differential is equal to one, North will also be competitive
in producing old goods, while if it is larger than one, North will only produce new
goods. OA represents the northern labor force and initially, Krugman assumes wn/ws >
1, so North specializes in new products. The DEF-curve shows the demand for northern
labor at different relative wage levels, and as can be seen, the lower the relative wage
the higher the demand is for northern labor. This remains until wn/ws reaches one, where
demand becomes infinitely elastic as Northern and Southern labor are perfect substitutes
15
in producing old products (Krugman, 1979b). If a technology transfer between North
and South happens, the demand for northern labor will drop and move left in figure 3.1,
to the line D’E’F. This will narrow the wage differential between the two countries,
making the labor in North worse off, if not matched with new product development in
North (Krugman, 1979b). Already at this stage in Krugman’s model it is clear why
innovation is important for the advanced economies. If the advanced economies do not
introduce new products at a pace that corresponds to the rate of technology transfer,
workers are worse off due to the narrowing of the wage differential as well as to the
movement of production to South. If they on the other hand do innovate and manage to
increase the number of new products, the wage rate for Northern labor will increase
because of the increase in demand. Krugman goes on to discuss innovation and
technology transfer by looking at the steady state.
The steady state equilibrium
The term “steady state” was developed by Solow in 1956 and states that in the absence
of technological progress; output, consumption and capital per worker are constant in
the long-run (steady state) (Snowdon and Vane, 2002). Innovation and technology
transfer determines the amount of products in North and South over time. Assuming
that all technological change comes from the introduction of new products, Krugman
defines the rate of innovation as:
(3.1)
𝑛̇ = 𝑖𝑛,
meaning that innovation is proportional to the products already existing. Furthermore,
the rate of technology transfer is defined as:
(3.2)
𝑛𝑆̇ = 𝑡𝑛𝑁
Technology transfer is thus modeled as new products becoming old products after a
time period, t (Krugman, 1979b). The rate of change of the number of new products in
the world follows from this as being the difference between innovation and technology
transfer:
(3.3)
𝑛𝑁̇ = 𝑖𝑛 − 𝑡𝑛𝑁
16
Krugman argues that the system of these three equations is unstable, since it will grow
with continuing technological progress. But the composition of the world stock of
products will move toward a stable mix. This can be seen by letting 𝜎 = 𝑛𝑁 /𝑛 be the
share of new products, which in turn means that the change in the share of new products
in the world will be:
𝜎̇ =
(3.4)
̇
𝑛𝑁
𝑛
−
𝜎𝑛̇
𝑛
= 𝑖 − (𝑖 + 𝑡)𝜎,
where the first term after the equal sign is the change in the share of new products out of
the total product stock, while the second term is the share of new products in the change
of products in the world in total. This equation can be transformed to look like:
𝜎 = 𝑖/(𝑖 + 𝑡),
(3.5)
which is the expression for the share of new products, and the ratio of new to old
products then is
𝑛𝑁
𝑛𝑆
𝑖
= 𝑡 (Krugman, 1979b). This thus determines the steady state of the
model, where relative wages, determined by the ratio of new to old products, are
constant. Moreover, there is a fixed differential in favor of North, equation (3.5), which
is an increasing function of the rate of innovation and a decreasing function of the rate
of technology transfer (Krugman, 1979b). This means that trade will be of Vernon’s
product-cycle type; new products are first produced in the advanced economy and after
a lag, the technology becomes commonly available leading to the production being
moved to less-advanced economies. Krugman elaborates further on the effects of
innovation and technology transfer where he asserts that an increase in innovation will
increase world productivity, as the number of products will go up. Furthermore,
innovation benefits the developed North disproportionately more than South, since, as
the number of new products increases, the wage differential will rise, moving the terms
of trade in the favor of North (Krugman, 1979b). Likewise, an increase in technology
transfer will lead to an increase in world productivity, but through a different channel;
when it is assumed that North only produces new goods and South only produces old
goods as Krugman does, then an increase in technology transfer will make it possible to
produce the same basket of goods as before the transfer of technology, but now at
reduced production costs using cheaper Southern labor, in turn making it possible to
expand world output. Moreover, technology transfer makes the terms of trade move in
South’s favor, as the wage differential between North and South will decrease. On
17
innovation and technology transfer, Krugman concludes: “(…) the incomes of Northern
residents depend in part on the rents from their monopoly of newly developed products.
This monopoly is continually eroded by technological borrowing and must be
maintained by constant innovation of new products. Like Alice and the Red Queen, the
developed region must keep running to stay in the same place.” (Krugman, 1979b). As
already mentioned, the advanced economies must keep innovating in order to uphold
their income level.
Finally, Krugman expands his model and introduces a second factor of production,
capital. He assumes that both new and old products are now produced using both labor
and capital, and that there is a fixed stock of capital in the world which is perfectly
mobile between countries, while labor is immobile. This can be illustrated by the
following figure:
Figure 3.2 Capital and innovation
Source: Krugman, 1979b
In figure 3.2, the horizontal axis represents capital in North/South, the vertical axis
represents the price of capital measured in terms of old products, DsDs is the marginal
product of capital in South and DnDn is the marginal value product of capital in North,
again measured in terms of old products at a given relative price of new goods. At this
18
given relative price of new products, the equilibrium return on capital is r2, with Kn and
Ks being the stock of capital allocated in North and South, respectively (Krugman,
1979b). If the price of new products rise, this would increase the marginal value product
of capital in North and shift the curve DnDn to D’nD’n. This in turn results in a shift in
the capital stock from South to North, making the labor in North better off, as income
has been redistributed to them. The relation between this and innovation and technology
transfer can be better illustrated by an example: if e.g. the rate of technology transfer
increases, the demand for old products will as a result increase, attracting capital to
South. This will make the labor in South better off because of a price increase in the
products they produce and because their relative wages go up. An increase in innovation
in North instead, would lead to similar effects here.
The conclusions following from this model must then be, that the advanced economies
must continue to innovate in order to sustain their current incomes and to grow further.
Likewise, it is important for the less advanced economies to adopt the technologies of
the advanced economies in order for them to grow. The innovation and adaptation of
products gives countries the possibility of exporting, which in turn will raise their
respected incomes. Thus, Krugman’s model of exogenous growth argues that
innovation leads to exports.
3.2.3 An endogenous product-cycle model
As mentioned above, the other major trend in the literature on exports and innovation is
the endogenous growth models. The main contributors to this theory are Grossman and
Helpman in a series of articles and books (1989, 1990, 1991a, 1991b) and Segerstrom
et. al. (1990). In the following, Grossman and Helpman, 1991a and 1991b will be used
as the basis for presenting these models.
Similar to Krugman (1979b), the model is a two-country model with product-cycle
features, where North again is the developed economy, and the only country able to
innovate and hence produce new products. South is less developed, making it only
capable of imitating the products from North. The model is first explained assuming
19
that a Northern firm being the only firm able to produce a product, hence enjoying
monopoly power, faces a demand structure derived from the utility function as in the
Krugman model, and acting as a monopolist, sets its price at a fixed markup over unit
costs (pn = wn/α) so that it maximizes profits. The profit obtained is therefore:
(3.6)
𝜋 𝑛 = (1 − 𝛼)𝑝𝑛 ∙ 𝑥 𝑛
Where π is profit, (1 − 𝛼) is markup, p is unit price, and x is the equilibrium output. The
possibility of two Northern firms producing the same product is ruled out in this setting.
This is because two Northern firms competing in a market for the same brand are
assumed to compete under Bertrand competition acting as price-setting oligopolists.
This implies that firms will set the price equal to wn, thus earning zero profits, meaning
that the costs of innovation will not be covered. Hence, a firm that realizes that another
firm is producing the same product will not enter the market, as a profit of zero is
expected. The same logic applies if two firms in the South compete in imitating the
same Northern product; the costs of imitation will not be covered with zero profits,
hence, two firms in South will never copy the same product in the model (Grossman
and Helpman, 1991a).
A sole Southern firm that imitates the Northern firm becomes a rival for the latter.
Grossman and Helpman operate with three scenarios of price setting, depending on the
level of costs for the Southern firm; Southern firms facing higher unit costs than the
Northern producer; Southern firms facing marginal costs well below those of the
Northern producer; and Southern firms facing unit costs just below those of the
Northern producer (Grossman and Helpman, 1991b, p. 285). The first scenario can be
ruled out, since the firm would not be able to compete on these terms. If the Southern
firm has marginal costs well below that of the Northern firm (scenario 2), it will set its
price equal to the monopoly price, ps = ws/α, as if it faced no competition from the
Northern producer, making profits equal to: (1- α)ps ∙ xs (Grossman and Helpman,
1991b, p. 285). If the marginal costs of the firm in South are just below the costs of the
Northern firm (scenario 3), the Southern firm could charge the monopoly price (ps).
However, this could lead the Northern firm to undercut the Southern firm, by setting its
price lower, driving the Southern firm’s sales down. Instead, the Southern firm will
20
choose a price at or just below the marginal costs of the Northern firm, wn, creating
profits of (1– ws/wn)ps ∙ xs (Grossman and Helpman, 1991b, p. 285).
Grossman and Helpman expand the model by introducing knowledge capital, created by
innovation and imitation, into the model. In North, the knowledge capital stock is given
by KN = n, which means that the larger the number of invented products in North, the
larger is the knowledge capital stock. The development of new products requires a/Kn
units of labor, where “a” is a fixed productivity parameter (Grossman and Helpman,
1991a). With no barriers to enter into R&D, the value of a product in North that has not
yet been imitated is:
(3.7)
𝑣𝑁 ≤
𝑤𝑛𝑎
𝑛
.
The profit of a firm in North producing such a product is equal to πNdt, where dt is the
time interval. All Northern firms risk having their product imitated during this time
interval, with the probability 𝑛̇ 𝑠 𝑑𝑡/𝑛𝑁 , which corresponds to the rate of South’s ability
to imitate over the North’s ability to innovate. If the product is imitated, the Northern
firm will lose capital of vN. However, if the product is not imitated, they gain the capital
𝑣̇ 𝑁 𝑑𝑡. This means that the total expected return on shares in a Northern firm equals:
(3.8)
𝜋 𝑁 𝑑𝑡 −
𝑛̇ 𝑆 𝑑𝑡
𝑛𝑁
𝑣 𝑛 + (1 −
𝑛̇ 𝑆 𝑑𝑡
𝑛𝑁
)𝑣̇ 𝑁 𝑑𝑡,
where the first term is the profit in the period, the second term is the capital loss if the
product is imitated, and the last term is the capital gain if the product is not imitated.
After some mathematical transformations4, an expression for the yield on a Northern
bond can be derived as:
(3.9)
𝜋𝑁
𝑣𝑁
𝑣̇ 𝑁
𝑛̇ 𝑆
+ 𝑣 𝑁 − 𝑛𝑁 = 𝑟 𝑁 ,
where the first term is equal to the profit rate, the second term is the rate of increase in
the value of Northern products, the third term is the rate of productivity growth in South
and rN is the yield on a bond in the Northern financial market. Grossman and Helpman
refer to this as a “no-arbitrage” condition, which means that the return on investment in
4
The reader is referred to (Grossman and Helpman, 1991b p. 287 ff. or 1991a) for details.
21
the Northern firm must be equal to the yield of a Northern bond to avoid the
displacement of the Northern producer from the market (Grossman and Helpman,
1991b, p. 288).
In South, a firm randomly chooses a product to imitate. The knowledge capital stock is
given by Km = ns, that is, the more products South successfully imitates, the larger the
capital stock5. The imitation of a product requires am/Km units of labor, where am is a
fixed productivity parameter for South (Grossman and Helpman, 1991a). The value of a
Southern product is then: 𝑣 𝑠 ≤
𝑤 𝑠 𝑎𝑚
𝑛𝑠
. The successful firm in South will earn an infinite
stream of oligopoly profits, after the imitation has been completed, which is equal to
πSdt in the time interval dt, and the capital gain is 𝑣̇ 𝑆 𝑑𝑡. The capital invested in the firm
must necessarily be equal to the opportunity cost of investing this capital in something
else (Grossman and Helpman, 1991b, p. 287ff.). This gives a no-arbitrage condition for
the firm in South, similar to (3.9) equal to:
(3.10)
𝜋𝑆
𝑣𝑆
𝑣̇ 𝑆
+ 𝑣𝑆 = 𝑟 𝑆 ,
where the first term is the profit rate, the second term is the rate of increase in the value
of Southern products and rS is the yield on a bond in the Southern financial market.
In both South and North, labor is employed in manufacturing and research and it is
necessary to assume labor-market clearing conditions to look at the steady state
(Grossman and Helpman, 1991a). This is done by setting Xi ≡ nixi, and letting this
denote the aggregate output in i = N,S. The labor-market equilibrium in North then
becomes:
(3.11)
𝑎
𝐿𝑁 = 𝑛 𝑛̇ + 𝑛𝑁 ∙ 𝑋𝑁 ,
where the first term on the right-hand side is the labor employed in research, while the
second term is the labor employed in manufacturing. LN is the total supply of labor in
North. In the same manner, the labor-market equilibrium in South becomes:
5
Grossman and Helpman also discuss the case of K m = Km(nS,nN) but this is not touched upon here. The
details can be found in Grossman and Helpman, 1991b, p. 307ff.
22
𝐿𝑆 =
(3.12)
𝑎𝑚
𝑛𝑆
𝑛̇ 𝑆 + 𝑛 𝑆 ∙ 𝑋𝑆 ,
where the first term is labor employed in imitation while the second term represents
labor employed in manufacturing, and LS is the total supply of labor in South
(Grossman and Helpman, 1991b, p. 288).
The steady state equilibrium
In order to conclude on the effects of international trade on the growth rates of the
economies, it is necessary to look at the long-run equilibrium growth paths of the two
economies, represented by the steady state.
Grossman and Helpman introduce a few supplementary variables before evaluating the
steady state. First, 𝜉 𝑖 ≡
𝑛𝑖
𝑛
, 𝑖 = 𝑁, 𝑆 is region i’s total share of products in the world, and
it follows that, because of the steady state characteristics, 𝜉N and 𝜉S must approach
constants in the long run. For this to be realized, the growth rates of the number of
varieties produced in the two regions, 𝑔, must converge, meaning that 𝑔𝑁 = 𝑔 𝑆 , where
𝑛̇ 𝑖
𝑔𝑖 ≡ 𝑛𝑖 , 𝑖 = 𝑁, 𝑆. Furthermore, as the total number of products is equal to the sum of
products in North and South, it follows that 𝑔 = 𝜉 𝑁 𝑔𝑁 + 𝜉 𝑆 𝑔 𝑆 , and because of the
steady state, 𝑔 = 𝑔𝑁 = 𝑔 𝑆 (Grossman and Helpman, 1991b, p. 289). The rate of
𝑔𝑆 𝜉 𝑆
imitation of the Southern firms can be defined as 𝑚 = (1−𝜉𝑆 ) which means that in the
Steady-State,
(3.13)
𝑚
𝜉 𝑆 = 𝑔+𝑚 , when 𝑔 = 𝑔 𝑆 .
This means that the higher the rate of imitation is, relative to the rate of innovation, the
higher is the share of Southern products out of the total product stock (Grossman and
Helpman, 1991b, p. 289).
It is now possible to derive a relationship between innovation and imitation that reflects
market clearing in North. Keeping in mind that the value of an average Northern firm
23
will fall at the rate of product development in the steady state (since increasing product
development leads to less labor employed in manufacturing, which in turn leads to
lower sales and profit), which changes the sign of the second term in the no-arbitrage
condition above (3.9) to a minus, the no-arbitrage condition for a Northern firm in the
steady state can now be written as (Grossman and Helpman, 1991b, p. 289):
(3.14)
𝜋𝑁
𝑣𝑁
= 𝜌 + 𝑔 + 𝑚,
where the term on the left-hand side is the profit rate, the first term on the right-hand
side is the long-run interest rate, the second term is the long-run growth rate and the
third term is the long-run rate of imitation. Substituting the monopoly price and
equation (3.11) (labor-market clearing condition) into (3.6), the expression for profits
for a Northern firm becomes:
(3.15)
(1−𝛼)𝑤 𝑁
𝜋 𝑁 = 𝛼(1−𝜉𝑆 )𝑛 (𝐿𝑁 − 𝑎𝑔)
Finally, combining (3.13), (3.14), (3.15) and (3.7), the relationship between innovation
and imitation for a Northern firm in the steady state can be written as:
(3.16)
1−𝛼 𝐿𝑁
𝛼
( 𝑎 − 𝑔)
𝑔+𝑚
𝑔
= 𝜌 + 𝑔 + 𝑚,
Where the left-hand side is again an expression for the profit rate, and the right-hand
side is an expression for the real cost of capital (Grossman and Helpman, 1991a). The
equation can graphically be seen in figure 3.3:
24
Figure 3.3 Innovation and imitation in the steady state
Source: Grossman and Helpman (1991b, p. 290)
The equation (3.16) is represented by the curve NN. The implications of the equation
and the reason for the upward slope of the NN curve can be discussed looking at the
rates of innovation and imitation, separately. Starting with imitation, it can be seen from
equation (3.16) that the higher m (rate of imitation) is, the higher the real cost of capital
in the Steady-State is (as the right-hand side of the equation increases). This is so
because a higher m increases the risk for Northern firms to be displaced and production
being moved to South. At the same time, it can be seen from the left-hand side of the
equation, that this closure of more Northern firms leads to a higher profit rate for the
remaining firms in North, because they are able to hire more workers and increase their
sales. With CES (Constant Elasticity of Substitution) of demand, the effect of m on
profit rates (the left-hand side of equation 3.16) is higher than on the cost of capital
(Grossman and Helpman, 1991b, p. 291).
Comparing instead two steady states of different rates of innovation, a higher rate of
innovation, g, will lead to a higher real cost of capital (right-hand side), because more
innovation means more competition, hence a higher rate of capital loss for a Northern
firm. At the same time, a higher rate of innovation will cause a decrease in the profit
25
rate per variety. This happens because the firms will spend more resources on R&D and
fewer resources on manufacturing, decreasing output and sales. Moreover, a rise in the
rate of innovation will increase the number of products in North, hence decreasing the
output per firm here (Grossman and Helpman, 1991a). As a result, a higher rate of
innovation necessitates a higher rate of imitation to have equality between the two sides
of the equation. From this it becomes evident, that imitation in South actually leads to a
higher profit rate for firms in North, which is why the slope of the NN curve in figure
3.3 is upward sloping.
By comparing autarky scenarios with trade scenarios, it is possible to draw further
conclusions on the relationship between innovation/imitation and trade. In autarky,
North introduces products at a rate that corresponds to the growth rate with free trade if
m = 0. This point is represented by the intersection of the NN curve with the vertical
axis in figure 3.3. However, with m > 0, the equilibrium in trade lies along the NN curve
to the right of the intersection with the vertical axis, hence North grows faster with trade
than without. As discussed above, this is because imitation will force some firms in
North out of the market, letting the existing Northern firms enjoy higher rates of profit
until imitated. The surviving Northern firms will be able to hire the workers from their
closed competitors, in turn expanding sales and profits (Grossman and Helpman, 1991b,
p. 295). For South, comparing an autarky-situation with no ability to invent new
products with a trade situation with the ability of imitation, it is clear that South will
grow faster with trade than without, because there will be no products to imitate in
autarky. Even with the possibility of inventing new products, South would grow at a
slower rate in autarky than with trade, as it would demand more labor to produce a new
product, than to do reverse engineering and imitate an existing product (Grossman and
Helpman, 1991a). The conclusion of the endogenous product cycle model by Grossman
and Helpman is therefore, that it is free trade which causes growth, determined by
innovation in North and imitation in South in the model.
This finding is in contrast with Krugman’s view discussed above, who concludes that a
country must have innovation to be able to grow and even maintain its level of income.
As previously mentioned, this thesis wishes to contribute to the empirical literature by
26
examining Krugman’s view that the causality runs from innovation to export.
Nevertheless, the endogenous growth model is important in the sense that it provides
foundation for the possible presence of reverse causality between trade and innovation.
As a result, it is important to control for this in the analysis to come.
The next section will present the previously established empirical evidence on the link
between innovation and exports.
27
4. Empirical evidence
The relationship between innovation and exports has been studied extensively during
many years and the empirical results are diverse. This section is dedicated to reviewing
some of these results.
Keesing (1967) was one of the first to study the relationship between innovation and
exports. He uses export data from the US to the then ten leading industrial nations from
1962 and investigates the correlation of this with the share of scientists and engineers in
R&D out of total employment per industry in 1961. He finds a strong correlation
between these. Furthermore, he tests the correlation of exports with two other proxies of
innovation, company financed R&D and federally financed R&D, both from 1960 and
again finds positive correlations. He also reports the correlation of total R&D (company
financed and federal financed added) with exports to be very high. Knowing that there
is a likelihood of cross-relationships with other explanatory factors, Keesing tests the
correlation of these with US exports and finds that his proxies of R&D explain trade
better than any other variable tested.
Soete (1981) is one of the first to use patents as a proxy for innovation, and regresses
variations in export performance in 1977 across OECD countries on variations in
innovativeness in 1963-1977 for 40 industrial sectors, including several other
explanatory variables6. Soete finds a significant positive relationship between
innovation and trade for almost all sectors investigated.
Wanting to prove the exogenous product-cycle model of trade as is the purpose of this
thesis as well, Hirsch and Bijaoui (1985) investigate the export performance of Israeli
firms, using the change in exports between 1975-1977 and 1979-1981 as the dependent
6
Actually Soete performs a regression very close to what would be described as a gravity equation
approach, but never defines it as this himself. The method he uses differs from the method applied in this
thesis in a number of ways; Soete uses cross-sectional data while this thesis uses panel data, Soete uses
variations in export performance as the dependent variable while this thesis uses plain export numbers in
millions and Soete does not control for as much heterogeneity as is controlled for in this thesis.
28
variable and percentage of R&D employees in 1977 as (one of more) explanatory
variables. Hirsch and Bijaoui hereby introduce lags of 4 years, recognizing that some
R&D projects take time before they are marketable, an approach that similarly will be
tested in this thesis. They conclude that firms engaged in R&D have a higher propensity
to export than firms in the same industry which do not engage in R&D, and argue that
their results confirm the exogenous product-cycle models.
Kumar and Siddharthan (1994) also try to empirically prove the exogenous productcycle models, especially the Krugman model as presented in section 3.2.2 by looking at
Indian firms. They use in-house R&D activity as a proxy for innovation, and also
introduce a measure of informal innovation proxied by skill intensity and a measure of
the import of technology which, according to Kumar and Siddharthan often occurs in
Indian firms. Their panel dataset covers 406 companies in 13 industries which are
followed over three years, 1987-88, 88-89 and 89-90. They find support for the
prediction of the product-cycle models in which less-developed countries will adopt
products of maturity from developed countries for the case of Indian firms. They
interpret low and medium technology industries as being mature in terms of
technological opportunities, and these are the sectors for which the proxies of
innovation are significant. Their results thus confirm what both Krugman’s and
Grossman and Helpman’s models have incorporated; that less-developed countries will
adopt the technologies of the developed countries.
In their study of UK export, Greenhalgh et. al. (1994) use industry-level data on exports
from the period 1954-1985 along with output proxies for innovation, patents granted
and a survey of patents used and produced. They create 2 regression equations, one for
the effect on net export volumes and one for export prices and find that innovation
“(…)improves the average quality and the variety of products on offer which attracts
more demand (…)” and that “The most common overall finding is that of successful
innovation whereby both trade volumes and the balance of trade were improved.”
(Greenhalgh et. al., 1994).
29
Bernard and Wagner (1997) study German manufacturing companies in the state of
Lower Saxony from 1978-1992, and more specifically, investigate the characteristics
and performance of exporters and non-exporters. They conclude that exporting
companies are larger, more capital-intensive, employ more white-collar workers and are
more productive than companies not exporting. Their results show that good firms selfselect into exporting, and that good firms become exporters and they find “(…) little or
no evidence that exporting by itself enhances performance”, which supports the
Krugman hypothesis. In another article on the same topic by Bernard, now with Jensen,
(Bernard and Jensen, 1999), US firm level data from 1984-1992 is used to establish the
causality between exporting and good firms. They document that exporting firms are
outperforming non-exporters, with regards to total employment, total shipments, labor
productivity and capital intensity. Additionally, they find that exporters are more
successful than non-exporters several years prior to the start of their export, and that
exporting firms grow faster with regards to plant size, shipments and total employment,
than non-exporters in the years prior to the year in which they become exporters.
Furthermore, Bernard and Jensen test whether exporting could lead to better firm
performance and their results do not suggest this, finding, however, an increasing
probability of survival among exporting firms.
Wakelin (1998a) studies the relationship between innovation and exports in 9 OECD
countries’ bilateral trade, with data on exports from 1988 and data on the explanatory
variables being averages from 1980-88. At country-level, she reports a positive and
significant coefficient of innovation using both R&D and patents as proxies for
innovation. At industry-level, Wakelin similarly finds a positive and significant
relationship between innovation and bilateral trade performance in 15 out of the 22
sectors investigated, using one of the two proxies of innovation. The use of the two
different proxies gives rather diverging results, in the sense that some sectors expressing
a positive relationship using R&D as proxy, shows a negative relationship using patents.
Wakelin explains this by the fact that the two measures express different aspects of the
innovation process, and e.g. concludes that patents seem to explain innovation better
than R&D in high technology industries. Interestingly, she goes one step further in her
analysis and divides sectors into users or producers of innovation and find that the R&D
variable is only positive for producers. In another article, (Wakelin, 1998b), she
30
investigates the relationship between innovation and exports in the UK, using a panel
data-set on firm-level from 1988-1992. Separating the firms into groups of innovating
and non-innovating firms, she finds that innovation, proxied by the number of
innovations used in a firm, is positively and significantly related to the probability of
exporting, but significantly negatively related to the propensity of exporting.
Furthermore, she finds that the number of innovations a firm has produced has a
positive and significant affect on probability of exporting. Finally, she is able to
separate the firms with respect to size, and concludes that large innovative firms are
more likely to export, and that the more innovations they had in the past the higher the
probability of exporting is. Moreover, small innovative firms are less likely to export
and hence more likely to concentrate on the home market (Wakelin, 1998b). Wakelin’s
results can thus be said to be supportive of the link between innovation and exports, but
they do not offer proof regarding the causality.
Sterlacchini (1999) investigates 143 small Italian firms (less than 200 employees) in
non-R&D intensive industries in the period 1994-96. As these smaller companies often
do not have formal R&D departments he uses a dummy variable for firms being
innovative or not and three alternative innovation proxies: innovative content of the
capital stock; ratio of expenditure on design, engineering and trial production to sales;
and finally the share of costs for acquiring innovative capital goods on sales. Using a
tobit model, Sterlacchini concludes that the ratio of expenditure on design, engineering
and trial production to sales as well as the dummy variable for firms being innovative or
not, show significant and positive influence on export performance. When looking only
at the innovative firms who export he finds that the same before mentioned ratio as well
as the innovative content of the firms’ capital stock shows positive and significant
relationships with exports. Likewise, Basile (2001) studies the export behavior of Italian
firms in the years 1991, 1994 and 1997, and is able to divide innovations into processor product-innovations. He concludes that firms having process- and/or product
innovations are more likely to export, than firms without.
Looking at firm-level data from UK and German manufacturing plants, Roper and Love
(2002) use export propensity and probability of the firms in 1991 and 1993 and relate it
31
to a substantial number of innovation proxies. They use a dummy variable indicating
whether there has been a product innovation in the company, a variable for innovation
intensity measured as number of product changes per employee and a variable for
innovation success measured as the share of sales coming from new products.
Furthermore, they introduce three other proxies for innovation; spill-over effects of
being in an innovative sector, spill-over effects of being located in an agglomeration of
innovative firms and spill-over effects of the supply-chain. These are estimated as the
average level of innovation intensity in the sector, region or the sectors supplying each
plant. They find that in the UK, being a product innovator and having innovation
success are positively and significantly related to the propensity and the probability of
exporting, while in Germany, product innovation is showing a positive relationship with
the probability of exporting, but innovation success shows a negative relationship. They
interpret this as UK and German manufacturing firms being in different markets with
regards to quality, with German firms having a home market where quality is an
important factor meaning that they already invest heavily in R&D, making further
increases in innovation less profitable. This is opposed to UK firms, where quality is not
as important a factor in the home market. Alternatively, they do offer a second
explanation of this being a product-cycle issue, suggesting that German firms initially
earn greater returns on the home market, before the export market over time becomes
more profitable. Sectoral spill-overs are found to be positive and significant with the
probability and propensity to export in the UK but show no effect in Germany.
Surprisingly, locational effects (agglomeration) show lower export probability in
Germany and lower export propensity in the UK. The authors explain this by suspecting
that export-oriented plants will locate in more remote areas where factor prices are
lower, but are, unfortunately, not able to test this. Finally, they find some effects of
supply-chain spill-overs on export probability in Germany and export propensity in the
UK.
In an article with the direct purpose of testing the causal relationship between
innovation and exports, Lachenmaier and Wößmann (2006) claim that most of earlier
research on the relationship between innovation and exports can only be interpreted as
descriptive and not causal. They argue that the data and approaches used are not taking
the endogeneity of innovation with respect to export into account, but that their own
32
alternative strategy to identify exogenous variation in innovation may create a new
understanding. As measures of innovation, Lachenmaier and Wößmann use an annual
innovation survey among German firms in manufacturing, representing all German
states and 15 sectors. The companies are asked to not only report whether they have
introduced an innovation (product or process), but also from where the innovation stems
(innovation “impulses”), making it possible to use strictly exogenous innovation
measures. The authors e.g. mention innovation stemming from the marketing
department as being endogenous to exporting, since the innovations are directly focused
on the costumers. “Reading the technological literature” is mentioned as exogenous to
the firm’s export performance, as the impulses will affect exporters and non-exporters
alike. Controlling for industry sectors, they are able to retrieve within-sector effects and
their results show that innovators export more and are thus supportive of the theory of
exogenous product-cycle models.
Tomiura (2007) investigates the effects of R&D on the export decision of Japanese
firms, performing a cross-sectional analysis on a dataset containing 118.300 firms.
Relating the probability of a firm being an exporter with various firm-level
characteristics, Tomiura finds that internal R&D is significantly positively related with
exporting. As a robustness check, a variable for patenting is also included, providing
similar positive results.
In a study comparing UK and Irish firms, Girma, et al. (2008) use firm-level databases
covering 1996-2003 for the UK and 2000-2003 for Ireland, to determine if innovation
causes exports (Krugman hypothesis) or exports cause innovation (Helpman and
Grossman hypothesis). Interestingly, using a probit model to determine the probability
of exporting, they conclude that Irish firms show learning-by-exporting characteristics,
while this is not the case for UK firms. Their study also finds that lagged R&D status
has a positive and significant effect on exports, supporting the Krugman model. Thus
their results support both hypotheses.
33
Examining the determinants of exporting in the UK, Harris and Li (2009) use survey
data from 2001 to estimate their model. They find that when treating R&D as
endogenous, it plays an important role for firms to enter into internationalization, but
when the firms have entered the export markets, they do not find that endogenous R&D
increases export intensity.
Overall, there is a substantial literature that supports the relationship between
innovation and export. However, as Lachenmaier and Wößmann point out, much of this
research does not control for the direction of the causality, and most of the studies are
thus unable to determine whether it is innovation that creates export, or export that
creates innovation. This thesis will therefore contribute to the literature on innovation
and exports, by empirically testing the theory of the exogenous product-cycle models,
controlling for reverse causality. Furthermore, by using an augmented version of the
gravity equation this thesis will bring new use to the model. The next section introduces
the gravity equation and leads the way to the empirical part of this thesis.
34
5. The gravity equation
In this section, the gravity equation is presented. This is done be reviewing the
evolution of the model and its different applications over time. Additionally, more
recent estimation issues are discussed and leads to the empirical analysis.
5.1 The evolution and foundation of the gravity equation
As mentioned, the model used in this paper for analysing the effects of innovation on
trade, is the gravity equation. The model has its inspiration from physics, more
specifically Newtonian physics, where Newton was the first to formulate a theory of the
force of gravity. The theory states that the force of gravity between two objects is
proportional to the product of their masses, divided by the square of the distance
between them (Baldwin and Taglioni, 2006). When using the model within international
economics, the force of gravity is replaced by bilateral trade flows, and the masses are
replaced by the GDP of each country. Distance is measured as the distance between
countries and is used as a proxy for transportation costs, with increasing distances
between trading partners having a negative effect on the trade between them.
The model was first developed by Tinbergen (1962) and Pöyhönen (1963) to explain
international trade, but lacked clear theoretical foundations, although producing good
explanatory fits. Anderson (1979) was the first to provide theoretical foundations for the
model, followed by Bergstrand (1984, 1989, 1990), Deardorff (1995), Evennett and
Kneller (2002) among others. They show that the gravity equation is consistent with
theories of international trade, e.g. the Heckscher-Ohlin model. Other authors included a
number of additional variables in the model as to measure the effect of these;
population, GDP per capita, Free-trade agreements (FTAs), common border,
landlocked, common language, transportation infrastructure etc. (see Yamarik and
Ghosh (2005) for a sensitivity analysis of 47 potential variables). The model has also
been used to predict trade flows, see e.g. Christerson (1994) and Sohn (2005), migration
flows, see e.g. Helliwell (1997) and FDI flows, see e.g. Brenton et al. (1999). Moreover,
the model has been used in the recent discussion of intensive margins (change in trade
35
between partners that already trade) and the extensive margin (change in trade between
two countries that do not currently trade), see e.g. Felbermayr and Kohler (2006). The
usually applied gravity equation looks like (5.1) (time subscripts omitted):
(5.1)
𝑙𝑛𝑇𝑖𝑗 = 𝛽0 + 𝛽1 ln(𝑌𝑖 ∗ 𝑌𝑗 ) + 𝛽2 𝑙𝑛𝑑𝑖𝑗 + 𝛽3 𝑙𝑛𝑉𝑖𝑗 + 𝜀𝑖𝑗
where T is the averaged bilateral trade flows between country i and j, Yi and Yj are the
GDPs of country i and j, d is the distance between country i and j, V is a subset of other
variables of interest and εij is the error term. Usually, the natural log is taken to the
variables in order to impose a linear relationship and to be able to directly interpret the
coefficients.
5.2 Recent estimation issues
Anderson and van Wincoop were still not satisfied with the justification of the model
and published an article (Anderson and van Wincoop, 2003), that created a new strand
of literature focusing on the econometric specifications of the model. In the article, the
authors criticize McCallum (1995), who investigated the effect of the Canadian-US
border and found very large effects on intra-national (provincial) trade in Canada.
Anderson and van Wincoop point out that McCallum (and subsequent authors) fail to
specify the model correctly, making their analyses invalid. They argue that the model
suffers from omitted variables, and is missing what they call the multilateral resistance
variables. These variables refer to the fact that bilateral trade not only depends on the
trade barriers between two trading partners, but also on the trade barriers they face with
the rest of their trading partners. Anderson and van Wincoop (2003) state that the border
effect is high for Canada in McCallum’s results, since he excludes the multilateral
resistance terms and since Canada is a small economy compared to the US. When
testing the effect of the border on the US, Anderson and van Wincoop (2003) find a
much smaller increase in US intra-national (state) trade. To correct for these omitted
variables they develop an estimation method which involves solving a number of
equations, making the approach rather complex and rarely used. Alternatively, they
suggest using country-specific dummies as a more simple approach which gives
36
consistent estimates of the model parameters (Anderson and van Wincoop, 2003).
Anderson and van Wincoop (2003) estimate their model for only one year, thus having
a cross-sectional dataset. The present paper, however, uses a panel dataset making it
insufficient to only apply the method of Anderson and van Wincoop, as country
dummies would remove the cross-sectional bias, but not the time-series bias.
Baldwin and Taglioni (2006) and Baldwin (2006) published articles similar to Anderson
and van Wincoop (2003), in which they investigate an article by Rose (2000), who adds
a currency union dummy variable to the gravity equation and finds a very large positive
and significant effect. However, Baldwin and Taglioni (2006) show that Rose’s
estimation method produces biased estimates. They identify some mistakes which could
be found in most of published work on the gravity equation until recently. The first
mistake is identical to the above mentioned pointed out by Anderson and van Wincoop,
and Baldwin and Taglioni (2006) also suggest using nation dummies but mention that
this is not sufficient when working with panel data. Instead, they suggest using timeinvariant country-pair fixed-effects, as they also have the effect of eliminating the crosssectional bias, controlling for time-invariant determinants, and are found to perform
better than nation dummies. However, controlling for time-invariant country-pair fixedeffects imply that no time-invariant parameters (e.g. distance) can be estimated, as the
fixed-effects estimation technique does not allow for it.
Another mistake identified by Baldwin and Taglioni (2006) is likewise a common
mistake in the gravity equation literature. The authors show that the often used method
of averaging the trade flows of the dependent variable (e.g. the average of US exports to
Canada and Canadian exports to the US) is wrong because in many of the papers, the
authors take the log of these averages instead of taking the average of the logs. This is
not a problem when countries in the dataset have similar trade flows, but if there is a
larger difference in the trade flows this will bias the coefficients on the included
variables (Baldwin and Taglioni, 2006). Also, they question the approach of averaging
the trade flows, as no theoretical foundation for doing so exists. Instead, they suggest
using unidirectional trade flows, a suggestion that will be followed below. This method
37
also conveniently allows for testing the effect of the innovation effort in one country on
its own export performance.
Hence, the gravity equation has so far been improved to look like the following (no time
subscripts):
(5.2)
𝑙𝑛𝑇𝑖𝑗 = 𝛽0 + 𝛽1 ln𝑌𝑖 + 𝛽2 𝑙𝑛𝑌𝑗 + 𝛽3 𝑙𝑛𝑑𝑖𝑗 + 𝛽4 𝑙𝑛𝑉𝑖𝑗 + 𝛼𝛿𝑖𝑗 + 𝜀𝑖𝑗
where Tij now represents the uni-directional trade flows from country i to j, Yi and Yj
are estimated separately, as uni-directional data makes this plausible, and 𝛼𝛿𝑖𝑗 is a
country-pair dummy (α for exporter and δ for importer).
In specification (5.2), there is still some heterogeneity unaccounted for. This takes the
form of exporter- or importer-specific time-varying effects, such as a country’s business
cycles, political or institutional factors or other unobserved factor endowment variables
(Baltagi et al., 2003). Baltagi et al. (2003) therefore suggest including time-varying
country dummies instead of time-invariant country dummies, to control for the timeseries correlation. Moreover, arguing that as much heterogeneity as possible should be
controlled for, they suggest keeping the time-invariant pair dummies as these control for
the bias stemming from the correlation between included parameters of bilateral trade
and parameters that are unobservable. This approach is supported by Baldwin and
Taglioni (2006), Baier and Bergstrand (2007), Baier et al. (2008) and Stack (2009).
Finally, a dummy variable for time is included in Baltagi et al. (2003) and Stack (2009)
to control for common shocks affecting all countries in the sample, an approach which
is followed in the analysis below.
It should be mentioned however, that the creation of the time-varying country-dummy
variables decreases the amount of degrees of freedom in the analysis. The degrees of
freedom measure the number of values in the final calculation that are free to vary, and
38
is measured by the number of independent values available in the estimation of a
parameter, minus the number of underlying parameters necessary to calculate the
parameter itself (Keller and Warrack, 2003, p. 363). Including the dummy variables
above will create 2NT dummies, which corresponds to 2 * 36 nations * 14 years =
1.008 dummies. This results in the loss of 1.008 degrees of freedom, but Baldwin and
Taglioni (2006) point out that this is not a problem when having a large dataset since
there will be many observations. In the dataset used, the number of observations varies
depending on the innovation proxy used, but in the fixed effects estimations in section
6.3 and 6.4 there is always between app. 6-14.000 observations, meaning that the
amount of dummy variables should not create a problem. Finally, it should be
mentioned that all models have been run using clustered standard errors, as
recommended by Stock and Watson (2006).
In the next section the discussed estimation issues are taken into consideration and the
final model specification is presented in details, and followed by the empirical analyses.
39
6. Analysis
In this section, the specifications of the model as well as the data used in the analyses
are presented. Hereafter, some descriptive statistics are discussed, in order to get a first
glance on the dataset. This is followed by the analysis at country-level, starting with
estimations of the gravity equation including the innovation variables separately and
subsequently together. Finally, the section includes an analysis of sector-level data.
6.1 Model specification and data
The gravity equation used in this thesis follows the discussion of the previous section
and looks like the following:
(6.1) 𝑙𝑛𝑇𝑖𝑗𝑡 = 𝛽0 + 𝛽1 𝑙𝑛𝑌𝑖𝑡 + 𝛽2 𝑙𝑛𝑌𝑗𝑡 + 𝛽5 𝑙𝑛𝑑𝑖𝑗 + 𝛽6 𝑙𝑎𝑛𝑔𝑖𝑗 + 𝛽7 𝐸𝑈𝑖𝑗𝑡 + 𝛽8 𝑙𝑛𝐼𝑛𝑛𝑜𝑖𝑡 +
𝛾𝑡 + 𝛼𝛿𝑖𝑗 + 𝛼𝛾𝑖𝑡 + 𝛿𝛾𝑗𝑡 + 𝜀𝑖𝑗𝑡
Where Tijt is unidirectional export flows from country i to country j at time t, lnYit and
lnYjt are exporter and importer GDPs at time t, lndij is the distance between country i
and j, langij is a dummy variable indicating whether country i and j share a common
official language, EUijt is a dummy variable indicating whether country i and j are both
members of the EU at time t, lnInnoit is a proxy for innovation for country i at time t, 𝛾𝑡
captures time effects, 𝛼𝛿𝑖𝑗 is the time invariant pair dummy, 𝛼𝛾𝑖𝑡 + 𝛿𝛾𝑗𝑡 are the timevarying country dummies, and finally 𝜀𝑖𝑗𝑡 is the error term. The model will be tested on
a sample of 36 countries7, of which most are from the EU, over a time period that varies
depending on data availability of the innovation proxy variables. The variables included
are discussed further in the following:
7
A list of countries can be found in appendix A. The countries are selected according to availability of data for the
innovation proxies.
40
Trade flows
As this thesis investigates the effect of innovation on exports, the dependent variable
consists of unidirectional export flows from country i to j. The export flows have been
retrieved from the IMF direction of trade statistics (DOTS), are measured in millions of
dollars and have been deflated with the US price index, year 2000 as the base year.
GDP
According to the theoretical foundation developed by Anderson (1979), the exporter
GDP is a proxy for the production of traded goods, while the importer GDP is a proxy
for expenditure on traded goods. In both cases, a positive relationship with exports is
expected, meaning that a larger GDP would result in larger export flows. The data on
GDP is in million dollars and in constant prices with year 2000 as the index year and
have been taken from the World Development Indicators database by the World Bank.
Distance and common language
The distance and common language variables are often used as proxies of trade and
transaction costs. The distance variable is a key variable in the gravity equation and the
interpretation is that the further away from each other two countries are, the less likely
they are to trade. The common language dummy variable is a proxy for transaction
costs, and a value of one in this variable, meaning the trade partners share a common
official language, should result in lower transaction costs, hence more trade (Yamarik
and Ghosh, 2005). The distance and common language variables are taken from the
CEPII institute.
EU
The EU dummy variable measures the effect of membership on trade in the European
Union. A value of one indicates that both countries are members of the EU. The effect
of EU, or more widely CUs (Currency Unions), FTAs (Free Trade Agreements), RTAs
(Regional Trade Agreements) and IEAs (International Economic agreements), in the
gravity equation has its own entire strand of literature and has been discussed for many
41
years. Going into the details of this could potentially be a thesis topic of its own.
Accordingly, the reader is referred to Baldwin (2006), Baier and Bergstrand (2007),
Baier et al. (2008) and Stack (2009) for recent discussions and results. Nevertheless, the
variable is generally expected to show a positive relationship with exports, meaning that
EU-membership should result in higher trade.
Innovation
The key parameter of interest in this thesis is the innovation variable. To measure the
amount of innovation in a country can be a difficult task, as the definition of innovation
goes from “all things new” to a more narrow definition of different innovation types, as
mentioned in section 2. Different authors have used different proxies for capturing the
effects of innovation and Roper and Love (2002) emphasize the importance of using
several innovation indicators. This is why in this study, a range of different
measurements have been selected as proxies for innovation, all taken from Eurostat.
The first proxy is R&D as a percentage of the country’s GDP, measured in millions of
dollars, and is a measure of innovation-input. R&D has been used in many papers as a
proxy for innovation, see e.g. Soete (1981) and Wakelin (1998a). R&D is expected to
be positively related to exports as a high intensity of R&D will tend to introduce new
products to the market and increase existing products’ quality, reinforcing the
competitive advantage of firms, in turn increasing trade performance. Furthermore,
R&D expenditure could also lead to process innovation, improving the cost structure
and competitiveness of the firm (Wakelin, 1998a). Greenhalgh et al. (1994) however,
argue that the use of R&D causes problems for empirical work. First of all, they argue
that there might be a considerable lag between R&D expenditure and the actual
production of marketable products, which is why the lagged effect of R&D will be
tested in the following analysis. Another reason to lag the effect of R&D is the potential
causality problem between innovation and exports, i.e. exporting creating a “learning”
effect, as in Grossman and Helpman’s model. By including lagged R&D it is insured
that the causality goes from innovation to exports, resulting in an unbiased estimate of
the effect of innovation on exports. Another concern rising when using R&D is pointed
out by Greenhalgh et al. (1994) and Lachenmaier and Wößmann (2006) who mention
42
that not all R&D expenditure leads to commercially successful products. This could
potentially lead to an overestimation of the effect of innovation. Wakelin on the other
hand, argues that R&D expenditure might underestimate the contribution of small firms
which do not have the capacity to set up a separate research department but do engage
in innovative activity (Wakelin, 1998a), or firms in sectors where innovations are
produced as part of the production process, e.g. the engineering or instrumentation
sectors (Wakelin, 1998b). The data for R&D expenditure is available for the period
1997-2008.
Since R&D represents the “input” of innovation it is necessary to also include a
measure of the “output”; hence three different counts of patents have been chosen: the
number of patent applications per country to the European Patent Office (EPO) per
million inhabitants, the total number of applications per country (not scaled) to the EPO
and the number of patents granted per country by the United States Patent and
Trademark Office (USPTO) per million inhabitants. The first two measures thus
represent patent applications and the difference between them is the scaling of one of
them which Wakelin (1998b) mentions as important in order to reduce
heteroskedasticity. The third patent variable measures actual patents granted in the US
and hence differs from the two other variables in both the choice of market and that it
measures “successful” applications. The first two patent variables are available from
1995-2006 while the last is available from 1995-2003. The lagged effect of all three
patent-measures will be tested, referring to the above discussion regarding potential
reverse causality between innovation and exports and the potential lag between
patenting and actually producing/selling.
Wakelin (1998a) argues that direct counts of innovations, e.g. patents, are better at
catching small firms’ innovation activity and she finds close correlation between patents
and innovations produced. Lachenmaier and Wößmann (2006) argue that also the use of
patents may be flawed, since a lot of innovations are never patented and that some firms
use patents as a strategic tool to prevent competitors from using the same technology.
They argue instead that innovation surveys should be used, where relevant persons in
companies are asked to fill out questionnaires regarding the innovation activities of the
43
firm. This has often been used in the literature, see e.g. Greenhalgh et al. (1994),
Wakelin (1998a and b), Sterlacchini (1999), Roper and Love (2002) and Lachenmaier
and Wößmann (2006). The downsides of this sort of measure is pointed out by
Lachenmaier and Wößmann (2006) who states that this typically only allows for
creating a dummy variable (has the company had an innovation or not) whereas the
other variables mentioned has the advantage of providing the ability to apply the actual
value of resources used or count the number of innovations produced. Furthermore,
innovation surveys are based on very subjective and arbitrary answering which can
seriously bias estimates. A third downside of innovation surveys is the significant
amount of time one would have to use to create, carry out and analyze the survey.
Overall, the author recognizes the advantages and disadvantages of the different proxies
(and underlines that they are in fact only proxies). However, using several proxies for
innovation will make it possible to assess the robustness of the findings and provide
elements for discussion.
6.2 Descriptive statistics
As a first glance on the dataset, the following table summarizes the relevant variables:
44
Table 6.1 Summation of the variables of interest
T
Variable
Obs
17.133
Mean
3.310,086
Std. Dev.
13.961,26
Min
7,50e-06
Max
443.326,8
Yi
17.395
699.148,3
1.807.970
3.099
1,16e+07
Yj
17.395
699.148,3
1.807.970
3.099
1,16e+07
dist
17.640
2.464,57
2.419,296
59,61723
11.156,39
lang
17.640
0,0507937
0,2195823
0
1
eu_dummy
17.640
0,3502268
0,4770544
0
1
rdi
11.585
17.279,65
50.303,98
10,1868
294.021,7
eu_pat_scale
14.455
79,70254
92,38413
0,08
430,65
eu_pat_noscale
14.980
2.821,927
6.515,857
0,67
35.054,22
pat_us
10.885
63,05904
84,41798
0,09
378,74
Note: T is the export flows from country i to country j, Yi and Yj are the GDPs of the countries, dist is the distance
between them, lang indicates if they share a common official language, eu_dummy indicates if both countries are
members of the EU, Rdi indicates spending on R&D per country, eu_pat_scale indicates number of patent
applications per million inhabitants per country to the EPO, eu_pat_noscale indicates total number of patent
applications per country to the EPO while pat_us indicates patents granted per country by the USPTO per million
inhabitants.
First, it can be seen that there is 17.640 observations in the dataset. It can also be seen
that there is missing data for some of the variables, and that it is the data on the
innovation variables that determines how many observations the model applied will
have, since these have the smallest number of observations. However, all the innovation
variables still hold more than 10.000 observations, which, as previously mentioned,
should not cause problems with the degrees of freedom in the analysis, and should make
it possible to retrieve solid, robust results.
Looking at the variable for total number of patent applications per country to the EPO
(eu_pat_noscale) it might seem odd that it shows observations with decimals, since total
applications not scaled should not exhibit numbers between 0 and 1. However, the EPO
uses fractional counting if e.g. multiple investors are involved, to avoid double
counting, making it possible to have observations between 0 and 1 and decimal numbers
in general. Other than this, the variables take on values as expected.
Since the relationship between innovation and exports is the topic of interest for this
thesis, it is interesting to map the relationship between these two variables. It is
45
expected that the plots will exhibit an upward trend, indicating that more innovation
will lead to more exports. The following four figures show this:
-10
-5
0
Exports
5
10
15
Figure 6.1 Scatter plot of R&D and exports
2
4
6
8
10
12
ln_rdi
T
Fitted values
Note: The X-axis represents the log of R&D while the Y-axis represents the log of exports.
-10
-5
0
Exports
5
10
15
Figure 6.2 Scatter plot of EU scaled patent applications and exports
-2
0
2
ln_eu_pat_scale
4
6
T
Fitted values
Note: The X-axis represents the log of patent applications to the EPO by million inhabitants while the Y-axis
represents the log of exports.
46
-10
-5
0
Exports
5
10
15
Figure 6.3 Scatter plot of EU not-scaled patent applications and exports
0
2
4
6
ln_eu_pat_noscale
8
10
T
Fitted values
Note: The X-axis represents the log of patent applications to the EPO while the Y-axis represents the log of exports.
-10
-5
0
Exports
5
10
15
Figure 6.4 Scatter plot of US patents granted and exports
-2
0
2
ln_pat_us
4
6
T
Fitted values
Note: The X-axis represents the log of patents granted to the USPTO by million inhabitants while the Y-axis
represents the log of exports.
As can be seen from graphs 6.1-6.4, the data point to the expected relationship between
innovation and exports; the more innovation the higher volume of exports, and all four
47
variables indicate this. In the following, it will be investigated whether the regression
analysis can confirm this.
6.3 Results at the country-level
As a test to see if the model is correctly specified, an OLS estimation has been applied
to equation (6.1). Model A is included to see if the variables usually included in the
gravity equation behave as expected. Models B-E include the innovation proxies one by
one and are expected to show positive significant results. These models are estimated
using time-varying country dummies and a time trend. The results can be seen in the
following table:
Table 6.2 OLS estimations
ln_Yi
ln_Yj
lnd
lang
eu_dummy
Model A
Model B
Model C
Model D
Model E
1,018***
0,533***
0,938***
1,120***
0,930***
(0,940)
(0,179)
(0,035)
(0,058)
(0,056)
0,940***
0,957***
0,942***
0,955***
0,965***
(0,051)
(0,045)
(0,052)
(0,052)
(0,044)
-1,526***
-1,508***
-1,506***
-1,537***
-1,534***
(0,063)
(0,067)
(0,063)
(0,064)
(0,065)
0,094
0,022
0,123
0,094
0,124
(0,153)
(0,175)
(0,149)
(0,151)
(0,140)
-0,014
-0,041
-0,010
-0,041
-0,033
(0,087)
(0,090)
(0,086)
(0,088)
(0,091)
ln_rdi
0,360**
(0,146)
ln_eu_pat_scale
0,139***
(0,048)
ln_eu_ pat_noscale
0,005
(0,040)
ln_pat_us
0,198***
(0,044)
Observations
16666
11227
13662
14161
10113
R-squared
0,936
0,896
0,895
0,892
0,903
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using time-varying country dummies and year dummies (not shown). Rdi indicates
spending on R&D per country, eu_pat_scale indicates number of patent applications per million inhabitants per
country to the EPO, eu_pat_noscale indicates total number of patent applications per country to the EPO while pat_us
indicates patents granted per country by the USPTO. All variables are in natural logarithm, except lang and
eu_dummy.
48
As can be seen from table 6.2, models A-E show coefficients of GDPi, GDPj, and
distance that are all of the expected signs and magnitudes of app. 1 for the GDPs and
between -1 to -2 for the distance. The dummy variable for common official language is
insignificant in all 5 models and the same is true for the EU-dummy. Several
explanations can be given for the insignificance of the EU-dummy: first, Stack (2009)
argues that if most countries already joined the EU before the start period of the sample,
then the only effect of the EU is from the countries joining during the sample period.
The present sample includes the ten countries that joined in 2004 (Cyprus, Malta,
Hungary, Poland, Slovakia, Czech Republic, Slovenia, Latvia, Lithuania, Estonia) as
well as the two countries joining in 2007 (Bulgaria and Romania). However, the time
period of the innovation proxies varies considerably: for R&D, both of these years are
included, the two patent variables for EPO includes 2004 but not 2007, while the US
patent variable does not include any of the years. It can furthermore be argued that the
effect of entering the EU has a substantial lag and that the effect is only visible after a
number of years, and it is therefore not possible to determine the effect until a number
of years have passed. Stack (2009) further argues that many countries have trade
agreements with the EU before officially entering or that trade expansion has been
anticipated before entering, resulting in rising trade before actual entry, in turn resulting
in a smaller effect of the EU. Moreover, most of the remaining countries included in the
sample are larger countries due to the availability of the data, like the US and Japan,
who demonstrate a high level of trade without being related to the EU.
The variables of interest, the innovation proxies, overall show results as was expected.
The coefficient for R&D expenditure is positive and significant with a point estimate of
0,360, indicating that a 1% increase in R&D results in a 0,360% increase in exports for
the given country. The patent variables for patent applications to the EPO per million
inhabitants and patent granted by the USPTO indicate positive significant results, with
point estimates of 0,139 and 0,198 respectively. The variable for patent applications to
the EPO (not-scaled) has an insignificant coefficient. The R-squared for all the models
are high, indicating that the models explain close to 90% of the variation in the export
flows between the countries included.
49
It should be noted however, that the models A-E are estimated using the OLS estimation
technique and therefore do not control for country-pair fixed effects, which results in
biased estimates when uncontrolled time-constant effects are correlated with the
explanatory variables. As previously mentioned, Baltagi et al. (2003) argue that as much
heterogeneity as possible should be controlled for by setting up the most general within
estimator to be able to come up with reliable parameter estimates. In the following
analysis, this is done by applying the fixed-effects estimation described above,
controlling for country-pair fixed effects, as well as including time-varying country
dummies and year dummies. Additionally, models A-E do not control for the issue of
reversed causality discussed above. Therefore, the estimates found in table 6.2 cannot
be interpreted as the clean effect of innovation on exports. As a result, each of the
innovation proxies is lagged one period in the subsequent analysis. The results can be
seen in the following tables 6.3 and 6.4:
Table 6.3 FE estimations with R&D and EPO scaled patent applications
Model 1
Model 2
Model 3
Model 4
lnd
(dropped)
(dropped)
(dropped)
(dropped)
lang
(dropped)
(dropped)
(dropped)
(dropped)
ln_rdi
0,143
(0,134)
ln_rdi_lagged_1
-0,190
(0,111)
ln_eu_pat_scale
0,215***
(0,050)
ln_eu_pat_scale_lagged_1
-0,004
(0,055)
Observations
11308
10438
13984
13015
Within R-squared
0,700
0,707
0,602
0,616
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
50
Table 6.4 FE estimations with EPO not-scaled patent applications and USPTO patents granted
Model 5
Model 6
Model 7
Model 8
lnd
(dropped)
(dropped)
(dropped)
(dropped)
lang
(dropped)
(dropped)
(dropped)
(dropped)
ln_eu_pat_noscale
0,172***
(0,067)
ln_eu_pat_noscale_lagged_1
0,097**
(0,047)
ln_pat_us
0,027
(0,032)
ln_pat_us_lagged_1
0,017
(0,031)
Observations
14488
13385
10437
9394
Within R-squared
0,578
0,587
0,400
0,380
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
From both tables it can be seen that the estimated models have dropped the variables
distance and common language since the fixed effects estimation does not allow for
time-invariant variables. Furthermore, the EU-dummy has been excluded for reasons
discussed above8. The GDP variables have also been excluded from the regression
following Baier and Bergstrand (2007), who refer to the theoretical derivation of the
gravity model in a panel framework made by Anderson and van Wincoop (2003) stating
that the GDP variables should be forced to unity, and therefore exclude them from their
analysis.
When looking at the different innovation proxies, it can be seen in table 6.3 that model
1 and 2 with R&D is showing an insignificant relationship with exports both when notlagged and when lagged. The coefficient for unlagged patent applications to the EPO
per million inhabitants (model 3) is positive and significant at the 1% level, with a point
estimate of 0,215. The corresponding coefficient for the lagged effect in model 4 is
insignificant. Table 6.4 shows the results when using the total number of patent
8
The EU-dummy would have automatically been dropped out of models 5-7 since the data on us patents does not
cover years in which the EU dummy changes. In appendix B, estimations including the EU-dummy can be found.
51
applications to the EPO (not scaled) and patents granted by the USPTO. As can be seen
in model 5 and 6, the first variable is positive and significant at the 1% level when
unlagged and positive and significant at the 5% level when lagged two periods, with
point estimates of 0,172 and 0,097 respectively. The variable for patents granted by the
USPTO, models 7 and 8, does not show significant effects.
The R-squared indicate that the models using R&D explain the variation in the data best
followed by the EPO scaled patent variable models and the models with EPO not-scaled
patent variables. Models using the USPTO patent variable show a rather low R-squared.
No negative significant relationship between innovation and exports has been found
using any of the variables.
In total, the results confirm what has previously been found in the literature.
Specifically, both the EPO patent application unlagged variables show positive
significant effects on exports, and the unscaled EPO variable also confirms this when
lagged. It should be noted however, that the un-lagged innovation variables do not take
the possible problem of causality into consideration, and that it is only the variable for
not-scaled EPO applications that shows a positive and significant relationship with
exports when lagged. The variables for R&D, scaled EPO patent applications and
USPTO patents granted are not significant when lagged. These results suggest the
possible presence of reverse causality between innovation and exports. Consequently,
the results of the un-lagged innovation variables should be interpreted with caution.
Therefore, it appears that the results are not robust to the different proxies and to the
possible presence of reverse causality. Again, however, it should be noted that evidence
of the reverse, a significant negative relationship, has not been found.
As a robustness check to the analysis, models including two innovation variables at the
time will be analysed in the following section. This is done to be able to determine
whether there is a difference in importance between innovation input and innovation
output.
52
6.4 Analysis with two innovation variables
As previously mentioned, R&D expenditure is an input-measure of innovation while
patent counts are a measure of innovation-output. To further test the different variables’
influence on export performance and to determine the importance of innovation input
vs. output, models with both R&D, representing innovation-input, and patent counts,
representing innovation-output have been tested. In tables 6.5-6.7 below, the results can
be seen:
Table 6.5 FE estimations with R&D and EPO scaled patent applications
ln_rdi
Model 9
Model 10
-0,088
0,420**
(0,157)
(0,175)
ln_rdi_lagged_1
ln_eu_pat_scale
Model 11
Model 12
-0,309**
-0,410***
(0,140)
(0,154)
0,492***
0,234***
(0,069)
(0,050)
ln_eu_pat_scale_lagged_1
0,272***
0,187***
(0,048)
(0,073)
Observations
10365
9492
9285
9355
Within R-squared
0,649
0,641
0,649
0,650
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
Table 6.6 FE estimations with R&D and EPO not-scaled patent applications
ln_rdi
Model 13
Model 14
-0,397***
-0,002
(0,108)
(0,118)
ln_rdi_lagged_1
ln_eu_pat_noscale
Model 15
Model 16
0,151
-0,639***
(0,139)
(0,143)
0,182***
0,272***
(0,055)
(0,049)
ln_eu_pat_noscale_lagged_1
0,416***
0,135**
(0,063)
(0,055)
Observations
10400
9492
9355
9355
Within R-squared
0,650
0,641
0,650
0,650
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
53
Table 6.7 FE estimations with R&D and USPTO patents granted
ln_rdi
Model 17
Model 18
-0,006
0,097
(0,108)
(0,216)
ln_rdi_lagged_1
ln_pat_us
Model 19
Model 20
0,033
-0,475***
(0,113)
(0,143)
0,086***
0,082**
(0,030)
(0,041)
ln_pat_us_lagged_1
0,075**
-0,068***
(0,034)
(0,026)
Observations
6992
6118
5878
5912
Within R-squared
0,422
0,408
0,415
0,415
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
As seen from the above tables, the coefficients for the patent variables nearly all
indicate a positive significant relationship with exports. The magnitude of the point
estimates lies between 0,075 and 0,492. The only exception is model 20 with the lagged
US patent variable which indicates a negative relationship with exports. It should
however be noted that the models 17-20 including the US patent variable have
substantially fewer observations than the other models tested and a lower R-squared.
The data for this variable is only present from 1995-2003 and it could have been
interesting to perform an analysis with up-to-date data, potentially resulting in a higher
R-squared and different coefficients.
Surprisingly, the coefficients on R&D expenditure in half of the models indicate a
negative significant relationship with exports, and the point estimates span between
-0,309 and -0,639, while the other half reports insignificant effects. Model 10 is the
exception, being the only model showing a significant and positive effect on exports
from both R&D and the patent variable. R&D with a point estimate of 0,420 and lagged
EU patent applications with 0,272. However, the R&D variable in this model is not
lagged, hence not taking the aspect of reverse causality into account. Interestingly,
comparing the models 9, 14 and 18 (with unlagged R&D), with the corresponding
models 11, 16 and 20 (with lagged R&D), the coefficients on R&D actually go from
54
being insignificant to indicating a significant negative relationship with exports when
changing the R&D variable from not-lagged to lagged. Hence, when taking the aspects
of reverse causality and the possible time-delay before R&D has an effect into
consideration, R&D is shown to have a significant negative effect on exports. Thus at
first, the results of the R&D variable seem to reject the hypothesis of the Krugman
model.
However, the author believes that these results, combined with the insignificant results
for R&D of section 6.3, instead suggest that innovation-output is more important than
innovation-input. This in turn means that the results indicate that it is not so much the
quantity or magnitude of R&D that matters, but more the quality of the R&D and what
comes out of it. This result has very interesting policy implications, as it advocates for a
more targeted government effort when distributing funding and support for R&D rather
than maximizing it. Specifically, this means that a government wishing to promote and
support innovation to create a higher volume of exports should focus on industries that
have the ability to convert this support into patented products, or focus on heightening
the quality of R&D by e.g. creating better environments for it, in turn leading to an
increase in exports. This point is supported by the positive and significant coefficients
on the patent variables in section 6.3 and in this section, which indicates that the more
patents a country gets, the higher the level of exports will be. Hence, a high level of
quality in the R&D effort, which will lead to more patented products, will in turn lead to
a larger export volume.
Two things should, however, be noted about the data. First, as mentioned previously,
there is a potential lag between the expenditure on R&D and the actual measurable
effect on exports. Lagging the variable one period did not indicate any support for this
suggestion, it rather indicated the opposite. Models with the innovation proxies lagged
two periods have also been tested and can be found in appendix C. These results show
fewer significant relationships when including the R&D variable, but when they do,
they again indicate a negative relationship with exports. Greenhalgh et al. (1994)
suggest an even longer time period between the expenditure of R&D and an actual
visible effect, but this has not been tested with the current dataset. It could have been
55
interesting to perform an analysis with more lags using a dataset spanning over more
years. Another important consideration is that typically there is a large difference in the
amount of R&D expenditure across sectors in an economy. The clothing sector of an
economy will e.g. normally show smaller amounts of expenditure than the
pharmaceutical sector, see e.g. Greenhalgh et al (1994) and Wakelin (1998a) for
discussions and evidence on this. This implies that country-level data could be too
aggregated to be able to identify the true effect of innovation on exports. This is why in
the following section sector-level data will be used for estimating equation (6.1) to see
whether less-aggregated data shows different results.
6.5 Sector-level analysis
Wakelin (1998a) argues that a country level study of innovation might be too
aggregated, and suggests using less aggregated data. Data on a sector-level is likely to
explain the true effect of innovation on export better, as the difference between sectors
with high and low efforts in the area of innovation can be separated. This section
therefore, presents an analysis of sector-level data from the US on innovation and
exports in a manner similar to the above, to test whether this will result in different
conclusions.
6.5.1 Data
The data used for the analysis in this section covers 12 manufacturing sectors in the US,
over the years 2000-20079. The sectors follow the North American Industry
Classification System (NAICS) and sectors are chosen according to data availability.
The export data stems from the US Census Bureau’s foreign trade statistics, and is
recorded as one-way export flows in million dollars from each sector to the 35 countries
also used in the country-level analysis. The innovation variable used is R&D by sector
in million dollars funded by companies or other donors (not federal) and the data for
this is retrieved from the National Science Foundation (NSF).
9
A list of the sectors can be found in appendix D.
56
6.5.2 Methodology
The methodology used in the sector-level analysis follows the methodology used in the
country-level analysis and the following equation is estimated:
(6.2)
𝑙𝑛𝑇𝑖𝑗𝑡 = 𝛽0 + 𝛽1 𝑙𝑛𝐼𝑛𝑛𝑜𝑖𝑡 + 𝛾𝑡 + 𝛼𝛿𝑖𝑗 + 𝛼𝛾𝑖𝑡 + 𝛿𝛾𝑗𝑡 + 𝜀𝑖𝑗𝑡
Where Tijt is unidirectional and one-way trade flows from the selected sectors to the
selected countries, innoit is the spending on R&D in sector i at time t, 𝛾𝑡 captures time
effects, 𝛼𝛿𝑖𝑗 is the time invariant sector-country pair dummy, 𝛼𝛾𝑖𝑡 represents the timevarying sector dummies, 𝛿𝛾𝑗𝑡 is the time-varying country dummies and 𝜀𝑖𝑗𝑡 is the error
term. As can be seen, the model only includes the innovation proxy variable, besides the
year-dummies, time-varying country-dummies and the country-pair fixed effects. As in
the country-level study, all country-, time- and pair-specific effects are captured by the
dummies included or the fixed effects. Furthermore, including the GDP in country i in
the regression as a proxy for the sectors’ level of production would not be justifiable, as
the GDP measures the total production of the country and not a specific sector’s.
Likewise, including the GDP of country j as a proxy for the expenditure on traded
goods would probably not be a realistic measure of the expenditure of country j on a
specific sector’s products in country i. As mentioned above, another issue with the GDP
variables is the theoretical derivation in which they are forced to unity. Baier and
Bergstrand (2007) exclude the GDP variables for this reason, an approach which again
is followed here.
6.5.3 Descriptive statistics
As in the country-level analysis, the following table summarizes the two variables of
interest:
Table 6.8 Summation of the variables of interest
Variable
T
Obs
3.356
Mean
975,8698
Std. Dev.
3.876,839
Min
0,0082254
Max
78.629,32
rdi
3.358
11.572,01
16.177,38
259,0534
66.613,48
Note: T is the export flows from sector i to country j, Rdi indicates spending on R&D per sector funded by companies
or other donors (not federal).
57
As can be seen, the dataset contains app. 3.300 observations, making it a somewhat
smaller dataset than the one used for the country-level analysis. Again, the relationship
between exports and innovation is of interest and expected to be positive. The following
graph depicts this:
-5
0
5
Exports
10
15
Figure 6.5 Scatter plot of R&D and exports
6
7
8
9
10
11
ln_rdi
ln_Tsi
Fitted values
Note: The X-axis represents the log of R&D expenditure by sector while the Y-axis represents the log of exports by
sector.
As can be seen from the graph, the relationship between innovation and exports is again
positive. In the following, it will be investigated whether the regression analysis can
confirm this.
6.5.4 Results at the sector-level
The results of the fixed effects estimation of equation (6.2) can be seen in table 6.9
below:
58
Table 6.9 FE estimations using sector-level data
Model 21
ln_rdi
Model 22
-0,063
(0,075)
ln_rdi_lagged_1
-2,975***
(1,147)
Observations
3356
2936
Within R-squared
0,462
0,473
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
Both models have been estimated using country/sector-pair fixed effects, including time-varying country- and sectordummies and year dummies (not shown). Rdi indicates R&D expenditure by the companies and other donors (not
federal) in the different sectors.
As can be seen, the coefficient for R&D spending when un-lagged shows insignificant
results. When lagged one period, R&D spending is found to have a significant negative
effect on exports. These results do not confirm the positive relationship seen in figure
6.5, but point in the direction of the above conclusions for the country-level analysis;
that innovation-input is not as important as innovation output. Unfortunately, it has not
been possible to back this result up by estimating the model with an innovation-output
variable like patents, due to data-unavailability.
The data available for the analysis causes some considerations. First, the analysis builds
on a relatively low number of sectors’ export flows over 8 years. As can be seen from
the table, this amounts to 3.356 observations when using not-lagged R&D expenditure,
while using lagged R&D expenditure decreases the number of observations to 2.936.
These are relatively low numbers and increasing the time period, the number of sectors
or the number of destination countries could lead to different results. Baldwin and
Taglioni (2006) show that this is plausible when they increase the time period in their
analysis, which results in lower standard errors and a change in the coefficient of their
key variable. Another cause of consideration is the aggregation-level of the data. It is
plausible that sector-level data is still too aggregated and that firm-level data would
show more convincing results. Later publications on innovation and exports do show an
increase in the use of firm-level data, see e.g. Wakelin (1998b), Roper and Love (2002)
and Lachenmaier and Wößmann (2006). An analysis using firm-level data could have
59
been interesting to perform and compare with the above. A third consideration is that
the data for R&D only includes the expenditure from the companies themselves and
other donors, but not the federally funded R&D, which is disclosed by the NSF for
competition reasons. Not being able to include federal R&D spending might cause a
serious bias in the results, as including government spending potentially could alter the
results
6.6 Conclusion on the data analyses
The results found in section 6 can be divided into two parts. The R&D variable first
showed a positive significant influence on exports when the model was estimated using
an OLS estimation. However, the OLS estimation of the model does neither control for
country-pair fixed effects nor the issue of reversed causality, potentially causing biased
estimates. The fixed effects estimations first showed insignificant results for the R&D
variable, unlagged and lagged, when estimating the model using only one innovation
variable at the time. When including two innovation variables at the time and estimating
models with R&D as a proxy for innovation input and patent variables as innovation
output, the coefficient for R&D expenditure generally showed a negative and significant
influence on exports. Hence, the results of the fixed effects estimations at first did not
seem to support the Krugman model, but rather refuted it.
However, when looking at the patent variables, the results point in the opposite
direction. The OLS estimations again resulted in a positive influence of innovation on
exports. When estimating the models using the fixed effects approach, the results were
again positive, although with some models giving insignificant results. The results of
the patent variables thus seemed to support the theory of Krugman. When estimating the
models with both R&D and patents, the patent variables showed significant positive
results (with one exception in model 20). The analysis on the sector-level confirmed the
negative result of the R&D variable, however without the possibility of confirming the
results with a patent-variable.
60
These results do not suggest that expenditure on R&D should be neglected as a way of
increasing the export volume of a country, since R&D generally is a prerequisite for
being able to “produce” patents (Wakelin, 1998a). Instead, the results suggest that
innovation-output is more important than innovation-input. This in turn implies that the
quality and what comes out of R&D is more beneficial for trade than the quantity. Thus,
based on these results, a government seeking to increase its export volume by
supporting innovative activity should target its funding towards heightening the quality
of R&D, in turn creating better and more output. Overall, the results of the analyses of
this thesis support the hypothesis of the Krugman model that innovation leads to an
increase in the export volume, but specifies that innovation-output is the direct driver of
export intensity.
61
7. Conclusion
The developing countries are increasingly becoming “the world’s factory”, producing at
costs with which the developed countries cannot compete. This has led to a shift in the
developed countries’ focus towards becoming knowledge-creating economies, which
has been shown to create competitive advantages on individual-, as well as on firm- and
country-level (OECD, 1997). Knowledge, in the form of innovative activity, has
therefore attracted the attention of many researchers, including its link with international
trade, which has been the focus of this thesis.
Within this research area, two types of theoretical models have attracted particular
interest among the scholars; exogenous product-cycle models and endogenous productcycle models. The former predicts that innovation influences exports through a causal
effect, treating innovation as an exogenous variable, while the latter treats the rate of
innovation as endogenous and predict dynamic effects of international trade on
innovative activity, a so-called “learning-by-exporting” effect. This thesis seeks to
prove the exogenous product-cycle models by using an approach that has not been done
before.
The model used in this thesis is the gravity equation, a model which is normally used to
explain flows of international trade between countries by regressing a number of
explanatory variables on the trade flows between them. This thesis takes the latest
econometric issues discussed by researchers into consideration and applies an
augmented version of the traditional model in which a variable for innovation is
included. Innovation is proxied by four different variables: R&D as a percentage of the
country’s GDP, the number of patent applications per country to the European Patent
Office (EPO) per million inhabitants, the total number of applications per country (not
scaled) to the EPO and the number of patents granted per country by the United States
Patent and Trademark Office (USPTO) per million inhabitants. Two datasets have been
used; a country-level dataset, containing more than 17.000 observations, and a sectorlevel dataset, containing more than 3.000 observations.
62
Under the country-level fixed-effects model specification, the R&D variable, measuring
innovation-input, appeared to be insignificant when included as the only innovation
regressor. This result remains robust also after taking the aspect of reverse causality into
consideration. Instead, when including the patent variables the results were mostly
significantly positive, and never significantly negative. The model was then estimated
with both R&D and one of the three patent variables, in order to have proxies of both
innovation-input and –output in the model. The results were generally negative and
significant for the R&D variable, but positive and significant for the patent variables
(with one exception). Overall, these results on country-level confirm the predictions of
the exogenous product-cycle models: innovation causes exports, but suggest that
innovation-output is more important than innovation-input. When testing the model on
sector-level data, although only with a variable for R&D available, the results confirm
the findings from the country-level analysis.
The results do not suggest that R&D should be dismissed as a target for innovation
funding by governments. They do however suggest a government policy in which
innovation-output, in the form of patents, should be the focus. This is off course a
difficult task, as funding is typically needed to be able to create a product before it can
be patented, and not after the patent is awarded. A potential focus could therefore be on
creating better environments in which R&D can be performed, in turn increasing the
quality of R&D, and thus creating more patents.
For future research, it could be interesting to expand the dataset on sector-level,
including more sectors, years and countries as well as including a patent variable to see
whether this would alter the results. Furthermore, in the light of newer research trends
focusing on firm heterogeneity within sectors, testing the model on a firm-level dataset
would be ideal to be able to shed more light on the topic. Finally, using the present
dataset for testing the endogenous product-cycle models could also be an appealing
idea. Thus, instead of using the gravity equation, one could reverse the model and
regress exports on innovation, too see if the prediction by the endogenous product-cycle
models can be confirmed.
63
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72
Appendix
Appendix A – List of countries ......................................................................................................................
Appendix B – FE estimations including the EU-dummy ...............................................................................
Appendix C – FE estimations with 2-period lags ...........................................................................................
Appendix D – List of sectors ..........................................................................................................................
73
Appendix A – List of countries
EU countries
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
UK
Other countries
Canada
Croatia
Iceland
Japan
Macedonia
Norway
Schwitzerland
Turkey
USA
Appendix B – FE estimations including the EU-dummy
Table A.1 FE estimations with R&D, EPO scaled patent applications, and the EU-dummy
eu_dummy
ln_rdi
Model A1
Model A2
Model A3
Model A4
0,033
0,026
0,067
0,034
(0,051)
(0,051)
(0,057)
(0,056)
-0,143
(0,113)
ln_rdi_lagged_1
-0,001
(0,100)
ln_eu_pat_scale
0,211***
(0,044)
ln_eu_pat_scale_lagged_1
0,217***
(0,035)
Observations
11308
10438
13984
13015
Within R-squared
0,699
0,707
0,602
0,616
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
Table A.2 FE estimations with EPO not-scaled patent applications, USPTO patent granted and the
EU-dummy
eu_dummy
ln_eu_pat_noscale
Model A5
Model A6
Model A7
Model A8
0,031
0,007
(dropped)
(dropped)
(0,059)
(0,057)
0,068
(0,059)
ln_eu_pat_noscale_lagged_1
0,278***
(0,055)
ln_pat_us
0,027
(0,032)
ln_pat_us_lagged_1
0,017
(0,031)
Observations
14488
13385
10437
9394
Within R-squared
0,578
0,587
0,390
0,380
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
Appendix C – FE estimations with 2-period lags
Table A.3 FE estimations with R&D and EPO scaled patent applications, unlagged, lagged one and
two periods
Model A9 Model A10 Model A11
ln_rdi
Model A12 Model A13
Model A14
0,143
(0,134)
ln_rdi_lagged_1
-0,190
(0,111)
ln_rdi_lagged_2
-0,254*
(0,144)
ln_eu_pat_scale
0,215***
(0,050)
ln_eu_pat_scale_lagged_1
-0,004
(0,055)
ln_eu_pat_scale_lagged_2
0,189***
(0,029)
Observations
11308
10438
9392
13984
13015
11937
Within R-squared
0,700
0,707
0,705
0,602
0,616
0,611
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies (not shown).
Table A.4 FE estimations with EPO not-scaled patent applications and USPTO patents granted,
unlagged, lagged one and two periods
Model A15 Model A16
ln_eu_pat_noscale
Model A17 Model A18
Model A19
Model A20
0,172***
(0,067)
ln_eu_pat_noscale_lagged_1
0,097**
(0,047)
ln_eu_pat_noscale_lagged_2
0,203***
(0,055)
ln_pat_us
0,027
(0,032)
ln_pat_us_lagged_1
0,017
(0,031)
ln_pat_us_lagged_2
-0,079*
(0,041)
Observations
14488
13385
12276
10437
9394
8315
Within R-squared
0,578
0,587
0,581
0,400
0,380
0,367
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels.
All models have been estimated using country-pair fixed effects, including time-varying country-dummies and year
dummies
(not
shown).
Table A.5 FE estimations with R&D and EPO scaled patent applications together, unlagged lagged one and two periods
ln_rdi
Model A21
Model A22
Model A23
-0,088
0,420**
0,549***
(0,157)
(0,175)
(0,116)
ln_rdi_lagged_1
Model A24
Model A25
Model A26
-0,309**
-0,410***
0,570***
(0,140)
(0,154)
(0,199)
ln_rdi_lagged_2
ln_eu_pat_scale
Model
A28
Model A29
-0,267
-0,204
-0,130
(0,192)
(0,180)
(0,128)
0,492***
0,234***
0,209***
(0,069)
(0,050)
(0,081)
ln_eu_pat_scale_lagged_1
0,272***
0,187***
(0,048)
ln_eu_pat_scale_lagged_2
Observations
Model
A27
10365
9492
0,218***
(0,073)
(0,067)
0,182***
0,122**
0,112**
(0,054)
(0,053)
(0,056)
8613
9285
9355
8443
8169
8239
8239
Within R-squared
0,649
0,641
0,643
0,649
0,650
0,649
0,649
0,650
0,650
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels. All models have been estimated using country-pair fixed effects,
including time-varying country-dummies and year dummies (not shown).
Table A.6 FE estimations with R&D and EPO not-scaled patent applications together, unlagged lagged one and two periods
ln_rdi
Model A30
Model A31
Model A32
-0,397***
-0,002
0,230**
(0,108)
(0,118)
(0,110)
ln_rdi_lagged_1
Model A33
Model A34
Model A35
0,151
-0,639***
-0,121
(0,139)
(0,143)
(0,141)
ln_rdi_lagged_2
ln_eu_pat_noscale
Model
A37
Model A38
-0,471***
-0,498***
-0,413***
(0,149)
(0,119)
(0,102)
0,182***
0,272***
0,280***
(0,055)
(0,049)
(0,050)
ln_eu_pat_noscale_lagged_1
0,416***
0,135**
(0,063)
ln_eu_pat_noscale_lagged_2
Observations
Model
A36
10400
9492
0,189*
(0,055)
(0,097)
0,300***
0,264***
0,197***
(0,060)
(0,052)
(0,062)
8613
9355
9355
8443
8239
8239
8239
Within R-squared
0,650
0,641
0,643
0,650
0,650
0,649
0,650
0,650
0,650
Note: Clustered standard errors given in parentheses. ***, **, * indicates significance at the 1%, 5% and 10% levels. All models have been estimated using country-pair fixed effects,
including time-varying country-dummies and year dummies (not shown).
Table A.7 FE estimations with R&D and USPTO patents granted together, unlagged lagged one and two periods
ln_rdi
Model A39
Model A40
Model A41
-0,006
0,097
(dropped)
(0,108)
(0,216)
ln_rdi_lagged_1
Model A42
Model A43
Model A44
0,033
-0,475***
(dropped)
(0,113)
(0,143)
ln_rdi_lagged_2
ln_pat_us
Model
A45
Model
A46
Model A47
-0,289
0,224
-0,264
(0,194)
(0,151)
(0,201)
0,086***
0,082**
-0,065
(0,030)
(0,041)
(0,059)
ln_pat_us_lagged_1
0,075**
-0,068***
(0,034)
ln_pat_us_lagged_2
0,088***
(0,026)
(0,034)
-0,027
0,039
-0,003
(0,026)
(0,028)
(0,042)
Observations
6992
6118
5239
5878
5912
5000
4901
4901
4901
Within R-squared
0,422
0,408
0,425
0,415
0,415
0,429
0,437
0,437
0,437
Appendix D – List of sectors
Sector code
311
313
314
315
316
322
323
324
325
326
332
333
334
335
336
339
Sector name
Food manufacturing
Textile mills
Textile product mills
Apparel manufacturing
Leather and allied product
manufacturing
Paper manufacturing
Printing and related support
activities
Petroleum and coal products
Chemicals
Plastics and rubber products
Fabricated metal products
Machinery
Computer and electronic
products
Electrical equipment, appliances
and components
Transportation equipment
Miscellaneous manufacturing
Note that the sectors 313-316 and 322-323 are grouped due to data availability. This
gives 12 sectors in total.