IMPACTS OF INTELLECTUAL PROPERTY RIGHT PROTECTION IN
FOREIGN COUNTRIES ON KOREA’S EXPORTS
Nguyen K. Doanh
Yoon Heo 
Nguyen T. Gam 
Abstract
This paper investigates the impacts of IPR protection in foreign countries on Korea’s total exports and exports
by commodity. Using the modified gravity equation with fixed effects and random effects models for the panel
data, our results are summarized as follows. First, reinforced IPR protection in foreign countries has a positive
effect on Korea’s total exports, indicating the dominance of market expansion effects. Second, stronger
protection of IPRs induces Korea’s exports to all foreign countries regardless of their level of development. The
effects are stronger in medium-income and high-income countries, followed by low-income countries where the
effect is not clear. Third, Korea tends to export more to countries with strong imitative ability when the IPR
protection in these countries is strengthened, suggesting the market expansion effects. Finally, stronger
protection of IPRs in foreign countries with weak imitative ability leads to ambiguous reduction in Korea’s
exports, demonstrating no market power effects. Efforts to increase the GDP, improve social infrastructure,
accelerate domestic reforms (openness to trade) and importantly strengthen IPR protection in foreign countries
are suggested as a remedy for obstacles to Korea’s exports.
1. Introduction
Over the past decade, the protection of intellectual property rights (IPRs) has become one of the most
important issues. Indeed, economists have recognized that the protection of Intellectual Property
Rights (IPR) has a significant impact on trade flows (See, e.g., Segerstrom et al., 1990; Grossman and
Helpman, 1991; Helpman, 1993). The preliminary conjecture is that weak IPR protection distorts
natural trade patterns and the ability of firms to transfer technology abroad. Thus, differences in
national norms regarding IPR protection are thought to negatively affect freer flows of international
trade. This could be one of the reasons why the regulation of national regimes of intellectual property
rights has recently become a contentious issue.

Vice-Director of the International Cooperation Center for Training and Study-Abroad, Thai Nguyen University
of Economics and Business Administration, Vietnam. Tel: 0084-977-242-268; Fax: 0084-280-647 684; E-mail:
nkdoanh@yahoo.com.

Professor, Graduate School of International Studies, Sogang University, Korea. Tel: 0082-2-705-8948; Fax:
0082-2-705-8755. E-mail: hurry@sogang.ac.kr.

Vice-Director of the Department of Training, Scientific Research and International Relations, Thai Nguyen
University of Economics and Business Administration, Vietnam. Tel: 0084-912-805 980; Fax: 0084-280-647
684; E-mail: ntgam@yahoo.com.
1
The results of the Uruguay Round were, however, extremely controversial for many WTO member
countries 1 . From the developed countries’ point of view, lack of IPR protection in developing
countries constitutes an unfavorable trade environment that could reduce their firms’ competitive
positions. Thus, they called for multilateral rules and enforcement of IPR. On the other hand, many
developing countries tend to argue in favor of weak IPR regime. According to them non-protection of
IPRs on their part had a negligible impact on producers in OECD countries, and that adoption of
stronger IPRs would increase the profitability of foreign firms at the expense of domestic producers
and thus would be detrimental to their welfare and development prospects (See Hoekman & Kostecki,
2001).
Theoretically, economic analysis is unable to predict the direction of the impacts of IPR protection on
bilateral trade flows2. The existence of such ambiguity is due to the fact that the strengthening of IPRs
would simultaneously create two effects working in opposite directions (see, e.g., Schwartz, 1991;
Taylor, 1993; Taylor, 1994; Maskus and Penubarti, 1995; Smith, 1999). On the one hand, stronger
protection of IPRs in the importing countries grants monopoly power to the exporting countries. Also,
the level of IPR protection may affect firms to choose to serve a foreign market by FDI or licensing
rather than exporting (Ferrantino, 1993). For that reason, the imports may decrease if exporters
exercise their enhanced market power by reducing output and charging higher prices to segments of
their foreign markets3. On the other hand, greater protection of IPRs in the importing country reduces
local firms to imitate foreign technologies. This leads to an increase in the net demand for the
protected products. Accordingly, the increase in demand induces the exporting firms to supply more
exports in the local market.
Since these two effects are offsetting, no clear prediction can be made regarding the nature and
direction of the impacts of IPR protection on trade. This theoretical ambiguity regarding the impact of
IPR protection on international trade has led to several empirical attempts. Recently, a growing body
of literature on the nature and direction of the effects of IPR protection on international trade flows
suggested that the relationship between IPRs and trade cannot be generalized (see Maskus and
Penubarti, 1995; Frink and Primo-Braga, 2005; Smith, 1999; Rafiquzzaman, 2002; Smith, 2002; Oh
and Won, 2005). Results of these studies show that the impact of stronger protection of IPRs on trade
1
As explained in Hoekman and Kostecki (2001), an intellectual property system seeks to create a balance
between the need for a temporary monopoly to create incentives for innovation and the benefits of free access
knowledge.
2
Maskus (2000) noted that theoretical models do not clearly predict the impacts of variable patent rights on
trade volumes. Much depends on local market demand, the efficiency of imitative production, and the structure
of trade barriers. Also important are the reactions of imperfectly competitive firms. Thus, a clear picture can
emerge only from empirical studies.
2
is an empirical issue. This has induced us to concentrate on the empirical analysis of the issue on
Korean case.
2. Research objective
This study aims at promoting the understanding of IPR protection and its impacts on international
trade, taking Korea as a case study. Therefore, it is guided by the following specific objectives:

To analyze the impacts of IPR protection in foreign countries on Korea’s total exports and exports by
commodity.

To analyze the impacts of IPR protection in foreign countries grouped by development levels and imitation
abilities on Korea’s total exports and exports by commodity.

To derive policy implications based on this study.
The empirical analysis in this paper differs from the previous studies in several aspects. First, this
study provides new evidence regarding the linkage between IPRs and trade with a focus on Korea.
Little evidence has ever been documented on the experiences of Korea and in that sense, this study
would provide important insights into Korea and the rest of the world where level of economic
development and imitation capacity differs across countries. Second, the study is based on the analysis
of the most recent panel data which allow the patent regime to change over time 4. Third, the impact of
IPR is firstly forced to be uniform across sectors and then is allowed to differ across sectors so that
industry-specific evidences can be documented. Since many of the previous studies focus on
industries at relatively high levels of aggregation, our industry-level analysis is particularly
advantageous because the effects of IPR protection on trade can be washed out at the aggregate level.
Fourth, in order to analyze the impact of IPR protection on trade, we use a set of models, including
the fixed effects model and random effects model. Finally, to measure the status of an IPR regime, the
IPR index developed by Park and Ginarte is used5.
3. Literature review
The linkage between IPR protection and trade has been discussed at length in the literature. There is a
growing body of literature in which the nature and direction of the effects of stronger protection of
IPR on trade (See, e.g., Primo Braga and Frink, 1997; Maskus and Penubarti, 1997). Although it is
unambiguous that IPR protection can influence trade flows, the net impact on trade flows of
strengthening protection of IPRs remains theoretically ambiguous (See, e.g., Maskus, 2000; Maskus
4
Most of the previous studies examine single points in time.
A number of studies have attempted to measure IPR cross-nationally, among them are Rapp and Rozek (1990),
Seyoum (1996) and Sherwood (1997). However, the IPR index developed by Park and Ginarte (1997) is the
most appropriate in the present context because it has the broadest country coverage. Moreover, it allows for a
much more fine-tuned ranking of national IPR system.
5
3
& Penubarti, 1995). Stronger protection of IPRs in importing countries allows the foreign exporters to
behave more monopolistically and to choose to serve the exporting market by foreign direct
investment or by licensing its intellectual asset to a foreign firm (Ferrantino, 1993; Lee & Mansfield,
1996; Maskus, 1998; Seyoum, 1996), which is known as the market power effect. Simultaneously, a
stronger level of IPR protection in importing countries encourages the foreign exporters to export
more to the foreign market due to the shrinkage of imitative activities in importing countries, which is
known as the market expansion effect. Naturally, the importance of these effects is likely to depend on
specific products and market characteristics. Certainly, some products are easier to imitate than
thothers, and some products have closer substitutes than others. In addition, the impacts of IPR
protection also depend on the exporters. If the exporter is not an innovator, the imports from this
exporter’s country are less likely to be new technology-intensive. So the protection of IPR is not
important for trade in this case.
The observation that theory indicates the relationship between stronger IPR protection and trade could
have either sign, depending on product and market characteristics, has led to attempts to resolve this
ambiguity empirically. To date, a number of studies have attempted to estimate the effects of IPR
protection on trade flows (e.g., Primo Braga and Frink, 1997; Al-Mawali, 2005 Wen-Hsien & Ya-Chi,
2005). Maskus and Penubarti (1995) provided the first systematic evidence on the linkage between
IPRs and trade, and demonstrated that national differences in PRs distort trade flows. They found that
a stronger protection of IPRs increases trade flows - that is the market expansion effect tends to
dominate the market power effect - when all industries are pooled.
A number of previous studies focused on the imitative abilities, threat of imitation and R&D abilities
of the importing countries in analyzing the impacts of IPR protection on exports (See Maskus and
Penubarti, 1995; Smith, 1999; Smith, 2002; Lui and Lin, 2005). For example, Ferrantino (1993)
studied the effect of IPR regimes on exports. Using the US export data, he found that importing
countries’ patent regimes do not affect total exports. Smith (1999) qualified these results by showing
that the market expansion effect of IPRs depends on whether local firms are capable of imitating the
exporter’s technology. The importing countries are divided into four groups according to threat of
imitation, which is defined as R&D expenditure as a percentage of GNP. The dummies for four
groups were then interacted with the IPR variable. The study indicated that US exports are sensitive to
patent rights in importing countries, and the direction of the relationship rests with the threat of
imitation6. Specifically, Smith found that there is a negative relationship between IPR protection and
imports of those countries with weakest imitative abilities, and positive relationship between IPR
protection and imports of those countries with strongest imitative abilities. Rafiquzzaman (2002)
found similar results, indicating that stronger patent rights is seen to increase Korean exports to those
countries that pose a strong threat of imitation and to reduce exports to countries that pose weakest
The threat of imitation may be viewed as a reflection of an importing country’s ability to imitate technologies
embodied the imported goods.
6
4
threat of imitation. This result is also confirmed by Smith (2002), which showed that stronger foreign
patent rights stimulate the market expansion of US drug exports across countries with strong imitative
abilities, but enhance the marker power of US drug exporters across countries with weak imitative
abilities. More recently, Frink and Primo Braga (2005) found a positive link between IPRs and trade
flows for total non-fuel trade, but a weak link between IPRs and high technology trade7.
Other studies have emphasized on the level of development in analyzing the impacts of IPR protection
on trade. For example, Smith (1999) introduced the interaction terms that are the product of
interactions between IPR and four dummy variables based on the per capita income of the importers,
including low income, lower middle income, upper middle income and high income. Smith found that
US exporters respond positively to the strength of IPR protection in countries classified as low
middle-income countries, but negatively to other country groups. Rafiquzzaman (2002) used similar
methodology to construct the three development dummy interaction variables. These variables are the
products of interaction between IPR and development dummies, which are constructed by classifying
the importing countries into three groups by their level of economic development, including high
income, middle income and low income. The results showed that, at aggregate manufacturing level,
Canadian exports respond positively to the strength of IPRs in countries within all levels of
development groups.
A review of the previous literature on the subject leads to the following conclusions. First,
theoretically, there is a link between IPRs and trade flow. More specifically, IPRs do affect trade.
However, the nature and the direction of the impact are ambiguous, depending on the interaction
between market expansion (which increases trade) and market power effects (which reduces trade). If
market expansion effects dominate, the stronger protection of IPR would enhance trade. In contrast, if
the market power effects dominate, stronger protection of IPR would reduce trade. Second,
empirically, evidence on the linkage was mixed, suggesting that the impact of IPR protection on trade
flows is an empirical issue and can only be assessed on a case-by-case basis. Third, past empirical
evidence showed that industrial countries and importing countries with significant threat of imitation
and relatively weak IPR regimes tended to experience an increase in bilateral trade8. In contrast, in
underdeveloped and developing countries with weak patent rights and weak imitation capacities, the
market power effect tended to dominate9. Finally, the response of trade in R&D intensive products to
increased IPR protection may be difficult to predict. The reason is that these products are particularly
The authors’ possible explanations are that the market power effect of IPRs could very well dominate in hightechnology sectors, that stronger IPRs lead to a switch from exporting to FDI, or that technology exports depend
on alternative means fro appropriation (such as first-mover advantages or reputation.
8
Industrial countries have strong imitation abilities. So to a large extent, their markets might be served by
imitated goods. Stronger IPR regimes would reduce the level of local infringement, and imitated goods are
replaced by foreign patented goods, generating a market expansion effect.
9
Initially, markets of these countries might already be served by foreign exporting firms. Since the imitation
abilities in these countries are often weak, the strengthening of IPR regime in these countries would not create
the market expansion effect large enough to outweigh the market power effect.
7
5
difficult to imitate anyway, and the producers of these products might choose to serve foreign
countries through FDI and licensing. These hypotheses are among those being explored further below.
4. Theoretical framework
4.1. Analytical model
The topic being explored is most suited to a quantitative approach. In seeking to empirically estimate
the impacts of increased IPR protection on trade flows, a gravity model is adopted. The gravity model
is commonly applied in the international trade literature to analyze trade distortions associated with
policy differences across countries. For the estimation purpose, the gravity equation is expressed in loglinear form as follows:
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7 LOCKj +
β8IPRjt + eijt
(1)
32
Where:

Eijt is the Korea’s exports to country j at the time t.

GDPit is the gross domestic products (GDP) of Korea at the time t.

GDPjt is the gross domestic products (GDP) of the importing country (country j) at the
time t.

POPit is the population of Korea at the time t.

POPjt is the population of the importing country (country j) at the time t.

DISTij is geographical distance, measured as the crow flies, between the capital city of
Korea and the capital city of the importing country (country j).

OPENjt is openness to trade of the importing country (country j) at the time t, measured as
the dollar value of exports plus imports as a percent of gross domestic product (GDP).

LOCKj is a dummy variable that equals 1 if the importing country is landlocked and zero
otherwise.

IPRj is the IPR index of the importing country at the time t.

eij is an error term.
The inclusion of supply factor of the exporting country (GDPit) and demand factor of the importing
country (GDPjt) is justified on the ground that higher level of exporting country’s GDP indicates
higher level of production for exports, while higher level of importing country’s GDP suggests higher
level of demand for imports. Therefore, it is expected that trade increases with the country size, as
measured by GDP, with other factors kept constant (See e.g., Chionis and Liargovas, 2002; Frankel,
1993). In other words, the gravity theory predicts that parameters on GDP are positive.
6
The theoretical justification for population variables (POPit and POPjt) is somewhat imprecise. On the
one hand, large population could promote a division of labor and allow more industries to reach
efficient economies of scale. Thus, opportunities for trade with foreign partners in a wide variety of
goods will increase, suggesting a positive impact of population on bilateral trade (See Oguledo and
Macphee, 1994). On the other hand, populous countries are assumed to be larger in area and thus
endowed with a greater quantity and variety of natural resources. The bigger absorption effect of this
domestic market causes less reliance on international trade transactions, indicating a negative impact
of population on bilateral trade (See Endoh, 1999; Endoh, 2000; Martinez-Zarzoso and NowakLehmann, 2003). Therefore, the coefficients for population could be positive or negative, depending
on which effect, absorption effect or economies of scale effect, is dominant.
Distance between trading partners (DISTij) is used as a proxy for several distance-related variables
such transport cost, cost of time, “psychic distance” or “cultural cost”, and access to relevant market
information (See Linenman, 1966) 10. All of these factors reflect the cost of international transactions
of goods and services and are expected to affect trade negatively (See, e.g., Al-Mawali, 2005; Clarete
et al., 2003; Deardorff, 1995; Geraci and Prewo, 1977; Martinez-Zarzoso, 2003; Sohn, 2005).
Therefore, we expect that the sign of the coefficient for DISTij variable is negative.
Following Smith (2001) we incorporate the OPEN variable into the equation (2) because countries
with higher level of openness tend to trade more. Therefore, we expect that the coefficient on OPENjt
is positive (See Smith, 2001; Smith, 2002).
The inclusion of the variable LOCKj is justified on the ground that being landlocked is generally
considered to reduce international trade. The reason is that the number of border-crossings, which
implies a transport cost burden, can explain a major part of the extra cost of overland transport in
comparison with maritime transport (See Raballand, 2003). Therefore, we expect that the coefficient
on LOCK is negative.
In line with the existing literature, IPR has an indeterminate effect on bilateral trade. This is because
stronger protection of IPRs simultaneously increase trade through market expansion effects and
reduce trade through market power effect. Therefore, the sign of the coefficient of the IPR index could
be positive (reflecting the dominance of market expansion effect) or negative (reflecting the
dominance of market power effect), indicating that the impact of IPRs on trade is an empirical issue.
In order to capture the sensitivity of Korea’s exports to the strength of IPRs in the importing countries
grouped by the level of economic development and imitative ability the equation (1) is written as
follows 11:
10
Psychic distance indicates the lack of familiarity with another country’s laws, institutions, and habits.
11
We adopt Rafiquzzaman (2002) and Smith (2002) for the empirical specification.
7
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7LOCKj +
β8IPRjt*D1 + β9IPRjt*D2 + β10IPRjt*D3 + eijt
(2)
32
D1, D2, and D3 in the equation (2) are dummy variables.
First, to analyze the sensitivity of Korea’s exports to the strength of national IPRs, in countries
grouped by level of development we let the development dummy variables interact with IPR. The
development dummy variables are constructed based on classifying the importing countries in our
sample into three groups by their level of economic development: high income (HDjt), middle income
(MDjt), and low income (LDjt)12. For the estimation purpose, the equation (2) is re-written as follows:
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7LOCKj +
β8IPRjt*LDjt + β9IPRjt*MDjt + β10IPRjt*HDjt + eijt
32
(3)
A positive value of an interaction variable indicates that, within a given level of development,
stronger protection of IPRs increase Korean exports to these countries through the market expansion
effects. In contrast, a negative value indicates that, within a given level of development, strong IPR
protection tends to reduce Korea’s exports to these countries via the market power effect.
Second, to capture the effect of imitative ability, we let imitative ability dummy variables interact
with IPR variable. The imitative ability dummy variables are constructed by classifying the importing
countries in our sample into two groups by their level of imitative abilities: Weak imitative ability
(WIjt) and strong imitative ability (SIjt). Drawing on Smith (2002), we use four alternative measures of
national imitative ability in order measure a country’s ability to imitate technology: (i) R&D
expenditure as the percentage of GNP; (ii) R&D scientists and engineers per million population, (iii)
R&D technicians per million population, and (iv) educational attainment. For the estimation purpose,
the equation (2) is re-written as follows:
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7LOCKj +
β8IPRjt*WIjt + β9IPRjt*SIjt + eijt
32
(4)
A positive value of the coefficient of these interaction terms indicates that, within a given level of
imitative ability, stronger IPR protection tends to increase Korea’s exports to these countries through
market expansion effect. A negative value indicates that, within a given level of imitative ability,
stronger protection of IPRs tends to reduce Korea’s exports to these countries via market power effect.
4.2. Model specification
First, the regression equation with respect to Korea’s total exports to the Rest of the World (ROW) is
estimated. This means that, by pooling the panel data on exports, we force the impact of IPRs on
12
Our classifications are based on the World Bank categorization.
8
Korea’s exports to be uniform. Second, the regression model with respect to Korea’s exports to ROW
(classified by Korea’s exports by commodity) is estimated. It means that we allow the impacts of IPR
protection to differ across the industries.
The rationale for setting up different specifications is as follows. First, using the data of total exports
allows us to see the overall impact of IPRs on exports regardless of industries. Second, using the same
gravity equation for different sectors allows us to capture the distinctive features of each sector in
terms of IPR-sensitivity.
In this study, two techniques are employed, including the fixed effects model and random effects
model. The fixed effects model allows for country-pair heterogeneity and gives each country-pair its
own intercept. The equations for fixed effects model is expressed in the following form:
lnEijt= β0ij + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7LOCKj +
β8IPRjt*LDjt + β9IPRjt*MDjt + β10IPRjt*HDjt + eijt
(5)
lnEijt= β0ij + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6OPENjt + β7LOCKj +
β8IPRjt*WIjt + β9IPRjt*SIjt + eijt (6)
Where:

β0ij indicates that each country-pair has its own intercept.
The fixed effects estimates can help us reduce potential specification errors from omitting important
variables. One shortcoming of this model, however, is that it does not allow for time-invariant
variables13 to be included. Therefore, we include the random effects model in order to incorporate
differences between cross-sectional entities by allowing the intercept to change, as in the fixed effects
model, but the amount of change is random. The random effects model is expressed as follows:
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6lnOPENjt + β7LOCKj +
β8IPRjt*LDjt + β9IPRjt*MDjt + β10IPRjt*HDjt + wijt
(7)
lnEijt= β0 + β1lnGDPit + β2lnGDPjt + β3lnPOPit + β4lnPOPjt + β5lnDISTij + β6lnOPENjt + β7LOCKj +
β8IPRjt*WIjt + β9IPRjt*SIjt + wijt
(8)
Where:

β0 is the mean intercept, and wijt is composite error term (wijt = μij + uijt). μij is a random
unobserved bilateral effect (which is cross-section or country-pair error component), and
13
Examples of time-invariant variables include distance, ex-colonial relationship, etc.
9
uijt is the remaining error (which is the combined time series and cross-section error
component).
The random effects model requires that μij ~ (0,  2 ), uijt ~ (0,  u2 ), the μij is independent of the uijt,
and the explanatory variables have to be independent of the μij and the uijt for all cross-sections (ij)
and time periods (t). The advantage of random effects model is that both time-series and crosssectional variations are used.
4.3. Data sources
The purpose of this section is to summarize the data to be used in the estimation of the regression
equations. While a large number of studies using the gravity equation to predict trade flows employ
cross-section data, the use of panel data allows us to capture the relationship between IRPs and trade
over a longer period of time; to account for changing IPR regimes and imitative ability; to control for
overall business cycle and disentangle the time invariant country-specific effects (Egger, 2000); and
to control for unmeasured country and time-specific heterogeneity (Co, 2004). The basic unit of
analysis is the industry. In this study, we use the 1990, 1995, 2000 and 2005 data on Korea’s exports
(total exports and exports by commodity classified into 2-digit SITC 14 ). 95 importing countries are
included in our sample, leading to 380 observations.

Export data (Eijt): The data on Korea’s exports, as measured in millions US$, come from
Korea International Trade Association (KITA), UN Comtrade, and the IMF Direction of
Trade Statistics (CD-ROM).

GDP data (GDPit and GDPjt), as reported in US$ millions are extracted from the IMF World
Economic Outlook Database and the Economist Intelligence Unit.

Population data (POPit and POPjt), as reported in million people, are extracted from the IMF
World Economic Outlook Database and the Economist Intelligence Unit.

Distance data (DISTij), as measured in kilometer, are collected from Indo.com
(http://www.indo.com/distance/).

Land-lock data (LOCKj) comes from the Economist Intelligence Unit.

Openness data (OPENjt): The openness index is measured as the dollar value of exports plus
imports as a percent of gross domestic product (GDP). The data on exports plus imports are
extracted from IMF-Direction of Trade Statistics (sum of exports and imports). The data on
GDP come from IMF-World Economic Outlook Database (GDP).

Level of development: The classification of economies according to the level of income is obtained from
the World Bank’s Classification.
14
The data on IPR index is up to the year 2005.
10

Imitative ability: Data on R&D expenditure as the percentage of GNP; R&D scientists and
engineers per million population, R&D technicians per million population are obtained from
OECD website, Statistical Yearbook published by UNESCO and various national statistics. Data on
educational attainment are obtained from various issues of Human Development Report
published by the United Nations.
Finally, to capture the effect of IPRs on bilateral trade flows, we use the IPR index. As indicated
above, a number of studies have attempted to measure IPR cross-nationally (See Rapp and Rozek,
1990; Seyoum, 1996; Sherwood, 1997). For example, Rapp and Rozek (1990) constructed the index
based on the adherence of each country’s patent laws in 1984 to the minimum standards proposed by
the U.S. Chamber of Commerce. These standards include guidelines for patent examination
procedures, term of protection, compulsory licensing, coverage of invention, transferability of patent
rights, and effective enforcement against infringement. The index is a sum of dummy variables that
take the value of one if one of the standards applies and zero otherwise. So, the index ranks the level
of patent protection for each country on a scale of zero to five. The index takes a value of zero when
there are no national patent laws, a value of one when a country has inadequate patent protection and
no laws prohibiting piracy, a value of two when a country has seriously flawed laws, a value of three
when a country has flawed laws with some enforcement of laws, a value of four when a country has
generally good laws, and a value of five when national protection and enforcement are fully consistent
with the minimum standards proposed by the U.S. Chamber of Commerce.
In this study, however, the IPR index developed by Park and Ginarte is the most appropriate because
it has the broadest country coverage. Moreover, it allows for a much more fine-tuned ranking of
national IPR system developed by Park and Ginarte (1997). The index takes on value between zero
(no protection) and five (maximum protection). The higher the numbers are, the stronger the
protection of IPR is. To construct the index, they examine five categories of the national patent laws:
(1) Extent of coverage (patentability), (2) membership in international patent agreement, (3)
restriction on patent rights, (4) enforcement mechanism and (5) duration. Each category takes on a
value between zero and one. The sum of these five values gives the overall value of the patent rights
index15.
5. Empirical results
5.1. Overview of Korea’s exports to the rest of the world
Korea’s international trade has been one of consistent growth. The US dollar value of exports of
goods and services now account for 44.26 percent of the GDP. The Korea’s commodity exports (by
SITC section) at 1-digit level are presented in Table 1.
15
The latest available data on IPR index, developed by Park Walter G. and Juan C. Ginarte, at the time of
writing is up to 2005.
11
As shown in Table 1, there has been a change in the commodity composition of exports. While
manufactured products dominate Korea’s exports, the exports of primary products have been growing
slightly. Among Korea’s exports, the share of basic and miscellaneous manufactures in total exports
has been declining over the years, while the exports of chemicals, machines and transport equipment
have been growing. This indicates that Korea has moved into producing patent-sensitive products.
Table 1: Korea’s Commodity Composition of Exports
Exports, by SITC section (in percent)
1990
1995
2000
2005
2008
Primary products
5.92
5.67
8.03
7.58
11.24
Food and live animals
3.13
2.12
1.39
0.87
0.72
Beverage and tobacco
0.19
0.12
0.12
0.18
0.19
Crude materials excluding fuels
1.52
1.43
1.06
1.00
1.21
Mineral fuels, etc.
1.07
1.98
5.44
5.52
9.11
Animal, vegetable oil, and fats
0.00
0.02
0.01
0.01
0.01
93.80
92.39
91.06
92.31
88.43
3.86
7.15
8.00
9.76
10.12
Basic manufactures
22.08
22.04
17.64
14.42
14.11
Machines, transport equipment
39.29
52.49
58.21
61.00
55.38
Miscellaneous manufactured goods
28.57
10.70
7.22
7.13
8.82
0.27
1.95
0.91
0.11
0.33
100.00
100.00
100.00
100.00
100.00
Manufactured products
Chemicals
Unclassified goods
Total
Source: The authors’ computation based ADB and KITA databases
Table 2 presents the Korea’s direction of exports. As the data in Table 2 reveal, Asian countries have
been important export markets for Korea. From 2005, Asian economies have accounted for around 50
percent of Korea’s exports. Although the portion of Korea’s exports to Middle East, Latin America,
Africa, Oceania and others his minimal, it has been increasing over time. In contrast, the importance
of North America and Europe has been declining overtime.
12
Table 2: Korea’s Direction of Export 1990-200816
(In millions of USD)
1990
1995
2000
2005
2008
24,638.76
61,564.55
81,092.61
146,913.69
214,051.13
2,619.02
4,880.49
7,586.47
12,241.03
26,647.35
Europe
12,001.47
20,854.18
28,141.38
52,853.26
76,696.71
North America
21,090.79
25,921.90
40,037.36
44,788.81
50,433.84
Latin America
2,102.08
7,370.29
9,368.96
14,986.88
33,267.32
Africa
892.35
2,227.47
2,239.51
6,202.51
9,386.09
Oceania
892.35
2,227.47
2,239.51
6,202.51
9,386.09
Others
892.35
2,227.47
2,239.51
6,202.51
9,386.09
65,129.16
127,273.81
172,945.29
290,391.19
429,254.63
37.83
48.37
46.89
50.59
49.87
4.02
3.83
4.39
4.22
6.21
Europe
18.43
16.39
16.27
18.20
17.87
North America
32.38
20.37
23.15
15.42
11.75
Latin America
3.23
5.79
5.42
5.16
7.75
Africa
1.37
1.75
1.29
2.14
2.19
Oceania
1.37
1.75
1.29
2.14
2.19
Others
1.37
1.75
Source: The authors’ computation based on KITA database
1.29
2.14
2.19
Asia
Middle East
Total
(in percent of total exports)
Asia
Middle East
5.2. Basic results
The summary of statistics for the data used in the estimation is presented in Appendix 1. The basic
results for the regression equation with respect to Korea’s total exports to ROW are shown in Tables
3-A and 3-B17. The first row shows the estimates of coefficients for all industries. As indicated in
Table 3-A, the gravity model fits the data well, explaining a major part of the variation in bilateral
trade. The conventional variables behave very much as the model predicts. All estimated coefficients,
except for the POPit and POPit, are statistically significant at the 0.05 significance level. The estimate
of the intercept term at the aggregate level is negative and statistically significant, indicating that
unmeasured trade distortions reduce Korea’s exports.
GDP turned out to be an important explanatory variable. GDPs of both exporting and importing
countries register positive impacts on bilateral trade, and the impacts are highly significant. The
coefficient on GDPit indicates that one percent increase in the GDP of the exporting country (Korea)
Korea’s direction of exports is displayed in detail in the Appendices 4-A and 4-B.
According to Hausman (1978) specification test, the random effects model is more relevant than fixed effects
model. Co (2004) also largely relies on the random effects model because, with only one exporter, the fixed
effects model would exclude time-invariant variables such as distance and land-lock, etc. Therefore, the analysis
in this paper is mainly based on the results of the random effects model.
16
17
13
increases the exporting country’s exports by 0.502 percent. The coefficient on the importing country’s
GDP (GDPjt) is also positive with elasticity of 0.748.
The coefficient on POPit is positive, while the coefficient on POPjt is negative. However, all of them
are statistically insignificant. This could be due to the fact that the economies of scale effects are
cancelled out by the absorption effects.
We also found the traditional negative sign on distance (DISTij) and land-lock (LOCKj), and positive
signs on openness (OPENjt). In our estimation, an increase in the log of bilateral distance between
Korea and its trading partners by 1 percent would lead to a decline of 0.92 percent in Korea’s exports.
Being a landlocked country would lead to a decrease in Korea’s exports by approximately 35 percent.
Finally, our primary interest is in the impact of IPRs on Korea’s exports. The coefficient on the patent
rights index of the importing country is positive and statistically significant. This indicates that, on
average, across sectors and countries the stronger protection of IPRs in foreign countries does raise
Korea’s exports. Therefore, at the aggregate level, Korea tends to exports more to countries with
relatively strong IPR protection, supporting the view of market expansion effects. This result is
consistent with the of results of Maskus &Penubarti (1997), Smith (1999), Rafiquzzaman (2002), Oh
and Won (2005), Jung (2007) and Doanh and Heo (2007).
On the sectoral level, it is clear that the above effects vary across sectors. GDP of the importing
countries (GDPjt) continues to be the important explanatory variable. The effects of distance between
Korea and its trading partners are negative and statistically significant in 9 out of 10 sectors, implying
that longer distance reduces trade.
When the impacts of IPR protection are allowed to differ across sectors, we observe that, all
coefficients are positive, with exception of Animal, vegetable oil and fat for which an insignificant
negative coefficient is found. The coefficients are statistically significant only in such sectors as
chemicals and miscellaneous manufactured goods. We also found stronger effects of IPRs in IPRsensitive sectors such as chemicals. This finding supports the view that, in these industries, the
stronger protection of patent right does enhance trade through market expansion. In other industries,
the coefficients are not significantly different from zero. Thus, across all industries and across all
countries, the exercise of enhanced market power does not play any role in reducing Korea’s exports.
Table 3-B displays the results from the fixed effects model. At the aggregate level, the results are
similar to those from the random effects model. Both GDP of Korea and GDP of the Korea’s trading
partners play important roles in Korea’s exports. Distance continues to be obstacles to Korea’s exports.
A landlocked country is likely to import less from Korea, and countries with greater openness to trade
14
tend to import more from Korea. Turning to IPR variable, again the coefficient on the patent rights
index of the importing country is positive and statistically significant, indicating the market expansion
effects. On the sectoral level, all coefficients, except Animal and Unclassified Manufactured Goods,
are positive. Five of them are statistically significant. They include Crude Materials, Mineral Fuels,
Chemicals, Basic Manufactures and Miscellaneous Manufactured Goods. At this point, based on the
results from fixed effects and random effects models, we could conclude that the strengthening of IPR
protection would raise exports to all countries
15
Table 3-A: Determinants of Korea’s Exports - Random Effects Model
PRODUCT SECTORS
Total exports
0. Food and live animals
1. Beverage and tobacco
2. Crude materials excluding fuels
3. Mineral fuels, etc.
4. Animal, vegetable oil, and fats
5. Chemicals
6. Basic manufactures
7. Machines, transport equipment
8. Miscellaneous manufactured goods
9. Unclassified goods
lnDISTij
OPENjt
LOCKj
IPRjt
R2
-0.045
-0.962**
0.027**
-0.426**
0.090**
0.759
(0.71)
(-0.50)
(-4.95)
(3.77)
(-3.00)
(2.96)
0.921**
-1.080
-0.146
-1.568**
0.087
-.2796079
0.014
(-0.43)
(7.35)
(-0.22)
(-0.95)
(-5.04)
(1.46)
(-1.09)
(0.20)
-8.195
-0.831
0.705**
9.971
0.0558
-1.487**
0.0200
-0.400
0.047
(-1.01)
(-1.15)
(4.51)
(1.45)
(0.30)
(-3.88)
(0.91)
(-1.43)
(0.50)
-6.506
0.181
0.516**
6.689
0.608**
-1.599**
0.036
-0.541*
0.137
(-1.05)
(0.33)
(3.94)
(1.29)
(3.72)
(-5.04)
(0.57)
(-2.26)
(1.80)
-27.904**
-0.586
0.345
25.305**
0.511*
-3.015**
0.006
-0.891*
0.177
(-2.75)
(-0.66)
(1.68)
(2.97)
(2.01)
(-5.76)
(0.23)
(-2.33)
(1.48)
-10.268*
-0.442
0.236**
11.995**
0.108
-1.975
0.019
-0.162
-0.038
(-2.05)
(-0.98)
(2.65)
(2.81)
(1.04)
(-9.51)
(1.43)
(-1.07)
(-0.67)
-22.864**
-0.337
0.522**
18.095**
0.537**
-1.222**
0.025*
-0.315
0.132*
(-5.30)
(-0.89)
(5.64)
(5.02)
(4.67)
(-5.13)
(2.11)
(-1.81)
(2.50)
7.817*
0.271
0.562**
-2.150
0.192
-1.074**
-0.009
-0.495**
0.069
(2.56)
(1.05)
(7.24)
(-0.86)
(1.81)
(-4.64)
(-0.96)
(-2.93)
(1.86)
-2.201
1.022**
0.886**
0.295
-0.120
-0.833**
0.039**
-0.495**
0.048
(-0.63)
(3.39)
(10.76)
(0.10)
(-1.11)
(-3.64)
(3.89)
(-2.97)
(1.10)
13.807**
-0.109
0.858**
-5.719*
-0.018
-0.978**
-0.027**
-0.155
0.083*
(4.30)
(-0.39)
(11.70)
(-2.16)
(-0.19)
(-4.95)
(-2.98)
(-1.07)
(2.05)
-59.411**
-3.398**
0.658**
52.296**
-0.153
-2.539**
0.041*
0.172
0.021
(-8.12)
(-5.23)
(4.66)
(8.47)
(-0.90)
(-7.31)
(2.09)
(0.68)
(0.24)
CONST
lnGDPit
lnGDPjt
lnPOPit
lnPOPjt
(Korea)
(Importing)
(Korea)
(Importing)
-2.850
0.502*
0.748**
1.443
(-1.14)
(2.37)
(11.59)
7.686
-0.224
(1.29)
** Significant at the 0.01 level
* Significant at the 0.05 level
16
0.610
0.429
0.638
0.467
0.526
0.738
0.678
0.726
0.784
0.5681
Table 3-B: Determinants of Korea’s Exports - Fixed Effects Model
PRODUCT SECTORS
Total exports
0. Food and live animals
1. Beverage and tobacco
2. Crude materials excluding fuels
3. Mineral fuels, etc.
4. Animal, vegetable oil, and fats
5. Chemicals
6. Basic manufactures
7. Machines, transport equipment
8. Miscellaneous manufactured goods
9. Unclassified goods
CONST
lnGDPit
lnGDPjt
lnPOPit
lnPOPjt
(Korea)
(Importing)
(Korea)
(Importing)
-7.641**
0.544**
0.788**
2.674
-1.420**
(-3.11)
(2.66)
(6.81)
(1.26)
(-2.92)
-2.859
-0.189
0.749**
2.845
-1.299
(-0.44)
(-0.35)
(2.06)
(0.51)
(-0.89)
2.490
-1.920
0.771
0.961
3.396
(0.21)
(-1.84)
(1.01)
(0.10)
(1.13)
-7.750
0.266
0.304
3.144
1.474
(-1.15)
(0.47)
(0.85)
(0.54)
(0.95)
-37.349**
-1.059
1.709**
23.882*
-3.080
(-3.45)
(-1.18)
(3.36)
(2.57)
(-1.44)
-18.180**
-0.606
0.707**
12.295
-1.394
(-3.27)
(-1.31)
(2.70)
(-1.27)
(-1.27)
-21.044**
-0.397
0.571*
12.562**
2.632**
(-4.57)
(-1.03)
(2.44)
(3.17)
(2.77)
3.899
0.271
0.590**
-2.010
-.546
(1.24)
(1.03)
(3.97)
(-0.74)
(-0.88)
-6.365
1.195**
0.689**
1.649
-1.512*
(-1.82)
(4.09)
(4.07)
(0.55)
(-2.11)
11.343**
-0.123
0.935**
-6.897*
0.173
(3.31)
(-0.43)
(5.60)
(-2.34)
(0.24)
-49.896**
-1.545
0.515
37.352**
-2.437
(-3.07)
(-1.17)
(0.61)
(2.97)
-0.52
** Significant at the 0.01 level
* Significant at the 0.05 level
17
lnDISTij
OPENjt
LOCKj
IPRjt
R2
Dropped
0.010
Dropped
0.118**
0.661
(1.47)
Dropped
-0.229
(3.88)
Dropped
(-0.88)
Dropped
0.740
0.070
Dropped
-0.010
Dropped
0.015
Dropped
0.027*
Dropped
-0.008
Dropped
0.008
Dropped
-0.035**
Dropped
-0.388
(-0.74)
-0.002
0.158
0.200**
0.574
0.108*
0.257
0.087
0.565
(1.94)
Dropped
(-3.46)
Dropped
0.330
(2.78)
(0.77)
Dropped
0.312*
(3.37)
(-0.84)
Dropped
0.517
(-0.03)
(1.99)
Dropped
0.215*
(2.34)
(0.94)
Dropped
0.193
(2.41)
(-0.31)
Dropped
0.070
(0.45)
(0.35)
Dropped
0.028
(0.5)
(1.53)
Dropped
0.041
0.100*
0.736
(2.24)
Dropped
-0.179
(-0.73)
0.116
5.3. Development interaction results
In this section, we focus our analysis on the response of the Korea’s exports to the variations in IPR of
foreign countries at similar levels of economic development. The effect is captured by the estimates of the
interaction between IPR and development dummy variables in equations 6 and 8. The development
dummy variables are constructed by classifying the importing countries in our sample into three groups
according to their level of economic development. These three groups include high income countries
(HDjt), middle income countries (MDjt) and low income countries (LDjt). The dummy variables take a
value of one for countries with a given level of development and zero otherwise. Each of these dummy
variables is interacted with IPR index. Therefore, the interaction variables are the product of these
development dummy variables and IPR variables.
Table 4 displays the estimates of equation 8 that includes the interactions of IPRs and the development
dummy variables.18 The first row reports parameter estimates for aggregate exports (total export in this
case) while subsequent rows contain estimates for disaggregated 2-digit level of SITC product categories.
In general, the coefficients on traditional variables (GDP, population, distance, openness to trade and
landlocked status) have expected signs and are statistically significant at 0.05 level. Our results show that
Korea tends to export more to countries with higher level of GDP and Openness, and export less to
countries, which are distant from Korea and landlocked.
As stated above, one of our major interests is on the impact of variations in strength of national IPRs in
countries grouped by the level of economic development. As the results reveal, all coefficients for the
development dummy variables (IPRjt*LDjt, IPRjt*MDjt and IPRjt*HDjt) are positive, and two them are
statistically significant (IPRjt*MDjt and IPRjt*HDjt), exhibiting the market expansion effects on Korea’s
exports to countries in all development groups. This result is consistent with that of Smith (1999). The
effect is stronger in high- and middle-income countries than in low-income countries. This indicates that
Korea’s exporters tend to respond positively to the strength of IPRs in foreign countries within all groups,
regardelss of development level. In other words, IPRs have market expansion effects on Korea’s exports
to all foreign countries grouped by level of economic development. The effect is most prevalent in
middle-income countries, followed by high-income countries and then low-income countries.
However, within a given income group, the effects vary across industries. For example, within countries
in the high-income group, the effects of IPR protection appear to be relatively stronger. As the results in
Table 4 reveal, stronger protection of IPR in foreign countries has positive and significant effects on
Korea’s exports in four product categories (Plastics in Non-primary Form, Cork and Wood Manufactures,
Iron and Steel, and Manufactures of Metals). Although, in some product categories, the coefficients are
negative they are statistically insignificant (Organic Chemicals, Medical and Pharmaceutical Products,
18
The development interaction results (at 1-digit level) are presented in Appendix 2.
18
Fertilizers, Plastics in Primary Forms, Paper, Textile Yarn, Non-metallic Mineral Manufactures,
Machinery Specialized for Particular Industry, Telecommunication, Furniture, and Footwear).
Within the middle-income group, the coefficients for development dummy interaction variable
(IPRij*MDjt) in most of the product categories are positive, and seven of them are statistically significant
(Dyeing Tanning and Coloring Materials, Plastics in Primary Forms, Plastics in Non-primary Forms,
Rubber Manufactures, Iron and Steel, Manufactures of Metals, Electrical Machinery, and Travel Goods).
This indicates that stronger protection of IPRs induces Korea’s exports to countries in middle-income
countries. In addition, this market expansion effect tends to be stronger in IPR-sensitive industries (e.g.,
Plastics, Electrical Machinery, etc.) where manufacturing process usually requires significant investment
in R&D.
Within the low-income group, the coefficients on the interaction term (IPRjt*MDjt) are negative for nine
product categories (Essential Oils, Textile Yarn, Non-metallic Mineral Manufactures, Metalworking
Machinery, General Industrial Machinery and Equipment, Telecommunication, Road Vehicles, Other
Transport Equipment, and Professional, Scientific and Controlling Apparatus). Although being
statistically insignificant, negative parameter estimates could suggest that Korea’s exports of these
commodity groups are influenced by the market power effects of strengthening IPRs across countries
within low-income group.
5.4. Imitative ability results
In the above section, we analyzed the response of the Korea’s exports to variability in IPR of foreign
countries at similar levels of development. In this section, we analyze the effects of the importing
countries’ imitative ability on the relationship between Korea’s exports and the strength of the importing
countries’ IPR. In order to do so, we interact the strength of IPRs and imitative abilities. Specifically, the
importing countries in our sample are split into two groups based on the imitative abilities: (i) weak
imitative ability (WIjt) and (ii) strong imitative ability (SIjt). Within this framework, we expect that
Korea’s exports would be biased against countries with relatively strong IPR and weak imitative ability,
and biased toward countries with relatively strong imitative ability.
Our point of departure is to focus more on those industries that are sensitive to imitative ability. They are
IPR-sensitive industries, which require large investment in R&D. These industries also have propensity to
IPR. They include plastics, machinery, chemical and chemical products, fabricated metal products,
transport equipment, electrical and electronic products and other manufacturing industries.
19
The imitative ability results are presented in Table 519. Again the first row is for Korea’s total exports,
whereas the subsequent rows display the estimates for disaggregated 2-digit level of SITC product
categories. With regard to Korea’s total exports, all basic variables behave as much as the model predicts.
The results confirm the fact that Korea tends to exports to those countries with higher level of GDP and
higher degree of openness to trade and export less to those countries, which are landlocked and distant
from Korea. Regarding the interaction of IPR and strong imitative ability (IPR jt*SIjt), the coefficient is
positive and highly significant. This probably indicates that market expansion effects apply to countries
within strong imitative ability group, suggesting that Korea tends to export more to countries with strong
imitative ability when the protection of IPRs in these countries is stronger. In contrast, the coefficient on
interaction of IPR and weak imitative ability (IPRjt*WIjt) is negative, but not statistically significant20.
Nevertheless, this could suggest that market power effects apply to countries within weak imitative ability.
On the sectoral level, the effects vary across industries within a given group of imitative ability. For
example, within weak imitative ability group, the coefficients on interaction of IPR and weak imitative
(IPRjt*WIjt) are negative in 16 product categories, but only significant for Telecommunication. The
insignificance of negative coefficients in most industries could suggest that, in the case of Korea’s exports,
the market power effects have been very minimal and unclear. One possible explanation is that Korea has
not exercised the market power effects because foreign countries could buy substitutes from other
exporters. In contrast, the coefficients are positive and statistically significant in three product categories
(Plastics in Non-primary Forms, Cork and Wood Manufactures, and Travel Goods). One interesting point
is that the coefficients of interaction terms for IPR-sensitive industries are larger than for total exports.
This indicates Korea’s exports of IPR-sensitive products are more sensitive to IPRs than are Korea’s total
exports.
Within countries classified as strong imitative abilities, the coefficients on the interaction terms
(IPRjt*SIjt) are negative in four product categories (Essential Oils, Iron and Steel, Telecommunication,
and Other Transport Equipment). However, all of them are statistically insignificant. The coefficients are
positive in other product categories, and four out of them are statistically significant (Plastics in Primary
Forms, Manufactures of Metals, Electrical Machinery, and Trade Goods). This finding indicates strong
support for the market expansion hypothesis. In addition, since these sectors are among knowledgeintensive sectors, it is not surprising that the impact of strengthening IPRs is larger in these sectors. At
this point, our sectoral results are supportive of the theoretical prediction that the market expansion effect
is likely to be predominant in larger markets with high imitative abilities. This finding is consistent with
that of Smith (1999).
19
20
The imitative ability interaction results (at 1-digit level) are presented in Appendix 3.
Since it is not statistically significant, caution must be taken when interpreting the results.
20
Table 4: Development Interaction Results (2-digit level) - Random Effects Model
PRODUCT SECTORS
All industries
51. Organic chemicals
52. Inorganic chemicals
53. Dyeing tanning and coloring materials
54. Medical and pharmaceutical products
55. Essential oils and resinoids perfume materials
56. Fertilizers
57. Plastics in primary forms
58. Plastics in non primary forms
59. Chemical materials and products, n.e.s.
61. Leather
62. Rubber manufactures
63. Cork and wood manufactures
64. Paper
65. Textile yarn, fabrics, made-up articles
66. Non-metallic mineral manufactures
67. Iron and steel
69. Manufactures of metals
CONST
lnGDPi
(Korea)
lnGDPj
(Importing)
lnPOPi
(Korea)
lnPOPj
(Importing)
lnDISTij
lnOPENjt
LOCKj
-2.815
(-1.12)
-14.766**
(-3.20)
-4.842
(-0.69)
-8.179
(-1.62)
-21.321**
(-3.59)
-15.014*
(-2.29)
20.326
(1.37)
-29.729**
(-5.36)
-6.793
(-1.47)
-9.735
(-1.82)
36.667**
(4.69)
3.140
(1.00)
30.589**
(3.96)
3.658
(0.69)
9.741*
(2.32)
-10.820*
(-1.99)
-13.162*
(-2.09)
13.021*8
(3.17)
0.527*
(2.47)
-0.896*
(-2.24)
-0.016
(-0.03)
-0.161
(-0.37)
-0.916
( -1.74)
-0.027
(-0.05)
0.223
(0.17)
-0.074
(-0.15)
-0.065
(-0.16)
0.384
(0.81)
1.437
(2.02)
0.564*
(2.06)
0.265
(0.37)
0.318
(0.69)
-0.278
(-0.77)
-0.977*
(-2.06)
-0.573
(-1.05)
0.340
(0.95)
0.699**
(8.59)
0.989**
(7.15)
0.504*
(2.34)
0.685**
(4.34)
0.796**
(4.74)
0.222
(1.12)
0.058
(0.13)
0.701**
(4.25)
0.679**
(4.70)
0.783**
(4.99)
0.621*
(2.53)
0.610**
(6.42)
0.390
(1.80)
0.931**
(5.56)
0.604**
(4.56)
0.796**
(4.79)
0.853**
(4.48)
0.869**
(7.05)
1.439
(0.71)
14.413**
(3.75)
5.366
(0.91)
6.408
(1.53)
16.814**
(3.36)
12.823*
(2.35)
-7.539
(-0.60)
20.630**
(4.46)
6.157
(1.61)
6.651
(1.48)
-23.562**
(-3.53)
-3.625
(-1.39)
-17.422**
(-2.61)
-0.401
(-0.09)
-2.258
(-0.65)
11.622*
(2.57)
13.551**
(2.58)
-6.471
(-1.89)
0.012
(0.12)
0.249
(1.50)
0.464
(1.86)
0.575**
(2.96)
0.210
(1.08)
0.434
(1.92)
0.248
(0.48)
0.419*
(2.22)
0.378*
(2.16)
0.432*
(2.36)
0.207
(0.73)
0.067
(0.59)
0.298
(1.32)
-0.023
(-0.11)
0.242
(1.46)
0.241
(1.25)
0.314
(1.42)
-0.000
(-0.00)
-0.961**
(-4.90)
-1.548**
(-6.10)
-1.297**
(-4.11)
-0.864**
(-2.87)
-0.797**
(-2.70)
-1.406**
(-4.22)
-1.860**
(-2.83)
-1.196**
(-4.30)
-1.142**
(-4.00)
-1.451**
(-5.61)
-1.862**
(-4.91)
0.047
(0.26)
-1.064**
(-4.50)
-1.670**
(-5.13)
-0.845**
(-2.91)
-1.175**
(-3.94)
-1.773**
(-5.40)
-1.341**
(-5.72)
0.027**
(3.70)
0.265**
(2.67)
0.568**
(3.78)
0.492**
(3.63)
0.294*
(2.32)
0.030
(1.78)
-0.343
(-1.19)
0.100*
(2.28)
0.347**
(3.24)
0.423**
(3.93)
0.493**
(2.68)
-0.006
(-0.73)
.1396**
(3.09)
0.189
(1.58)
0.004
(0.39)
0.218**
(3.92)
0.119
(1.94)
-0.010
(-0.86)
-0.414**
(-2.92)
-0.297
(-1.65)
-0.244
(-0.96)
-0.471*
(-2.18)
-0.185
(-0.90)
-0.192
(-0.72)
Dropped
Dropped
-0.216
(-1.06)
-0.511*
(-2.56)
-0.353
(-1.95)
-0.290
(-0.89)
-0.211
(-1.58)
-0.174
(-0.83)
-0.593*
(-2.51)
-0.562**
(-2.67)
-0.595**
(-2.68)
-0.760**
(-3.02)
-0.577**
(-3.36)
21
Development interaction
IPRjt*LD
IPRjt*MD
IPRjt*HD
0.047
0.092**
0.096**
(1.07)
(2.96)
(2.76)
0.065
0.028
-0.051
(0.87)
(0.51)
(-0.85)
0.146
0.087
0.071
(1.33)
(1.03)
(0.79)
0.059
0.130*
0.037
(0.71)
(2.13)
(0.56)
0.104
0.051
-0.041
(1.10)
(0.72)
(-0.56)
-0.035
-0.009
0.008
(-0.32)
(-0.11)
(0.09)
0.118
0.098
-0.059
(0.57)
(0.48)
(-0.28)
0.139
0.165*
0.013
(1.52)
(2.38)
(0.18)
0.103
0.210**
0.151*
(1.34)
(3.72)
(2.43)
0.095
0.076
0.032
(1.12)
(1.17)
(0.46)
0.308*
0.080
0.008
(2.40)
(0.85)
(0.08)
0.020
0.101**
0.062
(0.38)
(2.60)
(1.48)
0.086
0.102
0.180*
(0.79)
(1.17)
(2.03)
0.128
0.056
-0.020
(1.43)
(0.85)
(-0.29)
-0.014
0.055
-0.039
(-0.19)
(1.06)
(-0.68)
0.074
0.056
0.063
(0.83)
(0.86)
(0.89)
-0.102
-0.021
-0.062
(-0.99)
(-0.28)
(-0.76)
0.123
0.164**
0.132*
(1.78)
(3.24)
(2.43)
R2
0.763
0.771
0.579
0.667
0.559
0.405
0.383
0.663
0.722
0.727
0.552
0.681
0.558
0.618
0.565
0.653
0.629
0.776
71. Power generating machinery and equipment
72. Machinery specialized for particular industry
73. Metalworking machinery
74. General industrial machinery and equipment
75. Office machines
76. Telecommunication
77. Electrical machinery
78. Road vehicles
79. Other transport equipment
81. Prefabricated building sanitary, plumbing, n.e.s.
82. Furniture and parts thereof
83. Travel goods, handbag, similar
84. Articles of apparel and clothing accessories
85. Footwear
87. Professional, scientific, controlling apparatus
88. Photographic apparatus, n.e.s, watches, clocks
89. Miscellaneous manufactured articles
-19.519**
(-3.11)
-7.558
(-1.63)
-9.084
(-1.19)
-16.242**
(-4.15)
-1.936
(-0.36)
2.501
(0.42)
5.405
(1.42)
-4.910
(-0.92)
6.499
(0.49)
-0.482
(-0.07)
-11.956
(-1.85)
24.774**
(3.82)
10.218*
(2.03)
52.633**
(7.69)
4.056
(0.89)
4.329
(0.90)
(12.425)**
(4.14)
-0.383
(-0.69)
1.056**
(2.62)
0.975
(1.39)
-0.013
(-0.04)
0.238
(0.51)
0.124
(0.24)
0.066
(0.20)
2.268**
(4.77)
0.648
(0.55)
-0.437
(-0.65)
-1.255*
(-2.17)
-2.393**
(-4.17)
-1.370**
(-3.16)
0.527
(0.86)
0.770
(1.89)
0.191
(0.45)
0.193
(0.75)
1.050**
(5.80)
0.808**
(5.55 )
0.801**
(3.94)
0.777**
(6.52)
1.558**
(9.71)
1.242**
(7.40)
0.838**
(7.10)
0.835**
(5.60)
0.743
(1.82)
0.568**
(2.62)
1.142**
(5.87)
1.384**
(7.35)
0.975**
(6.01)
1.244**
(6.75)
0.879**
(6.89)
1.059**
(7.53)
0.813**
(8.60)
14.159**
(2.68)
3.139
(0.82)
4.107
(0.63)
11.953**
(3.68)
0.303
(0.07)
-1.392
(-0.28)
-0.242
(-0.08)
-4.326
(-0.96)
-4.029
(-0.36)
3.162
(0.51)
11.481*
(2.10)
-7.914
(-1.44)
-1.215
(-0.29)
-33.119**
(-5.69)
-5.154
(-1.33)
-2.446
(-0.60)
-6.155*
(-2.50)
0.131
(0.64)
0.286
(1.67)
0.501*
(2.25)
0.216
(1.52)
-0.322
(-1.75)
-0.057
(-0.30)
0.011
(0.08)
-0.052
(-0.31)
-0.311
(-0.68)
0.242
(0.99)
-0.298
(-1.31)
-0.619**
(-2.90)
-0.007
(-0.04)
-0.381
(-1.84)
0.472**
(3.21)
-0.102
(-0.65)
0.000
(0.00)
** Significant at the 0.01 level
* Significant at the 0.05 level
22
-1.105**
(-3.78)
-1.119**
(-4.08)
-1.395**
(-5.60)
-0.988**
(-4.30)
-0.987**
(-3.66)
-0.627*
(-2.31)
-1.388**
(-5.59)
-0.135
(-0.57)
-0.859
(-1.50)
-1.006**
(-3.52)
-0.821**
(-2.68)
-0.637*
(-2.27)
-0.569
(-1.70)
-0.884**
(-3.31)
-0.541*
(-2.54)
-0.970**
(-4.32)
-0.899**
(-4.38)
0.124
(2.20)
0.041**
(3.04)
0.172**
(2.64)
-0.004
(-0.36)
0.133*
(2.19)
0.042**
(2.66)
0.005
(0.46)
0.006
(0.41)
0.071*
(2.37)
0.232
(1.62)
0.297*
(2.01)
0.237*
(1.97)
-0.002
(-0.17)
0.084
(1.76)
0.583**
(6.00)
0.065
(1.84)
-0.007
(-0.90)
-0.596**
(-2.66)
-0.649**
(-3.24)
-0.226
(-1.06)
-0.673**
(-4.02)
0.271
(1.39)
-0.174
(-0.88)
-0.557**
(-3.09)
-0.266
(-1.55)
-1.462*
(-2.42)
-0.452*
(-2.04)
-0.123
(-0.58)
0.110
(0.54)
-0.377
(-1.54)
0.234
(1.11)
0.054
(0.37)
0.059
(0.36)
-0.296*
(-1.99)
0.015
(0.15)
0.023
(0.29)
-0.013
(-0.12)
-0.035
(-0.54)
0.049
(0.57)
-0.166
(-1.77)
0.053
(0.81)
-0.024
(-0.29)
-0.219
(-1.02)
0.108
(0.96)
0.195
(1.88 )
0.429**
(4.31)
0.169
(1.94)
0.067
(0.64)
-0.040
(-0.57)
0.153*
(1.99)
0.052
(1.00)
0.039
(0.52)
0.052
(0.91)
-0.005
(-0.07)
0.077
(1.57)
0.062
(0.96)
-0.014
(-0.20)
0.124**
(2.64)
0.093
(1.46)
-0.262
(-1.65)
0.099
(1.15)
0.122
(1.59)
0.167*
(2.24)
0.115
(1.84)
0.005
(0.07)
0.052
(0.98)
0.090
(1.56)
0.060
(1.62)
0.017
(0.22)
-0.021
(-0.33)
0.061
(0.70)
0.007
(0.14)
0.033
(0.47)
-0.056
(-0.75)
0.092
(1.78)
0.028
(0.42)
0.090
(0.53)
0.155
(1.70)
-0.101
(-1.24)
0.113
(1.43)
0.131
(1.89)
-0.018
(-0.23)
0.032
(0.56)
0.072
(1.18 )
0.021
(0.52)
0.685
0.705
0.698
0.770
0.780
0.711
0.755
0.653
0.337
0.576
0.508
0.677
0.630
0.629
0.792
0.733
0.765
Table 5: Imitative Ability Interaction Results (2-digit level) - Random Effect Model
PRODUCT SECTORS
Total exports
51. Organic chemicals
52. Inorganic chemicals
53. Dyeing tanning and coloring materials
54. Medical and pharmaceutical products
55. Essential oils and resinoids perfume materials
56. Fertilizers
57. Plastics in primary forms
58. Plastics in non primary forms
59. Chemical materials and products, n.e.s.
61. Leather
62. Rubber manufactures
63. Cork and wood manufactures
64. Paper
65. Textile yarn, fabrics, made-up articles
66. Non-metallic mineral manufactures
67. Iron and steel
69. Manufactures of metals
CONST
lnGDPi
(Korea)
lnGDPj
(Importing)
lnPOPi
(Korea)
lnPOPj
(Importing)
lnDISTij
lnOPENjt
LOCKj
-3.628
(-1.48)
-15.353**
(-3.30)
-4.417
(-0.64)
-9.091
(-1.80)
-22.007**
(-3.71)
-15.090*
(-2.32)
19.479
(1.36)
-31.457**
(-5.62)
-7.857
(-1.69)
-10.187*
(-1.91)
37.987**
(4.80)
2.410
(0.76)
30.765**
(4.07)
3.251
(0.62)
8.659*
(2.04)
-10.658*
(-1.97)
-14.421*
(-2.29)
12.153**
0.579**
(2.79)
-0.832*
(-2.06)
0.032
(0.05)
-0.176
(-0.40)
-0.887
(-1.69)
-0.058
(-0.10)
0.521
(0.41)
-0.102
(-0.21)
-0.102
(-0.25)
0.449
(0.95)
1.587*
(2.20)
0.516
(1.85)
0.119
(0.17)
0.428
(0.93)
-0.268
(-0.73)
-0.994*
(-2.11)
-0.605
(-1.10)
0.387
0.614**
(8.73)
0.804**
(6.97)
0.421**
(2.67)
0.575**
(4.28)
0.650**
(4.73)
0.296
(1.82)
-0.359
(-0.91)
0.575**
(4.22)
0.672**
(5.50)
0.626**
(4.95)
0.262
(1.32)
0.654**
(8.16)
0.722**
(4.54)
0.631**
(4.46)
0.482**
(4.14)
0.827**
(6.02)
0.862**
(5.51)
0.750**
1.839
(0.93)
14.696**
(3.78)
5.085
(0.86)
6.873
(1.63)
17.208**
(3.44)
12.861*
(2.36)
-7.329
(-0.61)
21.615**
(4.63)
6.687
(1.73)
6.882
(1.54)
-24.243**
(-3.58)
-3.253
(-1.23)
-17.618**
(-2.70)
-.167
(-0.04)
-1.730
(-0.50)
11.545*
(2.56)
14.263**
(2.72)
-5.997
0.085
(0.92)
0.452**
(3.34)
0.560**
(3.13)
0.696**
(4.24)
0.386*
(2.48)
0.357*
(1.98)
0.653
(1.54)
0.578**
(3.81)
0.382**
(2.61)
0.594**
(4.12)
0.612**
(2.76)
0.023
(0.25)
-0.015
(-0.10)
0.291
(1.72)
0.371*
(2.54)
0.219
(1.39)
0.300
(1.69)
0.107
-0.905**
(-4.74)
-1.448**
(-5.87)
-1.288**
(-4.26)
-0.723*
(-2.36)
-0.709*
(-2.43)
-1.423**
(-4.36)
-1.773**
(-2.64)
-1.035**
(-3.71)
-1.040**
(-3.71)
-1.385**
(-5.50)
-1.830**
(-4.95)
0.103
(0.58)
-1.133**
(-4.74)
-1.559**
(-4.85)
-0.707*
(-2.43)
-1.190**
(-4.02)
-1.708**
(-5.27)
-1.271**
0.025**
(3.58)
0.280**
(2.83)
0.573**
(3.85)
0.509**
(3.68)
0.297*
(2.32)
0.031
(1.86)
-0.365
(-1.24)
0.092*
(2.06)
0.354**
(3.29)
0.430**
(4.01)
0.512**
(2.79)
-0.006
(-0.72)
0.148**
(3.28)
0.205
(1.72)
0.002
(0.19)
0.222**
(3.96)
0.114
(1.85)
-0.013
-0.408*
(-2.93)
-0.326
(-1.85)
-0.238
(-0.96)
-0.524*
(-2.36)
-0.230
(-1.12)
-0.188
(-0.71)
Dropped
Dropped
-0.301
(-1.46)
-0.568**
(-2.87)
-0.367*
(-2.07)
-0.255
(-0.79)
-0.248
(-1.91)
-0.133
(-0.63)
-0.594*
(-2.53)
-0.617**
(-2.90)
-0.588**
(-2.65)
-0.800**
(-3.20)
-0.593**
23
Imitative ability interaction
IPRjt*WIjt
IPRjt*SIjt
-0.018
0.104**
(-0.48)
(3.48)
-0.010
0.000
(-0.15)
(0.00)
0.100
0.090
(1.01)
(1.13)
0.080
0.094
(1.04)
(1.56)
0.056
0.007
(0.64)
(0.11)
-0.001
-0.011
(-0.02)
(-0.14)
-0.051
0.045
(-0.19)
(0.24)
0.151
0.083
(1.79)
(1.23)
0.162*
0.179**
(2.28)
(3.21)
0.022
0.066
(0.28)
(1.04)
0.097
0.091
(0.81)
(0.98)
0.083
0.072
(1.72)
(1.89)
0.242*
0.117
(2.54)
(1.44)
-0.021
0.048
(-0.26)
(0.75)
-0.014
0.023
(-0.21)
(0.44)
0.081
0.054
(1.01)
(0.86)
-0.066
-0.053
(-0.71)
(-0.72)
0.070
0.160**
R2
0.771
0.771
0.578
0.649
0.553
0.408
0.354
0.651
0.715
0.727
0.552
0.680
0.559
0.614
0.537
0.652
0.623
0.773
71. Power generating machinery and equipment
72. Machinery specialized for particular industry
73. Metalworking machinery
74. General industrial machinery and equipment
75. Office machines
76. Telecommunication
77. Electrical machinery
78. Road vehicles
79. Other transport equipment
81. Prefabricated building sanitary, plumbing, n.e.s.
82. Furniture and parts thereof
83. Travel goods, handbag, similar
84. Articles of apparel and clothing accessories
85. Footwear
87. Professional, scientific, controlling apparatus
88. Photographic apparatus, n.e.s, watches, clocks
89. Miscellaneous manufactured articles
(2.98)
-20.455**
(-3.28)
-8.445
(-1.83)
-8.967
(-1.18)
-17.466**
(-4.42)
-2.937
(-0.56)
0.002
(0.00)
4.332
(1.15)
-7.397
(-1.42)
8.463
(0.64)
0.313
( 0.04)
-13.964*
(-2.11)
27.351**
(4.20)
10.411*
(2.07)
52.819**
(7.74)
2.562
(0.57)
4.564
(0.95)
12.210**
(4.05)
(1.08)
-0.339
(-0.61)
1.142**
(2.85)
0.976
(1.40)
-0.024
(-0.07)
0.375
(0.81)
0.232
(0.45)
0.138
(0.43)
2.380**
(5.09)
0.640
(0.54)
-0.431
(-0.64)
-1.216*
(-2.05)
-2.306**
(-3.98)
-1.328**
(-3.07)
0.602
(0.97)
0.817*
(2.04)
0.259
(0.61)
0.181
(0.70)
(7.27)
0.956**
(6.56)
0.583**
(4.72)
0.849**
(5.58)
0.733**
(7.15)
1.290**
(10.05)
1.031**
(7.70)
0.694**
(6.86)
0.603**
(5.09)
1.408**
(4.30)
0.639**
(4.02)
0.735**
(4.78)
1.143**
(7.73)
0.914**
(6.52)
1.072**
(7.45)
0.782**
(7.64)
0.910**
(7.93)
0.785**
(9.58)
(-1.77)
14.706**
(2.80)
3.588
(0.94)
4.108
(0.63)
12.552**
(3.84)
0.894
(0.20)
-0.034
(-0.01)
0.267
(0.09)
-2.979
(-0.68)
-5.442
(-0.49)
2.692
(0.43)
12.701*
(2.27)
-9.292
(-1.68)
-1.286
(-0.31)
-33.158**
(-5.69 )
-4.30
(-1.13)
-2.534
(-0.63)
-6.044*
(-2.44)
** Significant at the 0.01 level
* Significant at the 0.05 level
24
(0.89)
0.213
(1.34)
0.503**
(3.49)
0.434**
(2.78)
0.254*
(2.09)
-0.085
(-0.59)
0.104
(0.70)
0137
(1.10)
0.142
(1.09)
-1.055**
(-2.94)
0.156
(0.93)
0.179
(1.02)
-0.318*
(-1.97)
0.055
(0.33)
-0.211
(-1.39)
0.535**
(4.59)
0.048
(0.39)
0.037
(0.37)
(-5.49)
-1.062**
(-3.71)
-1.001**
(-3.65)
-1.456**
(-5.99)
-0.877**
(-3.76)
-0.912**
(-3.50)
-0.491
(-1.83)
-1.293**
(-5.24)
0.010
(0.04)
-1.294*
(-2.16)
-1.077**
(-3.88)
-0.547
(-1.79)
-0.665*
(-2.39)
-0.587
(-1.77)
-0.866**
(-3.36)
-0.475*
(-2.23)
-0.956**
(-4.30)
-0.857**
(-4.25)
(-1.09)
0.116*
(2.07)
0.037**
(2.77)
0.174**
(2.69)
-0.005
(-0.53)
0.122*
(2.04)
0.036*
(2.33)
0.002
(0.20)
-0.000
(-0.03)
0.080**
(2.63)
0.217
(1.52)
0.350*
(2.32)
0.238*
(1.96)
-0.003
(-0.20)
0.081
(1.71)
0.572**
(5.83)
0.064
(1.79)
-0.008
(-0.96)
(-3.48)
-0.601**
(-2.71)
-0.686**
(-3.40)
-0.187
(-0.88
-0.730**
(-4.27)
0.263
(1.39)
-0.212
(-1.07)
-0.577**
(-3.20)
-0.311
(-1.82)
-1.347*
(-2.10)
-0.434*
(-1.96)
-0.219
(-1.01)
0.138
(0.67)
-0.353
(-1.45)
0.242
(1.17)
0.029
(0.20)
0.067
(0.41)
-0.316*
(-2.15)
(1.13)
-0.034
(-0.38)
-0.056
(-0.79)
-0.039
(-0.42)
0.004
(0.08)
-0.097
(-1.27)
-0.258**
(-3.11)
-0.007
(-0.13)
-0.128
(-1.74)
-0.155
(-0.73)
0.113
(1.15)
0.081
(0.86)
0.308**
(3.41)
0.103
(1.30)
-0.029
(-0.33)
-0.078
(-1.25)
0.067
(0.96)
0 .063
(1.33)
(3.28)
0.032
(0.44)
0.043
(0.76)
0.028
(0.36)
0.044
(0.92)
0.076
(1.23)
-0.020
(-0.30)
0.126**
(2.78)
0.072
(1.19)
-0.145
(-0.93)
0.131
(1.59)
0.025
(0.33)
0.186**
(2.58)
0.129*
(2.11)
0.016
(0.21)
0.047
(0.92)
0.100
(1.80)
0.043
(1.19)
0.686
0.691
0.698
0.757
0.788
0.707
0.756
0.655
0.271
0.573
0.480
0.673
0.626
0.631
0.785
0.730
0.764
6. Conclusion
This paper contributes to the body of literature on the effect of stronger IPR protection on the flow of
traded goods. Using the gravity equation with fixed effects and random effects model, it analyzes the
impacts of IPR protection in foreign countries on Korea’s exports classified by total exports and 2-digit
commodity groups. Our empirical results are robust. Major findings are summarized as follows:
First, since the coefficient on the IPR index is positive and statistically significant in the case of Korea’s
total exports, we conclude that stronger protection of IPRs in foreign countries (the rest of the world)
induces Korea’s exports, exhibiting the prevalence of the market expansion effects. When the impacts of
IPR protection are allowed to differ across sectors, stronger effects are found in IPR-sensitive tech-sectors
such as chemicals.
Second, stronger protection of IPRs induces Korea’s exports to all foreign countries regardless of their
level of development. The effects are stronger in medium-income and high-income countries, followed by
low-income countries, which is not clear. When the impacts of IPR protection are allowed to differ across
sectors, again stronger effects are noticed in IPR-sensitive sectors such as plastics, iron and steel,
manufactures of metal, electrical machinery, etc.
Third, Korea tends to export more to countries with strong imitative ability when the IPR protection in
these countries is strengthened, indicating the market expansion effects. Stronger effects are found in IPRsensitive sectors such as plastics in non-primary form, manufactures of metal, electrical machinery, etc.
Fourth, although the coefficient on IPRjt*WIjt is statistically insignificant, the negative value could
indicate that stronger protection of IPRs in foreign countries with weak imitative ability leads to
ambiguous reduction in Korea’s exports. When we allow the impacts of IPRs differ across industries, the
stronger effect is found in IPR-sensitive sector such as telecommunication.
Finally, the signs of the coefficients on traditional variables suggest that Korea tends to export more to
countries with higher level of GDP and higher degree of openness to trade, and tends to export less to
landlocked countries and countries which are distant from Korea.
The implication is that Korea’s exports would increase further if appropriate measures are taken in order
to strengthen IPR protection in foreign countries. However, these measures must be accompanied by the
efforts to increase the GDP, improve social infrastructure and accelerate domestic reforms (openness to
trade) as well.
25
Appendices
Appendix 1: Summary of Statistics
Variables
Count
Mean
Std. Dev.
Variance
Minimum
Maximum
Range
lnEijt
380
2.233831
1.005208
1.010443
0.029384
4.791796
4.762412
lnGDPit
380
5.685683
0.170887
0.029203
5.421339
5.898490
0.477151
lnGDPjt
380
4.602252
0.896820
0.804286
2.130334
7.094608
4.964275
lnPOPit
380
1.660228
0.019159
0.000367
1.632143
1.682488
0.050345
lnPOPjt
380
1.157829
0.620215
0.384666
0.000434
3.116462
3.116027
lnDISTij
380
3.948605
0.243309
0.059199
2.983175
4.292101
1.308926
OPENjt
380
0.856290
2.779975
7.728260
0.074000
49.173000
49.09900
LOCKj
380
0.105263
0.307297
0.094431
0.000000
1.000000
1.00000
IPRjt
380
2.923658
1.066599
1.137633
0.590000
4.880000
4.29000
LD*IPR
380
0.582605
1.018284
1.036902
0.000000
3.760000
3.76000
MD*IPR
380
1.153395
1.449397
2.100752
0.000000
4.540000
4.54000
HD*IPR
380
1.190000
1.869174
3.493812
0.000000
4.880000
4.88000
WI*IPR
380
0.961658
1.204002
1.449620
0.000000
3.520000
3.52000
380
1.962000
1.863200
3.471515
0.000000
4.880000
4.88000
SI*IPR
Source: Statistical result
26
Appendix 2: Development Interaction Results (1-digit level) - Random Effects Model
PRODUCT SECTORS
Total exports
0. Food and live animals
1. Beverage and tobacco
2. Crude materials excluding fuels
3. Mineral fuels, etc.
4. Animal, vegetable oil, and fats
5. Chemicals
6. Basic manufactures
7. Machines, transport equipment
8. Miscellaneous manufactured goods
9. Unclassified goods
CONST
-2.815
(-1.12)
7.230
(1.21)
1.362
(0.14)
-5.968
(-0.96)
-27.070**
(-2.70)
-10.322*
(-2.05)
-21.689**
(-5.10)
8.431**
(2.80)
-2.161
(-0.61)
14.406**
(4.52)
-9.063
(-0.64)
lnGDPi
lnGDPj
(Korea) (Importing)
0.527*
(2.47)
-0.239
(-0.46)
-1.806*
(-1.99)
0.154
(0.28)
-0.791
(-0.90)
-0.477
(-1.05)
-0.358
(-0.96)
0.299
(1.17)
1.048**
(3.44)
-0.108
(-0.39)
-1.472
(-1.32)
0.699**
(8.59)
0.936**
(4.97)
0.690**
(2.61)
0.686**
(3.48)
1.025**
(3.47)
0.357**
(2.58)
0.735**
(5.41)
0.605**
(6.21)
0.816**
(7.18)
0.926**
(9.02)
1.215**
(3.72)
lnPOPi
lnPOPj
(Korea) (Importing)
1.439
(0.71)
-0.849
(-0.17)
8.379
(0.99)
6.305
(1.22)
24.611**
(2.93)
12.006**
(2.81)
17.421**
(4.92)
-2.419
(-0.99)
0.314
(0.11)
-6.028*
(-2.29)
12.873
(1.10)
0.012
(0.12)
-0.165
(-0.76)
-0.371
(-1.30)
0.417
(1.80)
-0.238
(-0.69)
-0.020
(-0.13)
0.303
(1.92)
0.143
(1.14)
-0.038
(-0.28)
-0.096
(-0.78)
-0.323
(-0.88)
** Significant at the 0.01 level
* Significant at the 0.05 level
27
lnDISTij
-0.961**
(-4.90)
-1.541**
(-4.81)
-1.547**
(-5.35)
-1.681**
(-5.19)
-3.225**
(-6.06)
-2.015**
(-9.49)
-1.382**
(-5.74)
-1.181**
(-5.10)
-0.828**
(-3.61)
-1.056**
(-5.19)
-1.934**
(-5.27)
lnOPENjt
0.027**
(3.70)
0.084
(1.40)
0.106
(1.63)
0.041
(0.65)
0.015
(0.55)
0.021
(1.58)
0.029*
(2.45)
-0.007
(-0.83)
0.038**
(3.86)
-0.026**
(-2.86)
0.816**
(4.75)
R2
LOCKj
-0.414**
(-2.92)
-0.287
(-1.10)
-0.755**
(-2.35)
-0.507*
(-2.09)
-0.854*
(-2.22)
-0.147
(-0.96)
-0.247
(-1.42)
-0.442**
(-2.63)
-0.485**
(-2.93)
-0.123
(-0.84)
0.405
(1.51)
IPRjt*LD
0.047
(1.07)
0.043
(0.42)
0.221
(1.52)
0.196
(1.88)
0.508**
(3.08)
0.001
(0.02)
0.175*
(2.40)
0.020
(0.38)
-0.004
(-0.07)
0.084
(1.50)
-0.124
(-0.67)
IPRjt*MD
IPRjt*HD
0.092**
(2.96)
0.003
(0.04)
0.117
(1.04)
0.163*
(2.08)
0.227
(1.85)
-0.033
(-0.55)
0.182**
(3.40)
0.098**
(2.62)
0.049
(1.09)
0.106**
(2.59)
-0.127
(-0.89)
0.096**
(2.76)
0.018
(0.23)
0.072
(0.63)
0.085
(0.99)
-0.001
(-0.01)
-0.077
(-1.23)
0.049
(0.84)
0.021
(0.50)
0.059
(1.20)
0.048
(1.07)
-0.251
(-1.68)
0.763
0.608
0.467
0.638
0.476
0.528
0.746
0.697
0.730
0.783
0.612
Appendix 3: Imitative Ability Interaction Results (1-digit level) - Random Effect Model
PRODUCT SECTORS
Total exports
0. Food and live animals
1. Beverage and tobacco
2. Crude materials excluding fuels
3. Mineral fuels, etc.
4. Animal, vegetable oil, and fats
5. Chemicals
6. Basic manufactures
7. Machines, transport equipment
8. Miscellaneous manufactured goods
9. Unclassified goods
CONST
lnGDPi
(Korea)
lnGDPj
(Importing)
lnPOPi
(Korea)
lnPOPj
(Importing)
lnDISTij
lnOPENjt
LOCKj
-3.628
(-1.48)
7.403
(1.25)
1.549
(0.16)
-6.307
(-1.02)
-38.801**
(-4.38)
-10.442*
(-2.09)
-22.603**
(-5.24)
7.334*
(2.42)
-3.364
(-1.01)
13.672**
(4.26)
-6.329
(-0.44)
0.579**
(2.79)
-0.128
(-0.24)
-1.818
(-2.01)
0.082
(0.15)
-0.581
(-0.65)
-0.392
(-0.87)
-0.374
(-0.99)
0.322
(1.25)
1.173**
(4.06)
-0.087
(-0.32)
-0.989
(-0.88)
0.614**
(8.73)
0.749**
(4.99)
0.651**
(3.29)
0.672**
(4.26)
0.572*
(2.24)
0.148
(1.36)
0.598**
(5.38)
0.472**
(5.46)
0.608**
(6.70)
0.821**
(9.59)
0.776**
(3.55)
1.839
(0.93)
-0.872
(-0.17)
8.316
(0.99)
6.599
(1.28)
32.027**
(4.03)
12.094**
(2.85)
17.924**
(4.99)
-1.904
(-0.77)
0.962
(0.35)
-5.645*
(-2.13)
10.050
(0.85)
0.085
(0.92)
-0.001
(-0.01)
-0.293
(-1.45)
0.468**
(2.58)
0.306
(1.01)
0.183
(1.55)
0.469**
(3.68)
0.278
(2.46)
0.136
(1.24)
0.014
(0.14)
0.097
(0.44)
-0.905**
(-4.74)
-1.542**
(-5.06)
-1.532**
(-5.49)
-1.625**
(-5.13)
-3.067**
(-5.93)
-1.956**
(-9.29)
-1.238**
(-5.17)
-1.037**
(-4.46)
-0.754**
(-3.46)
-0.969**
(-4.86)
-1.757**
(-5.02)
0.025**
(3.58)
0.080
(1.38)
0.110
(1.71)
0.041
(0.67)
0.015
(0.55)
0.016
(1.28)
0.026*
(2.20)
-0.009
(-1.10)
0.034**
(3.63)
-0.027**
(-3.05)
0.842**
(4.94)
-0.408*
(-2.93)
-0.286
(-1.14)
-0.787
(-2.48)*
-0.554*
(-2.32)
-0.753*
(-2.01)
-0.158
(-1.03)
-0.318**
(-1.82)
-0.483**
(-2.85)
-0.472**
(-2.98)
-0.152
(-1.05)
0.329
(1.24)
** Significant at the 0.01 level
* Significant at the 0.05 level
28
Imitative ability interaction
IPRjt*WIjt
IPRjt*SIjt
-0.018
0.104**
(-0.48)
(3.48)
-0.097
0.031
(-1.08)
(0.44)
0.205
0.092
(1.66)
(0.87)
0.232*
0.117
(2.49)
(1.53)
-0.148**
-0.223*
(-1.84)
(-2.20)
-0.091
-0.025
(-1.34)
(-0.45)
0.182**
0.124*
(2.77)
(2.34)
-0.002
0.078*
(-0.04)
(2.12)
-0.151**
0.077
(-2.88)
(1.86)
0.057
0.086*
(1.14)
(2.14)
-0.218
-0.129
(-1.28)
(-0.92)
R2
0.771
0.620
0.474
0.639
0.489
0.524
0.734
0.679
0.751
0.784
0.606
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CD-ROM
IMF-Direction of Trade Statistics - CD-ROM
Internet websites
Asian Development Bank: http://www.adb.org/Statistics/default.asp
KITA: http://global.kita.net/
OECD: http://www.oecd.org/home/
UN Comtrade: http://comtrade.un.org/db/mr/rfCommoditiesList.aspx?px=S3&cc=
The Economist Intelligence Unit: http://www.eiu.com/index.asp?rf=0
The World Bank: http://www.worldbank.org/
World Economic Outlook Database: http://www.imf.org/external/pubs/ft/weo/2008/02/weodata/index.aspx
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