Download The Impact of Free Trade Agreements on Foreign Direct Investment

The Impact of Free Trade Agreements on Foreign Direct
Investment
Yong Joon Jang
Indiana University
Department of Economics
Apr 6, 2007
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Abstract
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I. Introduction
The first Regional Trade Agreement (RTA), EC (Treaty of Rome) came into
being in 1958. Ever since then about 220 RTAs have been signed. The year of 1992
witnessed a sharp increase in the growth rate in the number of RTAs. After that RTAs
have kept increasing at a steady rate of 16%. (See Figure 1).
According to WTO, RTA can be classified into the following four kinds of
treaties. The first one is Free Trade Agreement (FTA), which is the lowest level of RTA.
With FTA, a country should reduce or eliminate a tariff for member countries but impose
a different tariff on non-member countries. The typical example is North American Free
Trade Agreement (NAFTA). Under NAFTA, there are no tariffs for trades among Canada,
Mexico and U.S., but each country imposes different tariffs on non-member countries.
The second one is Customs Union (CU). Under CU, there is no tariff between member
countries, but each member country imposes a common tariff on non-member countries.
Mercado Comun del Cono Sur (MERCOSUR) is one example. The third one is Common
Market. Under Common Market, member countries carry out common monetary and
fiscal policies. One example is European Community (EC). The last one is Single Market,
which achieves a political and economic unity with common currency and common
assembly. One example is Europe Union (EU).
I will focus on the lowest level of RTA, FTA in this paper. As the other three
RTAs involve much more restrictions, it would be rather complicated to analyze their
impact on economic activities.
Foreign Direct Investment (FDI) has also increased over the past two decades.
The total amount of FDI increased from 7.4% of the world GDP in 1982 to 22.1% in
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2002 (See Figure 2). (I will compare the FDI growth between developed-developing
countries and between developed-developed with the figure in my future draft)
Obviously descriptive data shows that both the number of FTA and amount of
FDI increased in the past twenty years. How does FTA affect the economic activities in
the member countries? In particular, what effects does FTA have on foreign direct
investment (FDI)? Previous research has found a positive effect of FTA on FDI between
developed countries and less developed countries. But is this effect always positive
between different country-pairs? What will happen when host and parent countries are
both developed? Unfortunately no research has ever addressed this question. The
objective of this paper is to fill in this blank in research. I attempt to empirically analyze
the impact of FTA on FDI among 23 OECD countries from 1982 to 2004 using the
Knowledge-Capital model as my theoretical framework.
II. Relationship between FTA and FDI
Recently many theoretical and empirical works in International trade have
focused on motives for FDI of Multinational enterprises (MNEs). These works are
divided into three main categories: the horizontal motivations (Markusen, 1984;
Markusen & Venables, 1998), the vertical motivations (Helpman, 1984; Helpman &
Krugman, 1985) and the Knowledge-Capital model (Markusen & Maskus, 2001) that
combines both the horizontal and vertical models.
Horizontal FDI & Vertical FDI
Horizontal FDI is designed to place production close to consumers and thereby
avoid trade costs. Multinationals have a plant and a headquarters in a home country (or
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source country) and other plants in each host country. So each production facility
supplies each individual market. However, there exists a trade-off between realizing
economies of scale at the firm level and using tariff-jumping strategies.
The main factors which affect the incentives for horizontal FDI are trade cost and
market size of host country. When trade costs in host country rise, exporters to this
country would encounter a higher marginal cost. Hence, they have higher incentive to
build a plant in the host country and sell their products directly. If the market size of the
host country expands, firms would have more incentives to build production facility there
because the associated fixed cost would be covered by the revenue generated.
Consequently horizontal FDI will increase as trade cost and market size in host country
increase. However, if trade costs decreases, then multinationals with higher fixed costs
may concentrate their activity in one country and develop trade flows with host countries
rather than open plants in each country (Yeyati, Stein and Daude, 2003 and Lesher and
Miroudot, 2006)
On the other hand, vertical FDI is driven by the desire to carry out unskilled-labor
intensive production activities in locations with relatively abundant unskilled labor.
Multinationals have a headquarters in a home country and a plant in a host country. In
this case, a home country is relatively abundant in skilled labor and a host country is
relatively abundant in unskilled labor. After producing goods in a host country, firms
with vertical FDI should import them to the home country to supply their home
consumers.
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The main factors which affect the intensives for vertical FDI are trade costs and
skill difference between home and host countries. As trade cost increases in host country,
firms with vertical FDI will have to import goods from the host country at a higher cost.
However, as the skill difference between home and host countries increases,
relative wage for low skilled labor will decrease. So firms would have more incentives to
produce their goods with lower production costs in the host country. Consequently,
vertical FDI will increase as trade costs decrease and skill difference increases (Yeyati,
Stein and Daude, 2003 and Lesher and Miroudot, 2006)
Knowledge-Capital model
Markusen et al. (1996) and Markusen (1997) built the Knowledge-Capital model
with two countries, two factors, and two goods. In this setup, they compare the incentives
for three different types of firms: horizontal firms with a plant in each country and a
headquarters in the home country, vertical firms with a plant in the host country and a
headquarters in the home country; and national firms with a plant and a headquarters in
the home country.
Markusen et al. (1996) analyzed various factors that affect FDI, including market
size, skill difference, distance between two countries, trade cost and their intersections. I
will pay more attention to the relationship between trade costs and FDI in my paper (I
will fully explain this model in my future draft).
FTA & FDI
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Carr et al. (2001) empirically test the Knowledge-Capital model. Their results
show that trade costs have positive effects on FDI when there exists a small skill
difference between parent and host countries but negative effects when the difference is
large. Since they use affiliates’ sales in host country to proxy FDI, insufficient
information exists to distinguish vertical FDI from horizontal FDI. However, their results
seem to show that in the case of small skill difference between parent and host countries,
when trade cost rises, the increase in horizontal FDI will dominate the decrease in
vertical FDI and vice versa when the skills difference is big.
Also, this may imply that the decrease in horizontal FDI will dominate the
increase in vertical FDI in case of small skill difference between parent and host
countries when trade cost falls and vice versa when the skills difference is big. This is my
hypothesis for the effects of decreased trade costs on FDI in an OECD - OECD countrypair and an OECD - non-OECD country-pair. In this paper, I want to test these
relationships by defining decreased trade costs as FTA.
As is well known, FTA is made to reduce trade cost. That is, when two countries
agree to form FTA, trade cost would fall or diminish between them. As a result, firms
with vertical FDI will benefit from this and hence have more incentive to increase
vertical FDI. On the other hand, there will be less tariff-jumping incentive for horizontal
FDI.
Vertical FDI dominates horizontal FDI in countries where skill difference is large.
Hence FTA should have a positive effect on FDI where member countries have large
different skill levels.
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However, the reduced trade cost will discourage firms from building plants with
high sunk cost in host country. In other words, firms with horizontal FDI will have more
benefit from economic of scales rather than tariff-jumping strategies. This leads to a
decrease in horizontal FDI. Since horizontal FDI dominates vertical FDI in countries
where skill difference is small, FTA should have a negative effect on FDI with similar
skill levels.
This is exactly my hypothesis – When the skill difference between parent and host
countries is small, FTA is going to have a negative impact on FDI and vice versa1.
III. Literature Review
All the previous empirical works on the relationship between FTA and FDI are
summarized and compared in Table 1. They all find RTA to have positive effects on FDI
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. Most authors use the gravity model. (I will expand this Table 1 into a detailed
literature review later).
I will follow the previous scholars and use the Knowledge-Capital model. By
considering trade costs and skill difference between member countries at the same time,
the Knowledge-Capital model provides a good framework to analyze the effects of FTA
on separated FDI: Horizontal FDI and vertical FDI.
My paper differs from the previous works in the following important ways: First,
unlike the previous empirical works, I will consider not only the impact of FTA on FDI
among member countries, but also the impact of FTA on FDI from non-member
countries to member countries. The first effect is called “intra- regional effect”. I expect
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In this paper, I analyze the effect of FTA only on the intra-regional FDI. In other words, I exclude the
effects of FTA on FDI from non-member countries in the main regression. Yeyati et al. (2003) define the
changes of FDI from non-member countries as redistributive effects, FDI diversion and FDI dilution. In
sensitivity analysis, I will control for this effect.
2
Some authors defined RTA as Regional Integration (RI) or (multilateral) FTA.
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FTA to affect horizontal FDI negatively and vertical FDI positively among member
countries. The second is called “extra-regional effect”. I will consider the second one
after the first draft of the third-year paper. I want to examine if this “extra-effect” is
positive or negative.
Second, all the previous works use “multilateral” RTA as a key independent
variable, while the dependent variable is “bilateral” FDI. This would be problematic
because there exists redistributive effects among member countries and the bilateral FDI
cannot control for this effect. In this paper, I consider only the bilateral FTA between
1982 and 2004. There is no bilateral FTA before 1982.
Third, previous studies dealing with the relationship between FDI and FTA
usually assume all independent variables to be exogenous and neglect the possible
endogeneity problem. For example, a higher FDI between two countries can affect their
economic growth positively. So I introduce GMM to this area to solve the endogeneity
problem.
Finally, I consider the dynamic specifications of FTA on FDI. In the previous
study, only Velde and Bezemer (2004) conducted time series analysis. They studied the
effects of specific investment-related provisions in RTAs on FDI from US and UK to
developing countries from 1980 to 2001. But since many gaps exist in their data, they
confined their analysis to including time dummies and using “error correction form”. In
my paper, I will use First Difference estimator, ECM estimator, and DID (Difference-indifference) estimator.
IV. Model Specification and Econometric Methodology
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To analyze the impact of bilateral FTA on bilateral FDI, I followed Carr et al.
(2001) and Egger and Pfaffermayr (2004) in setting up my key regression models, which
are fixed-effects OLS. Carr et al. (2001) empirically tested the Knowledge-Capital model
by estimating how FDI is influenced by country characteristics including economic sizes,
relative endowment differences, trade and investment costs, and certain interaction
among these variables.
Egger & Pfaffermayr (2004) added Bilateral Investment Treaties (BIT) to Carr et
al. (2001)’s regression model and changed the dependent variable into bilateral FDI.
They found a significant positive relationship between BIT and FDI, because BITs would
reduce the costs of investing abroad, including risk premium.
Equation (1) shows my major regression model. I use outward FDI of each OECD
country to another country ( Fijt ) as the dependent variable. For the independent variables,
I basically follow what Carr et al (2001) and Egger & Pfaffermayr (2004) used. I replace
BIT with RTA and tertiary school enrollment share in Egger & Pfaffermayr (2004) with
per capita GDP (“Percapita GDP”) for relative endowment differences. I add a new
variable trade openness (“OPEN”) to control for the effects of trade on FDI. Please see
table 2 for a detailed explanation of the dependent and independent variables in my
model.
Fijt  1  GDPijt   2 SIMI ijt   3 SK ijt   4 DISTij  SK ijt
  5  GDPijt  SK ijt   6 FTAijt   7 FTAijt  DISTij   ij  t   ijt
Table 3 summarizes the expected signs of the variables on horizontal FDI in this
paper. Below is a rationale for my expectations.
(1)
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First, I expect FTAijt to have a negative effect on Fijt in OECD – OECD country
pairs (intra OECD) and a positive effect in OECD – non-OECD country pair (extra
OECD). Carr et al.(2001) shows that as trade costs in host country increase, horizontal
FDI tends to increase whereas vertical FDI tends to decrease. To avoid increased trade
cost in host country, exporters from parent country are likely to build plant in host
country and sell their goods there. Thus Horizontal FDI increases. However, vertical FDI
tends to decrease as parent country has to encounter an increased cost when importing
from host country.
Carr et al. (2001) show that horizontal FDI dominates vertical FDI when skill
difference between home and host countries is small. Egger & pfaffermayr (2004) show
that skill difference between OECD countries is relatively much smaller than other
countries3. Hence, I expect horizontal FDI to dominate vertical FDI in the intra OECD
dataset and vertical FDI to dominate horizontal FDI in the extra OECD dataset.
As FTA eliminates various trade barriers such as tariff, and horizontal FDI
dominates vertical FDI among developed countries, the coefficient sign of FTAijt should
be negative in the intra OECD dataset. However, as vertical FDI dominates horizontal
FDI among developed-undeveloped county-pair, the coefficient sign of FTAijt should be
positive in the extra OECD dataset. As a result, the coefficient sign of FTAijt is
ambiguous in the whole dataset.
Second, according to Carr et al.(2001), as market size of host country increases,
exporters to this country acquire more consumers but simultaneously face higher
marginal trade costs. So they have more incentive to build a production plant in the host
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In my dataset, skill difference in intra OECD is much smaller than in extra OECD. Please see Table 6
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market, even though they have to pay the big fixed costs. Therefore, market sizes
(  GDPijt ) have positive effects on horizontal FDI. I expect the coefficient of
GDP
ijt
to be positive in intra OECD.
However, vertical FDI is not related to market size. As there still exists horizontal
FDI in extra OECD, the coefficient of
Overall, I expect of the coefficient of
GDP
ijt
GDP
ijt
would be also positive in extra OECD.
to be positive in the all the country-pairs
in the dataset.
Third, by simulation of the Knowledge-Capital model, Carr et al. (2001) show
that moderate difference in sizes of both parent and host countries encourage MNEs to
run a plant in a host market. Therefore, similarity in market sizes of parent and host
countries ( SIMI ijt ) have positive effects on horizontal FDI. Hence, I expect the
coefficients of SIMI ijt to be positive in intra OECD. Again, as vertical FDI is not related
to market size and horizontal FDI still exist in extra OECD, the coefficient of SIMI ijt
would be also positive in extra OECD. Overall, the coefficient of SIMI ijt would be
positive in all the country-pairs in the dataset.
Fourth, as Markusen and Venables (2000) show that dissimilarity in relative
endowments reduces horizontal FDI, SK ijt should be negatively related to FDI in intra
OECD country. On the other hand, as vertical FDI is driven by the desire to carry out
unskilled-labor intensive production activities in location with relatively abundant
unskilled labor, the relative skilled-labor difference between parent and host countries
( SK ijt ) should affect vertical FDI positively. In other words, the coefficient of SK ijt
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is positive in extra OECD as vertical FDI dominates horizontal FDI. As a result, the
coefficient sign of SK ijt is ambiguous in all the country pairs in the dataset.
Fifth, Egger et al.(2004) show that the difference in the skilled to unskilled labor
endowment supports vertical FDI to a lesser extent if the bilateral distance is large. So
DISTij  SK ijt would be negative in extra OECD. However, the effect of this
intersection term of the skilled-labor difference and the distance between two countries
( DISTij  SK ijt ) is positive on horizontal FDI. As horizontal FDI is expected to
dominate vertical FDI among OECD countries, the coefficient sign of DISTij  SK ijt
would be positive in intra OECD. Overall, the coefficient sign of DISTij  SK ijt would be
ambiguous in the whole country dataset.
Sixth, the variable  GDPijt  SK ijt represents the simultaneous effects of skill
difference and market size of both parent and host countries on FDI. Egger et al. (2004)
shows that this affects horizontal FDI negatively and vertical FDI positively. So the
coefficients of
 GDP
ijt
 SK ijt would be negative in intra OECD and positive in extra
OECD. Overall, the coefficient sign of
 GDP
ijt
 SK ijt would be ambiguous in the
whole OECD dataset.
Finally, I obtain trade openness ( OPEN jt ) by dividing the sum of exports and
imports by current GDP, i.e. the total trade as a percentage of GDP. As vertical FDI is
positively related to the quantity of exports in host and imports in home countries, the
effect of trade openness of host country is positive on vertical FDI. However, horizontal
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FDI has little to do with the volume of trade and thereby is not affected by trade openness.
Hence, I expect that the coefficient of OPEN jt is always positive.
I use Pooled OLS, Between estimator, Within (fixed effect) estimator, Random
effect-GLS estimator and Random effect-MLE estimator for the main regression. All
regressions are based on country-fixed effect.4
V. Data
Table 4 lists the variables and their respective sources. My database consists of
7053 observations of 23 OECD countries from 1982 to 2004. I dropped 7 countries from
a total of 30 OECD countries because they do not have bilateral FDI data. In my sample
there are 13 OECD home countries, 23 OECD host countries and 14 non-OECD host
countries (See Table 5). So there exist 468 country-pairs from 286 intra OECD countrypairs and 182 extra OECD country-pairs.
Table 6 shows the respective bilateral free trade agreements and the member
countries between 1982 and 2004. Before 1982, only multilateral FTAs existed including
EC, EU and EFTA. To avoid the problem of redistributive effect that I mentioned in
Section 3, I include the bilateral FTAs between 1982 and 2004 in the dataset of RTA.
But I also include FTAs between a single country and several other countries such as ECTurkey and EFTA-Mexico, because they have bilateral characteristics. In other words,
the FTA between EC-Turkey functions like bilateral FTA between anyone of the EC
countries and Turkey, for example, UK and Turkey.
Table 7 reports summary statistics for the key variables. I found them similar to
the values obtained by Egger et al. (2004). The mean value of skill difference ( SK ijt ) in
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The results from year-fixed effect are that all the coefficients of FTA are insignificant.
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intra OECD is 0.58 much lower than 2.01 in extra OECD. This is congruent to my
expectation, which is that the skill difference among OECD countries should be relatively
small.
VI. Results
Full Sample
Table 8 reports the regression results for Pooled OLS, Between estimator, Within
(fixed effect) estimator, Random effect-GLS estimator and Random effect-MLE
estimator in the entire dataset.
The coefficient estimates of
GDP
ijt
, SIMI ijt and OPEN jt are statistically
significant with expected signs in all regressions except for Between estimator.
The coefficient estimates of FTAijt is negative and statistically significant in
Within estimator, RE-GLS estimator and RE-MLE estimator regressions. This indicates a
negative effect of FTAijt on FDI among all the country pairs from 1982 to 2004. The
coefficient estimate of FTAijt is positive but statistically insignificant in Pooled OLS and
Between estimator regressions.
Since the dependent variable is logarithm and FTAijt is a dummy variable, I need
recalculate the value of the coefficient to get the effect of implementing FTA. I can use
the formula, 100  exp(  6  0.5  Var ( 6 )  1) , proposed by Kenney (1981) and van
Garderen and Shah (2002). As a result, the impact of RTA is -68.92% on FDI in all the
country pairs between 1982 and 2004 on average using Within estimator, RE-GLS
estimator and RE-MLE estimator.
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Among all these estimates, I trust Within estimator most. First, all independent
variables are time-varying. Second, R 2 is highest among all the models. Finally, I
conducted the Hausman-test for whether Fixed or Random estimator is appropriate. The
result shows that the null hypothesis was rejected at 1% significant level. So Fixed effects
are present.
Sub-Sample
To analyze why bilateral FTA affects FDI negatively, I divide the whole sample
into two sub-samples: intra OECD and extra OECD. I obtained Within estimator, REGLS estimator and RE-MLE for the two sub-samples. Pooled OLS is not appropriate here
as the errors for a country-pair are almost positively correlated over the years. So the
coefficient estimator in Pooled OLS would be inconsistent if Within estimator is
appropriate. Moreover, the Between estimator is inconsistent as the independent variables
are time-varying.
Table 9 shows the regression results of Within estimator, RE-GLS and RE-MLE
regressions in intra OECD and extra OECD sub-samples. The coefficient estimates of
GDP
ijt
, SIMI ijt and OPEN jt are statistically significant with expected signs in all
regressions for both intra OECD and extra OECD sub-samples. All values of
GDP
ijt
and SIMI ijt in intra OECD sub-sample are much greater than those in extra OECD subsample, indicating the effect of market size is larger for intra OECD sub-sample than for
extra OECD sub-sample. Since horizontal FDI has a positive relationship with market
size while vertical FDI does not related to, I expect horizontal FDI among intra OECD
countries to be greater than that among extra OECD countries.
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The coefficient estimates of SK ijt has expected signs but insignificant in all
regressions for both intra OECD and extra OECD sub-samples. This might lend some
support to the assertion that horizontal FDI dominates vertical FDI in intra OECD
countries where skill difference is relatively small and vice versa in extra OECD
countries where skill difference is relatively big .
The coefficient estimates of FTAijt and DISTij  FTAijt are statistically significant
with the expected negative signs in all three regressions for intra OECD. The coefficient
estimate of FTAijt in extra OECD is statistically significant with the expected positive
sign only in Within estimator regression. Actually the model of Within estimator has the
highest R 2 among the three for both sub-samples, indicating this model is most
appropriate to use in this study. Hence, the sub-sample regression results suggest that
FTA affects FDI negatively in intra OECD county-pairs but positively in extra OECD
country-pairs.
The Within estimator of FTA is -3.451 for intra OECD country-pairs and 3.635
for extra OECD country-pair. As there are much more country pairs for intra OECD
(4589) than those in extra OECD (2464), the Within estimator of FTA is negative (2.599) in the full sample. This shows that the negative effects of FTAijt comes mainly
from intra OECD country-pairs where horizontal FDI dominates vertical FDI. (I am still
looking for the reason why there is the negative effect of FTA on FDI in the full sample.)
VII. Sensitivity analysis
Endogeneity problem
The fixed-effects estimators assume all the independent variables to be exogenous.
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Our key variable FTA, however, may be endogenous. The more FDI, the more trade
activities will result between the two countries. Consequently, this increased trade across
the border may increase the demand to get rid of the existing trade barriers and to form
FTA. Hence, FTA can be an endogenous variable. However, no one has considered the
possibility of FDI to affect FTA. Meanwhile, all the other independent variables can be
endogenous. Since they are measured in the same year as the dependent variable FDI, it
is possible that they may either be correlated with the error term. Moreover, independent
variables such as GDP and per-capita GDP may be affected by the dependent variable
FDI. Therefore, endogeneity seems to be a serious problem that would make the fixedeffects estimators inconsistent.
To solve the endogeneity problem, I set up two-step GMM models. ( I am still
trying this estimation.)
Time series analysis
1) Unit root test for panel data
To test whether outward FDI is non-stationary, I first conducted two unit-root
tests for outward FDI: LLC test (Levin, Lin and Chu, 2002) and IPS test (Im, Pesaran and
Shin, 1995). Table-10 shows the results from the two tests. Although the results from
LLC test show outward FDI is stationary for most country-pairs, we cannot determine
whether non-stationarity exists from IPS test.
The important assumption of the LLC test is the independence of individuals,
that is, the independence of country-pairs in my case. So I checked if there is any
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relationship across country-pairs in the same home country by analyzing the scatter plot
of two coefficients on AR1 and AR2.
The regression equation is as follows: Fijt    1ij Fijt1   2ij Fijt 2   ijt .
The results from the scatter plot show that most of points on the plots are
located near or just below the line 1   2  1 , which is the line corresponding to unit
root AR(2) models. Given the well-established fact that standard unit root tests tend to be
downward biased for small T, evidence in favor of unit roots in FDI is pretty strong.
Hence, if I have to choose a single hypothesis that would provide a better description of
outward FDI for the majority of country pairs, I will choose the unit root I(1) process.
2) First-difference estimator
As outward FDI is non-stationary process, I estimate the first difference estimator as
follows:
dFijt  1d  GDPijt   2 dSIMI ijt   3 d SK ijt   4 d ( DISTij  SK ijt )
  5 d ( GDPijt  SK ijt )   6 dFTAijt   7 d ( FTAijt  DISTij )  (t  t 1 )  d ijt
Table-11 shows the results from the first difference estimator with the full sample,
intra OECD and extra OECD.
The coefficient estimates of d  GDPijt and dSIMI ijt are still positive and
statistically significant in all the models. Meanwhile, the values of d  GDPijt and
dSIMI ijt in intra OECD are greater than those in in extra OECD, indicating the first
differenced market size is larger for intra OECD sub-sample than for extra OECD subsample. Since horizontal FDI has a positive relationship with market size while vertical
(2)
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FDI does not related to, I expect horizontal FDI among intra OECD countries to be
greater than that among extra OECD countries.
The coefficient of dOPEN jt is positive and statistically significant for the full sample
and the sub-sample of extra OECD.
All the coefficient of dFTAijt is positive but statistically significant only in extra
OECD at 10% significant level.
(I am still looking for the appropriate first-difference estimator with a dummy
variable. One candidate is ECM regression, which I will try later.)
3) Difference-in-differences estimator (DID estimator)
I estimated the Difference-in-difference by regressing the following equation;
F     1 Dt   2 fta   3 Dt  fta   t
1 if year  t
1 if fta has been forced between two countries
, where Dt  
& fta  
0 otherwise
 0 otherwise
Thus, Dit  ftai = 1 if FTA has been forced between countries in the post periods.
(i.e. Dt represents whether F is in the post period.)
The coefficient,  3 , on Dt  fta is the Difference-in-difference estimator. (Trivedi
(2004), p.891)
Since the sample periods are from 1982 to 2004, I estimated the Difference-indifference by each year between 1983 and 2004.
Table-12 shows the estimate results in the full sample.
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The coefficient estimates for Dt  fta are statistically significant only in 1983,
1998, 1999, 2000 and 2001. However, the DID estimator in 1983 is positive, while those
in each period between 1998 and 2001 are negative.
Among the significant values in Table 12, I do not trust the result in 1983
because R 2 is lowest among the coefficients and only one FTA was signed in this period
(that is, CER, which is the treaty between Australia and New Zealand).
The results in the Difference-in-difference for each period between 1998 and
2001 are consistent with those in the previous panel analysis. But the values in the
Difference-in-difference are smaller than in the panel analysis.
VIII. Limitations of Analysis
VII. Conclusion
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Kennedy, E., 1981. Estimation with correctly interpreted dummy variables in semi
logarithmic equations. American Economic Review 71, 801.
22
Lesher, M. and Miroudot, S. 2006. Analysis of the Economic Impact of Investment
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Cambridge, United States: National Bureau of Economic Research Department.
Markusen, J. 1984. Multinationals, Multi-Plant Economies, and the Gains from Trade.
Journal of International Economics 16 205-26
Markusen, J. and K. Maskus. 2001. General-Equilibrium Approaches to the Multinational
Firm: A Review of Theory and Evidence. NBER Working Paper 8334. Cambridge,
United States: National Bureau of Economic Research Department.
Markusen, J. and Venables, A. 1998, Multinational Firms and the New Trade Theory.
Journal of International Economics 46 183-203
Markusen, J. and Venables, A. 2000, The theory of Endowment, Intra-Industry and
Multinational Trade. Journal of International Economics 52(2), 209-34.
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Horizontal Direct Investment, Vertical Direct Investment, and the Pattern of Trade in
Goods and Services. NBER working paper No. 5696 Cambridge, United States: National
Bureau of Economic Research Department.
23
Trivedi. P., 2004. “Microeconometrics”, Cambridge
Van Garderen, J., and C. Shah. 2002. Exact interpretation of dummy variables in simi
logarithmic equations. Econometrics journal 5, 149-159.
Velde, D. and Bezemer. D. 2004. Regional Integration and Foreign Direct Investment In
Developing Countries.
Yeyati, E., Stein, E. and Daude, C. 2003. Regional Integration and the Location of FDI.
IADB Draft.
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an empirical comparison of moment-condition estimators. Journal of Business and
Economic Statistics, 15, 419-431
24
Figure 1- The Notifications of RTAs to the World Trade Organization (WTO) between
1958 and 2004.
Source: WTO Secretariat
Figure 2- Average Outward FDI of 13 OECD countries
FDI
25000
20000
15000
FDI
10000
5000
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
0
Table-1 Previous empirical researches
Study
Topic
Dataset of FDI
Yeyati et al. (2003)
Regional Integration
and the Location of
FDI
The bilateral
outward FDI stocks
in the OECD
countries over 19821999
Velde & Bezemer
(2004)
The effects of
specific investmentrelated provisions in
RTAs on FDI in
developing
countries
The real stock of
UK and US in
developing
countries over 19802001
Panel fixed effect,
RE-GLS,
Error correction
Kang and Park
(2004)
Korea-U.S. FTA:
Trade and
Investment Creation
Effects and Trade
Structure.
The bilateral
outward FDI stocks
in the OECD
countries over 19802000
Gravity model,
Panel fixed effect,
Panel random effect.
Estimating Regional
Trade Agreement
Effects on FDI an
Interdependent
World
The bilateral
outward FDI stocks
in to Europe over
1989-2001
Analysis of the
Economic Impact of
Investment
Provisions in
Regional Trade
Agreements
The bilateral
outward FDI stocks
in the OECD
countries over 19902004
Baltagi et a. (2005)
Leshier and
Miroudot (2006)
Methodology
Explanatory Variables
Gravity model,
Panel fixed effect
Multilateral FTA, GDP,
Distance, Common
border, Colonial,
Common language
RTA- Regional
Investment Provision /
Regional Trade
Provision, GDP,
Education enrolment,
Inflation, Roads, Region
Multilateral FTA, GDP,
Distance, per Capita
GDP, Openness,
Common border,
Colonial , Common
language.
Findings
The regional integration, on average,
contributes to attracting FDI but the
benefits are unlikely to be distributed
evenly.
The type of region matters for
attracting FDI.
The position of countries within a
region matters for attracting FDI.
FTA increases FDI by 14-35% from
member countries and by 28%-35%
form non-member countries.
Spatial GM methods
Europe agreements
between EU and CEE
members, GDP, per
capita GDP
RTA increases FDI up to by 78%
among European countries.
Gravity model,
Tobit regression,
OLS, Time varying
country fixed
effects, Panel fixed
effect. Poisson
regression
RTA, GDP, Distance,
Tariff, per capita GDP,
Exchange rate, Common
Language, Common
border, Colonial
Investment provisions are positively
associated with trade and, to an even
greater extent, investment flows
24
Table 2- Variable Definitions
Main variables
Definition
I
J
T
FDI ijt
Parent country
Host country
Year (1982-2004)
Outward FDI stocks of i to j at t in US dollars
Fijt
ln FDI ijt
GDPjt (GDPjt )
Current GDP of i (j) at t in 2000 US dollars
 GDPijt
ln( GDPit  GDPjt )
ln{1  [GDPit /(GDPit  GDPjt )]2  [GDPjt /(GDPit  GDPjt )]2 }
SIMI ijt
PercapitaGDPit
Per capita GDP in i (j) in year t in 2000 US dollars
( PercapitaGDPjt )
SK ijt

ln( percapitaGDPit )  ln( percapitaGDP. jt )
5
DISTij
ln(Bilateral distance (kilometers) between home country i and host country j)
DISTij  SK ijt
[ln( Dist ij )   [ln( percapitaGDPit )  ln( percapitaGFDPjt )]]
 GDP
[ln( GDPit )  ln( GDPjt )]   [ln( percapitaGDPit )  ln( percapitaGFDPjt )]
ijt
 SK ijt
FTAijt
1 after the FTA has been signed between country i and j at t, 0 otherwise
Fixed country-pair effects
it
Table 3- Signs of independent variables on horizontal FDI
 GDP
Whole countries
+
Expected signs
Intra-OECD
+
Extra-OECD
+
SIMI ijt
+
+
+
SK ijt
+/−
−
+
DISTij  SK ijt
+/−
+
−
+/−
−
+
FTAijt
+/−
−
+
FTAijt  DISTij
+/−
+
−
OPEN jt
+
+
+
Independent variables
ijt
 GDP
ijt
5
 SK ijt
For the robust test, I used secondary school enrollment share and tertiary school enrollment share for the
skill difference. But the result was that all the coefficients of FTA were insignificant.
25
Table 4- Data sources
Variable
Bilateral FDI (real outward position)
GDP (constant 2000 US $)
Per capita GDP (constant 2000 US$)
Trade openness
Bilateral distance (miles)
FTA
Source
International
Source OECD
Direct Investment
Statistics (2006)
International Financial Statistics (IFS) of IMF
International Financial Statistics (IFS) of IMF
Penn world table 6.2
Time & Date AS (http://www.timeanddate.com)
WTO Regional Trade Association (2006)
Table- 5 Country coverage
Home (13 countries)
Host (37 countries)
OECD (23 countries)
Non-OECD (14 countries)
Australia, Austria, Canada
Denmark, Finland, France
Australia, Austria, Canada
Germany, Greece, Ireland
France, Germany, Italy
Italy, Japan, Mexico
Japan, Korea (Republic of)
Korea (Republic of)
Netherlands, Norway, Sweden
Netherlands, New Zealand
United Kingdom
Norway, Portugal, Spain
United States
Sweden, Switzerland
Turkey, United Kingdom
United States
Argentina, Brazil, Chile
China, Egypt, Hong Kong
India, Indonesia, Israel
Malaysia, Philippines
Singapore, Taiwan
Thailand
26
Table-6 Bilateral FTAs between 1982 and 2004
Member countries
Home
Host
Australia
New Zealand
Unite States
Israel
CER
US-Israel
Date of entry into
force
1983
1985
EFTA-Turkey
1992
Austria, Norway Sweden
Turkey
EFTA-Israel
1993
Austria, Norway, Sweden
Israel
EC-Turkey
1996
Canada-Israel
Canada-Chile
1997
1997
EC-Israel
2000
EC-Mexico
2000
Austria, France Germany,
Italy Netherlands, Sweden
United Kingdom
Mexico
EFTA-Mexico
Japan-Singapore
EFTA-Singapore
2000
2002
2003
Norway
Japan
Norway
Mexico
Singapore
Singapore
Australia-Singapore
2003
Australia
Singapore
US-Singapore
US-Chile
Korea-Chile
2004
2004
2004
United States
United States
Korea (Rep. of)
Singapore
Chile
Chile
EC-Egypt
2004
Austria, France Germany,
Italy Netherlands, Sweden
United Kingdom
Egypt
EFTA-Chile
2004
Norway
Chile
FTA
France, Germany
Italy, Netherlands United
Kingdom
Canada
Canada
France, Germany
Italy, Netherlands United
Kingdom
Turkey
Israel
Chile
Israel
Table- 7 Descriptive Statistics
Variable
Full sample
(7074 obs.)
Mean
Std.dev.
Min
Max
Skew
Kurt
Med
6.55
2.46
-2.30
12.78
-0.46
3.15
6.79
GDP
7.17
1.15
4.34
10.06
0.10
2.25
7.13
SIMI ijt
-1.62
0.93
-4.86
-0.69
-1.16
3.58
-1.30
SK ijt
1.13
1.13
0.00
4.70
1.10
3.11
0.64
DISTij  SK ijt
9.97
10.19
0.00
41.69
1.11
3.14
5.48
 GDP
8.00
8.28
0.00
40.41
1.33
4.03
4.38
FTAijt
0.03
0.17
0.00
1.00
5.66
33.09
0.00
DISTij  FTAijt
0.23
1.35
0.00
9.82
5.75
34.41
0.00
6.89
2.50
-2.30
12.78
-0.51
3.18
7.11
GDP
7.30
1.13
4.39
10.06
0.03
2.24
7.25
SIMI ijt
-1.50
0.84
-4.63
-0.69
-1.28
4.02
-1.21
SK ijt
0.58
0.58
0.00
2.88
1.51
4.64
0.37
DISTij  SK ijt
4.84
4.96
0.00
26.17
1.51
4.66
2.97
 GDP
4.08
4.12
0.00
26.00
1.71
5.94
2.68
FTAijt
0.03
0.17
0.00
1.00
5.63
32.66
0
DISTij  FTAijt
0.23
1.32
0.00
9.24
5.71
34.01
0
5.91
2.25
-2.30
10.95
-0.61
3.14
6.21
GDP
6.97
1.15
4.34
9.52
0.23
2.33
6.86
SIMI ijt
-1.79
1.02
-4.86
-0.69
-0.93
2.91
-1.48
SK ijt
2.01
1.23
0.00
4.70
0.07
1.83
2.03
DISTij  SK ijt
18.04
11.03
0.02
41.69
0.06
1.84
18.29
 GDP
14.16
9.36
0.01
40.41
0.37
2.26
13.67
FTAijt
0.03
0.16
0.00
1.00
5.78
34.43
0
DISTij  FTAijt
0.23
1.39
0.00
9.82
5.83
35.18
0
Fijt
ijt
ijt
 SK ijt
Intra-OECD
(4589 obs.)
Fijt
ijt
ijt
 SK ijt
Extra-OECD
(2485 obs.)
Fijt
ijt
ijt
 SK ijt
28
Table- 8 Empirical results in Full sample
GDP
ijt
SIMI ijt
SK ijt
DISTij  SK ijt
 GDP
ijt
 SK ijt
FTAijt
DISTij  FTAijt
OPEN jt
Observations
R2
Log-likelihood
F-tests:
Country-pair
effects
p-value
Hausman-test (FE
vs RE)
p-value
POLS
Between
Within
RE-GLS
1.343***
(0.044)
1.052 ***
(0.038)
 0.617 **
(0.286)
 0.049*
(0.025)
0.103***
(0.027)
1.486
(1.659)
-0.149
(0.197)
0.666***
(0.042)
7053
0.276
1.959 ***
(0.182)
0.890***
(0.168)
0.455
(1.288)
-0.137
(0.118)
0.053
(0.095)
0.918
(7.920)
-0.225
(0.964)
0.221
(0.198)
7053
0.182
2.076 ***
(0.034)
0.967 ***
(0.077)
0.024
(0.517)
0.093*
(0.055)
 0.133***
(0.022)
 2.599 ***
(0.647)
0.324***
(0.080)
1.417 ***
(0.061)
7053
0.609
2.050 ***
(0.032)
1.134 ***
(0.061)
0.262
(0.467)
0.061
(0.049)
 0.119 ***
(0.021)
 2.466 ***
(0.647)
0.308***
(0.080)
1.358***
(0.056)
7053
0.243
REMLE
2.053***
(0.032)
1.122 ***
(0.063)
0.238
(0.470)
0.064
(0.049)
 0.120***
(0.021)
 2.479 ***
(0.644)
0.310***
(0.079)
1.363***
(0.056)
7053
-8459
378.28
0.00
Notes. 1. The figures in parentheses are standard errors.
2. * Significance at the 10% level, ** Significance at the 5% level,
*** Significance at the 1% level.
Sign as
predicted
?
Y
Y
Y
Table-9 Empirical results in Intra-OECD and Extra-OECD
Intra-OECD
GDP
ijt
SIMI ijt
SK ijt
DISTij  SK ijt
 GDP
ijt
 SK ijt
FTAijt
DISTij  FTAijt
OPEN jt
Observations
R2
Log-likelihood
Extra-OECD
Within
RE-GLS
RE-MLE
Sign as
predicted?
2.101***
(0.040)
1.164 ***
(0.098)
-1.040
(0.642)
0.155**
(0.067)
-0.045
(0.043)
 3.451***
(0.728)
0.413***
(0.092)
1.489 ***
(0.083)
4589
0.626
2.056 ***
(0.039)
1.255***
(0.083)
-0.370
(0.614)
0.055
(0.063)
-0.026
(0.042)
 3.196***
(0.730)
0.379***
(0.092)
1.508***
(0.079)
4589
0.248
2.064 ***
(0.039)
Y
1.142 ***
(0.085)
-0.481
(0.639)
0.072
(0.066)
-0.029
(0.044)
 3.241***
(0.725)
0.385***
(0.092)
1.050 ***
(0.080)
4589
-5361
Notes. 1. The figures in parentheses are standard errors.
2. * Significance at the 10% level, ** Significance at the 5% level,
*** Significance at the 1% level.
Y
Y
Y
Y
Y
Y
Y
Within
RE-GLS
RE-MLE
1.859 ***
(0.077)
0.835***
(0.142)
1.369
(1.026)
-0.101
(0.109)
 0.090***
(0.034)
3.635*
(2.008)
-0.371
(0.233)
1.808***
(0.072)
1.180 ***
(0.093)
0.403
(0.813)
0.005
(0.085)
-0.052
(0.320)
2.319
(1.997)
-0.225
(0.232)
1.811***
(0.072)
1.161***
(0.097)
0.479
(0.832)
-0.003
(0.088)
 0.054*
(0.032)
2.486
(1.988)
-0.244
(0.231)
1.442 ***
(0.095)
2464
0.587
1.260 ***
(0.079)
2464
0.176
1.274 ***
(0.081)
2464
-3066
Sign as
predicted?
Y
Y
Y
Y
N
Y
Y
Y
Table-10 The results of two unit tests
Home Country
Australia
Austria
Canada
France
Germany
Italy
Japan
Korea, Rep. of
Netherlands
Norway
Sweden
United Kingdom
United States
LLC test
Stationary
Stationary
Stationary
Stationary
Non-stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Non-stationary
IPS test
Non-stationary
Stationary
Non-stationary
Stationary
Non-stationary
Non-stationary
Stationary
Non-stationary
Non-stationary
Stationary
Stationary
Non-stationary
Non-stationary
Table-11 First Difference estimator
d  GDPijt
dSIMI ijt
d SK ijt
d ( DISTij  SK ijt )
d ( GDPijt  SK ijt )
dFTAijt
d ( DISTij  FTAijt )
dOPEN jt
Observations
R2
Full sample
0.763***
(0.078)
0.562***
(0.094)
0.288
(0.488)
0.023
(0.049)
-0.053
(0.035)
1.029
(0.908)
-0.131
(0.120)
0.128*
(0.078)
6380
0.023
Intra OECD
0.760***
(0.093)
0.589***
(0.125)
0.093
(0.642)
0.063
(0.060)
-0.061
(0.069)
1.341
(1.451)
-0.178
(0.198)
0.068
(0.116)
4173
0.025
Notes. 1. The figures in parentheses are standard errors.
2. * Significance at the 10% level, ** Significance at the 5% level,
*** Significance at the 1% level.
Extra OECD
0.749***
(0.155)
0.408**
(0.166)
0.536
(1.179)
-0.030
(0.122)
-0.045
(0.054)
1.368*
(0.714)
 0.154**
(0.079)
0.233**
(0.109)
2207
0.023
31
Table- 12 Difference-in-difference in the full sample (n=7074)
t
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
 3t
0.326
0.237
0.033
-0.256
-0.435
-0.636
-0.813
-0.941
-0.909
-1.031
-0.708
-0.212
-0.389
-0.519
-0.614
-0.703
-0.744
-0.857
-0.583
-0.477
-0.112
0.829
Std.Err.
0.166
2.466
1.747
1.239
1.013
0.879
0.789
0.722
0.694
0.648
0.581
0.522
0.450
0.410
0.377
0.356
0.342
0.335
0.334
0.356
0.429
0.684
t
1.96
0.10
0.02
-0.21
-0.43
-0.72
-1.03
-1.30
-1.31
-1.59
-1.22
-0.41
-0.86
-1.26
-1.63
-1.97
-2.18
-2.56
-1.75
-1.34
-0.26
1.21
P > |t|
0.050
0.924
0.985
0.837
0.668
0.469
0.302
0.193
0.191
0.112
0.222
0.684
0.387
0.206
0.104
0.048
0.030
0.010
0.081
0.181
0.794
0.225
F
5.03
4.89
7.74
19.86
35.58
48.75
59.28
68.75
74.52
83.45
97.01
112.26
122.98
123.14
111.53
100.42
88.82
100.42
69.41
62.99
43.81
16.49
P>F
0.007
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
R2
0.0014
0.0021
0.0033
0.0084
0.0149
0.0203
0.0245
0.0283
0.0306
0.0342
0.0395
0.0455
0.0496
0.0497
0.0452
0.0409
0.0363
0.0409
0.0286
0.0260
0.0183
0.0069