CESSA WP 2014-01

The Nature of FDI Competition in East Asia:
Crowding-out or Crowding-in?
Chan-Hyun Sohn
Kangwon National University, Korea
Yokohama National University, Japan

January 2014

Center for Economic and Social Studies in Asia (CESSA) Working Paper
Downloadable from:http://www.econ.ynu.ac.jp/cessa/publication/workingpaper.html

Center for Economic and Social Studies in Asia, Department of Economics
Yokohama National University

The Nature of FDI Competition in East Asia:
Crowding-out or Crowding-in?*
Chan-Hyun Sohn
Kangwon National University, Korea &Yokohama National University, Japan<Abstract>
This paper aims to empirically investigate the so-called China effect, namely, how China’s
increasing attraction of FDI affects the FDI inflows of neighboring ASEAN countries.
Special attention will be given to whether China and ASEAN countries are crowding-out(competing) or crowding-in (helping) each other for global FDI inflows into East Asia.
Recognizing that all previous studies employed gravity models, which have no theoreticalfoundation to explain FDI inflows, this study instead utilizes a theory-based KnowledgeCapital Model. Using country-pair FDI data for China and six major ASEAN countries(Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam) with 31 FDI source
countries during1985–2010, panel analyses of each country are carried out to identify thepossible influence of the other countries’ FDI performance.
The empirical results suggest that China’s rise does not pose a threat to neighboring ASEAN
countries, but that it induces a strong synergetic effect on the FDI inflows into neighboring
ASEAN countries. Thus in attracting global FDI, China and the ASEAN countries are notfoes, but friends.
JEL Code: F13, F21, O53
Key Words: Knowledge-Capital Model, China Effect, Country-Pair Analysis, FTA, BIT*Comments and suggestions are welcome. To cite this work, please contact the author.
**(Correspondence) Professor, Department of International Trade and Business, College ofBusiness Administration, Kangwon National University, Chuncheon 200-701, Korea
E-mail: chsohn@kangwon.ac.kr
Guest Professor, Faculty of Economics, International Graduate School of Social Science,
Yokohama National University, 79-3 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan
E-mail: chsohn@ynu.ac.jp

I. INTRODUCTION
The importance of foreign direct investment (FDI) on a nation’s economic growth has been
widely recognized in recent studies, and statistics on FDI and economic growth have beenfound to be significant. The critical role of FDI on a nation’s economic growth has led to thedevelopment of various FDI-attracting policies in many developing countries. Malaysia in
1988–1998, Vietnam in the 1990s, and particularly China after 2000 are examples in which
FDI made an important contribution to economic growth.
Figure 1 shows the FDI inflows of six major ASEAN countries and China for the period of
1990–2010. FDI inflows in China increased rapidly since Deng Xiaoping’s reforms andopening-up policies at the beginning of the 1990s. With its 2002 accession into the WTO,
China has become even more attractive to FDI inflows. In contrast, most ASEAN countries
except Singapore registered drops in their FDI inflows after the Asian financial crisis in 1997.
<Figure 1> FDI flows to China and ASEAN countries
Unit: billion U.S. dollars

Source: http://unctadstat.unctad.org/

Figure 2 shows FDI inflows into China as compared to six other ASEAN countries in 1990,
2000, and 2010. It illustrates why China’s neighboring countries feel threatened by China’s
growth. The figures show that while FDI inflows into Southeast Asian countries were in
decline (Indonesia even registered net outflows), FDI inflows into China surged. Was there
any causal relationship between these contrasting trends?

<Figure 2> FDI flows to China and ASEAN countries: 1990, 2000, 2010
Unit: million U.S. dollars

Source: http://unctadstat.unctad.org/

China has particularly accelerated its economic growth since it joined the WTO in 2002. The
strong drive to attract FDI into China has been fruitful, and has expedited the growth of theeconomy. This rise of China, particularly in the area of FDI, has received a mixed response
from its neighboring ASEAN countries. One point of view is that China may monopolize all
FDI inflows to East Asia, thereby crowding-out its neighboring ASEAN countries fromreceiving FDI. The opposing view is that China’s rise may lead to close businesssynchronization and interdependence between China and the ASEAN, thereby facilitating aconcurrent crowding-in of FDIs into neighboring ASEAN countries. This is the question of
the so-called China effect.
The objective of this paper is simple and straightforward: to examine whether China’s rapid
economic growth comes at the expense of its ASEAN neighbors in terms of attracting FDI.
Put differently, this paper empirically tests the relationship between the FDI inflows of
ASEAN countries with that of China to identify whether crowding-out or crowding-in istaking place.
This paper is organized as follows: Section II reviews the literature on the so-called China
effect. Section III explains the theoretical foundation of FDI determination, the KnowledgeCapital Model. Section IV explains the empirical models and data used herein. Section V
shows the empirical results. Section VI concludes the paper.

II. LITERATURE REVIEWThe so-called China Effect has become a fascinating subject, particularly since the strong
critical comment made in 2002 by the Lee Hsien Loong, the Deputy Prime Minister ofSingapore, who stated that Southeast Asian countries had reached the point wherecompetition with China had become extremely difficult, and that FDI to China deprived other
Southeast Asian countries of investment flows (China Online, November 14, 2002). As such,the question of whether global FDIs are diverted to China from neighboring ASEAN
countries has become one of great concern.
Investigation of the China effect was first elaborated in Chantasasawat et al. (2004 and 2008)
and Fung (2008). These papers divided the China effect into three possibilities: diversion,
creation, or a neutral effect. The diversion effect was defined as where the growth of China’s
inward FDI resulted in a simultaneous decrease of the neighboring ASEAN countries’ FDI
inflows, which we call crowding-out. The creation effect appears when ASEAN countries’
FDI inflows concurrently increase with China’s FDI inflows; we call this crowding-in. The
neutral effect exists when the ASEAN countries’ FDIs are not affected in any systematic way
by FDI inflows into China.
Chantasasawat et al. (2004, 2008) provided empirical results that the China effect encourages
FDI flows into the East Asian countries; the only negative correlation found was for China’s
FDI and the share of FDI inflows into the East Asia region. A similar simulation was
conducted for China and two other regions: Latin America and Eastern Europe. The results
show that the potential China effect has no vital relation in these cases. Together, thesestudies suggest that a country’s FDI attractiveness is strongly influenced by the FDI inflows
in the region to which it belongs. These results are further confirmed in Fung et al. (2008),
who considered a possible global supply chain consisting of China and Central and Eastern
European countries. All of these analyses utilized a gravity model for the crowd-out orcrowd-in of FDIs.
Considered an attractive pioneering study, the method of Fung’s empirical analysis was usedby many studies that followed. The panel analyses by Zhou and Lall (2005) and Wang et al.
(2007) indicated that inward FDI into China has had a partly positive influence on other East
Asian countries, excluding Indonesia, Malaysia, and Taiwan. Mercereau (2005) concluded
that China’s FDI inflows have no significant effect on neighboring East Asian countries;
however, it diverted the FDI inflows of Singapore and Myanmar.
As such, the gravity estimation of Fung became widely pervasive in econometric testing of
the China effect. Eichengreen and Tong (2007) found an opposite result from Fung: China
had a negative influence for European countries’ inward FDI. Cravino et al. (2007) filled thegap between the role of China’s development in the FDI environment with that in Latin
American and Caribbean countries and found no significant effect, supporting the regional
characteristic of FDI: that a country’s FDI inflows are affected by the region to which itbelongs, and not by other regions. Resmini and Siedschleg (2008), however, showed an interregional effect of China’s rise on European countries’ FDI performance.
Most previous studies of the China effect utilize a country-specific dataset and conduct
ordinary least squares (OLS) with fixed effects to estimate the China effect. Chantasasawat etal. (2004) used data for eight Asian economies—Hong Kong, Taiwan, the Republic of Korea,
Singapore, Malaysia, the Philippines, Indonesia, and Thailand—from 1985 to 2001. They
added China’s inward FDI as an indicator for the China effect, while the annual data for theother eight countries are pooled and treated in a panel. They found that China’s FDI receipts
and other Asian countries’ receipts are positively correlated. They also found that the China
effect is not the most important determinant of the inward direct investment of these
economies: policy and institutional factors such as openness, corporate tax rates, andcorruption are more important.
Eichengreen et al. (2005) estimated a gravity equation using bilateral FDI inflows between 29
source countries and 63 recipient countries during 1998–2003. They used OLS and countrypair fixed effects and found a complementary relationship between FDI in China and that inother Asian countries, but a substituting relationship with the FDI of OECD countries. They
also found that China’s rapid growth and attractiveness as an FDI destination encouraged FDI
inflows into other Asian countries that might belong to a common supply chain.
Zhou and Lall (2005) utilized panel data analysis for the period 1986–2001 using fixed-effectestimation and found no competition between China and its neighbors for FDI in all activities.
In contrast, the complementary relationship between China and its neighboring countries in
the regional production network was projected to grow.
Mercereau(2005) used FDI inward data for 14 Asian countries for1984–2002 and found that
China did not seem to have diverted FDI inflows from countries in Asia, with the exceptionsof Singapore and Myanmar only. He used country-specific data using OLS with fixed country
effects to estimate the impact of China’s emergence on FDI inflows to Asian countries.
Further, he divided the impact into two parts: the average diversion across Asian countries
and the diversion for each Asian country. He also pointed out that the fundamentals of acountry, such as a balanced government budget, an appreciating real exchange rate, and a low
inflation rate seem to be associated with increased FDI inflows.
In sum, the previous studies found evidence for both crowding-out and crowding-in by the
China effect. Most of the models employed use a gravity equation, and most studies used acountry-specific dataset except Eichengreen et al. (2005) which used a country-pair dataset.
Analytical methods used were mostly OLS with some fixed effects.

III. THE MODELAs reviewed in the above section, most previous studies on the China effect employed agravity equation for the determination of FDI. Namely, the volume of FDI is dependent on
the well-known gravity variables, which include the products of GDPs, the distance between
countries, common languages, and geographic characteristics. The use of the gravity equation
for the FDI, however, has a serious theoretical shortcoming: thusfar, we have no theoreticalfoundation for using the gravity equation to explain the FDI flows. The gravity equation fits
the FDI data well, as it does trade volume data. While the gravity model could betheoretically derived from trade models—both intra-industry and inter-industry trade—as
explained in Sohn (2005), there is no theoretical explanation for using the gravity model for
FDI flows. This paper, therefore, introduces the Knowledge-Capital Model for FDI flows
across countries. This model is supposed to fit particularly well in a country pair.
The previous empirical and theoretical papers on the determinants and motives of FDI flows
can be divided into the three categories: (1) horizontal motivation, (2) vertical motivation,and (3) knowledge-capital motivation (Carr et al. 2001, 2003; Blonigen et al. 2003). If there
is any certain FDI flow data set for distinguishing the vertical and horizontal FDI, theknowledge-capital model could be separated into vertical and horizontal types of FDI.
The estimation using the knowledge-capital model (CMM model) was originally developed
and applied by Carr, Markusen, and Maskus (2001). Instead of considering FDI as a macro
phenomenon, they focused on the activity of multinational enterprises (MNEs). FDI is closely
related to emerging MNE activities, and current theoretical developments have incorporated
multinational firms that produce in various countries into trade models, such as firm
heterogeneity, fragmentation and overseas outsourcing, and production networking acrossnational borders. Both horizontal and vertical production activities and their corresponding
FDI arise from the MNE’s optimization process for producing goods (Carr et al., 2001). The
horizontal type of MNE activity was introduced by Markusen (1984); Helpman (1984)
showed the vertical type of MNE activity.
Markusen (1984) introduced the horizontal model, which he described as a multinational
firm’s location choice for the same activities in multiple regions. The horizontal activity
comes from the interaction between the trade cost and economies of scale that eventually
generate FDI flows across countries. According to this model, the horizontal FDI could be
predicted from the skill differences of production locations, which represent the knowledge
level of countries. (Markusen and Venables, 2000).
Helpman (1984) introduced the vertical model, which he described as the activities of afirm’s location choice in different countries based on the motivation to exploit the factor costadvantages. The model predicts that FDI should flow into unskilled countries from skilledcountries. Therefore, the FDI will diminish where countries have comparable skill levels
because there are no cost advantages (Blonigen et al., 2003).

The CMM model provides the theoretical predictions of the horizontal and vertical activitiesof FDIs: horizontal activities arise among countries that are relatively similar in size andendowments, while vertical activities arise from the skill differences between countries.
These are the characteristics of the model developed by Carr et al. (2001).
The knowledge-capital model can be simply explained through the combination of motives of
the horizontal and vertical models. According to Carr et al., the knowledge-capital model
includes three important assumptions. First, knowledge-generating activities and knowledgebased activities can be located in different regions of production. Second, in the framework of
production, these knowledge-capital activities are related with skilled-labor intensity. Third,
knowledge-capital activities can be utilized by multiple types of production at the same time.
In Carr et al. (2001), horizontal FDI arises from trade cost and market size incentives. If
knowledge-generating and knowledge-based activities can be simultaneously utilized, these
activities could supply extra production facilities with lower cost. This means thatknowledge-capital activities can produce the same commodities in various locations in order
to avoid trade costs. Generally, when the trade cost increases, exporters in the home country
will face a high marginal cost. In this case, there will be growing incentives to produce andsell directly in a host country. If MNEs produce in a host country and sell there at the sametime, they have trade cost advantages because they can avoid import tariffs and transportation
costs. As a result, the horizontal FDI will be greater when the home and host countries are
relatively similar in size and have relatively similar factor endowments under a high tradecost.
On the other hand, vertical FDI increases because of trade costs and relative factor prices.
According to the CMM model, vertical FDI is generated when knowledge-capital activities
are located in different countries and provide production facilities at zero or low cost (Carr etal., 2001). In this case, vertical FDI has incentives for skilled-labor-intensive countries with
low trade costs. When the countries of similar size have relatively different endowments,
there will also be incentives to operate in skilled-labor-scarce countries. In effect, vertical
FDI will be generated when the home country is small and skilled-labor-intensive. In
conclusion, the knowledge-capital model provides the following estimation equation:
Real Sales∗ GDP sum∗ GDP difference squared
GDP difference ∗∗ Skill difference∗
Skill defference∗ Investment cost host∗ Trade cost hostTrade cost host ∗∗∗ Trade cost parent
Skill difference squared∗ Distance

Each variable indicates the determinants of each type of investment. For example, relatively
similar market size relates to horizontal FDI, while relatively different labor endowments (thedifference between skilled labor and unskilled labor) and trade costs relate to vertical FDI.
The results of OLS, weighted least squares, fixed effects, and Tobit analysis are used toestimate the knowledge-capital model.
By the result of regressing Eq.(1), Carr et al. found that trade cost is unimportant to the homecountry, but is important to the host country. This means that vertical investment diminishes
according to the home country’s trade cost. As a result, if the trade cost decreases investment
flows, then trade and investment complement each other. In contrast, if the trade cost
increases investment, then trade and investment are substitutes for each other.
Moreover, the result suggests that the sales of affiliates are significantly related with bilateral
economic activities. The regression is comprised of the GDP difference, squared GDP
differences, differences in skilled-labor endowments, and the relationship between
endowment and size differences. According to the result of Carr et al., investment from thehome country to the host country increases by the sum of market size, skilled-laborabundance in the home country, and the relationship between endowment and size differences.
These findings are consistent with the earlier work of Brainard (1997) and Ekholm (1997).
The findings of Carr et al. show that the increase of the skill endowment of the home country
encourages the affiliate’s sales when the parent is small. Moreover, an increase in the skill
endowment in the home country brings a decrease in the GDP difference. Thus, authors havesuggested that these results can support the appropriateness of the knowledge-capital model.
However, arguments arise because of the incorrect specification of the skill difference. Since
the knowledge-capital model is derived from a combination of horizontal and vertical FDI
motivation, the skill difference can have either a positive or a negative effect on FDI(Blonigen et al., 2003). Therefore, the knowledge-capital model would have an incorrect
specification in the regression equation. Blonigen et al. (2003) therefore suggested a revised
knowledge-capital model as follows:
RSALESRSALES
GDP

GDP

GDP

GDP

SK
SK
SK
SK ∗ GDP GDP
Investment cost hostTrade cost hostTrade cost host ∗ Squared skill difference
Trade cost parent
Distance
In response to this criticism, Carr et al. (2003) reevaluated the knowledge-capital model.
They insisted that the alternate-term absolute difference is not reasonable to use in the theory.
However, they estimated the model again and found identical results (Carr et al., 2003).

IV. DATA AND THE EMPIRICAL MODELAs shown in the equations, the knowledge capital model determines the real sale or real
production of an overseas plant, subsidy, or affiliate. Thus, it explains the production volumeof an overseas branch of a multinational enterprise. After critical debate, it has been widely
recognized that the model is a unique and appropriate formulation for distinguishing between
the horizontal and vertical overseas activities of multinational firms.
Thus, if we accept the assumption that the FDI volume remains proportional to the volume ofthe overseas production of a multilateral firm, the knowledge-capital model can be used toeffectively determine FDI volumes across countries.
The Data
In terms of how China’s FDI inflows affect the FDI inflows of neighboring ASEAN countries,
one of the distinguishing features of this paper might be that all data are arranged by countrypairs and that all empirics are based on country-pair analyses. In its empirical investigation,
this paper deals with six ASEAN countries and China as FDI recipients and 38 FDI-sourcing
countries. The FDI recipients, usually called the host countries, include Indonesia, Malaysia,
the Philippines, Singapore, Thailand, Vietnam, and China. Four other ASEAN countries are
not included because of a lack of data. The FDI sourcing countries, called the home countries,
include 31 OECD member countries, six ASEAN countries, and China. The period studied is
26 years, from 1985 to 2010. Thus, the number of observations in this study is theoretically[7 host countries][38 home countries][26 years].
FDI Data: The bilateral flow of FDI (FDIijt) from country i (i=1,2,..,38) to country j (j=1,2,..,7)
is collected from OECD investment data set for the period 1985–2010.
Other Data: Other variable data including FDIijt are explained in detail in Table A1.
The Empirical Models(1) The Basic Knowledge-Capital model:
FDI

GDPSUM
GDP differece square
GDP difference ∗ Skill difference
Trade costln Distance

Skill difference
Investment costε------- (1)where j represents the host countries, which are Indonesia, Malaysia, the Philippines,
Singapore, Thailand, and Vietnam.
GDPSUM denotes the market size expansion. This variable indicates the joint market size ofthe home and host countries. It is captured by the sum of the GDPs in the home and hostcountries. Its coefficient is expected to show a positive sign.

GDP Difference Square denotes the GDP difference between the home and host countries atyear t, which represents the proxy of economic size of the country-pair. This variable isimportant because it captures the horizontal motivation of FDI. According to the knowledgecapital model, if countries have similar endowments, then the GDP difference will show anegative sign because of the relationship between the skill difference and the GDP difference.
Furthermore, when home countries’ GDPs are constant, the market size difference will be
determined by the GDP of the host countries. Hence, if the host country’s GDP is larger, then,investment from the home country will increase because of the increase of the homecountry’s GDP. In this case, the horizontal FDI will increase, and thereby, the coefficient is
expected to be negative. In contrast, the horizontal FDI will decrease when the homecountry’s GDP is larger than the host country’s GDP. Thus, the sign of the coefficient is
expected to be negative.
Skill Difference denotes the skill difference between the home and host countries. Thisvariable indicates the vertical motivation of FDI. It represents the factor intensity difference
between the home and host countries. According to the knowledge-capital model, a positive
skill difference represents an inequality of skill endowments between the home and hostcountries. When the skill difference term lies in the positive range, an increase in the variablecorresponds to a greater inequality in the relative skill endowments. Moreover, as mentioned
earlier, the vertically motivated FDIs occur when MNE production is located in several
countries but the headquarters are located in a country with cheap skilled labor. Thisphenomenon is well known as the production fragmentation of MNEs (Markusen and Maskus,
2002). A negative sign shows a convergence of skill intensity between the home and hostcountries. Therefore, its sign is expected to be positive.
GDP difference∗Skill differencedenotes the interaction term for the relationship between theskill difference and the GDP difference. This variable influences the vertical motivation of
FDI to some extent. However, the sign of the coefficient is expected to have a negative value
because vertical FDI is encouraged when the home country is small and highly skilled.
Affiliation production is highest when the home country is moderately small and has a large
amount of skilled labor.
Investment Cost and Trade Cost
In this paper, all variables are kept exactly the same as in the original knowledge-capitalmodel of Carr et al.; however, trade and investment cost variables have been introduced.
Although these variables are very important in empirical estimation, not much attention has
been given to the development of their measurement. In this paper, these two variables are
introduced to reflect the cost of trade and FDI as realistically as possible. In fact, it is worth
noting that they are one of the distinguishing features of this study.
Investment cost denotes the investment cost into the host country. A proxy of the variable isused from the financial risk of the host country. It combines the financial risk with several
indices reported from the PRS Group’s International Country Risk Guide. The financial risk

is a compound of several indices, including exchange stability, foreign debt as a percentage ofGDP, current account as a percentage of exports of goods and services, foreign debt service
as a percentage of exports of goods and services, and net international liquidity of importcover. Its value ranges from zero to 50; a higher score indicates greater financial risk.
Furthermore, we combine this variable with the BIT (bilateral investment treaty) below. We
expect its sign to be negative.
Investment Cost = [Investment cost measure] x BIT
where BIT = 1 if no BIT between countries i and j at time t
BIT = 0, if BIT implemented between countries i and j at time t
Investment cost measure = Financial risk index
Trade Cost denotes the trade costs from the home country to the host country. This variable isdesigned to capture whether the host country’s trade cost will affect investment inflows. Thisvariable is a combination of the host country’s free trade index and binary free trade
agreement (FTA). FTA eliminates tariffs and other trade barriers between member countries.
Therefore, when an FTA comes into force, the host country’s tariffs and other trade barriers
turn become zero, and the trade cost drops. We expect the sign of this variable to be negative
because trade costs diminish the incentive to locate plants abroad for shipment back to thehome market.
Trade Cost = [Tariff rate] x FTA
where FTA

= 1 if no FTA between countries i and j at time t

FTA = 0, if FTA implemented between countries i and j at time t
Tariff rate = applied simple mean, all productsDistance denotes the geographical distance between the home and host countries. Thisvariable represents both investment and trade cost. Most previous studies achieved
ambiguous results; therefore, we expect mixed signs throughout the country-pairs.
Finally,
While

and

indicate the home and host country-year fixed effects, respectively.

captures the year dummy,

represents the white error.

(2) The Basic China Effect model:
FDIα

Skill difference

China

ε

------- (2)wherestands for all the explanatory variables as in the basic model.
represents the skill difference between China and each ASEAN country. This paper adds thisvariable to capture whether the skill difference between China and ASEAN countries could
affect investment inflows between them.

denotes the FDI into China. This variable is added to capture the so-called China
effect on neighboring ASEAN countries. It will identify how China’s FDI inflows affectneighboring ASEAN countries, whether positively or negatively. It could explain how andwhy some Asian countries have perceived China’s rapid growth as a threat, whereas others
have perceived this growth as beneficial to the Asian region (Mercereau, 2005). Thus, we
expectto be negative, but the coefficient for China may be ambiguous.
andcapture the host country-year fixed effect and the year dummy, respectively,
whereasrepresents the error term.
<Figure 1> China’s Effect on ASEAN(3) The Country Effect on Neighbors model:
ASEANFDI
where

α

FDI

China

μ

ε ----------- (3)denotes FDI inflows to country k from country i at time t, where k

represents the host countries (k= 1,2..,j,..,6 and k≠j). This variable represents the sum of thefive host countries’ inward FDI, excluding that of the host country j.
This model is designed to capture the possible effect of the FDI inflows of one host country
to the other host countries in the region. For example, this model estimates the effect ofIndonesia’s FDI inflow to neighboring ASEAN countries’ FDI inflows. To be more specific,
for the Indonesia effect, the dependent variable would be the inward stock of FDI into
Malaysia, the Philippines, Singapore, Thailand, and Vietnam. As such, this equation can be
estimated for each of the ASEAN countries; this means there are six equations because kranges from 1 to 6 and k≠j.
This equation is used by Fung to identify the China effect. The model can include the FDI
inflows of China to estimate the China effect under an assumed structural and behavioral
framework effect for each ASEAN host country. This paper presumes that each country canaffect the other five countries as well as the entire ASEAN region. Therefore, the Indonesia
effect, for instance, affects FDI inflows into Malaysia, the Philippines, Singapore, Thailand,
and Vietnam; as such, this equation can be used for other countries.
<Figure 2> A Country’s Effect on Its Neighbors(4) The Neighbors’ Effect on a Country model:
FDI

A1FDI
A4FDI

α

A2FDI
LnA5FDI

A3FDI
China

ε --- (4)This model is designed to capture the neighboring host countries’ effect on a host country.
For example, in the individual estimation, all six ASEAN countries could be affected by theother five countries simultaneously. Thus, this model allows us to incorporate the possible
interrelationship of a host country with the other five countries. To find the China effect inthis structural and behavioral framework, we simply add the China FDI variable.
<Figure 3> Neighbors’ Effect on a Country(5) The Regional Agglomeration Effect model
FDI

α

RAE

China

ε

---------- (5)wheredenotes the sum of all six ASEAN countries.

The model examines the possibility of the regional agglomeration effect (which is also
known as the clustering effect). If the effect exists, there will be a crowding-in effect within
ASEAN on a host country’s FDI performance.
<Figure 4> Regional Agglomeration

V. THE EMPIRICAL RESULT
Econometric Tools
Since our data set is in a panel (7 host countries
38 home countries 26 years) ofcountry-pairs and there exists the possibility of endogeneity and heteroskedasticity errors in
OLS, this paper adds host and year effects into the panel analysis. The knowledge-capitalmodel usually uses OLS; however, FDI has intrinsic endogeneity problems. For this reason,
Baier and Bergstrand (2011) provided a better econometrical method to avoid the problem. In
this paper, we follow their approach and adapt the host-country fixed effect and year fixed
effect in order to avoid endogeneity and self-selection problems. We do not accept the homecountry fixed effect because the home country’s specific characteristics do not affect its
investment decision; rather, the host country’s specific characteristics affect that decision.
As the log of FDI stock is the dependent variable in this paper, there exists the possibility of
autocorrelation and data selection problems in country pairs. For this reason, this paper uses
the system GMM (generalized method of moments) estimator, which is a well-known
econometric method for estimation when the dependent variable is correlated with theprevious period and possible current realizations of the error as well as heteroskedasticity andautocorrelation within individuals (Roodman, 2009). Furthermore, GMM is supposed to curb
such econometric problems as weak instruments and measurement errors and to reconcile
time-invariant individual specific effects, such as distance (Kukenova and Monteiro, 2008)1.
1Y

γY
where γ

ε
1

ε

μ

ν

Empirical Results
Table 1reports the results of OLS and panel regression for the basic model. In this table, theChina effect consistently shows a strong positive and significant effect. Thus, China’s FDI
has a positive effect on the investment inflows of ASEAN countries.
The sign of the GDP sum is constantly positive and significant. This result indicates that ajoint market has a positive effect on ASEAN countries. This result is expected from theprevious literature. A higher total income should lead to a shift from national firms, which are
high-marginal-cost suppliers to foreign markets, to horizontal multinationals, which are highfixed-cost suppliers. The greater the skill difference between China and ASEAN countries,
the more the FDI flows into ASEAN countries decrease.
The coefficient of the GDP difference is negative and significant. This result indicates that thehome country’s GDP is larger than the host country, which discourages the horizontal
motivation of FDI. Furthermore, the result implies that an increase in a parent country’s GDP
will increase its affiliate sales abroad only if it is small and skilled-labor-scarce (Carr et al.,2001). Here, an increase in affiliate sales means an increase of overseas direct investment in
the country. In contrast, in the basic knowledge-capital model, the signs of the skill difference
imply that greater skill inequality between the home and host countries brings greater
investment. However, this effect becomes mixed or insignificant under the China effect.
China’s FDI inflow becomes more important than the skill difference between the home andhost countries. Therefore, this result indicates that investors who consider investing in
ASEAN countries are not doing so to exploit the skill level difference.
The interaction term of GDP and skill difference is constantly negative. It indicates that

denotes the log of FDI stock, while
denotes the combinationof

stands for

and .

.

indicates all the explanatory variables explained before.

represents the time-invariant individual specific effect andrepresents the stochastic error term.
represents the short-run effects on the dependent variable,
andrepresents endurance in the process of adjustment toward equilibrium. Arellano and Bond (1991)
suggested that by utilizing the first-difference form, the system GMM can eliminate both the constant term andthe individual effect as follows:

Y

where

γ

represents the first-difference form andY

εimplies thatis the first difference ofthe dependent variable: FDI inflows from the home country to the host country at time t.

denotes the one-

. Utilizing system GMM, this paper also provides an Arellano-Bondyear lag of the dependent variable .
second-order correlation test for autocorrelation and a Sargan test for over-identifying restrictions.

foreign investment will increase when the home country is small and highly skilled labor isabundant. The coefficient of investment cost shows a consistent negative and significant
value, as expected. This result indicates that a decrease of the investment cost in the hostcountry will encourage investment inflows. The coefficients of trade cost are constantly
negative and significant. The lower the trade cost, the greater is the increase of investment
inflows into host countries in the ASEAN region.
In Table2, the results of the panel regression for FDI inflows into each ASEAN country arereported. The China effect is positive and significant for all individual countries. This result
implies that China’s FDI affects ASEAN countries positively. Therefore, FDI into China has
an apparently positive impact on investment in each ASEAN country.
Panel results for the country effect are shown in Table3. Controlling the fundamental
knowledge-capital model, in this estimation, the empirical results show that each ASEAN
country exercises a positive effect on neighboring ASEAN countries’ FDI inflows. The China
effect here also shows a strong positive and very significant value.
Table 4 shows an individual host country is affected by neighboring countries. That is, themodel identifies whether a single country is affected by the other six countries in the ASEAN
region. For FDI inflow to Indonesia, Thailand has a strong and significant positive effect,
Vietnam has a positive effect, and China has an insignificant result. For Malaysia, Singapore
has a strong positive effect, while Vietnam shows a negative effect; the China effect has apositive value. In the case of the Philippines, Thailand shows a strong positive effect, while
Vietnam has a strong negative effect; China shows an insignificant effect. Malaysia andVietnam have positive effects on Singapore’s FDI inflows, while the China effect is negative.
The FDIs into Thailand are positively affected by the Philippines, Singapore, Vietnam, andChina. Finally, Vietnam’s FDI inflows are negatively affected by Malaysia, but positively bySingapore. Thus, the relationships among the FDIs of each ASEAN country are, in general,more cooperative than competitive.
The empirical results on the regional agglomeration effect are presented in Table5. The resultshows a strong positive regional agglomeration effect throughout the ASEAN countries. This
indicates that each ASEAN country is reinforced in its FDI inflows by the overall regional
FDI performances. Moreover, countries in the ASEAN region have a regional synergy effect.
In this case, foreign investors consider the entire region when they determine where to invest
because the ASEAN countries are closely interrelated and intertwined. However, the result of
the regional agglomeration effect indicates that cooperation with other countries is more
beneficial than treating them as potential competitors. Finally, the China effect shows an
ambiguous result: it has a positive effect on Indonesia, the Philippines and Thailand, but anegative effect on Singapore and Vietnam and an insignificant effect on Malaysia.

VI. CONCLUSIONSThe empirics confirm that knowledge-capital model seems quite appropriate in examining thedeterminants of FDI in East Asia.
In all empirics, the China effect remains positive and significant: it is not a crowding-out, but
a crowding-in. This means that there may exist a co-movement or synchronization between
China’s FDI inflows and each ASEAN country’s FDI inflows. This may be the outcome ofthe ever-increasing interdependence between China and the ASEAN countries, or their
expanding supply-chain networks, deepening production networks, and increasing
fragmentation and diversification of the overseas business activities of multinational firms in
East Asia, which eventually affect the FDI performance of each ASEAN country.
China’s rise in attracting FDI is not only a crowding-in of the FDI inflows for the ASEAN
region as whole, but it also positively affects the FDI inflows of various individual ASEAN
countries. In other words, China’s rise generates co-movement for each ASEAN country as
well as for the ASEAN region as a whole.
In conclusion, in FDI competition, China is a friend, not a threat, to neighboring ASEAN
countries.

REFERENCES
Blonigen, B. A., R. B. Davies, and K. Head (2003) “Estimating the Knowledge-Capital
Model of the Multinational Enterprise: Comment”, The American Economic Review, Vol. 93,
No.3, pp. 980-994
Braconier, H., P. Norbäck and D. Urban (2005), “Reconciling the Evidence on the
Knowledge-capital Model”, Review of International Economics, Vol. 13, Issue. 4, pp. 770786
Brainard, S. L. (1997), “An Empirical Assessment of the Proximity-Concentration Trade-off
Between Multinational Sales and Trade”, The American Economic Review, Vol. 87, No. 4, pp.
520-544
Carr, D. L., J. R. Markusen and K. E. Maskus (2001), “Estimating the Knowledge-Capital
Model of the Multinational Enterprise”, The American Economic Review, Vol. 91, No. 3, pp.
693-708________ (2003), “Estimating the Knowledge-Capital Model of the Multinational Enterprise:
Reply”, The American Economic Review, Vol. 93, No. 3, pp. 995-1001

Chantasasawat, Busakorn Beam., Hitomi lizaka., K.C. Fung and Alan Siu (2004), “The Giant
Sucking Sound: Is China Diverting Foreign Direct Investments from Other Asian
Economies?” UC Santa Cruz International Economics Working Paper No. 04-25; UC Santa
Cruz Economics Working Paper, No. 594
Chantasasawat, B.; Fung K.C.; Iizaka H. & Siu A. (2008) “Multinational enterprises in China,
East Asia, Latin America and Eastern Europe: Moving out or moving in?” Journal of Chinese
Economic and Foreign Trade Studies, Emerald Group Publishing 1(1): 122-135.
Eichengreen, Barry, Yeongseop Rhee & Hui Tong (2004), “The Impact of China on the
Exports of the Other Asian Countries”, NBER working Paper, No.10768, pp.2-11________ (2007), “China and the Exports of Other Asian Countries”, Review of World
Economic, Vol. 143, No.2, pp.201-226
Eichengreen, Barry and Hui Tong (2005), “Is China’s FDI coming at the expense of othercountries”, Journal of the Japanese and International Economies, pp.153-172
Ekholm, K. (1997), “Factor Endowments and the Pattern of Affiliate Production by
Multinational Enterprises” CREDIT Working paper, No. 97
Helpman, E. (1984), “A Simple Theory of International Trade with Multinational
Corporations”, Journal of Political Economy, Vol. 92, No. 3, pp. 451-471
Fung, K.C.; Korhonen, I.; Li, K., & Ng, F. (2008) “China and Central and Eastern European
Countries: Regional networks, global supply chain or international competitors?” BOFIT
Discussion Papers 9/2008. Bank of Finland, Institute for Economies in Transition.
Kukenova, M. and J. Monteiro (2008), “Spatial Dynamic Panel Model and System GMM: A
Monte Carlo Investigation”, Working papers
Markusen, J. R. (1984), “Multinationals, Multi-plant Economies, and the Gains from Trade”,
Journal of International Economics, Vol. 16, pp. 205-226
Markusen, J. R. and K. E. Maskus (2002), “Discriminating Among Alternative Theories ofthe Multinational Enterprise”, Review of International Economics, Vol. 10, No. 4, pp. 694707
Maskus, J. R. and A. J. Venables (2000), “The theory of endowment, intra-industry and multinational trade”, Journal of International Economics, Vol. 52, pp. 209-234
Mercereau, Benoit (2005), “FDI Flows to Asia: Did the Dragon Crowd out the Tigers?” IMF
Working Paper, No.05, pp. 189-219
Roodman, D. (2009), “How to do xtabond2: An introduction to difference and system GMM
in STATA”, The STATA Journal, Vol. 9, No. 1, pp. 86-136
Sohn, Chan-Hyun (2005) “Does the Gravity Model Explain Korea’s Trade Flows?” The
Japanese Economic Review, Volume 56, Issue 4, Page 417-430, Dec. 2005

Sohn, Chan-Hyun and Hongsik Lee (2010) “Trade Structure, FTAs, and Economic Growth”
Review of Development Economics, Volume 14, No. 3, Page 683-698, Aug. 2010
Wang C.; Wei Y. & Liu X.(2007) “Does China Rival its Neighboring Economies for Inward
FDI.” Transnational Corporation 16(3): 35-60.
Zhou, Yuping and Sanjaya Lall (2005), “The impact of China’s FDI surge on FDI in SouthEast Asia: panel data analysis for 1986-2001”, Transnational Corporations, pp.41-65

<Table 1> Basic Model for ASEAN

∗

_cons
Year effect
Host country fixed effectHome country fixed effectNumber of obs.
R-squared

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.182***(26.867)-0.103***(-18.111)
0.441***(9.801)-0.808***(-5.780)-0.126***(-18.153)-0.547***(-14.797)-15.824***(-6.867)

0.088***(13.671)-0.052***(-10.279)
0.296***(6.311)-0.469***(-3.924)-0.043***(-6.173)-0.370***(-10.884)-2.870(-1.446)-0.290***(-4.567)
0.574***(25.442)6.585***(3.863)
No
No
No
1,756
0.669

0.190***(28.873)-0.112***(-20.302)
0.410***(9.128)-0.808***(-5.882)-0.105***(-14.265)-0.505***(-13.361)-15.606***(-6.986)

0.061***(8.574)-0.027***(-4.905)
0.184***(3.938)-0.462***(-3.994)-0.060***(-9.096)-0.469***(-13.431)-0.764(-0.384)-0.011(-0.177)
0.706***(24.586)4.701***(2.740)
Yes
No
No
1,756
0.692

0.184***(29.905)-0.109***(-21.439)
0.632***(11.807)-0.669***(-5.129)-0.128***(-15.245)-0.188**(-2.308)-21.475***(-9.476)

0.066***(9.909)-0.030***(-5.894)
0.219***(4.797)-0.431***(-4.399)-0.083***(-10.780)-0.228***(-2.748)-5.672**(-2.380)
1.518***(3.283)0.693***(22.916)
8.667***(4.354)
No
Yes
No
1,756
0.731

-0.535***(-4.313)-1.207***(-7.827)-0.213***(-3.756)-0.078(-0.727)-0.019***(-2.633)-0.531***(-17.380)-22.138***(-3.995)-0.628***(-5.019)-1.139***(-7.755)
16.717***(13.713)-0.165(-1.573)-0.023***(-3.188)-0.534***(-17.815)-24.033***(-4.534)-16.871***(-13.722)
23.076***(13.525)-184.414***(-12.629)
No
No
Yes
1,756
0.852

24.173***(13.176)
No
No
No
1,941
0.484

22.288***(12.362)
Yes
No
No
1,941
0.504

26.297***(14.418)
No
Yes
No
1,941
0.560

29.465***(6.612)
No
No
Yes
1,941
0.844

Note: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference
square rescaled by 10 , (GDP difference * Skill difference) rescaled by 10 . All FDIs are measured in stock.

<Table 2> Basic Model for Each Country
Indonesia
(1)
(2)

∗

_cons

Host country fixed
effectNumber of obs.
R-squared

Malaysia(3)(4)

Philippines(5)(6)

Singapore(7)(8)

Thailand(9)(10)

Vietnam(11)(12)

0.115***

0.086***

0.103***

0.113***

0.208***

0.190***

0.084***

0.061***

0.290***

0.273***-0.0050.008(4.875)(3.571)(6.469)(5.829)(9.031)(8.063)(4.492)(2.948)(12.573)(11.377)(-0.101)(0.160)-0.413***-0.295*-0.436***-0.514***0.059

0.291*-0.0430.118-0.404***-0.268**0.601*

0.636*(-2.785)(-1.956)(-3.246)(-3.223)(0.366)(1.765)(-0.309)(0.740)(-3.681)(-2.417)(1.896)(1.950)0.233*

0.211*

0.097

0.077

1.029***

1.078***

0.607***

0.524***

1.009***

0.984***

0.284

0.355

(1.905)(1.731)(0.796)(0.584)(0.829)(1.015)-0.168***-0.169***-0.585***-0.583***-0.143-0.183(-1.324)(-0.844)(-2.758)(-2.675)(-9.250)(-9.296)(-0.792)(-1.002)-0.031***-0.050***-0.073**-0.078**-0.158***-0.192***-0.244***-0.260***(-3.107)(-4.668)(-2.492)(-2.421)(4.194)0.227***(-3.036)
0.095***(-4.120)(9.279)-0.071(7.230)0.653***(-6.950)
0.093***(-5.999)(9.666)-0.111(6.823)0.624***(-6.570)0.078***(-5.546)(-10.870)(-11.679)(-5.353)(-5.316)0.253**-0.180

0.058

0.333

-0.107-0.364*0.209**-0.190-0.0540.078(-1.235)(2.385)(-1.447)(-0.333)(0.402)

6.183

10.853***14.116***-13.665***-14.590***(2.539)(-1.016)(0.319)(1.257)(-0.757)(-1.773)(5.208)0.256***(-3.547)0.065***(-3.324)
0.947***(-2.905)-7.968***-6.893**-6.545*-7.392*

1.572

2.335

8.003**-0.620(-2.854)(-2.432)(-1.729)(-1.823)(0.811)(1.201)(2.116)(1.549)(4.615)(6.012)(-3.162)(-3.215)0.758***

0.803***

0.538***

0.508***

0.400***

0.493***

0.614***

0.672***

0.529***

0.600***

0.795***

0.781***(21.352)(21.011)(12.021)(6.974)(7.739)(8.573)(15.041)(13.723)(14.279)(14.313)(7.696)(7.099)17.399***17.449***18.330**18.725**0.932-0.087-13.233-8.757-22.319***-27.567***

29.120***31.503***(2.720)(2.721)(2.164)(2.058)(0.205)(-0.019)(-1.544)(-0.963)(-4.275)(-5.386)(2.933)(3.061)

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

333
0.834

333
0.852

314
0.648

314
0.662

299
0.705

299
0.744

388
0.674

388
0.687

329
0.845

329
0.867

93
0.722

93
0.743

Note: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference
square rescaled by 10 , (GDP difference * Skill difference) rescaled by 10 . All FDIs are measured in stock.

<Table 3> Country effect: Panel
Indonesia
Indonesia

Malaysia

Philippines

Singapore

Thailand

Vietnam

0.337***(5.727)0.509***(11.922)

Malaysia

0.335***(6.524)

Philippines

0.437***(12.486)

Singapore

0.534***(9.174)Thailand

0.342***(3.944)

Vietnam∗_cons
Host country fixed
effectNumber of obs.
R-squared

0.103***-0.005-0.0010.079***

0.035

0.046

(4.356)(-0.375)(-0.047)(6.487)(1.228)(1.286)0.240

0.190*

0.230*-0.219**0.315***

0.193(1.624)(1.662)(1.654)(-2.334)(2.894)(0.793)0.380***

0.198**-0.0260.217***

0.219*-0.105(3.212)(2.087)(-0.185)(2.638)(1.790)(-0.410)-0.300***-0.077*-0.037-0.147***-0.170**-0.150(-3.693)(-1.698)(-0.426)(-3.151)(-2.406)(-1.120)-0.044***0.019-0.016-0.061***-0.033*-0.067(-4.081)(0.822)(-1.107)(-4.198)(-1.666)(-1.592)-0.011-0.1450.068

0.673**-0.271**-0.117(-0.064)(-0.804)(0.397)(2.213)(-2.169)(-0.852)

9.576***

0.547

0.115

-10.785***

4.477*

14.335***(3.506)(0.192)(0.071)(-4.484)(1.871)(4.082)0.364***

0.351***

0.624***

0.319***

0.373***

0.824***(6.150)(5.534)(11.294)(7.534)(6.843)(8.166)-17.466***

2.468

2.684

26.292***-4.967-30.673***(-2.806)(0.384)(0.696)(4.767)(-0.964)(-3.829)

Yes

Yes

Yes

Yes

Yes

Yes

317

297

289

326

313

90

0.815

0.811

0.815

0.883

0.862

0.858

Note: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects included
at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill
difference) rescaled by 10 . All FDIs are measured in stock<Table 4>Neighboring effect: Panel
Indonesia

Malaysia

Philippines

Singapore

Thailand

Vietnam-0.080

0.148

0.186

0.075

0.258

(-0.648)(1.276)(1.366)(1.019)(1.345)-0.161

0.077

0.343***

0.041-0.418*(-1.253)(0.632)(2.720)(0.529)(-1.923)

Indonesia
Malaysia
Philippines
Singapore
Thailand
Vietnam∗_cons
Host country fixed
effectNumber of obs.
R-squared

0.112

0.019

-0.0200.375***

0.005(0.915)(0.156)(-0.141)(5.909)(0.015)0.060

0.223*-0.0960.240***

0.561***(0.536)(1.924)(-0.879)(3.501)(2.688)0.457***

0.180

0.772***

0.311-0.122(2.656)(1.008)(5.379)(1.613)(-0.290)0.164*-0.175**-0.312***0.163*

0.192***(1.849)(-2.095)(-4.054)(1.777)(3.740)-0.612

0.175

0.275

0.076

1.579***-0.230(-1.184)(0.490)(0.733)(0.369)(5.819)(-0.435)-0.632**-0.4800.681*-0.180

0.177

0.340

(-1.985)(-1.317)(1.908)(-0.754)(0.938)(0.835)-0.509-0.5800.610-0.3280.630***

0.233(-1.421)(-1.576)(1.487)(-1.159)(3.446)(0.555)0.414*

0.335-0.3840.158-0.486***-0.099(1.899)(1.535)(-1.566)(0.990)(-4.530)(-0.438)

0.014

0.160

0.005

-0.146***-0.094***0.004(0.734)(1.467)(0.185)(-2.850)(-4.347)(0.049)

0.061

0.035

-0.0600.133-0.030-0.063(0.483)(0.226)(-0.509)(0.233)(-0.367)(-0.379)-18.752***-6.894-5.732*

1.173

19.933***-13.460**(-4.081)(-0.812)(-1.824)(0.186)(7.153)(-2.191)-0.0810.651**-0.064-0.608***0.453***

0.771*(-0.466)(1.966)(-0.314)(-3.488)(1.943)48.420***15.20214.0048.410(4.230)(0.723)(1.621)(0.544)(3.289)49.330***(-7.231)

Yes

Yes

Yes

Yes

Yes

Yes

99

99

99

99

99

62

0.871

0.883

0.909

0.895

0.957

0.758

25.309(1.353)

Note: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects included
at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill
difference) rescaled by 10 . All FDIs are measured in stock<Table 5> Regional Agglomeration Effect: Panel
Indonesia
Ln(RAE)∗_cons
Host country fixed
effectNumber of obs.
R-squared

Malaysia

Philippines

Singapore

Thailand

Vietnam

0.372***

0.862***

0.587***

1.040***

0.505***

0.642***(6.986)(14.800)(8.609)(30.800)(11.028)(5.519)0.041*

0.062***

0.140***-0.0010.181***-0.021(1.858)(4.076)(6.405)(-0.047)(8.388)(-0.517)-0.402***-0.407***0.099-0.049-0.360***0.399(-2.962)(-3.353)(0.659)(-0.591)(-3.937)(1.464)-0.001-0.164

0.793***

0.037

0.623***

0.339(-0.013)(-1.608)(5.661)(0.551)(6.461)(1.169)0.585-0.027-4.615***0.065-3.365***-0.610(0.768)(-0.054)(-5.286)(0.164)(-5.910)(-0.400)-0.032***-0.069***-0.059***0.008-0.128***-0.168***(-3.093)(-2.799)(-4.059)(0.684)(-8.503)(-3.827)-0.1480.174-0.336*-0.738***-0.0090.093(-0.954)(0.878)(-1.836)(-2.917)(-0.084)(0.593)-8.583***-5.690*1.765

12.388***8.670***-18.919***(-3.427)(-1.850)(1.011)(6.103)(4.361)(-5.081)0.523***-0.0070.080-0.097***0.238***

0.076(10.628)(-0.097)(1.118)(-2.799)(5.088)(0.529)20.061***

10.943-1.947-27.417***-18.809***40.934***(3.535)(1.581)(-0.471)(-5.901)(-4.412)(4.848)

Yes

Yes

Yes

Yes

Yes

Yes

321

307

292

320

90

0.882

0.817

0.797

0.911

0.814

0.918

Note: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects
included at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDPdifference * Skill difference) rescaled by 10 . All FDIs are measured in stock<Table 6> Basic Model For ASEAN: System GMM
(1)

(2)

(3)

(4)

(5)

(6)

1985~99

1990~94

1995~99

2000~04

2005~09

2006~100.175-0.017

0.039

0.209

0.024

0.021

(0.807)(-0.306)(0.537)(1.627)(0.505)(0.230)-0.1540.032-0.033-0.099*-0.036-0.006(-0.519)(0.735)(-0.831)(-1.666)(-0.968)(-0.120)-0.074-0.100

0.465

0.486

-0.793**-0.902**(-0.044)(-0.298)(1.328)(1.621)(-2.244)(-2.490)

0.307

0.096

0.031

-0.0890.022-0.051(0.232)(0.963)(0.148)(-0.495)(0.171)(-0.463)-0.068

0.010

0.021

0.008

0.007

0.020

(-1.123)(0.645)(1.518)(0.292)(0.231)(0.591)-0.182-0.1520.138-0.200-0.028-0.041(-0.292)(-0.923)(0.528)(-0.819)(-0.661)(-1.111)-13.914-5.98828.885215.447

25.404**15.501(-0.427)(-0.246)(1.508)(1.020)(2.428)(0.415)

0.010

0.064

0.301

-0.0020.202***

0.369***(0.034)(1.224)(1.597)(-0.010)(3.190)(4.700)0.416

0.502***

0.730***

0.839**

0.246***

0.272*(1.213)(4.712)(3.992)(2.121)(2.579)(1.849)35.39518.525-66.064-476.092-47.871**-28.543(0.481)(0.342)(-1.548)(-1.014)(-2.092)(-0.360)

Number of obs.

94

193

288

308

378

382

Sargan8.330

22.1470.671

11.01812.87817.552

sargan(p-value)(0.304)(0.002)(0.999)(0.138)(0.075)(0.014)abond1-0.709-1.312-2.247-1.217-2.100-1.610abond1(p-value)(0.478)(0.190)(0.025)(0.224)(0.036)(0.107)abond2-0.430

0.221

1.368

0.778

0.576

-0.062abond2(p-value)(0.667)(0.825)(0.171)(0.436)(0.565)(0.950)∗

FDI (t-1)_consNote: Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, * p<0.1. Time specific effects included atPanel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill
difference) rescaled by 10 . All FDIs are measured in stock<Table 7>Basic Model for each Country: System GMM∗

Indonesia
(1)
(2)
0.349
-0.931(1.320)(-0.385)

Malaysia
(1)
(2)
0.127
-0.917(0.836)(-0.492)

Philippines(1)(2)
0.491**
3.129*(2.157)(1.833)-0.0292.235***(-2.999)(-0.004)
0.101
0.208
(0.846)(0.299)-0.789-0.895-0.1044.471(-0.827)0.119(0.826)(-0.063)-0.355(-0.151)(-0.103)-0.050(-0.437)(0.300)-0.288(-0.234)-0.0620.255

0.102**

0.304-0.066(-0.745)0.009(1.173)(0.313)0.013(0.170)(2.030)-0.082***(-4.738)(0.465)0.049(0.072)0.078-0.042-0.0640.089(1.579)(-0.418)

Singapore
(1)
(2)
0.047
1.518
(0.273)(0.468)Thailand(1)(2)
0.461**
1.505(2.333)(1.284)-7.7971.549***(-2.671)(-1.142)
0.114-0.443(1.334)(-1.063)

0.106-6.766(0.121)0.258**(2.255)(-0.380)-0.715(-1.083)-0.970*-0.0560.110-0.044(-0.977)-0.014**(-2.312)(-1.886)
0.061(1.030)(0.353)0.063(0.596)-0.079*-0.033(-0.992)-0.012(-1.007)0.523***(-4.212)-3.201

Vietnam
(1)
(2)
0.636
0.621
(0.951)(0.485)-6.747-0.871(-1.484)-0.048(-0.157)(-0.080)-1.582(-1.286)

0.081

0.048

-0.155(-0.881)-0.004(-0.601)(0.371)-0.037(-0.899)(0.490)-0.051(-1.410)(-0.580)-0.059(-0.213)-0.045-0.079-0.064-0.038(-1.201)(0.559)(-1.719)(-0.505)(-1.143)(-1.338)(-0.833)(-1.192)(-0.528)2.624 -116.572-23.837-1.848-7.5200.454-26.786-6.291*-22.672-1.862-16.591
16.948***(0.611)(-1.418)(-2.818)(-0.123)(-0.805)(-0.665)(0.085)(-0.226)(-1.868)(-0.629)(-0.229)(-1.253)0.129***
0.586**
0.116***
0.279
0.015
0.235 0.116***0.467**
0.047**
0.167 0.226***
0.595**(3.683)(2.086)(4.342)(1.320)(0.553)(1.393)(3.388)(1.985)(1.999)(1.396)(2.786)(2.505)
FDI (t-1)
0.759***
0.265
0.671***
0.836** 0.722***0.809* 0.774*** 0.763*** 0.718***0.370 0.496***-0.043(21.095)(0.638)(18.270)(2.236) (22.209)(1.855) (25.537)(2.935)(25.607)(1.099)(8.358)(-0.417)_cons-5.325260.343 40.052***
51.1377.177
14.1560.678
56.375 16.321**
55.253
6.310
43.828(-0.550)(1.412)(3.013)(0.121)(1.384)(0.497)(0.057)(0.219)(2.171)(0.665)(0.353)(1.357)
Number of obs.
302
69
290
60
277
56
361
80
307
65
82
52
Sargan314.673
12.022324.2075.261
287.0643.362
334.8103.539
309.6247.471
68.807
2.124
sargan(p-value)(0.042)(0.100)(0.003)(0.628)(0.082)(0.850)(0.108)(0.831)(0.045)(0.382)(0.350)(0.953)abond1-2.612-0.542-1.707-1.080-2.350-1.199-2.664-1.326-1.958-1.525-1.194-0.818abond1(p-value)(0.009)(0.588)(0.088)(0.280)(0.019)(0.231)(0.008)(0.185)(0.050)(0.127)(0.233)(0.413)abond2-0.116-1.041
1.297
0.505
1.682
1.233
1.583
1.696
0.185
-0.0981.214-0.224abond2(p-value)(0.908)(0.298)(0.195)(0.614)(0.093)(0.218)(0.113)(0.090)(0.854)(0.922)(0.225)(0.823)
Note: Column (1) indicates periods of 1985 to 2010 while Column (2) indicates period of 2006 to 2010 ; Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05, *p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill difference) rescaled by
10 . All FDIs are measured in stockIndonesia

Indonesia(1)(2)
0.120***
0.101(3.738)(0.697)<Table 8> Country effect: System GMM
Malaysia
Philippines
Singapore
(1)
(2)
(1)
(2)
(1)
(2)
0.097***(2.594)

Malaysia

Thailand
(1)
(2)

0.119
(0.601)0.036(1.591)

Philippines

0.035(0.174)0.131***(5.197)Singapore

0.184(1.200)0.189***(4.871)Thailand

0.466(1.534)0.451(0.314)-0.363(-0.356)-1.044(-0.681)0.048(1.025)-0.481(-1.244)0.237(1.095)-0.021(-0.138)-0.129(-1.574)-0.731(-0.884)0.305(0.544)-1.403(-1.139)

0.026

0.065

0.162

(0.062)(0.828)0.050
0.002(0.614)(0.084)-0.021-0.045(-0.143) (-1.152)-66.577-3.967(-1.148) (-0.587)
0.208 0.159**(0.978)(2.241)0.167 0.690***(0.557) (11.725)
152.126
11.030(1.170)(0.741)63
88
5.073
62.022(0.717)-0.266*(-1.754)-0.017(-0.318)-28.963(-0.893)
0.418**(2.134)0.221(0.584)74.987(0.971)54
2.975

Vietnam
0.341**(2.088)0.138*(1.951)0.261***(2.617)∗

FDI (t-1)_cons
Number of obs.
Sargan0.126(0.065)-0.614(-0.539)-1.459(-1.146)

Vietnam(1)(2)-0.075(-0.536)0.099(1.070)-0.236**(-2.002)2.356(0.797)-1.425(-0.731)-1.265(-0.746)-0.081(-0.709)-0.045(-1.061)-0.023(-0.283)-0.877(-0.484)-0.262(-0.185)-1.145(-0.572)0.400***(3.093)-0.182***(-2.919)0.172**(2.262)-0.140***0.285 0.106***

0.367

0.059

0.451

-0.028(-2.694)-0.001(-0.211)0.001(0.021)2.057(0.499)0.011(0.370)0.784***(22.646)-3.521(-0.382)302
323.637(0.546)(2.617)0.016-0.029(0.143) (-1.374)-0.000-0.048(-0.003) (-0.872)-10.071-8.047(-0.446) (-1.404)
0.335 0.116***(1.482)(3.909)0.704** 0.606***(2.221) (15.853)
23.83720.933(0.446)(1.632)69284
1.405 329.523(0.469)(1.463)-0.129 0.009**(-0.159)(2.468)0.058
0.025(0.303)(0.892)17.998-2.412(0.232) (-1.350)
0.182 0.088***(0.496)(4.245)0.824*** 0.763***(3.007) (26.171)-43.574
7.104*(-0.246)(1.762)64277
14.534 367.401(0.408)-0.010(-0.138)-0.000(-0.003)
5.035(0.059)0.371(1.489)0.572(1.395)-8.245(-0.044)60
2.759(-0.728)-0.002(-0.325)
0.033(0.421)-7.454*(-1.843)
0.062**(2.561)0.575***(17.333)
19.176**(2.125)310
320.0420.246
0.324*(0.223)(1.798)-0.028-0.023(-0.031) (-0.429)
0.352 0.248***(0.312)(3.015)-0.013-0.092**(-0.061) (-2.104)
0.024
0.007
(0.131)(1.168)4.165 0.078**(1.218)(2.386)-35.995
1.867(-0.837)(0.461)0.123 0.081***(0.769)(3.041)0.863* 0.628***(1.693) (18.291)
74.757-3.094(0.745) (-0.344)
65
298
7.958 393.625

sargan(p-value)(0.021)(0.985)(0.001)(0.043)(0.000)(0.906)(0.032)(0.336)(0.000)(0.651)(0.649)(0.887)abond1-1.652-1.865-2.460-1.921-2.030-1.996-1.887-0.023-2.227-0.636-1.605-0.924abond1(p-value)(0.099)(0.062)(0.014)(0.055)(0.042)(0.046)(0.059)(0.982)(0.026)(0.525)(0.108)(0.356)abond20.779-0.334-0.935-0.5330.729-0.1610.271-1.3640.406-0.926-1.109-1.215abond2(p-value)(0.436)(0.739)(0.350)(0.594)(0.466)(0.872)(0.786)(0.173)(0.685)(0.355)(0.267)(0.224)
Note: Column (1) indicates periods of 1985 to 2010 while Column (2) indicates period of 2006 to 2010 ; Shown in parentheses are t-statistics, *** p<0.01, **p<0.05, * p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill difference)rescaled by 10 . All FDIs are measured in stock<Table 9> Neighboring effect: System GMM
Indonesia(1)(2)
Indonesia
Malaysia
Philippines
Singapore
Thailand
Vietnam∗

FDI (t-1)_cons
Number of obs.

-0.133*(-1.944)0.071(1.024)-0.073(-0.707)
0.026(0.219)0.146**(2.210)0.279(0.617)-0.116(-0.442)0.060(0.220)-0.014(-0.102)0.010(0.933)-0.008(-0.111)4.269(0.742)0.115(0.886)0.550***(6.530)-5.562(-0.439)
99

Malaysia
(1)
(2)
0.052
0.025
(0.683)(0.132)0.445(0.865)-0.127-0.061(-0.116) (-1.221)-0.027 0.202***(-0.101)(2.681)-0.208-0.064(-0.459) (-0.641)
0.123-0.014(0.314) (-0.241)
1.220 -0.447**(0.354) (-2.049)-0.982 0.394**(-0.553)(2.432)-1.031
0.197(-0.613)(0.649)

Philippines
(1)
(2)
0.034
0.102
(0.433)(0.684)0.026
0.127(0.390)(0.534)Singapore
(1)
(2)
0.019
-0.100(0.255) (-0.624)
0.121**
0.092(2.084)(0.379)0.085-0.047(1.533) (-0.168)

0.358(1.160)0.053(0.325)-0.042(-0.193)
0.363**(2.038)0.447(0.338)-0.521(-0.541)-0.767(-0.550)-0.079(-0.935)
0.259**(2.434)-0.083(-1.559)
0.516(1.552)0.209(1.082)0.396(1.517)0.069(0.562)-0.011(-0.052)-0.206(-1.275)
1.992(1.426)0.586(1.148)0.754(0.629)-0.0180.360-0.280**-0.7990.121*(0.103) (-0.126)-0.031
0.118(-0.104)(1.462)-0.059 -0.183***(-0.404) (-4.042)-36.487
3.085(-0.284)(0.702)0.153 0.302***(0.366)(3.256)-0.199 0.284***(-0.648)(4.671)93.983-2.983(0.344) (-0.304)
38
97(0.783)0.011(0.030)-0.003(-0.025)-4.562(-0.052)-0.129(-0.582)0.359(1.597)
10.791(0.056)38(-2.132)-0.013(-1.496)-0.059(-1.149)1.994(0.675)0.048(0.494)0.752***(14.184)-4.466(-0.655)
99(-1.337)-0.112**(-2.177)-0.040(-0.743)-19.447(-0.506)
0.192(1.159)0.472(1.218)43.341(0.486)38(1.805)-0.035*(-1.792)
0.045(0.298)8.237**(2.020)0.111(1.217)0.542***(7.951)-18.115**(-2.018)98

0.118

0.259**
0.359*(2.565)(1.743)-0.004 0.673***(-0.083)(4.276)-0.684**1.240(-2.549)(1.312)
0.341*-0.685(1.732) (-1.177)-0.441*** -0.976**(-2.877) (-2.537)
0.223

Thailand
Vietnam
(1)
(2)
(1)
(2)
0.069**-0.2760.049
0.149(2.055)(-1.626)(0.487)(0.565)0.035
0.226-0.073 0.933***(1.181)(0.681) (-0.874)(2.657)0.024
0.928***
0.246* -1.095**(0.863)(2.847)(1.822) (-2.308)
0.074**
0.163 0.395*** 0.341**(2.174)(1.261)(3.829)(2.056)-0.056-0.376(-0.304) (-1.166)
0.022-0.054(0.907)(-0.282)
0.157-1.391-0.4733.141(0.673)(-1.465) (-1.234)(1.190)-0.0460.529-0.040-0.905(-0.498)(0.746) (-0.180) (-0.459)
0.022
3.704***-0.2881.283*(0.208)(2.584) (-1.188)(1.669)-0.026(1.207) (-0.478)
0.120-0.004(0.593) (-0.689)
5.260-0.019(1.534) (-0.713)-51.4310.398(-1.094)(0.211)-0.205-0.044(-0.983) (-1.042)
0.041 0.708***(0.329) (13.245)
115.7210.796(1.112)(0.189)37
97-0.1240.075-1.176**(-0.382)(0.793)0.015
0.002(0.222)(0.051)-0.091-0.035(-0.964) (-0.664)-149.915***-3.702(-2.787) (-0.771)
0.095 0.519***(0.413)(3.049)-0.645 0.387***(-1.265)(4.399)328.733***
0.912(2.768)(0.080)36
56(-2.136)
1.831(1.175)-0.072(-0.925)-19.234(-1.141)0.453*(1.793)-0.206(-1.054)47.595(1.171)36

Sargan
78.5318.643
88.6669.268
126.1736.063
75.8904.851 111.176
5.157
32.065
6.272
sargan(p-value)(0.840)(0.279)(0.550)(0.234)(0.009)(0.532)(0.873)(0.678)(0.074)(0.641)(0.777)(0.508)abond1-1.443-1.330-1.584-1.603-1.517-1.340-1.763-1.330-1.999-1.417-1.473-1.966abond1(p-value)(0.149)(0.184)(0.113)(0.109)(0.129)(0.180)(0.078)(0.183)(0.046)(0.157)(0.141)(0.049)abond2-1.010-0.7250.250
1.091-0.0170.948
0.586-0.036-1.795-0.1880.571-0.285abond2(p-value)(0.313)(0.468)(0.803)(0.275)(0.987)(0.343)(0.558)(0.971)(0.073)(0.851)(0.568)(0.776)
Note: Column (1) indicates periods of 1985 to 2010 while Column (2) indicates period of 2006 to 2010; Shown in parentheses are t-statistics, *** p<0.01, ** p<0.05,* p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill difference) rescaledby 10 . All FDIs are measured in stock<Table 10>Regional Agglomeration Effect: System GMM
RAE (log)∗

Indonesia
Malaysia
Philippines
(1)
(2)
(1)
(2)
(1)
(2)
0.166*** 0.469** 0.325*** 0.651*** 0.121***0.012(3.718)(2.056)(6.492)(2.940)(2.706)(0.070)0.010-0.1230.015-0.225 0.045**
0.310*(0.394) (-0.545)(1.118) (-1.250)(2.146)(1.805)-0.179**0.118-0.051
1.022 -0.233***
0.005(-1.987)(0.102) (-0.564)(0.825) (-3.402)(0.007)-0.089-0.031-0.037-0.6360.056
0.244(-0.647) (-0.019) (-0.342) (-0.641)(0.497)(0.329)
0.460

2.302

0.635

2.946

-0.546-9.639*Singapore
Thailand
Vietnam
(1)
(2)
(1)
(2)
(1)
(2)
0.483*** 1.237*** 0.113*** 0.282** 0.348*** 1.072***(14.914) (13.914)(3.040)(2.363)(3.243)(4.153)-0.0040.043 0.053***
0.147
0.026
0.059
(-0.336)(0.437)(2.952)(1.454)(0.427)(0.599)-0.029-0.039 -0.167***-0.723-0.406
0.196(-0.453) (-0.072) (-3.165) (-1.145) (-0.993)(0.230)-0.096-0.048 0.163**-0.157-0.0910.004(-1.128) (-0.230)(2.095) (-0.365) (-0.335)(0.004)-0.025-1.086-0.800*

0.135

0.405

-3.446(0.588)(0.324)(1.342)(0.581) (-0.873) (-1.874)(-0.061) (-1.129) (-1.748)(0.070)(0.471) (-1.604)
0.004
0.016 -0.089***-0.073 -0.012**
0.060
0.003
0.045
-0.005-0.004-0.0210.076(0.585)(0.148) (-5.023) (-0.160) (-2.078)(0.997)(0.351)(1.380) (-0.836) (-0.090) (-0.661)(0.337)0.059-0.029-0.0570.071 -0.107**-0.032 -0.306***
1.652*-0.007-0.069-0.087*-0.016(1.285) (-0.308) (-1.169)(0.548) (-2.489) (-0.486)(-3.397)(1.789) (-0.215) (-0.784) (-1.812) (-0.281)
0.077 -110.386-7.777 182.682-1.128-8.056 12.625***-53.311-3.569-21.653-6.355-14.452(0.020) (-1.293) (-1.408)(1.105) (-0.518) (-0.702)(3.194) (-1.451) (-1.130) (-0.707) (-0.869) (-1.382)
0.072**
0.456*
0.004
0.054-0.0190.234-0.001-0.0890.041*
0.127 0.171**
0.203(2.027)(1.667)(0.140)(0.275) (-0.677)(1.290)(-0.037) (-1.035)(1.797)(1.132)(1.965)(0.987)
FDI (t-1)
0.660***-0.090 0.541***0.351 0.689***
0.779 0.536***-0.144 0.675***
0.257 0.355***-0.117(16.826) (-0.232) (14.153)(1.079) (20.503)(1.624)(20.107) (-1.633) (21.884)(0.817)(5.549) (-1.439)_cons
0.493 245.177
18.225 -403.861
4.860
15.481 -28.079*** 115.999
9.174
50.23713.81125.684(0.056)(1.312)(1.484) (-1.116)(0.977)(0.536)(-3.185)(1.455)(1.289)(0.717)(0.863)(0.994)
Number of obs.
293
68
288
62
272
56
355
80
301
64
81
51
Sargan343.682
10.830 344.043
6.069 312.644
3.434
422.259
9.157 331.845
6.070
74.967
5.266
sargan(p-value)(0.001)(0.146)(0.000)(0.532)(0.004)(0.842)(0.000)(0.242)(0.003)(0.532)(0.144)(0.628)abond1-2.078-0.605-1.622-0.015-2.406-1.277-2.8310.868-1.593-1.286-1.0041.005
abond1(p-value)(0.038)(0.545)(0.105)(0.988)(0.016)(0.202)(0.005)(0.385)(0.111)(0.199)(0.316)(0.315)abond2-0.129-1.1501.370-0.199
1.820
1.229
1.513
1.293
-0.310-1.3061.102
0.801
abond2(p-value)(0.897)(0.250)(0.171)(0.842)(0.069)(0.219)(0.130)(0.196)(0.757)(0.192)(0.271)(0.423)
Note: Column (1) indicates periods of 1985 to 2010 while Column (2) indicates period of 2006 to 2010 ; Shown in parentheses are t-statistics, *** p<0.01, **p<0.05, * p<0.1. Time specific effects included at Panel; GDPSUM rescaled by 10 , GDP difference square rescaled by 10 , (GDP difference * Skill
difference) rescaled by 10 . All FDIs are measured in stock<Table A1> Data Sources
Variable

Description

Definition

FDI

Bilateral stock of FDI from i to j at time t

1,000USD, bilateral FDI

ln FDI

log (Bilateral stock of FDI from i to j at time t)

Log form of bilateral FDI

China

log of FDI into China from i at time t

1,000USD, China effect

GDPSUM

GDP

GDP

GDP differece square

GDP

GDP

Skill difference

Skll

Skill

GDP

GDP

GDP difference∗ Skill difference

Million USD in constant 2000 prices, sumof GDP between i and j
Million USD in constant 2000 prices,
GDP squared difference between i and j∗ SkllSkill

Investment const ∗ BIT

Trade cost

Trade cost ∗ FTA

ln Distancelog (Bilateral distance between i and j)

OECD-International Direct Investment
Data
OECD-International Direct Investment
Data
OECD-International Direct Investment
Data
World Bank-WDI
World Bank-WDI

Skill difference between i and j

Barro and Lee(2010) - Average years of
secondary schooling for pop
lation over age 15

Interaction term between GDP difference
and Skill difference

World Bank-WDI
Barro and Lee(2010)

BIT = 0, if no BIT
Investment cost

Source

BIT = 1, if BIT implemented
Financial Risk index
FTA = 0, if no FTA
FTA = 1, if FTA implmented
Free trade index of c
untry j at time t
Geographical distance between i and j

ICSID, UNCTAD,Country Risk Guide(ICRG)
Freedom to trade internationally of
Economic Freedom of the World of Fraser
Institute, WTO
CEPII-‘Etudes Prospectives etd’Informations Internationales

< Table A2> Composition of FDI Data

<Table A3> List table of Home countries

Note: The data excluded with Israel, Mexico, and Canada
for home countries because of data availability