SOURCES OF FOREIGN DIRECT INVESTMENT

Transcription

China Economic Policy Review
Vol. 1, No. 2 (2012) 1250013 (16 pages)
c World Scienti¯c Publishing Company
°
DOI: 10.1142/S1793969012500136
SOURCES OF FOREIGN DIRECT INVESTMENT
FLOWS TO DEVELOPING ASIA: THE IMPORTANCE
OF TIME ZONE¤
RABIN HATTARI
Asian Development Bank
Indonesia Resident Mission, Gedung BRI II
7th Floor, JI. Jend. Sudirman Kav. 44-46
Jakata 10210, Indonesia
[email protected]
RAMKISHEN S. RAJAN†
School of Public Policy
George Mason University
3351 Fairfax Dr., Arlington VA 22201, USA
[email protected]
This paper investigates the determinants of foreign direct investment (FDI) °ows to developing
Asia using bilateral FDI °ows for the period 1990 to 2005. We pay particular attention to
possible di®erences in the determinants of FDI °ows to developing Asian economies from the
rest of the Asia-Paci¯c region compared to those from nonregional OECD economies, with an
emphasis on the role of distance and time zone. We ¯nd that the elasticity of distance is much
greater for FDI from the nonAsia-Paci¯c OECD economies than intraregional Asian °ows.
However, this di®erence disappears when one accounts for di®erences in time zones. The world is
not °at; di®erences in time zones appear to act as a hindrance to FDI.
Keywords: Developing Asia; distance; foreign direct investment (FDI); gravity
model; time zone.
JEL Classi¯cation: F21, F23, F36
1. Introduction
With the rapid growth of countries such as the People's Republic of China
(PRC) and India, and the resurgence of Southeast Asia after the 19971998
currency crisis, developing Asia has once again become one of the most
dynamic economic regions in the world. It would not be an exaggeration to
say that international trade and foreign direct investment (FDI) is a key
*An
initial draft of the paper was drawn while the second author visited the Asian Development Bank Institute (ADBI).
† Corresponding author.
1250013-1
R. Hattari & R. S. Rajan
determinant of trade and growth in much of the developing Asian region.
While there have been detailed studies on the sources and determinants of
international trade °ows to developing Asia at the bilateral level, less
research has been done on bilateral FDI °ows, a gap that this paper aims to
¯ll. The focus of this paper is on FDI °ows to developing Asian economies
from the main OECD economies as well as from other developing Asian
economies using data from UNCTAD.1
Analyzing bilateral FDI °ows data is not a straighforward exercise, as
in°ows and out°ows data do not accurately match. While some source
countries have relatively complete FDI out°ows data, it is apparent that for
many countries, source country data is incomplete or nonexistent. Numerous
practices of describing FDI data create bilateral discrepancies between FDI
°ows as reported by source and host countries, some of which can sometimes
be quite large. Faced with these concerns, we drew inferences on FDI °ows by
examining FDI in°ow data reported in the host economies, as they are
relatively more complete and are available for all developing Asian economies
under consideration. In other words, we focused on the sources of in°ows
rather than the destination of out°ows.
Figure 1 highlights the period from 19902004, during which, Japan has
been the single largest investor into developing Asia, accounting for 1718%
of total °ows, and showing an increase from 1314% in 19901994. The US
was the second largest investor in the region, accounting for 9% of total
EU-5
2.7%
Others
40.5%
US.
4.6%
US.
9.3%
EU-5
16.3%
Others
19.5%
EU-5
12.1%
Others
30.5%
US.
8.9%
Japan
13.5%
Developing
Asia
38.6%
(1990−94 averaged)
Japan
17.9%
Developing
Asia
37.0%
Japan
16.9%
Developing
Asia
31.6%
Fig. 1. Sources of FDI In°ows to Asia, 19902004. Developing Asia consists of newly
industrialized Asian economies (NIEs), ASEAN-4, People's Republic of China (PRC), India,
Low-income Asia, and other parts of Asia. EU-5 consists of France, Germany, Italy, Netherlands, and the United Kingdom.
Source: UNCTAD.
1 We
follow the United Nations Conference on Trade and Development (UNCTAD) policies in
categorizing Republic of Korea as part of developing Asia.
1250013-2
Sources of FDI Flows to Developing Asia: The Importance of Time Zone
in°ows, up from 45% in 19901994. The EU has averaged around 14%
of total in°ows over the period 19952004, while intradeveloping Asian
°ows has accounted for an average of 35%, with a slightly declining trend
over the last 15 years. The main EU sources of FDI °ows to Asia have been
the United Kingdom, Netherlands, and Germany. Intradeveloping Asian
°ows have been largely from the PRC; Hong Kong, China; Singapore; and
Taipei, China.
Table 1 summarizes the top 40 bilateral FDI °ows to developing Asia
for the last two sub-periods. Flows from Hong Kong, China, to the PRC, and
vice versa, stand out in this regard. Part of this is due to round-tripping,
which signi¯cantly in°ates the amount of outward FDI from the PRC (Xiao,
2004). Flows from Japan, the US, and Singapore to the PRC and Hong Kong,
China also stand out. Also noteworthy are FDI °ows from US and Japan
to the Republic of Korea, Malaysia, Thailand, and in particular, Singapore.
Overall, FDI in°ows are particularly pronounced between and within
East Asian economies and Southeast Asian economies. India is the only
South Asian country that enters the top 40 bilateral FDI °ows to developing
Asia.2
The paper is organized as follows: Sections 2 and 3 respectively outline and
estimate an augmented gravity model framework to examine the main
determinants of FDI °ows to the region using a panel dataset. We pay particular attention to whether there are di®erences in the determinants of FDI
°ows from the nonAsia-Paci¯c OECD economies compared to those coming
from intraregional °ows, with particular emphasis on the role of distance and
time zone di®erences. Section 4 presents a summary and a few concluding
remarks. To preview the main conclusion, we ¯nd the elasticity of distance to
be greater for FDI from nonAsia-Paci¯c OECD economies than for intradeveloping Asian °ows. However, this di®erence disappears when one
accounts for di®erences in time zones in the manner of Stein and Daude
(2006).
2 While Japan, North America, and the EU-5 (i.e., France, Germany, Italy, Netherlands, and
UK) and intraregional (Asian) economies, as well as Australia and New Zealand, have together
constituted around 70% of total in°ows to developing Asia over the last 15 years, that still
leaves a substantial portion of in°ows unaccounted for. While regions such as Russia, Latin
America, and the Middle East have invested in the region, they have been relatively small
players to date. A signi¯cant portion of FDI to the region is from o®shore ¯nancial centers
(OFCs) such as the British Virgin islands, Bermuda, Cayman Islands, Mauritius, and Western
Samoa. Insofar as some part of in°ows from the OFCs involve FDI that originated from other
Asian economies and are not intended for the originating country (i.e., trans-shipping as
opposed to round-tripping), we may be undercounting the size of intra-Asian FDI °ows.
1250013-3
Hong Kong, China
People's Republic of China (PRC)
Japan
United States
Taipei, China
Singapore
Netherlands
Japan
Japan
United States
United States
Singapore
United States
Japan
United Kingdom
Germany
Singapore
Netherlands
United States
Singapore
United States
Germany
Netherlands
Japan
Source
A.
Host
B.
(PRC)
(PRC)
(PRC)
(PRC)
1250013-4
China (PRC)
China (PRC)
China (PRC)
China
China
China
China
China (PRC)
17,750.8
7266.9
3276.2
3774.7
2774.8
2706.3
1929.0
1347.0
1417.6
1915.1
1840.4
2835.3
1293.6
1281.5
1305.4
995.1
844.1
1350.1
1429.8
441.7
631.3
486.9
590.2
607.8
(19972000)
17,819.1
5459.4
5194.5
4107.0
3361.3
2136.7
2011.5
2324.9
2044.6
1521.3
1506.5
353.1
1571.4
1276.6
893.4
1146.4
1133.8
573.4
428.8
1381.9
852.7
957.0
801.7
717.3
(20012005)
Average
Top 40 bilateral °ows to developing Asia (in millions of US$).
People's Republic of
Hong Kong, China
Hong Kong, China
Thailand
Hong Kong, China
Hong Kong, China
Singapore
Hong Kong, China
Republic of Korea
Singapore
Malaysia
Republic of Korea
Malaysia
Thailand
India
Singapore
Republic of Korea
Table 1.
16.0
6.5
3.0
3.4
2.5
2.4
1.7
1.2
1.3
1.7
1.7
2.6
1.2
1.2
1.2
0.9
0.8
1.2
1.3
0.4
0.6
0.4
0.5
0.5
(19972000)
16.8
5.2
4.9
3.9
3.2
2.0
1.9
2.2
1.9
1.4
1.4
0.3
1.5
1.2
0.8
1.1
1.1
0.5
0.4
1.3
0.8
0.9
0.8
0.7
(20012005)
In percent to Asia
1250013-5
Host
B.
Malaysia
Republic of Korea
Philippines
Hong Kong, China
Thailand
Philippines
India
Malaysia
Thailand
Singapore
India
India
Republic of Korea
Note: Based on FDI in°ow data in host economy.
Source: UNCTAD FDI database.
France
Germany
Germany
United States
Taipei, China
Australia
United Kingdom
Japan
Malaysia
United Kingdom
Hong Kong, China
Hong Kong, China
France
Japan
Netherlands
France
Source
A.
701.4
316.0
681.9
658.8
268.9
278.2
273.9
232.9
290.8
134.3
272.3
360.1
303.8
249.3
130.0
382.2
594.8
852.2
248.3
250.5
446.6
400.7
363.5
377.5
316.7
443.4
296.5
160.8
211.5
244.7
350.9
97.4
(20012005)
Average
(19972000)
Table 1. (Continued )
0.6
0.3
0.6
0.6
0.2
0.3
0.2
0.2
0.3
0.1
0.2
0.3
0.3
0.2
0.1
0.3
(19972000)
0.6
0.8
0.2
0.2
0.4
0.4
0.3
0.4
0.3
0.4
0.3
0.2
0.2
0.2
0.3
0.1
(20012005)
In percent to Asia
2. Determinants of FDI Flows to Developing Asia
This section undertakes an empirical investigation of some of the possible
determinants of FDI °ows to developing Asia from the OECD and the rest of
the region over the period 19902005 using an augmented gravity model
framework.
2.1. The model
Our aim is to develop a relatively parsimonious model that includes speci¯c
bilateral variables as well as selected host country policy variables. In view of
this, we followed the basic gravity-type framework, which argues that market
size and distance are important determinants in the choice of the location of
source countries for direct investments.3
The basic speci¯cation of our estimated model is outlined below:
lnðFDIijt Þ ¼ 0 þ 1 lnðGDPit Þ þ 2 lnðGDPjt Þ þ 3 LANGij
þ 4 lnðDISTij Þ þ 5 Xijt þ j þ t þ ijt
ð1Þ
where FDIijt is the real FDI °ow from source country i to host country j in
time t; GDPit and GDPjt are real GDPs in US dollars for the source country i
and the host country j in time t; LANG is a binary variable equal to 1 if the
source and host countries have a common o±cial language; DISTij is the
geographical distance between the host and source countries; Xijt is a vector
of control variables in°uencing FDI out°ows; j denotes the unobservable
type of source country e®ects (source country dummies are used); t denotes
the unobservable time e®ects (year dummies are used); and ijt is a nuisance
term. Our baseline gravity model is augmented with measures of trade
openness and ¯nancial openness of the host country as well as bilateral
imports between the two countries.
We assumed the coe±cients of the real GDP of the source and destination
countries to be both positive, as they proxy for important masses in gravity
models. A destination country that has a large market tends to attract more
FDI. The coe±cient of the source country size could either be negative or
positive. While large real GDP indicates greater aggregate income and/or
more companies, and therefore higher ability to invest abroad, small real
GDP in the source implies limited market size and consequent desire by
3 The
theoretical basis for the gravity model of FDI has recently been proposed by Head and
Ries (2008). The competing model is the capital-knowledge model of multinational activity
developed by (Carr, Markusen and Maskus, 2001), which is arguably more appropriate if one
uses FDI stock data. In addition, some of the variables required to operationalize the CMM
model are not easily available for smaller developing Asian economies.
1250013-6
companies to expand their wings overseas in order to gain market share. The
sign for common language ought to be positive, while the sign for distance
from the source to the host country should be negative, as greater distance
between countries makes a foreign operation more di±cult and expensive to
supervise and might therefore discourage FDI.4
We also added a measure for bilateral trade (i.e., imports). The idea here is
that a source country could either import from the host country or choose to
establish a production base there in order to sell directly to their home
market. Alternatively, insofar as companies in the home market are losing
market share to \cheap" imports, they may choose to relocate overseas in
order to counter this competition. There may be issues of reverse causality
between FDI and imports, so we lagged imports by one period. In addition to
bilateral trade, in general, the more open the economy is to trade and capital
°ows, the more likely it is to attract FDI.5 We used total trade to GDP ratio
as a measure of trade openness and used a normalized ChinnIto index
(see Chinn and Ito, 2007) as a proxy for ¯nancial openness.6 Clearly, there
may be a number of other host-country determinants of FDI (for instance,
see Hattari and Rajan, 2009). However, since our aim is to focus on the basic
gravity model, and in particular, to compare the di®erence between OECD
and developing Asian sources of FDI, we have kept the regressions fairly
economical. We also included time (year) dummies to account for global
changes in FDI trends and also controlled for other source-country e®ects to
account for unobservable or omitted factors.
2.2. Data and methodology
Tables A.1 and A.2 summarize the data sources that are used. The FDI
data are based on the UNCTAD FDI/TNC and EIU's World Investment
Service databases in millions of US dollars. We de°ated the FDI data by
using the 1996 US consumer price index (CPI) for urban consumers. Data
for real GDP and real GDP per capita are taken from the World Bank's
4 However, if the foreign ¯rm is looking to service the destination country's market, a longer
distance also makes exporting from source countries more expensive and might therefore make
local production more desirable and encourage investment. This argument is not unlike the
tari®-jumping one.
5 We could have included bilateral exports rather than imports. However, the nexus between
FDI and trade is ambiguous a priori. Insofar as both are a means of servicing a market, they
could be competitive in nature (i.e., market-seeking FDI). On the other hand, their relationship could be complementary if FDI is export-oriented, or if greater exports increase familiarity
with a country, hence stimulating FDI in°ows as well.
6 We normalized the ChinnIto index from 0 to 100.
1250013-7
World Development Indicators database. Imports data from the source
countries to the host countries are taken from the International Monetary
Fund's (IMF) Direction of Trade and Statistics (DOTS) database (although the data are limited to merchandise trade). We also de°ated our
export data using the 1996 US CPI for urban consumers. Data on distance
and common o±cial language are taken from the CEPII (see http://www.
cepii.fr/).
Our sample is based on a balanced panel dataset of annual data on 187
sourcehost country pairs, which consist of 24 source countries and 12 host
countries between 1990 and 2005 (Table 2). The dataset contains a large
number of missing variables for bilateral FDI (roughly 40% of the total
observations) and a small number of disinvestment ¯gures shown in the data
as negative (188 observations). Excluding missing and negative observations,
our panel consists of around 1600 observations. In all of our estimations, we
dealt with the issue of censored data using the Tobit model, a commonly used
approach to dealing with censored data (for instance, see Stein and Daude,
2006; Loungani et al., 2002).7 We followed di Giovanni (2005) by computing
Table 2.
Host and source economies.
Source
Source
Host
OECD
Australia
Canada
France
Germany
Italy
Japan
Netherlands
New Zealand
United Kingdom
United States
Developing Asia
Bangladesh
People's Republic of China
Hong Kong, China
India
Indonesia
Republic of Korea
Malaysia
Pakistan
Philippines
Singapore
Sri Lanka
Taipei, China
Thailand
Viet Nam
Bangladesh
People's Republic of China
Hong Kong, China
India
Indonesia
Republic of Korea
Malaysia
Pakistan
Philippines
Singapore
Thailand
Viet Nam
Source: Authors.
7 An
alternative suggested by Santos Silva and Tenreyo (2006) is to use the Poison pseudo
maximum likelihood method. This methodology has been recently applied to FDI by Head and
Ries (2008).
1250013-8
a Tobit model using the two-step procedure: ¯rst, a probit model is estimated
based on whether a deal is observed to be conditional or not on the same
right-hand variables as in Eq. (1), and the inverse Mills' ratio is constructed
from the predicted values of the model. Second, a regression is run to estimate
Eq. (1), including the inverse Mills ratio as a regressor.8
3. Empirical Results
3.1. Baseline results
We started with the baseline regression (regression 1) and went on to add
dummy interaction terms in order to di®erentiate between developing Asia
and the OECD. The results are summarized in Table 3.
Our baseline regression (regression 1) suggests that larger countries
received greater volumes of FDI and that the results are statistically signi¯cant. However, the coe±cient of the source country is negative and
economically and statistically signi¯cant. This result is not completely
unexpected, as major source economies, such as Japan and the US, and
smaller source economies, such as the Netherlands; Hong Kong, China; and
Singapore are both major sources of FDI to the region. Possessing a common
language is positively associated with increased FDI in°ows, though it is not
statistically signi¯cant. This may be because English is the de facto language
of economic transactions in most of Asia. Greater distance between the host
and source country appears to hinder bilateral FDI and this result is strongly
signi¯cant, with the distance elasticity at about 0.4. Bilateral imports are
positive and statistically signi¯cant. There is also evidence that a country
that is more open to international trade and capital °ows (based on the
ChinnIto index) receives more FDI.
3.2. Robustness checks
We undertook three robustness checks. First, given that annual data could be
volatile, we re-estimated regression 1 using 3-year data averages (regression 2).
The results are quite close to the baseline, with the exception that ¯nancial
openness in the host country loses its statistical sign¯cance. Second, given
the importance of the Hong Kong, ChinaPRC bilateral FDI °ows, and
8 The
standard errors are corrected for heteroskedasticity and we use an estimated parameter
of an exogenous variable (the inverse Mills' ratio) in the second stage. See di Giovanni (2005)
for details.
1250013-9
Table 3.
Gravity models on the determinants of bilateral FDI °ows to developing Asia.
Dependent variable:
Ln of
bilateral real
FDI out°ows
Regression
(1)
Regression
(2)
Regression
(3)
Regression
(4)
Two-stage
Two-stage
Two-stage tobit OLS, 1 þ FDI
tobit
tobit
Hong Kong, China (annual data)
(annual data) (three year
dummy
average)
(annual data)
Ln(real GDP i)
1.203***
(0.076)
1.237***
(0.109)
1.190***
(0.0074)
1.113***
(0.062)
Ln(real GDP j)
1.243***
(0.074)
1.263***
(0.102)
1.246***
(0.072)
1.136***
(0.053)
Common language
0.273
(0.188)
0.163
(0.213)
0.126
(0.176)
0.421***
(0.104)
0.376***
(0.117)
0.476***
(0.181)
Lag of import from i to j
0.197**
(0.078)
0.174**
(0.078)
0.114
(0.095)
0.143***
(0.038)
Trade openness in j
0.008***
(0.001)
0.008***
(0.002)
0.008***
(0.001)
0.006***
(0.001)
Financial openness in j
0.008**
(0.004)
0.004
(0.005)
0.010**
(0.004)
3.579***
(0.245)
1589
0.69
0.008***
(0.002)
Ln distance
Hong Kong, China
Dummy
Observations
Adjusted R-squared
1589
0.67
706
0.70
0.319***
(0.114)
0.469***
(0.084)
1589
0.70
*Signi¯cant at 10%.
**Signi¯cant at 5%.
***Signi¯cant at 1%.
Note: Robust standard errors in parentheses. Year and source dummies, inverse Mills' ratio,
and constant not shown.
Source: Authors.
the likelihood that a large part of that may be round-tripping, we re-estimated the regression by including a Hong Kong, ChinaPRC dummy
(regression 3). Once again the results are robust with the exception being
the lag of imports, which now loses its statistical sign¯cance. Third, we reestimated the regression by using OLS and converted the dependent variable
to (1 þ FDI) (regression 4). The results remain robust, with the exception of
the common language dummy, which becomes statistically signi¯cant.
Overall, the results are highly robust.
1250013-10
3.3. Role of time zone di®erences
Could the distance variable be capturing factors other than physical distance? In a recent paper, Stein and Daude (2006) emphasized the importance
of time zone di®erences using OECD data for 17 OECD source economies and
58 host economies. According to the authors:
(t)he transaction costs associated with the di®erence in time zones
should be important in activities that are intensive in information
and require a great deal of interaction in real-time. Frequent realtime communications should be particularly important between
headquarters and their foreign a±liates, as well as between a ¯rm
and its prospective foreign partners (p. 97).
We therefore re-estimated regression 1 by including the di®erence in time
zone between the host and source countries (regression 5 in Table 4). The
source data on time zone di®erences is from Stein and Daude (2006), and
Table 4.
Gravity models on the determinants of bilateral FDI °ows to developing Asia.
Dependent variable:Ln of
bilateral real FDI out°ows
Regression
(5)
Regression
(6)
Regression
(7)
Regression
(8)
Two-stage tobit Two-stage tobit Two-stage tobit Two-stage tobit
(annual data) (annual data) (annual data) (annual data)
Ln (real GDP i)
Ln (real GDP i)
Common language
Ln distance
Lag of import
from i to j
Trade openness in j
Financial openness
in j
Time zone
di®erence
OECD
Ln(real GDP i)*
OECD
1.248***
(0.077)
1.278***
(0.074)
0.117
(0.198)
0.204*
(0.120)
0.182**
(0.078)
1.466***
(0.140)
1.505***
(0.156)
0.412*
(0.240)
0.653***
(0.164)
0.026
(0.108)
1.279***
(0.099)
1.275***
(0.108)
0.164
(0.233)
0.401***
(0.133)
0.195**
(0.090)
1.234***
(0.104)
1.255***
(0.112)
0.163
(0.228)
0.314**
(0.134)
0.167**
(0.083)
0.008***
(0.001)
0.009**
(0.004)
0.009***
(0.002)
0.008*
(0.004)
0.007***
(0.002)
0.007
(0.004)
0.007***
(0.002)
0.006
(0.004)
0.176***
(0.040)
0.143
(0.094)
4.384
(3.103)
0.647***
(0.221)
1250013-11
18.202***
(4.522)
0.523**
(0.206)
1.885
(4.824)
0.161
(0.194)
Table 4. (Continued )
Dependent variable:Ln of
bilateral real FDI out°ows
Regression
(5)
Regression
(6)
Regression
(7)
Regression
(8)
Two-stage tobit Two-stage tobit Two-stage tobit Two-stage tobit
(annual data) (annual data) (annual data) (annual data)
Ln(real GDP j)*
OECD
Common language*
0.554***
(0.212)
0.067
OECD
(0.290)
Ln distance* OECD
0.509**
(0.247)
Lag of import from
i to j* OECD
0.403***
(0.128)
0.394**
(0.193)
0.071
0.146
(0.181)
0.166
(0.276)
(0.285)
1.407***
(0.499)
0.360
(0.544)
0.405***
(0.141)
0.479***
(0.137)
Trade openness in
j* OECD
0.002
(0.003)
0.000
(0.002)
0.000
(0.002)
Financial openness
in j* OECD
0.003
(0.005)
0.004
(0.006)
0.006
(0.006)
Time zone
di®erence* OECD
Observations
Adjusted R-squared
1589
0.68
1589
0.68
1589
0.68
0.281**
(0.141)
1589
0.68
*Signi¯cant at 10%.
**Signi¯cant at 5%.
***Signi¯cant at 1%.
Note: Robust standard errors in parentheses. Year and source dummies, inverse Mills' ratio,
and constant not shown. The OECD interaction terms exclude Japan, Australia, and New
Zealand.
Source: Authors.
similar to their approach, we extracted the absolute di®erence of time between
the host and source countries. Interestingly, with the inclusion of the time zone
di®erence variable, the distance elasticity declines by about half in absolute
terms to 0.2. The time zone di®erence variable almost wholly captures this
decline in elasticity. In particular, the time zone elasticity is estimated at
0.18 and it is strongly statistically signi¯cant. All the other estimated
coe±cients are the same as in regression 1. It clearly appears that physical
distance is partly a®ected by the di®erences in time zones between countries.
3.4. Intraregional versus extraregional FDI °ows
While we do not have sectoral data on FDI °ows, we recognize that there
could be di®erences in the determinants of between FDI from the OECD and
1250013-12
that from other developing Asian countries, particularly in consideration of
distance and time zone variables. To determine this we re-estimated regression
1 by including an interaction term for all the dependent variables with the
OECD economies (regression 6). Compared to the baseline regressions, the
elasticities of host and source GDPs increase somewhat and remain statistically
signi¯cant. Interestingly, although the elasticity of the source countries' GDP
remains negative, the elasticity of the distance variable rises sharply to 0.65
and remains highly statistically signi¯cant, while the lag of imports becomes
economically and statistically insigni¯cant.
The OECD interaction terms o®er some noteworthy ¯ndings, including
the decline of elasticity of GDPs (in absolute terms) of the host and source
countries. Another notable ¯nding is the statistical signi¯cance and sharp rise
of elasticity of imports, which imply that bilateral imports tend to strongly
attract bilateral FDI from the OECD economies, but not from developing
Asian sources. This may suggest that FDI °ows from OECD may be relatively export oriented, while those from developing Asia are focused on the
domestic market. This said, the distance elasticity of FDI from OECD
sources sharply declines (0:653 þ 0:509 ¼ 0:142), appearing quite counterintuitive and requiring further examination. To this end, we re-estimated
regression 2 but only included the interaction terms for the nonAsia-Paci¯c
OECD economies (i.e., excluding Japan, Australia, and New Zealand). The
results are outlined in regression 7. Interestingly, the noninteraction terms
are quite similar to the baseline in regression 1, with the exception of ¯nancial
openness, which is no longer statistically signi¯cant. With regard to the
interaction terms, it is notable that the distance elasticity is much higher (in
absolute terms). Speci¯cally, intraregional FDI is 0.4, while extraregional
°ows is a rather high 1.8.
Accordingly, we included the time zone di®erence variable and re-estimated the regression (regression 8). Notably, the time zone di®erence variable is no longer statistically signi¯cant, as expected a priori, since these are
only intraregional °ows. The interaction terms, economic masses, trade and
¯nancial openness, and common language elasticities, are all statistically
insigni¯cant, suggesting no di®erence between nonAsia-Paci¯c OECD FDI
sources and intraregional sources. Interestingly, the distance variable is also
statistically insigni¯cant.9 The time zone di®erence is statistically and
9 While not statistically signi¯cant, the net elasticity of distance from extraregional sources is
0.056. Positive distance elasticity may have some economic basis. For instance, if the foreign
¯rm is looking to service the destination country's market, a longer distance also makes
exporting from source countries more expensive and might therefore make local production
more desirable and thus, encourage investment.
1250013-13
economically signi¯cant (entering with a negative coe±cient), suggesting
that there is no obvious di®erence in distance elasticity between intraregional
and extraregional FDI °ows to developing Asia. However, there is clearly a
time zone di®erence e®ect that, ceteris paribus, reduces the amount of FDI
°ows to developing Asia from extraregional sources. One other interesting
discovery is the signi¯cant rise in import elasticity (0:167 þ 0:479 ¼ 0:666).
This suggests that extraregional FDI is much more sensitive to bilateral
import °ows than intraregional FDI, and that FDI from extraregional sources
uses developing Asia relatively more intensively as a source of imports (i.e., as
an export platform back to the home country). This is also consistent with
the fact that many of the developing Asian economies run bilateral trade
de¯cits with the US and EU.
4. Concluding Remarks
This paper investigated the sources and determinants of FDI °ows to
developing Asia using bilateral FDI °ows for the period 19902005. Our
panel dataset allowed us to take advantage of the time series as well as crosscountry variations in the data. An augmented gravity model for FDI °ows to
developing Asia ¯ts the data well, regardless of the FDI source. However, the
analysis clearly emphasizes that there are notable di®erences in the determinants of FDI °ows from the OECD to the region as compared to intraregional °ows. In particular, we ¯nd that the elasticity of distance is greater
for FDI from nonAsia-Paci¯c OECD economies than for intradeveloping
Asian °ows. However, this distance e®ect disappears when the di®erences in
time zones are accounted for. Stein and Daude (2006) appear to be correct;
the world is not °at. Di®erences in time zones appear to act as a hindrance
to FDI.
1250013-14
Appendix A. Data Sources
Table A.1.
Variables included in the dataset.
Variables
Source
FDI In°ows
UNCTAD FDI/TNC database and the EIU's
World Investment Service databases
World Development Indicators, World Bank
International Financial Statistics, IMF
Direction of Trade Statistics, IMF
CEPII
CEPII
ChinnIto index
Stein and Daude (2006)
Real GDP in US dollar
Real GDP per capita in US dollar
Consumer price indices
Imports of goods
Distance
Common O±cial Language
Trade Openness
Financial Openness
Time Zone di®erences
Source: Authors.
Table A.2.
Bilateral FDI °ows
from i to j
Real GDP country i
Real GDP country j
Common o±cial
language
Dummy
Distance
Imports from i to j
Average annual US
CPI
Trade Openness
Index in j
Normalized Financial
Openness Index in j
Time Zone di®erences
Summary statistics.
Units
Obs.
Mean
Std. dev.
Min.
Max.
US$ millions
1792
412.2
1,672.7
6359.5
20,809.7
US$ billions
US$ billions
1 ¼ yes
2992
2992
2992
157.6
38.9
0.3
767.4
133.4
0.4
0.0
0.0
0.0
10,995.8
1893.4
1.0
0 ¼ no
Kms
2992 6185.6
US$ millions
2985 3176.7
Index: 2000 ¼ 100 2985 103.5
3,992.4
8,812.4
14.5
315.5 16,180.3
0.0 169,085.6
83.3
128.5
In % of GDP
2992
111.75
93.5
13.1
368.2
100 ¼ Max
0 ¼ Min
In hours
2992
50.6
33.6
0.0
100.0
2992
4.0
4.0
0.0
15.0
Source: Authors.
1250013-15
References
Carr, D. L., J. R. Markusen, and K. E. Maskus. 2001. \Estimating the
knowledge-capital model of multinational enterprise." American Economic
Review, 91: 693708.
Chinn, M. and H. Ito. 2007. \A new measure of ¯nancial openness." Mimeo.
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di Giovanni, J. 2005. \What drives capital °ows? The case of cross-border activity
and ¯nancial deepening." Journal of International Economics, 65: 127149.
Hattari, R. and R. S. Rajan. 2009. \Understanding FDI °ows in developing
Asia." Asia Paci¯c Economic Literature, 23: 7393.
Head, K. and J. Ries. 2008. \FDI as an outcome of the market for corporate
control: Theory and evidence." Journal of International Economics, 74: 220.
Loungani, P., A. Mody, and A. Razin. 2002. \The global disconnect: The role of
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foreign direct investment." Journal of International Economics, 71: 96112.
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reportFolders.aspx
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