Exchange Rate, Exchange Rate Volatility and Foreign Direct
Investment∗
∗∗
Shujiro URATA
and
Kozo KIYOTA∗∗∗
Summary and Policy Recommendations
In the light of the importance of foreign direct investment (FDI) for the promotion
of economic development, this paper examines the impact of the changes in the
exchange rate of the currency of the FDI host country and its volatility on FDI flows
to that country. Using the three groups of data on FDI flows, namely those from
OECD countries, Japan, and the United States, we found that depreciation of the
currency attracts FDI inflows while high volatility of the exchange rate discourages
FDI inflows.
These findings are consistent with the prior expectation and the
previous studies. Depreciation of the currency in the host country reduces the cost
of production and the prices of assets for foreign investors, who are interested in
achieving low cost production and obtaining assets at low prices. High volatility of
the exchange rate of the currency in the host country would discourage investment
by foreign firms as it increases uncertainty regarding the future economic and
business prospects of the host country. Our findings indicate the need to maintain
stable macroeconomic environment on the part of potential FDI host countries and
avoid over-valuation of the exchange rate in order to maintain the economic
environment, which is attractive to foreign investors. Moreover, our findings argue
for the establishment of an exchange rate system, under which the stability of the
exchange rates are achieved and maintained.
We also found that openness, low wages, and past FDI in the potential host
country attract new FDI.
These findings appear to indicate that foreign firms seek
for an open and free environment with availability of low wage labor for their FDI
destinations.
This kind of behavior can be expected form the firms that are
∗
Prepared for the Kobe Project.
Waseda University
∗∗∗
Yokohama National University
∗∗
20
interested in maximizing profits or minimizing costs.
The finding on the positive
impact of past FDI on new FDI confirms the importance of the agglomeration effect,
which can be realized by the presence of many foreign firms.
Indeed, the
agglomeration effect may give rise to a virtuous cycle, under which FDI flows into
an attractive country continuously as FDI leads to more FDI.
It should be noted
that this finding also indicates the possibility of a vicious cycle for a country, which
is not successful in attracting FDI. Based on these findings, we recommend the
potential host countries to promote trade and FDI liberalization to achieve an FDI
friendly environment.
21
1.
Introduction
Globalization of economic activities has been accelerating rapidly as cross border
movements of goods, services, money, information and people have expanded in
recent years. In the post WWII period until the early 1980s foreign trade was a
major means of globalization of economic activities, while foreign direct investment
(FDI), another means of globalization, did not play a significant role. However,
since the mid-1980s FDI has increased its importance as a means of globalization.
Indeed, the value of world FDI outflows increased 31 times in 18 years from 1982 to
2000, while world exports increased 3.3-fold3. Despite the rapid expansion of FDI
in recent years, its magnitude in flow terms is still significantly smaller than
foreign trade.
In 2000 the magnitude of world FDI outflows was approximately
one-sixth of world exports of goods and non-factor services. 4
Having noted a
relatively small magnitude of FDI in relation to foreign trade, it is important to be
reminded that a large portion of foreign trade is conducted by multinational
enterprises (MNEs), which undertake FDI.
Indeed, in 2000 exports of foreign
affiliates of MNEs accounted for approximately 50 percent of world exports.5
FDI has significant impacts on economic activities, because it transfers not only
financial resources, but also technology and managerial know-how from investing
countries, or the home countries, to the recipient countries, or the host countries.
Financial resources are largely used to expand productive capabilities by increasing
fixed investment in the host countries, while transfer of technology and managerial
know-how improves productive capability.
Furthermore, FDI brings in various
networks such as sales and procurement networks to the host countries, which can
be used to expand their business opportunities.
FDI also increases competitive
pressures on local firms to result in an improvement in technical and allocative
efficiency in the host country. It is important to note that FDI also benefits the
home country, and investing firm, because it enables them to use their resources
efficiently.
In the light of important contributions that FDI delivers to both home and host
countries, it is useful to discern the factors that would promote FDI.
Identification
of the determinants of FDI would help the policy makers formulate policies, which
would create a pro-FDI environment to promote economic growth. A variety of
3
4
5
United Nations (2001), Table 1.1.
ibid.
ibid.
22
factors, both those in the home country and host countries, are considered to
influence FDI flows. For example, the business environment, which is determined
by the factors such as cost of doing business and government policies, is an
important factor. We examine the effect of the exchange rate and its volatility in
the determination of location of FDI with a focus on East Asia.
Since the
exchange rate has important impacts on cost conditions in the country in relation to
other countries, it is likely to have significant impact on the FDI decision by MNEs.
The exchange rate volatility, which has increased its magnitude under the floating
exchange rate system, is likely to affect FDI decision because it increases
uncertainty in business environment.
The structure of the paper is the following. Section II presents the framework
for the analysis, while section III reviews previous studies. Section IV presents the
results of our statistical analysis and finally section V concludes the paper.
2.
Theoretical Framework and Data Used for the Estimation
We present the framework for the analysis of the impact of exchange rate and its
volatility on FDI, which is used for the empirical analysis in section IV.
construct a model for the determination of FDI location by a firm.
We
A firm is
assumed to maximize its profits given wages, exchange rate and its volatility for a
potential host country with respect to the FDI home country 6 .
Under this
framework the depreciation of the currency of the host country is likely to attract
FDI inflows at least from the following two reasons.
First, the currency
depreciation reduces production costs in the host country vis-à-vis other countries
including the home country, thereby making it attractive for FDI seeking for
production efficiency. Second, the currency depreciation lowers the value of assets
in the depreciating host country in terms of other currencies including the currency
of the home country.
Accordingly, the cost of undertaking FDI declines in terms of
foreign currency, making FDI in the depreciating country attractive.
High
volatility in the exchange rate is likely to discourage FDI inflows because it
increases uncertainty in business environment in the host country.
In addition to
these variables we consider the characteristics of trade regime and the magnitude of
past cumulative FDI in the host country, which are often considered as important
determinants of FDI location.
See Bebassy-Quere, Fontagne, and Lahreche-Revil (2001) for the theoretical model
of the firm behavior, which is similar to the one considered in this paper.
6
23
In the estimation we use the following specification.
FDI ti
ln
= β1 ln RERti + β 2 ln VOLit + β 3 ln OPENNESSti + β 4 RPGDPt i + β 5 FDISTOCK ti + ε ti
i
GDPt
(Subscript 'i' refers to the host country)
(1)
The dependent variable is FDI / FGDP, where FDI is outward FDI from the home
country (OECD countries, Japan, and the US in this paper) to respective countries
measured in 1995 prices, and GDP is the host country’s GDP in 1995 prices.
Nominal FDI data are obtained from OECD (2000) and they are converted into US
dollars.
In our analysis, we follow Bayoumi and Lipworth (1998) in obtaining
real FDI by deflating nominal FDI value by the GDP deflator.
The nominal
exchange rate is based on IMF (2001) International Financial Statistics on
CD-ROM, Washington, D.C.: IMF, line [rf]. For Taiwan, the Council for Economic
Planning and Development, Republic of China (2000) Taiwan Statistical Databook,
Taipei: Council for Economic Planning and Development.
The source of GDP
deflators is the World Bank (2001) World Development Indicators on CD-ROM,
Washington, D.C.: World Bank.
For Taiwan, the Council for Economic Planning
and Development, Republic of China (2000) Taiwan Statistical Databook, Taipei:
Council for Economic Planning and Development.
Independent variables are the real exchange rate of the currency of the host
country vis-à-vis that of the home country (RER), the volatility of RER (VOL),
openness of the host country (OPEN), relative labor costs (RPGDP), and cumulative
FDI (FDISTOCK).
RER is defined as (2).
RER =
S / P SP *
=
1 / P*
P
(2)
S is the nominal exchange rate of the host country currency against the US dollar
or Japanese yen. P (P*) is the producer price index (PPI) or wholesale price index
(WPI) of the host country (home country). We used the PPI or WPI data, reported
in IFS line 63.
The RER is normalized assuming a value in 1995 of 100.
The
exchange rates are annual averages expressed in local currency units against the
U.S. dollar [IFS line rf ].
Exchange rates of the host currencies against the yen are
obtained by applying the yen/dollar rates. The computed values of RER are shown
24
in Table 2.
VOL is measured by the standard deviation of RER for the period preceding one
year by using monthly data (Table 3). OPENNESS is measured by the ratio of
total trade (exports plus imports) to GDP.
The variables used for the construction
of OPENNESS are taken from the World Bank (2001) World Development
Indicators on CD-ROM, Washington, D.C.: World Bank. The values for Taiwan are
taken from the Council for Economic Planning and Development, Republic of China
(2000) Taiwan Statistical Databook, Taipei: the Council for Economic Planning and
7
RPGDP is a ratio of GDP per capita of the host country to that of
Development.
home country. We used per capita GDP as a proxy for average wage. Although we
realize that the ILO Wage index would be more desirable for our analysis, the
limited availability of data for the countries under study precludes us from using
the indicator.
FDISTOCK is computed by summing annual FDI, starting 1980.
The data sources for RPGDP and FDISTOCK are already given above.
According to the hypotheses discussed above, we expect the following signs for the
exchange rate of the host country and its volatility.
β1 > 0,
β2 < 0,
For the remaining coefficients the following signs are expected.
β3 > 0,
β4 > 0,
β5 > 0
RPGDP (β 3 ) is expected to have a positive impact on FDI, as low cost of
production would attract FDI. OPENNESS (β4 ) is likely to attract FDI, as an
open and free business environment without government intervention allows a firm
to make business decision based on economic rationale, thus possibly enabling it to
increase profits.
FDISTOCK (β5 ) is supposed to have a positive impact on FDI,
for several reasons.
A potential investor may regard the host country with
substantial amount of FDI stock as a safe place for its investment. Since FDI
incurs substantial cost, security consideration is very important.
A potential
investor may find a lot of business opportunities in a potential host country with a
huge FDI stock.
7
Tables 4 and 5 give summary statistics and correlation matrix of the variables used
in the analysis.
25
3.
The Previous Studies
A number of studies have examined the determinants of FDI, reflecting the
increasing interest in FDI not only by researchers but also by policy makers. One
can divide the previous studies into two types. One type of studies examined the
determinants of the magnitude of FDI.
These studies mainly focus on
macroeconomic variables such as the exchange rates as the determinants of FDI.
For example, Froot and Stein (1991) investigated the impact of real exchange rates
on FDI flows from several European countries to the US by using annual and
quarterly data covering the 1974-87 period. They found that the depreciation of
the European currencies vis-à-vis the US dollar discourages their FDI to the US.
The similar relationship was found by several other studies including Klein and
Rosengren (1994), Bayoumi and Lipworth (1998), Goldberg and Klein (1998), Ito
(2000), and Sazanami, Yoshimura, and Kiyota (2001).
Specifically, the
appreciation of the home currency vis-à-vis the host currency was found to
encourage FDI from the home country to the host country. Klein and Rosengren
(1994) analyzed FDI flows from Canada, Japan, and several European countries to
the US for the 1979-91 period, while Bayoumi and Lipworth (1998), Goldberg and
Klein (1998), Ito (2000) and Sazanami, Yoshimura, and Kiyota (2001) examined the
impacts of exchange rate on Japanese FDI for different periods.
Most studies examined the relationship between the exchange rates and overall
FDI flows with the exceptions of Froot and Stein (1991) and Sazanami el al (2001),
which also examined FDI for several sectors.
Several studies considered other
variables besides the exchange rates as explanatory variables.
For example, Klein
and Rosengren (1994) included labor cost and wealth, while Sazanami et. al (2001)
included labor cost and cumulative FDI from the home country to the host country
under study. Both of them found that high labor of the host country discourages
FDI to that country.
Sazanami et.al. (2001) found that cumulative FDI encourages
FDI to the country under study.
Only few studies examined the impact of exchange rate volatility, besides
exchange rates, on FDI flows. Goldberg and Kolstad (1995) examined the impact
of exchange rate volatility on bilateral FDI flows from Canada, Japan, and the UK
to the US for the 1978-99 period by using quarterly data.
They measured the
exchange rate volatility by computing standard deviation of the real exchange rate
over the 12 quarters, prior to and inclusive of each period.
impact of exchange rate volatility on FDI.
They found the positive
Benassy, et. al (2001) investigated the
26
impacts of exchange rate volatility, which is measured by the coefficient of variation
of quarterly nominal exchange rate over the past three years, on FDI from
developed countries to developing countries for the 1984-96 period by using annual
data. They found that high exchange rate volatility discourages FDI. These two
studies show different impacts of exchange rate volatility on FDI.
It should be
noted that both studies obtain expected relationship between the level of exchange
rate and FDI as they found the appreciation of the currency of the home country
vis-à-vis the currency of the host currency would promote home country's FDI to
host country.
The other type of studies attempted to reveal the characteristics of host countries,
which would attract FDI.
These studies investigated foreign firm's decision as to
whether it invests in a particular host country or not.
did not address the issue of the magnitude of FDI.
Accordingly, these studies
It should be noted that these
two types of studies are closely related. One could think of these two types of
studies as reflecting firm's sequential decision making.
A firm may make a
decision on the location of FDI first, and then decides on the magnitude of FDI.
Alternatively, a firm may make a joint decision on the location and the magnitude of
FDI.
The structural model as well as the estimation procedure differ, depending
on the characteristics of the models. Although we understand the need to consider
the mechanism of firm's decision making process on FDI, we continue to review the
previous studies without considering the linkage between the two types of decision
making discussed above.
When compared to the empirical studies, which can be classified under the first
type discussed above, the number of empirical studies classified under the second
type is much smaller.
Wheeler and Mody (1992) examined the locational
determinants of US FDI, while several studies including Woodward (1992), Head et.
al (1995), Fukao and Chung (1996), and Urata and Kawai (2000) investigated
Many studies examined the importance of structural characteristics of the host
country such as the level of education, infrastructure, country risks, market size by
using cross-country data, and they do not consider the changes in macroeconomic
variables such as exchange rate.
These cross-country studies found that low
country risk, large market, outward-oriented trade policies as important factors
attracting FDI.
Urata and Kawai (2000) is one exception to other papers in this group, as they
included exchange rates and their volatility in their study of Japanese firms'
decision on the location of their FDI.
By analyzing a panel of annual data covering
27
1980-94 for 117 countries, Urata and Kawai found that exchange rate volatility
discourages FDI.
4.
The Results
We tested the effects of exchange rate changes on FDI by conducting statistical
analyses.
We examined the relationship for FDI from three different sources, that
is, FDI from OECD countries, Japan, and the US. To shed lights on the possible
regional differences concerning the determinants of FDI, we analyzed the
relationship for FDI in East Asia and in Latin America separately. The analysis
was conducted for the 1989-98 period because of data availability.
We adopted the random effect model for the estimation and the results are shown
in Tables 6-8.
To begin with Table 6, where the results for FDI from OECD
countries are shown, we find that the explanatory variables explain approximately
70 percent of variations in the dependent variable.
The levels of exchange rates
vis-à-vis Japanese yen and US dollar show expected signs, indicating that
depreciation of the host country currency promotes FDI into that country.
This
relationship was found statistically significant for the exchange rate vis-à-vis US
dollar for FDI in the world as well as that in East Asia and Latin America.
However, this relationship was found statistically significant for the exchange rate
vis-à-vis Japanese yen for FDI in the world and in Latin American but not for FDI
in East Asia.
These findings indicate that FDI in East Asia is sensitive to the
changes in the exchange rates vis-à-vis US dollar but not to the changes in the
exchange rate vis-à-vis Japanese yen.
As to the impact of exchange rate volatility on FDI, the results show that
exchange rate volatility with respect to Japanese yen discourages FDI from OECD
countries regardless of the destination of FDI.
The discouraging impact of
exchange rate volatility with respect to the US dollar was found only for FDI in East
Asia and in Latin America but not for FDI in the world.
We found that exchange
rate volatility discourages FDI from OECD countries in developing countries.
Based on this observation, we may argue that stability in exchange rates
contributes to the promotion of FDI inflows.
In addition to the impacts of exchange rate and its volatility on FDI inflows, we
find that openness, low wages, and past FDI promote FDI inflows. These findings
are consistent with our expectation and they give policy makers important
messages for formulating policies to attract FDI.
Specifically, it is important to
28
establish and maintain the open trade regime, under which foreign firms can
manage their operation efficiently.
Low labor cost by achieving and maintaining
flexible labor market appears important for attracting FDI, especially the
export-oriented FDI. The positive impact of cumulative FDI on new FDI appears
to indicate the presence of agglomeration effect resulting form close inter-firm
linkages with other foreign firms.
Indeed, a virtuous cycle has been formed in such
a way that FDI attracts new FDI.
Tables 7 and 8 show the results of the estimation for FDI from Japan and the US,
respectively.
According to the results, FDI not only from the US but also from
Japan is more sensitive to the level of exchange rates of the host countries vis-à-vis
the US dollar than Japanese yen.
Specifically, depreciation of the host currency
vis-à-vis the US dollar is shown to promote not only US FDI in the world and in
Latin America (Table 8), but also Japanese FDI in the world and in East Asia (Table
7).
The level of exchange rates of the host currency vis-à-vis Japanese yen does
affect significantly either Japanese FDI or US FDI.
These findings appear to
reflect the behavior of Japanese and US firms who make FDI decisions by
considering the movements of exchange rates of the host country vis-à-vis the US
dollar.
As to the effect of the exchange rate volatility on FDI, we find that in many cases
volatility has a discouraging impact on FDI. However, this impact is rather weak,
as the estimated coefficients are not statistically significant in many cases with the
exceptions of US FDI in the world and in Latin America.
The volatility in the
exchange rate of the host currency vis-à-vis the US dollar is shown to have a
statistically significantly negative impact on FDI inflows from the US.
Openness is shown to have a positive impact on FDI inflows for both FDI from
Japan and from the US, although the levels of statistical significance differ by the
cases. Openness is significantly positive for Japanese FDI only in East Asia but it
is significantly positive for US FDI not only in East Asia, but also in the world and
in Latin America.
These differences appear to reflect the differences in the
regional strategies of Japanese and US firms. US firms' FDI strategy is strongly
export-oriented regardless of their location, while Japanese firms' FDI strategy is
strongly export-oriented in East Asia but not in other regions.
It is interesting to find that low labor cost attracts Japanese FDI in the world and
in East Asia, while it attracts US FDI in the world only. These results indicate
that Japanese firms are more sensitive to labor costs than US firms, probably
reflecting the fact that Japanese firms are engaged in labor-intensive production.
29
Agglomeration, which is captured by cumulative FDI, is shown to have an
significantly positive impact on promoting Japanese and US FDI in the world and
in Latin America.
Although positive, its impact on Japanese and US FDI is not
statistically significant.
Considering that agglomeration has a significantly
positive impact on FDI inflows in even East Asia from OECD countries, the
agglomeration effect does not result from FDI from the same sources such as Japan
and the US but from FDI from a variety of OECD countries.
5.
Conclusions
We examined the impacts of the exchange rate and its volatility on FDI flows from
OECD countries as well as FDI from Japan and from the US separately.
We found
generally that the depreciation of the host country currency attracts FDI inflows
while large volatility of the exchange rates discourages FDI inflows.
These
findings are consistent with our expectation and also with many previous studies.
Our findings have important policy implications.
First, overvaluation of the
currency, which often results from inappropriate macroeconomic policies,
discourages FDI inflows, and therefore the government should pursue sound
economic policies.
The same policy implications may be obtained from the result
indicating the negative impact of exchange rate volatility on FDI inflows.
In
addition, one may argue based on the finding the need for the exchange rate system,
under which exchange rate volatility is minimized.
We hope that our analysis contributes to the discussion on the appropriate
exchange rate system, but at the same time we realize the need for further research
on this subject.
For example, we should consider the role of expectation on
exchange rate explicitly in the analysis, as the expectation plays an important role
in the determination of the exchange rate.
It is also expected to expand the
coverage of the analysis, for example, by examining FDI by different sectors, as the
firms in different sectors appear to have different FDI strategies, thereby likely to
respond differently to the changes in the exchange rate.
30
References
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32
1
T re n d s o f F D I flo w s fro m J a p a n , U n ited S ta tes a n d E U -1 5 , 1 9 8 5 -1 9 9 8
rd F D I flo w s (U S $ m illio n )
W ORLD
Jap an
U n ite d
S ta tes
E U -1 5
E ast an d S o u th -E a st A sian C o u n tries
C h in a
H o n g K o n g In d o n esia
100
131
408
349
1 ,7 8 5
1 ,1 0 5
3 ,8 3 4
982
1 ,3 7 4
1 ,0 4 1
588
1 ,0 5 3
K o re a
134
284
384
296
M a lay sia P h ilip p in e s
79
61
725
258
493
614
503
371
S in g ap o re
339
840
1 ,0 1 5
623
1985
1990
1995
1998
d S tates
1985
1990
1995
1998
1 2 ,2 1 7
5 6 ,9 1 5
4 4 ,0 0 2
3 9 ,8 5 4
---------
5 ,3 9 5
2 6 ,1 2 8
1 9 ,3 9 2
1 0 ,0 8 9
1 ,8 5 7
1 3 ,3 5 4
7 ,0 5 3
1 3 ,5 4 5
1 2 ,7 2 0
3 0 ,9 8 2
9 2 ,0 7 4
1 2 1 ,6 4 4
333
984
2 ,3 3 6
3 ,8 4 4
---------
6 ,8 5 6
4 ,2 7 5
3 7 ,9 2 4
6 6 ,4 6 1
174
30
261
1 ,4 9 0
18
352
631
1 ,5 7 1
220
691
519
384
42
330
1 ,0 5 1
665
36
175
1 ,0 3 7
-3 0 2
-2 4 2
177
269
121
-7 9
620
947
1 ,8 9 5
1985
1990
1995
1998
2 6 ,3 9 6
1 2 9 ,2 1 2
1 4 5 ,7 3 9
3 6 1 ,1 9 3
224
1 ,3 4 4
1 ,7 2 9
1 ,3 2 8
9 ,7 5 4
1 4 ,7 4 9
4 0 ,9 0 8
1 2 9 ,1 7 2
8 ,3 0 7
7 8 ,6 5 6
7 7 ,4 1 2
1 3 0 ,2 4 0
44
14
1 ,0 1 8
1 ,0 9 2
157
-4 4 4
1 ,8 4 2
-1 ,3 1 4
-1 8
-2 9
933
224
29
-8 9
505
2 ,3 0 4
61
354
-4 5
4 ,5 8 9
35
37
112
1 ,0 4 0
279
691
624
86
E ast an d S o u th -E a st A sian C o u n tries
C h in a
H o n g K o n g In d o n esia
0 .8 %
1 .1 %
3 .3 %
0 .6 %
3 .1 %
1 .9 %
8 .7 %
2 .2 %
3 .1 %
2 .6 %
1 .5 %
2 .6 %
K o re a
1 .1 %
0 .5 %
0 .9 %
0 .7 %
M a lay sia P h ilip p in e s
0 .6 %
0 .5 %
1 .3 %
0 .5 %
1 .1 %
1 .4 %
1 .3 %
0 .9 %
S in g ap o re
2 .8 %
1 .5 %
2 .3 %
1 .6 %
(1 9 8 5 = 1 0 0 )
W ORLD
Jap an
U n ite d
S ta tes
E U -1 5
1985
1990
1995
1998
d S tates
1985
1990
1995
1998
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
---------
4 4 .2 %
4 5 .9 %
4 4 .1 %
2 5 .3 %
1 5 .2 %
2 3 .5 %
1 6 .0 %
3 4 .0 %
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
2 .6 %
3 .2 %
2 .5 %
3 .2 %
---------
5 3 .9 %
1 3 .8 %
4 1 .2 %
5 4 .6 %
1 .4 %
0 .1 %
0 .3 %
1 .2 %
0 .1 %
1 .1 %
0 .7 %
1 .3 %
1 .7 %
2 .2 %
0 .6 %
0 .3 %
0 .3 %
1 .1 %
1 .1 %
0 .5 %
0 .3 %
0 .6 %
1 .1 %
-0 .2 %
-1 .9 %
0 .6 %
0 .3 %
0 .1 %
-0 .6 %
2 .0 %
1 .0 %
1 .6 %
1985
1990
1995
1998
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
1 0 0 .0 %
0 .8 %
1 .0 %
1 .2 %
0 .4 %
3 7 .0 %
1 1 .4 %
2 8 .1 %
3 5 .8 %
3 1 .5 %
6 0 .9 %
5 3 .1 %
3 6 .1 %
0 .2 %
0 .0 %
0 .7 %
0 .3 %
0 .6 %
-0 .3 %
1 .3 %
-0 .4 %
-0 .1 %
0 .0 %
0 .6 %
0 .1 %
0 .1 %
-0 .1 %
0 .3 %
0 .6 %
0 .2 %
0 .3 %
0 .0 %
1 .3 %
0 .1 %
0 .0 %
0 .1 %
0 .3 %
1 .1 %
0 .5 %
0 .4 %
0 .0 %
1 ) E U -1 5 : A u stria , B elg iu m -L u x , D e n m a rk , F in lan d , F ran ce , G e rm an y , G reec e, Ire la n d , Ita ly , N eth e rlan d s, P o rtu g al,
S p ain , S w ed e n a n d th e U n ited K in g d o m .
2 ) V a lu es a re n e t F D I o u tflo w s, w h ich im p lie s th at, fo r so m e c o u n tries, th e F D I n e t o u tflo w s co u ld b e n e g ativ e v a lu e s.
O E C D (2 0 0 0 ) In tern a tio n a l D irec t In vestm e n t (B ey o n d 2 0 /2 0 ) D a ta b a se , P aris: O E C D .
33
T a b le 1 (c o n tin u e d )
L a tin A m e ric a n C o u n trie s
A rg e n tin a
B ra z il
8
314
213
615
98
255
125
456
T a iw a n
114
446
390
219
T h a ila n d
48
1 ,1 5 4
1 ,0 6 2
1 ,3 4 1
2
222
419
396
-4 3
316
686
1 ,3 3 3
2
379
2 ,0 4 8
1 ,2 3 8
21
146
506
167
-1 9
161
807
533
83
156
841
4 7 ,7 8 9
T a iw a n
0 .9 %
0 .8 %
0 .9 %
0 .6 %
T h a ila n d
0 .4 %
2 .0 %
2 .4 %
3 .4 %
0 .0 %
0 .7 %
0 .5 %
0 .3 %
- 0 .3 %
1 .0 %
0 .7 %
1 .1 %
0 .0 %
1 .2 %
2 .2 %
1 .0 %
0 .1 %
0 .1 %
0 .3 %
0 .0 %
- 0 .1 %
0 .1 %
0 .6 %
0 .1 %
0 .3 %
0 .1 %
0 .6 %
1 3 .2 %
0
30
121
0
0
0
0
0
M e x ic o
101
168
179
81
V e n e z u e la
0
0
0
24
134
876
6 ,9 5 4
3 ,7 9 0
46
520
1 ,2 9 1
612
0
0
164
406
136
1 ,9 2 6
2 ,9 8 3
2 ,5 3 3
0
0
654
786
337
627
957
1 7 ,4 3 4
74
96
363
2 ,0 0 0
5
14
357
2 ,1 9 0
56
346
1 ,4 6 9
1 ,5 3 8
3
12
314
1 ,6 3 0
C h ile
0 .0 %
0 .1 %
0 .3 %
0 .0 %
C o lo m b ia
0 .0 %
0 .0 %
0 .0 %
0 .0 %
M e x ic o
0 .8 %
0 .3 %
0 .4 %
0 .2 %
V e n e z u e la
0 .0 %
0 .0 %
0 .0 %
0 .1 %
1 .1 %
2 .8 %
7 .6 %
3 .1 %
0 .4 %
1 .7 %
1 .4 %
0 .5 %
0 .0 %
0 .0 %
0 .2 %
0 .3 %
1 .1 %
6 .2 %
3 .2 %
2 .1 %
0 .0 %
0 .0 %
0 .7 %
0 .6 %
1 .3 %
0 .5 %
0 .7 %
4 .8 %
0 .3 %
0 .1 %
0 .2 %
0 .6 %
0 .0 %
0 .0 %
0 .2 %
0 .6 %
0 .2 %
0 .3 %
1 .0 %
0 .4 %
0 .0 %
0 .0 %
0 .2 %
0 .5 %
L a tin A m e ric a n C o u n trie s
A rg e n tin a
B ra z il
0 .1 %
2 .6 %
0 .4 %
1 .1 %
0 .2 %
0 .6 %
0 .3 %
1 .1 %
C h ile
C o lo m b ia
34
T ab le 2 T ren d s of R eal E xch an ge R ate (1995= 100), 1985-2000.
C ountry
Japan
EU -15
1985
204.8
1986
146.5
1987
130.3
1988
119.2
1989
131.6
1990
141.2
1991
132.6
1992
126.3
1993
112.7
1994
105.6
1995
100.0
1996
118.9
1997
133.0
1998
145.2
A ustria
189.2
B elgium
178.1
D enm ark
178.5
Finland
142.1
France
165.1
G erm any
181.1
G reece
179.3
Ireland
142.6
Italy
139.3
Lux em bourg
177.9
N etherlands
173.3
P ortugal
192.6
S pain
169.4
S w eden
141.1
U nited K ingdom
136.9
East and S outh-East A sian C ountries
C hina
n.a.
H ong K ong
n.a.
Indonesia
76.6
K orea, R ep.
140.0
M alaysia
93.0
P hilippines
119.5
S ingapore
132.5
Taiw an
n.a.
Thailand
117.7
S elected Latin A m erican C ountries
A rgentina
256.9
B razil
196.4
C hile
133.8
C olom bia
102.2
M exico
89.2
V enezuela
90.1
139.8
134.8
133.9
115.1
126.4
136.2
150.4
109.9
104.7
135.9
130.0
154.2
130.7
114.2
118.0
118.5
115.1
112.9
99.4
110.2
116.7
129.7
100.2
90.2
118.0
112.3
137.7
113.6
101.2
105.4
118.1
116.5
110.5
93.6
110.6
117.1
124.5
99.5
89.6
119.1
113.2
133.6
106.3
96.1
96.0
129.3
126.9
120.1
94.4
120.0
127.8
131.4
107.6
93.2
129.4
125.9
136.0
106.0
99.6
101.5
113.4
109.7
104.4
83.6
104.4
112.8
112.3
94.1
80.6
111.5
111.2
114.5
90.2
87.2
90.1
117.5
113.2
109.9
88.5
109.3
118.7
112.6
97.7
81.8
115.2
115.4
108.6
90.4
85.0
89.3
109.5
107.2
104.6
98.5
103.2
109.5
104.8
92.3
79.7
108.3
108.4
96.0
86.7
82.4
89.1
115.2
115.6
114.3
126.6
111.3
114.3
113.4
108.0
100.3
115.9
114.9
110.2
106.1
108.4
105.7
112.7
112.0
112.8
117.5
110.1
112.0
111.0
106.9
101.3
112.5
112.4
111.3
109.4
107.9
103.7
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
106.1
105.9
104.3
107.6
103.4
106.6
98.8
101.4
93.8
106.6
105.9
101.9
101.0
96.3
101.6
123.6
123.2
119.0
123.0
119.3
123.3
108.7
108.0
103.8
124.4
122.8
116.0
117.1
111.6
96.1
126.1
125.8
120.4
126.8
121.7
125.9
114.1
114.0
105.4
126.9
124.3
117.8
119.2
118.2
93.3
n.a.
n.a.
85.2
140.6
97.7
133.8
135.5
n.a.
114.0
n.a.
n.a.
103.6
132.1
98.7
136.0
135.2
n.a.
112.8
n.a.
n.a.
102.3
114.1
104.0
129.2
132.4
n.a.
111.2
n.a.
n.a.
105.8
103.8
109.7
125.3
131.4
n.a.
112.4
n.a.
134.3
107.7
106.2
112.5
130.5
124.4
n.a.
111.3
n.a.
125.1
108.6
105.0
114.2
129.8
119.5
n.a.
109.4
n.a.
117.4
108.3
108.4
104.1
114.3
113.5
n.a.
107.8
n.a.
112.4
104.5
109.5
104.6
117.0
113.3
n.a.
107.1
n.a.
106.0
102.3
105.9
105.4
108.0
106.6
n.a.
103.9
n.a.
100.0
100.0
100.0
100.0
100.0
100.0
n.a.
100.0
n.a.
96.8
99.3
102.3
99.9
96.3
101.0
n.a.
98.9
n.a.
93.7
118.3
118.6
111.4
104.6
106.7
n.a.
118.7
n.a.
92.6
262.2
165.0
149.9
134.4
122.5
n.a.
147.0
215.7
178.3
135.6
119.5
116.1
88.7
219.9
161.9
133.4
125.6
117.1
128.8
210.8
154.3
135.1
125.7
93.8
103.4
336.1
114.3
131.8
134.0
88.7
140.6
169.0
95.2
125.9
143.5
84.4
142.5
126.8
110.9
123.5
144.6
76.9
134.1
108.7
120.6
114.4
140.7
70.4
126.5
102.0
120.0
116.5
134.3
66.4
125.2
100.5
112.5
111.5
108.9
69.0
130.6
100.0
100.0
100.0
100.0
100.0
100.0
102.8
97.4
99.6
97.2
90.7
121.5
104.6
100.0
97.7
92.1
80.2
97.1
105.3
105.9
103.6
97.2
81.0
81.3
N otes:
S ource:
1) n.a.: not available.
2) R eal E x change R ate (R E R ) is defined as: N ER (dc/U S $) * (P d/P us), w here N ER is nom inal ex change rate, dc is dom estic currency, P d is dom estic consum er price (C P I) index an
U S C P I index.
IM F (2002) International Financial Statistics on C D -R O M , W ashington, D .C .: IM F .
35
T ab le 3 T rend s of R eal E xchange R ate V olatility, 1988-2000.
C ountry
Japan
EU -15
1988
0.029
1989
0.021
1990
0.037
1991
0.023
1992
0.022
1993
0.025
1994
0.019
1995
0.048
1996
0.015
1997
0.030
1998
0.046
1999
0.029
2000
0.023
A ustria
0.025
Belgium
0.028
D enm ark
0.029
Finland
0.027
France
0.027
G erm any
0.028
G reece
0.034
Ireland
0.030
Italy
0.029
Luxem bourg
0.028
N etherlands
0.031
P ortugal
0.026
S pain
0.026
S w eden
0.024
U nited K ingdom
0.037
East and S outh-East A sian C ountries
C hina
n.a.
H ong K ong
n.a.
Indonesia
0.004
K orea, R ep.
0.013
M alaysia
0.006
P hilippines
0.009
S ingapore
0.012
Taiw an
n.a.
Thailand
0.006
Selected Latin A m erican C ountries
A rgentina
n.a.
Brazil
n.a.
C hile
0.012
C olom bia
0.012
M exico
0.016
V enezuela
0.024
0.032
0.030
0.029
0.026
0.029
0.031
0.026
0.025
0.025
0.030
0.028
0.027
0.035
0.022
0.024
0.020
0.018
0.019
0.017
0.019
0.022
0.019
0.016
0.019
0.019
0.019
0.021
0.019
0.013
0.032
0.038
0.039
0.038
0.026
0.036
0.038
0.035
0.033
0.035
0.039
0.037
0.037
0.036
0.034
0.033
0.035
0.033
0.032
0.046
0.031
0.033
0.029
0.029
0.045
0.033
0.033
0.035
0.040
0.049
0.049
0.022
0.024
0.027
0.034
0.026
0.023
0.029
0.041
0.030
0.025
0.028
0.027
0.033
0.031
0.031
0.019
0.017
0.017
0.023
0.017
0.017
0.016
0.018
0.017
0.016
0.019
0.017
0.017
0.019
0.014
0.028
0.027
0.025
0.024
0.023
0.027
0.021
0.015
0.019
0.028
0.028
0.019
0.018
0.019
0.013
0.017
0.017
0.014
0.018
0.013
0.016
0.015
0.013
0.011
0.016
0.015
0.013
0.014
0.018
0.016
0.023
0.022
0.024
0.022
0.023
0.022
0.028
0.021
0.025
0.023
0.024
0.024
0.025
0.026
0.022
0.021
0.022
0.022
0.022
0.021
0.021
0.025
0.022
0.021
0.021
0.023
0.019
0.022
0.021
0.014
n.a.
n.a.
0.019
n.a.
n.a.
n.a.
0.017
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.015
0.020
n.a.
n.a.
0.032
n.a.
n.a.
n.a.
0.031
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.027
0.021
n.a.
n.a.
0.005
0.007
0.008
0.021
0.010
n.a.
0.008
n.a.
0.005
0.009
0.006
0.007
0.028
0.013
n.a.
0.009
n.a.
0.007
0.008
0.006
0.009
0.007
0.014
n.a.
0.008
n.a.
0.008
0.007
0.004
0.014
0.015
0.010
n.a.
0.009
n.a.
0.007
0.006
0.006
0.016
0.020
0.009
n.a.
0.006
n.a.
0.004
0.004
0.005
0.009
0.013
0.006
n.a.
0.006
n.a.
0.003
0.008
0.011
0.014
0.015
0.011
n.a.
0.005
n.a.
0.004
0.007
0.009
0.008
0.005
0.007
n.a.
0.005
n.a.
0.003
0.188
0.100
0.051
0.046
0.021
n.a.
0.067
n.a.
0.007
0.169
0.041
0.057
0.037
0.028
n.a.
0.061
n.a.
0.005
0.070
0.019
0.004
0.012
0.011
n.a.
0.022
n.a.
0.003
0.044
0.019
0.004
0.014
0.012
n.a.
0.014
0.278
n.a.
0.012
0.005
0.011
0.214
0.173
n.a.
0.019
0.007
0.009
0.018
0.050
n.a.
0.011
0.010
0.007
0.014
0.007
n.a.
0.019
0.009
0.007
0.021
0.004
0.206
0.013
0.008
0.008
0.010
0.003
0.028
0.009
0.041
0.091
0.071
0.003
0.019
0.023
0.020
0.072
0.108
0.004
0.008
0.007
0.014
0.018
0.074
0.003
0.006
0.017
0.023
0.020
0.008
0.002
0.059
0.011
0.035
0.033
0.015
0.005
0.083
0.023
0.033
0.016
0.006
0.004
0.019
0.039
0.019
0.022
0.007
N otes:
Source:
1) n.a.: not available.
2) R eal exchange rate (R ER ) volatility is defined as the standard deviation of the R ER changes (last 12 m onths).
IM F (2002) International Financial Statistics on C D -RO M , W ashington, D .C .: IM F.
36
Table 4 Summary Statistics
World
Variable
lnFDIGDP
lnRERus
lnRERjp
VOLRERus
VOLRERjp
OPENNESS
lnRPGDP
lnFDIStock
No. of Obs
16,353
16,436
16,436
15,512
15,512
16,240
17,120
17,909
Mean
-19.401
4.646
4.436
0.027
0.037
0.669
-0.870
-2.427
S.D.
East Asia
Variable
lnFDIGDP
lnRERus
lnRERjp
VOLRERus
VOLRERjp
OPENNESS
lnRPGDP
lnFDIStock
No. of Obs
2,303
2,100
2,100
2,100
2,100
2,156
2,408
2,356
Mean
-19.128
4.722
4.512
0.019
0.035
1.015
-1.564
-2.355
S.D.
Latin America
Variable
lnFDIGDP
lnRERus
lnRERjp
VOLRERus
VOLRERjp
OPENNESS
lnRPGDP
lnFDIStock
No. of Obs
2,349
2,772
2,772
2,604
2,604
2,464
2,408
2,705
Mean
-19.750
4.672
4.461
0.024
0.044
0.466
-1.496
-3.003
S.D.
5.039
0.275
0.294
0.041
0.040
0.465
1.489
2.951
Min
-27.492
2.108
1.875
0.001
0.010
0.108
-4.975
-8.008
Max
-6.230
5.817
5.543
0.621
0.634
4.641
2.925
7.977
5.180
0.148
0.133
0.031
0.027
0.756
1.573
2.854
Min
-25.273
4.528
4.155
0.003
0.012
0.351
-4.880
-8.008
Max
-7.358
5.569
5.196
0.188
0.188
3.077
2.143
5.479
4.564
0.214
0.235
0.046
0.043
0.246
0.854
2.503
Min
-25.110
4.196
4.020
0.001
0.014
0.108
-3.077
-7.691
Max
-6.230
5.817
5.543
0.278
0.290
1.032
1.072
6.374
Notes:
1) The unit of FDI and GDP is US$ million. GDP is 1995 prices.
2) lnFDIGDP: natural log of real FDI flows divided by host country's GDP. Real FDI is nominal
FDI flows divided by host country's GDP deflator.
lnRERus and lnRERjp: natural log of RER US$ base and RER Japanese yen base,
respectively.
VOLRERus and VOLREjp: volatility of RERus and RERjp, respectively.
OPENNESS: exports plus imports divided by GDP. lnFDI stock.
lnRPGDP: natural log of relative (host to home) per-capita GDP.
lnFDIStock: natural log of FDI stock (cumulative value of real FDI from 1980).
3) East Asia: China, Hong Kong, China, Indonesia, Japan, Korea, Rep., Malaysia,
the Philippines, Singapore, Taiwan, Thailand
Latin America: Argentina, Brazil, Cayman Islands, Chile, Colombia, Costa Rica
Mexico, Netherlands Antilles, Panama, Venezuela, RB,
Sources: IMF (2002) International Financial Statistics on CD-ROM , Washington, D.C.: IMF.
OECD (2000) International Direct Investment (Beyond 20/20) Database , Paris: OECD.
37
Table 5 Correlation Matrix of Regressors
World
lnFDIGDP
lnRERus(-1)
lnRERjp(-1)
VOLRERus(-1)
VOLRERjp(-1)
OPENNESS(-1)
lnRPGDP(-1)
lnFDIStock(-1)
East Asia
lnFDIGDP
lnRERus(-1)
lnRERjp(-1)
VOLRERus(-1)
VOLRERjp(-1)
OPENNESS(-1)
lnRPGDP(-1)
lnFDIStock(-1)
Latin America
lnFDIGDP
1.000
0.053
0.077
-0.030
-0.037
0.107
-0.158
0.829
lnRERus(-1)
lnFDIGDP
1.000
-0.058
0.050
0.035
0.025
0.129
-0.272
0.841
lnRERus(-1)
1.000
0.908
0.163
0.163
0.025
0.074
0.018
1.000
0.270
0.279
0.231
-0.136
-0.059
-0.128
lnRERjp(-1)
1.000
0.117
0.143
0.056
0.068
0.055
lnRERjp(-1)
1.000
-0.020
0.076
0.032
0.019
0.046
VOLRERus(-1) VOLRERjp(-1) OPENNESS(-1)
1.000
0.959
-0.096
-0.111
-0.064
1.000
-0.073
-0.182
-0.076
1.000
0.187
0.063
VOLRERus(-1) VOLRERjp(-1) OPENNESS(-1)
1.000
0.922
-0.112
-0.085
0.066
1.000
-0.069
-0.068
0.061
1.000
0.529
0.139
lnFDIGDP
lnRERus(-1)
lnRERjp(-1)
VOLRERus(-1) VOLRERjp(-1) OPENNESS(-1)
lnFDIGDP
1.000
lnRERus(-1)
-0.062
1.000
lnRERjp(-1)
-0.019
0.879
1.000
VOLRERus(-1)
-0.031
0.523
0.518
1.000
VOLRERjp(-1)
-0.036
0.528
0.527
0.980
1.000
OPENNESS(-1)
0.028
-0.390
-0.384
-0.308
-0.295
1.000
lnRPGDP(-1)
-0.319
0.032
0.056
0.074
0.080
-0.164
lnFDIStock(-1)
0.807
-0.116
-0.048
0.032
0.021
-0.180
Notes: 1) (-1) indicates the 1-year lag. For the definition of variables, see Table 4.
2) For East Asia and Latin America, see Table 4.
Sources:IMF (2002) International Financial Statistics on CD-ROM , Washington, D.C.: IMF.
OECD (2000) International Direct Investment (Beyond 20/20) Database , Paris: OECD.
lnRPGDP(-1)
1.000
-0.001
lnRPGDP(-1)
1.000
-0.179
lnRPGDP(-1)
1.000
-0.213
lnFDIStock(-1)
1.000
lnFDIStock(-1)
1.000
lnFDIStock(-1)
1.000
38
Table 6 Regression Results: FDI Flows from OECD Countries
RERjp(-1)
VOLjp(-1)
RERus(-1)
VOLus(-1)
Openness(-1)
lnRPGDP(-1)
lnFDISTOCK(-1)
Constant
Observations
R-squared
FDI flows to
World
0.319***
[3.29]
-0.318
[0.57]
0.147
[0.71]
-17.106***
[8.28]
0.387***
0.341
[3.53]
[1.45]
0.760
16.801***
[1.40]
[8.32]
0.742***
0.738***
0.749***
[6.53]
[6.56]
[6.51]
-0.622*** -0.618*** -0.628***
[17.49]
[17.79]
[17.56]
1.285***
1.310***
1.318***
[90.85]
[91.87]
[90.98]
-18.405*** -18.739*** -18.954***
[41.23]
[35.65]
[35.77]
11,332
11,301
11,087
0.710
0.720
0.720
East Asia
0.613
[0.93]
-3.742
[1.50]
0.306
[0.44]
-11.061
[1.64]
3.198***
2.969***
[3.48]
[3.09]
-4.779**
4.566
[2.00]
[0.74]
0.542***
0.747***
0.757***
[2.68]
[3.67]
[3.75]
-0.646*** -0.688*** -0.694***
[5.32]
[5.87]
[5.96]
1.423***
1.455***
1.450***
[32.08]
[33.03]
[32.76]
-19.527*** -32.023*** -32.145***
[6.47]
[7.31]
[6.69]
1,408
1,408
1,408
0.730
0.740
0.740
Latin America
1.528***
[4.50]
-3.704***
[2.58]
1.803***
[4.98]
-3.838***
[2.87]
2.446***
2.660***
[6.42]
[6.96]
-0.798*** -0.744***
[7.30]
[6.92]
1.363***
1.397***
[42.43]
[42.89]
-24.578*** -26.134***
[15.91]
[15.30]
1,881
1,881
0.700
0.710
0.685
[1.35]
-3.536
[0.60]
1.297**
[2.38]
-1.004
[0.18]
2.697***
[7.09]
-0.751***
[7.02]
1.394***
[42.60]
-26.803***
[15.28]
1,881
0.710
Notes:
1) Regressand is lnFDIGDP. Absolute values of t-statistics are in brackets. Random-effect GLS is used for estimation.
2) * significant at 10%; ** significant at 5%; *** significant at 1%
3) For the definition of variables, see Table 4.
4) For East Asia and Latin America, see Table 4.
Sources: IMF (2002) International Financial Statistics on CD-ROM , Washington, D.C.: IMF.
OECD (2000) International Direct Investment (Beyond 20/20) Database , Paris: OECD.
39
Table 7 Regression Results: FDI Flows from Japan
RERjp(-1)
VOLjp(-1)
RERus(-1)
VOLus(-1)
Openness(-1)
lnRPGDP(-1)
lnFDISTOCK(-1)
Constant
Observations
R-squared
FDI flows to
World
0.341
[0.58]
-4.956
[1.44]
East Asia
-2.191*
0.330
[1.67]
[0.40]
-37.248*** -0.644
[2.88]
[0.20]
0.879
3.371**
[1.34]
[2.31]
-2.466
31.539**
[0.73]
[2.50]
0.475
0.544
0.596
1.047***
[0.99]
[1.09]
[1.23]
[7.21]
-0.617*** -0.747*** -0.766*** -0.819***
[3.39]
[4.16]
[4.12]
[8.44]
1.146*** 1.220*** 1.204*** 0.064
[18.71]
[19.37]
[19.33]
[0.51]
-18.714*** -21.761*** -23.123*** -14.710***
[7.09]
[6.97]
[7.48]
[3.83]
455
455
445
58
0.640
0.660
0.650
0.610
Latin America
-0.106
1.836
[0.12]
[0.87]
-0.493
-0.748
[0.06]
[0.08]
2.212**
2.249*
3.300
[2.00]
[1.91]
[1.43]
-2.647
-2.272
-3.058
[0.88]
[0.30]
[0.36]
1.039*** 1.041*** 1.137
1.930
[7.40]
[7.25]
[0.56]
[0.92]
-0.847*** -0.847*** 0.416
0.246
[8.94]
[8.76]
[0.31]
[0.18]
0.176
0.176
1.107*** 1.178***
[1.31]
[1.28]
[7.34]
[7.29]
-24.108*** -23.803*** -23.531** -31.418***
[4.38]
[3.92]
[2.19]
[2.58]
58
58
74
74
0.640
0.640
0.620
0.620
-1.402
[0.42]
-15.937
[0.41]
4.534
[1.23]
11.752
[0.32]
1.940
[0.92]
0.419
[0.30]
1.170***
[7.10]
-30.192**
[2.41]
74
0.630
For notes and sources, see Table 6.
40