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 Aizenman, Joshua (1992) “Exchange Rate Flexibility, Volatility and the Patterns of Domestic and Foreign Direct Investment,” IMF Working Paper. No.20 Bayoumi, Tamim and Lipworth, Gabrielle (1998) “Japanese Foreign Direct Investment and Regional Trade,” Journal of Asian Economics, vol.9 - 4, Winter, pp.581-607. Bennasy-Quere, Agnes, Lionel Fontagne and Amina Lahreche-Revil (2001) "Exchange Rate Strategies in the Competition for Attracting Foreign Direct Investment, " Journal of Japanese and International Economies, 15, pp. 178-198. Compa, Jose Manuel (1993) "Entry by Foreign Firms in the United States under Exchange Rate Uncertainty," Review of Economics and Statistics, 75 (4), pp. 614-622. Cushman, D. (1985) “Real Exchange Rate Risk, Expectations, and the Level of Direct Investment,” Review of Economics and Statistics 67, pp.297-308. Froot, K and Stein, J. (1991) “Exchange Rates and Foreign Direct Investment: An imperfect capital markets approach,” Quarterly Journal of Economics 106, pp.1191-1217. Fukao, Kyoji and Ximing Yue (1997) "Denki Meka no Ricchi Sentaku" [The Locational Selection of Japanese Electronics Firms], Mita Gakkai Zasshi [Mita Journal of Economics], vol.90, No.2, July, Keio University, pp. 11-39. [in Japanese] Goldberg, Linda S. and Charles D. Kolstad (1995) "Foreign Direct Investment, Exchange Rate Variability and Demand Uncertainty," International Economic

Review, 36 (4), pp. 855-873. ________________ and Michael Klein (1998) "Foreign Direct Investment, Trade, and Real Exchange Rate Linkages in Developing Countries," Reuven Glick ed.,

Managing Capital Flows and Exchange Rates: Perspectives from the Pacific Basin, Cambridge, UK: Cambridge University Press. Head, Keith, John Reis, and Deborah Swenson (1995) “Agglomeration benefit and location choice: Evidence from Japanese manufacturing investments in the United States,” Journal of International Economics 38, pp. 199-222.

31

Ito, Takatoshi (2000) “Capital Flows in Asia,” pp.255-297,in Capital Flows and the

Emerging Economies eds. Edwards, Sebastian, The University of Chicago Press. Klein, Michael.W. and Rosengren, Eric (1994) “The Real Exchange Rate and Foreign Direct Investment in the United States: Relative Wealth vs. Relative wage effects,” Journal of International Economics 36, pp.373-389. Sazanami, Yoko, Seiji Yoshimura, and Kozo Kiyota (2001) "Japanese Foreign Direct Investment Flows to East Asia and the Real Exchange Rate: Lessons from the Asian Financial Crisis," presented at the conference titled "Regional Development and the Global Economy: European and East Asian Experience, University of Le Havre, Le Havre, France, September. United Nations (2001) World Investment Report 2001, New York and Geneva. Urata, Shujiro and Hiroki Kawai (2000), "The Determinants of the Location of Foreign Direct Investment by Japanese Small and Medium-sized Enterprises,"

Small Business Economics 15, pp. 79-103. Wheeler, David and Ashoka Mody (1992) "International Investment Location Decisions: The Case of U.S. Firms," Journal of International Economics, 33, pp. 57-76. Woodward, Douglas P. (1992) "Locational Determinants of Japanese Manufacturing Startups in the United States," Southern Economic Journal, January, pp.690-708.

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