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Page 1: Effects of regional integration on FDI: An empirical approach

Effects of regional integration on FDI: An empirical approach

Mordechai E. Kreinin a, Michael G. Plummer b,*a Department of Economics, Michigan State University, East Lansing, MI, United Statesb School of Advanced International Studies, The Johns Hopkins University, SAIS-Bologna, Bologna, Italy

1. Introduction

While much has been written about the effect of regional integration on trade flows (i.e., estimates of trade creation anddiversion as well as of dynamic effects), little work has been done on its effect on foreign direct investment (FDI) flows and itis these effects that have always concerned government officials and private observers. For example, during the final stagesof the North American Free Trade Area (NAFTA) negotiations, officials and observers from Southeast Asia expressed concernabout diversion of Japanese and U.S. investments from, say, Thailand and Malaysia, to Mexico in order to obtain free access tothe U.S. market for the plant’s output. It has been argued that the 1992 Association of Southeast Asian Nations (ASEAN) Free-Trade Agreement (AFTA) was designed more as a means to increase FDI inflows to Southeast Asia than to stimulateintraregional trade (Naya & Plummer, 1997).

Table 1 summarizes international FDI inflows for the period 1995–2005 based on UNCTAD statistics. It shows that worldFDI inflows almost tripled from 1995 to 2005, reaching $916 billion, although this is less than in the 1998–2000 period; FDIinflows peaked at $1.4 trillion in 2000. This investment was concentrated in developed countries; the United States and theEuropean Union together have consistently accounted for more than half of global FDI, with the exception of 2004, whentheir share was 47%. During the entire period their combined share came to almost two-thirds the total.

FDI in Japan and South Korea has been insignificant; together, FDI in these two large OECD countries constituted less than2% of global inflows (and a small a fraction of flows to ASEAN and China). FDI inflows to ASEAN as a share of total worldinflows have dropped from their highs in the mid-1990s, when ASEAN countries accounted for about 8% of world FDI inflows,to the current 4%. The cause for this decline is related to the Asian financial crisis, which affected not only ASEAN MemberCountries but also other East Asian countries.

This chapter is organized as follows. The next section considers theoretical approaches to the effects of regional economicintegration on FDI – in particular, investment creation and diversion – followed by a review of the (scarce) empirical

Journal of Asian Economics 19 (2008) 447–454

A R T I C L E I N F O

JEL classification:

F15

F21

Keywords:

Foreign investment

Trade

A B S T R A C T

This study uses an augmented gravity model to capture the effect of regional economic

integration on Foreign Direct Investment (FDI) flows in the cases of the EU, NAFTA,

MERCOSUR, and ASEAN. Three important conclusions emerge: (i) regional integration has

had a positive and significant effect on FDI, which is a combination of investment creation

and diversion; (ii) investment diversion does occur in a significant number of cases, and

hence it is a legitimate cause for concern, especially among developing countries that are

not part of a regional grouping with at least one developed country; and (iii) FDI acts as a

substitute for trade in a significant number of cases, although in some cases, it

complements trade.

� 2008 Published by Elsevier Inc.

* Corresponding author. Tel.: +39 051 2917 822; fax: +39 051 228505.

E-mail address: [email protected] (M.G. Plummer).

Contents lists available at ScienceDirect

Journal of Asian Economics

1049-0078/$ – see front matter � 2008 Published by Elsevier Inc.

doi:10.1016/j.asieco.2008.09.005

Page 2: Effects of regional integration on FDI: An empirical approach

literature. Section 4 develops our econometric approach to estimating the effects of regionalism on FDI, using the UnitedStates, Japan, France and Germany as source countries. We are also able to address the question of whether trade and FDI aresubstitutes or complements. The final section offers concluding remarks.

2. Theoretical framework

Theoretically, we distinguish between investment creation and investment diversion (to parallel trade creation anddiversion).1 In the context of NAFTA, investment creation occurs when a U.S. multinational corporation (MNC) or its affiliatesinvest in Mexico or Canada, because the output of such factories can be exported to the United States duty-free, whereaswithout integration such investment would have occurred domestically (in the United States). Before NAFTA, the goodsproduced in Mexico could not be marketed by MNCs competitively in the United States because of trade restrictions. Theabolition of U.S. tariffs on Mexican and Canadian goods made it profitable to invest there and ship the products to the U.S.market. In other words, investment in Mexico substituted for investment in the home country (the United States). This effectis favorable to welfare, because it moves the configuration of production (and global resource allocation) in the direction ofincreased efficiency.

On the other hand, NAFTA introduces tariff discrimination against nonmember countries. Hence, some MNC investmentsthat would have flowed to third-country hosts (such as South East Asia), when all countries were subject to the same MFNtariff in the United States, would now be diverted to Mexico-Canada, because of the tariff discrimination. This is investmentdiversion, and it moves the world away from optimal resource allocation. The optimal configuration would have dictatedthat such FDI be made elsewhere. Hence, it is unfavorable to global welfare, with the bulk of the cost being borne by the moreefficient third country where FDI would have been more profitable.

It bears emphasis that any increase in U.S. FDI in Mexico caused by integration (i.e., a NAFTA effect) is investment creationand diversion combined. To distinguish between the two, one would have to analyze the investments of U.S. FDI innonmembers of NAFTA, especially countries that compete with Mexico, and subtract these from the total effect.

While conceptually clear, empirically it is difficult to separate out investment creation and diversion because suchseparation would require knowing the motives for MNC decisionmaking. Moreover, since we expect trade creation anddiversion to be highly correlated with investment creation and diversion, the trade–investment link also enters the equation.Indeed, this study will shed light on the age-old question of whether trade and FDI are complements or substitutes for eachother.

3. Literature on investment creation and diversion

There exists a large literature on the determinants of FDI.2 In particular, the ‘‘eclectic theory’’ of Dunning (1977) suggestedthat FDI is a function of three clusters of variables: firm-specific, ‘‘internalization,’’ and locational advantages. That is, thefirm must possess (firm-specific) ‘‘ownership advantages’’ over other firms; it must find it beneficial to utilize theseadvantages directly instead of selling or leasing them (‘‘internalization advantages’’); and the firm must find it profitable tocombine these advantages with at least one factor input abroad so that local production dominates exporting (‘‘locationaladvantages’’). If all three types of incentives are not in place, an MNC is better off exporting, licensing, franchising, etc., thanengaging in FDI. While conceptually clear, these determinants are difficult to estimate empirically.3

Table 1

Share of world FDI inflows, 1995–2005 (selected countries and regions, percentage share).

Host 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

United States 17.30 21.50 21.10 24.50 25.80 22.30 19.20 12.10 9.53 17.2 10.90

European Union 25 37.90 31.80 29.2 39.80 45.70 49.40 45.90 49.70 45.50 30.10 46.00

Japan 0.01 0.06 0.66 0.45 1.16 0.59 0.75 1.50 1.13 1.10 0.30

PRCa 11.00 10.60 9.24 6.38 3.67 2.89 5.63 8.54 9.59 8.53 7.90

South Korea 0.37 0.51 0.54 0.71 0.88 0.61 0.46 0.49 0.70 1.09 0.79

East Asiab 21.70 21.90 19.40 12.20 9.58 9.87 11.70 13.20 15.80 18.30 16.80

ASEANc 8.27 7.77 7.01 3.13 2.62 1.67 2.34 2.55 3.57 3.61 4.05

World FDI inflow (US$ trillions) 0.34 0.39 0.49 0.71 1.10 1.41 0.83 0.62 0.56 0.71 0.92

Source: UNCTAD FDI Statistics Online.a The figures for PRC do not include inflows to Hong Kong, China and Macao.b East Asia includes PRC, Hong Kong, China; Taipei, China; South Korea, Cambodia, Indonesia, Malaysia, Myanmar, Philippines, Singapore, Thailand, and

Vietnam.c ASEAN includes Brunei, Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

1 For more on this, see Kreinin (1964).2 See, for example, Lizondo (1991) and Petri and Plummer (1999).3 See Lee and Roland-Holst (1999) for a review of empirical FDI models.

M.E. Kreinin, M.G. Plummer / Journal of Asian Economics 19 (2008) 447–454448

Page 3: Effects of regional integration on FDI: An empirical approach

The effect of regionalism on FDI can be incorporated into a traditional eclectic model in which the changes in externalcommercial policies alter the locational advantages. As Dunning and Robson (1987) and Blomstrom and Kokko (1997) havepointed out, the effect of regional integration on FDI is a priori ambiguous: on the one hand, what we have defined asinvestment creation and diversion lead to an increase in FDI to a member state of a preferential trading agreement. On theother hand, to the extent that there existed a ‘‘tariff hopping’’ incentive to existing FDI flows prior to integration, there wouldbe a decrease in FDI outflows from partner countries (i.e., a member state in a preferential trading agreement to which thehome country belongs) when the tariff disappears.4

However, there is scant empirical literature that tests the effect of regionalism on FDI in the context of this theory. This isan important drawback, as policymakers have been motivated mainly by the ‘‘dynamic’’ effects of regional integration, ofwhich attraction of FDI inflows is a major component.

One way of integrating investment into models of economic regionalism is to link discriminatory trade liberalization tochanges in relative factor remunerations, which, in turn, cause changes in investment flows. A relatively capital-abundantcountry would experience a net inflow of investment as rents rise relative to wages, and a relatively labor-abundantcountry would experience the opposite effect. Such a trade-induced, investment-led effect has been used in dynamicComputational General Equilibrium (CGE) models.5 However, relative factor prices are only one determinant of FDI; othersinclude, for example, economic size, per capita income, economic dynamism in the source and host countries, thegeographic distance between them, commercial policy-related variables, and other locational advantage factors.Moreover, the relative factor renumeration incentive suggests that FDI would flow from relatively low-rent to high-rentcountries, while no such effect is evident. Indeed, Table 1 shows that the same developed countries are the largest sourcesand hosts of FDI.

Another possible approach is to interpret ex-post the effects of a regional trading area on FDI patterns using ad hoctechniques, such as identifying structural breaks in FDI shares that might be attributable to a trading block. Kreinin andPlummer (2002) used this approach for the EC Single Market Program and NAFTA and Blomstrom and Kokko (1997) forthe U.S.–Canada Free-Trade Area, NAFTA, and MERCOSOR. The former find no evidence of investment diversion andsome evidence of investment creation, whereas the latter find that the effect of regionalism on FDI flows depends uponthe agreement’s effect on the commercial policy environment and locational advantages in the integrating countries.However, these studies are limited in scope, as neither attempt to model a counterfactual scenario nor take aneconometric approach to FDI determinants.6 Pain (1996) used disaggregated, panel-data to estimate the determinantsof U.K. investment in the European Union and finds evidence of a statistically significant positive impact of EC-92 (SingleMarket Program) on FDI outflows to the rest of the EU, as well as investment diversion away from the United States.However, these results are based on only one source country (the United Kingdom) and one nonpartner country (theUnited States).

4. Effects of regional integration on FDI

We propose to approach the determinants of FDI flows directly by the use of a gravity model. The model incorporates asmany of the determinants as theory suggests and data permits, and then grafts onto them binary variables for regionalintegration. Gravity models have been used extensively in empirical studies of international trade flows7 and althoughsubject to criticism concerning their theoretical foundation, they have been successful in unraveling stable relationshipsbetween variables.

We attempt to gauge the effects of regionalism on FDI outflows from the Triad countries, namely, the United States, Japan,and, for the European Union, France and Germany.

4.1. The United States as a source country

In what follows, we model first U.S. (source country) outflows of FDI to 39 host countries over an 18-year period spanningfrom 1982 to 1999. Ordinary least square regressions are run on host country—annual combinations yielding 702observations.8 Annual dollar outflow of U.S. FDI is the dependent variable, while the independent variables (aside from aconstant) are the GDP and per capita GDP of the source and host countries, and the geographic distance between their

4 This would not be true for nonpartner countries, as trade barriers applied to them would presumably remain. Hence, Dunning and Robson (1987)

concluded that the EC1992 program would lead to an increase in FDI inflows from nonpartners — especially the United States and Japan — but the effect on

intraregional FDI flows in the EU would be ambiguous.5 For example, see Baldwin and Seghezza (1996).6 It should be noted that these studies also used data capturing the entire integrating period for NAFTA (Kreinin and Plummer) and MERCOSOR

(Blomstrom and Kokko).7 Frankel (1998) provided several examples of gravity model specifications in estimating the effects of regional trade groupings on trade.8 Natural numbers were used. We experimented with logarithmic transformation, but did not obtain satisfactory estimates.

M.E. Kreinin, M.G. Plummer / Journal of Asian Economics 19 (2008) 447–454 449

Page 4: Effects of regional integration on FDI: An empirical approach

capitals. Bilateral trade between the source and host countries is added as a second stage. To these, we added binary variablesto represent four regional groupings as follows:� North American integration:� 1982–1988: zero for all countries� 1989–1992: one for Canada; zero otherwise� 1993–1999: one for Canada and Mexico9; zero otherwise� EU integration:� 1982–1985: one for the EC-10; zero otherwise� 1986–1994: one for the EC-12; zero otherwise� 1995–1999: one for the EU-15; zero otherwise� Mercusur (two countries):� 1982–1988: zero for all countries� 1989–1999: one for Brazil, Argentina; zero otherwise� ASEAN (5 countries)10

� 1982–1991: zero for all countries� 1992–1999: one for the ASEAN-5 members; zero otherwise.

Table 2 presents the determinants of U.S. FDI. The estimated coefficients on the GDP variables for the source and hostcountries are both, as expected, positive and statistically significant. However, the estimated coefficient on per capita GDP isnegative, a result that can be subject to multiple interpretations. The estimated coefficient on distance is negative andstatistically significant. When it comes to the effect of regionalism, the estimated coefficients on NAFTA and the EU areoverwhelmingly significant (but not the MERCOSOR11 or ASEAN12), and positive. This means that regional integration ofNAFTA and the EU had a strong positive effect in attracting U.S. FDI, controlling for other factors. However, it is important torecall that this effect is investment creation and diversion combined.

Table 2

Determinants of U.S. FDI (1982–1999).

Variable Coefficient t-Statistic

C 1232.797 0.961763

GDPSa 2.011316 2.228950

GDPHb 0.666750 4.128523

PCSc �516.2779 �1.829378

PCHd 3.720695 0.733562

DISTe �0.071644 �2.256678

Binary NAFTAf 3408.920 5.040187

EUg 708.8086 3.007064

MERh 848.2397 1.498663

ASEANi �24.60852 �0.055470

R-squared 0.219095 Mean dependent variable 1128.953

Adjusted R-squared 0.208939 S.D. dependent variable 2682.614

S.E. of regression 2385.960 Akaike info criterion 18.40673

Sum squared resid 3.94E+09 Schwarz criterion 18.47160

Log likelihood �6450.763 F-statistic 21.57243

Durbin-Watson stat 2.162283 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 702.a GDP of source country.b GDP of host country.c Per-capital GDP of source country.d Per-capital GDP of host country.e Distance between source and host country.f North American Free Trade Agreement.g European Union.h Mercursur: Brazil and Argentina.i The ASEAN-5: Thailand, Malaysia, Indonesia, the Philippines, and Singapore.

9 NAFTA did not come into effect until 1994. However, we switch the binary variable in 1993 to capture a possible anticipatory effect, as suggested by

Freund and McLaren (1999).10 ASEAN-5 refers to the founding members of ASEAN: Indonesia, Malaysia, the Philippines, Singapore and Thailand.11 Only Brazil and Argentina are included in MERCOSOR, because of a lack of trade data for Uruguay and Paraguay. They are, however, the two main

members.12 Included are Thailand, Malaysia, Indonesia and the Philippines.

M.E. Kreinin, M.G. Plummer / Journal of Asian Economics 19 (2008) 447–454450

Page 5: Effects of regional integration on FDI: An empirical approach

In order to separate the two, in the case of NAFTA, we ran separate U.S. FDI regressions using a binary variable forcountries thought to be competitive with Mexico, i.e., developing countries in our database. Table 3 presents one of severalregressions run to unravel this effect, with the LDC estimated coefficient listed last. The results show a statistically significantand negative effect of FDI in ‘‘competing countries,’’ indicating the presence of investment diversion. Variations of thisregression yield similar and even more statistically significant results.

As a next step, we added bilateral trade between the source and host countries to the regression equation. Table 4 showsthat trade is positive and statistically significant, while the other variables retain their statistically significant status. (Theadjusted R2 rises from 0.2 to 0.3 with the addition of trade to the equation.) However, the striking observation is that theestimated coefficient on the NAFTA variable turns from positive to negative (both are statistically significant). This switchalso occurs in specifications using the logs of the dependent and independent variables. These results suggest that in the caseof NAFTA, trade and FDI are substitutes. The estimated coefficients on EU and MERCORSUR retain their positive signs,indicating complementarity between trade and FDI. Finally, the estimated coefficient on the ASEAN variable becomesstatistically significant (and negative), suggesting a substitute relationship. Thus, the answer to the age-old question: ‘‘aretrade and investment substitutes or complements?’’ is that the relation can go either way.13 All results are largely confirmedin a sample of more countries but fewer years (1991–1999) yielding a total of 414 observations.14

Table 3

NAFTA effect on U.S. FDI in competing LDCs.

Variable Coefficient t-Statistic

C �1150.161 �2.575300

GDPS 0.432455 6.492983

GDPH 0.634193 3.941925

PCS-PCH 0.087880 0.463612

DIST �0.074208 �2.506114

EU 504.9883 2.072833

NAFTA 3099.252 4.560056

LDC �483.7990 �1.615340

R-squared 0.216340 Mean dependent variable 1128.953

Adjusted R-squared 0.208436 S.D. dependent variable 2682.614

S.E. of regression 2386.719 Akaike info criterion 18.40456

Sum squared resid 3.95E+09 Schwarz criterion 18.45645

Log likelihood �6452.000 F-statistic 27.36972

Durbin-Watson stat 2.080986 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 702.

Table 4

Determinants of U.S. FDI with trade added, 1982–1999.

Variable Coefficient t-Statistic

C �50.29435 �0.041644

TRADE 0.088467 9.880026

GDPS 1.794000 2.121657

GDPH �0.446459 �2.366842

PCS �464.4306 �1.756445

PCH 4.777033 1.005172

DIST 0.026169 0.834890

NAFTA �3083.392 �3.377950

EU 1280.675 5.610492

MER 1381.642 2.592510

ASEAN �565.9709 �1.350222

R-squared 0.315756 Mean dependent variable 1128.953

Adjusted R-squared 0.305854 S.D. dependent variable 2682.614

S.E. of regression 2235.032 Akaike info criterion 18.27744

Sum squared resid 3.45E+09 Schwarz criterion 18.34880

Log likelihood �6404.383 F-statistic 31.88736

Durbin-Watson stat 2.105297 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 702.

13 Possibly, trade is a substitute for FDI in some industries, and a complement in others.14 The 41 countries in the regression are Argentina, Australia, Austria, Belux, Brazil, Canada, Chile, China, Colombia, Costa Rica, Denmark, Egypt, Finland,

France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Korea, Malaysia, Mexico, Morocco, Netherlands, New Zealand,

Norway, Panama, the Philippines, Poland, Portugal, Romania, Saudi Arabia, Singapore, Spain, Sweden, Switzerland, Thailand and Turkey.

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4.2. Japan as a source country

Japan is the only source country in our sample that is not itself a member of any regional grouping throughout our timeperiod, at least at the time of our writing.15 Table 5 shows a significant positive effect of NAFTA and ASEAN on Japan’s FDI, buta negative effect of the EU and MERCORSOR. Could it be that FDI in the EU and MERCOSOR were diverted to Canada–Mexico,to gain free access to the U.S. markets, but FDI in ASEAN was not so affected? In another table (not shown in this study)containing ‘‘all LDCs’’ as an independent variable, the estimated coefficient for that variable is negative and statisticallysignificant, suggesting investment diversion (as in the case of U.S. FDI outflows).

Table 6 adds a bilateral trade variable to the equation. The estimated coefficient on the ASEAN variable turns negative,suggesting a possible substitution relation between Japanese trade and FDI in that region. However, the negative coefficientis not statistically significant.

4.3. Germany and France as source countries

Table 7 shows that only EU integration had a statistically significant, and positive, effect on German FDI. However, whentrade was added to the regression equation (results not reported in table), the estimated coefficient for the EU variableturned negative and was far less statistically significant. This suggests that FDI was a substitute for trade. Finally, when the

Table 5

Determinants of Japan’s FDI 1982–1995.

Variable Coefficient t-Statistic

C �3.6873.76 �2.584768

GDPS �0.486125 �2.499352

GDPH 2.452288 25.86354

PCS 314.8060 2.607550

PCH �13.86015 �1.467606

DIST �0.032214 �1.557430

NAFTA 2134.862 4.749990

EU �405.36438 �2.305220

MER �209.4647 �0.376111

ASEAN 623.5206 1.602614

R-squared 0.743283 Mean dependent variable 907.7259

Adjusted R-squared 0.737972 S.D. dependent variable 2998.567

S.E. of regression 1534.927 Akaike info criterion 17.53257

Sum squared resid 1.02E+09 Schwarz criterion 17.62466

Log likelihood �3890.997 F-statistic 139.9415

Durbin-Watson stat 1.764214 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 445; excluded observations: 3 after adjusting endpoints.

Table 6

Determinants of Japan’s FDI with trade added, 1982–1995.

Variable Coefficient t-Statistic

C �34623.96 �2.736308

TRADE 0.155199 10.91151

GDPS �0.520845 �3.018951

GDPH 0.063926 0.272627

PCS 288.1584 2.690604

PCH �3.756863 �0.445832

DIST 0.058873 2.921164

NAFTA 1986.481 4.980772

EU 246.9859 1.477819

MER 15.00385 0.030351

ASEAN �162.3823 �0.460678

R-squared 0.798548 Mean dependent variable 907.7259

Adjusted R-squared 0.793906 S.D. dependent variable 2998.567

S.E. of regression 1361.276 Akaike info criterion 17.29464

Sum squared resid 8.04+08 Schwarz criterion 17.39594

Log likelihood �3837.057 F-statistic 172.0361

Durbin-Watson stat 1.619789 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 445.

15 Japan has subsequently concluded several free-trade areas, including with Singapore, Mexico, and most recently, with Thailand.

M.E. Kreinin, M.G. Plummer / Journal of Asian Economics 19 (2008) 447–454452

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LDC variable was added, its estimated coefficient was negative (in all permutations of the estimating equation), but notstatistically significant. Possible investment diversion could have occurred from North America, and indeed the estimatedcoefficient for NAFTA is negative, but not statistically significant. Table 8 shows that the pattern for France – also a member ofthe EU – closely follows that of Germany.

5. Conclusion

This study uses gravity regression equations to assess the effect of regional integration on FDI flows. Four regionalgroupings – EU, NAFTA, MERCUSUR, and ASEAN – were grafted as binary variables to a regression equation containing theusual determinants of FDI. Because they appear as binary variables, only their sign and statistical significance can be used, sono quantitative measures are obtained. Nevertheless, three important conclusions emerge: (i) regional integration has apositive and significant effect on FDI, which is viewed as a combination of investment creation and diversion; (ii) investmentdiversion does occur in a significant number of cases, and hence it is a legitimate cause for concern, especially amongdeveloping countries that are not part of a regional grouping with at least one developed country; and (iii) FDI acts as asubstitute for trade in a significant number of cases, although in some cases, it complements trade.

Acknowledgements

This chapter was originally presented at the American Economic Association Meetings and has been substantially revised.The authors would like to thank participants at that session, in particular our discussant, Robert Lipsey, for their useful input.We are grateful to Rima Kohli and Theresa Beltramo for excellent research assistance.

Table 7

Effect of integration on German FDI, 1982–1999.

Variable Coefficient t-Statistic

C �721.8044 �1.344365

GDPS 0.736108 2.091437

GDPP 2.220509 13.41928

PCSPCP �0.708168 �0.810463

DIST �0.066968 �1.539626

NAFTA �604.7959 �0.558200

EU 1234.435 2.653387

MER �322.5369 �0.303614

ASEAN �137.1313 �0.170461

R-squared 0.265859 Mean dependent variable 1226.999

Adjusted R-squared 0.256906 S.D. dependent variable 5118.445

S.E. of regression 4412.249 Akaike info criterion 19.63560

Sum squared resid 1.28E+10 Schwarz criterion 19.69650

Log likelihood �6519.836 F-statistic 29.69510

Durbin-Watson stat 1.620338 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 665; excluded observations: 1 after adjusting endpoints.

Table 8

Effect of integration on French FDI, 1982–1999.

Variable Coefficient t-Statistic

C �2462.952 �1.676334

GDPS 3.192097 2.475000

GDPP 4.826575 13.04895

PCSPCP �2.477218 �1.245469

DIST �0.162988 �1.646676

NAFTA �1129.824 �0.469483

EU 4208.270 3.885132

MER 208.0047 0.089536

ASEAN 167.1278 0.095936

R-squared 0.262994 Mean dependent variable 2888.493

Adjusted R-squared 0.254424 S.D. dependent variable 11435.42

S.E. of regression 9874.110 Akaike info criterion 21.24605

Sum squared resid 6.71E+10 Schwarz criterion 21.30476

Log likelihood �7395.248 F-statistic 30.68835

Durbin-Watson stat 1.895324 Prob (F-statistic) 0.000000

Dependent variable: FDI; method: least squares; included observations: 697 after adjusting endpoints.

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