catherine co, 2001

International Journal of Industrial Organization19 (2001) 163183

www.elsevier.com/ locate /econbase

Trade, foreign direct investment and industryperformance

*Catherine Y. CoDepartment of Economics, University of Central Florida, Orlando, FL 32816-1400, USA

Received 1 September 1997; received in revised form 1 February 1998; accepted 1 July 1999

Abstract

This paper investigates the US margins effect of imports and FDI using a panel of 448manufacturing industries for 19821990. In the single equation two-way fixed effectsregressions, greenfield FDI has no significant effect on margins; while there is someindication that non-greenfield FDI affect margins and the effect is found to depend on thelevel of industry concentration. When potential endogeneity among variables are taken intoaccount, both types of FDI are found to increase margins. However, for non-greenfield FDI,the effect appears with a lag of two periods. For the most part, the results indicate that thepositive effect on margins holds for industries with low levels of concentration. Beyondsome critical level, the competitive effect of FDI predominates. 2001 ElsevierScience B.V. All rights reserved.

Keywords: Foreign direct investment; Margins; Technology sourcing; Spillovers

JEL classification: F23; F10; L60; C30

1. Introduction

One of the stylized facts that characterize the 1980s is the rapid increase in theamount of foreign direct investment (FDI) into the US. The share of US affiliates

*Tel.: 11-407-823-3763; fax: 11-407-823-3269.E-mail address: [email protected] (C.Y. Co).

0167-7187/01/$ see front matter 2001 Elsevier Science B.V. All rights reserved.PI I : S0167-7187( 99 )00042-9

164 C.Y. Co / Int. J. Ind. Organ. 19 (2001) 163 183

of foreign companies in total US manufacturing value added increased from 3.6%in 1977 to 15.0% in 1992; further, their share in total manufacturing employment

1also increased from 3.5% in 1977 to about 11.6% in 1992. Observations on thegrowing importance of foreign firms have sparked debates as to the costs andbenefits of foreign firms direct participation in US industries. Proponents of FDIpoint to the additional employment or the superior technology that foreign firmsbring into the US. Those worried about the growing presence of foreign firmsargue that these employment effects tend to be small since most FDI are throughthe buy-out of existing US firms; there is also concern that FDI into the US meansforeign appropriation of US technology. Studies on the effect of FDI on the USeconomy mostly focus on employment, technology and trade balance issues (see,e.g., Young and Steigerwald, 1990; Kogut and Chang, 1991; Orr, 1991). There hasbeen little done to study the effect of FDI on margins.

The margins effect of imports into the US is well studied. Empirically, there isconsensus that imports are a source of competitive discipline (see Caves, 1985, fora survey). In particular, this discipline is larger in more concentrated industries.This result is robust in most single- and simultaneous equations models using US

2and non-US data. Except for de Ghellinck, Geroski and Jacquemins (GGJhereafter) (1988) study of a panel of 82 three-digit Belgian manufacturingindustries for 19731978, the imports-as-market discipline hypothesis has notbeen extended to account for FDI. This paper makes its contribution byincorporating FDI into the imports-as-market discipline literature. Perhaps onefactor that may have hindered our ability to address the relationship between FDIand margins is the lack of detailed and usable data on foreign affiliates share in

3US output or value added. The International Trade Administration (ITA) has anannual listing of FDI occurrences at the four-digit SIC level which dates back to1974. This data set can serve as a good proxy for foreign firms direct participationin US production and allows one to investigate the US product market competitioneffects of imports and FDI.

In the single equation two-way fixed effects regressions, greenfield FDI has no

1 The Bureau of Economic Analysis defines a US affiliate as a US business enterprise which asingle foreign person owns or controls, directly or indirectly, 10% or more of the voting securities of anincorporated business enterprise or the equivalent interest in an unincorporated business enterprise.

2 For example, Domowitz et al. (1986) (DHP hereafter) and Katics and Petersen (1994) use US data.Levinsohn (1993), Jacquemin and Sapir (1991), Stalhammar (1991) and De Ghellinck et al. (1988) use

non-US data.3 The US Bureau of Economic Analysis has made available four-digit SIC level data on the

value-added share of US affiliates of foreign firms in domestic production starting in 1988. This dataset would have been the ideal data for our analysis. However, data are suppressed in several cases andare consistently available only for about 110 four-digit SIC industries.

C.Y. Co / Int. J. Ind. Organ. 19 (2001) 163 183 165

significant effect on margins. There is some indication that non-greenfield FDIaffect margins and the effect is found to depend on the level of industryconcentration. Since some authors have found simultaneity between margins, tradeand concentration ratios, the margins equation is also estimated using two-stage

4least-squares for fixed effects panel. When endogeneity is taken into account, bothgreenfield and non-greenfield FDI occurrences significantly affect margins. Newplant FDI seems to increase the margins of industries with low levels ofconcentration. Greenfield FDI typically involves new facilities and entrants wouldadopt the best available technology which makes them more efficient andcontribute to an increase in industry margins. However, beyond some criticallevel, the competitive effect of FDI predominates (margins decline). This isconsistent with the findings of the imports-as-market discipline literature where . . . the competitive discipline of imports is conditional upon potentially non-competitive conditions among domestic producers. There has to be something todiscipline. (See Caves, 1985, p. 379) With regard to lagged FDI, only itsinteraction with CR is significant and the coefficient happens to be positive.Margins rise with industry concentration. Perhaps this is indicative of foreign firmsdeploying firm-specific assets upon entry and this knowledge spills over domesti-cally with a lag and the potential technological spillovers may be larger inconcentrated industries. The results for non-greenfield FDI indicate that the effecton margins is less immediate; it appears with a lag of two periods. One possibleexplanation for this is that it takes time for firms to integrate their existing andacquired human and technological resources. Any efficiency benefits appear with alag. The interaction between one-period lagged FDI and CR is significantlynegative. This again is consistent with the findings of the imports-as-marketdiscipline literature. Although some interesting findings are found, the issuerequires more study because the interpretations are based on assumptions madeabout the motivations behind FDI; no attempt is made to test these motivations. Abetter understanding of the motives will strengthen the conclusions found in thispaper.

To put the empirical analysis in perspective, the next section reviews sometheoretical models that address the question of why firms invest abroad. This isbecause the effect of FDI on product market competitiveness may depend onfirms motivations for entering foreign markets. In Section 3, some of theeconometric issues involved in the empirical investigation will be reviewed andthen the models estimated will be discussed. Results are presented and analyzed indetail in Section 4. Section 5 contains some concluding comments.

4See, for example, Martin (1979), Geroski (1982) and Stalhammar (1991). These papers are briefly

reviewed in Section 3.


2. FDI and host country industry margins: the connection

Firms entry mode choices into foreign markets depend on the interactions5

among ownership, location and internalization factors. Firms are typicallyassumed to possess firm-specific advantages (e.g., superior technology) that allowthem to overcome the operational advantages of host country firms. These firmshave the option of deploying these firm-specific advantages themselves orlicensing these out. The former is typically preferred because the latter optioninvolves additional costs, such as, contract enforcement cost. These two factorsinteract with location factors. These location factors can be policies that encourageFDI (e.g., investment incentives), or location-specific disadvantages (e.g., tariffs)that induce firms to engage in FDI production instead of exporting. When foreignentrants deploy their firm-specific advantages, the effect of FDI on marginsdepends on whether some incumbents exit. If there is no exit by incumbents (anddemand is stable), new plant FDI can lead to lower margins because this type ofFDI increases domestic capacity and production, ceteris paribus, leads to lower

6prices and margins. On the other hand, margins may rise if foreign entrants adoptthe latest technology which can translate to an increase in industry margins.Benefits from superior foreign technologies can also spillover to domestic firms;

7contributing to a rise in margins too.

Besides these traditional explanations, policymakers in the US and Europe haverecently raised the possibility that firms may engage in FDI to take advantage ofexisting host country technologies or technical capabilities. That is, foreign firmsmay be sourcing host country technologies. The sourcing argument is made giventhe observation that the bulk of FDI is through mergers and acquisitions. Forexample, about 70% of the FDI inflows to developed countries during 198690 isthrough acquisitions (UNCTAD, 1994). The academic profession has also lookedat the issue and there are some empirical evidence supporting the sourcing motive

8for some types of FDI. As Neven and Siotis (1996) point out, firms would

5 This is Dunnings (1988) OLI paradigm. See Caves (1996) and Markusen (1995) for recent surveysof the FDI literature.

6 This result follows from Horstmann and Markusens (1992) model where firms are assumed tocompete in quantity.

7 Wang and Blomstrom (1992) develop a model that endogenizes the age and rate of foreign firmtechnology (assumed to be superior) transfer. One implication of their model is that spillovers from FDIcan make domestic firms more efficient; hence industry margins can rise with FDI. See Blomstrom andKokko (1996) for an extensive review of the FDI and spillover literature.

8 To test the sourcing argument, Kogut and Chang (1991) used the difference in industry R&Dintensities between host and source countries. A significant positive coefficient is taken to be anindication of sourcing as the motive for FDI. Controlling for other factors, they find some evidence thatthe Japanese enter into joint ventures with US companies to source some US technological advantage.Following Kogut and Chang (1991), Neven and Siotis (1996) also find some indication that technologysourcing may be the motive behind US and Japanese FDI into four EC member countries for 198489.


probably engage in non-greenfield FDI (e.g., acquisitions, joint ventures) to takeadvantage of spillovers emanating from the local industry. Under the sourcingmotive, domestic technology is assumed to be superior, hence, industry-widemargins are expected to fall initially if existing host country assets are taken overby less knowledgeable foreign firms. Margins may rise once foreign firms are

9able to learn the technology that they have acquired.

One approach in getting a handle on the issue is to look into the temporal natureof FDI, the industries and countries involved. These details may give insights as toboth the motives for FDI and their consequent effects on margins. The annualbreakdown of the FDI counts for selected industries appear in Table 1. For theperiod 198290, almost 5271% of new plant FDI are concentrated in food

Table 1aNumber of FDI by type of investments

bFood and Chemicals Industrial Electronic Transport TotalKindred & Allied Mach. Oth Elec Equip.SIC 20 SIC 28 SIC 35 SIC 36 SIC 37

Greenfield FDI1982 2 14 7 7 2 521983 7 21 15 14 11 1071984 5 16 11 20 10 901985 2 11 8 10 12 631986 6 16 9 13 13 921987 6 23 20 12 22 1411988 2 7 9 12 15 861989 7 6 9 13 12 801990 9 9 4 8 19 69

Total, 198290 46 123 92 109 114 780

Non-Greenfield FDI1982 12 25 34 26 9 1671983 11 29 41 47 9 2461984 18 38 28 49 17 2451985 23 46 39 38 4 2891986 33 50 50 50 9 3351987 41 53 84 89 22 4761988 38 52 74 51 20 4071989 34 66 45 61 19 3801990 21 48 40 54 14 305

Total, 198290 231 407 435 465 123 2850a Note: These are calculated from ITAs Foreign Direct Investment in the United States, various

years.b Includes these five industries and all other manufacturing SIC industries.

9 I wish to thank an anonymous referee for suggesting that I consider the possible motivations behindFDI for explaning the observed margins effect of FDI.


(SIC20), chemicals (SIC28), machinery (SIC35), electrical (SIC36) and transport10(SIC37) equipments. The same five two-digit SIC industries received the most

non-greenfield FDI. They comprised about 5263% of the total non-greenfieldFDI for the period. The same sets of countries are involved in both greenfield and

11non-greenfield FDI. An interesting pattern is that non-greenfield FDI (from allsource countries) does not seem to precede greenfield FDI. That is, we do notobserve a concentration of non-greenfield entries first, then followed by greenfieldentries. Foreign firms taken as a whole seem to use both modes of entry at the

12same time. These industries differ in that the number of greenfield and non-greenfield FDI counts are almost the same in transport equipment (SIC37) while inthe other industries, greenfield FDI is between 20 and 30% of non-greenfield FDIcounts. Interestingly, in 1987, the four-firm concentration rate for transportequipment is about 52%, while the rates for the other industries fall between 11and 19%. This pattern suggests that the margins effect of FDI may depend on theindustrys concentration rate. An industrys foreign entry mode mix may be afunction of the industrys concentration rate. This suggests that even if foreignfirms have firm-specific advantages, if they are to compete effectively in highlyconcentrated industries, they may need to complement their new plant entries byentering into joint ventures with (or acquiring) domestic incumbents. It does seemnatural then to test whether the practice of the imports-as-market disciplineliterature of including an interaction term between import share and concentrationrate applies to FDI too. This and other issues are explored in the next section. Theeconometric models used are also discussed in detail.

3. Empirical issues and model specification

A two-way fixed effects panel model is employed in the estimation of themargins equation. Data used cover the period from 19821990 for 448 four-digit

10 One should note that using some other measure (e.g., the share of US affiliates to total US valueadded) will show high foreign concentration in chemicals (SIC28), stone, clay and glass (SIC32),electronics (SIC36) and primary metals (SIC33), so these numbers should be interpreted with caution(See Graham and Krugman, 1995, p. 43).

11 The top greenfield investors with the number of occurrences for the period in parentheses are:Japan (271), Germany (49), UK (35), Canada (24), France (23) and The Netherlands (10). Fornon-greenfield, the country rankings are: Japan (614), UK (309), Germany (166), Canada (126), France(105) and The Netherlands (67).

12 The identities of the entrants need to be known for this statement to be precise; perhaps differentfirms are involved in the different entry modes. However, it does seem quite interesting that a simplepanel regression of current greenfield FDI against lagged non-greenfield FDI (up to three periods) has

2an R value of 0.17 and positive significant coefficients; and a similar panel regressing current

2non-greenfield FDI on lagged greenfield FDI (up to three periods) has an R value of 0.28 andsignificant positive coefficients. These results taken together seem to suggest complementaritybetween both modes.


13SIC industries. This approach mitigates the concern raised against the use ofcross-section data in industry analysis which result in biased coefficient estimatesbecause of omitted variables and/or measurement errors (See Schmalensee, 1989,for a survey). The specification used is similar to that used by DHP (1986) and

14includes a measure of foreign participation via FDI. This single margins equationis

PCM 5 a 1 g 1 b9X 1 , i 5 1, . . . ,N; t 5 1, . . . , T (1)it i t it itwhere PCM is price-cost margins, the matrix X contains industry four-firmconcentration rates (CR), growth of industry sales (GROWTH), investmentoutput ratio (KO), an openness measure (OPEN), the number of FDI occurrencesfor the period (FDI), one- and two-period lagged FDI (LFDI and L2FDI) andinteraction terms between CR and import shares (IMP CR), between CR and

]GROWTH (GR CR) and between CR and KO (KO CR). Each of the FDI

] ]variables is also interacted with CR. is an error term identically and in-dependently distributed over i and t. a and g represent industry-specific fixed (atthe two-digit SIC level) and time effects, respectively.

Although the relationship between PCM and CR is found to be weakempirically (but the relationship is statistically significant), there is a well

15established theoretical relationship between the two variables. For example,under the assumption of some barriers to entry, it is possible to observe highermargins in more concentrated industries. Alternatively, higher margins could result

Table 2aDescriptive statistics

Variables Mean Standarddeviation

PCM 0.289 0.096CR 39.55 20.52OPEN 58.98 27.17IMPSH 12.98 14.78AD 0.048 0.214GROWTH 4.210 13.78KO 3.012 2.012SCALE 10.47 56.01SKILL 21.90 6.619

a Source: authors calculations.

13 All data are based on 1972 SIC definition which are available for 448 (out of 450) industries.These are described in detail in Appendix A. Table 2 contains the descriptive statistics of the data used.Unavailability of data for some years meant unbalanced panels must be employed.

14 See Table 7 in DHP (1986, p. 13).15 As most industrial economists now realize (see Schmalensee, 1989, for a review), these two

variables are endogenous to each other and the direction of causality is not as clear as once thought.


from a smaller number of firms sharing the market which perhaps can makecollusion easier, hence margins are expected to be larger in more concentratedindustries. GROWTH is included to account for the possibility that in theshort-run, increases in demand may lead to higher prices and margins. Thisvariable is interacted with CR to test if the effect of demand changes on margins islikely to be larger in more concentrated industries. KO is included to control fordifferences in margins due to investmentoutput intensity. This is used as a proxy

for capital intensity following Martin (1979), DHP (1986) and Stalhammar (1991)to name a few. This effect may differ across industries with different concentrationlevels, hence, it is interacted with CR.

Because of trade- and/or FDI-related discipline, margins are expected to belower in industries which are more open. OPEN (5imports / imports1exports) is

16included to capture the effect of both exports and imports on margins. While theeffect of OPEN on margins is ambiguous by construction, it nonetheless can giveus impressions as to how changes in trade- related discipline can affect margins.As pointed out by GGJ (1988), an industry exposed to foreign competition isuncompetitive when imports comprise a large share of external transactions(imports1exports). For example, as the variable approaches one (OPEN in-creases), exports approach zero (one reason could be higher domestic relative toworld prices so more output is sold domestically). But it is unclear how a drop inexports affects margins. The effect also depends on what happens to the level ofimports. If imports rise, margins can decline. As OPEN approaches zero (OPENdeclines), imports by the industry approach zero (one reason could be becausetariffs increase the cost of exporting to the market), ceteris paribus, margins canrise. However, if this is accompanied by changes in the export and domesticmarket sales mix, the effect on margins depends on the price differential in the twomarkets.

A common practice in the imports-as-market discipline literature is the inclusionof an interaction term between import share (IMPSH) and CR, call this IMP CR. It

]is included to capture differences in the effect of imports across industries withvarying concentration rates. A negative coefficient implies that imports wouldhave larger competitive effects in more concentrated industries. This non-lineareffect has been empirically confirmed (see Geroski and Jacquemin, 1981, for atheoretical treatment). For example, using a panel consisting of 185 four-digit SICUS industries from 1958 to 1981, DHP (1986) find that import competitionreduces pricecost margins significantly in concentrated industries using OLS.

16 Ideally, we would like to include imports as a percentage of total shipments and exports as apercentage of total shipments separately. These two variables are treated as endogenous variables insimultaneous equation systems studying margins. Because of the limited number of exogenous industrycharacteristics available, identification concerns resulted in the creation of the OPEN variable. Asimilar approach is used by GGJ (1988).


That is, the interaction term between industry import share and four-firmconcentration ratio is negative.

17Three definitions of the FDI variable are used in the estimation. Thisdistinction is made since greenfield FDI involves the building of new plants thatexpand production capacity. Other types of investments, such as mergers /acquisi-tions, involve only the transfer of ownership from existing domestic producers toforeign firms. As reviewed in the previous section, different types of FDI may bemotivated by different factors and thereby may have different effects on margins.Current, one- and two-period lagged FDI are included to account for the nature ofhow the FDI data are collected and to allow for the possibility that projects maytake 13 years from project initiation to completion, hence the effects of FDI

18appear with a lag. Following the imports-as-market discipline practice ofincluding an interaction between IMPSH and CR, it seems natural to introducevariables where FDI is interacted with CR. These variables would capture thedifferential impacts of FDI on industries with different concentration rates.

The second econometric issue to consider is the potential endogeneity betweensome of the variables. For example, industry concentration has been found toinfluence and is influenced by industry margins; and there is some evidence ofsimultaneity between imports and margins (see Martin, 1979; Geroski, 1982; andSchmalensee, 1989, for a survey). Further, there is some evidence that import andexport shares are endogenous in a system of equations investigating industrymargins. Geroski (1982) finds that import and export shares are endogenous using

US and UK data, respectively; while Stalhammar (1991) cannot reject the nullhypothesis that export and import shares are exogenous using Swedish industrydata. A four-equation simultaneous panel is used to account for the potentialsimultaneity between margins, concentration ratios and industry exposure toforeign competition. In addition to the margins equation presented above, thefollowing equations are included in the system:

9CR 5 a 1 g 1 b S 1 i 5 1, . . . ,N; t 5 1, . . . , T (2)it 2i 2t 2 it 2it9OPEN 5 a 1 g 1 b Z 1 (3)it 3i 3t 3 it 3it

17 The FDI data are taken from ITAs Foreign Direct Investment in the United States. Thispublication lists FDI projects that show signs of completion (not actual completions); for example,ground breaking in the case of new plants. See Appendix A for a detailed discussion of this data set.

18 The time it takes to complete projects vary from industry to industry; and, it also varies accordingto the type of transaction. For example, according to JAMA (Japan Automobile ManufacturersAssociation), Toyota Motor Manufacturing, USA, Inc., was founded on January 1986 and theproduction start-up date for their Georgetown, Kentucky, plant was July 1988. This investment isrecorded in 1986 by ITA as new plant investment. New United Motor Manufacturing, Inc. (NUMMI),a joint venture between Toyota and General Motors was founded on February 1984 and the productionstart-up date for the Chevrolet Nova was December 1984. This investment is recorded in 1984 by theITA as a joint venture.


9FDI 5 a 1 g 1 b Q 1 , (4)it 4i 4t 4 it 4itwhere the variables on the left hand side are assumed to be endogenous variablesin the system. The definitions of , a and g are similar to those in Eq. (1).

Matrix S in Eq. (2) includes PCM, GROWTH and average plant scale(SCALE). A positive relationship between margins and concentration is possible.Industries with high margins have the most incentive to maintain these margins bykeeping potential competitors out; hence, one would more likely observe theseindustries to be more concentrated than otherwise. If growth in demand facilitatesentry into an industry, then CR may decline with industry growth. In the empiricalimplementation, SCALE is defined as value added per plant; this is the bestavailable proxy for plant level scale-related entry barrier. The effect of SCALE onconcentration is ambiguous. Larger plant level entry barriers (SCALE) maysustain high concentration levels. However, a negative relationship is equallyprobable. Industries with low SCALE values appear concentrated perhaps becauseof the importance of firm level barriers to entry (e.g., advertising) relative to plantlevel barriers. It then appears that industries with relatively small plants (SCALE)are highly concentrated. However, as Schmalensee (1989) points out, in theconcentration equation, the more appropriate proxy may be firm level entrybarriers rather than plant level entry barriers. This suggests that in addition toSCALE, one should perhaps include a variable that controls for firm-level entrybarriers. The unavailability of firm-level data, such as advertising, call for somecaution in interpreting the results.

Matrix Z in Eq. (3) contains PCM, FDI, LFDI, L2FDI, the number ofanti-dumping petitions in year t (AD) and t21 (LAGAD). Industries with highermargins are expected to attract more competition from abroad. Current and laggedFDI are included since trade and FDI can be either substitutes or complements.

19AD and LAGAD are included to control for the effects of trade restraints. Theeffect of AD petitions on imports is ambiguous. Prusa (1997) finds that USimports from countries named in the petitions decline, but imports from unnamedcountries rise. The sign of the coefficient depends on the magnitudes of these twoeffects.

Finally, in Eq. (4), matrix Q contains PCM, payroll cost per employee (SKILL),KO and SCALE. Industries with higher margins are expected to attract more FDI.The other three factors represent barriers to entry for any firm. Multinationalenterprises (MNEs) usually have firm specific advantages (e.g., advance tech-nologies) which require a more skilled workforce. A positive coefficient is

19 Ideally, effective protection rates are desired. But these measures are available only for a limitedset of four-digit SIC industries (See Hufbauer et al., 1986). The passage of the Trade Agreement Act of1979 resulted in significant changes in anti-dumping laws. For the AD data to be consistent, datastarting in 1980 are used (See Staiger and Wolak, 1994, for a review of AD laws in the US).


expected from SKILL, a proxy for what Markusen (1995) calls knowledge capital.MNEs are usually found in technically complex industries which normally wouldrequire a relatively large amount of physical capital investment. The investmentoutput ratio (KO) is used as a proxy for industry physical capital investmentrequirements. Industries which are capital intensive would attract more FDI.SCALE is included to control for industry differences in plant-level fixed costrequirements. A positive coefficient is expected. As a Commerce Departmentstudy (US, 1993) points out, FDI is more likely in industries that require largeplant-level fixed costs because having a large operation allows them to spreadcosts (such as learning the host countrys language and business practices) over alarger output volume.

The results for both the single equation fixed effects and the two-stageleast-squares fixed effects are presented and discussed in the next section.

4. Analyses of results

Table 3 contains the single equation two-way fixed effects regressions for the20

margins equation. Model A includes all types of FDI occurrences; model B usesnew plant FDI counts only; and model C includes other types of investments. Bothcurrent and lagged FDI are included as regressors.

FDI and its lagged values are insignificant in Model B; it appears that greenfield21FDI has no statistically significant effect on margins. It was argued that under the

assumption of no exit by incumbents, new plant FDI should lead to lower industrymargins because this type of FDI increase domestic capacity and production. Ifsome domestic establishments (or plants) close given foreign entry, supply will notincrease and margins may not be affected. There is some evidence of plantclosures. Table 4 lists changes in the number of establishments between 1982 and1987 for industries which received large number of FDI occurrences. The tablealso contains the average margins in 197881 and 198891 for these industries.No clear pattern emerges as to the relationship between FDI occurrences, thechange in the number of establishments and margins. Margins are not significantlyaffected by greenfield FDI possibly because the expected fall in margins due toincreases in capacity is countered by a rise in margins in those industries thatexperienced net declines in the number of plants.

The coefficients for FDI and LFDI are both positive and statistically significant

20 Greenes Limdep Version 7 is used in all regressions. The two-way fixed effects model is judgedappropriate versus OLS using likelihood ratio tests.

21 Other lagged structures (one period lag only and up to three period lags) for FDI were tried and theresults are qualitatively the same as those in Table 3. The results are also qualitatively similar if allinteraction terms are excluded from the regression. These results are available upon request.


Table 3aFixed effects regression results: pricecost margins

Model A Model B Model C

CR 1.5577* 1.4531* 1.5726*(0.1398) (0.1398) (0.1400)

OPEN 0.5432* 0.5216* 0.5471*(0.0617) (0.0620) (0.0616)

IMP CR 20.0201* 20.0196* 20.0202*] (0.0024) (0.0025) (0.0024)

GROW 0.1206 0.1945 0.1277(0.2412) (0.2430) (0.2408)

KO 10.4410* 12.1690* 10.7280*(1.8577) (1.8783) (1.8470)

GRO CR 0.0183* 0.0170* 0.0182*] (0.0047) (0.0048) (0.0047)

KO CR 20.1331* 20.1612* 20.1387*] (0.0376) (0.0380) (0.0374)

FDI 5.3663** 2.4529 7.1444*(2.4225) (6.010) (2.7790)

LFDI 6.5460* 7.8482 7.5342*(2.5312) (6.099) (2.8969)

L2FDI 3.5603 5.4185 4.8737(2.4524) (5.0576) (2.9870)

FDICR 20.1175** 20.0917 20.1470**(0.0543) (0.1238) (0.0644)

LFDICR 20.1160** 20.1237 20.1396**(0.0586) (0.1285) (0.0678)

L2FDICR 20.0280 20.0706 20.0414(0.0554) (0.1126) (0.0690)

2Adjusted R 0.363 0.353 0.365F-stat 55.66 53.26 56.10No. of observations 3833 3833 3833

a Notes: The coefficients and their standard errors above are scaled by 1000. The numbers inparentheses are standard errors. *, ** and *** denote significant at 1, 5 and 10%, respectively. InModel A, FDI, LFDI, L2FDI include all types of FDI occurrences; in Model B, only new plant FDIoccurrences are counted. The FDI data used in estimating Model C include the following types:merger /acquisition, equity increase, joint venture, plant expansion, and other FDI types. Estimatedcoefficients for the industry / time dummy variables are excluded in the table. Likelihood ratio testsindicate that the two-way fixed effects model is appropriate.

and their interactions with concentration are significantly negative in Models Aand C (see Table 3). The significant interaction terms imply that the effect of FDIdepends on the level of concentration. Let us focus on the results for non-greenfield FDI. In model C, at concentration levels below 48.60 and 53.97%, for

22FDI and LFDI, respectively, the values of the partial derivatives are positive.

22 For model C, PCM/FDI50.007144420.0001473CR; and PCM/LFDI50.007534220.00013963CR. Setting each equal to zero result in these critical concentration rates.


Table 4aAverage industry margins, plant closures and FDI occurrences

bSIC Average PCM Number of establishments FDI occurrences (8191)7881 8891 Change (198287) Change (%) NEWFDI OTHFDI

3714 0.22478 0.21039 387 15.99 82 61c3674 0.37292 0.44430 87 11.36 27 71c2821 0.25921 0.31332 40 9.09 23 60

3079 0.27813 0.28666 383 3.29 21 53c3711 0.12748 0.23177 58 16.34 16 24d3651 0.24221 0.15906 2100 220.92 15 37c3861 0.48060 0.54816 28 21.01 14 14c2869 0.31481 0.38435 211 21.60 12 27c3662 0.31653 0.34176 77 3.22 11 48c2819 0.36566 0.42546 17 2.64 11 48c3841 0.39831 0.44501 277 32.25 10 36c2813 0.47338 0.52154 31 5.51 10 13d2833 0.44285 0.39871 23 21.32 10 14

2911 0.12083 0.14372 290 220.79 10 31d3541 0.33613 0.26128 2525 255.73 10 22

3679 0.27619 0.28798 389 10.32 9 70c2834 0.52464 0.61223 49 7.17 9 44

3573 0.33337 0.32165 113 6.50 9 923585 0.27902 0.27058 27 3.12 9 103621 0.30401 0.30210 210 22.12 9 10

a Authors calculations. Source of the original data: ITA, Foreign Direct Investment in the UnitedStates, various years. Feenstras US Exports and Imports Database; Bartelman Becker-Grays IndustryProductivity Database; 1987 and 1992 Census of Manufactures Subject Series-Concentration Ratios inManufacturing.

b Establishments by the Census definition is a single physical location where business is conductedor where services or industrial operations are performed.

c A significant increase at the 5% level.d A significant decrease at the 5% level.

Margins rise with FDI at relatively low levels of concentration. For an industrywith a concentration rate of 39.55% (the average rate), every one non-greenfieldFDI (LFDI) lead to an increase in current (lagged) period margins of about 0.0013(0.0020), statistically controlling for everything else. These changes translate toabout 0.45% [(0.0013/0.2890)3100] and 0.69% [(0.0020/0.2890)3100] of theaverage industry margins.

As mentioned, some of the regressors used in Eq. (1) may themselves beendogenous. If so, these panel estimates may be biased if simultaneity amongvariables is not accounted for. A series of exogeneity tests using Spencer andBerks (1981) methodology is performed since it is generally the case that thecoefficient estimates in simultaneous systems are highly dependent on assumptionsmade about variable endogeneity. This methodology is a single equation testwhere the null hypothesis is that a variable x* is exogeneous. It requires twoseparate regressions: first, x* is treated as an endogenous variable in the system


(call the coefficient estimates b ); then, x* is treated as exogenous (call the coefficient estimates b ). If the calculated Wald statistic, (b 2 b )9hVar[b ] 2

21 Var[b ]j (b 2 b ), is significant, then the null of exogeneity is rejected. The

exogeneity tests indicate that concentration rate and openness are endogenous in23the margins equation; FDI is found to be endogenous only in model B. For

completeness, the endogeneity of PCM in Eqs. (2)(4) need to be confirmed. Thenull of exogeneity is rejected in all models; there is evidence that PCM isendogenous in Eqs. (2)(4).

The regression results for Eq. (1) under the above assumptions appear in Table244. The presence of interaction terms involving endogenous and exogenous

variables complicate its estimation. Kelejian (1971) derived a variant of two-stageleast-squares (TSLS) in systems that include nonlinear endogenous variables asregressors. In general, these interaction terms are considered endogenous and willrequire instruments; however, the number of equations in the system will notincrease (see Greene, 1997, p. 733). The rank condition for identification is three,which Eq. (1) satisfies. The instruments used are the linear, square and crossproducts of all exogenous variables in the system (stage one). These instruments

25are then used in the second stage estimation of Eq. (1). The correlations betweenthe endogenous variables and their corresponding instruments range from 0.50 to0.96, indicating the appropriateness of the instruments.

When endogeneity is taken into account, there is some evidence that both typesof FDI affect margins (see Table 5). When significant, the coefficient estimates ofFDI and its lagged values are positive; whereas the coefficient estimates of theinteractions between FDI and CR for the most part are negative. The marginseffect of greenfield FDI seems to be more immediate than that of non-greenfieldFDI (that is, FDI is significant in model B, whereas L2FDI is significant in modelC); the potential differential impact of FDI due to industry concentration suggestsa similar story (that is, FDICR and LFDICR are significant in model B, whereasLFDICR is significant in model C). These results seem counter-intuitive at first

23 A detailed explanation of the exogeneity tests is available from the author. As suggested by ananonymous referee, a possible reason why FDI is endogenous only in model B is because new plantFDI alters the domestic market structure, whereas acquisitions (a component of non-greenfield FDI)only involves the transfer of ownership of existing facilities.

24 Limdeps two-stage least-squares for fixed effects model is used in the estimation (see Greene,1995, p. 302). To focus discussions, only the estimates of Eq. (1) will be presented below. The interestin Eqs. (2)(4) are not the coefficient estimates, but the fitted values of regressing each of theirdependent variables against all exogenous variables in the system. These fitted values are then used asinstruments in the second stage estimation of Eq. (1). Results for Eqs. (2)(4) are available from theauthor.

25 In models with non-linear endogenous variables, Kelejian (1971) showed that a polynomial of acertain degree exists such that the instruments from the first stage are linearly independent of the otherexogenous regressors found in the equation of interest.


Table 5aTwo-stage fixed effects panel regression results: pricecost margins

Model A Model B Model CbCR 0.8288** 0.7868*** 0.8482**

(0.4482) (0.4708) (0.4479)bOPEN 1.3711* 1.2107** 1.5066*

(0.4615) (0.5221) (0.4780)bIMP CR 20.0708* 20.0609* 20.0752*

] (0.0115) (0.0119) (0.0113)GROW 21.2317** 20.5567 21.329**

(0.5885) (0.6143) (0.5834)KO 28.0465 210.905 26.2788

(6.1357) (6.7157) (5.8491)bGRO CR 0.0452* 0.0320** 0.0472*

] (0.0121) (0.0127) (0.0120)bKO CR 0.2742** 0.3388** 0.2374***

] (0.1327) (0.1448) (0.1264)cFDI 0.0137 79.724* 0.0377

(0.0254) (30.668) (0.0310)LFDI 9.5244** 216.062 7.6454

(4.8414) (14.170) (5.5548)L2FDI 12.1800* 210.143 13.726*

(4.5337) (11.554) (5.3264)bFDICR 20.1706 22.0386* 20.1250

(0.1197) (0.6328) (0.1394)bLFDICR 20.2495** 0.6506** 20.2719***

(0.1218) (0.3315) (0.1479)bL2FDICR 0.0330 0.4363 0.0332

(0.0492) (0.2765) (0.0672)2Adjusted R 0.2307 0.2207 0.2245

No. of observations 3833 3833 3833a Notes: The coefficients and their standard errors above are scaled by 1000. The numbers in

parentheses are standard errors. *, ** and *** denote significant at 1, 5 and 10%, respectively. Seenotes for Table 1 for definitions of FDI data.

b Instrumental variables.c Instrumental variable for Model B only. Estimated coefficients for the industry / time dummy

variables are excluded in the table. Likelihood ratio tests indicate that the two-way fixed effects isappropriate.

considering that the lag from project initiation to completion is longer forgreenfield FDI.

Consider the results for greenfield FDI. The coefficient for FDI is significantlypositive; and the interaction term between current FDI and CR is significantlynegative. The partial derivative of the margins equation with respect to currentFDI, PCM/FDI50.07972420.00203863CR, indicates that margins rise withFDI in industries with relatively low levels of concentration (at concentrationlevels below 38.91%). A possible explanation for this result is as follows.Greenfield FDI involves new facilities, hence foreign entrants would adopt the best


available technology. This makes them more efficient and their efficiency mayincrease industry margins. However, beyond some critical concentration level,the competitive effect of FDI predominates (margins decline). This result isconsistent with the findings of the imports-as-market discipline literature where thedisciplining effect of imports is conditional upon the existence of potentiallynon-competitive conditions, such as high concentration (See Caves, 1985). Toget a sense of the economic significance of this effect, the partial derivative isevaluated at the average concentration rate (39.55%). For an industry with thisconcentration level, the result shows that for every one greenfield FDI, marginsfall by about 0.0009; this translates to about 0.31% [(0.0009/0.2890)3100] for anindustry with average margins. With regard to lagged FDI, only its interaction withCR is significant and the coefficient happens to be positive. Margins rise withindustry concentration. Perhaps this is indicative of foreign firms deployingfirm-specific assets upon entry and these knowledge spill over domestically with alag. The result further implies that potential technological spillovers may be largerin more concentrated industries. This is not surprising considering that theseindustries tend to invest more in research and development (R&D) and may have

26more innovations that can spill over into the host market. Given the shortness ofthe period considered, the spillover part of the story should be viewed with caution

27and deserves more study; especially since we do not yet completely understandthe real motivations behind firm entry into foreign markets.

To get a better appreciation of the results, consider the breakdown of the overallindustry margins into the margins of purely domestic and purely foreign plants.Ideally, a comparison between the margins of domestic and foreign plants must bemade in the pre-FDI surge (197881) period. If domestic margins are significantlylarger (suggestive of superior domestic technology), then succeeding foreign entrymay be due to sourcing of US technology. If the pre-FDI surge margins of foreignplants are significantly higher (suggestive of superior foreign technology), thensucceeding foreign entry may be due to deployment of foreign firm-specificadvantages. Unfortunately, such direct comparisons are not possible since data for

28purely foreign plants are not available for years earlier than 1988. A second-bestapproach is the following exercise. Consider industries that experienced anincrease in the overall margins in the post-FDI surge (198891) period. This

26 This is a controversial point. Several studies have empirically confirmed a small positive impact ofconcentration on R&D; however, a negative impact has also been found (see Kamien and Schwartz,1982, for a review). That is, while the benefits to innovation may be large in concentrated industries, itis also possible that firms in concentrated industries may not feel the pressure to innovate.

27 Although, Mansfield (1985) estimates that around 70% of technical innovations diffuse in theindustry within a year.

28 See Footnote 3. Information are available only for 201 four-digit SIC industries, hence succeedingcomparisons and discussions are based only on these industries.


Table 6aFDI in these industries are potentially motivated by deployment of foreign firm-specific advantages

SIC Average PCM Number of establishments FDI occurrences(8191)

7881 8891 8891 8891 Change ChangeOverall Overall Domestic Foreign (198287) (%) New Other

2816 0.29341 0.48585 0.36087 0.55915 214 213.21 5 82821 0.25921 0.31332 0.30048 0.35041 40 9.09 23 602824 0.26489 0.38578 0.32451 0.42013 2 2.86 2 132844 0.58244 0.62058 0.61563 0.63956 55 8.61 5 232869 0.31481 0.38435 0.36049 0.41018 211 21.60 12 27

a Source: Authors calculations. Source of the original data: ITA, Foreign Direct Investment in theUnited States, various years. Feenstras US Exports and Imports Database; Bartelman Becker-GraysIndustry Productivity Database; 1987 and 1992 Census of Manufactures Subject Series-ConcentrationRatios in Manufacturing.

coupled with a significantly higher margins for foreign plants in the post-FDIsurge period imply that FDI may be motivated by firm-specific advantages. Table

296 contains a partial list of industries where these conditions are satisfied. Logicsuggests that there should be enough foreign presence for FDI to affect overallindustry margins. The list could be shortened to include only those industries thatreceived substantial new plant FDI for the period. These industries are plasticsmaterials and resins (SIC2821) and industrial organic chemicals, n.e.c. (SIC2869).

Interestingly in model C, current and one-period lagged FDI are not statisticallysignificant (see Table 5). Logic suggests that if foreign firm non-greenfield FDI ismotivated by sourcing, the (negative) effect on margins would be immediate;margins are expected to decline because US assets are taken over by lessknowledgeable foreign firms. One possible explanation why the coefficients forboth current and one-period lagged FDI are insignificant is that sourcing may nothave been the motive behind FDI; otherwise a decline in margins should havebeen observed. The positive effect on margins appears with a two-period lagbecause foreign acquirees need time to learn the technology that they haveacquired and/or integrate their human and technological resources. In some cases,there may be a clash of corporate cultures that needs ironing out. All these needtime to resolve. The only interaction term between FDI and CR that is statisticallysignificant is the one for one-period lagged FDI. The negative coefficient impliesthat the competitive effect of FDI appears with a lag and that the effect is larger inmore concentrated industries. The explanations offered should again be viewed as

29 Due to space limitations the table includes those four-digit SIC industries that belong to food,chemicals, machinery, electrical and transport equipment. The complete list of industries is availablefrom the author upon request.


tentative because it assumes that the motivation behind non-greenfield FDI is nottechnology sourcing; since there is some evidence that some types of FDI aremotivated by sourcing, more research is needed in this area.

Turning to the coefficients of other variables, in the three models estimated,controlling for an industrys openness to foreign competition, margins are larger inconcentrated industries; this is consistent with the findings of most of the studiesreviewed in Section 3. The OPEN variable is positive and statistically significant.As OPEN increases, that is, as it moves towards one, margins rise. As mentionedin the previous section, as OPEN fi 1, exports fi 0 and presumably more is sold inthe local market. Depending on what happens to the level of imports, the effect onmargins is theoretically ambiguous. If imports decline at a faster rate than exportsthen margins can rise, as indicated by the result. The interaction term IMP CR is

]significantly negative. This implies that imports disciplining impact is larger inmore concentrated industries. In model A, for an industry with an averageconcentration rate, the marginal effect of a one percentage point increase in import

30share is about a 0.0008 drop in margins, controlling for other factors. This resultis consistent with the findings in the imports-as-market discipline literature.Finally, the interaction between the investmentoutput ratio and the concentrationrate (KO CR) has the expected positive sign and is statistically significant in all

]models. Recall that the investmentoutput ratio is used as a proxy for barriers toentry. The result implies that keeping capital intensity constant, margins rise withindustry concentration. This contrasts with the estimates found when the endo-geneity of the concentration rate, openness and FDI were ignored.

5. Conclusion

The results using both single equation fixed effects and two-stage least-squaresfixed effects for panels on the margins equation are consistent with earlier findingsthat did not include FDI. For example, imports have a larger competitive effect inprecisely those industries that need disciplining (those with high concentration).The paper offers a much needed extension to the imports-as-market disciplinehypothesis by taking account of FDI using US data. The results from the two-stageleast-squares for a fixed effects panel indicate that the effect of FDI depends on theindustrys concentration rate. It was suggested that the observed margins effectmay be indicative of the motivations behind FDI. One of the interesting pattern inthe data is that the greenfield and non-greenfield FDI mix of an industry is a

30 Take the derivative of the margins equation with respect to IMPSH, PCM/IMPSH520.00002013CR; at the average concentration rate, this is 0.000794. Katics and Petersen (1994) find acomparable figure for this variable; from their results, a one percentage point increase in imports isassociated with a decline in margins of 0.00158.


function of the industrys concentration rate, consistent with our findings that themargins effect of FDI depends on concentration. This warrants further inves-tigation. Another possible extension to the current paper is to test whether theR&D activities of US firms in the 1980s is in response to the surge in FDI into theUS during the same period.

Acknowledgements

I would like to thank Bruce Blonigen, Ira Gang, Tom Prusa, Charles Romeo,Mark Strazicich, participants at the UCF Department of Economics brown bagseminar and the 1996 Southeastern International Trade Conference for usefuldiscussions, comments and suggestions. I am also grateful to Bruce for sharing theanti-dumping data and to an anonymous referee whose comments and suggestionshave greatly improved this paper. All remaining errors are mine.

Appendix A. Data Sources

Pricecost margins, capitaloutput ratio, skill level and plant scale measures arecalculated using data from Bartelsman, E., Becker, R. and Gray, W., 1996, NBERProductivity Database, NBER Technical Working Paper No. 205. This data setuses the 1972 SIC definitions. Pricecost margins are calculated using DHPs(1986) definition: PCM5(value of sales1change in inventories2payroll2cost ofmaterials) /(value of sales1change in inventories). Using Census data, this isequivalent to (value added2payroll) /(value added1cost of materials). Capitaloutput ratio is new capital expenditures divided by industry shipments; skill levelis payroll cost per employee (in thousand dollars) and plant-scale is value addedper establishment (in million dollars).

The US affiliates and US owned businesses margins are calculated using datafrom the US Department of Commerce, Bureau of Economic Analysis, variousyears, Foreign Direct Investment in the United States: Establishment Data forManufacturing (GPO, Washington, DC).

Four-firm concentration measures are from the Census of Manufactures SubjectSeries- Concentration Ratios in Manufacturing. CR and the number of establish-ments are available only in Census years. CR series for years other than Censusyears are derived using simple interpolation techniques. These also use 1972 SICdefinitions.

Import and export values at the four-digit SIC (1972 SIC definition) level aretaken from Feenstra, R., 1996, US Imports, 1972 1994: Data and Concordances,NBER Working Paper 5515 and Feenstra, R., 1997, US Exports, 1972 1994: WithState Exports and Other US Data, NBER Working Paper 5990.

Anti-dumping petition counts are from the International Trade Administration.


The antidumping data I use start in 1980 because of significant changes in AD lawwith the passage of the Trade Agreement Act of 1979.

The FDI data are taken from US Department of Commerce, International TradeAdministration, various years, Foreign Direct Investment in the United States(GPO, Washington, DC). This publication lists all completed FDI transactions forthe year identified from public sources. The ITA identifies the type of investmentsmade: merger /acquisition, equity increase, joint venture, plant expansion, newplant or others. It further identifies the four-digit SIC code of the investment, thenationality of the investor, the location of the investment and in some cases, thetotal amount of the investment.

References

Blomstrom, M. and Kokko, A., 1996, Multinational corporations and spillovers, CEPR DiscussionPaper No. 1365.

Caves, R., 1996. In: Multinational enterprise and economic analysis, 2nd ed.. Cambridge UniversityPress, New York.

Caves, R., 1985. International trade and industrial organization: problems, solved and unsolved.European Economic Review 28, 377395.

De Ghellinck, E., Geroski, P., Jacquemin, A., 1988. Inter-industry variations in the effect of trade onindustry performance. Journal of Industrial Economics 37, 119.

Domowitz, I., Hubbard, G., Petersen, B., 1986. Business cycles and the relationship betweenconcentration and price-cost margins. Rand Journal of Economics 17, 117.

Dunning, J., 1988. The eclectic paradigm of international production: a restatement and some possibleextensions. Journal of Business Studies 19, 131.

Geroski, P., 1982. Simultaneous equations models of the structure-performance paradigm. EuropeanEconomic Review 19, 145158.

Geroski, P., Jacquemin, A., 1981. Imports as a competitive discipline. Recherches Economique deLouvain 47, 197208.

Graham, E., Krugman, P., 1995. In: Foreign Direct Investments. Institute for International Economics,Washington.

Greene, W., 1997. In: Econometric Analysis, 3rd ed. Prentice Hall, New Jersey.Greene, W., 1995, LIMDEP Version 7 Users Manual, Econometric Software, Inc., New York.Horstmann, I., Markusen, J., 1992. Endogenous market structures in international trade (Natura facit

saltum). Journal of International Economics 32, 109129.Hufbauer, G., Berliner, D., Elliot, K., 1986. In: Trade protection in the United States. Institute for

International Economics, Washington.Jacquemin, A., Sapir, A., 1991. Competition and imports in the European market. In: Winter, A.,

Venables, A. (Eds.), European Integration: Trade and Industry. Cambridge University Press,Cambridge.

Kamien, M., Schwartz, N., 1982. In: Market Structure and Innovation. Cambridge University Press,Cambridge.

Katics, M., Petersen, B., 1994. The effect of rising import competition on market power: a panel datastudy of US manufacturing. Journal of Industrial Economics 42, 277286.

Kelejian, H., 1971. Two-stage least squares and econometric systems linear in parameters butnon-linear in the endogenous variables. Journal of the American Statistical Association 66,373374.


Kogut, B., Chang, S.J., 1991. Technological capabilities and Japanese FDI in the US. Review ofEconomics and Statistics 73, 401413.

Levinsohn, J., 1993. Testing the imports-as-market discipline hypothesis. Journal of InternationalEconomics 35, 122.

Mansfield, E., 1985. How rapidly does new industrial technology leak-out? Journal of IndustrialEconomics 34, 217223.

Markusen, J., 1995. The boundaries of multinational enterprises and the theory of international trade.Journal of Economic Perspectives 9, 169189.

Martin, S., 1979. Advertising, concentration and profitability: the simultaneity problem. Bell Journal ofEconomics 10, 639647.

Neven, D., Siotis, G., 1996. Technology sourcing and FDI in the EC: an Empirical Evaluation.International Journal of Industrial Organization 14, 543560.

Orr, J., 1991. The trade balance effects of foreign direct investment in US manufacturing. FederalReserve Bank of New York Quarterly Review 16, 6376.

Prusa, T., 1997. The trade effects of US antidumping actions. In: Feenstra, R. (Ed.), The Effects of USTrade Protection and Promotion Policies. The University of Chicago Press, Chicago.

Schmalensee, R., 1989. Inter-industry studies of structure and economic performance. In: Schmalensee,R., Willig, R. (Eds.), Handbook of Industrial Organization, vol. 1. North Holland, Amsterdam.

Spencer, D., Berk, K., 1981. A limited information specification test. Econometrica 49, 10791083.Staiger, R. and Wolak, F., 1994, Measuring industry specfic protection: anti-dumping in the United

States, Brookings Papers on Microeconomics, 51-118.Stalhammar, N., 1991. Domestic market power and foreign trade. International Journal of IndustrialOrganization 9, 407424.

UNCTAD, 1994, World Investment Report, 1994, United Nations Geneva.US Department of Commerce, Economics and Statistics Administration, 1993, Foreign direct

investments in the United States: an update, GPO, Washington, DC.Wang, J., Blomstrom, M., 1992. Foreign investment and technology transfer. European Economic

Review 36, 137155.Young, K., Steigerwald, C., 1990. Is foreign investment in the US transferring US technology abroad?

Business Economics 25, 2830.

catherine co, 2001

Documents

us margins effect of

us industries

competitive effect of

types of fdi

nongreenfield fdi

significant effect

existing us firms

thepositive effect