Preliminary Draft

Horizontal versus Vertical Multinationals ∗

Kazuhiko Yokota†

May, 2005

Abstract

A method to break down foreign direct investment (FDI) samples into eitherhorizontal or vertical FDI is proposed. Using the implications of multinationalenterprize (MNE) theories, samples of U.S. MNE activities are separated intoeither horizontal or vertical FDI. The results show that a large share of the U.S.FDIs is horizontal and that the types of FDIs vary with industries. The resultsof U.S. FDI strategies indicate that the vertical FDI tend to export back to theU.S. market more than the horizontal FDI. Skill difference plays an importantrole to determine the firm’s strategy.

Keywords: Horizontal Multinational; Vertical Multinationals;

JEL Classification: F23

∗I am grateful to Jim Markusen and Keith Maskus for their helpful comments and suggestions forthe earlier draft. All remaining errors are, of course, mine.

†The International Centre for the Study of East Asian Development. 11-4 Otemachi, Kokurakita,Kitakyushu, Fukuoka, 803-0814, Japan. Phone: 81-93-583-6202, E-mail: yokota@icsead.or.jp

1

1 Introduction

A broad definition of horizontal multinational is that it maintain the whole production

process in both home and host countries with headquarters in the home country. On the

other hand, vertical multinational is a firm that divide the production process into more

than two parts and maintain a single plant in host country keeping headquarter in home

country. Furthermore, horizontal multinationals are more likely to be substituted for

international trade while vertical multinationals are complement to trade. Horizontal

multinationals generally have more job creation effects on host economy than vertical

multinationals.

The first trade-theoretic model in the line of capital flow in Heckscher-Ohlin model

was developed by Mundell (1957). He concludes that trade in factors is a substitute

for trade in goods. Substitutability or complementarity of foreign direct investment

(FDI) for international trade in goods was analyzed as a location choice problem of

MNEs in 1984. Trade-theoretic multinational enterprize (MNE) theories have identified

differences in the roles of horizontal and vertical MNEs. The first formal horizontal

model originates from Markusen (1984) that shows the roles of firm specific fixed cost

and trade costs in the one factor, labor, framework. More recently Markusen and

Venables (1998) which is now a standard model of trade-theoretic horizontal model in

general equilibrium with two factors of production, skilled and unskilled labor force,

show that the similarity in size and endowments in two countries likely to lead more

foreign direct investment and MNEs and national firms arise endogenously.

On the other hand, the trade-theoretic vertical-MNE model originates from Help-

man (1984). He shows in a two-factor framework with monopolistic competition that

without trade costs, MNE builds plants in different countries according to the compar-

ative advantages. In other words, the incentive to operate headquarters in one country

and production in a other arises from factor price differences across the countries.

2

Combined the horizontal motives together with he vertical motives, Markusen

(1997, 2002) models ”knowledge capital” model in which horizontal and vertical MNEs

arise endogenously according to the difference in size and relative factor endowments

in two countries.

In empirical studies, however, little attention have been paid for the different roles

of the horizontal and the vertical MNEs. A few exceptions, for example, are Carr,

Markusen and Maskus (2001), Aizenman and Marion (2004), and Hanson Mataloni,

and Slaughter (2001). Carr et al. (2001) estimate the ”knowledge capital model”

and confirm the theory. Aizenman and Miron (2004) focus on the different impacts

of supply uncertainty on two types of foreign direct investment (FDI) activities, i.e.,

horizontal and vertical. Hanson et al. (2001) show that both horizontal and vertical

FDI are important and it is difficult to distinguish these two motives from data clearly.

However, they categorize three important activities by foreign affiliates; importing

intermediates from their U.S. parents for further processing (vertical FDI); selling

locally within the host countries (horizontal FDI); exporting from the host countries

(either).1

The purpose of this paper is to break the U.S. MNEs into two types of motives, i.e.,

market oriented (horizontal) and comparative advantage (vertical) motives. Making

use of implications of theories, including Helpman (1984), Markusen (1984), Markusen

and Venables (1998), and Markusen (2002) as well as empirical findings by Hanson et

al. (2001), the sample of U.S. FDI is separated into two groups of FDI and estimate

the determinants of FDI strategies and the impacts on the host economies.

This paper is organized as follows. Section 2 describes the methodology of separat-

ing sample into the horizontal and the vertical FDIs. Section 3 shows the estimation

results. Section 4 examines the determinants of U.S. MNE strategies. Section 5 esti-

1See Feenstra (2004) for the survey of empirical studies on FDI.

3

mates the spillover effects of U.S. FDI on host economies. Section 6 concludes.

2 Separating Horizontal and Vertical FDIs

There are two steps in separating samples into two types of FDI activities, i.e., hori-

zontal and vertical. In the first step, making use of the broad definition of FDI and

empirical findings, sample data on U.S. FDI will be sorted in order of ex post character-

istics. Ex post characteristics include the empirical findings that the horizontal MNEs

tend to sell their products mainly in local (host) markets while the vertical MNEs tend

to import inputs from their parent companies in home for further processing. In the

second stage of separating samples, the determinants of real total sales by U.S. FDI are

estimated based on the implications of trade-theoretic models. Some important factors

of determinants play a crucial or an opposite role on FDI activities. This second stage

estimation is also the test of horizontal and vertical FDI theories.

Using the broad definitions of horizontal and vertical multinationals and Hanson et

al. (2001) findings, a simple indicator to distinguish two groups of FDIs is developed.

Since no firm level data on multinationals are available, it is impossible to distinguish

between horizontal and vertical MNEs. Hence the results in this paper should be

interpreted at the industry level. Hence, to be consistent with the theoretical literature,

I will hereafter use the term horizontal (vertical) FDI to refer to the industry which

has horizontal (vertical) MNE characteristics.

As models by Helpman (1984) and Markusen (1984) and the empirical study by

Hanson et al. (2001) indicate that the horizontal multinationals are basically trade

substitute and their main concern is to sell the products in the host economy. Hence

it is plausible to assume that horizontal multinationals sell their products in the host

country’s market with high proportions. In this case, the ratio of domestic sales to the

total affiliate’s sales is high. On the other hand, vertical strategy of a firm is likely

4

to more associated with the export of intermediate goods from home (in many cases,

from parent company) to host country. Consequently, the ratio of exports from home

to host over total affiliate’s sales becomes high. Let D, M , and S be domestic sales,

affiliate’s imports from home country, and affiliate’s total sales, respectively. As D/S

increases, a firm becomes more horizontal while as M/S increases, a firm becomes more

vertical. Combining these two indices into one, D/M indicates the ex post measure

of horizontal nature of a firm. As D/M increases, a firm can be said to become more

horizontal and vice versa.

The first step, thus, is to sort the FDI data by the measure, D/M in descending

order. Samples with relatively higher D/M may contain more horizontal FDI na-

ture and samples with relatively lower D/M are expected to show the vertical FDI

characteristics.

The second step is to estimate the determinants of FDI using sorted data. Let

us discuss implications of the models first. Markusen and Venables (1998) have the

following testable propositions:

proposition 1: multinationals become more important relative to trade as countries

become more similar in size.

proposition 2: multinationals become more important as world income grows.

proposition 3: multinationals become more dominant as host countries become more

similar in relative endowments.

On the other hand, the model of vertical multinationals by Helpman (1984)2 hy-

2See also Feenstra (2004), chapter 11, for the comparison of theoretical models of FDI.

5

pothesizes that vertical FDI is higher when the factor endowments of the countries

differ more. Helpman’s model also predicts that neither differences in country size nor

the magnitude of the world income affects the FDI outflow. In other words, In Help-

man’s vertical multinational model does not indicate Proposition 1 and proposition

2, while it has the opposite prediction of proposition 3.

Estimated equation, thus, is:

SALEijt = αi + αj + αt + β1GDPSUMjt + β2|MKTDIFijt|+

β3|SKILLDIFjt|+ β4LENDRATEjt + β5DSPEAKjt +

β6TCOSTijt + β7DADJj + β8DISTANCEj + εijt (1)

SALE represents the affiliate’s real sales (both in the host market and exports to the

world). As Feenstra (2004) describes, the dependent variable should be affiliates’ sales

in host market for horizontal FDI while affiliates’ export sales for vertical FDI. The

first three terms in the right hand side represent the constant dummies, capturing

industry, country and year fixed effect respectively. Term GDPSUM represents the

world income and should have a positive coefficient if the sample is dominated by

horizontal FDI, according to Markusen and Venables (1998). However, as Helpman

(1984) predicts, if the sample is dominated by vertical FDI, the coefficient would be

zero, because the difference of development stages between two countries plays no

role for determining the magnitude of affiliate’s exports in his model. |MKTDIF |represents the similarity of market sizes between the U.S. and host country which is

defined as the ratio of host country’s market size to the U.S. market size and the

coefficient should be negative.3 |SKILLDIF | represents the difference in skilled labor

abundance between the U.S. and host country which is defined as the ratio in absolute

3Since |MKTDIF | is defined, in absolute term, as the ratio of host economy’s market size to theU.S. market size, the smaller the value means the similarity between the two markets. Market size inturn is defined as the total output minus exports plus imports by country and industry.

6

term of skilled labor abundance in host economy to the skilled labor abundance in the

U.S. Skilled labor abundance by country is defined as the number of skilled labor over

the total labor force. From proposition 3, the more similar the factor endowments, the

more horizontal FDI occurs. Hence, the expected sign is negative for the horizontal

FDI. However, the coefficient on |SKILLDIF | has two aspects. The vertical FDI

reacts the relative costs of production factors and is sensitive to the skilled labor

abundance in the host economy. For the vertical FDI, the more dissimilar in skill

endowment, the more vertical FDI tends to occur. The expected sign for the vertical

FDI is, thus, positive. |SKILLDIF | plays a key role to distinguish the horizontal

from the vertical FDI. To sum up, the sign conditions of estimated coefficients are,

β1 > 0, β2 < 0, β4 < 0, β5 < 0, β6 > 0, and the sign of β3 is negative for horizontal

FDI and positive for vertical FDI.

Last four variables are used to control the estimation. LENDRATE is a lending in-

terest rate by country which is a proxy for the FDI cost. The higher the LENDRATE,

the less FDI. Hence, the expected sign is negative both for the horizontal and the ver-

tical FDI. DSPEAK is a dummy variable with value 1 if the host country is a English

speaking country, otherwise zero. This is interpreted as another proxy for the FDI

cost and the expected sign is negative. TCOST is a proxy for trade cost defined as

the ratio of CIF value to FOB value of international trade by country and industry.

Thus, this variable directly represents freight and insurance costs. The expected sign

of this variable is positive for the horizontal FDI (substitute case). However, the sign

is ambiguous in the vertical FDI case, because it is generally recognized that the ver-

tical FDI is complement with the international trade. DADJ is the adjacent dummy

which takes value 1 if the host country is adjacent to the U.S. It is expected that if the

host country is the neighbor of the U.S., FDI flows more. Hence, the expected sign is

positive. Last dummy variable DISTANCE is a distance between the U.S. and the

7

host country. The expected sign is negative.

Sample data cover U.S. manufacturing sectors which is composed of six industries.4

Basic data come from the Bureau of Economic Analysis (BEA), Department of Com-

merce. Detailed descriptions on data are found in appendix. 5

To distinguish the horizontal from the vertical FDI, I estimate the equation (1)

for separated samples and check the sign condition of coefficient on |SKILLDIF |.The samples containing mainly the horizontal FDI would have a negative estimated

coefficient on |SKILLDIF |, while in the samples containing mainly the vertical FDI

a positive sign is expected. At the same time, the magnitude and the significance of

the coefficient on TCOST will be checked carefully.

It is expected that as the separating point of the sample moves from (A) to (G),

sign conditions, statistical significance,and the magnitude may vary. First, GDPSUM

is expected positive for horizontal type of multinationals but ambiguous for vertical

multinationals. Second, and more importantly, |SKILLDIF | is positive for horizontal

but turns to be negative for vertical multinationals. This simply reflects the fact that

market motive (similarity) is important for horizontal multinationals while comparative

advantage motive (dissimilarity) is important for vertical multinationals. Third, trade

cost, TCOST , is more important for the horizontal FDI than for the vertical FDI.

Hence, it is expected to observe that the magnitude of the coefficient is larger in the

horizontal FDI case than that in the vertical case.

3 Results of Estimations

Table 1 shows the estimation results of estimated equation (1) with various fixed effects,

such as industry, year and country. All equations, except for equations (4) and (6),

4Six industries are, food, chemicals, metals, machinery, electric equipment and transportation,5Since there are many missing values in BEA data, available data account for about 40% of total

U.S. manufacturing FDI activities.

8

satisfy the sign conditions and many of them are highly statistically significant. The

finding that the coefficients on GDPSUM are positive and those on |SKILLDIF |are negative for equations (1), (2), (3), and (5) cases suggests that the horizontal

FDI dominate the U.S. FDI activities. Comparing equations (1) and (3), and (2) and

(5), it is obvious that year dummy plays no role in explaining affiliates’ real sales.

Comparing equations (1) and (2), and (3) and (5), it is observed that industry dummy

greatly improves results. Estimations (4) and (6) which include country fixed effects

have opposite signs of coefficients on |SKILLDIF | and DSPEAK. Since they have

the higher adjusted R−squared and relatively low t−statistics, the existence of severe

multicolinearity is suspected. So I will adopt only industry dummy in this section.

Table 2 shows the estimation results when the sample is divided into two groups,

one has higher D/M and the other has lower D/M . Top 20%, for example, is the

sample with top 20% which is arranged in descending order along with D/M . It is

assumed that top part of the sample contains the horizontal FDI, while the bottom

part of the sample contains the vertical FDI. In other words, the horizontal (vertical)

motive becomes weak (strong) as horizontal index (D/M) decreases. For example, all

the estimated coefficients in estimation (A) (the first column covers top 20%, and the

second column covers bottom 80%) satisfy the expected signs and are highly statisti-

cally significant for both samples. Both the coefficients on |SKILLDIF | are negative

and highly significant, that means both samples contain a large amount of horizontal

FDI observations.

The coefficient on GDPSUM in the top sample (left column) remains positive

and statistically significant for all equations that is consistent with the horizontal

FDI, while that in the bottom sample (right column) is positive in from (A) to (D)

losing significance, and turns negative after (E) although that are not significant. This

indicates that the lower D/M samples may contain the vertical FDI observations.

9

Table 1 Deternimants of Real Sales of U.S. Affiliates with Various Dummies

(1) (2) (3) (4) (5) (6)

GDPSUM (+) 0.373 0.427 0.284 0.763 0.357 0.831

(6.70)** (8.18)** (4.40)** (6.71)** (5.84)** (8.43)**

|MKTDIF| ( - ) -0.379 -0.349 -0.473 -0.509 -0.422 -0.439

(7.82)** (7.77)** (8.34)** (9.98)** (7.97)** (9.78)**

|SKILLDIF| ( - ,+) -0.227 -0.280 -0.216 0.605 -0.265 0.088

(2.12)* (2.81)** (2.00)* (1.22) (2.65)** (0.21)

LENDRATE ( - ) -0.181 -0.194 -0.119 -0.094 -0.143 -0.065

(2.31)* (2.68)** (1.41) (0.87) (1.83) (0.70)

DSPEAK ( - ) -0.760 -0.782 -0.752 0.486 -0.774 0.147

(7.92)** (8.89)** (7.74)** (0.54) (8.65)** (0.19)

TCOST (+) 0.564 0.586 0.544 0.067 0.570 0.096

(5.40)** (6.12)** (5.19)** (0.63) (5.92)** (1.04)

DADJ (+) 1.077 0.776 1.134 0.823

(4.64)** (3.64)** (4.78)** (3.77)**

DISTANCE ( - ) -0.295 -0.406 -0.275 -1.149 -0.388 -0.869

(2.34)* (3.49)** (2.13)* (2.62)** (3.27)** (2.30)*

Obs 1108 1108 1108 1108 1108 1108

Adj R-sqd 0.50 0.58 0.51 0.64 0.59 0.74

F-value 139.1 119.6 47.7 56.2 54.4 76.1

Industry no yes no no yes yes

Year no no yes no yes no

Country no no no yes no yes

Dependent variable is real sales of U.S. affiliates.

Absolute value of t-statistics in parentheses

* significant at 5%; ** significant at 1%

Coefficients of constants and dummies are suppressed.

10

Table

2 E

stim

ation b

y S

ep

aation b

y S

epara

ted S

am

ple

s

(A

) (B

) (C

) (D

)

Top 2

0%

Bot 80%

Top 3

0%

Bot 70%

Top 4

0%

Bot 60%

Top 5

0%

Bot 50%

GD

PS

UM

(+)

0.8

22

0.3

12

0.4

49

0.2

34

0.3

29

0.0

99

0.3

48

0.0

55

(9.4

8)*

*(4

.90)*

*(5

.99)*

*(3

.39)*

*(4

.94)*

*(1

.25)

(5.4

2)*

*(0

.62)

|MK

TD

IF|

( -

)-0

.100

-0.3

81

-0.1

83

-0.4

23

-0.1

84

-0.5

2-0

.194

-0.5

32

(2.2

6)*

(7.0

9)*

*(3

.94)*

*(7

.29)*

*(4

.38)*

*(7

.74)*

*(4

.45)*

*(7

.28)*

*

|SK

ILLD

IF|

( -

,+)

-0.4

75

-0.3

13

-0.6

22

-0.3

01

-0.7

28

-0.1

89

-0.6

12

-0.0

58

(3.5

8)*

*(2

.71)*

*(5

.01)*

*(2

.44)*

(6.4

5)*

*(1

.39)

(5.6

8)*

*(0

.39)

LE

ND

RA

TE

( -

)-0

.009

-0.2

05

-0.1

14

-0.2

44

-0.1

78

-0.2

78

-0.1

62

-0.3

56

(0.1

0)

(2.3

4)*

(1.4

9)

(2.5

1)*

(2.4

3)*

(2.5

6)*

(2.2

1)*

(2.8

8)*

*

DS

PE

AK

( -

)-0

.637

-0.7

46

-0.2

20

-0.7

62

-0.0

71

-0.8

12

-0.2

21

-0.8

76

(4.8

6)*

*(7

.46)*

*(1

.94)

(7.0

4)*

*(0

.67)

(6.9

8)*

*(2

.23)*

(6.8

5)*

*

TC

OS

T(+

)0.4

19

0.6

12

0.4

41

0.5

52

0.5

84

0.5

07

0.6

10

0.4

81

(3.8

9)*

*(5

.54)*

*(3

.57)*

*(4

.89)*

*(4

.67)*

*(4

.30)*

*(5

.13)*

*(3

.84)*

*

DA

DJ

(+)

-0.4

37

0.9

82

0.6

12

1.1

81

0.7

66

0.9

11

0.8

62

1.0

70

(1.5

4)

(3.4

4)*

*(2

.63)*

*(3

.42)*

*(3

.41)*

*(2

.39)*

(3.8

2)*

*(2

.52)*

DIS

TA

NC

E(

- )

-0.8

54

-0.3

75

-0.1

91

-0.3

97

-0.2

11

-0.4

57

-0.1

92

-0.5

31

(4.7

9)*

*(2

.87)*

*(1

.34)

(2.8

3)*

*(1

.57)

(3.0

1)*

*(1

.44)

(3.2

2)*

*

Obs

222

886

332

776

443

665

554

554

Adj R

-sqd

0.8

40.4

40.7

60.3

90.7

0.3

90.6

60.4

1

F-v

alu

e92.4

753.8

881.9

238.4

881.5

834.1

883.1

230.1

2

Dependent variable

is r

eal sale

s o

f U

.S. affili

ate

s.

Absolu

te v

alu

e o

f t-

sta

tistics in p

are

nth

eses

* sig

nific

ant at 5%

; **

sig

nific

ant at 1%

11

(E

) (F

) (G

)

Top 6

0%

Bot 40%

Top 7

0%

Bot 30%

Top 8

0%

Bot 20%

GD

PS

UM

(+)

0.3

34

-0.0

42

0.3

51

-0.1

39

0.3

60

-0.1

11

(5.3

6)*

*(0

.44)

(5.9

5)*

*(1

.24)

(6.7

2)*

*(0

.70)

|MK

TD

IF|

( -

)-0

.209

-0.4

97

-0.2

39

-0.5

22

-0.2

67

-0.4

97

(4.6

5)*

*(6

.26)*

*(5

.38)*

*(5

.77)*

*(6

.14)*

*(4

.31)*

*

|SK

ILLD

IF|

( -

,+)

-0.5

62

0.2

17

-0.4

67

0.5

29

-0.4

57

0.5

33

(5.4

8)*

*(1

.25)

(4.6

5)*

*(2

.59)*

(4.6

7)*

*(2

.12)*

LE

ND

RA

TE

( -

)-0

.195

-0.5

01

-0.2

07

-0.5

57

-0.1

88

-0.4

93

(2.6

8)*

*(3

.61)*

*(2

.85)*

*(3

.35)*

*(2

.67)*

*(2

.23)*

DS

PE

AK

( -

)-0

.378

-0.9

8-0

.522

-1.1

53

-0.4

77

-1.5

36

(3.8

9)*

*(7

.14)*

*(5

.54)*

*(7

.49)*

*(5

.42)*

*(6

.84)*

*

TC

OS

T(+

)0.6

77

0.4

16

0.7

34

0.3

19

0.7

70

0.2

17

(5.8

0)*

*(3

.15)*

*(6

.60)*

*(2

.14)*

(6.9

8)*

*(1

.31)

DA

DJ

(+)

0.8

68

1.2

83

0.9

33

1.2

34

1.2

46

0.6

14

(3.8

6)*

*(2

.68)*

*(4

.20)*

*(1

.31)

(6.0

0)*

*(0

.46)

DIS

TA

NC

E(

- )

-0.2

34

-0.6

18

-0.1

99

-0.6

68

-0.0

51

-0.8

24

(1.7

7)

(3.4

4)*

*(1

.54)

(3.1

9)*

*(0

.43)

(3.0

2)*

*

Obs

665

443

776

332

886

222

Adj R

-sqd

0.6

0.4

30.5

90.4

30.5

90.4

7

F-v

alu

e77.4

526.7

087.1

820.3

497.7

816.1

5

Dependent variable

is r

eal sale

s o

f U

.S. affili

ate

s.

Absolu

te v

alu

e o

f t-

sta

tistics in p

are

nth

eses

* sig

nific

ant at 5%

; **

sig

nific

ant at 1%

Coeffic

ients

of consta

nts

and d

um

mie

s a

re s

uppre

ssed.

12

Focusing on the coefficients on |SKILLDIF |, it is observed that the coefficients

on |SKILLDIF | are all negative and statistically significant (high D/M case) for all

cases, while the magnitude of the estimated coefficient becomes greater for all cases

as the D/M is decreasing (right column). Especially in cases (F) and (G), coefficients

on |SKILLDIF | are statistically significant. This result clearly shows that for all

cases (from (A) to (G)), the characteristic of horizontal FDIs appears in higher D/M

samples, while that of the vertical FDIs appears in cases (F) and (G).

As trade cost increases, the horizontal FDI substitute for trade while vertical FDI

prefer low trade cost because of intermediate goods transaction between parents and

affiliates. As a result, horizontal FDI tend to have higher coefficient of trade cost

variable (TCOST ) and vertical multinationals are likely to have lower or even negative

coefficient. The results confirm this: the coefficient on TCOST in top 20% of (A) is

0.418 with highly statistical significance, while that in bottom 20% in (G) is 0.217

without statistical significance. This is again consistent with the prediction of the

previous arguments.

Judging from these exercises, I conclude that the horizontal FDI in the U.S. man-

ufacturing sectors dominate vertical FDI and the former accounts for more than 70%

of total multinational activities.

To look into the results more carefully, I calculate the summary statistics for both

the horizontal and the vertical FDI observations. Table 3A shows the summary sta-

tistics for total samples, horizontal FDI which account for top 70%, and the vertical

FDI which account for bottom 30% of the sample. The first observation is that the

real sales, the sum of GDPs, the trade cost variables are higher in the horizontal FDI

than in the vertical FDI. The sales of the horizontal FDI are larger than the those

of the vertical means that in general the large plants are more likely to be horizontal

FDI. The higher sum of GDPs in horizontal FDI indicates that the FDI expands as

13

world income grows for the horizontal case. This is consistent with the proposition 2.

That trade cost is also higher in the horizontal than the vertical FDI suggests that the

horizontal FDI are more sensitive to the trade cost than the vertical ones.

The mean of market difference is larger in the vertical FDI is also consistent with

the proposition 1. Although there seems little difference in the factor endowment

variable, |SKILLDIF |, the previous regression results clearly show that the factor

endowment plays an opposite role in the horizontal and the vertical FDI.

Next table (Table 3B) shows the number of observations in two categories, i.e., by

host country’s development level and by industry. The upper part of the table shows

the numbers of FDI which are included in either developing countries or developed

countries. 64% are horizontal and 35% are vertical in developing samples, while 72%

are horizontal and 27% are vertical in developed samples. The lower panel of the table

shows the number of observations by industry and by country’s development level.

Machinery, metals, and electric equipment industries have relatively higher shares of

vertical FDI than food, chemicals, and transportation industries.

Table 3C shows the detailed information about the number of FDI by industry, by

types of FDI, and levels of host economies. For food and chemical industries, there is

no clear evidence that horizontal and vertical FDI are different by FDI destinations,

i.e., developing and developed countries. However, metal, machinery, electric, and

transportation industries have relatively larger number of horizontal FDI than vertical

FDI. An interesting finding is found in electric apparatus industry: in horizontal FDI

case, 110 industries are operated in developed countries, while only 15 cases are oper-

ated in developing countries. On the other hand, in vertical FDI case, 39 industries are

in developed, while 33 are in developing countries. This means that electric apparatus

industry is very sensitive to the host market and cost advantage motives. In other

words, electric industry changes their strategies of FDI to suit the occasion.

14

Tbale 3A Summary Statistics

Total Sample Mean Std. Dev. Min Max Obs.

HOR.INDEX 7.072 2.223 0.000 13.339 1108

SALE 6.613 1.771 1.674 11.027 1108

GDPSUM 41.830 1.245 38.615 44.736 1108

|MKTDIF| 3.637 1.432 0.021 8.633 1108

|SKILDIF| 0.635 0.420 0.001 1.844 1108

LENDRATE 2.520 0.628 0.770 4.475 1108

DSPEAK 0.605 0.489 0 1 1108

TCOST 0.081 0.377 -4.576 0.583 1108

DADJ 0.127 0.333 0.000 1 1108

DIATANCE 8.407 0.609 6.981 9.154 1108

Horizontal Mean Std. Dev. Min Max Obs.

HOR.INDEX 8.230 1.323 6.159 13.339 776

SALE 7.210 1.495 3.618 11.027 776

GDPSUM 42.291 1.054 38.800 44.736 776

|MKTDIF| 3.230 1.329 0.021 8.633 776

|SKILDIF| 0.637 0.438 0.001 1.844 776

LENDRATE 2.522 0.650 0.770 4.475 776

DSPEAK 0.634 0.482 0 1 776

TCOST 0.089 0.333 -2.641 0.510 776

DADJ 0.179 0.384 0 1 776

DIATANCE 8.356 0.645 6.981 9.154 776

Vertical Mean Std. Dev. Min Max Obs.

HOR.INDEX 4.364 1.390 0.000 6.158 332

SALE 5.216 1.569 1.674 8.868 332

GDPSUM 40.752 0.961 38.615 43.746 332

|MKTDIF| 4.588 1.194 0.755 7.661 332

|SKILDIF| 0.630 0.377 0.015 1.803 332

LENDRATE 2.516 0.575 1.666 4.097 332

DSPEAK 0.536 0.499 0 1 332

TCOST 0.060 0.463 -4.576 0.583 332

DADJ 0.006 0.077 0 1 332

DIATANCE 8.524 0.498 6.981 9.148 332

15

Tbale 3B Number of FDI

TOTAL HORIZONTAL VERTICAL

DC 323 208 115

64.4% 35.6%

LDC 785 568 217

72.4% 27.6%

FOOD DC 87 64 23

73.6% 26.4%

LDC 82 61 21

74.4% 25.6%

CHEM DC 180 146 34

81.1% 18.9%

LDC 135 110 25

81.5% 18.5%

META DC 117 69 48

59.0% 41.0%

LDC 61 40 21

65.6% 34.4%

MACH DC 125 79 46

63.2% 36.8%

LDC 20 6 14

30.0% 70.0%

ELEC DC 149 110 39

73.8% 26.2%

LDC 48 15 33

31.3% 68.8%

TRAN DC 73 56 17

76.7% 23.3%

LDC 9 9 0

100.0% 0.0%

Developing: Low income, lower and Upper middle

income countries by World Bank classification

Developed: High income coutries by World Bank classification

16

Table 3C Difference in Mean

TOTAL FOOD CHEM META MACH ELEC TRAN

HOR.INDEX 32.61** 11.59** 16.42** 24.72** 13.75** 16.06** 17.33**

SALE 13.18** 8.41** 5.22** 9.26** 8.05** 4.25** 4.28**

GDPSUM 22.22** 11.16** 13.86** 10.94** 9.22** 7.68** 11.15**

|MKTDIF| -12.42** -6.52** -8.01** -4.27** -6.29** -4.46** -3.35**

|SKILDIF| 0.60 4.35** 3.70** 1.10 -6.95** -10.62** 5.34**

LENDRATE 0.25 1.39 1.73 0.48 -3.72** -7.81** 2.99**

DSPEAK 6.27** 2.84** 2.20 9.73** 2.25* -3.81** 2.49**

TCOST 3.11** 0.65 -1.45 6.97** 4.09** 7.31** -7.04**

DADJ 25.11** 9.53** 9.95** 11.87** 13.43** 9.41** 6.72**

DIATANCE -6.97** -3.63** 1.89 -3.57** -5.51** -2.46** -3.99**

** significant at 1%, and * significant at 5% level.

2,

21

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17

In the next section, using the results of this section, I estimate the determinants of

U.S. FDI strategies, that is either targeting the host market, aiming to export back to

the U.S. or export platform strategy.

4 Determinants of U.S. Affiliate Strategies

Next exercise is to compare the determinants of the U.S. affiliate strategies, that is,

targeting the host country’s market, exporting back to the U.S. market, or exporting

to the other countries (export platform strategy). The method used for this purpose

is to estimate the following equation;

SALE(h)ijt = α + D∗α + β1MKTijt + β2USMKTjt + β3|SKILLDIFijt|

+β4LENDRATEjt + β5TCOSTjt + γ1D ∗MKTijt

+γ2D ∗ USMKTjt + γ3D ∗ |SKILLDIFijt|

+γ4D ∗ LENDRATEjt + γ5D ∗ TCOSTjt + εijt (2)

This formulation is slightly different from equation (1). First, dependent variables

are U.S. affiliate sales in the host market, exports back to the U.S. market, and the

other world market. Second, equation (2) does not include the world income proxy,

GDPSUM . Since the purpose of this section is to estimate the determinants of the

U.S. affiliate sales in the host market, the U.S. market and the other world market, the

world income proxy seems not to play an important role. Hence, I drop that variable

and instead add the market size of home and the U.S. separately.6 Third, to detect the

significance of the difference between the horizontal and the vertical FDI strategies,

I include both constant dummy and slope dummy which are value 1 if it drops in

the area of bottom 30% (vertical FDI) and otherwise zero. Thus, I can discuss the

6Since all variables are expressed in natural log form, ln(MKT/USMKT ) in equation (1) equalsln(MKT )− ln(USMKT ) in equation (2). The interpretation should be the same as before.

18

difference not only between three strategies but also between the horizontal and the

vertical FDI.

The results of estimation in Table 4 reveal large differences both among strategies

and between the horizontal and the vertical FDI. Let us discuss first the differences

between the horizontal and the vertical FDI with respect to each strategy. Then, the

discussion on the differences among strategies are followed.

In the column (1), judging from the magnitude and the significance of the coeffi-

cients on constant (Constant and D), there is no significant difference in level of local

sales between the horizontal and the vertical FDI. The horizontal FDI has a higher

elasticity of market size (MKT) to local sales which is .562 than the vertical FDI which

is .393 (.562-.169). The coefficient on |SKILLDIF | of the horizontal FDI has a neg-

ative sign (-.619) while a positive (.162=-.619+.781) for the vertical FDI. This means

that the similarity in factor endowment promotes local sales of FDI in the horizontal

FDI case but deter the local sales in the vertical FDI case.

As for the export to the U.S., column (2), large difference between the horizontal

and the vertical FDI is a coefficient on constant. The coefficient on constant for the

horizontal FDI is negative while that of the vertical FDI is positive and the difference

is statistically significant.7 This means that the vertical FDI are more likely to export

back their products to the U.S. market than the horizontal FDI in level. This is

consistent with Helpman (1984)’s view that the main function of vertical FDI is to

export back to the home market.

The coefficient on |SKILLDIF | is larger than the horizontal case with 1% sta-

tistical significance. The vertical FDI are more sensitive to the factor endowment in

the host economy regarding the export back to the U.S. strategy. Interestingly, the

7The coefficient of the horizontal FDI is -14.25 while the coefficient of the vertical FDI is 7.609(=-14.25+21.859). The difference between them is thus 21.850 which is statistically significant at 1%level.

19

Table 4 Determinants of U.S. MNE Strategies

(1) (2) (3)

Local Sales Exports to US Export to

Other Countries

MKT 0.562 0.578 0.26

(15.92)** (6.02)** (2.31)*

USMKT 0.052 0.488 0.385

(0.82) (2.77)** (1.91)

|SKILLDIF| -0.619 -1.623 -1.03

(6.34)** (6.22)** (3.45)**

LENDRATE 0.093 0.112 -0.331

(1.22) (0.52) (1.34)

TCOST 0.01 -0.537 1.479

(0.08) (1.50) (3.43)**

D30_MKT -0.169 -0.562 -0.151

(2.36)* (2.79)** (0.65)

D30_USMKT 0.19 -0.635 0.269

(1.22) (1.55) (0.58)

D30_SKILLDIF 0.781 3.534 0.982

(3.97)** (6.39)** (1.55)

D30_LENDRATE 0.116 -1.282 -2.628

(0.77) (2.79)** (4.79)**

D30_TCOST -0.006 -0.141 -1.676

(0.03) (0.20) (2.01)*

D30 -4.118 21.859 2.549

(1.39) (2.76)** (0.28)

Constant -3.182 -14.25 -5.209

(2.67)** (4.18)** (1.32)

Obs 1115 707 684

Adj R-sq 0.66 0.17 0.13

F-value 195.44 14.16 10.44

D30s stand for dummy variables with value 1 when it drops

in the area of bottom 30% of the sample.

Absolute value of t-statistics in parentheses

* significant at 5%; ** significant at 1%

20

coefficient on LENDRATE is positive in the horizontal FDI while negative in the ver-

tical FDI with statistically significant difference,8 indicating that the vertical FDI are

sensitive to the FDI cost in the host country while the FDI cost plays little important

roles in horizontal FDI with respect to the export to the U.S. market strategy.

Export platform (exporting to the other market) strategy, column (3), shows the

almost same trend with the export to the U.S. market strategy but weaker connections

between dependent and independent variables. Crucial difference from export to the

U.S. strategy is appeared in the coefficient on TCOST . The horizontal FDI has a

positive coefficient on TCOST (1.479) and the vertical has a negative coefficient on

TCOST (-0.197). The similarity in factor endowment spurs the export to the other

market strategy for the horizontal FDI while the similarity deters that strategy in the

vertical FDI.

Let us discuss the differences among three strategies, that is, local sales, exports to

the U.S., and exports to the other countries. The coefficient on MKT is a positive and

statistically significant for all strategies but larger in both local sales and export back

to the U.S. strategies indicating a large market size is still important for export back

to the U.S. strategy. This is consistent with Yeaple (2003) on the U.S. MNE strategy.

Only the export to the U.S. strategy has a significant coefficient on USMKT . This

indicates that the larger the U.S. market, the more likely the FDI takes “export back

to the U.S.” strategy. As I discussed previously, this is true for both the horizontal and

the vertical FDI. The coefficient on |SKILLDIF | is negative and significant for all

three strategies in the horizontal FDI while positive in the vertical FDI with respect to

local sales and export back to the U.S. strategies. It indicates that both the horizontal

and the vertical FDI are more sensitive to the factor endowment similarity of host

economy in export strategy than in local sale strategy.

8The difference is -1.282, and the coefficient of the horizontal FDI is 0.112. Hence the coefficientof the vertical FDI is -1.17.

21

5 Spillover Effects of FDI

In this section, I estimate the effects of spillovers by FDI on the host economy. The

following equation is estimated:

TFPitj = α + β1SKILLjt + β2SIZEijt + β3EMPLOY EEijt + εijt. (3)

All variables are in natural logarithmic forms. The dependent variable is the level of

total factor productivity by industry and country, which is defined as

ln TFPijt = ln GDPijt − sijt ln Kijt − (1− sijt) ln Lijt,

where Kijt is capital stock and Lijt is the number of labor force in country i and

industry j in time t. sijt is the capital expenditure share. All necessary data for TFP

calculation are obtained from the World Bank. Independent variable SKILL is defined

as the share of skilled labor to the total labor force and SIZE is the average output

per firms (including both local and MNEs), which is in turn defined as

ln SIZE = ln(output)− ln(number of firms).

EMPLOY EE is the number of employees (in the industry) who are working for MNEs.

Table 5 shows the estimation results for two samples, top 70% and bottom 30%.

As discussed, the former sample is likely contain more horizontal FDI and the latter

more vertical FDI. Estimation group (1) excludes EMPLOY EE as an explanatory

variable, while group (2) includes EMPLOY EE. The reason is twofold: first to check

the multicollinearity between EMPLOY EE and other variables, and the second to

check the robustness of estimation. Judging from the results, it seems that there is no

multicollinearity and estimates are robust.

The estimation results for group (1) reveal that skill abundance positively affects

the levels of productivity in both horizontal and vertical cases. This is consistent with

22

Table 5 Spillover Effects of MNEs

(1) (2)

Horizontal MNEs Vertical MNEs Horizontal MNEs Vertical MNEs

Top 70% Bot 30% Top 70% Bot 30%

SKILL 1.061 1.656 1.012 1.751

(6.15)*** (8.72)*** (5.71)*** (8.24)***

SIZE 0.218 -0.204 0.185 -0.234

(4.21)*** (2.93)*** (3.33)*** (3.03)***

EMPLOYEE 0.118 0.098

(2.01)** (1.75)*

Constant 3.849 8.329 3.011 8.009

(6.51)*** (11.97)*** (4.07)*** (11.33)***

Observations 469 229 457 218

Adj. R-sq 0.11 0.26 0.11 0.25

F-value 28.91*** 42.08*** 19.92*** 25.45***

Dependent variable is the level of TFP.

See text for the definitions of variables.

Absolute value of t-statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

23

the previous literatures9; the host country benefits more from FDI when it is more

skilled labor abundant. However, one striking result is that horizontal and vertical

samples have the opposite signs of estimated coefficients on SIZE. This means that

as output per firm increases, the level of productivity increases in that industry for the

horizontal case. The reverse is true for the vertical sample. This is also consistent with

Aitkin and Harrison (1999) finding, that the host economy benefits more from vertical

FDI if the industry is relatively small.

These results do not change if the EMPLOY EE variable is added as an indepen-

dent variable in the right hand side as shown in group (2). The results of group (2)

show that the number of workers employed by MNEs positively affects the level of

productivity. This finding in turn may support the formulation of spillover. As the

number of workers hired by MNEs increases, the possibility that those workers move

to local firms increases, thereby increasing the total productivity of the industry. 10

6 Conclusions

This paper proposed a methodology to distinguish the horizontal FDI from the vertical

FDIs, making use of implications of trade-theoretic models as well as empirical studies.

Estimated results clearly showed the differences in characters between horizontal and

vertical FDI. In separating the sample into two groups of FDI, factor endowment played

a crucial role. Basic idea to distinguish two types of FDI is that factor endowment

similarity spurs horizontal FDI while dissimilarity spurs vertical FDI.11 One of this

paper’s novel contributions to the literature is to reveal that the horizontal and vertical

9See Yokota (2004) for a detailed survey.10Since the data on skilled labor employed by MNEs are not available, total number of employees

employed by MNEs is used.11It should be noted again that this is an implication from trade-theoretic FDI models, not the

result of my exercise.

24

FDI coexist in U.S. manufacturing sector, which is consistent with the findings of

Hanson, Mataloni, and Slaughter (2001). And the horizontal FDI dominates U.S.

MNE activities, which in turn is consistent with Brainard (1993).

This paper also identified that the U.S. horizontal FDI tend to go developed coun-

tries with 72% share while the U.S. vertical FDI go developed countries with 64%

share. These figures indicate that the vertical FDI do not necessarily a phenomenon of

the North-South only while a horizontal production integration occurs even between

dissimilar endowment countries.

Another exercise revealed that the vertical FDI are more likely to export back to the

U.S. market. This is consistent with the definition of vertical FDI by Helpman (1984).

On the other hand, the horizontal FDI are more likely to target the local market of

the host country depending on the magnitudes of market size and factor endowment of

the host economy. These results are almost consistent with the fact findings by Yeaple

(2003), although he did not estimated the horizontal and the vertical FDI separately.

As I have shown, in determining the MNE strategy, U.S. horizontal FDI have quite

different motives from the vertical FDI.

Then, the determinants of the level of productivity in the host economy was iden-

tified. Interesting results are: firstly, skill abundance in both the horizontal and the

vertical FDI positively affect host country’s productivity. This is consistent with the

previous literatures. Secondly, the number of workers in MNEs also positively affects

the productivity. Thirdly, average output per firm positively affects productivity in

the horizontal FDI case, but negatively affects productivity in the vertical FDI case.

This is also consistent with the previous finding discussed earlier.

As shown, the horizontal and the vertical FDI behave quite differently. This is a

reason why the separation of groups of FDI is necessary to understand the MNE issues

empirically.

25

A Definitions and Data Sources of Variables*

Variable Dimension DefinitionSALE i× j × t sales by majority-owned non-bank U.S. affiliate in host country (BEA),

measured in 1995 constant price using GDP deflator (IFS, IMF).GDPSUM j × t sum of U.S. real GDP and host country’s real GDP, constant prices,

Laspeyles. (PWT)|SKILLDIF | j × t absolute value of host country’s skilled labor share over U.S. skilled labor

share. Skilled labor is defined as the sum of occupational categories0/1 (professional, technical, and kindred workers) and 2 (administrativeworkers) divided by total number of workers. The nearest values are usedfor fulfill the missing values for avoiding the loss of degree of freedom.(ILO)**

|MKTDIF | i× j × t absolute value of market size difference between host and U.S. market.Market size (total output minus exports plus imports) in host countryminus market size in U.S. (WB)

LENDRATE j × t proxy for investment cost defined as lending rate of host country. (IMF)DSPEAK j × t another proxy for investment cost defined as a dummy variable with 1

for non-English speaking host countries.TCOST j × t proxy for trade cost defined the freight costs by country and industry,

CIF/FOB. (Feenstra)DADJ j dummy variable with value 1 if the host country is adjacent to the U.S.

DISTANCE j distance between U.S. and the host country.GDPPC j × t real GDP per capita in host countries. constant prices, Laspeyles.

(PWT)SIZE i× j × t proxy for scale economy. Output per firm by industry in the host country.

(WB)DFOOD i dummy variable with value 1 if the industry is manufacturing foods.

ISIC code 311 and 313.DCHEM i dummy variable with value 1 if the industry is manufacturing chemicals

and products. ISIC code 351 and 352.DMETA i dummy variable with value 1 if the industry is manufacturing metal and

metal products. ISIC code 372 and 381.DMACH i dummy variable with value 1 if the industry is manufacturing machinery.

ISIC code 382.DELEC i dummy variable with value 1 if the industry is manufacturing electric

equipments and electronics. ISIC code 383.D i× j × t dummy variable with value 1 when the observation drops in the area in

bottom 30% in the sample, i.e., it is vertical industry.TFP i× j × t level of total factor productivity, proxy for the industry productivity.

See text for the definition.EMPLOY EE i× j × t the number of workers hired by MNEs. (BEA)

*All variables are in natural logarithmic forms.** The number of skilled labor in U.S. affiliates by industry and country is available only for 1989,1994, and 1999 (these are benchmark survey conducted by BEA), I used 1989 data for the periods be-tween 1983 and 1991, 1994 data for between 1992 and 1995, and 1999 data for between 1996 and 2000.

26

BEA: (http://www.bea.gov/bea/di/di1usdop.htm)PWT: Penn World Table (http://pwt.econ.upenn.edu/php site/pwt index.php)ILO: International Labor Organization (http://laborsta.ilo.org/)WB: World Bank data base, “Trade and Production, 1976-1999.”IMF: International Financial Statistics data base.Feenstra: World Trade Flows, 1980-1997

B Country, Industry and Data Periods

Data have three dimensions, i.e., country, industry and time. Data includes 44 countries, 6 industriesand 17 years (1983 - 1999):

Countries:Argentina, Australia, Austria, Canada, Chile, China, Colombia, Costa Rica, Denmark, Ecuador,Egypt, Finland, France, Germany, Greece, Guatemala, Honduras, Hong Kong, India, Indonesia, Ire-land, Italy, Japan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Panama, Peru,Philippines, Portugal, Singapore, South Africa, Spain, Sweden, Taiwan, Thailand, Trinidad and To-bago, Turkey, United Kingdom, Venezuela.

Industries:Food, Chemicals, Metal, Machinery, Electric Machinery, and Transportation.

Time Periods:Data availability varies greatly depending on country. So panel is unbalanced.

27

References[1] Aitkin, Brian and Ann E. Harrison (1999)“Do Domestic Firms Benefit from Foreign Investment?

Evidence from Venezuella,” American Economic Review, 89, 605-618.

[2] Aizenman, Joshua, and Nancy Marion (2004), “The Merits of Horizontal versus Vertical FDI inthe Presence of Uncertainty,” Journal of International Economics, 62, 125-148.

[3] Brainard, S. Lael (1993), “An Empirical Assessment of the Factor Proportions Theory,” NBERworking paper no. 4269

[4] Carr, David, James Markusen, and Keith Maskus (2001), “Estimating the Knowledge-CapitalModel of the Multinational Enterprise,” American Economic Review, 91, 691-708.

[5] Feenstra, Robert C. (2004), Advanced International Trade, Princeton University Press, Princeton.

[6] Hanson, Gordon, Raymond Mataloni, and Mathew Slaughter (2001), “Expansion Strategies ofU.S. Maultinational Firms,” NBER working paper no. 8433.

[7] Helpman, Elhanan (1984), “A imple Theory of Trade with Multinational Corporations,” Journalof Political Economy, 92, 451-471.

[8] Markusen, James (1984) “Multinationals, Multi-plant Economies, and the Gains from Trade,”Journal of International Economics, 16, 205-226.

[9] Markusen, James (2002), Multinational Firms and the Theory of International Trade, MIT Press,Cambridge.

[10] Markusen, James and Anthony Venables (1998), “Multinational Firms and the New Trade The-ory,” Journal of International Economics, 46, 183-204.

[11] Yeaple, Stephen (2003), “The Role of Skill Endowments in the Structure of U.S. Outward ForeignDirect Investment,” Review of Economics and Statistics, 85(3), 726-734.

[12] Yokota, Kazuhiko (2004), “Comparative Advantage and Vertical Multinaitonals,” ICSEADWorking Paper series, 2004-34.

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