Preliminary Draft
Horizontal versus Vertical Multinationals
∗
Kazuhiko Yokota†
May, 2005
Abstract
A method to break down foreign direct investment (FDI) samples into either
horizontal or vertical FDI is proposed. Using the implications of multinational
enterprize (MNE) theories, samples of U.S. MNE activities are separated into
either 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 results
of U.S. FDI strategies indicate that the vertical FDI tend to export back to the
U.S. market more than the horizontal FDI. Skill difference plays an important
role 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 for
the 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 Helpman (1984). He shows in a two-factor framework with monopolistic competition that
without trade costs, MNE builds plants in different countries according to the comparative 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 separating 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 esti1
See 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., horizontal 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 characteristics. 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 nature 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 hy2
See 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 Helpman’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 + β1 GDP SU Mjt + β2 |M KT DIFijt | +
β3 |SKILLDIFjt | + β4 LEN DRAT Ejt + β5 DSP EAKjt +
β6 T COSTijt + β7 DADJj + β8 DIST AN CEj + ²ijt
(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 GDP SU M 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. |M KT DIF |
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
3
Since |M KT DIF | is defined, in absolute term, as the ratio of host economy’s market size to the
U.S. market size, the smaller the value means the similarity between the two markets. Market size in
turn 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. LEN DRAT E is a lending interest rate by country which is a proxy for the FDI cost. The higher the LEN DRAT E,
the less FDI. Hence, the expected sign is negative both for the horizontal and the vertical FDI. DSP EAK 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. T COST is a proxy for trade cost defined as
the ratio of CIF value to F OB 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 vertical 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 DIST AN CE 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 Commerce. 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 T COST 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, GDP SU M
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, T COST , 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),
4
Six industries are, food, chemicals, metals, machinery, electric equipment and transportation,
Since there are many missing values in BEA data, available data account for about 40% of total
U.S. manufacturing FDI activities.
5
8
satisfy the sign conditions and many of them are highly statistically significant. The
finding that the coefficients on GDP SU M 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 DSP EAK. 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 statistically 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 GDP SU M 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
GDPSUM
(+)
|MKTDIF|
(-)
|SKILLDIF|
( - ,+)
LENDRATE
(-)
DSPEAK
(-)
TCOST
(+)
DADJ
(+)
DISTANCE
(-)
Obs
Adj R-sqd
F-value
Industry
Year
Country
(1)
(2)
(3)
(4)
(5)
(6)
0.373
(6.70)**
-0.379
(7.82)**
-0.227
(2.12)*
-0.181
(2.31)*
-0.760
(7.92)**
0.564
(5.40)**
1.077
(4.64)**
-0.295
(2.34)*
0.427
(8.18)**
-0.349
(7.77)**
-0.280
(2.81)**
-0.194
(2.68)**
-0.782
(8.89)**
0.586
(6.12)**
0.776
(3.64)**
-0.406
(3.49)**
0.284
(4.40)**
-0.473
(8.34)**
-0.216
(2.00)*
-0.119
(1.41)
-0.752
(7.74)**
0.544
(5.19)**
1.134
(4.78)**
-0.275
(2.13)*
0.763
(6.71)**
-0.509
(9.98)**
0.605
(1.22)
-0.094
(0.87)
0.486
(0.54)
0.067
(0.63)
0.831
(8.43)**
-0.439
(9.78)**
0.088
(0.21)
-0.065
(0.70)
0.147
(0.19)
0.096
(1.04)
-1.149
(2.62)**
0.357
(5.84)**
-0.422
(7.97)**
-0.265
(2.65)**
-0.143
(1.83)
-0.774
(8.65)**
0.570
(5.92)**
0.823
(3.77)**
-0.388
(3.27)**
1108
0.50
139.1
1108
0.58
119.6
1108
0.51
47.7
1108
0.64
56.2
1108
0.59
54.4
1108
0.74
76.1
no
no
no
yes
no
no
no
yes
no
no
no
yes
yes
yes
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
-0.869
(2.30)*
11
(-)
( - ,+)
(-)
(-)
(+)
(+)
(-)
|MKTDIF|
|SKILLDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DISTANCE
0.312
(4.90)**
-0.381
(7.09)**
-0.313
(2.71)**
-0.205
(2.34)*
-0.746
(7.46)**
0.612
(5.54)**
0.982
(3.44)**
-0.375
(2.87)**
886
0.44
53.88
0.822
(9.48)**
-0.100
(2.26)*
-0.475
(3.58)**
-0.009
(0.10)
-0.637
(4.86)**
0.419
(3.89)**
-0.437
(1.54)
-0.854
(4.79)**
222
0.84
92.47
Dependent variable is real sales of U.S. affiliates.
Absolute value of t-statistics in parentheses
* significant at 5%; ** significant at 1%
Obs
Adj R-sqd
F-value
(+)
GDPSUM
(A)
Top 20% Bot 80%
332
0.76
81.92
0.449
(5.99)**
-0.183
(3.94)**
-0.622
(5.01)**
-0.114
(1.49)
-0.220
(1.94)
0.441
(3.57)**
0.612
(2.63)**
-0.191
(1.34)
776
0.39
38.48
0.234
(3.39)**
-0.423
(7.29)**
-0.301
(2.44)*
-0.244
(2.51)*
-0.762
(7.04)**
0.552
(4.89)**
1.181
(3.42)**
-0.397
(2.83)**
(B)
Top 30% Bot 70%
443
0.7
81.58
0.329
(4.94)**
-0.184
(4.38)**
-0.728
(6.45)**
-0.178
(2.43)*
-0.071
(0.67)
0.584
(4.67)**
0.766
(3.41)**
-0.211
(1.57)
665
0.39
34.18
0.099
(1.25)
-0.52
(7.74)**
-0.189
(1.39)
-0.278
(2.56)*
-0.812
(6.98)**
0.507
(4.30)**
0.911
(2.39)*
-0.457
(3.01)**
(C)
Top 40% Bot 60%
Table 2 Estimation by Sepa
ation by Separated Samples
554
0.66
83.12
0.348
(5.42)**
-0.194
(4.45)**
-0.612
(5.68)**
-0.162
(2.21)*
-0.221
(2.23)*
0.610
(5.13)**
0.862
(3.82)**
-0.192
(1.44)
554
0.41
30.12
0.055
(0.62)
-0.532
(7.28)**
-0.058
(0.39)
-0.356
(2.88)**
-0.876
(6.85)**
0.481
(3.84)**
1.070
(2.52)*
-0.531
(3.22)**
(D)
Top 50% Bot 50%
12
(-)
( - ,+)
(-)
(-)
(+)
(+)
(-)
|MKTDIF|
|SKILLDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DISTANCE
665
0.6
77.45
0.334
(5.36)**
-0.209
(4.65)**
-0.562
(5.48)**
-0.195
(2.68)**
-0.378
(3.89)**
0.677
(5.80)**
0.868
(3.86)**
-0.234
(1.77)
443
0.43
26.70
-0.042
(0.44)
-0.497
(6.26)**
0.217
(1.25)
-0.501
(3.61)**
-0.98
(7.14)**
0.416
(3.15)**
1.283
(2.68)**
-0.618
(3.44)**
776
0.59
87.18
0.351
(5.95)**
-0.239
(5.38)**
-0.467
(4.65)**
-0.207
(2.85)**
-0.522
(5.54)**
0.734
(6.60)**
0.933
(4.20)**
-0.199
(1.54)
332
0.43
20.34
-0.139
(1.24)
-0.522
(5.77)**
0.529
(2.59)*
-0.557
(3.35)**
-1.153
(7.49)**
0.319
(2.14)*
1.234
(1.31)
-0.668
(3.19)**
(F)
Top 70% Bot 30%
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.
Obs
Adj R-sqd
F-value
(+)
GDPSUM
(E)
Top 60% Bot 40%
886
0.59
97.78
0.360
(6.72)**
-0.267
(6.14)**
-0.457
(4.67)**
-0.188
(2.67)**
-0.477
(5.42)**
0.770
(6.98)**
1.246
(6.00)**
-0.051
(0.43)
222
0.47
16.15
-0.111
(0.70)
-0.497
(4.31)**
0.533
(2.12)*
-0.493
(2.23)*
-1.536
(6.84)**
0.217
(1.31)
0.614
(0.46)
-0.824
(3.02)**
(G)
Top 80% Bot 20%
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 (T COST ) and vertical multinationals are likely to have lower or even negative
coefficient. The results confirm this: the coefficient on T COST 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. manufacturing 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 statistics 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 operated 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
HOR.INDEX
SALE
GDPSUM
|MKTDIF|
|SKILDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DIATANCE
Mean
7.072
6.613
41.830
3.637
0.635
2.520
0.605
0.081
0.127
8.407
Std. Dev.
2.223
1.771
1.245
1.432
0.420
0.628
0.489
0.377
0.333
0.609
Min
0.000
1.674
38.615
0.021
0.001
0.770
0
-4.576
0.000
6.981
Max
13.339
11.027
44.736
8.633
1.844
4.475
1
0.583
1
9.154
Obs.
1108
1108
1108
1108
1108
1108
1108
1108
1108
1108
Horizontal
HOR.INDEX
SALE
GDPSUM
|MKTDIF|
|SKILDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DIATANCE
Mean
8.230
7.210
42.291
3.230
0.637
2.522
0.634
0.089
0.179
8.356
Std. Dev.
1.323
1.495
1.054
1.329
0.438
0.650
0.482
0.333
0.384
0.645
Min
6.159
3.618
38.800
0.021
0.001
0.770
0
-2.641
0
6.981
Max
13.339
11.027
44.736
8.633
1.844
4.475
1
0.510
1
9.154
Obs.
776
776
776
776
776
776
776
776
776
776
Vertical
HOR.INDEX
SALE
GDPSUM
|MKTDIF|
|SKILDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DIATANCE
Mean
4.364
5.216
40.752
4.588
0.630
2.516
0.536
0.060
0.006
8.524
Std. Dev.
1.390
1.569
0.961
1.194
0.377
0.575
0.499
0.463
0.077
0.498
Min
0.000
1.674
38.615
0.755
0.015
1.666
0
-4.576
0
6.981
Max
6.158
8.868
43.746
7.661
1.803
4.097
1
0.583
1
9.148
Obs.
332
332
332
332
332
332
332
332
332
332
15
Tbale 3B Number of FDI
DC
LDC
FOOD
DC
LDC
CHEM
DC
LDC
META
DC
LDC
MACH
DC
LDC
ELEC
DC
LDC
TRAN
DC
LDC
TOTAL HORIZONTAL VERTICAL
323
208
115
64.4%
35.6%
785
568
217
72.4%
27.6%
87
64
23
73.6%
26.4%
82
61
21
74.4%
25.6%
180
146
34
81.1%
18.9%
135
110
25
81.5%
18.5%
117
69
48
59.0%
41.0%
61
40
21
65.6%
34.4%
125
79
46
63.2%
36.8%
20
6
14
30.0%
70.0%
149
110
39
73.8%
26.2%
48
15
33
31.3%
68.8%
73
56
17
76.7%
23.3%
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
HOR.INDEX
SALE
GDPSUM
|MKTDIF|
|SKILDIF|
LENDRATE
DSPEAK
TCOST
DADJ
DIATANCE
TOTAL
FOOD
CHEM
META
MACH
ELEC
TRAN
32.61**
13.18**
22.22**
-12.42**
0.60
0.25
6.27**
3.11**
25.11**
-6.97**
11.59**
8.41**
11.16**
-6.52**
4.35**
1.39
2.84**
0.65
9.53**
-3.63**
16.42**
5.22**
13.86**
-8.01**
3.70**
1.73
2.20
-1.45
9.95**
1.89
24.72**
9.26**
10.94**
-4.27**
1.10
0.48
9.73**
6.97**
11.87**
-3.57**
13.75**
8.05**
9.22**
-6.29**
-6.95**
-3.72**
2.25*
4.09**
13.43**
-5.51**
16.06**
4.25**
7.68**
-4.46**
-10.62**
-7.81**
-3.81**
7.31**
9.41**
-2.46**
17.33**
4.28**
11.15**
-3.35**
5.34**
2.99**
2.49**
-7.04**
6.72**
-3.99**
** significant at 1%, and * significant at 5% level.
tn1 n2 2
x1 x2
u
n1n2
,
n1 n2
17
u
n1S12 n2S22
n1 n2 2
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 + β1 M KTijt + β2 U SM KTjt + β3 |SKILLDIFijt |
+β4 LEN DRAT Ejt + β5 T COSTjt + γ1 D ∗ M KTijt
+γ2 D ∗ U SM KTjt + γ3 D ∗ |SKILLDIFijt |
+γ4 D ∗ LEN DRAT Ejt + γ5 D ∗ T COST jt + ²ijt
(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,
GDP SU M . 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
6
Since all variables are expressed in natural log form, ln(M KT /U SM KT ) in equation (1) equals
ln(M KT ) − ln(U SM KT ) 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 coefficients 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 negative 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% statistical 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
7
The 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
MKT
USMKT
|SKILLDIF|
LENDRATE
TCOST
D30_MKT
D30_USMKT
D30_SKILLDIF
D30_LENDRATE
D30_TCOST
D30
Constant
Obs
Adj R-sq
F-value
(1)
Local Sales
(2)
Exports to US
(3)
Export to
Other Countries
0.562
(15.92)**
0.052
(0.82)
-0.619
(6.34)**
0.093
(1.22)
0.01
(0.08)
-0.169
(2.36)*
0.19
(1.22)
0.781
(3.97)**
0.116
(0.77)
-0.006
(0.03)
-4.118
(1.39)
-3.182
(2.67)**
0.578
(6.02)**
0.488
(2.77)**
-1.623
(6.22)**
0.112
(0.52)
-0.537
(1.50)
-0.562
(2.79)**
-0.635
(1.55)
3.534
(6.39)**
-1.282
(2.79)**
-0.141
(0.20)
21.859
(2.76)**
-14.25
(4.18)**
0.26
(2.31)*
0.385
(1.91)
-1.03
(3.45)**
-0.331
(1.34)
1.479
(3.43)**
-0.151
(0.65)
0.269
(0.58)
0.982
(1.55)
-2.628
(4.79)**
-1.676
(2.01)*
2.549
(0.28)
-5.209
(1.32)
1115
0.66
195.44
707
0.17
14.16
684
0.13
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 LEN DRAT E is positive in the horizontal FDI while negative in the vertical 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 T COST . The horizontal FDI has a
positive coefficient on T COST (1.479) and the vertical has a negative coefficient on
T COST (-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 M KT 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 U SM KT . 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.
8
The difference is -1.282, and the coefficient of the horizontal FDI is 0.112. Hence the coefficient
of 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:
T F Pitj = α + β1 SKILLjt + β2 SIZEijt + β3 EM P LOY EEijt + ²ijt .
(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 T F Pijt = 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 T F P
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 f irms).
EM P LOY 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 EM P LOY EE as an explanatory
variable, while group (2) includes EM P LOY EE. The reason is twofold: first to check
the multicollinearity between EM P LOY 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)
Horizontal MNEs
Top 70%
Vertical MNEs
Bot 30%
1.061
(6.15)***
0.218
(4.21)***
1.656
(8.72)***
-0.204
(2.93)***
Constant
3.849
(6.51)***
Observations
Adj. R-sq
F-value
469
0.11
28.91***
SKILL
SIZE
(2)
Horizontal MNEs
Top 70%
Vertical MNEs
Bot 30%
8.329
(11.97)***
1.012
(5.71)***
0.185
(3.33)***
0.118
(2.01)**
3.011
(4.07)***
1.751
(8.24)***
-0.234
(3.03)***
0.098
(1.75)*
8.009
(11.33)***
229
0.26
42.08***
457
0.11
19.92***
218
0.25
25.45***
EMPLOYEE
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 EM P LOY EE variable is added as an independent 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.
6
10
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
9
See Yokota (2004) for a detailed survey.
Since the data on skilled labor employed by MNEs are not available, total number of employees
employed by MNEs is used.
11
It should be noted again that this is an implication from trade-theoretic FDI models, not the
result of my exercise.
10
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 countries 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 identified. 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
SALE
Dimension
i×j×t
GDP SU M
j×t
|SKILLDIF |
j×t
|M KT DIF |
i×j×t
LEN DRAT E
DSP EAK
j×t
j×t
T COST
j×t
DADJ
DIST AN CE
GDP P C
j
j
j×t
SIZE
i×j×t
DF OOD
i
DCHEM
i
DM ET A
i
DM ACH
i
DELEC
i
D
i×j×t
TFP
i×j×t
EM P LOY EE
i×j×t
Definition
sales by majority-owned non-bank U.S. affiliate in host country (BEA),
measured in 1995 constant price using GDP deflator (IFS, IMF).
sum of U.S. real GDP and host country’s real GDP, constant prices,
Laspeyles. (PWT)
absolute value of host country’s skilled labor share over U.S. skilled labor
share. Skilled labor is defined as the sum of occupational categories
0/1 (professional, technical, and kindred workers) and 2 (administrative
workers) divided by total number of workers. The nearest values are used
for fulfill the missing values for avoiding the loss of degree of freedom.
(ILO)**
absolute value of market size difference between host and U.S. market.
Market size (total output minus exports plus imports) in host country
minus market size in U.S. (WB)
proxy for investment cost defined as lending rate of host country. (IMF)
another proxy for investment cost defined as a dummy variable with 1
for non-English speaking host countries.
proxy for trade cost defined the freight costs by country and industry,
CIF/FOB. (Feenstra)
dummy variable with value 1 if the host country is adjacent to the U.S.
distance between U.S. and the host country.
real GDP per capita in host countries. constant prices, Laspeyles.
(PWT)
proxy for scale economy. Output per firm by industry in the host country.
(WB)
dummy variable with value 1 if the industry is manufacturing foods.
ISIC code 311 and 313.
dummy variable with value 1 if the industry is manufacturing chemicals
and products. ISIC code 351 and 352.
dummy variable with value 1 if the industry is manufacturing metal and
metal products. ISIC code 372 and 381.
dummy variable with value 1 if the industry is manufacturing machinery.
ISIC code 382.
dummy variable with value 1 if the industry is manufacturing electric
equipments and electronics. ISIC code 383.
dummy variable with value 1 when the observation drops in the area in
bottom 30% in the sample, i.e., it is vertical industry.
level of total factor productivity, proxy for the industry productivity.
See text for the definition.
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 between 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 industries
and 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, Ireland, Italy, Japan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Panama, Peru,
Philippines, Portugal, Singapore, South Africa, Spain, Sweden, Taiwan, Thailand, Trinidad and Tobago, 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 in
the Presence of Uncertainty,” Journal of International Economics, 62, 125-148.
[3] Brainard, S. Lael (1993), “An Empirical Assessment of the Factor Proportions Theory,” NBER
working paper no. 4269
[4] Carr, David, James Markusen, and Keith Maskus (2001), “Estimating the Knowledge-Capital
Model 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 of
U.S. Maultinational Firms,” NBER working paper no. 8433.
[7] Helpman, Elhanan (1984), “A imple Theory of Trade with Multinational Corporations,” Journal
of 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 Theory,” Journal of International Economics, 46, 183-204.
[11] Yeaple, Stephen (2003), “The Role of Skill Endowments in the Structure of U.S. Outward Foreign
Direct Investment,” Review of Economics and Statistics, 85(3), 726-734.
[12] Yokota, Kazuhiko (2004), “Comparative Advantage and Vertical Multinaitonals,” ICSEAD
Working Paper series, 2004-34.
28