KEIO/KYOTO JOINTGLOBAL CENTER OF EXCELLENCE PROGRAM
Raising Market Quality-Integrated Design of “Market Infrastructure”

KEIO/KYOTO GLOBAL COE DISCUSSION PAPER SERIES
DP2009-002
How Does FDI Affect Per for mance at Home? An Activity-level
Analysis for J apanese Electrical Machinery and Electronics Fir ms

Toshiyuki MATSUURA*
Kazuyuki MOTOHASHI **Kazunobu HAYAKAWA***
Abstract
Focusing on firms from the electrical machinery and electronics industry, this paper investigates
the impact of foreign direct investment (FDI) on home productivity using activity-level datarather than, as most previous studies have done, firm-level data. Distinguishing between
horizontal and vertical FDI (HFDI and VFDI), it is argued that VFDI changes the activities thatfirms are engaged in at home after the relocation of production, altering firms’ production
function. This means that a direct comparison of productivity at the firm-level before and after
the relocation of production is inappropriate. Instead, in order to assess the impact of FDI onhome productivity, it is necessary to use activity-level data. Doing so, we obtain results that areconsistent with theoretical predictions: HFDI does not necessarily have a significant positiveeffect on home productivity in activities that parallel those conducted by affiliates abroad. On
the other hand, VFDI significantly enhances productivity in activities that have an input-output
relationship with the activity relocated abroad.

*Toshiyuki MATSUURAThe Institute for Economic and Industrial Studies, Keio University, Japan**Kazuyuki MOTOHASHI
Department of Technology Management for Innovation, School of Engineering,
The University of Tokyo, Japan
Research Institute of Economy, Trade and Industry, Japan***Kazunobu HAYAKAWA
Inter-Disciplinary Studies Center, Institute of Developing Economies, Japan
Research Institute of Economy, Trade and Industry, Japan

KEIO/KYOTO JOINT GLOBAL COE PROGRAM
Raising Market Quality-Integrated Design of “Market Infrastructure”
Graduate School of Economics and Graduate School of Business and Commerce,
Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
Institute of Economic Research,
Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan

How Does FDI Affect Performance at Home? – An Activity-level
Analysis for Japanese Electrical Machinery and Electronics Firms

Toshiyuki MATSUURA * §The Institute for Economic and Industrial Studies,
Keio University, Japan

Kazuyuki MOTOHASHI
Department of Technology Management for Innovation, School of Engineering,
The University of Tokyo, Japan
Research Institute of Economy, Trade and Industry, Japan

Kazunobu HAYAKAWA
Inter-Disciplinary Studies Center, Institute of Developing Economies, Japan
Research Institute of Economy, Trade and Industry, Japan

Abstract: Focusing on firms from the electrical machinery and electronics industry, this paper
investigates the impact of foreign direct investment (FDI) on home productivity using
activity-level data rather than, as most previous studies have done, firm-level data. Distinguishing
between horizontal and vertical FDI (HFDI and VFDI), it is argued that VFDI changes theactivities that firms are engaged in at home after the relocation of production, altering firms’
production function. This means that a direct comparison of productivity at the firm-level before
and after the relocation of production is inappropriate. Instead, in order to assess the impact of FDI
on home productivity, it is necessary to use activity-level data. Doing so, we obtain results that areconsistent with theoretical predictions: HFDI does not necessarily have a significant positive effecton home productivity in activities that parallel those conducted by affiliates abroad. On the other
hand, VFDI significantly enhances productivity in activities that have an input-output relationshipwith the activity relocated abroad.
Keywords: Productivity; FDI; Japan; electrical machinery and electronics industry
JEL Classification: F23; H32; O53*

Corresponding author. Toshiyuki Matsuura, Address: Institute of Economic and Industrial
Studies, Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345 Japan. Phone:
81-3-5427-1479; Fax: 81-3-5427-1640. E-mail: matsuura@sanken.keio.ac.jp§
We are grateful to the Ministry of Economy, Trade and Industry for providing us with the micro
data used in this study. The opinions expressed in this paper are those of the authors and do not
represent the views of any of the institutions with which we are affiliated. We would like to thankMasahisa Fujita, Kyoji Fukao, Ken Itakura, Fukunari Kimura, Hiroyuki Odagiri, Sadayuki Takii,
Eiichi Tomiura, Hiroshi Yoshikawa, and participants of the 2008 spring meeting of the Japanese
Economic Association and seminars at RIETI, Nagoya City University, and Yokohama National
University for their valuable comments. Finally, we would like to give special thanks to Ms.
Yurika Uchida for excellent research assistance.
11. Introduction
A topic that has received considerable attention in recent years is the impact ofJapanese multinational firms’ foreign direct investment (FDI) in East Asia on theirperformance at home. Much of the focus has been on firms from the machinery
sector, which have invested in countries throughout the region and have built
extensive production and distribution networks. These international production
networks forged by Japanese firms have come to play a significant role in their hosteconomies, span a wide range of countries, and represent a sophisticated web ofdifferent types of transactions ranging intra-firm to arm’s-length transactions.
However, the impact of the formation of such international production
networks on performance at home is not quite clear. On the one hand, the relocationof production has forced Japanese firms to concentrate their domestic activities on
specific production processes such as upstream processes and to shut down activities
engaged in processes that have been moved abroad. The latter development has
attracted much public attention, giving rise to concerns over the “hollowing out” ofdomestic industry that peaked around the year 2000, when Japanese firms
accelerated the transfer of production to China. On the other hand, against suchconcerns over “hollowing out” must be weighed the potential improvements in firm
performance and productivity at home resulting from outward FDI. Indeed, the active
vertical division of labor with Japanese affiliates in China has resulted in a rapid
increase in Japanese exports to China.
Against this backdrop, a substantial body of empirical literature has grown up
examining the impact of FDI on firm performance and productivity at home,although the results obtained remain inconclusive. Examples of studies in this field
include Navaretti et al. (2004, 2006) for Italy, Hijzen et al. (2007) and Ito (2007) forJapan, and Hijzen et al. (2006) and Navaretti et al. (2006) for France. Hijzen et al.
(2007) and Ito (2007), both analyzing the impact of Japanese FDI on domestic
productivity at the firm level, do not find a robust improvement.
Another consideration in this context is that the effect of FDI on productivity
at home may differ depending on the type of FDI, i.e., whether it is vertical FDI(VFDI) or horizontal FDI (HFDI). From a theoretical point of view, the impact of
HFDI on productivity at home is ambiguous. On the one hand, HFDI may have a2

positive effect on home productivity through knowledge or technologies gained fromoperating abroad. On the other hand, the relocation and hence fragmentation of
production potentially reduces economies of scale. In contrast, the effect of VFDI
unambiguously should be positive due to the vertical division of labor, making it
possible to locate processes in countries that have a comparative advantage in them.
Based on this reasoning, there are several papers that have explored the effectsof HFDI and VFDI separately, but their results seem to be inconsistent with theabove theoretical predictions. Navretti et al. (2006), for instance, distinguish between
FDI in developed and developing economies, with the former assumed to represent
HFDI and the latter VFDI. Hijzen et al. (2006), in contrast, define VFDI as
investments in developing countries by firms in industries in which the investing
country has a comparative disadvantage, and HFDI as investments in developed
countries by firms in industries in which the investing country has a comparativeadvantage. Using French firm-level data in both cases, the two studies find that HFDI
significantly enhances firms’ domestic productivity, while VFDI has no significant
effect.
A possible reason for these counterintuitive results lies in the data they use. An
important difference between the two types of FDI is whether there is a change ininvestors’ main activity at home after the investment abroad. In contrast with HFDI,
VFDI changes firms’ main activity at home, such as from a downstream activity toan upstream activity. Since such a change in activities implies a change in firms’
home production function per se, it is inappropriate to compare firms’ productivity
before and after the VFDI is conducted. This means that the firm-level analyses ofthe previous studies do not present an adequate picture of the impact of VFDI.
The aim of this paper is to re-examine the impact of FDI on productivity at homeby employing data on FDI in the Japanese electrical machinery and electronics
industry. In contrast with previous studies relying on firm-level data, this paper
employs activity-level data that are constructed for this purpose, not ready-madefirm-level data. For example, if a firm has more than one activity such as an upstream
activity and a downstream activity, we treat these activities as different observations.
The use of activity-level data enables us to compare productivity in an activity before
and after an overseas investment was conducted and thus to eliminate any influence of3

a change in activity in the assessment of the impact of FDI. In order to highlight theadvantages of using activity-level data in the measurement of the productivity impactof FDI, we also conduct a sensitivity analysis by estimating the same model using
firm-level data. Doing so, we find that the choice of the data unit makes a significant
difference in our estimate of the impact of FDI.
The rest of this paper is organized as follows. The next section theoretically
discusses the impact of HFDI and VFDI on productivity at home, while Section 3
outlines our empirical methodology. In Section 4, we provide our empirical results,
and Section 5 concludes.

2. The impact of FDI on domestic performanceThis section provides theoretical considerations on the impact of FDI on the
productivity of domestic activities. We begin by discussing the nature of the different
types of FDI and then consider the ways in which horizontal and vertical FDI affectproductivity at home.
The literature on FDI distinguishes two kinds of FDI, horizontal and vertical.
HFDI essentially is conducted to gain access to local markets. Especially in the
manufacturing sector, it often aims at avoiding trade costs in the broadest sense by
setting up production facilities within the target market/country rather than byexporting from the home country. In the case of HFDI, production activities in the hostcountry are more or less the same as those in the home country.
On the other hand, VFDI seeks to exploit international factor price differences.
This means that firms relocate those activities or production processes that are
relatively intensive in the factor in which the host country has a comparativeadvantage. Thus, from a theoretical point of view, there should be little or no overlap
between activities at home and abroad in the case of VFDI. In addition, HFDI and
VFDI differ in terms of the destination of final sales: while HFDI targets the local
market of the host country (or region), the final sales destination of VFDI is third
countries and/or the home country. Although in practice multinational enterprises(MNEs) pursue a variety of motives when investing abroad and the activities ofoverseas affiliates consequently cannot always be easily classified as clearly falling4

into one or the other category, the distinction between horizontal and vertical FDI isvery useful for the analysis of MNE behavior.
Whether firms engage in FDI, and whether such FDI is of the horizontal orvertical type, is likely to be determined by a number of factors, including relative
factor prices, the production technology (returns to scale), fixed investment costs
abroad, and broadly-defined trade costs. In the absence of factor cost differentials, FDI
is likely to be of a horizontal nature. Firms supply their products through HFDI rather
than exporting from the home country when broadly defined trade costs are high.
On the other hand, when there are substantial factor cost differentials between
the host and the home country, FDI is likely to be of a vertical nature. Firms can benefit
from fragmenting their production processes and relocating labor intensive processes
to countries with lower factor prices. Since relocated labor intensive processes are
typically downstream activities such as the assembly of final products, international
fragmentation often requires intra-firm trade between upstream activities such as the
production of parts and components at home and relocated downstream activities.
Thus, low shipment costs may be another determining factor of VFDI.
Next, we consider to the different ways in which FDI affects the productivity ofhome activities. HFDI changes the average cost of home activities through thefollowing channels. First, HFDI unambiguously decreases the level of home
production because it replaces the production of goods designated for the host country.
If we assume firms use an increasing-returns-to-scale technology, this decreaseobviously raises the average cost as depicted in Figure 1, where Xpre and Xpost are thequantities of home production before and after the relocation of production abroad. In
this case, the productivity of home activities unambiguously decreases. 1 Second,
however, there may be knowledge or technology spillovers from the overseas activity
to the home activity, as pointed out in previous studies such as Navaretti et al. (2006).
If such spillover effects exist and the home activity enjoys a sufficient decrease in
marginal costs, the average cost declines, as depicted in Figure 2. In sum, the impact of
HFDI on the productivity of home activities depends on the existence and magnitudeof knowledge/technology spillovers from host countries.

1

Home activity fixed costs rise even further if the costs of establishing the overseas affiliate arealso born by the unit, in which case the negative effect on productivity would be even greater.
5

===Figures 1and 2 ===In contrast, the impact of VFDI on productivity at home is less ambiguous. We
restrict our attention only to the cost structure of an upstream activity at home which is
linked with a downstream activity relocated abroad as part of international
fragmentation. VFDI affects the average cost through its impact on the quantity of
production of upstream products. International fragmentation with intra-firm tradethrough VFDI enables firms to reduce their total operating cost and consequently
reduces the price of their final products as a result of the relocation of production
processes to countries with low factor prices. The decrease in the price of final
products increases the total production quantity of such firms and thus also increases
the production of upstream activities at home, because the increase in downstream
production abroad leads to an increase in the export of upstream parts and components
for use in downstream activities. Again, assuming an increasing-returns- to-scaletechnology, the average cost of home upstream activities decreases, as depicted in
Figure 3, and the productivity in such activities thus rises.
===

Figure 3 ===So far, we have only examined the impact of FDI on the level of productivity inhome activities. In fact, almost all preceding studies have focused exclusively on theimpact on the productivity level. However, FDI might also affect the growth of
productivity. HFDI, for example, as mentioned above, may involve knowledge and/or
technological spillovers, which may affect productivity growth. A substantial numberof studies on spillover effects have shown that the presence of MNEs in a country has
a positive effect on the productivity growth of host country firms (e.g., Görg andGreenaway, 2004; Crespo and Fontoura, 2007). It may therefore be hypothesized that
there are also knowledge and/or technology spillovers from the host country via theforeign affiliate to activities at home affecting not only the level but also the growth of
productivity
VFDI may similarly affect productivity growth through offshoring. Hijzen et al.
(2008), for example, suggest that specializing in skill-intensive production stages

6through

offshoring

generates

higherproductivity

growthdue

tolargerlearning-by-doing effects than in the case of no offshoring. Consequently, both HFDI
and VFDI may affect not only the level but also the growth of productivity in activities
at home. Given these considerations, in the empirical analysis we examine the impactof outward FDI on both the level and the growth of productivity at home, focusing on
Japanese manufacturing firms.

3. Empirical Issues
In this section, we begin by explaining our empirical methodology to examine
the impact of FDI on performance at home. We then briefly describe our data sourcesand explain how we construct our productivity measure.

3.1. Empirical Methodology
To examine the impact of FDI on home productivity, we conduct our analysis at
the most detailed level possible. This means that instead of the firm, our unit ofanalysis is the production activities in a specific line of business. As mentioned earlier,in the case of VFDI, a certain part of the production process is relocated abroad,meaning that the production activities at home before and after the relocation of
production differ. This, in turn, implies that investigations examining the impact of
VFDI on productivity at the firm-level in fact miss a key element, namely that changes
in productivity reflect changes in activities in addition to any learning effects.
Therefore, in order to measure the learning effects only, it is necessary to focus on the
productivity change only in those activities conducted at home both before and after
the relocation of production. It is for this reason that in our analysis we employ not
firm-level but activity-level data.
To examine the impact of FDI on home performance, it is necessary to address
the issue of endogeneity between productivity and FDI. Previous studies have tended
to adopt one of two approaches: instrumental variable methods and propensity scorematching methods. Particularly when micro data are available, the latter, which
requires a larger number of observations, is usually the preferred method. However,our data are highly disaggregated, and this high degree of disaggregation prevents us
from using the type of matching methods typically employed in preceding studies such7

as the ones mentioned at the outset. The purpose of such matching methods is to
overcome the endogeneity problem that firms that conduct FDI, by their very nature,
tend to be more productive than non-FDI firms. (Nearest) matching methods usuallychoose non-FDI firms that not only have the closest probability of conducting FDI butthat also operate in the same industry as the FDI firms that they are matched with.
However, the high degree of disaggregation in our study means that the number of
non-FDI firms engaged in the same industry/production process as FDI firms is smalleven though our dataset is one of the largest datasets available in Japan. Thus, the lackof a sufficient number of observations prevents us from obtaining a good matching.
Instead, following Castellani et al. (2007) and Hijzen et al. (2008), we therefore
specify a linear equation with a lagged dependent variable in order to control forfluctuations caused by elements not adequately measured by our productivity index.
We estimate two kinds of equations at the activity-level: level equations and growthequations. The specifications are as follows:
TFPij (t) = ρ TFPij (t-1) + β1 Horizontalij (t-1) + β2 Verticalij (t-1) + δ(t) + ηij + εij (t),∆TFPij (t) = λ ∆TFPij (t-1) + γ1 Horizontalij (t-1) + γ2 Verticalij (t-1) + δ(t) + ηij + εij (t),where TFPij (t) and ∆TFPij (t) denote respectively the level and the first-difference of
productivity in firm i’s activity j in year t. Productivity is measured using a total factor
productivity index, the construction of which will be explained below. Horizontalij andVerticalij represent the magnitude of firm i’s horizontal and vertical FDI in activity j.
We take the lagged dependent variable and the two FDI variables as predetermined. To
control for any endogeneity of these predetermined variables, we employ the System
GMM (general method of moments) proposed by Blundell and Bond (1998). We use
the second and third lagged observations of the dependent variable and of the FDI
variables as instruments. In both equations, the error term is modeled as a time fixed
effect (δ), a firm activity fixed effect (η), and the usual disturbance (ε).
In order to pinpoint the impact of FDI on home productivity in the activity ofinterest, it is necessary to gauge the magnitude of HFDI and VFDI in these activities,
which the variables Horizontalij and Verticalij do. These variables are constructed as8

follows. As outlined in Section 2.1, in the case of HFDI, overseas units engage in thesame activity as units at home, while in the case of VFDI, activities abroad differ fromthose at home and often form an input-output relationship with home activities. Thus,
firm i’s HFDI in activity j, i.e, Horizontalij, represents the scale of production abroad in
the same activity that units at home are engaged in. On the other hand, firm i’s VFDI inactivity j, Verticalij, represents the scale of production abroad in activities which form
an input-output relationship with home activities. In practice, many firms have foreign
affiliates in different countries. To give an example, suppose that an MNE with both
upstream and downstream activities at home also has downstream activities in country
1 and country 2 as well as upstream activities in country 2. Table 1 shows ahypothetical example of such a distribution of production activities. In this example,
both A and C are upstream activities, so C represents a horizontal investment for A. On
the other hand, assuming the overseas downstream activities D and E form an
input-output relationship with upstream activity C at home, D and E represent verticalinvestments for A. We measure the scale of firms’ overseas activities using the numberof employees, so in the case of our hypothetical firm here, Horizontal for A measuresthe employment in C, while Vertical for A measures the sum of the employment in D
and E.
===

Table 1===Furthermore, it is necessary to normalize the scale of the two FDI variables,
Horizontal and Vertical. For the HFDI variable, we do so by dividing the scale of firm
i’s overseas production in activity j by its worldwide production in that activity, i.e., by
the sum of overseas and domestic production in activity j. In our example in Table 1,
Horizontal for home activity A thus is overseas upstream activity C adjusted by thesum of A and C. On the other hand, as for Vertical, we divide by the magnitude of firmi’s production in the whole world including home. That is, in Table 1, the VFDI
variable for upstream activity at home A is adjusted by the sum of A, B, C, D, and E. 2
2

The reason why we include the same activity as a home activity in the denominator of Vertical is
to distinguish platform type VFDI (the vertical division of labor between the host and other hostcountries) from pure VFDI (the vertical division of labor between the home and the host country).
For instance, in Table 1, if overseas downstream activities (D and E) purchase upstream products
9As already mentioned, in this paper we use overseas’ affiliates employment togauge the scale of firms’ overseas activities. Another measure that would potentiallybe more appropriate than employment is overseas affiliates’ value added. However,many firms do not report the cost of intermediate inputs, which would be necessary to
calculate value added. Consequently, we use the number of employees and define thetwo FDI variables as follows:∑
Horizontal =∑ijr ∈ ROr ∈R∑ ∑, Vertical =
L
∑ ∑
Lrij

r ∈RO

k ∈S j

j∈S

r ∈R

ij

r
ij

Lrik

Lrij

where Lrij represents the number of employees in firm i in activity j in country r; S
denotes the set of all activities in the industry to which activity j belongs; and R is theset of all countries: R ∈ {All countries including Japan}, RO ∈ {All countries except
for Japan}. Sj denotes the set of activities with an input-output relationship withactivity j. For example, if activity j is the manufacture of “electrical machinery,equipment and supplies,” Sj is the manufacture of “electronic parts and devices.”
Details on the activities used in our analysis are provided in the next subsection.
In addition, we construct two further variables by decomposing the variable
Vertical as follows. If activity j is an upstream activity, then∑ ∑Upstream =
∑ ∑
r∈RO

k ∈S j

Lrik

j∈S

r ∈R

Lrij

ij

,

and Upstreamij = 0 otherwise. If activity j is a downstream activity, then∑ ∑Downstream =
∑ ∑
r∈RO

k ∈S j

Lrik

j∈S

r ∈R

Lrij

ij

.

and Downstreamij = 0 otherwise. Returning to the example in Table 1, while thevariable Downstream for home activity A refers to the sum of D and E, there are nofurther upstream activities for home activity A. Thus, we set Upstream to zero for thehome activity, i.e., A, in the case of this example. Similarly, while Upstream for home
activity B is C, there are no further downstream activities for B, thus, Downstream for
not only from the home activities (A) but also from country 2 (C), the use of indicator(D+E)/(A+D+E) as the VFDI variable for A overestimates the impact of overseas production(D+E) on the productivity of activity A. In this case, the more appropriate indicator for the VFDI
variable for A is (D+E)/(A+C+D+E). Therefore, we normalize the VFDI variable by the sum of
the scale of all activities in the world.
10

B is defined as zero. Therefore, using Upstream and Downstream in place of Vertical,
we distinguish between the impact of the relocation of an upstream activity and adownstream activity.
Finally, two more points should be noted. First, our variables representing FDI
are continuous, while most previous studies use binary variables, i.e., variables thattake unity if a firm conducts FDI and zero otherwise. 3 Since most other studies use
propensity score matching methods, their FDI variables are restricted binary variables.
However, since we use the GMM approach here, this enables us to use continuous
variables. Given that it takes time for overseas affiliates to ramp up production after
the initial investment, using continuous variables has the advantage that we canmeasure the incremental spillovers from the division of labor through FDI. Using
continuous variables representing affiliates’ activities enables us to take this kind oftime lag into consideration. However, employing continuous variables, we cannot
clearly distinguish between the impacts of two separate investments if these occur inshort succession and production is still being ramped up in the first affiliate wheninvestment in the second takes place. Therefore, the variables representing firms’
overseas production activities measure these not for individual affiliates but for allaffiliates in one particular region.
Second, a possible concern is the double counting of learning effects. Let us
return to the example presented in Table 1. In our methodology, for home upstream
activity A, the proxy for the scale of HFDI is foreign upstream activity C. At the sametime, for home downstream activity B, foreign upstream activity C is regarded as theproxy for the scale of VFDI. That is, the effect of relocating production shows up in
both Horizontal and Vertical if an MNE has both downstream and upstream activities
at home (i.e., the MNE is an integrated MNE). Such double counting produces noise in
the coefficients for both Horizontal and Vertical. However, since there are few
integrated MNEs, 4 the influence of such double counting on our estimates is likely to
be trivial.

3.2. Data Issues
3

One preceding study using continuous variables is Hijzen et al. (2008).
The share of integrated MNEs in all Japanese MNEs in our dataset is around 10 percent. Thatis, most firms investing abroad engage only in one type of activity overseas.

4

11

Our primary data sources are the linked longitudinal data sets of the Census of
Manufactures and the Basic Survey of Overseas Business and Activities for the period
1985-2003. 5 The Census of Manufactures provides data on establishments located inJapan (including, e.g., their location, number of employees, tangible assets, and valueof shipments). The Basic Survey of Overseas Business and Activities, on the other
hand, provides data on Japanese overseas affiliates for the period from 1985 and 2003.
Finally, information on the parent firms of establishments/affiliates, such as their
number of employees, we obtain from the Basic Survey of Japanese Business Structure
and Activities. In our analysis, we exclude establishments with fewer than 9
employees, because such establishments do not provide information on capital,
which is indispensible for calculating our productivity measure, total factor
productivity (TFP). Information on capital data is also not available for 2001 and2002 for establishments with less than 29 employees. Therefore, our linked panel
data set covers the years from 1985 to 2000 and 2003.
We calculate the TFP index following Caves et al. (1982, 1983) and Good et al.
(1983). The TFP index at the activity-level is calculated as:

(

)

(

)(

)

1
s ijft + s ft ln X ijft + ln X ft
2
t
t
F 1
+ ∑ s =1 ln Qs − ln Qs −t − ∑ s =1 ∑ f =1 s fs + s fs −1 ln X fs − ln X fs −1
2

TFPijt = ln Qijt − ln Qt − ∑ f =1

(

F

)

(

)(

) ,

where Qijt denotes the gross output of firm i’s activity j in year t, sijft stands for thecost share of input f for firm’s activity j in year t, and Xijft is the input of factor f in
firm’s activity j in year t. Variables with an upper bar denote the industry average ofthat variable. We define a hypothetical (representative) firm for each year andindustry. Its input and output are calculated as the geometric means of the input andoutput of all establishments in the industry. The first two terms on the right-hand sideof the equation denote the cross-sectional TFP index based on the Theil-Tornqvist
specification for each firm and year relative to the hypothetical establishment. Since
the cross-sectional TFP indexes for t and t-1 are not comparable, we adjust thecross-sectional TFP index with the TFP growth rate of the hypothetical firm, which iswhat the third and fourth terms in the equation represent. For more details on theconstruction of these variables, see Appendix B.
5For details on the construction of our dataset, see Appendix A.
12This paper focuses on the seven activities in the electrical machinery andelectronics manufacturing industry with the largest outward FDI stock. Five of these
are categorized as downstream activities, while the other two are upstream activities.
The classification into upstream and downstream activities is based on the
input-output relationships among them, which are determined on the basis of the
Input-Output Tables published by the Ministry of Internal Affairs andCommunications. We do so by first defining upstream activities as activities in which
the share of manufacturers’ intermediate demand in total domestic demand is greaterthan 90 percent. This is the case for the manufacture of “electronic parts and devices”
and “semiconductors.” Next, the five remaining activities are defined as downstream
activities. They include “office machinery,” “household electronic equipment,”“industrial electrical apparatus,” “electronic data processing machines,” and“communication equipment.”
Using this classification, Table 2 shows the distribution of firms, for the year
2000, in terms of the combination of their activities at home and abroad. The figure
113 in the right-most column, for example, indicates that there are 113 firms that are
engaged in upstream activities at home and also abroad. The figures in parentheses
show the distribution when we focus only on whether firms have activities in East
Asia (i.e., ignoring activities in other parts of the world). The table allows two
observations. First, there are more firms that engage in similar activities at home andabroad (i.e., downstream/downstream or upstream/upstream) than firms that engage
in different types of activities at home and abroad (i.e., downstream/upstream or
upstream/downstream). This suggests that there are more firms conducting HFDI
than VFDI. Second, the pattern is very similar if the criterion is whether firms haveactivities located in East Asia, which is the area that has attracted the greatest numberof Japanese overseas affiliates and accounts for the largest share of overseas
employment by Japanese firms.

===

Table 2 ===13

It is generally presumed that Japanese FDI in the electrical machinery andelectronics industry, especially when it is directed at East Asia, is motivated by the
exploitation of factor cost differences. This means that we would expect firms to movelabor-intensive downstream activities overseas, while they keep capital-intensive
upstream activities in Japan. However, in practice, the vertical division of labor inJapanese MNEs may be more complicated than this. The upper half of Table 3 shows
the share of imports from Japan in overseas affiliates’ total procurements. As can beseen, the share of imports from Japan tends to be higher for upstream than fordownstream affiliates, and in one case (semiconductors) substantially so. Next, thelower half of the table shows the share of exports to Japan in overseas affiliates’ total
sales. The figures indicate that upstream affiliates, particularly those in the ASEAN
countries and China, tend to export more of their output to Japan than downstream
affiliates. In the case of semiconductor manufacturing affiliates in ASEAN and China,
more than 60 percent of their products are exported to Japan. Since around 90 percent
of affiliates’ exports to Japan are intra-firm trade, 6 such upstream products are likely to
be further assembled in home activities, and the assembled products then arepotentially again exported abroad. This pattern reflects the complex nature of
production systems in the electrical machinery and electronics industry. For example,
the manufacturing process of semiconductors can be decomposed into two types ofprocesses, capital-intensive processes such as lithography and etching andlabor-intensive processes such as packaging and inspection. A large share of the latterlabor-intensive processes is conducted in China, and the completed semiconductor
products are then imported back to Japan to be used for the further assembly ofelectronics products. Due to this complex division of labor in the electrical machineryand electronics industry, we may find that VFDI may have an impact on productivity
not only in upstream activities but also in downstream activities.
===

Table 3 ===6

According to Kiyota et al. (2008), the average intra-firm trade ratio for Japanese multinationals
in the electrical machinery and electronics industry exceeds 90 percent.
14

4. Empirical Results
We can now begin the empirical analysis. 7 The system GMM regression results
are reported in Table 4. Introducing only the first-lagged dependent variable in thelevel equation, we can see that the results are not consistent with the assumption of the
System GMM: the AR(2) test and Hansen’s J test are rejected. Based on the rejectionof the AR(2) test, we introduce both the second- and third-lagged dependent variables
as independent variables in the level equation. The results are as follows. First, thecoefficients on Horizontal are insignificant in the level equation, while in the growthequation, the coefficients are positive and significant. These results may be evidenceof weak spillover effects from host countries to activities at home. Second, thecoefficients on Vertical are significantly positive in both the level and growthequations. We may say that, while the positive coefficient in the level equationsuggests that VFDI raises productivity through the vertical division of labor, that in thegrowth equation suggests that VFDI generates learning-by-doing effects. Thisdifference in the productivity impact of HFDI and VFDI is consistent with ourtheoretical discussion in Section 2.
In addition, we decomposed the variable Vertical into Upstream andDownstream, corresponding to the relocation abroad of upstream and downstream
activities, respectively. We find that the coefficients for both Upstream andDownstream are significant and positive. This is a somewhat surprising result. We had
expected a significant positive effect on home productivity of the relocation ofdownstream activities, which typically consist of labor-intensive assembly processes,
and the significant coefficient on Downstream confirms the presence of such an effect.
On the other hand, the significant result for Upstream is somewhat unexpected, but islikely to reflect the complex nature of production systems in the electrical machineryand electronics industry described earlier: certain upstream activities (such as
inspection and packaging) also tend to be labor intensive and their relocation raises
productivity in activities that remain at home.
===

7

Table 4 ===For basic statistics of the variables we used, see Table A1 in the Appendix.
15In order to further investigate the effect of FDI on home productivity, we focus on
Japanese overseas activities in East Asian countries (ASIA). 8 The FDI variables for
East Asia are defined as follows:∑
Horizontal =
∑
ij

∑ ∑
, Vertical =
L
∑ ∑

Lr
r∈ASIA ijr∈R Asiar∈ ASIAk ∈S jLrik

j∈Sr∈R Asia

Lrij

ij

r
ij

,

where RAsia is a set consisting of East Asian countries and Japan. While in the previous
analysis, these variables were based on MNEs’ overseas employment worldwide, here
they are restricted to overseas employment in East Asia only.
Focusing on FDI in East Asia is useful for two reasons. First, as shown in
Table 2, East Asia is by far the most important region for overseas production by
Japanese firms; concentrating on East Asia only thus allows us to examine whether
there are any qualitative differences in the impact of FDI in this region. Second,
focusing on FDI in East Asia helps to control for differences in skill levels across hostcountries. For example, the skill level of workers in OECD countries tends to be higher
than that of workers in East Asian countries. Restricting the two FDI variables to East
Asian countries thus enables us to avoid the aggregation of employees with different
skill levels.
Since the above two variables include only employment in East Asian
countries, to control for the different [???] impacts of MNEs’ activities in developed
countries (Developed) and developing countries excluding East Asian countries(Other) on home productivity, 9 we also prepared the following two variables:

FDI Developed,ij∑ L , FDI∑=
∑ ∑ L
r∈Developed
j∈S

j∈S
r
r∈R ijrij∑ ∑ L .
=∑ ∑ L
r∈Other

Other,ij

j∈S

r
ij

j∈S

r∈R

r
ij

The numerator for these variables is the number of employees at Japanese affiliates inthe developed countries and in all other countries apart from the East Asian countries,
respectively, while the denominator is the same as for Vertical.
The regression results are reported in Tables 5. 10 The table shows, first, that the
8

In this paper, East Asia includes South Korea, Taiwan, Hong Kong, Singapore, Malaysia, thePhilippines, Thailand, Indonesia, and China.
9
Developed countries here consist of the United States, Canada, the Western European countries,
Australia, and New Zealand. And other developing countries are rest of countries except for East
Asia.
10
Basic statistics are again presented in Table A1 in the Appendix.
16coefficient on Horizontal is insignificant in all specifications. This result is consistent
with the fact that we would expect any impact of HFDI on home productivity to be theresult from knowledge spillovers from the host country. Such knowledge spillovers areless likely from East Asian countries, which have a lower knowledge base thandeveloped countries. Second, although the results for the VFDI-related variables are
qualitatively unchanged from those in Table 5, the size of the coefficients is
considerably greater. This result is consistent with the general argument that the larger
the differences in factor prices between home and host countries, the larger are thebenefits from VFDI. Third, as in Table 5, the coefficients on both Upstream (t-1) and
Upstream (t-1) are positive and significant.
===Table 5 ===Finally, in order to check the validity of using activity-level data, we also run thesame regressions using firm-level data. 11 The equations that are estimated are:
TFPi (t) = ρ TFPi (t-1) + β1 Horizontali (t-1) + β2 Verticali (t-1) + δ(t) + ηi + εi (t),∆TFPi (t) = λ ∆TFPi (t-1) + γ1 Horizontali (t-1) + γ2 Verticali (t-1) + δ(t) + ηi + εi (t),where TFPi (t) and ∆TFPi (t) denote the level and the first-difference of productivity infirm i in year t, respectively. Horizontali and Verticali are calculated as:∑
Horizontal =∑ir ∈ ROr∈R∑ ∑, Vertical =
L
∑ ∑
Lrih

i

r
ih

Lrik

r ∈ ROk ∈S hh∈S

Lrr ∈R ih,r

where L ih represents firm i’s employment in activity h in country r. Activity h is theactivity with the largest sales share in all of firm i’s activities. As is shown in Table 6,
the coefficients for Horizontal and Vertical become insignificant. Similarly, thecoefficients on Upstream and Downstream now are also insignificant with the
exception of that on Downstream in the growth equation. In sum, the results of thefirm-level estimation are strikingly different from the activity-level estimations,11

The firm-level data were constructed by aggregating the activity-level data for each firm. Thus,
our observations do not include the cost of headquarter services.
17

confirming our hypothesis that the inconclusive results of preceding studies on therelationship between FDI and productivity at home owe to data problems. As we argue
in Section 1, VFDI may change the activities that firms are engaged in at home and theinconclusive results on the effect of FDI on home productivity may be a result of thenoise caused by such changes in home activities. Thus, activity-level data is more
appropriate to compare productivity before and after the relocation.
===

Table 6 ===5. Concluding Remarks
Focusing on Japanese electrical machinery and electronics firms, the purposeof this study was to examine the impact of HFDI and VFDI on productivity at home
separately by using activity- rather than firm-level data. The difference between these
two types of FDI lies in the way they firms’ activities at home change as a result.
Although there are a substantial number of studies that have sought to compare theproductivity impact of HFDI and VFDI, their results have been inconclusive. In
particular with regard to the productivity impact of VFDI, the evidence tends to be
weak. A likely reason for this is that all of these studies have used firm-level data. Yet,in the case of VFDI, firms’ domestic activities may change significantly as a result,
so that simply comparing the performance of the firm before and after the investment
will lead to inconclusive conclusions. Therefore, it is important to address the impactof foreign investment in downstream (or upstream) activities on those activities thatremain at home.
In order to address this issue and overcome the shortcomings of previous
research, the present study employed activity-level data, which makes it possible tocompare the productivity in an activity before and after an overseas investment. The
results we obtained are consistent with the theoretical predictions, namely, that VFDI
significantly raises productivity in activities that remain at home, while HFDI has noclear impact on productivity. Furthermore, focusing on FDI in East Asia, which is thearea that has attracted the largest number of Japanese affiliates, we found that the
productivity improvements at home as a result of VFDI are even greater, whichmight be due to the large differences in factor prices between Japan and host18

countries. Although the relocation of production to East Asian countries, i.e., VFDI,
has given rise to concerns over the “hollowing out” of domestic industry, ourempirical results suggest that the active vertical division of labor with Japaneseaffiliates in East Asia helps to enhancing the productivity of, and hence sustain, those
activities that remain at home.
These findings highlight a number of important issues and point to avenues
for further research. First, it is important to take into account the complexity of FDI.
Some recent studies have attempted to do so by developing theoretical models usinga three-country rather than the traditional two-country framework (Ekholm et al.,2007; Grossman et al., 2006; Yeaple, 2003). In these models, VFDI is conceptuallydivided into pure VFDI and complex VFDI, where the former consists of the divisionof labor between the home country and one host country, while the latter involves
more than one host country. While this paper does not distinguish between these two
types of VFDI, doing so may help to further refine the analysis and produce
interesting results. Second, although studies on the impact of FDI on productivity at
the firm level have produced inconclusive results, it is important to continue
examining firm-level effects. In the case of both HFDI and VFDI, the relocation ofparticular production activities results in a reallocation of labor resources withinfirms, and the speed with which firms are able to do so may be an important
determinant of productivity improvements at the firm-level. The dataset employed
for the analysis in this paper allows us to examine changes in employment acrossfirm activities, and investigating the effects of the reallocation of labor resources
within firms in response to the relocation of production through FDI represents animportant research task for the future.

19

Appendix A. Data Sources
Our primary data source in this paper is a database we constructed using the
Census of Manufactures (COM), the Basic Survey of Japanese Business Structure
and Activities (BSJBSA), and the Survey of Oversea Business Activity (SOBA) by
the Ministry of Economy, Trade and Industry (METI). In this appendix, we provide
some basic information on these surveys and briefly explain the procedure of data
construction.
A) Census of Manufacturers
The Census of Manufactures is a representative survey of economic activity
and its origins date back to 1868, the year of the Meiji Restoration. The census isconducted on all establishments in calendar years ending with 0, 3, 5 and 8. For otheryears, the census covers establishments with 4 or more employees. The census uses
two forms, one of which (Form A) is for establishments with 30 or more employees,
and another, simpler one (Form B), which is for establishments with 29 or fewer
employees. The total number of establishments covered in 2003 is 504,530, of which
about 46,284 fall into the Form A category.
Major items in the census are shipments, inventories, book values ofequipment and structures, employment, cost of materials, and energy usage. However,there are some limitations on the availability of information on book values ofequipment and structures, and depreciation for smaller establishments, because
establishments with 9 or fewer employees are not required to report these items andafter 2000, such information for establishments with 29 or fewer employees isavailable only every 5 years.
Since the identification numbers for establishments are revised every 5 years,
we linked the establishment data using matching tables provided by METI for theconstruction of our panel dataset. 12

12The compilation of the panel dataset of the Census of Manufactures was conducted by a groupof researchers as well as members of the Quantitative Analysis Database division at the ResearchInstitute of Economy, Trade and Industry (RIETI), including Kazushige Shimpo (Keio
University), Kazuyuki Motohashi (University of Tokyo), Toshiyuki Matsuura (Hitotsubashi
University), Kyoji Fukao (Hitotsubashi University), Hyeog Ug Kwon (Nihon University),
Mutsuharu Takahashi, and Tami Ohomori (RIETI). For details of the procedure, also see
Motohashi (2002), Shimpo et al. (2004), Fukao et al. (2006) and Matsuura et al. (2007).
20

B) Basic Survey of Japanese Business Structure and Activities
The Basic Survey of Japanese Business Structure and Activities (BSJBSA) is acomprehensive firm-level survey conducted by the Ministry of Economy, Trade andIndustry. The survey was first conducted in 1991 and then again in 1994, since when
it has been conducted annually. The main purpose of the survey is to gain an overall
picture of Japanese corporate firms’ activities in terms of their diversification,
globalization, and strategies on research and development as well as information
technology. The strength of the survey is its coverage and the reliability of its
information. The survey includes all firms with more than 50 employees and withcapital of more than 30 million yen. The survey covers the mining, manufacturing,and service sectors, although some services industries, such as finance, insurance,and real estate, are not included. Another feature of this survey is that each firm has
its own identification number (BSJBSA code) throughout. Therefore, it is easy for
researchers to construct a panel dataset. The limitation of the survey is that
information on financial and institutional features, such as whether firms belong to akeiretsu, are not available and small firms with fewer than 50 workers (or withcapital of less than 30 million yen) are excluded.
For our analysis, we decided to extend our definition of a firm to include
wholly-owned firms (subsidiaries). In Japan, manufacturing firms often relegate
production activities to their subsidiaries. However, since the firm-level data in the
BSJBSA is essentially on a non-consolidated basis, production activities by whollyor majority-owned domestic affiliates are excluded from the measurement of MNEs’
productivity. 13 This exclusion may result in significant measurement error. To avoid
such measurement error, we extend our definition of the firm as described above. In
practice, this means that we need to identify each firm’s parent and, moreover, theparent’s BSJBSA code. The BSJBSA reports the securities code of each firm’s parent,
and we obtained a converter table for the BSJBSA code and the securities code fromMETI for the period 1991-2000. Employing this converter table, we can identify the
BSJBSA code of firms’ parent even for the period 2001-2003 as long as the parent’s
securities code is available for the period 1991-2000.
13

To provide an example, according to Sony’s annual report, the domestic production ofbatteries, semiconductors, and video cameras is carried out by wholly-owned affiliates.
21C) Survey of Overseas Business Activities
The Survey of Overseas Business Activities (SOBA) is an affiliate-level survey
conducted by the Ministry of Economy, Trade and Industry. The aim of this survey is
to obtain basic information on the activities of foreign affiliates of Japanese firms.
The survey covers all Japanese foreign affiliates. The survey consists of two parts.
One is the Basic Survey, which is more detailed and is carried out every 3 years. The
other is the Trend Survey, which is less comprehensive and carried out between the
Basic Surveys. A foreign affiliate of a Japanese firm is defined as an overseas
subsidiary in which a Japanese firm holds 10 percent or more of the invested capital.
The SOBA provides, for example, the establishment year of a foreign affiliate, abreakdown of its sales and purchases, its employment, cost of labor, research anddevelopment expenditures, etc. For further information on the items in the SOBA,
refer to the “Survey Form for Overseas Affiliates” and the “Guide for Completing theSurvey.” 14
The micro data underlying the SOBA does not have any unique identification
number for affiliates. We therefore linked the data using the information on affiliates’
location, name, year of establishment, etc., to construct the panel data set. 15
D) Linking the databases
The three datasets just described were linked as follows. First, we linked theactivity-level data from the COM and firm-level data from the BSJBSA.

Althoughboth surveys are conducted by METI, each survey uses its own firm identification(ID) code and no matching table to match the COM and BSJBSA codes is available.
We therefore match firms by referring to their names, telephone numbers, and otherinformation such as addresses. Moreover, the firm ID numbers in the COM weredrastically revised in 1997, meaning that we needed to match the firm ID number
used before 1996 by referring to the list of name and addresses of firms. The result of
linking the COM and the BSJBSA in this way is satisfactory: the number ofmanufacturing firms for which we manage to match the data from the two sources is
14

Downloadable from the METI website at:
http://www.meti.go.jp/english/statistics/tyo/kaigaizi/index.html.
15
For details on the SOBA panel dataset, also see Kiyota et al. (2008).
22

more than 95 percent of the total number of manufacturing firms covered in the
BSJBSA. 16
Next, the SOBA is linked with the BSJBSA.

First of all, since METI revises

the parent firm code for the SOBA every year, we compiled a matching table for theparent firm codes and then constructed our panel dataset. Second, based on
information such as firms’ name, address, and number of employees, we matchedfirms in the BSJBSA and the SOBA. While the SOBA covers almost all Japanesefirms with foreign affiliates, the coverage of the BSJBSA is restricted to firms withmore than 50 employees or paid-in capital of more than 30 million yen. Therefore,
we were not able to link all foreign affiliates in the SOBA with the BSJBSA.

16

Note that since the BSJBSA covers only firms with more than 50 employees and 30 million
paid-in capital, establishments which belong to small firms cannot be linked with firm-level data.
The number of matched firms is about 10 percent of the total number of firms in the COM.
23

Appendix B. Construction of Variables Used for the TFP Index
Output, intermediate input, labor input, and deflators
Real value added is defined as real gross output minus real intermediate input.
Real gross output is measured as shipments deflated by the output deflator, whileintermediate input is the cost of materials deflated by the input deflator. Labor input
is measured by the total number of employees multiplied by the industry-levelworking hours from the System of National Accounts (Cabinet Office). All output
and input deflators are obtained from the JIP Database 2006 (Fukao et al., 2006).

Capital stock
Following Fukao et al. (2006), as for the capital stock, we used the nominal
book values of tangible assets by multiplying the ratio of the net stock with the bookvalue of industry-level capital. 17 Net capital stocks by industry are from the JIP
Database 2006, while the book values of capital by industry are obtained byaggregating the individual data from the Census of Manufactures.

Cost shares
To construct the TFP index, we need the shares of labor costs, intermediate
input costs, and capital costs in total costs. Labor costs are defined as total salaries
and intermediate input costs as the sum of raw materials, fuel, electricity andsubcontracting expenses for consigned production. Capital costs are calculated bymultiplying the real net capital stock with the user cost of capital, PK. The latter isestimated as follows:⎛⎞P&
PK = PI ⎜ rt + δ − I ⎟ ,P
I ⎠⎝where PI is the price of investment goods, r the interest rate, and δ the depreciation
rate. Data on the price of investment goods and the depreciation rate are calculated
17

Fukao, Kim and Kwon (2006) suggest that it is more appropriate to use the ratio of the net
stock to the book value of capital by type of asset. In the COM, however, the book values ofcapital by type of asset are available only for those establishments that have more than 30
employees. Therefore, in order to include small establishments in our sample, rather thancalculating the ratio of the net stock to the book value of capital by type of asset, we employed
the methodology described above.
24

using the investment and capital stock matrices from the JIP Database 2006. 18
Interest rates (10-year-bond yields) are from the Bank of Japan.

18

The JIP Database provides investment and capital stock matrices by 108 industries and 39
types of assets. We calculated the weighted averages of price indices for investment goods andthe depreciation rates by industry.
25

Appendix C. Basic Statistics of our Dataset
Table A1. Basic Statistics
N

Mean

Sd

p10

p90

Activity-level
32,8970.024
0.243-0.137
0.202
∆ TFP
TFP
32,897
0.949
0.695
0.000
1.785
Horizontal
32,897
0.100
1.654
0.000
0.000
Vetical32,897
0.030
0.663
0.000
0.000
Upstream
32,897
0.021
0.648
0.000
0.000
Downstream
32,897
0.009
0.143
0.000
0.000
Activity-level (East Asia estimation)
FDI Developed
32,897
0.085
1.285
0.000
0.000
FDI Other
32,897
0.024
0.308
0.000
0.000
Horizontal
32,897
0.019
0.103
0.000
0.000
Vetical32,897
0.007
0.107
0.000
0.000
Upstream
32,897
0.004
0.080
0.000
0.000
Downstream
32,897
0.003
0.072
0.000
0.000
Firm-level
29,3220.029
0.249-0.146
0.221
∆ TFP
TFP
29,322
0.942
0.668
0.000
1.725
Horizontal
29,322
0.097
1.678
0.000
0.000
Vetical29,322
0.013
0.316
0.000
0.000
Upstream
29,322
0.008
0.286
0.000
0.000
Downstream
29,322
0.005
0.134
0.000
0.000
Note: “East Asia estimation” refers to the variables used in Table 5. For details, see text.
Source: Authors’ calculations based on the Basic Survey of Overseas Business Activities andCensus of Manufactures (1985-2003).

26

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29

Figure 1. The Impact of HFDI on the Average Cost of Home Activities

Figure 2. The Impact of HFDI on the Average Cost of Home Activities, with
Spillovers

30Figure 3. The Impact of VFDI on the Average Cost of Home Activities

31

Table 1. Hypothetical Example of an MNE’s Distribution of Production Activities
Home
Country 1
Country 2

UpstreamA
C

Downstream
B
DE

Table 2. Distribution of Firms in Terms of Their Activities at Home and AbroadHome

No overseas
Abroadactivities Downstream Upstream
Downstream
1249
174
113
(1275)(148)(107)
Upstream
723
81
124
(737)
(69)
(112)
Source: Authors’ calculations based on the Basic Survey of Overseas Business Activities (2000).
Notes: The numbers in parentheses show the distribution if only overseas affiliates in East Asia
are considered. “No overseas activities” means that these firms have no overseas affiliates(have no affiliates in East Asia).

32

Table 3. Trade Between Japan and Overseas Affiliates
N. America Europe
A. Share of Imports from Japan in Total Procurements
Downstream
Office machines
44.7%
Household electric appliances
36.6%
Industrial electrical apparatus
32.9%
Electronic data processing machines 52.2%
Communication equipment
42.8%
Upstream
Electronic parts and devices
50.7%
Semiconductors
63.9%
B. Share of Exports to Japan in Total Sales
Downstream
Office machines
2.5%
Household electric appliances
1.6%
Industrial electrical apparatus
3.5%
Electronic data processing machines
4.1%
Communication equipment
4.8%
Upstream
Electronic parts and devices
7.3%
Semiconductors
16.4%

ASEAN

NIES

China

38.9%
44.0%
49.7%
42.4%
36.9%

37.9%
23.6%
47.1%
30.6%
33.0%

50.7%
32.1%
40.1%
47.5%
27.0%

24.0%
27.3%
49.0%
38.1%
39.4%

47.0%
70.4%

39.8%
84.5%

41.4%
75.1%

46.9%
90.9%

0.2%
12.2%
0.8%
1.4%
6.2%

24.6%
16.2%
34.3%
37.3%
33.4%

52.2%
22.8%
11.5%
25.5%
23.9%

32.2%
17.2%
23.6%
21.0%
24.5%

3.8%
9.2%

28.6%
50.7%

16.2%
23.6%

37.5%
54.8%

Source: Authors’ calculations based on the Basic Survey of Overseas Business Activities (2000).

33

Table 4. Activity-level Results
Level
Growth
(I)
(II)
(I)
(II)
Dependent Var. (t -1)
0.768
0.772
-0.158-0.152[44.62]*** [46.16]***[-9.38]*** [-9.08]***
Dependent Var. (t -2)
0.169
0.156
[8.60]*** [7.12]***
Dependent Var. (t -3)
0.131
0.135
[7.63]*** [7.44]***
Horizontal (t -1)
0.005
0.005
0.006
0.005
[1.64][1.47][1.96]*[1.67]*
Vetical (t -1)
0.005
0.004
[1.95]*[1.77]*
Upstream (t -1)
0.004
0.004
[1.98]**[2.01]**
Downstream (t -1)
0.023
0.029
[3.49]***[4.79]***
Time Fixed Effect
Yes
Yes
Yes
Yes
Activity Fixed Effect
Yes
Yes
Yes
Yes
Hansen J (p-value)
0.014
0.061
0.288
0.647
AR(2) (p-value)
0.533
0.369
0.322
0.418
No. of Observations
23,977
23,97727,98527,985
No. of Firms' Activities
3,242
3,242
3,682
3,682
Notes: z-values are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%
level, respectively.

34

Table 5. Activity-level Results for East Asia
Level
Growth
(I)
(II)
(I)
(II)
Dependent Var. (t -1)
0.770
0.773
-0.154-0.148[46.53]*** [46.98]***[-9.28]*** [-8.79]***Dependent Var. (t -2)
0.162
0.162
[7.60]*** [7.47]***
Dependent Var. (t -3)
0.120
0.117
[6.47]*** [6.43]***
FDI Developed (t -1)-0.004-0.004-0.003-0.003[-1.32][-1.13][-1.31][-1.30]
FDI Other (t -1)
0.039
0.036
0.042
0.040
[1.81]*[1.65]*[3.37]*** [3.07]***
Horizontal (t -1)-0.015-0.0190.004
0.003[-0.57][-0.70][0.16][0.12]
Vetical (t -1)
0.062
0.056
[4.06]***[5.15]***
Upstream (t -1)
0.076
0.067
[4.07]***[5.43]***
Downstream (t -1)
0.046
0.055
[6.03]***[6.00]***
Time Fixed Effect
Yes
Yes
Yes
Yes
Activity Fixed Effect
Yes
Yes
Yes
Yes
Hansen J (p-value)
0.102
0.218
0.495
0.667
AR(2) (p-value)
0.631
0.657
0.391
0.510
No. of Observations
23,977
23,97727,98527,985
No. of Firms' Activities
3,242
3,242
3,682
3,682
Notes: z-values are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%
level, respectively.

35

Table 6. Firm-level Results
Level
Growth
(I)
(II)
(I)
(II)
Dependent Var. (t -1)
0.768
0.771
-0.147-0.145[45.30]*** [45.16]***[-8.26]*** [-8.05]***
Dependent Var. (t -2)
0.158
0.159
[6.91]*** [6.85]***
Dependent Var. (t -3)
0.156
0.154
[9.22]*** [9.21]***
Horizontal (t -1)
0.003
0.002
0.004
0.003
[0.87][0.85][1.07][0.92]
Vetical (t -1)
0.006
0.010
[0.61][0.99]
Upstream (t -1)
0.006
0.009
[0.50][0.80]
Downstream (t -1)
0.011
0.022
[1.13][2.67]***
Time Fixed Effect
Yes
Yes
Yes
Yes
Firm Fixed Effect
Yes
Yes
Yes
Yes
Hansen J (p-value)
0.177
0.910
0.347
0.960
AR(2) (p-value)
0.261
0.279
0.390
0.436
No. of Observations
23,74423,744
26,417
26,417
No. of Firms' Activities
2,666
2,666
2,792
2,792
Notes: z-values are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10%
level, respectively.

36