Gains from Fragmentation at the Firm Level:
Evidence from Japanese Multinationals in East Asia
Kazunobu HAYAKAWA # §Inter-disciplinary Studies Center, Institute of Developing Economies, Japan

Fukunari KIMURA
Economic Research Institute for ASEAN and East Asia
Faculty of Economics, Keio University, Japan

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

Abstract: The unprecedented development of production networks in East Asia has been investigated,both theoretically and empirically, employing the conceptual framework of fragmentation theory and its
extensions. However, the benefits of production fragmentation at the firm level, particularly benefits
deriving from different location advantages, have never been directly measured empirically. This paper
presents the very first attempt, to the authors’ knowledge, to empirically capture the benefits offragmentation. Specifically, using Japanese firm-level data, we find that the larger the gap in thecapital-labor ratios between fragmenting firms’ home and overseas activities, the more greatly their cost
efficiency improves.

Keywords: Firm heterogeneity; multinational enterprises; fragmentation; factor intensity; micro data
JEL Classification: F21; F23#

Corresponding author. Kazunobu Hayakawa, address: Economic Integration Studies Group,
Inter-Disciplinary Studies Center, Institute of Developing Economies, 3-2-2 Wakaba, Mihama-ku,
Chiba-shi, Chiba 261-8545 Japan. Phone: 81-43-299-9754; Fax: 81-43-299-9763. E-mail:kazunobu_hayakawa@ide.go.jp§
This research was conducted as part of a project of the Economic Research Institute for ASEAN
and East Asia (ERIA) “Deepening East Asian Economic Integration Part II: Firm-Level Analyses”.
We thank the Ministry of Economy, Trade, and Industry of the Japanese government for providing
the micro data used in this study. The authors are deeply indebted to the members of this project for
their invaluable suggestions. The opinions expressed in this paper are the sole responsibility of theauthors and do not reflect the views of the ERIA.
11. Introduction
The fragmentation theory initiated by Jones and Kierzkowski (1990) has had agreat impact on the theoretical conceptualization of the production-process-wisedivision of labor developed between the North and South. 1 Notably, the unprecedented
formation of production networks in East Asia has been investigated, both theoreticallyand empirically, with employing the conceptual framework of fragmentation theory andits extensions. 2 The fragmentation theory has indeed become a strong theoreticalbackbone for understanding the recent phenomenon of active North-South intra-industry
trade. Applying gravity equations for bilateral trade data at the industrial level, someresearchers have found more active trade in parts and components in country-pairs withlarger differences in income in East Asia (see, for example, Athukorala and Yamashita,
2006; Kimura, Takahashi, and Hayakawa, 2007). However, the benefits of production
fragmentation at the firm level, particularly benefits derived from utilizing different
location advantages, have never been directly measured empirically. This short paper
presents the very first attempt, to the authors’ knowledge, to empirically capture thebenefits of fragmentation, using rigorous econometric methods.
The basic concept of fragmentation is illustrated as Figure 1. Suppose that a firm
originally has a large electronics factory that takes care of a long sequence of value
chains from upstream to downstream. The electronics industry as a whole isphysical-capital-intensive or human-capital-intensive, so that the factory is located in adeveloped country. If we take a look at the factory in details, we find that it consists ofvarious production processes; some production processes are purely
human-capital-intensive while others are labor-intensive. Thus, if the firm properly
divides a factory into multiple production blocks and places them in various locations
with different location advantages, the total production cost may be reduced. This isfragmentation. To make fragmentation economically viable, two conditions must be met.
First, there must be a large reduction in production cost in production blocks, achievedby utilizing different location advantages. Secondly, the cost of the service links thatconnect remotely located production blocks must be reasonably low.
===

Figure 1

1===

For the fragmentation theory and its applications, also see Arndt and Kierzkowski (2001), Cheng
and Kierzkowski (2001), and articles in the special issue of International Review of Economic andFinance on “Outsourcing and Fragmentation: Blessing or Threat” (Vol. 14, Issue 3, 2005).
2
Kimura and Ando (2005) extend the fragmentation framework to two dimensions, incorporating
fragmentation along the geographical distance axis and along the integration (intra-firm vs. arm’s
length) axis. Kimura (2006) summarizes the nature and characteristics of East Asian production
networks in the extended framework.
2

Note that the fragmentation theory does not directly include the mechanism bywhich the production blocks are separated. Suppose that production activities require
two primary inputs, capital (K) and labor (L), and differences in location advantages
between developed countries (DCs) and less developed countries (LDCs) arerepresented by differences in factor prices, r and w. If a firm could freely separate
production blocks, it would place a purely capital-using production block in a DC and apurely labor-using production block in a LDC, in order to fully exploit differences infactor prices. This, however, does not actually happen because a firm faces
technological and managerial constraints in separating production blocks. Casual
observations in a number of factory visits suggest that production blocks located in
LDCs tend to be more labor-intensive than those in DCs, as we would expect. However,gaps in factor intensity between production blocks in LDCs and DCs differ widely
across firms, and how far the differences in location advantages are exploited seems todetermine the extent of gains from fragmentation.
This is actually a testable hypothesis with the data of Japanese firms and theirforeign affiliates, though we have to tolerate various data limitations. What we will
demonstrate is as follows: suppose that two firms initially exist in an industry andoperate at home (a DC) with the same technology. They now draw lotteries as Melitz(2003), determine the magnitude of gaps in factor intensity between a production block
which remains at home and the other located in a foreign country (a LDC), and conduct
fragmentation. Applying a set of reasonable conditions proposed by Deardorff (2001),
we graphically demonstrate that a firm with a larger gap in factor intensity infragmentation presents better performance than the other. Our econometric exercise
provides a robust support for this claim.
The rest of this paper is organized as follows: the next section provides thetheoretical framework of our empirical analysis. Following the theoretical framework ofDeardorff (2001), we show that international fragmentation with a larger gap incapital-labor ratios (KL ratios) between two production blocks leads to a larger total
cost reduction. Section 3 specifies our empirical methodology and discusses data issues.
Some data overview on the capital-labor ratios of Japanese MNEs and the empiricalresults are reported in section 4, and section 5 concludes.

2. Theoretical Framework
This section summarizes how the benefit from fragmentation is related to the gap

3

in capital-labor ratios between activities at home and abroad. To do that, we employ thefragmentation model, particularly the theoretical framework proposed by Deardorff(2001).

2.1. Fragmentation and the Gap in KL Ratios
Consider two countries with different factor prices under free trade. The one is thecapital-abundant North, and the other is the labor-abundant South. We assume thatsufficiently different factor endowments between countries make factor priceequalization impossible. Unit isocost lines in both countries are shown in Figure 2 (lines
ACD and BCE for South and North, respectively). In this paper, we focus on good X,
which is assumed to be capital-intensive enough to be initially produced only in the
North. In this framework, we consider the total cost of a firm that tries to fragment
technology for producing X. The production of good X can be broken up into two
fragments, which are assumed to follow Leontief fixed-coefficient technologies. It isalso assumed that since the firm constitutes a sufficiently small part of the total
economies, it takes factor prices in the two countries as given, and its changing to thefragmented technology does not cause a noticeable change in the factor prices in either
country.
===

Figure 2===

We first consider the fragmentation that uses the same quantity of resources as theunfragmented technology. Deardorff (2001) calls such fragmentation “costlessfragmentation”. The amount of good X produced by the isoquant X=1/px can also be
produced using the capital-intensive fragment that requires the vector of factors shownas OZ and the labor-intensive fragment that requires the vector shown as ZY. Since thecapital-labor ratio of fragment OZ is above the cutoff line OC, the capital-intensive
fragment OZ will be produced in the North.
In this setting, Deardorff (2001) demonstrates that international fragmentation
leads to total cost reduction. To see this, it is useful to draw some lines. The lines
A’C’D’ and B’C’E’ are parallel to ACD and BCE respectively, and both lines arecontracted toward the origin by the same proportion. The line B’C’E’ passes through the
tip of the arrow OZ and thus shows the factor combinations that cost as much as factor
inputs in producing fragment OZ in the North. The amount of such cost becomes lessthan one dollar. Since point C’ is placed on both A’C’D’ and B’C’E’, the factor
combination at this point costs the same amount between the North and the South. Thus,4

drawing vector C’Y’ with the same length and direction as ZY, we can obtain the point
Y’, through which an isocost line shows the total cost for producing thecapital-intensive fragment in the North and the labor-intensive fragment in the South.
As a result, since the point Y’ lies inside unit isocost line ACD, the use of fragmented
technology reduces the cost if the fragments are produced in different countries. That is,in this setting, international fragmentation succeeds in reducing the total cost for the
production of good X. 3
We can derive a further meaningful result from the above framework. We
consider two firms. While one firm conducts fragmentation with a large gap incapital-labor ratios between fragments (KL gap), the other firm does so with a small KL
gap. Here we restrict our attention to the fragmentation in which the cost for producing
the capital-intensive fragment does not depend on its KL ratio. This ensures that the KL
gap uniformly expands as the KL ratio in the capital-intensive fragment rises. Thus, we
can easily compare fragmentation between large and small KL gaps. Graphically, even
if the KL ratio in capital-intensive fragment OZ changes, the tip of the arrow OZ isalways placed on the line B’E’. 4
The result is shown in Figure 3. Two fragments’ vectors in the large KL gap
fragmentation are shown as OZ and ZY, and those in the small KL gap fragmentation as
OZ’ and Z’Y. Notice that both points Z and Z’ are placed on the line E’B’ since the costof producing the fragment does not depend on its KL ratio. The rest of the figure
construction is the same as in Figure 1. Corresponding vectors to ZY and Z’Y are C’Y’
and C’Y’’, respectively. Because both vectors C’Y’ and C’Y’’ start from point C’, anddue to the order of KL ratio between ZY and Z’Y, point Y’’ always lies more inside theisocost line ACD than point Y’. This indicates that a large KL gap fragmentation leads
to more total cost reduction than a small gap fragmentation.
===

Figure 3===

2.2. Service Link Costs
So far, we have not considered costs to link two remotely located fragments, i.e.
3

If the fragment ZY is not so labor-intensive, that is, if Y’ is placed on the upper-left area of thepoint C, international fragmentation raises the total cost. In this paper, we assume that the fragment
ZY is sufficiently labor-intensive. At the same time, the fragment OZ is assumed to be sufficiently
capital-intensive that the good X is capital-intensive enough to be produced only in the North in theunfragmented technology.
4
Since allowing the dependence of the cost in the capital-intensive fragment on its KL ratio
prevents us from visualizing our claim, we do not cover such fragmentation in this paper.
5

service link costs. A service link would require additional supervision, coordination, andcontrol over the geographically diversified production activities. In this subsection, we
model such costs as additional factor use. As a result, international fragmentation isrequired to use more combined factors than could have produced the good before.
Specifically, we assume that such extra resources have the same KL ratio with thefragment at home and are inputted at home by a certain proportion of resources used in
the home fragment. 5 This type of fragmentation is qualitatively similar to the one that
Deardorff (2001) calls “costly fragmentation”.
In this framework, as in Deardorff (2001), we can see that international “costly”
fragmentation could still lead to a total cost reduction. Such a case is shown in Figure 4.
Since the capital-intensive fragment extends beyond the previous isocost line B’C’E’,
the labor-intensive fragment’s vector also reaches beyond the unit-isocost line BCE. As
a result, the same figure construction as before yields the point Y’, through which an
isocost line shows the total cost in international fragmentation. Thus, international
fragmentation can reduce the total cost even though it is costly in terms of factor use.
Consequently, as the fragmentation theory claims (see, for example, Arndt andKierzkowski, 2001; Cheng and Kierzkowski, 2001), whether international
fragmentation reduces total cost or not depends on the magnitude of service link costs(in our case, the amount of the extra resources). The longer the vector OZ, the more
likely it is that point Y’ will reach the right area of unit-isocost line ACD. Thus, thesmaller the service link cost, the larger total cost reduction the firms can enjoy.
===

Figure 4===

We again compare fragmentation between large and small KL gaps, incorporating
service link costs. As shown in Figure 5, the figure construction is basically the same as
in Figure 3. We extend the fragment OZ’ by the same proportion as in the case offragment OZ. Since we restrict ourselves to the fragmentation in which the cost for
producing the capital-intensive fragment does not depend on its KL ratio, the vectors
C’’Y’ and C’’Y’’ again start from the same point. Thus, we can confirm that a large KL
gap fragmentation leads to more total cost reduction than a small gap fragmentation.
Consequently, in this section, we obtain the following testable hypothesis:
Testable Hypothesis: The larger the gap in KL ratios between a Northern fragment and5

The following results are qualitatively unchanged even if we assume such extra resources are
inputted abroad (see Deardorff, 2001), or both at home and abroad.
6

a Southern fragment, the greater the total cost reduction in international fragmentation.
In other words, the larger the gap in KL ratios between an MNE’s home and overseas
activities, the larger its total profit is. As long as we assume Leontief technology, the
MNE’s profit has a positive linear relationship with the gap in KL ratios. From the next
section, we investigate whether this hypothesis is empirically valid or not.
===Figure 5===

3. Empirical Issues
3.1. Methodology
Our hypothesis to be tested is whether the larger the gap in KL ratios between afragmentation firm’s home and overseas activities, the better the performance of thefirm is. To empirically test this hypothesis, we regress the following simple linear
equation:
Performanceft = β0 + β1 Gapft + uft,where Gapft = (KftHome / LftHome) - (KftAbroad / LftAbroad).
β0 and β1 are coefficients to be estimated, and β1 is expected to be significantly positive.
u is disturbance. Gap is the difference in capital-labor ratios between home and abroad. 6
K and L are tangible fixed assets and labor, respectively. Subscripts f and t represent
firm and year, respectively. We do not take logs of Performance and Gap not only
because they can be negative but also because the theoretical framework does not
require us to take their logs. To keep consistency of our empirical framework with theabove theoretical prediction, we investigate MNEs’ profits on a consolidated basis, i.e.
the sum of home profit and overseas profit. 7 Thus the greater the total cost reduction in
6

In this paper, we use the difference in capital-labor ratios between home and abroad rather thantheir ratio. In the gravity analysis, the relationship between trade and wage gap is often examined(see, for example, Kimura, et al., 2007). All these studies use a difference in GDP per capita between
exporter and importer as the gap measure. Also in the studies of the knowledge-capital model, e.g.
Carr, et al. (2001), a difference in the share of the labor force in certain skilled occupations between
parent and host country is used in order to examine the relationship between affiliate sales and skill
difference. Our paper follows the formulation of gap in such studies. But, even in the case of theratio, we obtain qualitatively the same results, particularly in the case of labor productivity.
7
To our best knowledge, this paper is the first that explores the impacts of investing abroad on
investors’ consolidated performance.
7

international fragmentation, the larger their consolidated profits would be. To control
differences in scale among MNEs, we divide the consolidated profits by their total
assets. In addition to the profits, we also examine the impact on value added 8 on aconsolidated basis, which is further divided by their total employment. Firms withgreater total cost reduction in international fragmentation would gain larger value added.
In sum, our performance measures are return on assets (ROA) and labor productivity on
a consolidated basis.
Some other variables are included as independent variables for controllingfirm-specific characteristics and host country-specific characteristics. The first variable
is a firm-specific one. The amount of capital stock on a consolidated basis is included tofurther control MNEs’ scale. The other variables are host country-specific ones. In theabove theoretical framework, we confirm that the smaller the service link cost, thelarger the benefit from investing. To control differences in the service link cost withJapan among host countries, we include two variables on such cost: geographicaldistance between Japan and host country and the extent of country risk. As a result, ourbaseline regression equation is given by
Performanceft = β0 + β1 Gapft + β2 ln Capital Stockft + β3 ln Distancec + β4 ln Riskct + uft.
Subscript c represents host country. Year and industry dummies are also introduced.
In order to keep further consistency with the theoretical framework, we need torestrict our sample firms only to firms with fragmentation. To do that, we require
sample firms to meet the following five conditions. The first is to invest in East Asian
countries since many empirical papers such as Kimura (2006) show that Japan has
actively been engaged in international fragmentation primarily with East Asian
countries. Secondly, we restrict to firms with only one affiliate. Although it is an
important research topic to clarify the mechanics and consequences of operating
multiple affiliates, such examination is beyond our framework in section 2. 9 The third is
the firms of which activities at home are more capital-intensive than those abroad. Since
Japan is expected to serve as a country producing the more capital-intensive fragments
8

Due to the data limitation in this paper, value added is simply defined as total sales minus total
procurements.
9
We also conducted regression with the MNEs with multiple affiliates in East Asia. The gap
measure is constructed by using the weighted average of all East Asian affiliates’ KL ratio in each
MNE. We use affiliates’ sales as a weight. Their inclusion in the sample drastically increases thenumber of observations in regression, though the aggregation procedure is inevitably accompanied
by a looser link with our theoretical framework. As a result, we obtained qualitatively unchanged
results as reported in this paper.
8

than host countries in East Asia, firms with negative gaps are eliminated from oursample. Fourthly, we restrict our sample set to firms that are actually exporting theirproducts from home to their overseas affiliates, since the fundamental source of benefits
from international fragmentation is the intra-firm vertical division of labor between
home and abroad. As long as we assume that upstream processes are more
capital-intensive, Japanese MNEs should export their upstream products to their affiliate.
Lastly, our sample of overseas affiliates is restricted to affiliates in the same industry as
their parents, which enables us to compare KL ratios among fragments (production
processes) in an industry, as is consistent with our theoretical framework.

3.2. Data Issues
Our main data source is “The Basic Survey of Overseas Business and Activities(BSOBA),” which is a firm-level survey by the Ministry of Economy, Trade andIndustry, Government of Japan. The aim of this survey is to obtain basic information on
the activities of the overseas affiliates of Japanese firms. The survey has two versions.
One is the Basic Survey, which includes more detailed questions and is conducted every
three years. The other is the Trend Survey, which is an abbreviated version and is
carried out between the Basic Surveys. Both the Basic Survey and the Trend Survey
consist of two parts: one is for parent companies and the other is for their overseas
affiliates. 10 The parent companies are Japanese corporations which, as of the end of
March, own or have owned overseas affiliates in the past, excluding those in thefinancial and insurance industry or real estate industry. The information on parents
includes their employment, assets, exports, and so on. As for affiliates, theestablishment year of the affiliates, the breakdown of sales and purchases, employment,
costs, and research and development, and so forth are available. As a result, the BSOBA
provides us all necessary data on firm-specific variables.
As of 2008, micro data sets for the BSOBA are available between 1995 and 2005.
However, tangible fixed assets in parent firms and their overseas affiliates, which arenecessary to construct the Gap, are available only in the Basic Survey. Furthermore,such information turns out to be unavailable for 2004. Thus, our sample years are forced
to be only 1995, 1998, and 2001. For further information on the figures included in the
BSOBA, see “Survey Form for Oversea Affiliates” and “Guide for Completing the10

An overseas affiliate of a Japanese firm is defined as follows: a foreign affiliate in which aJapanese firm has the invested capital of 10% or more, a foreign affiliate in which a “subsidiary”
funded more than 50% by a Japanese firm has invested capital of more than 50%, and a foreign
affiliate in which a Japanese firm and a subsidiary funded more than 50% by a Japanese firm haveinvested capital of more than 50%.
9

Survey”. 11
Next, data sources of the country-specific variables are as follows: the data on
bilateral distance are drawn from the CEPII website. As a proxy for the country risk, we
use a country risk index which is drawn from Institutional Investor (Institutional
Investor, various issues). This index is formed from aggregates of bankers’ evaluations
on the risk of default, and a larger value indicates that the risk of default in the country
is smaller.
Lastly, it is worth noting one crucial limitation in our dataset. Our dataset ispooling data, not panel data. Although our data source includes firm identification codes
applicable over years, most of our sample firms appear only once, mainly due to thefrequent absence of data on tangible fixed assets. As a result, we are forced to treat oursample as a pooling set and could not introduce time-invariant firm-fixed effects into
our regression equation.

4. Empirical Results
4.1. Overview of KL Ratios in Japanese MNEs
We present some tables on capital-labor ratios in Japanese MNEs in 1998, in
which there are a largest number of observations for our sample period, i.e. 1995, 1998,
and 2001. The number of Japanese overseas affiliates reporting both K and L in 1998 isprovided by industry by region in Table 1. 12 As for regional definition, in this paper,
East Asia means ASEAN countries, China, and Asian NIEs, while developed countries
include European countries (both Western and Eastern European countries) plus North
American countries (Canada and the US).
===

Table 1===Three kinds of measures are presented. The first is the simple average of KL
ratios in Japanese affiliates. In most of the industries, affiliates in developed countries
have higher KL ratios than those in East Asian countries. This result implies thatJapanese MNEs investing in East Asia aim to utilize low-priced labor. Secondly, thesimple average of KL ratios in Japanese MNEs’ home activities is also presented.
11

Downloadable from the METI web site:
http://www.meti.go.jp/english/statistics/tyo/kaigaizi/index.html.
12This table includes the MNEs with multiple-affiliate or/and negative gap values and those without
exports to their affiliates.
10Compared with the results in the first measure, the table does not show clear differencesin KL ratios between the case of East Asia and that of developed countries, in most of
the industries. This result would indicate that Japanese MNEs investing in either East
Asia or developed countries have no choice but to get engaged in sufficiently
capital-intensive production activities at home due to the high wages in Japan. The lastis the gap in KL ratios between home and overseas activities. We find that, in almost allindustries, the gap is larger in the case of East Asian countries. Thus, we can say that, on
average, Japanese MNEs investing in East Asia cut out production blocks on the basisof factor intensities.

4.2. Regression Results
Next, we report our regression results. Basic statistics are provided in Table 2. 13
Table 3 tabulates the regression results.
===

Tables 2-3===

The baseline results are presented in equation (I). Three points are noteworthy inthe case of ROA. First, we can see that the coefficient for Gap is estimated to besignificantly positive at the five percent level, indicating that the larger the gap in KL
ratios between MNEs’ home and overseas activities, the higher their profitability.
Secondly, the insignificant result in capital stock would be because differences in scale
among MNEs are already adjusted by dividing their consolidated profits by their total
assets. Thirdly, as is consistent with our theoretical framework, the closer to Japan thehost country is, that is, the lower the distance-related charge, the significantly better the
performance. The results in the case of labor productivity are basically the same as in
the case of ROA. In particular, the coefficient for Gap is again positively significant at
the one percent level. The noteworthy difference with the case of ROA is that thecoefficient for the capital stock turns out to be significantly positive. This result may
indicate that total employment is not enough to control MNEs’ scale embodied by theircapital stock, in contrast to total assets.
To confirm the robustness of these results in Gap, we further conduct several
regressions. First, by introducing country fixed effects or country-year fixed effects, we
control host country characteristics in full detail, which include factor endowment,
technology, the magnitude of service link costs, and so on. Then, differences in factor
13

In this dataset, we exclude two obvious outliers in gap: they have abnormally large gaps of avalue greater than 1,000.
11

prices, that is, differences in the slope of the unit isocost line, are also controlled. In
these regressions, host country-specific variables are dropped. Their results are reported
in equations (II) and (III) and remained unchanged with baseline results. That is, weconsistently find positive estimators of the Gap coefficient.
The second robustness check is more important. Since our sample of hostcountries comprises countries with different levels of economic development, there
seem to be the large differences in labor quality. Although such differences may bepartly controlled by introducing country-year fixed effects, we also try to adjust suchdifferences more directly. Specifically, we multiply an affiliate’s employment by a ratioof the level of education (average schooling years in the total population) in the hostcountry to that in Japan. The data concerning the education level are drawn from “Data
Set for a Panel of 138 Countries” provided by Robert J. Barro and Jong-Wha Lee. 14 By
employing such adjusted labor in the host countries, we again calculate our gap measure
and labor productivity. Their basic statistics are reported in Table 2, and the regression
results are provided in Table 4. From this table, we again find qualitatively unchanged
results compared with Table 3 and confirm the significantly positive coefficients for
Gap.
===

Table 4===

5. Concluding Remarks
This paper empirically investigated gains from fragmentation at the firm level.
Examining corporate performance on a consolidated basis, we investigated, using
Japanese firm-level data, whether the heterogeneity in impacts of international
fragmentation on corporate performance across firms exists or not. We found that thelarger the gap in KL ratios between their home and overseas activities, the more greatly
their cost efficiency improves. Several estimations confirmed the robustness of thisfinding. Given this finding, our future research may be to clarify what firm
characteristics determine such a gap in KL ratios between home and overseas activities.

14

http://www.nber.org/pub/barro.lee/
12References
Arndt, S. W. and Kierzkowski, H., 2001, Fragmentation: New Production Patterns in
the World Economy, Oxford University Press, Oxford.
Athukorala, P.-C. and Yamashita, N., 2006, Production Fragmentation and Trade
Integration: East Asia in a Global Context, The North American Journal of
Economics and Finance, 17(3): 233-256.
Carr, D., Markusen, J., and Maskus, K., 2001, Estimating the Knowledge-Capital Modelof the Multinational Enterprise, American Economic Review, 91: 693-708.
Cheng, L. K. and Kierzkowski, H., 2001, Global Production and Trade in East Asia,
Kluwer Academic Publishers, Boston.
Deardorff, A., 2001, Fragmentation across Cones, In: SvenW. Arndt and Henryk
Kierzkowski, eds., Fragmentation: New Production Patterns in the World
Economy, Oxford University Press, pp. 35-51.
Kimura, F., 2006, International Production and Distribution Networks in East Asia:
Eighteen Facts, Mechanics, and Policy Implication, Asian Economic Policy
Review, 1: 326-344.
Kimura, F. and Ando, M., 2005, Two-dimensional Fragmentation in East Asia:
Conceptual Framework and Empirics, International Review of Economics andFinance, 14(3): 317-348.
Kimura, F., Takahashi, Y., and Hayakawa, K., 2007, Fragmentation and Parts andComponents Trade: Comparison between East Asia and Europe, North American
Journal of Economics and Finance, 18(1): 23-40.

13

Table 1. KL Ratios
Observations
Developed East
Countries
Asia
Textile5110Chemicals
157
262
Primary metal
28
90
Metals
14
42
General Mach.
134
153
Electrical Mach.
48
138
IT Mach.
145
298
Trasnport Equip.
187
221
Precision Mach.
23
43
Others176
347
Total Average
917
1,704

KL in affiliates
Developed East
Countries
Asia
15
3
30
19
16
14
13
6
17
4
11
6
4
5
19
9
6
5
20
23
18
11

KL at home
Developed East
Countries
Asia
48
13
25
27
23
27
10
9
13
11
11
10
11
12
12
12
9
11
24
22
17
17

Gap
Developed East
Countries
Asia
32
11
-5
8
6
13
-3
3
-4
7
0
5
6
7
-7
3
2
6
4
0
-1
5

Source: Authors’ calculation by using the Basic Survey of Overseas Business and Activities

Table 2. Basic Statistics
Variable
Return on assets
Labor productivity
Gap
log of Capital stock
log of Distancelog of Country risk
Adjusted Labor productivity
Adjusted Gap

Obs
204
204
204
204
204
204
204
204

Mean
7
13
9
6
8
4.06
8
13

Std. Dev.
14
19
16
2
1
0.26
16
19

Source: Authors’ calculation by using the Basic Survey of Overseas Business and Activities

14

Min
-18
-23
0
3
7
3.33
-4
-23

Max
108
145
198
10
9
4.42
197
144

Table 3. Baseline Results
Dependent variable
Equation
Gap
log of Capital stock
log of Distancelog of Country risk
Year dum.
Industry dum.
Country dum.
Country*Year dum.
Observations
R-squared

Return on Assets
(I)
(II)
(III)
0.108**
0.117*
0.121*[0.054][0.062][0.063]-0.249-0.376-0.570[0.748][0.697][0.747]-6.414**[2.989]-3.127[3.123]
YES
YES
NO
YES
YES
YES
NO
YES
NO
NO
NO
YES
204
204
204
0.188
0.206
0.250

Labor Productivity
(I)
(II)
(III)
0.495*** 0.450*** 0.457***[0.095][0.096][0.100]2.379*** 2.632*** 2.619***[0.613][0.632][0.690]-4.745[3.175]-1.477[5.167]
YES
YES
NO
YES
YES
YES
NO
YES
NO
NO
NO
YES
204
204
204
0.425
0.498
0.505

Notes: Heteroskedasticity-consistent standard errors (White) are in parentheses. ***, **, and * show 1%, 5%, and 10% significance, respectively.

15

Table 4. Regression Results: Adjusted Employments
Dependent variable
Equation
Gap
log of Capital stock
log of Distancelog of Country risk
Year dum.
Industry dum.
Country dum.
Country*Year dum.
Observations
R-squared

Return on Assets
(I)
(II)
(III)
0.113**
0.119*
0.123*[0.055][0.063][0.064]-0.226-0.349-0.539[0.750][0.698][0.745]-6.384**[2.974]-3.253[3.107]
YES
YES
NO
YES
YES
YES
NO
YES
NO
NO
NO
YES
204
204
204
0.189
0.207
0.251

Labor Productivity
(I)
(II)
(III)
0.545*** 0.500*** 0.509***[0.093][0.097][0.102]2.511*** 2.707*** 2.696***[0.593][0.620][0.674]-3.882[3.181]-3.326[5.215]
YES
YES
NO
YES
YES
YES
NO
YES
NO
NO
NO
YES
204
204
204
0.448
0.509
0.517

Notes: Heteroskedasticity-consistent standard errors (White) are in parentheses. ***, **, and * show 1%, 5%, and 10% significance, respectively.

16

Figure 1. The Fragmentation Theory: Production Blocks and Service Links

Source: Authors’ compilation

17

Figure 2. Fragmentation

Figure 3. Small KL Gap versus Large KL Gap

18

Figure 4. International Fragmentation with Service Link Costs

Figure 5. Small KL Gap versus Large KL Gap: Service Link Costs

19