Structural Decomposition Analysis Using Spectral Graph
Theory and Its Application to the Energy Issue in Japan
Yuko OSHITA1), Shigemi KAGAWA1), and Keisuke NANSAI2)
1) Kyushu University, 6-19-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
2) National Institute for Environmental Studies of Japan, 16-2, Onogawa, Tsukuba,
Ibaraki 305-0053, Japan
Keywords: Oil price pressure, Spectral graph theory, Structural decomposition analysis,
Input–output analysis
ABSTRACT
This paper presents a detection method reconciling input–output price analysis with
spectral graph analysis, which is useful in partitioning a whole network into
sub-networks under a certain cut criterion. The method is used to detect industrial
clusters in Japan that have been influenced remarkably by the rapid increase in crude oil
prices during 2000–2007. Furthermore, the structural decomposition analysis using the
cluster information enables us not only to visualize the structural changes in the
industrial clusters (i.e. costly influenced industrial clusters), but also to examine the
effects of the changes in the inter-industry transactions within the clusters on the
commodity prices. The empirical results obtained using the 1990–1995–2000 linked
Input–Output Tables of Japan show that the Japanese economy at least in 1990
comprises 45 industry clusters largely influenced by the oil price rise. In addition, the
clusters during 1990–2000, whose internal structural changes reduced the effects of the
increased oil price, include Research and Development cluster, Cement cluster,
Household air-conditioners cluster, Printing, Industrial Soda Chemicals cluster, Plate
Making and Book Binding cluster, and others, which can be considered superior clusters.
The Research and Development cluster are composed of many electric and machinery
sectors, whose input structural changes causing a decrease in the oil price pressure
contribute greatly to the reduction of the effects on the whole economy. Conversely,
those clusters during 1990–2000, whose structural changes increased the effects of the
rise in the oil price on the economy include Other Business Services cluster and Water
Supply cluster. The former has added its pressure within the cluster and influence on the
other clusters attributable to the development of an advanced information society.
Corresponding author: Yuko OSHITA, Kyushu University, 6-19-1 Hakozaki, Higashi-ku,
Fukuoka 812-8581, Japan, oshita-yuko@live.jp.
1. Introduction
Crude oil is a scarce resource that is used for various products aside from petroleum
products and the plastic products. The crude oil import rate of Japan is almost 100%.
Domestic prices of goods and services are strongly affected by unstable import prices of
crude oil. To reduce the effect of increased crude oil prices on production costs,
Japanese firms have attempted to reduce the use of energy-intensive materials that use
crude oil. However, it is also true that the efforts at the individual firm level and sector
level have already reached their limits.
This paper presents the argument about how to determine industrial groups which
can reduce oil price pressures effectively. Although the Japan Standard Industrial
Classification (JSIC), which is classified (defined) by similarities of production
technologies and product functions, is used as the policy unit for example in case of
providing subsidies for the promotion of saving energy and reducing oil price pressure
(see Cabinet Office, Government of Japan, 2007), energy policy that is based on the
JSIC does not give different industries incentives for mutual cooperation with the
intention of reducing oil price pressure. An important problem is the manner in which
we can objectively define the most effective policy units in the whole economy.
To detect the appropriate industrial groups (industrial clusters), this paper
reconciles input–output price analysis (e.g. Chapter 2 of Miller and Blair, 2009) using
spectral graph analysis (e.g., Shi and Malik, 2000; Zhang and Jordan, 2008), which is
useful in partitioning a network into sub-networks under a certain cut criterion and
which detects industrial clusters in Japan that have been influenced remarkably by the
rapid increase in the crude oil prices during 2000–2007. We propose the industrial
clusters detected analytically using the proposed method as the energy policy units
instead of using aggregated industrial sectors in JSIC codes.
Furthermore, the structural decomposition analysis (Dietzenbacher and Los, 1998,
Fujikawa et al., 2007; Hattori and Hitomi, 2007, Chapter 13 of Miller and Blair, 2009)
using the cluster information from the reconciled analysis enables us not only to
visualize structural changes in the industrial clusters, but also to examine the effects of
changes in inter-industry transactions within the clusters on both commodity prices and
the inflation rate in the whole economy.
This paper is organized as follows. Section 2 formulates the proposed method.
Section 3 provides data sources. Section 4 provides the results and discussions. Finally
Section 5 concludes this paper.
2. Method
In this section, we formulate the input–output price model and reconcile it with the
spectral graph model. Then the structural decomposition analysis is conducted based on
the information related to industrial clusters detected using the method described above.
2.1 Modified input–output price model
Following the standard price model (see Chapter 2 of Miller and Blair, 2009),
domestic commodity prices are estimated as p d  (p m Am  v)(I  Ad ) 1 , where p d
is the row vector of the domestic commodity prices, p m is the row vector of the
imported commodity prices, A m signifies the input coefficient matrix for the imported
commodities, v is the row vector of the value added, A d is the input coefficient matrix
for the domestic commodities, and I is the identity matrix. Accordingly, if only the
crude oil price (i.e., only the element of crude oil sector in the row vector of the
imported commodity prices) rises, then the domestic prices influenced by the oil price
increase can be estimated as
pd*  (pm*Am  v)(I  Ad )1 ,
where p m* denotes the row vector in which the rate of increase in crude oil price is
added only to the crude oil sector and the value of other sectors is 1.
Following Kagawa et al. (2009), we decompose the domestic input coefficient
matrix belonging to both energy-related product sectors and non-energy sectors as
follows.
(1)
Enegy Non energy
 Ad
0 
Ae,d ne   A e,d e A e,d ne  0
e,e
A  Non  Energy　

  
 0 A d  d
 Ad

d
A
0
A
ne,
e
ne,ne 

ne,ne 
 ne,e

 

A1d
Ad2
d
Energy
(2)
Substituting eq. (2) into eq. (1) and transforming it, p d * can be reformulated as
p d*  (p m*A m  v)(I  LA1d )(I  A d2 ) 1 ,
where L   I  A d 
1
(3)
represents the Leontief inverse matrix whose element lij denotes
the direct and indirect input of commodity i required for one unit of the production of
commodity j. Defining (p m* A m  v)(I  LA 1d ) from the right-hand side of eq. (3) as
w , then w  w j  represents the pressure from crude oil prices placed on the domestic
product prices in non-energy sectors j. Using Ŵ , which is the diagonal matrix of w j
elements, we obtain the following eq. (4):
ˆ (I  A d2 ) 1 ,
NP d*  W
(4)
where (I  A d2 ) 1 represents the direct and indirect input coefficient matrix for

non-energy sectors. Consequently, NP d*  NPijd*

is useful to represent the increase
in payments from non-energy sector i to non-energy sector j, which results from the
pressure associated with a hike in crude oil prices.
2.2 Reconciling modified price model with spectral graph model
The matrix NP d*  NPijd*  in eq. (4) can be interpreted as a directed graph whose
weights denote the effects of the increase in the crude oil price on the payments from
non-energy sector i to non-energy sector j. To obtain information related to the total oil
price pressure between non-energy sectors i and j, we further converted the directed
 
weighted graph into a undirected weighted graph Q*  qij*
by adding the effect on
the payment from non-energy sector i to non-energy sector j to the effect on the
payment from non-energy sector j to non-energy sector i, if i  j , as
q ij*  NPijd *  NPjid * i  j ,
(5)
q ij*  0　i  j .
It is noteworthy that qij* is set to zero if i  j .
Here, the weighted degree of each sector (i.e. vertex) is computable as di   j qij* .
 
This study examines the case such that the graph Q*  qij*
shown in eq. (5) is
partitioned into two sets A and B. For this study, we also define a cut criterion (cut
value) as follows (see von Luxbrug, 2007).
Cut( A, B)  Cut( B, A) 
 q*ij
i A, jB

 q*ij
j A,iB
(6)
Solving the minimization problem of eq. (6), we can find the two sets (i.e. two industrial
clusters) such that the sum of weights (i.e. oil price pressures) of edges (i.e. intermediate
transactions) between vertex i belonging to the set A (non-energy sector i belonging to
the cluster A) and vertex j belonging to the set B (non-energy sector j belonging to the
cluster B) is minimized. However, when the cut value in eq. (6) is employed and its
minimization problem is solved, a vertex is well known to be detected as a set. For the
mathematical problem, Shi and Malik (2000) proposed the normalized cut value (Ncut
value) to avoid the undesired solution. The Ncut value can be formulated as follows.
 q x x
d
*
ij
Ncut( A, B) 
i
i A, jB
i
i A
Here,

iA
d i and

iB
 q x x
d
*
ij
j

i
j A,iB
j
xi  1i  A, xi  1i  B 　(7)
i
iB
d i express the sum of the weighted degrees of vertexes
belonging respectively to sets A and B. They represent the respective cluster sizes of A
and B. It is noteworthy that the numerator in the first term (second term) of the
right-hand side of eq. (7) coincides with the first term (second term) of eq. (6).
Therefore, in eq. (7), the clustering problem is to find the two sets A and B such that not
only the cut value (the numerator) is minimized but also the sizes of both clusters (the
denominator) are maximized simultaneously. This optimization problem implies, in
terms of the economy, that sectors having trade relations that are under strong pressure
from oil price increases belong to the same cluster. However, this problem is well
known as an NP-complete problem. Then, Shi and Malik (2000) converted the indicator
vector x  xi  in the eq. (7) into y  i  x  bi  x where b  iA d i

iB
di
is the size of cluster A relative to the size of cluster B, and i is the vector of unities and
transformed eq. (7) into the following equivalent optimization problem (see p.890 of
Shi and Malik (2000) for more details).
Min .
y
yi 1,  b
T
D  Q  y   q  y  y 
y Dy
d y
*
ij
*
i
2
j
i j
T
i
2
i
(8)
i
s.t.　y T Di  0
Here, superscript T denotes the transpose of the vector, D is the diagonal matrix whose
diagonal elements are weighted degrees di, and D  Q* is often called a Laplacian
matrix (see von Luxbrug, 2007). It is noteworthy that the Laplacian matrix D  Q* can
also be reformulated as GEG T , where G is the rotation matrix whose rows are indexed
by the vertices and the whose columns are indexed by the edges such that each column
corresponding to an edge (i, j) has an entry 1 in the row corresponding to i, an entry -1
in the row corresponding to j, and has zeros otherwise. In addition, E is the diagonal
matrix whose kth diagonal element is the weight of kth edge between vertices i and j (see
Chung, 1997). Consequently, we have the following relation:
y T  D  Q*  y  y TGEG T y   qij*  yi  y j  . If yi is relaxed to take on ‘real value’, then eq.
2
i j
(8) engenders the Rayleigh quotient of the generalized eigenvalue problem
D  Q y  Dy , where
*
 is the eigenvalue (see chapter 7 of Bellman (1997) for the
Rayleigh quotient). It is evident that the smallest eigenvalue of the generalized
eigenvalue problem is 0 and the eigenvector corresponding to the smallest eigenvalue is
the unity vector i because, from the definitions of D and Q*, we have
 D  Q  i   Di  0 , where 0 is the null vector. Accordingly, the optimum solution of the
*
generalized eigenvalue problem with the constraint yTDi=0 is the eigenvector
corresponding to the second smallest eigenvalue. Considering two important features of
the generalized eigenvalue problem––that the second eigenvector values corresponding
to the vertices which belong to the same cluster take similar values and that the
weighted average of the second eigenvector values is zero from the constraint
yTDi=0––it can be understood that a network is partitioned into two sub-networks using
information related to the sign of the second eigenvector values. More concretely, the
vertices with the positive (negative) eigenvector values are grouped as a cluster.
However, an important problem is that even if the graph is partitioned, the Ncut value
might not be minimized. In this study, as in Shi and Malik (2000), we bi-partitioned the
graph by finding the splitting point of the second eigenvector values such that Ncut is
minimized. Industrial clusters with the higher oil price pressure were detected by
recursively using the bipartition method (see Kagawa et al. (2010) for a detailed
explanation of the algorithm of the recursive bipartition). The detected clusters are
named a representative sector, which has the largest degree (i.e. the largest oil price
pressure) in the cluster in question.
2.3 Structural Decomposition Analysis
According to structural decomposition analysis (Dietzenbacher and Los, 1997,
d*
d*
 p 90
1998), p d *  p 00
––which represents the changes in the price vector of domestic
goods and service, and which is affected by the oil price increase (as indicated in Eq.
(1)) that occurred during 1990–2000––can be decomposed into four effects as shown in
m*
m*
Eq. (9) below: the effect of p m*  p 00
, which are changes in the import prices; the
 p 90
m
m
 A 90
effect of A m  A 00
, which are changes in the import coefficient; the effect of
v  v 00  v 90 , which are changes in the value added coefficient, and the effect of
L  L 00  L90 , which are changes in the input coefficient of direct and indirect
domestic goods. Because the import prices in this study are identical in 1990 and 2000,
the effect of changes in the import price is zero.
p d *  p 0d 0* p d 9 *0
m* m
m* m
  p 00
A 00  v 00  L 00   p90
A 90  v 90  L90
1 m* m
1
p A  L90  L 00   p m*A m  L90  L 00 
2
2
IP
,
Imported goods effects: pIG
Import price effects : p
1
1
m* m
m* m

v  L90  L 00    p90
A 90  v 90    p 00
A 00  v 00  L
2
2
Value added effects: pVA
Leontief effects: pTE



where subscripts 90 and 00 respectively denote 1990 and 2000, A m 
pm 
(9)
m
m
A90
 A00
, and
2
m
m
p90
 p 00
.
2
Moreover, substituting the decomposition formula of the Leontief inverse matrix
presented by the following Eq. (10) (see Dietzenbacher and Los, 1997, 1998)
L 
1
 L90A d L00  L00A d L90 
2
(10)
for the fourth term of the right-hand side of eq. (9) enables estimation of the effects of
changes in the input structure of domestic goods on the equilibrium price of ith sector,


p TE  piTE , as shown in Eq. (11) below.
p TE 


1 m* m
m* m
p90 A90  v 90    p 00
A 00  v 00   L90 A d L00  L00 A d L90 

4
(11)
And, the change of the rate of equilibrium price increase in the entire domestic market
caused by oil price rise were estimated as shown below from eq. (9) by adding the
weight of domestic output to p d * to obtain the weighted average.
d*
d*
p d *  ω 00p 00
 ω 90p 90






1
1
d*
d*
ω p 90
 p 00
 ω 90  ω 00 p d *
2
2
1
1
d*
d*
 ω p 90
 p 00
 ω 90  ω 00  p IP  p IG  p VA  p TE
2
2
1
1
1
d*
d*
 ω p 90
 p 00
 ω 90  ω 00 p IP  ω 90  ω 00 p IG
2  
2 
2 


 



p

W

p IP

,
(12)
p IG
1
ω 90  ω 00 p VA  1 ω 90  ω 00 p TE
2 
2 

 


p VA
pTE
where ω 90  ω90, j  ( ω 00  ω00, j ) is the weight row vector indicating the rate of the
output of sector j in the total output in 1990 (2000), and ω  ω 00  ω 90 . We obtained
the weight row vector by using the sectoral outputs from the 1990 (2000) input-output
table.
This study will analyze, in particular, the effects of changes in the input structure of
industrial clusters that are affected significantly by pressure from the oil price increase
on commodity prices. The changes in the input coefficient of all industries that occurred
d
d
d
d
d
d
during 1990–2000 are expressed as A  A　00  A 90 (i.e., aij   a00    a90  ).
ij
ij
Therefore, the changes in the intermediate input coefficients among the sectors
belonging to the kth cluster identified in Section 2.2 can be expressed as shown below.
d
d
d

 i, j  Cluster k 
aij   a00 ij   a90 ij A  
d

a

0
i,
j

Cluster
k



 ij
d
k
(13)
Accordingly, as in eq. (11), the effect of changes in the Leontief inverse matrix caused
by changes in the intermediate input coefficients among the sectors belonging to the kth
cluster on the equilibrium price of ith sector, p TE,k   piTE,k  , is obtainable from
p TE,k 


1
m* m
m* m
p90
A90  v 90    p 00
A 00  v 00   L90 A dk L 00  L 00 A dk L90  .

4
(14)
The effects of changes in the internal input structure of the kth cluster on the rate of price
increase can be finally estimated as shown below.
p TE, k  ωp TE, k ,
where, ω 
1
ω 90  ω 90  .
2
(15)
3. Data
The data used for this study are from 1990, 1995, and 2000-linked Input–Output
Tables (395 sectors) published by the Japanese Ministry of Internal Affairs and
Communications. In addition, the crude petroleum and natural gas sector was
disaggregated into crude petroleum and natural gas sectors using 3EID data of National
Institute for Environmental Studies of Japan. Energy-related product sectors in eq. (2)
are Coal mining, Crude petroleum, Natural gas, Petroleum refinery products, Coal
products, Electric power for commercial use, On-site power generation, Gas supply, and
Steam and hot water supply. The other sectors are non-energy sectors.
As in Kagawa et al. (2009), the rate of increase in the crude oil import price index
(2.563) for 2000–2007 in the time series data of the Import Price Index published by the
Bank of Japan was used to represent the rate of increase in crude oil import prices.
Accordingly, the import product price vector, in which the price increase rate during
2000–2007 is only added to the crude oil sector and the value of the other sectors is zero,
m*
m*
is set as p 90
or p 00
.
4. Results and discussion
To analyze the effects of the crude oil price rise on the commodity prices, we
redefined w  (p m* A m  v)(I  LA1d ) in eq. (3) as w  p m* A m (I  LA1d ) by

ignoring the value added effects and constructed NP d*  NPijd*

in eq. (4) as
representing the oil price pressures in the payments from non-energy sector i to
 
non-energy sector j and the undirected weighted graph Q*  qij*
shown in eq. (5).1
The maximum number of sectors comprising the cluster was set to 20.2 The industrial
clusters in 1990 identified using the method in Section 2.2 were 45. Among these
clusters, the first cluster that was firstly detected standing out from other industrial
clusters, whose normalized cut value is the smallest (1.16), is the Marine Fisheries
cluster (#1), which consists of seven sectors including #25 Marine Fisheries, #26
Marine Culture, #39 Frozen Fish and Shellfish, #40 Salted, Dried or Smoked Seafood,
#41 Bottled or Canned Seafood, #42 Fish Paste, and #43 Other Processed Seafood (see
Fig. 1.) The line (i.e. edge) thickness represents the strength of the pressure from a rise
in the oil price; the absence of a line between sectors shows that no oil price pressure
exists between those sectors. The vertex size signifies the size of the degree within the
clusters in the sector. In this Fig.1, since #25 Marine Fisheries sector has the maximum
degree within the cluster, this cluster is called Marine Fisheries cluster in this paper.
1
Vertices corresponding to a total of 14 sectors, including three – Liquid Crystal Elements (#235), Nursing Care
(In-home) (#357), and Nursing Care (In-facility) (#358) – with zero output because of their nonexistence in 1990, two
of scrap and byproduct sectors – Steel Scrap (#165) and Non-ferrous Metal Scrap (#179) –, and nine of the
energy-related product sectors are excluded from the undirected weighted graph Q*   qij*  .
2
This study has adopted 20 as the maximum number of sectors constituting a cluster for the purpose of finding the
most effective unit for adopting energy policy such as providing government subsidies. The sensitivity analysis
associated with changes in this maximum number will be studied in the future. Setting the maximum number too
large would require extensive government support, which would not only make budget management difficult, but
would create drawbacks from the large scale of inter-sectoral collaboration in the industrial clusters. Setting the
maximum number too small would only require small-scale government support, which, however, would limit the
potential of inter-sectoral collaboration in the industrial clusters. For this reason, setting an appropriate maximum
number is necessary.
This Marine Fisheries cluster was also detected as the first cluster in 2000 which
composes of industrial sectors same with the cluster in 1990, in contrast to many other
clusters, which suggests that this cluster has a strongly-connected input-output structure
from the point of view of oil price pressure.
#26 Marine culture
#43 Other processed seafood
#39 Frozen fish and shellfish
#42 Fish paste
#40 Salted, dried or smoked seafood
#25 Marine fisheries
#41 Bottled or canned seafood
Figure 1. Marine fisheries cluster (#1) in 1990.
There are industrial clusters which comprise sectors forming a supply chain of raw
materials, intermediate products, final products, and service businesses. Examples are
the Paperboard cluster (#3) (Its constituent sectors are #95 Pulp, #96 Paper, #97
Paperboard, #98 Corrugated Cardboard, #99 Coated Paper and Building (Construction)
Paper, #100 Corrugated Card Board Boxes, #101 Other Paper Containers, #102 Paper
Textile for Medical Use, #103 Other Pulp, Paper and Processed Paper Products, #104
Newspapers, #106 Publishing, #319 Packing Services, and #394 Office Supplies) and
the Cement cluster (#16) (Its constituent sectors are #29 Materials for Ceramics, #30
Gravel and Quarrying, #31 Crushed Stone, #140 Paving Materials, #152 Cement, #153
Ready Mixed Concrete, #154 Cement Products, #284 Public Construction of Roads,
#285 Public Construction of Rivers, Drainages and Others, #286 Agricultural Public
Construction, #287 Railway Construction, #288 Electric Power Facilities Construction,
And #290 Other Civil Engineering and Construction). Figures 2 and 3 respectively
depict the Paperboard cluster and the Cement cluster in 1990. The sectors comprising
these clusters would not be grouped together based on the Standard Industrial
Classification. Industrial alliances using aggregated industrial sectors in JSIC codes that
forms groups based on similarity in industrial technology, however, differ completely in
nature from the industrial alliances through a supply chain from raw material
exploration to material production and processing, parts production, and final goods.
The strength of the pressure from the rising oil price is an issue that should be resolved
through collaborative efforts of the sectors with different technologies. The industrial
clusters identified in this study are grouped according to the definite criteria of the
strength of economic transactions involving the oil price pressure (see eq. (7)), which
are thereby related to the supply chains. We found the clear difference between the
constituent sectors of industrial clusters detected in this study and the Standard
Industrial Classification and proposed the alternative energy policy units such as Marine
Fisheries cluster including fisheries sector and food processing sector.
#106
Publishing
#100 Corrugated card board boxes
#319 Packing services
#96 Paper
#103 Other pulp, paper and processed paper …
#101 Other paper containers
#97 Paperboard
#394 Office supplies
#99 Coated paper and building (construction) …
#95 Pulp
#104 Newspaper
#102 Paper textile for …
#98 Corrugated cardboard
Figure 2. Paperboard cluster (#3) in 1990.
154 Cement products
#31 Crushed stones
#154 Cement products
#288 Electric power facilities …
#287 Railway construction
#153 Ready mixed concrete
#30 Gravel and quarrying
#290 Other civil engineering and construction
#286 Agricultural public
construction
#29 Materials for ceramics
#140 Paving materials
284 Public construction of roads
#285 Public construction…
#284 Public construction of roads
#152 Cement
Figure 3. Cement cluster (#16) in 1990.
The following describes the results of the structural decomposition analysis. This
analysis used cluster information acquired using data from the 1990 Input–Output
Table.
First, the change of the rate of equilibrium price increase in the entire economy
caused by oil price rise was p d*  0.0034 (see eq. (12)), and the influence of oil
price pressure on the whole economy was moderated 0.34% during ten years between
1990 and 2000. The changes in the input structure of domestic goods and services
contributed to reducing the rate of price increase influenced by the oil price rise and the
effect was p TE  0.0013 (see eq. (12)). Similarly, the effect of changes in the import
coefficient was p IG  0.0052 (see eq. (12)) and the effect of changes in the value
added coefficient was p VA  0.0082 , revealing a contribution of changes in the value
added coefficient direction to reduce the impact of rising oil prices, in the opposite the
changes in the import coefficient increased the impact of rising oil prices.3

Secondly, the result of p TE  piTE
 estimated with Eq. (11) indicates how the
rate of increase in the equilibrium price of ith sector caused by the rising oil price shifted
as a result of the changes in the input structure of the whole economy. If this value is
negative, the rate of increase in the commodity price in question would have been lower
in 2000 than in 1990 if a rise in the oil price equivalent to this oil price surge had
occurred both in 1990 and in 2000, and vice versa.
Those sectors indicating a particularly large negative value among all sectors are
presented in Table 1. From the result, we found that the equilibrium prices of #227
Cellular Phones and #223 Personal Computers declined significantly because of
3
In this study, since the intermediate inputs of scraps and byproducts are excluded from the input
structure and included in the value added category, the total effect p d * does not coincide with the
sum of the four effects of p IP , p IG , p VA , and p TE .
changes in the input structure of domestic goods and services. This might have resulted
from the considerable control of the oil price pressure in the electric and machinery
sectors enabled by rapid technological development and dematerialization through
miniaturization. In contrast to Table 1, Table 2 presents the high-ranking sectors which
have large positive value. From Table 2, it is remarkable that agriculture, forestry and
fisheries sectors and ships sectors have been increasingly pressured from the higher oil
price. This indicates that the cost structures of these sectors need to be monitored
carefully.
Table 1. Top 10 sectors for which the rate of equilibrium price caused by crude oil
price rises decreases because of input structural change during 1990–2000
Rank
No.
Sector name
1
2
3
227
223
224
Change of the price of the sector
including oil price pressure by
structural change during
1990–2000
Cellular phones
Personal computers
Electronic computing equipment
(except personal computers)
4
5
6
7
8
9
10
233
220
236
221
181
Integrated circuits
162
Ferro alloys
113
Petrochemical basic products
Video recording and playback equipment
Magnetic tapes and discs
Household air-conditioners
Optical fiber cables
-3.095
-1.310
-0.949
-0.900
-0.716
-0.474
-0.379
-0.374
-0.341
-0.338
Table 2. Top 10 sectors for which the rate of equilibrium price caused by crude oil price
rises increases because of input structural change in 1990–2000
Rank
No.
Sector name
1
2
3
255 Ships (except steel ships)
4
5
6
7
8
9
10
135 Agricultural chemicals
276 Tatami (straw matting) and straw products
71 Organic fertilizers, n.e.c.
254 Steel ships
251 Motor vehicle bodies
256 Internal combustion engines for vessels
332 Cable broadcasting
55 Animal oils and fats
277 Ordnance
Change of the price of the
sector including oil price
pressure by structural change
during 1990–2000
0.393
0.277
0.277
0.249
0.240
0.204
0.200
0.196
0.195
0.171
Table 3 presents the clusters which has the large negative value of p TE, k , the
Leontief effect caused by the alteration of the intermediate input coefficient belonging
to the clusters on equilibrium prices. In other words, Table 3 shows the clusters in which
the changes in the input structures among the constituent sectors during 1990–2000
reduced the rate of price increase caused by rising oil prices. These clusters are mainly
manufacturing clusters such as Cement cluster (#16), Household air-conditioners cluster
(#34), Industrial Soda Chemicals cluster (#39). Research and Development cluster (#5),
which most significantly reduces the effects on the whole economy is also dominated by
manufacturing sectors. Figure 4 portrays a diagram of the Research and Development
cluster detected using the 1990 Input–Output Table. Figure 5 is a diagram of the same
cluster in 2000. Comparison of these two diagrams reveals narrowing of the lines
among sectors––a decrease in the pressure from oil price increase––during 1990–2000.
Substantial reduction in the pressure between #347 Research and Development and
#223 Personal Computers, #224 Electronic Computing Equipment (except personal
computers), and #227 Cellular phones is evident. Decrease in the pressure between #347
Research and Development and #227 Cellular Phones was particularly large, and the
pressure decreased to approximately one-eleventh during 1990–2000. This might reflect
the lower demand for Research and Development sector in Cellular phone sector and a
decline in the pressure of Cellular phone sector itself attributable to its miniaturization
and dematerialization. Consequently, the pressure from the rise in the oil price in this
cluster decreased to approximately a third during 1990–2000. Furthermore, the marked
easing of influence of the oil price increase on the whole economy achieved by this
cluster is facilitated by the close trading relations of #347 Research and Development
with a broad spectrum of other sectors.4 In other words, the oil price pressure reduced
in this cluster spreads more widely to the entire economy through trading relations with
other sectors.
Table 4 presents the high-ranking clusters in which the changes in the input
structure during 1990–2000 increased the rate of price increase caused by the rise in oil
prices. These clusters require attention because they include many service businesses
such as Other business services cluster (#26), Wholesale trade cluster (#35) and Water
supply cluster (#42). Figures 6 and 7 respectively represent the diagrams of the Other
Business Services cluster in 1990 and 2000 whose input structural changes increased
the rate of price increase attributable to the rising crude oil price to the largest. These
diagrams present a particularly large increase in the pressure between #362 Information
Services and the other sectors. Actually, #372 Other Business Services, in which the
total pressure with other sectors in this cluster comprise slightly less than 30% of all
pressure in this cluster, reduced its pressure with other sectors to approximately 70%
during 1990–2000, whereas #362 Information Services comprising 10% of the pressure
in this cluster increased the pressure with other sectors to approximately 1.2 times. This
increase can be considered to reflect the increased demand for information services to
accommodate the rapid progress of advanced information society. As a results, the
pressure in this cluster increased by approximately 10% during 1990–2000.
For the Aluminum cluster (#7) (see Fig. 8), because most constituent sectors of the
cluster are automobile-related sectors, cooperation provided by the automobile sector
such as joint investment and development not only for the energy conservation in the
manufacturing process of aluminum products such as aluminum wheels required in
4
According to Kagawa et al. (2009), the size of the outdegree of #347 Research and Development Cluster sector,
which functions as an indicator of how many sectors are affected by the increase in the oil price, ranked fourth among
the 395 sectors (in the structure of year 2000).
automobile manufacturing, but for new material development using aluminum requiring
less environmental burden would be an effective in easing the impact on the whole
economy.
Table 3. Top 5 clusters decreasing the rate of the price increase because of oil price rise
by input structural change within the cluster during 1990–2000
Crude oil rise influence on the
Rank
Industrial cluster name
inflation rate by structural change
in the cluster during 1990–2000
1
2
3
4
Research and development cluster (#5)
Cement cluster (#16)
Household air-conditioners cluster (#34)
Industrial soda chemicals cluster (#9)
-0.00766
-0.00083
-0.00078
-0.00077
5 Printing, plate making, and book binding
cluster (#39)
-0.00077
Table 4. Top 5 clusters increasing the rate of the price increase because of oil price
rise by input structural change in the cluster during 1990–2000
Crude oil rise influence on the
Rank
Industrial cluster name
inflation rate by structural change
in the cluster during 1990–2000
1 Other business services cluster (#26)
0.00295
2
3
4
5
0.00114
0.00087
0.00074
0.00056
Wholesale trade cluster (#35)
Water supply cluster (#42)
Aluminum cluster (#7)
Pig iron cluster (#30)
#228 Radio communication … #227 Cellular phones
#229 Other communication …
#226 Wired communication equipment
#230 Applied electronic equipment
#225 Electronic computing equipment…
#224 Electronic computing equipment…
#231 Electric measuring instruments
#232 Semiconductor devices
#223 Personal computers
#233 Integrated circuits
#220 Video recording and playback …
#237 Other electronic components
#247 Other electrical devices …
#347 Research and …
#219 Radio and television sets
#218 Electric audio equipment
#216 Other office machines
#204 Industrial robots
Figure 4.
Research and development cluster in 1990.
#228 Radio communication … #227 Cellular phones
#229 Other communication …
#230 Applied electronic equipment
#231 Electric measuring instruments
#232 Semiconductor devices
#225 Electronic computing equipment…
#224 Electronic computing equipment…
#223 Personal computers
#233 Integrated circuits
#220 Video recording and playback …
#237 Other electronic components
#247 Other electrical devices …
#347 Research and …
#226 Wired communication equipment
#219 Radio and television sets
#218 Electric audio equipment
#216 Other office machines
#204 Industrial robots
Figure 5.
Research and development cluster in 2000.
#354 Social insurance …
#362 Information services
#353 Social insurance (public)
#364 Goods rental and leasing …
#340 Other educational and training …
#368 Building maintenance services
#337 Public administration (local)
#372 Other business services
#328 Telecommunication
#326 Travel agency and other services relating …
Figure 6.
Other business services cluster in 1990.
#354 Social insurance …
#362 Information services
#353 Social insurance (public)
#364 Goods rental and leasing …
#340 Other educational and training …
#368 Building maintenance services
#337 Public administration (local)
#372 Other business services
#328 Telecommunication
#326 Travel agency and other services relating …
Figure 7.
Other business services cluster in 2000.
#248 Passenger
motor cars
#177 Aluminum (inc. regenerated aluminum)
#183 Rolled
Rolled and
and drawn
drawn aluminum
aluminum
#183
#183 Rolled
and drawn
aluminum
#252 Internal combustion engines for …
#246 Electrical equipment for internal combustion …
#184 Non-ferrous metal castings …
#249 Trucks, buses and other cars
#250 Two-wheel motor vehicles
#253 Motor vehicle parts and accessories
Figure 8.
Aluminum cluster in 1990.
5. Conclusion
This paper proposed a detection method reconciling input–output price analysis
with spectral graph analysis and demonstrated that industrial clusters with strong oil
price pressure have a sector composition that differs considerably from the Japan
Standard Industrial Classification. Some clusters are formed as a supply chain, which
could not be grouped together according to the Standard Industrial Classification. For
situations in which the efforts of individual companies and industries are reaching their
limit, measures must be taken using units that are based not only on similarity in
production technology but on the criteria that are defined by factors related to demand
and supply relations and issues (e.g. the strength of economic relations involving oil
price pressure) in an attempt to achieve more efficient reduction of the pressure from the
rising oil price and effective use of crude oil. The industrial clusters identified in this
study represent one form of policy unit. The clustering method formulated in this study
is also effective for detecting the CO2 intensive, energy intensive, and resource intensive
industrial groups.
Another important finding was that the changes in the input structure of domestic
goods and services during 1990–2000 eased the impact of the oil price increase
throughout the whole economy. Especially, the Research and Development cluster,
Cement cluster, Industrial Soda Chemicals cluster, and Printing, Plate Making and Book
Binding cluster contributed to reducing the effect of the increasing oil price because of
internal structural changes within the clusters. Among them, the Research and
Development cluster was composed of many electric and machinery sectors, whose
input structural changes causing a decrease in the oil price pressure contribute greatly to
the reduction of the effects on the economy. Conversely, the Other Business Services
cluster and Water Supply cluster increased the effects of the oil price rise on the
economy because of inputl structural changes within the clusters during 1990–2000.
The former was increased the pressure within the cluster and influence on the other
clusters through the development of advanced information society in Japan.
At present, when economic transactions are forming complex networks, reducing
the use of crude oil is not an issue that affects only a single sector or industry. Forming
an alliance of the industrial clusters identified in this study enables the efficient
reduction of oil price pressures. Analyzing the changes occurring over time such as an
increase and decrease in the oil price pressure using units called industrial clusters
enables identification and sharing of responsibilities. For instance, in a cluster in which
the oil price pressure is neither decreasing nor increasing, transactions with sectors
under substantial oil price pressure and sectors in which the oil price pressure is not
changing or increasing are identifiable as targets for improvement. Furthermore,
cooperation among sectors belonging to the same cluster would be effective for such
improvement.
Reference literature
1) Bellman, R., 1997. “Introduction to Matrix Analysis: Second Edition,” Society for
Industrial and Applied Mathematics.
2) Cabinet Office, Government of Japan, 2007. Report of the Ministerial Meeting on
Emergency Measures Concerning the Sharp Increase in the Price of Crude Oil and
Subcontractors and Small and Medium-sized Enterprise (in Japanese).
3) Chung, F., 1998. Spectral Graph Theory, American Mathematical Society.
4) Dietzenbacher, E. and Los, B. (1997): Analyzing Decomposition Analysis, in: A.
Simonovits and A.E. Steenge (eds.), Prices, Growth and Cycles, pp.108-131
(London, Macmillan).
5) Dietzenbacher, E. and Los, B., 1998. “Structural Decomposition Techniques: Sense
and Sensitivity,” Economic Systems Research, vol.10, no.10, pp.307-323.
6) Feser, E. and Bergman, E., 2000. “National Industry Cluster Templates: A
Framework for Applied Regional Cluster Analysis,” Regional Studies, vol.34, no.1,
pp.1-19.
7) Freeman, C.L., 1978. “Centrality in Social Networks: Conceptual Clarification,”
Social Networks, vol.1, no.3, pp.215-239, 1978.
8) Fujikawa, K., Shimoda, M., and Watanabe, T., 2007. “A comparative analysis on
ripple effect of imported oil price hike in domestic prices between Japan and the
USA,” Journal of Socio Economic Research, no.55, pp.45-62.
9) Ghosh, S. and Roy, J., 1998. “Qualitative Input–Output Analysis of the Indian
Economic Structure,” Economic Systems Research, vol.10, no.3, pp.263-274.
10) Hattori, T. and Hitomi, K., 2007. “Japanese Industrial Prices Fluctuation in Soaring
Oil Prices Period – Comparative Analysis of Soaring Oil Price Period since 2002
and Oil Crisis Period by Polar Decomposition Method –,” Journal of Socio
Economic Research, no.55, pp.63-80.
11) Kagawa, S., Kondo, Y., Nansai, K., Oshita, Y. and Suh, S., 2010. “Detecting Energy
Clusters from the Product Supply Chain: Spectral Clustering Approach,” Working
paper.
12) Kagawa S., Oshita Y., Nansai K., Sangwon S., 2009. “How Has Dematerialization
Contributed to Reducing Oil Price Pressure?: A Qualitative Input–Output Analysis
for the Japanese Economy during 1990-2000,” Environmental Science &
Technology, vol.43, no.2, pp.245-252.
13) Miller, R.E, and Blair, P.D., 2009. “Input–Output Analysis: Foundations and
Extensions,” Cambridge University Press, Cambridge.
14) Muniz, A., Raya, A. and Carvajal, C., 2008. “Key Sectors: A New Proposal from
Network Theory,” Regional Studies, vol.42, no.7, pp.1-18.
15) OECD, 2004. “Oil Price Drivers, Economic Consequences and Policy Responses,”
OECD Economic Outlook, No. 76.
16) Oosterhaven, J., Eding, G. and Stelder, D., 2001. “Cluster, Linkages and
Interregional Spillover: Methodology and Policy Implications for the Two Dutch
Mainports and the Rural North,” Regional Studies, vol.35, no.9, pp.809-822.
17) Shi, J. and Malik, J., 2000. “Normalized Cut and Image Segmentation,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.8,
pp.888-905.
18) von Luxburg, U.,2007. “A tutorial on spectral clustering,” Statistics and Computing,
vol.17, no.4, pp.395-416.
19) Zhang, Z. and Jordan, M., 2008. “Multiway Spectral Clustering: A Margin-based
Perspective,” Statistical Science, vol.23, no.3, pp. 383–403.
Appendix 1. Selected cluster diagrams in 1990.
#112 Other industrial inorganic …
#129 Medicaments
#108 Industrial soda chemicals
#119 Oil and fat industrial chemicals
#350 Medical services (medical …
#348 Medical services (public)
#127 Rayon and acetate
#296 Industrial water supply
#349 Medical services (non-profit...
#111 Salt
#149 Sheet glass and…
#69 Manufactured ice
#134 Photographic sensitive …
Industrial soda chemicals cluster (#9)
#163 Crude steel (converters)
#161 Pig iron
Pig iron cluster (#30)
#282 Non-residential construction (non-wooden)
#245 Wiring devices and …
#272 Musical instruments
#267 Professional and scientific instruments
#262 Bicycles
#221 Household air-conditioners
#395 Activities not elsewhere…
#274 Stationery
#210 Semiconductor making equipment
#289 Telecommunication facilities construction
Household air-conditioners cluster (#34)
#270 Toys and games
#300 Wholesale trade
#159 Abrasive
#278 Miscellaneous
manufacturing products
#275 Jewelry and adornments
#280 Residential construction (non-wooden)
#171 Cast iron pipes and tubes
#314 Coastal and inland water transport
#86 Bedding
#367 Repair of machines
#215 Copy machine
#222 Household electric appliances …
#281 Non-residential
construction (wooden)
#198 Refrigerators and air conditioning …
#150 Glass fiber and glass fiber products …
#255 Ships …
#266 Watches and clocks
#268 Analytical instruments, testing machines, …
Wholesale trade cluster (#35)
#360 Private non-profit institutions …
#369 Judicial, financial and …
#344 Research institutes …
#308 Railway transport (passengers)
#311 Hired car and taxi transport
#83 Woven fabric apparel
#303 Life insurance
#334 Public administration (local)
#298 Waste management services (public)
#302 Financial services
#105 Printing, plate making and book binding
#376 Amusement and recreation …
#370 Civil engineering and construction …
#391 Miscellaneous repairs, n.e.c.
#304 Non-life insurance
#359 Private non-profit institutions serving ...
#81 Fabricated textiles for medical use
#342 Research institutes…
#345 Research institutes…
Printing, plate making, and book binding cluster (#39)
#295 Water supply
#341 Research institutes for natural science (public)
#388 Public baths
#336 School education (private)
#385 Cleaning, laundry and dyeing services
#355 Social welfare (public)
#356 Social welfare (private, …
#390 Ceremonial occasions
#297 Sewage disposal
#299 Waste management services …
#322 Services relating to water transport
#335 School education (public)
#383 Eating and drinking places for… #351 Health and hygiene (public)
#339 Other educational …
#384 Hotels and other lodging places
#352 Health and hygiene (profit-making)
Water supply cluster (#42)
Appendix 2. Sector classification.
No.
Sector Name
No.
Sector Name
1 Rice
32 Other non-metallic ores
2 Wheat, barley and the like
33 Coal mining
3 Potatoes and sweet potatoes
34 Crude petroleum
4 Pulses
Natural gas
5 Vegetables
35 Slaughtering and meat processing
6 Fruits
36 Processed meat products
7 Sugar crops
37 Bottled or canned meat products
8 Crops for beverages
38 Dairy farm products
9 Other edible crops
39 Frozen fish and shellfish
10 Crops for feed and forage
40 Salted, dried or smoked seafood
11 Seeds and seedlings
41 Bottled or canned seafood
12 Flowers and plants
42 Fish paste
13 Other inedible crops
43 Other processed seafood
14 Dairy cattle farming
44 Grain milling
15 Hen eggs
45 Flour and other grain milled products
16 Fowl and broilers
46 Noodles
17 Hogs
47 Bread
18 Beef cattle
48 Confectionery
19 Other livestock
49 Bottled or canned vegetables and fruits
20 Veterinary service
50
21
Agricultural services (except
veterinary service)
Preserved agricultural foodstuffs (other
than bottled or canned)
51 Sugar
22 Silviculture
52 Starch
23 Logs
53 Dextrose, syrup and isomerized sugar
24 Special forest products (inc. hunting)
54 Vegetable oils and meal
25 Marine fisheries
55 Animal oils and fats
26 Marine culture
56 Condiments and seasonings
27 Inland water fisheries and culture
57 Prepared frozen foods
28 Metallic ores
58 Retort foods
29 Materials for ceramics
59 Dishes, sushi and lunch boxes
30 Gravel and quarrying
60 School lunch (public)
31 Crushed stone
61 School lunch (private)
No. Sector Name
No. Sector Name
62 Other foods
88 Timber
63 Distilled alcohol
89 Plywood
64 Beer
90 Wooden chips
65 Whiskey and brandy
91 Other wooden products
66 Other liquors
92 Wooden furniture and fixtures
67 Tea and roasted coffee
93 Wooden fixtures
68 Soft drinks
94 Metallic furniture and fixture
69 Manufactured ice
95 Pulp
70 Feeds
96 Paper
71 Organic fertilizers, n.e.c.
97 Paperboard
72 Tobacco
98 Corrugated cardboard
73 Fiber yarns
99
74
75
76
Cotton and staple fiber fabrics (inc.
fabrics of synthetic spun fibers)
Silk and artificial silk fabrics (inc.
fabrics of synthetic filament fibers)
Woolen fabrics, hemp fabrics and other
fabrics
77 Knitting fabrics
78
Yarn and fabric dyeing and finishing
(processing on commission only)
Coated paper and building
(construction) paper
100 Corrugated card board boxes
101 Other paper containers
102 Paper textile for medical use
103
Other pulp, paper and processed paper
products
104 Newspapers
Printing, plate making and book
79 Ropes and nets
105
80 Carpets and floor mats
106 Publishing
81 Fabricated textiles for medical use
107 Chemical fertilizer
82 Other fabricated textile products
108 Industrial soda chemicals
83 Woven fabric apparel
109 Inorganic pigment
84 Knitted apparel
110 Compressed gas and liquefied gas
85
Other wearing apparel and clothing
accessories
binding
111 Salt
86 Bedding
112 Other industrial inorganic chemicals
87 Other ready-made textile products
113 Petrochemical basic products
No.
114
Sector Name
Petrochemical aromatic products
(except synthetic resin)
No.
Sector Name
143 Rubber footwear
115 Aliphatic intermediates
144 Plastic footwear
116 Cyclic intermediates
145 Other rubber products
117 Synthetic rubber
146 Leather footwear
118 Methane derivatives
147 Leather and fur skins
119 Oil and fat industrial chemicals
148 Miscellaneous leather products
120 Plasticizers
149 Sheet glass and safety glass
121 Synthetic dyes
150
122 Other industrial organic chemicals
151 Other glass products
123 Thermo-setting resins
152 Cement
124 Thermoplastics resins
153 Ready mixed concrete
125 High function resins
154 Cement products
126 Other resins
155 Pottery, china and earthenware
127 Rayon and acetate
156 Clay refractories
128 Synthetic fibers
157 Other structural clay products
129 Medicaments
158 Carbon and graphite products
130
131
Soap, synthetic detergents and surface
active agents
Cosmetics, toilet preparations and
dentifrices
Glass fiber and glass fiber products,
n.e.c.
159 Abrasive
160
Miscellaneous ceramic, stone and clay
products
132 Paint and varnishes
161 Pig iron
133 Printing ink
162 Ferro alloys
134 Photographic sensitive materials
163 Crude steel (converters)
135 Agricultural chemicals
164 Crude steel (electric furnaces)
136 Gelatin and adhesives
165 Steel scrap
137 Other final chemical products
166 Hot rolled steel
138
Petroleum refinery products (inc.
greases)
167 Steel pipes and tubes
139 Coal products
168 Cold-finished steel
140 Paving materials
169 Coated steel
141 Plastic products
170 Cast and forged steel
142 Tires and inner tubes
171 Cast iron pipes and tubes
No.
Sector Name
No.
Sector Name
172 Cast and forged materials (iron)
196 Engines
173 Iron and steel shearing and slitting
197 Conveyors
174 Other iron or steel products
198
175 Copper
199 Pumps and compressors
176 Lead and zinc (inc. regenerated lead)
200 Machinists' precision tools
177
Aluminum (inc. regenerated
aluminum)
201
Refrigerators and air conditioning
apparatus
Other general industrial machinery and
equipment
Machinery and equipment for
178 Other non-ferrous metals
202
179 Non-ferrous metal scrap
203 Chemical machinery
180 Electric wires and cables
204 Industrial robots
181 Optical fiber cables
205 Metal machine tools
182
Rolled and drawn copper and copper
alloys
183 Rolled and drawn aluminum
184
Non-ferrous metal castings and
forgings
construction and mining
206 Metal processing machinery
207 Machinery for agricultural use
208 Textile machinery
185 Nuclear fuels
209 Food processing machinery
186 Other non-ferrous metal products
210 Semiconductor making equipment
187 Metal products for construction
211
188 Metal products for architecture
212 Metal molds
189
Gas and oil appliances and heating and
cooking apparatus
190 Bolts, nuts, rivets and springs
191
192
Metal containers, fabricated plate and
sheet metal
Plumber’s supplies, powder metallurgy
products and tools
Other special machinery for industrial
use
213 Bearings
214 Other general machines and parts
215 Copy machines
216 Other office machines
193 Other metal products
217 Machinery for service industry
194 Boilers
218 Electric audio equipment
195 Turbines
219 Radio and television sets
No.
220
Sector Name
Video recording and playback
equipment
221 Household air-conditioners
222
Household electric appliances (except
air-conditioners)
223 Personal computers
224
225
Electronic computing equipment
(except personal computers)
Electronic computing equipment
(accessory equipment)
No.
Sector Name
244 Electric bulbs
245 Wiring devices and supplies
246
Electrical equipment for internal
combustion engines
247 Other electrical devices and parts
248 Passenger motor cars
249 Trucks, buses and other cars
226 Wired communication equipment
250 Two-wheel motor vehicles
227 Cellular phones
251 Motor vehicle bodies
228
Radio communication equipment
(except cellular phones)
252
Internal combustion engines for motor
vehicles and parts
229 Other communication equipment
253 Motor vehicle parts and accessories
230 Applied electronic equipment
254 Steel ships
231 Electric measuring instruments
255 Ships (except steel ships)
232 Semiconductor devices
256
233 Integrated circuits
257 Repair of ships
234 Electron tubes
258 Rolling stock
235 Liquid crystal elements
259 Repair of rolling stock
236 Magnetic tapes and discs
260 Aircrafts
237 Other electronic components
261 Repair of aircraft
238 Rotating electrical equipment
262 Bicycles
239 Relay switches and switchboards
263 Other transport equipment
240 Transformers and reactors
264 Cameras
241
Other industrial heavy electrical
equipment
265
Internal combustion engines for
vessels
Other photographic and optical
instruments
242 Electric lighting fixtures and apparatus
266 Watches and clocks
243 Batteries
267 Professional and scientific instruments
No.
268
Sector Name
Analytical instruments, testing
machines, measuring instruments
No.
Sector Name
296 Industrial water supply
269 Medical instruments
297 Sewage disposal
270 Toys and games
298 Waste management services (public)
271 Sporting and athletic goods
299 Waste management services (private)
272 Musical instruments
300 Wholesale trade
273
Audio and video records, other
information recording media
301 Retail trade
274 Stationery
302 Financial services
275 Jewelry and adornments
303 Life insurance
276
"Tatami" (straw matting) and straw
products
304 Non-life insurance
277 Ordnance
305 Real estate agencies and managers
278 Miscellaneous manufacturing products
306 Real estate rental services
279 Residential construction (wooden)
307 House rent
280 Residential construction (non-wooden)
308 Railway transport (passengers)
281 Non-residential construction (wooden)
309 Railway transport (freight)
282
Non-residential construction
(non-wooden)
310 Bus transport service
283 Building renovation and repair
311 Hired car and taxi transport
284 Public construction of roads
312 Road freight transport
285
Public construction of rivers, drainages
and others
313 Ocean transport
286 Agricultural public construction
314 Coastal and inland water transport
287 Railway construction
315 Harbor transport services
288 Electric power facilities construction
316 Air transport
289
290
Telecommunication facilities
construction
Other civil engineering and
construction
317 Freight forwarding
318 Storage facility services
291 Electric power for commercial use
319 Packing services
292 On-site power generation
320 Facility services for road transport
293 Gas supply
321 Port and water traffic control
294 Steam and hot water supply
322 Services relating to water transport
295 Water supply
323 Airport and air traffic control (public)
No. Sector Name
324
Airport and air traffic control
(industrial)
325 Services relating to air transport
326
Travel agency and other services
relating to transport
No. Sector Name
345
346
347
Research institutes for natural sciences
(profit-making)
Research institutes for cultural and
social sciences (profit-making)
Research and development
(intra-enterprise)
327 Postal services
348 Medical services (public)
328 Telecommunication
349
329
Other services relating to
communication
350
Medical services (non-profit
foundations, etc.)
Medical services (medical
corporations, etc.)
330 Public broadcasting
351 Health and hygiene (public)
331 Private broadcasting
352 Health and hygiene (profit-making)
332 Cable broadcasting
353 Social insurance (public)
333 Public administration (central)
354 Social insurance (private, non-profit)
334 Public administration (local)
355 Social welfare (public)
335 School education (public)
356 Social welfare (private, non-profit)
336 School education (private)
357 Nursing care (In-home)
337 Social education (public)
358 Nursing care (In-facility)
338 Social education (private, non-profit)
359
339
340
341
342
343
344
Other educational and training
institutions (public)
Other educational and training
institutions (profit-making)
Research institutes for natural science
(public)
Research institutes for cultural and
social sciences (public)
Research institutes for natural sciences
(private, non-profit)
Research institutes for cultural and
social sciences (private, non-profit)
360
Private non-profit institutions serving
enterprises
Private non-profit institutions serving
households, n.e.c.
361 Advertising services
362 Information services
363
364
News syndicates and private detective
agencies
Goods rental and leasing (except car
rental)
365 Car rental and leasing
No.
Sector Name
No.
Sector Name
General eating and drinking places
366 Repair of motor vehicles
381
367 Repair of machines
382 Coffee shops
368 Building maintenance services
383 Eating and drinking places for pleasure
369
370
Judicial, financial and accounting
services
Civil engineering and construction
services
(except coffee shops)
384 Hotels and other lodging places
385 Cleaning, laundry and dyeing services
371 Worker dispatching services
386 Barber shops
372 Other business services
387 Beauty shops
373
Motion picture and video production
and distribution
388 Public baths
374 Movie theaters
389 Photographic studios
375 Theaters and entertainment facilities
390 Ceremonial occasions
376 Amusement and recreation facilities
391 Miscellaneous repairs, n.e.c.
377
378
Stadiums and companies of bicycle,
horse, motorcar and motorboat races
Sport facility services, public gardens
and amusement parks
379 Theatrical companies
380
Other amusement and recreation
services
392 Places for private lessons
393 Other personal services
394 Office supplies
395 Activities not elsewhere classified