ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E13-7

Assessing Dynamic Externalities
from a Cluster Perspective:
The Case of the Motor Metropolis in Japanby
Eri Yamada
Tetsu Kawakami

August 2013

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Assessing Dynamic Externalities from a Cluster Perspective:
The Case of the Motor Metropolis in JapanEri Yamada and Tetsu Kawakami
Abstract

In this paper, we first apply the methods of exploratory spatial data

analysis (ESDA) and investigate the geographic concentration of interrelated growing
industries, or “growth clusters,” by using data from the Nagoya metropolitan area inJapan over the period 1986–2006. Second, by applying econometric models, we
examine whether and which type of knowledge externalities contribute to region–industry dynamics and to the formulation of the detected growth cluster. As amethodological contribution, spatial dependence caused by the geographical proximity
between regions and the technological proximity between industries is incorporated
into the empirical models. Combining the information obtained from the ESDA andeconometric analysis enables us to assess the role of knowledge externalities forregional growth from a cluster perspective. The empirical results identify the presenceof a growth cluster mainly driven by the automobile and associated industries. We findthat intra-industry externalities help the substantial growth of the automobile
industry and diffuse over a broader area in the cluster. In the core of the cluster, thediversified interrelated structure also contributes to the growth of both theauto-related and non-auto-related manufacturing sectors.
JEL Classifications
Keywords

C31, O18, R11

Dynamic externality, Knowledge spillover, Exploratory spatial data

analysis, Technological proximity, Growth cluster
Eri Yamada
Graduate School of Economics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya,
Aichi 464-8601, Japane-mail: yamadaeri@soec.nagoya-u.ac.jp
Tetsu Kawakami
Faculty of Economics, Kinki University, 3-4-1 Kowakae, Higashiosaka,
Osaka 577-8502, Japane-mail: tekawaka@kindai.ac.jp
Tel: +81(6)6721-2332

Fax: +81(6)6726-3213
1

1. Introduction
There is a growing body of work confirming that externalities of knowledge spillovers
resulting from industrial agglomeration drive economic growth in cities and regions.
Following the attention paid to industrial scope by Glaeser et al. (1992), empiricalstudies have investigated the role of these dynamic externalities for various
geographical scales and industrial aggregations. 1 What is still an on-going debate is theexact nature of spillovers that facilitate growth. Marshall–Arrow–Romer (MAR)externalities concern knowledge spillovers enhanced by the agglomeration of local firms
within the same industry (Marshall 1920; Arrow 1962; Romer 1986). On the other hand,
Jacobs (1969) argues that knowledge spillovers come from outside the industry andstresses agglomeration containing diverse industries for growth. The difference between
MAR and Jacobs’ externalities are also recognized in the local competition environment.
MAR predicts that a local monopoly restricting and internalizing the flow of ideas isbetter for growth, whereas Jacobs favors local competition to foster the pursuit andadoption of technology. The hypothesis proposed by Porter (1990) is a mix of MAR andJacobs, and describes knowledge spillovers that are enhanced within the same
competitive industries. De Groot et al. (2009) conclude that the evidence as to which
type of agglomeration externality is most beneficial for growth is rather mixed anddiffers across regions, sectors, and time periods.
From a policy perspective, these empirical studies on dynamic externalities provide asignificant basis for recent regional development policies. This is particularly true ofindustrial cluster policies, which have attracted policymakers and been launched invarious countries since the 1990s. However, to draw more meaningful policy
implications, a closer look at disaggregated spatial levels is required.2 Note that thespillover effects of knowledge externalities tend to grow stronger as the geographicalunit of reference becomes smaller (Jaffe et al. 1993; Baptista 2000). This implies that,when analyzed on smaller spatial scales, an analytical framework that explicitly focuseson geographical proximate dependence would be more informative. 3
In addition to geographical proximity, recent literature emphasizes the role of
1 See, for example, Henderson et al. (1995); Feldman and Audretsch (1999); Combes (2000); Drennan

et al. (2002); De Lucio et al. (2002); Henderson (2003); Suedekum and Blien (2005); Van Oort (2007);
Lim (2007); De Vor and De Groot (2010).
2 Van Oort (2007) applies a Dutch municipal data set on sectoral employment and specifies the
econometric models by hierarchical spatial regimes. De Vol and De Groot (2010) investigate theperformance of industrial sites in Amsterdam. Audretsch et al. (2012) apply West German planning
regions to test the interrelation between regional characteristics and entrepreneurial activities.
3 Van Oort (2007) and Lim (2007) introduce spatially lagged explained and explanatory variables
into the econometric models. Both studies find significant evidence of spatial dependence in regional
growth, but do not confirm clear evidence about geographic knowledge spillover effects.

2technological relatedness or proximity for regional growth. Porter (2003) claims that,from a cluster perspective, the hypotheses argued by MAR and Jacobs are too simple,
and that the relevant knowledge spillovers should be strongest within clusters andamong related industries. Previous studies have proposed several measures to establish
relatedness across products. The measures frequently used are based on standard
classifications of industries. Here, a subset of larger digit industrial codes within asmaller digit group is regarded as related, whereas industries classified into the othersmaller digits are unrelated (Henderson et al. 1995; Lim 2007; Frenken et al. 2007).
Boschma et al. (2012) show that the agnostic outcomes-based measures developed byPorter (2003) and Hidalgo et al. (2007) better capture the degree of relatedness to
explain regional growth than do the conventional ex ante classifications of relatedness.
An expanding body of literature suggests that spatial proximity in both geographicaland technological aspects matters to the knowledge transfers that contribute to growth.
However, these studies do not give adequate evidence capturing both proximity aspects
simultaneously. Yamada and Kawakami (2012) point out that considering geographicalproximity alone would fail to detect the significant associations of industries in clusters,
and argue for the need to incorporate both proximity aspects in empirical modeling. To
establish more reliable statistical inferences, we consider the geographical andtechnological proximities simultaneously by introducing the extensive spatial weightmatrix into the models. As an indicator of technological proximity in particular, weapply the average propagation length (APL) proposed by Dietzenbacher et al. (2005).
APL is an index that expresses the average number of steps it takes to transmit ademand-pull (or cost-push) from one sector to another. If knowledge transfers are more
likely to occur in the nearer rounds of input and output flows, APL is suitable for testing
the role played by industry relatedness in growth.

In view of these discussions, the purpose of this paper is twofold. The first is todetect the geographic concentration of interrelated growing industries in a particularlocation, or “growth clusters,” by using exploratory spatial data analysis (ESDA).
Finding growth clusters is becoming more important for policymakers, since, as Porter(1998) put it, government should reinforce and build on existing and emerging clusters
rather than attempt to create entirely new ones. Our second purpose is to examine
whether and which type of knowledge externalities contribute to the dynamics of aregion–industry and, in particular, to the detected growth cluster. We aim to contributeto the debate on dynamic externalities by elaborating on the importance of spillover
effects across geographical and technological proximate industries.
We analyze the manufacturing and service sectors in the detailed county-level3

regions of the Nagoya metropolitan area (hereafter, the Nagoya MA) in Japan, where
there is a prominent agglomeration of automobile enterprises, including Toyota Motor
Corporation and their related industries. The Nagoya MA has prospered, growing into
a global automotive manufacturing center, while at the same time, Metro Detroit has
slid into decline. Based on the Industrial Cluster Project, the Ministry of Economy,
Trade and Industry (METI) designated the Nagoya MA as a region to promote effective
innovations and new technology in manufacturing industries (METI, 2009). Therefore,clarifying the diffuse nature of externalities in this area is of great interest from bothan academic and a policy perspective.
The rest of the paper is structured as follows. Section 2 describes the data. Section 3
presents a formal definition of the extensive spatial weight matrix on which theproximity of region–industries relies, and implements ESDA to detect growth clusters
in the Nagoya MA. Section 4 specifies the econometric model used to examine thecausal relationship between a region–industry’s growth and knowledge externalities
and presents the results. Section 5 concludes the paper.

2. Study area and data description
The Nagoya MA, with a population of around 10 million as of 2012, is the third largest
metropolitan area in Japan after the Tokyo and Osaka MAs (see Figure 1). 4 This area
extends into three prefectures, Gifu, Aichi, and Mie, and is subdivided into 13 districts.
Each district is broken further into counties, shi, ku, cho, and mura, which are thesmallest administrative divisions in the Population Census of Japan. Usually, regional
economic development initiatives are planned and implemented according to the above
three administrative boundaries, in other words, prefectures, districts, and counties.
The following empirical studies are based on the data of the 118 counties included in
the Nagoya MA.
(Fig. 1 around here)
We briefly describe the data to provide an impression of the industrial structure in
the Nagoya MA. We use data extracted from the Establishment and Enterprise Census
of 1986, 1991, 1996, 1999, 2001, 2004, and 2006, produced by the Ministry of

The geographical coverage of this study is based on the Metropolitan Employment Area proposedby Kanemoto and Tokuoka (2002), which is defined in terms of population density and commuting
flows. The residential population is taken from the Basic Resident Register.

44

International Affairs and Communications. 5 Each contains information on the numberof employees and establishments on both a geographical and industrial level.
Table 1 presents the number of employees by industry in 1986 and 2006, together
with the compound annual growth rates (CAGR) over this period. The data sample
comprises 20 manufacturing (including construction) and 11 service sectors. It appears
that, in 1986, the manufacturing sectors (sectors 1–20) accounted for 43% of total
employment, while services (sectors 21–31) represented around 56%. The mostrepresented sector is wholesale and retail (sector 23), followed by personal services(sector 30). Over the period 1986–2006, most service employment grew, accumulating
to 66% of total employment, whereas manufacturing decreased to a share of 33%.6
However, among the manufacturing sectors, transportation (sector 19) showed a CAGR
of 1.2%, which subsequently stimulated growth in those sectors strongly related totransportation, such as plastic, rubber, and electrical (sectors 10, 11, and 18).
Two additional indicators are presented in Table 1 that reveals more detailed
characteristics of the Nagoya MA industries. According to the employment CAGR byindustry in the three major MAs (Tokyo, Osaka, and Nagoya), the trend of employment
growth in Nagoya appears similar to Tokyo and Osaka (i.e., growth in service sectors,
and shrinking of manufacturing sectors). However, the positive growth rates fortransportation, plastic, rubber and electronics are only reported in Nagoya. Anotherindicator that shows the degree of industrial concentration is the county–industry’s
share of county employment relative to the industry’s share of the three major MAs
employment in 1986. On the whole, the manufacturing sectors in Nagoya are moreconcentrated than those in Tokyo and Osaka. In particular, the degree of concentrationis remarkable for the textile and apparel, lumber, ceramic, and transportation sectors(sectors 3, 4, 13, and 19), while the service sectors other than utilities and public
services (sectors 21 and 31) are less concentrated in the Nagoya MAs.
(Table 1 around here)
Table 2 lists the top three industries that are highly concentrated in each district. As
shown, the manufacturing sectors dominate services in all districts other than Gifu,
Nagoya, and Chusei (districts 1, 5, and 12), which contain the prefectural capitals. Of
the concentrated manufacturing sectors, the textile and apparel sector is distributed
5 Since the past census surveys were not regularly conducted, our data set is based on a panel withirregular periods.
6 Of the service sectors, the only sector with a decrease in employment is finance and insurance(sector 24), presumably due to the burst of the Japanese bubble economy in the early 1990s.

5among the north western districts (districts 1, 2, and 6), while ceramics is concentrated
in the northern area (districts 2, 3, 4, and 7) and Chita (district 8). There is a
substantial concentration of transportation in West Mikawa (district 9), including aprominent agglomeration of transportation machinery and related industries. A
number of plants that process imported resources are located in Chita and Hokusei(districts 8 and 11), as this is where the international hub ports designated by
Japanese government are located.
(Table 2 around here)
Table 3 shows the county–industries with the ten highest and lowest CAGR of
employment. Seven of the ten highest CAGR sectors are business services and two are
medical, health care, and welfare. Rapidly declining sectors include textile and apparel,and some manufacturing sectors. To plan efficient regional development policies,
policymakers need to know if these county–industries are being affected by someexogenous (inter)national trends or by endogenous region-specific conditions.
(Table 3 around here)
In the following empirical studies, we manipulate the data from 777 region
industries, each of which employed over 1,000 people as of 1986, since we aim tohighlight long-term industrial growth due to the considerable scale of initial
agglomeration.

3. Detecting growth clusters
Applying the ESDA method, we explore the spatial pattern of industry dynamics in the
Nagoya MA counties. ESDA is a collection of techniques used to describe and visualize
spatial distributions; identify atypical locations or spatial outliers; discover patterns ofspatial association, clusters, or hot spots; and suggest spatial regimes or other forms ofspatial heterogeneity (Anselin 1994).

3.1. Spatial weight matrix
For ESDA purposes, we summarize the proximity of units under observations bydefining a spatial weight matrix. In this study, the growth of industry 𝑖 (𝑖 = 1, ⋯ , 𝑁𝑖 ) inregion 𝑟 (𝑟 = 1, ⋯ , 𝑁𝑟 ) is employed as the observation so that the scale of the spatial
6

weight matrix is extended to 𝑁𝑟 𝑁𝑖 × 𝑁𝑟 𝑁𝑖 . That is, an extensive spatial weight matrix

that reflects both the geographical and technological proximities is put forward. The

construction of the extensive spatial weight matrix builds on the work of Yamada and
Kawakami (2012).

The geographical proximity between industry 𝑖 in region 𝑟 and industry 𝑗 in region𝑠 is denoted by 𝑤𝑟𝑟,𝑠𝑠 , and defined as follows:
𝑔𝑔𝑔

𝑔𝑔𝑔

𝑤𝑟𝑟,𝑠𝑠 = 1/𝑑𝑟𝑟 𝑖𝑖 𝑟 ≠ 𝑠
𝑔𝑔𝑔

𝑚𝑚𝑚
𝑤𝑟𝑟,𝑠𝑠 = 1/0.5𝑑𝑟𝑟
𝑖𝑖 𝑟 = 𝑠(1)where 𝑑𝑟𝑟 is the transportation time by road between the municipal offices of counties

𝑟 and 𝑠. Using the National Integrated Transportation Analysis System developed by

the Ministry of Land, Infrastructure, Transport and Tourism, the values of 𝑑𝑟𝑟 of theroad networks in 1991 are measured so that the generalized cost of each connection is

at a minimum. The geographical linkages between industries 𝑖 and 𝑗 (with 𝑖 ≠ 𝑗)within the same county are given as half the time distance to the nearest neighbor,𝑚𝑚𝑚, as defined by the second equation of Eq. 1.
0.5𝑑𝑟𝑟Technological proximity is measured using the average propagation length (APL)

developed by Dietzenbacher et al. (2005). The APL is an index that measures how

closely the round flows of intermediate goods between industries arise, and can be used
to represent an “economic distance.” 7 In the conventional input–output model, we can
extend the Leontief inverse matrix with endogenized imports, say 𝐋, to a power series
after neglecting the initial exogenous injection:
� �𝐀 + {�𝐈 − 𝐌� �𝐀}2 + {�𝐈 − 𝐌� �𝐀}3 + ⋯𝐋 − 𝐈 = �𝐈 − 𝐌(2)where 𝐈 denotes the identity matrix and 𝐀 is the input coefficient matrix. The import� , has a diagonal element, which gives the import share of thecoefficient matrix, 𝐌
domestic products in each sector. The APL is defined as the weighted average of

production rounds in industry 𝑖 required for a demand-pull in industry 𝑗. Using theshare of the total effect required in each round as a weight, the APL between
industries 𝑖 and 𝑗, 𝑎𝑎𝑎𝑖𝑖 , is the element of the following matrix:� �𝐀 + 2 ∙ ��𝐈 − 𝐌� �𝐀�2 + 3 ∙ ��𝐈 − 𝐌� �𝐀�3 + ⋯ ]./(𝐋 − 𝐈)
𝐀𝐀𝐀 = [1 ∙ �𝐈 − 𝐌(3)𝐀𝐀𝐀 = 𝐋(𝐋 − 𝐈)./(𝐋 − 𝐈)(4)where “./” represents element-by-element division. After some matrix algebra, Eq. 3
can be rewritten as:

The APL can also be interpreted from the cost-push direction. In this case, 𝑎𝑎𝑎𝑖𝑖

measures the weighted average number of steps it takes an exogenous cost-push in
7

Antras et al. (2012) introduce a measure analogous to the APLs to describe industry“upstreamness,” or average distance from final use.

7

industry 𝑖 to affect industry 𝑗 (Dietzenbacher et al. 2005).

We also reflect the size of the linkage between industries in terms of thetechnological proximity. In line with the fact that the APLs can be interpreted fromdemand-pull and cost-push directions, the size of the linkages is given by the matrix 𝐅,with elements 𝑓𝑖𝑖 , which are defined as the average of the backward effect of ademand-pull and the forward effect of a cost-push:(5)

𝐅 = 0.5 × {(𝐋 − 𝐈) + (𝐆 − 𝐈)}

where 𝐆 denotes the Ghosh inverse.

Since we do not establish the direction of the technological proximity a priori, we

take the average of the size of the linkage between industries 𝑖 and 𝑗. The economic

distance between industries 𝑖 and 𝑗 is also given by the weighted average, each ofwhich is weighted by the respective size of the linkage:𝑓𝑖𝑖̅ = 𝑓𝑗𝑗̅ = 0.5 × �𝑓𝑖𝑖 + 𝑓𝑗𝑗 ������𝑎𝑎𝑎𝑖𝑖 = �����
𝑎𝑎𝑎𝑗𝑗 = (𝑓𝑖𝑖 ∙ 𝑎𝑎𝑎𝑖𝑖 + 𝑓𝑗𝑗 ∙ 𝑎𝑎𝑎𝑗𝑗 )/(𝑓𝑖𝑖 + 𝑓𝑗𝑗 )(6)

The technological proximity between industry 𝑖 in region 𝑟 and industry 𝑗 in region𝑡𝑡𝑡ℎ𝑠 is denoted by 𝑤𝑟𝑟,𝑠𝑠, and defined as follows:8
𝑡𝑡𝑡ℎ�����𝑖𝑖 ≥ 0.01
𝑤𝑟𝑟,𝑠𝑠= 𝑓𝑖𝑖̅ ������
𝑎𝑎𝑎𝑖𝑖 𝑖𝑖 𝑓𝑖𝑖̅ �𝑎𝑎𝑎
𝑡𝑡𝑡ℎ
𝑤𝑟𝑟,𝑠𝑠= 0 𝑖𝑖 𝑓𝑖𝑖̅ ������
𝑎𝑎𝑎𝑖𝑖 < 0.01

(7)

As with the relatedness measures introduced by Hidalgo et al. (2007) and Boschma et al.
(2012), we place the critical cutoff threshold on the technological proximity. We consider
that industries have an intra- or inter-sectoral linkage if their proximity is greater than
0.01. For our empirical analysis, the technological proximity between 31 industries ismeasured using the input–output table of the Chubu region for 1990, as developed by
the Chubu Bureau of Economy, Trade, and Industry. While the Chubu region isgeographically bigger than the Nagoya MA, the Nagoya MA has 74% of the number of
employees in the Chubu region, as of 1986. Figure 2 presents the distribution of theproximity values calculated from our sample, and shows that 27.6% of all industry
combinations meet the criterion.
(Fig. 2 around here)
Finally, each element of the extensive spatial weight matrix, 𝑤𝑟𝑟,𝑠𝑠 , is defined as the

product of the geographical weight and the technological weight, as follows:
𝑔𝑔𝑔

(8)

𝑡𝑡𝑡ℎ
𝑤𝑟𝑟,𝑠𝑠 = 𝑤𝑟𝑟,𝑠𝑠 × 𝑤𝑟𝑟,𝑠𝑠

8 Franco et al. (2010) analyze the impact on total factor productivity of trade-related R&D spillovers
by introducing a similar measure using the economic distance between countries.

83.2. Exploratory spatial data analysis
We conduct a detailed statistical analysis of local spatial association focusing on theregion–industry’s growth. One of the local spatial statistics that allows for testing ahypothesis about the spatial dependence of the variable is the local Moran, which is akind of local indicator of spatial association (LISA), as defined by Anselin (1995). In
this study, we extend the local Moran statistics of industry 𝑖 in region 𝑟 to ageographical and technological space context, as follows:𝑦𝑟𝑟 − 𝑦�𝐼𝑟𝑟 =
� � 𝑤𝑟𝑟,𝑠𝑠 (𝑦𝑟𝑟 − 𝑦�)𝑚𝑠where:

𝑚 = ��
𝑟

𝑖

𝑗

(9)

(𝑦𝑟𝑟 − 𝑦�)2
𝑁𝑟 𝑁𝑖

We focus here on the adjusted long-term employment growth of industry 𝑖 in region 𝑟,

𝑦𝑟𝑟 , which is not affected by the industry-mix or time-specific effects. 9 Further, toreflect the respective importance of region–industry to aggregate employment, weapply the observations weighted by the region–industry’s share of the aggregate

employment across all industries.
Note that a positive (negative) local Moran value indicates positive (negative) spatial
autocorrelation or spatial similarities (dissimilarities). Combining the information
obtained from the local Moran values and the Moran scatterplot, we can assess
whether each industry in any of the four quadrants is significantly associated withgeographical and technological proximate industries. 10 For the Nagoya MA as a whole,
297 industries (38.2% of the total sample) are significant at the 5% pseudo-significancelevel.11 Of these, 105 industries (13.5%) fall within quadrant I in the scatterplot, and
116 industries (14.9%) within quadrant III; 28.4% of the regional industries exhibit
significant positive spatial associations. Meanwhile, 76 industries (9.8%) exhibit
significant atypical patterns; 30 industries (3.9%) fall within quadrant IV and 46
industries (5.9%) within quadrant II.
We undertake a detailed analysis of whether the growing industries form a cluster
9 An error-component model that decomposes the growth rates into the contributions specific toregional, industrial, temporal, and their combination effects is specified. We then employ thedecomposed region and region–industry specific components as the ESDA observations. Refer to
Yamada and Kawakami (2012) for details of the estimation.
10 The Moran scatterplot splits the sample into the four quadrants: quadrant I (III) shows growing(declining) regional industries accompanied by their growing (declining) neighbors. Quadrant II (IV)shows growing regional industries accompanied by declining (growing) neighbors.
11 To implement statistical inferences, the empirical distribution function is derived using 9,999
conditional permutations (Anselin 1995) for each of the local Moran statistics.

9with geographical and technological neighbors. To detect growth clusters, we focus inparticular on the geographical distribution of the significant region–industries
classified into quadrant I. Figure 3 visualizes the result of the local spatial statistics onmaps, where each county is categorized using a color code according to the number of
local spatial statistics judged to be significant.
(Fig. 3 around here)
Figure 3 reveals that a growth cluster is formed as a core in West Mikawa, andextends, leaping over the center of the MA, to the peripheral counties in the Gifu andMie prefectures. 12 Regardless of the core or periphery of the cluster, all 45 shaded
counties in Figure 3 contain transportation equipment and/or transportation-relatedindustries as highly significant industries. In particular, transportation equipment,with significant local spatial statistics, is observed in the 29 counties. This result
confirms, with statistical significance, that the growth cluster is driven mainly by theautomobile and associated industries. The dark-shaded counties on the map are
Toyota-shi and Okazaki-shi, which contain 12 and 10 of the constituent industries,
respectively. It is worth noting that the core of the cluster contains not only
manufacturing (plastic, rubber, electrical machinery, transportation equipment,
fabricated metal, general machinery, and construction), but also several service sectors(transport, wholesale and retail, finance and insurance, education and research, andbusiness services). However, the periphery of the cluster has less variety, mostly
containing transportation equipment and/or a few manufacturing sectors that aretechnologically proximate to transportation, such as electrical machinery. In the next
section, we examine whether and which type of knowledge externalities contribute to
the formulation of this detected growth cluster by estimating econometric models.

4. Econometric analysis of dynamic externalities
4.1. Empirical specification
The presence of dynamic externalities associated with the location-specific industrial
agglomeration is investigated by estimating econometric models. Following theframework developed by Glaeser et al. (1992) and Henderson et al. (1995), the model
12 In Nagoya-shi (district 5 in Figure 1), located in the center of the MA, the significant geographic

concentration composed of shrinking manufacturing and service sectors is detected. For more details,
refer to Yamada and Kawakami (2012).

10specification is based on a reduced form equation. Here, the long-term employment
industry growth rates in regions are determined by, among others, the growth oflocation-specific technology. Specifically, the model under study hypothesizes that thegrowth of county-specific technology is an exponential function of the initial industrial
structure expressed by concentration (𝐶𝐶𝐶𝐶), diversity (𝐷𝐷𝐷), and competition (𝐶𝐶𝐶𝐶),
as follows:(10)

𝐴𝑟,𝑖,𝑡 = 𝐴𝑟,𝑖,𝑡−1 𝑒𝑒𝑒�𝑔�𝐶𝐶𝐶𝐶𝑟,𝑖,0 , 𝐷𝐷𝐷𝑟,𝑖,0 , 𝐶𝐶𝐶𝐶𝑟,𝑖,0 �𝑡�where 𝐴𝑟,𝑖,𝑡 represents the level of technology specific to region 𝑟 and industry 𝑖 atyear 𝑡.

We reflect the technological proximity in the usually used measures for testing

dynamic externalities in each region. Our measure of the extent of concentration is
given by:
𝐶𝐶𝐶𝐶𝑟,𝑖,0 = 𝑤𝑟𝑟,𝑟𝑟 ×

𝐸𝐸𝐸𝑟,𝑖,0 �∑𝑟 𝐸𝐸𝐸𝑟,𝑖,0(11)∑𝑖 𝐸𝐸𝐸𝑟,𝑖,0 �∑𝑟 ∑𝑖 𝐸𝐸𝐸𝑟,𝑖,0

where 𝐸𝐸𝐸 denotes employment. The second term of Eq. 11 is the fraction of region

𝑟’s employment share of industry 𝑖, relative to region 𝑟’s share of the overall industry.

Since we consider a relatively small area as a sample, local concentration is better

expressed by referring to the three major MAs (Tokyo, Osaka, and Nagoya), rather
than only to Nagoya. Multiplying this ratio by the extensive spatial weight (𝑤𝑟𝑟,𝑟𝑟 ), theregion–industries with strong intra-industrial proximities are evaluated as highly
concentrated.
The diversity index represents a variety of industries. It is measured by the inverseof the Hirschman–Herfindahl index (𝐻𝐻𝐻), which is a slight modification of the index
proposed by Adelman (1969) and Henderson et al. (1995). Formally, let 𝑆𝑟 denote theset of industries technologically proximate to the industry in question. 13 With theemployment share of industry 𝑖 of the technologically proximate industries (including
industry 𝑖 itself), the diversity index is given by:
𝐷𝐷𝐷𝑟,𝑖,0 = 1⁄𝐻𝐻𝐻𝑟,𝑖,0 with 𝐻𝐻𝐻𝑟,𝑖,0 = �

𝑗∈𝑆𝑟

�

𝐸𝐸𝐸𝑟,𝑖,0∑𝑗∈𝑆𝑟 𝐸𝐸𝐸𝑟,𝑗,0

�

2

(12)

For industry 𝑖, the larger this value, the more diverse the region.

To more closely inspect the competition hypothesis, we introduce the two measuresof intra- and inter-industry competition. Intra-industry competition is captured by the
number of establishments per employee in the region–industry relative to theestablishments per employee in this industry for all three major MAs:
13 As described in section 3, how technologically proximate industries 𝑗 is to industry

the threshold value of proximity being at least equal to 0.01.

11

𝑖 is judged on

𝑖𝑖𝑖𝑖𝑖
𝐶𝐶𝐶𝐶𝑟,𝑖,0= 𝑤𝑟𝑟,𝑟𝑟 ×

𝐸𝐸𝐸𝑟,𝑖,0 �𝐸𝐸𝐸𝑟,𝑖,0(13)∑𝑟 𝐸𝐸𝐸𝑟,𝑖,0 �∑𝑟 𝐸𝐸𝐸𝑟,𝑖,0

where 𝐸𝐸𝐸 denotes the number of establishments. As with the concentration index,
after multiplying the ratio by 𝑤𝑟𝑟,𝑟𝑟 , the region–industries with strong intra-industrial
proximities are evaluated to be highly competitive.

The measure of inter-industry competition for industry 𝑖 captures the extent towhich industry 𝑗 , which is closely related to, but not involving industry 𝑖 , iscompetitive in region 𝑟. We consider the number of establishments per employee inindustry 𝑗, which is technologically proximate to industry 𝑖, in terms of the overall

number in the three major MAs. Since the number of proximate industries, 𝑗, varies byindustry, the inter-industry competition is given by the window weighted average ofthe relative competition:
𝑖𝑖𝑖𝑖𝑖
𝐶𝐶𝐶𝐶𝑟,𝑖,0=�𝑗∈𝑆𝑟 ,𝑗≠𝑖�𝑤𝑟𝑟,𝑟𝑟 ×

𝐸𝐸𝐸𝑟,𝑗,0�𝐸𝐸𝐸𝑟,𝑗,0∑𝑟 𝐸𝐸𝐸𝑟,𝑗,0�∑𝑟 𝐸𝐸𝐸𝑟,𝑗,0���𝑗∈𝑆𝑟 ,𝑗≠𝑖𝑤𝑟𝑟,𝑟𝑟(14)

where using the extensive spatial weights within a region replaces the value for anindustry with an average based on the values of the proximate industries.
Except for the measures of intra- and inter-industry externalities, the following
control variables are included in the regression models: the log of initial employment ofindustry 𝑖 in region 𝑟 ( 𝐿𝐿𝐿𝐿𝐿𝑟,𝑖,0 ); the log of initial wage in industry 𝑖 at theprefectural level (𝐿𝐿𝐿𝐿𝐿𝐿𝑖,0); and the log of initial industry-wide wage in region 𝑟(𝐿𝐿𝐿𝐿𝐿𝐿𝑟,0 ). To correct for the industry-mix and temporal demand shifts, we also

incorporate the following national employment change: the employment CAGR in
industry 𝑖 in Japan during the period from 𝑡 − 1 to 𝑡 ( 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖,𝑡 ); theindustry-wide CAGR of employment in Japan during the period from 𝑡 − 1 to 𝑡

(𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑡 ); and the employment CAGR in industry 𝑖 in Japan over the entire period(𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖 ).

To explain employment growth across region–industries using the abovementioned

explanatory variables, we can specify the empirical model as follows:
𝑖𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖𝑖
𝐺𝐺𝐺𝐺𝐺𝐺𝑟,𝑖,𝑡 = 𝛽0 + 𝛽1 𝐶𝐶𝐶𝐶𝑟,𝑖,0 + 𝛽2 𝐷𝐷𝐷𝑟,𝑖,0 + 𝛽3 𝐶𝐶𝐶𝐶𝑟,𝑖,0+ 𝛽4 𝐶𝐶𝐶𝐶𝑟,𝑖,0+𝛽5 𝐿𝐿𝐿𝐿𝐿𝑟,𝑖,0 + 𝛽6 𝐿𝐿𝐿𝐿𝐿𝐿𝑖,0 + 𝛽7 𝐿𝐿𝐿𝐿𝐿𝐿𝑟,0(15)+𝛽8 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖,𝑡 + 𝛽9 𝐺𝐺𝑁𝑁𝑁𝑁𝑂𝑂𝑡 + 𝛽10 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖 + 𝜀𝑟,𝑖,𝑡where 𝐺𝐺𝐺𝐺𝐺𝐺𝑟,𝑖,𝑡 denotes the employment CAGR across region–industries in eachperiod, and 𝜀𝑟,𝑖,𝑡 is an idiosyncratic disturbance assumed to be orthogonal to the

explanatory variables. All independent variables other than the national employmentchanges were measured in 1986.

As suggested by Henderson et al. (1995), the regression parameters for the12externality indices could take on distinct values in subsets of industries. Given that thegrowth cluster appears to be driven by transportation equipment and its relatedindustries, the model considers the structural instability of the coefficients amongauto-related manufacturing (𝐴𝐴𝐴𝐴_𝑀𝑀𝑀𝑀 ), auto-related services (𝐴𝐴𝐴𝐴_𝑆𝑆𝑆𝑉 ), andother manufacturing ( 𝑂𝑂ℎ𝑒𝑒_𝑀𝑀𝑀𝑀 ) and services (𝑂𝑂ℎ𝑒𝑒_𝑆𝑆𝑆𝑆 ). 14 To address aninherent heteroskedasticity problem, the entire estimation model is then weighted by
the region–industry’s employment share of the aggregate employment across allindustries during the period. This weight also reflects each region–industry’s relative
importance to aggregate employment.

4.2. Estimation results
We pool the all the observations and start with an ordinary least squares (OLS)
estimate. As shown in column (1) of Table 4, the Lagrange multiplier tests indicate both
spatial-lag and spatial-error dependence in the OLS estimation. Following the outline
in Florax et al. (2003), we carry out the robust versions of the Lagrange multiplier tests
for spatial-lag and spatial-error dependence (Anselin et al, 1996), with the result thatthe spatial error model (SEM) seems to be the appropriate specification. The result
based on the SEM in column (2) differs very little from that estimated by the OLS. 15(Table 4 around here)
The effect of knowledge externalities does not necessarily diffuse only within acounty, and may transcend geographical boundaries. To conduct a detailed
investigation on geographical spillover effects, we also estimate the models that
replace the domestic externality measures in Eq. 15 with the locally weighted averageof these measures. 16

Note that, in contrast to standard practice in spatial

autocorrelation analysis, the location itself is included by setting the weights 𝑤𝑟𝑟,𝑟𝑟

when taking an average (Anselin et al. 2006). Each locally weighted measure ofexternalities is denoted by the prefix “𝐿𝐿”. The result estimated by OLS is shown in
14 For the definition of the sectoral subsets, see Appendix.

15 For the spatial weight matrix reflecting the geographical and technological dependence in the
disturbance term, say 𝐖, we replace the diagonal elements of the extensive spatial weight matrix byzero and convert it to have row sums of unity. Further, since we use the sample for six periods, thespatial weight matrix is extended by W ⊗ Ι 6 .
16 As an example, the locally weighted measure of concentration is given by:

𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0 = ∑𝑠 �𝑤𝑟𝑟,𝑠𝑠 ×

𝐸𝐸𝐸𝑠,𝑖,0�∑𝑟 𝐸𝐸𝐸𝑟,𝑖,0∑𝑖 𝐸𝐸𝐸𝑠,𝑖,0�∑𝑟 ∑𝑖 𝐸𝐸𝐸𝑟,𝑖,0��∑𝑠 𝑤𝑟𝑟,𝑠𝑠

Strictly speaking, since we take an average by using the extensive spatial weights, the locallyweighted measures also consider the technological proximity to geographical neighbors.

13

column (1) of Table 5. The result based on the SEM is shown in column (2).
(Table 5 around here)
In terms of the control variables, there is not much difference between the results
shown in Tables 4 and 5. The coefficients on the initial level of employment are
significantly negative, with the exception of non-auto-related manufacturing. In theauto-related industries, high wages lead to faster growth in employment. The
substantial positive coefficients on 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖,𝑡 indicate that employment growth in

the region–industries is associated with the national trends specific to the industries.

The coefficients of 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑡 and 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑖 are significantly positive in theservice sectors, whereas those of 𝐺𝐺𝑁𝑁𝑁𝑁𝑁𝑁𝑡 are negative in the manufacturingsectors. This suggests that national employment growth promotes a shift of regional
employment from manufacturing to services.

The estimated results on externalities unveil several interesting findings. Table 4shows that, for auto-related manufacturing, the effects of within-county concentration
𝑖𝑖𝑖𝑖𝑖(𝐶𝐶𝐶𝐶𝑟,𝑖,0) and intra-competition (𝐶𝐶𝐶𝐶𝑟,𝑖,0) are not significant in terms of growth.

However, as shown in Table 5, the coefficients of the locally weighted measures of𝑖𝑖𝑖𝑖𝑖
concentration (𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0 ) and intra-competition (𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0) are significantly

positive and negative, respectively. These results are consistent with the hypothesis

stressed by MAR when we consider the effects of externalities in a broader area thanthe county. The MAR-type externalities diffuse beyond the county boundaries and help
the substantial growth of transportation equipment in particular, which accounts forthe largest share of employment in the auto-related industries. Furthermore, thediversity of related industries (𝐷𝐷𝐷𝑟,𝑖,0 ) matters to the growth of the auto-relatedindustries. The effect of diversity becomes stronger when we use the locally weighted

measure (𝐿𝐿_𝐷𝐷𝐷𝑟,𝑖,0 ), suggesting that the externalities from outside the industry alsotranscend county boundaries. For non-auto-related manufacturing, the coefficient of
𝐿𝐿_𝐷𝐷𝐷𝑟,𝑖,0 is significantly positive, although that of 𝐷𝐷𝐷𝑟,𝑖,0 is not significant. The

diversified industrial structure in a broader area than the county is important for
growth of, in particular, fabricated metal and general machinery, for which the valuesof 𝐿𝐿_𝐷𝐷𝐷𝑟,𝑖,0 are substantial.

It is observed that the MAR hypothesis is significantly supported by the coefficients

𝑖𝑖𝑖𝑖𝑖
of 𝐶𝐶𝐶𝐶𝑟,𝑖,0 and 𝐶𝐶𝐶𝐶𝑟,𝑖,0
for the auto-related services, but not by those of𝑖𝑖𝑖𝑖𝑖. In contrast to auto-related manufacturing, this𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0 and 𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0

outcome confirms the MAR externality that diffuses only within the county. The
14

𝑖𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖𝑖
significant positive estimates of 𝐶𝐶𝐶𝐶𝑟,𝑖,0and 𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0
suggest that morecompetition among firms in related industries promotes growth in auto-related

services. For non-auto-related services, concentration within their own industries

retards their growth. The negative effect of concentration becomes much stronger
𝑖𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖𝑖
when we use 𝐶𝐶𝐶𝐶𝑟,𝑖,0
rather than 𝐿𝐿_𝐶𝐶𝐶𝐶𝑟,𝑖,0
as the competition measure.

Based on these econometric analysis results, we elaborate on how knowledge

externalities contribute to the formulation of the growth cluster shown in Figure 3.
Transportation equipment that is regionally concentrated over a broader area in thedetected cluster benefits from within-industry knowledge transfers, and realizes
steady growth. Transportation equipment located around the core (West Mikawa)grows faster than that in the periphery, because of its higher concentration andlarger-scale establishments around the core. A certain amount of both auto-related andnon-auto-related manufacturing sectors in the core enables them to share in thebenefits of the diversified productive structure and contributes to their growth. For thedevelopment of the periphery of the cluster, the formation of inter-related
concentrations, if not a diversified structure, should be reinforced.
According to the results of the ESDA, we also find some growing auto-related
services accompanied by geographical and technological neighbors in the core. The
econometric analysis proves that the auto-related services benefit from the MAR-type
externalities, but this is not the case for the growing services in the core. The extent of
the concentration and establishment size is far smaller in the core of the cluster thanin the center of the Nagoya MA. Hence, the growth of some services observed in thecore would not be attributed to knowledge spillovers within an industry, but would
rather depend on the demand induced by expanding manufacturing outputs and theassociated income growth. For further development in the core regions, our findings
reveal the need to enhance the productivity of services through the channels of intraand inter-sectoral knowledge spillovers.

5. Conclusions
This study applies the methods of ESDA and investigates the geographic concentrationof interrelated growing industries, or “growth clusters,” by using data from the Nagoya
MA over the period 1986–2006. Further, it examines the causal relationship between
region–industry dynamics and knowledge externalities by applying an econometricanalysis. As a methodological contribution, spatial dependence caused by thegeographical proximity among regions and the technological proximity of industrial
15

linkages is incorporated into the empirical models applied in this study. Combining theinformation obtained from the ESDA and econometric analysis enables us to assess therole played by knowledge externalities in regional growth from a cluster perspective.
The result of the ESDA identifies the presence of a growth cluster that is mainly
driven by the automobile and associated industries. What is noteworthy is that theconstituent industries of the core of the cluster (West Mikawa) are not only
manufacturing, but also include several service sectors. Meanwhile, the periphery of
the cluster is composed only of transportation equipment and/or a few auto-related
manufacturing.
The

econometricanalysis

reveals

evidenceof

MAR-type

externalities

intransportation equipment, which diffuse over a broader area in the detected cluster. In
the core regions, the diversified interrelated structure contributes significantly to thegrowth of the auto-related and non-auto-related manufacturing sectors. The result also
confirms that auto-related services benefit from the MAR-type externalities diffusing
within the county. However, the growth of the services in the core cannot be explainedby the positive endogenous effects of knowledge externalities. Instead, this growth
relies mainly on the exogenous demand induced by expanding manufacturing outputs.
These findings give useful information to policymakers for recent regional
development policies, such as the direct R&D and indirect networking/coordination
supports.
The issue that remains is how the size of establishments is related to theexternalities of knowledge spillovers. The Japanese automobile and related industries
are notably characterized by a vertical and hierarchical organizational structure. It
might be interesting to investigate, for example, whether the different nature of theknowledge externalities is confirmed in large-sized firms at the top of the hierarchyand small- and medium-sized firms at the bottom, and what type of externalities spillover between different sizes of firms. This will be elaborated on in future research.
Acknowledgements

The research for this paper is supported by the Japan Society for the Promotionof Science (Grant-in-Aid for Young Scientists (B) 22730195). The usual disclaimer applies.

(Appendix around here)

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18

Yamada E, Kawakami T (2012) Distribution of industrial growth in the Nagoya
metropolitan area, Japan: focusing on geographical and technological proximity.
Kinki working papers in economics E-24.
http://www.kindai.ac.jp/keizai/overview/pdf/wp/wp24.pdf.
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19

Accessed

12

AugustFig. 1The Nagoya metropolitan area(G), (A), and (M) denote the districts belonging to Gifu, Aichi, and Mie prefectures,
respectively.

NoteTable 1 Industrial structure in terms of the number of employees in the Nagoya metropolitan area
1986
No. Industry
1 Construction
2 Food and beverages
3 Textile and apparel4 Lumber
5 Furniture
6 Pulp and paper
7 Printing
8 Chemical
9 Petroleum and coal
10 Plastic
11 Rubber
12 Leather
13 Ceramic
14 Iron and steel
15 Non-ferrous metals
16 Fabricated metal
17 General machinery
18 Electrical
19 Transportation
20 Precision
21 Utilities
22 Transport
23 Wholesale and retail
24 Finance and insurance
25 Real estate
26 Information and communications
27 Education and research
28 Medical, health care and welfare
29 Business services
30 Personal sevices
31 Public services
Total Nagoya MA

Number of
employees
324,879 7.96%
106,325 2.61%
213,758 5.24%
29,406 0.72%
38,249 0.94%
27,378 0.67%
46,578 1.14%
39,818 0.98%
3,548 0.09%
57,825 1.42%
22,486 0.55%
3,647 0.09%
96,202 2.36%
49,165 1.21%
15,476 0.38%
113,478 2.78%
171,935 4.21%
135,554 3.32%
248,049 6.08%
21,238 0.52%
25,101 0.62%
196,205 4.81%
953,026 23.36%
121,748 2.98%
43,351 1.06%
23,916 0.59%
49,030 1.20%
137,800 3.38%
181,481 4.45%
487,028 11.94%
55,000 1.35%
4,079,438

2006

1986-2006
1986-20061986
Concentration
Employment
Employment
Number of
relative to
CAGR
CAGR
employees(Nagoya MA) (3 major MAs) the 3 majour MAs
323,212 6.83%-0.03%-0.54%
1.03
105,906 2.24%-0.02%
0.05%
1.26
63,799 1.35%-5.87%-5.19%
2.12
14,892 0.31%-3.34%-3.52%
2.14
23,682 0.50%-2.37%-2.62%
1.83
23,175 0.49%-0.83%-1.71%
1.02
43,163 0.91%-0.38%-0.86%
0.56
34,348 0.73%-0.74%-0.46%
0.77
2,702 0.06%-1.35%-1.90%
0.80
65,378 1.38%
0.62%-0.82%
1.38
24,327 0.51%
0.39%-2.03%
1.12
1,433 0.03%-4.56%-3.90%
0.29
59,593 1.26%-2.37%-2.54%
2.70
37,026 0.78%-1.41%-2.74%
1.24
14,833 0.31%-0.21%-1.78%
0.84
98,672 2.08%-0.70%-1.78%
1.15
154,845 3.27%-0.52%-1.33%
1.34
139,669 2.95%
0.15%-2.08%
0.80
315,176 6.66%
1.20%-0.42%
2.37
15,339 0.32%-1.61%-1.97%
0.70
32,276 0.68%
1.27%
0.94%
1.10
266,185 5.62%
1.54%
0.86%
0.86
1,015,518 21.46%
0.32%
0.05%
0.93
104,576 2.21%-0.76%-1.06%
0.74
60,387 1.28%
1.67%
1.45%
0.57
49,407 1.04%
3.69%
4.07%
0.89
78,951 1.67%
2.41%
2.03%
0.63
324,113 6.85%
4.37%
4.47%
0.90
481,166 10.17%
5.00%
4.18%
0.67
661,200 13.97%
1.54%
1.19%
0.89
55,605 1.17%
0.05%
0.40%
1.16
4,732,882
0.75%
0.58%

Source Author’s calculations based on the Establishment and Enterprise Census.

Table 2

Highly concentrated industries in each district in the Nagoya metropolitan area
Concentraion relative to the 3 major MAs in 1986.
Concentration
Industry
Concentration
Industry

No.

District1

Gifu

Textile2.44

Public Service

1.29

Finance

1.22

2

Seino

Textile2.19

Plastic

1.99

Ceramic

1.75

3

Chuno

Electoricalmachinery

2.67

Pulp

2.30

Ceramic

2.07

4

Tono

Ceramic

12.46

Pulp

2.93

Information

1.27

1.73

Business
service

1.60

Information

1.57

3.25

Leather

1.69

Plastic

1.64

3.55

Ceramic

3.18

Rubber

2.85

Iron

9.12

Petroleum

5.56

Ceramic

2.78

Transportation

3.58

Precision

2.38

Generalmachinery

1.49

Precision

4.07

Food

1.67

Lumber

1.55

5
6
7
8
9
10

Industry

NagoyaPrintingshi
WestTextile
Owari
East
Pulp
Owari
Chita
West
MikawaEast
Mikawa11

HokuseiPetroleum

6.06

Chemical

4.45

12ChuseiPublic Service

2.56

Lumber

2.46

13

NanseiRubber3.00

Electoricalmachinery2.37

Non-ferrous
MetalsElectoricalmachinery
Public Service

Concentration

2.53
2.31
2.00

Source Author’s calculations based on the Establishment and Enterprise Census.
Note The cord number identifying the location of the districts corresponds to that in Figure 1.

Table 3

Fastest and slowest growing county–industries in the Nagoya metropolitan area

10 fastest growing region-industries
No.

District

9
7
9
5
9
1
5
9
8
6

West Mikawa
East Owari
West Mikawa
NagoyaWest Mikawa
Gifu
NagoyaWest Mikawa
Chita
West Owari

CountyKariyaKomakiToyotaMidoriku
Anjo
Kakamigahara
Nakamuraku
Okazaki
Handa
Inazawa

Industry
Business services
Business services
Business services
Medical, health care and welfare
Business services
Business services
Business services
Business services
Transportation
Medical, health care and welfare

10 slowest growing region-industries
No.

District

8
8
8
5
8
8
9
13
6
2

Chita
Chita
Chita
NagoyaChita
Chita
West Mikawa
NanseiWest Owari
Seino

CountyHigashiura
Handa
Chita
Mizuhoku
Chita
Agui
Kota
Ise
Kiyosu
Tarui

Industry
Textile and apparelTextile and apparel
General machinery
Precision
Textile and apparelTextile and apparelTextile and apparelTextile and apparelElectrical
Textile and apparel

Source Author’s calculations based on the Establishment and Enterprise Census.
Note The cord number identifying the location of the districts corresponds to that in Figure 1.

Fig. 2Distribution of the technological proximity values

Fig. 3

Number of significant growing region–industries accompanied by the growing neighbors.

Source Author’s calculations based on the input–output table of the Chubu region for 1990.

Table 4

Region–industry’s employment growth between 1986 and 2006, explained by domestic externality measures
Dependent variable: GROWTHr,i,t

Independent variables
Constant
CONCr,i,0
DIVr,i,0
COMP r,i,0intra
COMP r,i,0inter
LNEMP r,i,0
LNWAGEi,0
LNWAGEr,0
GRNATIONit
GRNATIONt
GRNATIONi(1) OLS
Auto_MANU-0.590 ***(-3.354)-0.007(-0.983)
0.007 ***
(2.760)-0.135(-1.309)
0.000(0.097)-0.006 **(-2.533)
0.022 **
(2.227)0.018 *
(1.902)
1.035 ***
(9.522)-0.588 ***(-4.514)-0.173(-0.560)

Other_MANU(2) SEM

Auto_SERV

0.018(0.082)-0.026(-0.705)
0.003(1.113)-0.242(-1.197)
0.005(1.190)-0.000(-0.072)
0.011(1.064)-0.013(-1.304)
0.921 ***
(10.101)-0.125(-0.924)0.082(0.359)-0.142 *(-1.819)0.106 ***
(3.541)-0.001(-1.331)-0.238 ***(-2.803)
0.004 ***
(3.004)-0.006 ***(-6.933)
0.025 ***
(4.885)-0.013 ***(-5.053)0.477 ***
(7.714)0.817 ***
(11.879)
1.081 ***
(11.074)

Other_SERV-0.169(-1.202)-0.327 ***(-3.025)
0.003 *
(1.727)0.545(1.335)0.003(1.122)-0.003(-1.518)
0.008(1.178)0.003(0.661)0.352 ***
(5.516)0.284 ***
(3.459)0.770 ***
(3.629)Spatial error
Number of observations
2

Adjusted R /ML
Moran's I
LMerrorRobust LMerror
LMlag
Robust LMerror

4662
0.480
33.893986.641894.655
92.2440.259

Auto_MANU-0.583 ***(-26.417)-0.008(-1.266)
0.007 ***
(2.815)-0.115(-1.114)-0.000(-0.088)-0.006 **(-2.440)0.023 ***
(2.651)0.016 **
(2.420)0.955 ***
(8.378)-0.621 ***(-4.526)-0.162(-0.579)

Other_MANU

Auto_SERV-0.140 ***0.026(0.129)(-3.664)0.100 ***
-0.027(-0.734)(3.410)-0.001
0.002(-1.413)(0.981)-0.223 ***-0.208(-1.011)(-2.641)0.004 ***
0.005
(3.003)(1.278)-0.001-0.006 ***(-0.552)(-7.156)
0.024 ***
0.010
(1.059)(27.927)-0.012 ***-0.012(-1.240)(-5.068)
0.941 ***
0.514 ***
(8.699)(10.235)-0.1200.788 ***(-0.862)(11.932)
0.093
1.031 ***
(0.392)(10.780)0.544***(29.548)
4662
39654

Other_SERV-0.133(-0.963)-0.320 ***(-3.068)
0.003(1.602)0.536(1.354)0.002(1.082)-0.004 *(-1.935)
0.007(1.094)0.002(0.465)0.283 ***
(4.576)
0.356 ***
(4.342)0.866 ***
(4.143)Note t-values (asymptotic t-values) are in parentheses for OLS (SEM). * Significant at the 10 % level; ** at the 5% level; ***at the 1% level. LMerror (LMlag)
denotes the Lagrange multiplier statistics for spatial-error (spatial-lag) dependence.

Table 5

Region–industry’s employment growth between 1986 and 2006, explained by the locally weighted externality measures
Dependent variable: GROWTHr,i,t

Independent variables
Constant
LW_CONCr,i,0
LW_DIVr,i,0
LW_COMP r,i,0intra
LW_COMP r,i,0inter
LNEMP r,i,0
LNWAGEi,0
LNWAGEr,0
GRNATIONit
GRNATIONt
GRNATIONi(1) OLS
Auto_MANU-0.345 **(-2.124)0.006 **
(2.501)0.033 ***
(3.344)-0.023 ***(-2.899)-0.013(-1.062)-0.008 ***(-4.222)0.023 **
(2.467)0.001(0.137)1.027 ***
(9.437)-0.582 ***(-4.468)
0.942 ***
(2.596)

Other_MANU(2) SEM

Auto_SERV

0.074(0.347)-0.000(-0.080)0.014 ***
(2.730)-0.000(-0.054)-0.010(-1.085)-0.001(-0.554)0.012(1.132)-0.018(-1.489)
0.936 ***
(10.225)-0.137(-1.013)-0.146(-0.443)-0.406 ***(-4.620)-0.010(-0.993)
0.000(0.097)-0.004(-0.556)
0.009 **
(2.500)-0.004 ***(-3.465)
0.028 ***
(5.267)-0.001(-0.410)0.469 ***
(7.566)0.824 ***
(11.960)
1.022 ***
(10.040)

Other_SERV-0.014(-0.086)-0.024(-1.408)-0.002(-0.344)-0.008(-1.193)0.001(0.209)-0.004 **(-2.432)
0.007(0.976)-0.001(-0.240)0.361 ***
(5.622)0.277 ***
(3.356)0.480(1.478)Spatial error
Number of observations
Adjusted R2/ML
Moran's I
LMerrorRobust LMerror
LMlag
Robust LMlag

4662
0.479
33.644962.371867.134
0.000
0.056

Auto_MANU-0.357 ***(-2.772)
0.005 **
(2.070)0.030 ***
(3.486)-0.021 ***(-2.650)-0.010(-1.060)-0.008 ***(-3.844)0.022 ***
(2.732)0.003(0.842)
0.953 ***
(8.341)-0.621 ***(-4.515)0.896 ***
(2.661)Other_MANU

Auto_SERV

0.087-0.397 ***(0.466)(-4.632)-0.002-0.008(-0.413)(-0.859)0.014 ***
0.000
(2.687)(0.021)0.000-0.004(0.012)(-0.581)-0.0110.009 ***(-1.085)(3.658)-0.002-0.004 ***(-0.943)(-3.993)
0.011
0.028 ***
(1.100)(5.957)-0.017 *-0.001(-1.667)(-0.983)
0.957 ***
0.508 ***
(10.379)(8.566)-0.134
0.793 ***(-0.960)(11.990)-0.286
0.987 ***(-0.822)(10.082)0.540***(20.566)
4662
39650

Other_SERV
0.053(0.351)-0.029 *(-1.731)-0.003(-0.462)-0.005(-0.851)-0.000(-0.045)-0.005 ***(-2.627)0.007(0.989)-0.005(-0.846)
0.289 ***
(4.636)
0.351 ***
(4.268)0.605 *
(1.908)Note t-values (asymptotic t-values) are in parentheses for OLS (SEM). * Significant at the 10 % level; ** at the 5% level; ***at the 1% level. LMerror (LMlag)
denotes the Lagrange multiplier statistics for spatial-error (spatial-lag) dependence.

Appendix
Table 6
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

Sector classification

Industy nameConstruction
Food and beverages
Textile and apparel
LumberFurniture
Pulp and paper
Printing
ChemicalPetroleum and coal
Plastic
Rubber
Leather
Ceramic
Iron and steel
Non-ferrous metals
Fabricated metal
General machinery
ElectricalTransportation
Precision
Utilities
Transport
Wholesale and retail
Finance and insurance
Real estate
Information and communications
Education and research
Medical, health care and welfare
Business services
Personal sevices
Public services

Description
Construction
Manufacture of food, bevarages, tobbaco and feed
Manufacture of textile mill, apparel and other finished products
Manufacture of lumber and wood prodcts
Manufacture of furniture and fixtures
Manufacture of pulp, paper and paper products
Printing and allied industries
Manufacture of chemical and allied products
Manufacture of petroleum and coal products
Manufacture of plastic products
Manufacture of rubber products
Manufacture of leather tanning, leather products and fur skins
Manufacture of ceramic, stone and clay products
Manufacture of iron and steel
Manufacture of non-ferrous metals and products
Manufacture of fabricated metal
Manufacture of general machinery
Manufacture of electrical machinery
Manufacture of transportation equipment
Manufacture of precision instruments and machinery
Electricity, gas, heat supply and waterTransport
Wholesale and retail trade
Finance and insurance
Real estate
Information and communications
Education and research
Medical, health care and welfare
Business services
Personal sevices
Public services

Sectoral subset
Other_MANU
Other_MANU
Other_MANU
Other_MANU
Other_MANU
Other_MANU
Auto_MANU
Other_MANU
Other_MANU
Auto_MANU
Auto_MANU
Other_MANU
Auto_MANU
Auto_MANU
Auto_MANU
Other_MANU
Other_MANU
Auto_MANU
Auto_MANU
Other_MANU
Auto_SERV
Auto_SERV
Auto_SERV
Auto_SERVOther_SERVOther_SERV
Auto_SERVOther_SERV
Auto_SERVOther_SERVOther_SERV