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The Annals of Regional Science

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01.01.2015 | Original Paper

Assessing dynamic externalities from a cluster perspective: the case of the motor metropolis in Japan

verfasst von: Eri Yamada, Tetsu Kawakami

Erschienen in: The Annals of Regional Science | Ausgabe 1/2015

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Abstract

In this paper, we first apply the methods of exploratory spatial data analysis and investigate the geographical concentration of interrelated growing industries, or “growth clusters,” by using data from the Nagoya metropolitan area in Japan 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 formation of the detected growth cluster. As a methodological contribution, following Porter’s (Reg Stud 37:549–578, 2003) widely recognized cluster concept, we incorporate spatial dependence caused by the geographical proximity between regions and the technological proximity between industries into the empirical models. Combining the information obtained from the ESDA and econometric analysis enables us to assess the role of knowledge externalities for regional growth from a cluster perspective. The empirical results identify the presence of a growth cluster mainly driven by the automobile and associated industries. The core of the cluster is made up of manufacturing industries, but also includes several service sectors. The periphery of the cluster has less variety, mainly including the transportation equipment and electrical machinery industries. We find that intra-industry externalities that diffuse over a broader area within the cluster help the steady growth of the transportation equipment industry. In the core of the cluster, the diversified interrelated structure also contributes to the growth of the auto-related manufacturing sectors.

Vorheriger Artikel The factors determining firm start-ups in French regions and the heterogeneity of regional labor markets

Nächster Artikel Innovation in peripheral regions: Do collaborations compensate for a lack of local knowledge spillovers?

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See, for example, Henderson et al. (1995), Feldman and Audretsch (1999), Combes (2000), Drennan et al. (2002), De Lucio et al. (2002), Henderson (2003), Cingano and Schivardi (2004), Suedekum and Blien (2005), Van Oort (2007), Lim (2007), De Vor and De Groot (2010), Duranton et al. (2010).

The same definition of a cluster can be found in Porter’s other works (e.g., Porter 1998, 2000a, b).

According to Kanemoto and Tokuoka (2002), the densely inhabited district population of the core in the Metropolitan (Micropolitan) Employment Area is at least 50,000 (between 10,000 and 50,000). Adjacent counties not indicated in Fig. 1 are depopulated rural or mountainous areas.

The census also provides information on the number of employees and establishments, by firm size, classified into seven groups.

The number of employees in our study area accounts for 87 % of that in the Chubu region, as of 1986.

Corresponding to the magnitude of the interregional distance decay, \({\gamma }\), other formulae such as \(d_{rs}^{-{\gamma }} \hbox { or }\hbox { exp}\left( {-{\gamma }d_{rs}} \right) \) can be used. However, in our study, the growth clusters can be identified clearly from Eq. 1. Furthermore, we examine the sensitivity of the ESDA results for the various cutoff values of \(d_{rs}\). Since the results do not change much after setting \(d_{rs}= 120\) or more, our analysis does not place a threshold on \(d_{rs}\).

The proof is shown in Dietzenbacher et al. (2005).

We explore the value of the technological proximity threshold by specifying the first-order spatial autoregressive model to explain the region-industry’s growth rates, in which an extensive spatial weight matrix is applied. Refer to Yamada and Kawakami (2012) for more details.

We specify an error-component model that decomposes the growth rates into the contributions of regional, industrial, temporal effects, as well as effects of combinations of the three. We then employ the decomposed time invariant region-specific and region-industry-specific components as the ESDA observations. Refer to Yamada and Kawakami (2012) for details of the estimation.

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 declining (growing) regional industries accompanied by growing (declining) neighbors.

To implement statistical inferences, the empirical distribution function is derived using 9,999 conditional permutations (Anselin 1995) for each of the local Moran statistics.

We should treat the results of the neighboring areas of the Nagoya MA with caution, as they might be affected by neighbors other than the Nagoya MA.

As described in Sect. 3, the technological proximity of industries \(j\) and \(i\) is adjudged using a proximity threshold value greater than or equal to 0.01.

For the diversity index, we also consider the entropy within the technologically proximate set as an alternative, as proposed by Frenken et al. (2007) and Boschma et al. (2012). The derived entropy measure is highly correlated with the measure defined by Eq. 15, so did not change our results.

Since we do not have data on the region-industry’s wage, we use the two kinds of income per employee as proxies in the model. These values were measured in 1990.

We also considered accessibility to the Nagoya central business district instead of to the port. However, this did not change the results on the other explanatory variables.

For the spatial weight matrix reflecting the geographical and technological dependence in the disturbance term, we replace the diagonal elements of the extensive spatial weight matrix by zero and convert it to have row sums of unity.

As mentioned in Sect. 2, we need to be careful when interpreting results based on employment growth. In their meta-analysis, De Groot et al. (2009) suggest that the chances of finding significant positive effects tend to be larger when measuring growth in terms of employment than in terms of productivity. Employment growth with regard to transportation equipment may be associated with several pecuniary externalities, not just knowledge externalities.

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Titel: Assessing dynamic externalities from a cluster perspective: the case of the motor metropolis in Japan
verfasst von: Eri Yamada
Tetsu Kawakami
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: The Annals of Regional Science / Ausgabe 1/2015
Print ISSN: 0570-1864
Elektronische ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-014-0654-9

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