Abstract
Industrial clusters have attracted increasing attention as important locations of innovation. Therefore, several countries have started promotion policies for industrial clusters. However, there are few empirical studies on cluster policies. This paper examines the effects of the “Industrial Cluster Project” (ICP) in Japan on the R&D productivity of participants, using a unique dataset of 229 small firms, and discusses the conditions necessary for the effective organization of cluster policies. Different from former policy approaches, the ICP aims at building collaborative networks between universities and industries and supports the autonomous development of existing regional industries without direct intervention in the clustering process. Thus far, the ICP is similar to indirect support systems adopted by successful European clusters. Our estimation results suggest that participation in the cluster project alone does not affect R&D productivity. Moreover, research collaboration with a partner in the same cluster region decreases R&D productivity both in terms of the quantity and quality of patents. Therefore, in order to improve the R&D efficiency of local firms, it is also important to construct wide-range collaborative networks within and beyond the clusters, although most clusters focus on the network at a narrowly defined local level. However, cluster participants apply for more patents than others without reducing patent quality when they collaborate with national universities in the same cluster region.
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For example, Cooke (2002) indicates that local learning is crucial in defining a cluster, while other researchers regard effective links between the market processes and the institutional and cultural factors (social capital) as being central (Dei Ottati 2002). The variety of organizations and competitive or cooperative structures in industrial clusters is also considered in constructing a multitude of typologies (Paniccia 1998).
The Ministry of Education, Culture, Sports, Science and Technology (MEXT) also started the “Knowledge Cluster Initiative” in 2002. METI cooperates with MEXT in the cluster project.
Hospers et al. (2009) select several regions such as Baden-Württemberg, Emilia-Romagna, Jutland, and Manchester as the examples of successful clusters in Europe.
The ICP in the first period comprises 19 regional projects, most of which cover two or more prefectures (see Appendix 1).
Contrary to this discussion, Abramo et al. (2009) indicate the importance of information asymmetry in the market for UIPs. Their findings reveal that firms have the option of choosing more qualified research partners in universities located closer to the place of business.
Though his argument is focused on the agglomeration in the cities, we expect it to be applicable to wider geographical areas.
This program provides financial support only to the R&D consortia that include national university in the region. Thus, it aims to promote local UIP with national universities. There were approximately 1,130 R&D consortia by 2004 and approximately 60% of them involved the participants of the ICP.
Here, we define R&D-intensive firms as those that agreed to the following statement in our survey: “We appropriate R&D budgets every year.” The definition of SMEs follows that of the SME Basic Law.
We focus on the firms with UIP in order to measure knowledge flow using the characteristics of UIP, as mentioned in the introduction.
The industry classification in our survey roughly corresponds to the JSIC 2-digit level.
The estimation results using patent data of each year are available upon request from the authors.
Partner types are classified into the following categories according to the affiliation of the research partner: national university, other public university, private university, and public research institute. See Table 3 and the following section with regard to the purposes of UIP.
The results of these alternative estimations demonstrate no considerable differences from those of negative binomial regression. Therefore, we only provide the estimation results of the latter.
The effect of the participation in the ICP may take a long time to come out. Unfortunately, we cannot identify since when the firms have participated in the ICP. However, according to unofficial information from the METI, a majority of the participants, at least in the bio-clusters, were involved in the ICP from the very beginning of the project, i.e., since 2001.
It is noteworthy that our sample comprises SMEs up to 300 employees. Thus, with this variable, we check and control for the size effect among SMEs.
The UIP includes various patterns, such as joint R&D, commissioned R&D, technological consultation, technological licensing, and education/training. The baseline reference of joint R&D comprises any other patterns of the UIP.
The geographical area of a regional cluster is not clearly defined by METI, though, as mentioned before, the regional clusters usually cover two or more prefectures. Neither do we have a priori information on the optimal scope of an industrial cluster. Thus, in order to check whether or not we set appropriate criteria for the scope of an industrial cluster, we alternatively limit the cluster area to the same prefecture with the exceptions of Tokyo and Osaka, where they can easily collaborate with partners beyond the borders of the prefectures. Even by using this alternative definition of the cluster area, however, we obtained similar results.
We are very grateful to an anonymous referee for suggesting this point.
As mentioned in Sect. 4.3, average firm age is significantly different between the participants and non-participants of the cluster project at the 5% level (i.e., cluster participants are, on average, younger than non-participants). Moreover, the result of the first-stage estimation of the IV regression demonstrates that the coefficient of firm age is significant at the 1% level (see Table 5).
We also incorporated the interaction terms participant × sameregion, participant × national, and participant × jointrd in order to check the effect of cluster participation. However, the coefficients of these variables are not significant.
However, the results differ according to the technological focus of the regional clusters, such as biotechnology or IT. We will focus on this difference in another paper.
Some may insist that the effect of participation in the cluster projects may be canceled out when the non-participants receive knowledge spillovers from the cluster participants. However, we argue that such knowledge spillovers, which may occur through patent information, formal collaboration as well as various informal contacts between the participants and non-participants, are not substantial for the following reasons. First, we use the number of patent applications between 2003 and 2005 as the invention counts by the UIP between 2002 and 2004. Considering the time lag of 18 months between the application and the publication of patents, it seems difficult for the non-participants to absorb and utilize knowledge from the patent applications by the participants. Second, according to our survey data, only 30% of the participants collaborate with other firms within the same clusters. Moreover, we find from additional estimations that, unlike the UIP, the collaboration with other firms does not have a positive impact on the R&D productivity of our sample firms. Therefore, although we do not know about informal contacts between the cluster participants and non-participants, we consider it to be rather unlikely that the effects of the participation in the ICP are completely canceled out by knowledge spillovers between them.
Some firms did not apply for patents. In this case, we replace the average number of claims and citations with zero values. Estimation results remain unchanged when we omit them.
Using our dataset, we cannot identify which types of support programs are more effective for improving firm performance. Nishimura and Okamuro (2009) suggest that indirect support programs may be more effective for the enhancement of firm performance. Falck et al. (2009) also insist on the effectiveness of networking supports of the cluster policy in Germany.
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Acknowledgments
This research received financial support from the Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (C) (No. 16530147) and (A) (No. 20243018). The authors are grateful for this support. Earlier versions of this paper were presented at RENT (Research in Entrepreneurship and Small Business) XXII Conference in Covilha, Portugal, in November 2008; AEA (Applied Econometrics Association) 97th International Conference “Patent and Innovation: Econometric Studies” in Tokyo, Japan, in December 2008; DRUID Society Summer Conference 2009 on Innovation, Strategy, and Knowledge in Copenhagen, Denmark, in June 2009; the 36th Annual Conference of EARIE (European Association for Research in Industrial Economics) in Ljubljana, Slovenia, in September 2009, and the seminar at the Development Bank of Japan in Tokyo, Japan, in September 2009. The authors would like to express their gratitude to the participants of these conferences for their valuable comments and suggestions. The comments by the anonymous referees greatly contributed to improving this paper. The usual disclaimer applies.
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Nishimura, J., Okamuro, H. R&D productivity and the organization of cluster policy: an empirical evaluation of the Industrial Cluster Project in Japan. J Technol Transf 36, 117–144 (2011). https://doi.org/10.1007/s10961-009-9148-9
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DOI: https://doi.org/10.1007/s10961-009-9148-9