Making agglomeration ‘metabolised’ for innovation

Nobuaki Hamaguchi, Keisuke Kondo

07 February 2016

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Innovation is an important driver of economic growth. In particular, to acquire global competitiveness, the quality of innovation matters more than the quantity. Although innovative outcomes rest on individual efforts in research and development in firms and scientific organisations, economic research has also paid special attention to the agglomeration economy, which is expected to foster innovation through active knowledge spillovers (e.g. Carlino and Kerr 2015).

It is more likely that high-quality innovations are born in cities. The large number of specialised people in cities is not the only reason for such advantage – the greater diversity of knowledge also matters. It is often pointed out that proximity to a greater number of people facilitates face-to-face communication and fosters innovation. However, as analysed by Berliant and Fujita (2012), repeated interactions increase common knowledge and reduce knowledge diversity across workers, which limits opportunities for learning fresh ideas from each other. In fact, Huber (2012) indicates that technological knowledge spillover effects within the Cambridge Information Technology Cluster are very weak. In that sense, the effect of agglomeration on innovation is not sustainable just because an industrial cluster is established.

In this regard, we need to take a new look at the measurement of agglomeration economies to analyse their effects on innovation. Beside the size, we need to take into account how well the knowledge diversity is maintained. Concerning the latter, an attempt of our study (Hamaguchi and Kondo 2015) is to examine the effects of knowledge turnover on the quality of innovation.

How can we capture knowledge turnover in the real world? Our empirical strategy is to use interregional migration of university graduates. Thus, we examine whether patents invented in regions with bigger migration of university graduates have more citations after controlling for agglomeration, human capital, and industrial diversity.

Interregional migration and knowledge turnover ‘metabolised’ for innovation

The account of migration resembles the metabolism of the human body, which is the basis for a sound mind and ideas in a sound body. In other words, a metabolised agglomeration is supportive of innovation.

There are difficulties in measuring knowledge turnover in the real world. It might be measured by workers’ flows at the firm or establishment level. In this study, we consider knowledge turnover in a broader context to capture changes in human relationships. We would like to incorporate broader effects arisen from them such that even non-labour force would affect the invention process outside of firms.

Our idea is motivated by Faggian and McCann (2009), who criticise the existing literature on geography of innovation and mention that it tends to ignore the role played by the mobility of human capital. Their analysis demonstrates the statistically positive significance of university graduate human capital inflows on regional innovation performance.

Note that knowledge turnover differs from the common measure of diversity. The inverse of the Herfindahl-Hirschman index is often used as a diversity measure. However, it cannot capture a dynamic change arising at the individual level. For example, the commonly used diversity index is unchanged if migrants have the same characteristics (e.g. gender, age, education level, and occupation). However, interregional migration will generate a big impact on knowledge diversity if individuals have unobserved heterogeneous characteristics. Thus, we would like to capture changes in knowledge diversity arisen from interregional turnover of people under the condition in which individuals are heterogeneous.

Positive effects of knowledge turnover on quality of innovation

We empirically investigate whether interregional knowledge turnover has a positive impact on the quality of innovation. Our study uses the Japanese patent database of the Institute of Intellectual Property, which contains information on patent citation and inventors.1 We measure the quality of innovation by the number of forward patent citations by examiners. Inventors’ addresses are used to link regional characteristics with regions where inventions were created. Interregional migration of university graduates is calculated from the population census.

Figure 1 presents the relationship between the number of patent citations and interregional migration flows of university graduates (the sum of in- and out-migrations). Panels (a) and (b) of Figure 1 show a positive correlation between them in both 1980 and 2000. However, we should note that not all patents invented in regions with bigger knowledge turnover have a greater number of citations. There are also a large number of patents that have no citation in regions with bigger knowledge turnover. On the other hand, frequently cited patents are hardly observed in regions with smaller knowledge turnover.

Figure 1. Number of patent citations and gross migration flows

(a) 1980 (application year)                                                              (b) 2000 (application year)

Source: Hamaguchi and Kondo (2015)

The regression analysis also confirms a positive relationship between the number of patent citations and interregional knowledge turnover, even after controlling for other factors. More importantly, we find that agglomerated regions with active knowledge turnover tend to have a higher number of patent citations. Our results suggest that making agglomeration metabolised increases the quality of innovation.

Important messages for innovation policy

A new innovation strategy must be discussed going forward beyond short-term benefits from agglomeration. We need to know that the agglomeration economy reaches a mature stage (e.g. Japan is currently facing population decline and some OECD countries also will face it in the coming decades). The important question is how we can build a sustainable innovation system in a whole nation.

  • Our empirical findings suggest that industrial cluster policy aiming at active innovation does not necessarily work well if interregional migration of knowledge workers is inactive.

Urban policymakers should consider how to make agglomeration metabolised in order to incorporate fresh knowledge from outside cities. Although it is often considered that rural areas have difficulties in enjoying agglomeration benefits for innovation, our empirical findings shed light on the fact that rural industrial clusters also have opportunities for high-quality innovation through active knowledge workers’ mobility.

  • Thus, an important view for industrial cluster policy is mutual cooperation between urban and rural policymakers to facilitate interregional migration without burden, which will make the innovation system sustainable in the long run.

References

Berliant, M and M Fujita (2012), “Culture and Diversity in Knowledge Creation,” Regional Science and Urban Economics 42(4), pp. 648–662.

Carlino, G and W R Kerr (2015), “Agglomeration and Innovation,” in Duranton, G., Henderson, J. V. and Strange, W. C. eds. Handbook of Regional and Urban Economics Vol. 5, Amsterdam: Elsevier, Chap. 6, pp. 349–404.

Faggian, A and P McCann (2009), “Human Capital, Graduate Migration and Innovation in British Regions,” Cambridge Journal of Economics 33(2), pp. 317–333.

Hamaguchi, N and K Kondo (2015), “Fresh Brain Power and Quality of Innovation in Cities: Evidence from the Japanese Patent Database” RIETI Discussion Paper Series, 15-E-108

Huber, F (2012), “Do Clusters Really Matter for Innovation Practices in Information Technology? Questioning the Significance of Technological Knowledge Spillovers,” Journal of Economic Geography 12(1), pp. 107–126.

Footnote

1 See the web page of the Institute of Intellectual Property (URL: http://www.iip.or.jp/e/e_patentdb/)

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Topics:  Industrial organisation Productivity and Innovation

Tags:  innovation, knowledge turnover, agglomerations, interregional migration, patents

Faculty fellow, RIETI; professor at RIBE, Kobe University

Fellow, RIETI; junior research fellow at RIEB, Kobe University

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