by Pei Yu, Wuhan University of Technology, China - Jean-Louis Mucchielli, Sorbonne Center of Economics, University Paris 1 Panthéon

With acceleration of globalization, and by considering each location’s comparative advantages based on global value chain, Multinational Corporations (MNCs) tend to divide their different function units across the world, in order to maximize profits. Offshore R&D has become a topic with growing importance.

Traditionally, MNC’s innovation units were concentrated in the developed world, and nevertheless, they have increasingly shifted towards developing countries, such as China, in recent 10 years (Bruche 2009, Liu and Chen 2012).

Being different from production plants, MNC’s R&D units have specific “cluster” characteristics. Firstly, R&D unit is usually the last step of globalization and follows the production unit with a certain time-lag (Kumar 2001, Cantwell and Iammarino 2000, Cantwell and Piscitello 2002, 2005). In this case, R&D mostly tends to support local demand and Marshallian “industry-cluster” spillover effects have influences on MNC’s R&D location choice. Secondly, firm’s R&D is an urban phenomenon, and most R&D activities are concentrated in cities (Sheamur 2012). “Knowledge capital model” raised by Markusen (2002) predicts knowledge intensive activities may concentrate in small skills-intensive economies, and in this case, local innovative milieu (Camagni 1991), or what we call “knowledge cluster” , such as innovation capacity of local universities or institutes, forms firm-university linkages (Buckley and Ghauri 2004, Cantwell and Narula 2001, Park 2010).

The above mentioned R&D’s specific “cluster” characteristics may affect firm’s strategic behavior. In order to strengthen FSA, an MNC needs to benefit from clustering spillover effects, and then to gain “cluster specific advantage (CSA)”. “Cluster-seeking” strategies lead competition to upgrade from firm level to cluster level, and even to a metropolitan level.

Research on MNC’s offshore R&D mainly focus on Europe, US and Japan, while neglecting its recent expansion in China, especially quantitative studies. Therefore, as China’s increasingly integration to R&D globalization, it is urgent to improve the current research, by discovering the strategic framework of MNC’s offshore R&D in China. Our study brings following contributions on research of R&D globalization.

Firstly, we underline CSA rather than FSA which has been initiated by Hymer (1976) long time ago. By establishing a CSA-based conceptual framework, we discuss: what factors drive the location choice of MNCs’ R&D activities in a developing country?

Secondly, different from previous research focusing on Triad R&D flows (US, Europe and Japan), we apply our CSA framework by comparing offshore R&D location strategies in China, conducted by US and European MNCs. We analyze: Do US and European R&D affiliates conduct different “cluster-seeking” strategies when locating across China?

Thirdly, unlike previous case studies or those based on regional level, we use firm level data and quantitative methods, by taking Chinese city as a basic location unit, which takes in account regional heterogeneity within China and avoids aggregation bias.

We conduct discrete choice model to test the “cluster-seeking” strategies conducted by 275 US and 207 European R&D affiliates located China over the period 1992-2012, across 27 cities. Our main findings confirm MNC’s cross-country differences in clustering strategies: US firms adopt “knowledge cluster-seeking” strategy, namely, host knowledge  resources and technology infrastructure are important location determinants. Nevertheless, European firms prefer to seek “industrial cluster”. A city’s manufacturing base, industrial specialization and intra-firm forward linkage significantly affect their location choices. We also identify firm’s heterogeneity in scale and European country of origin. For instance, German R&D units pay much more attention on a city’s knowledge resources than other European firms, while French firms conduct stronger home agglomeration effects.

Acknowledgment
This research receives financial support from P.R. China’s 2013 National Social Science Foundation (Young Researchers’ Program:No.13CJY052) and the Fundamental Research Funds for the Central Universities (WUT: 2015VI003).

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