Technological spillovers and industrial growth in Chinese regions

The Impact of Coastal FDI on Inland Economic Growth in China

“…Ouyang and Fu (2012) use city-level data for 1996-2004 and find that FDI in coastal regions has positive inter-regional spillovers on the economic growth of inland regions. In contrast, using provincial industry-level data for the period 1990, Wang et al (2013 find that inter-regional FDI spillovers are consistently negative-that is, foreign investment in one region attracts resources away from regions with less FDI, and thus has a negative influence on the growth of industrial output in neighbouring regions. Additional study of the impact of FDI on inter-regional knowledge spillovers is evidently required in order to better understand its role in the economic growth of host countries.…”

Exploring the spatial dimensions of nanotechnology development in China: the effects of funding and spillovers

“…In our framework, interregional spillovers can stem from two sources: nanotechnology-related knowledge of regions measured by patents in this technological area; and the funding for nanotechnology research received by regions. Prior research has shown that R&D expenditure, used as a proxy for innovation activities, carried out in one location can generate spillovers in others through a variety of channels, such as proximity (Funke & Niebuhr, 2005;Wang, Meijers, et al, 2017) and collaboration networks (Fritsch & Franke, 2004;Jaffe, 1989). From this perspective, we suggest that funding, which represents subsidies provided for augmenting R&D expenditure on nanotechnology, can generate spillovers of knowledge to other locations.…”

“…To capture the former, this study uses the geographical proximity between regions to construct a set of spillover variables. The spatial proximity weight to capture spillovers from region j to region i can be expressed in three different ways depending on the different underlying assumptions (see also Ertur, Le Gallo, & Baumont, 2006;Ponds et al, 2010;Vinciguerra, Frenken, Hoekman, & Van Oort, 2011;Wang, Meijers, et al, 2017). Compared with the two other types of proximity weights (see Note A1 in Appendix A in the supplemental data online), the column standardization of the weight matrixes is considered more appropriate (Ponds et al, 2010).…”

“…Compared with the two other types of proximity weights (see Note A1 in Appendix A in the supplemental data online), the column standardization of the weight matrixes is considered more appropriate (Ponds et al, 2010). Following the existing literature (e.g., Ertur et al, 2006;Vinciguerra et al, 2011;Wang, Meijers, et al, 2017), this study adopts spatial proximity weights constructed through column standardization:…”

“…Geographical proximity facilitates spillovers of knowledge because, as knowledge is intrinsically sticky (Winter, 1987), it permits close and repeated interactions between actors, allowing them to understand, identify and integrate relevant knowledge (Antonelli, 2001;Arundel & Guena, 2004;Boschma, 2005;Breschi & Lissoni, 2009;Jaffe, 1989;Verspagen & Schoenmakers, 2004). Confirming this, a vast body of empirical research finds that geographical distance is a barrier to knowledge spillovers (Breschi & Lissoni, 2009;Verspagen & Schoenmakers, 2004;Wang, Meijers, & Szirmai, 2017). From the point of view of peripheral regions, particularly those located very far from centres of excellence, a greater degree of agglomeration economies implies lower prospects of benefitting from the technological advancements of core regions, causing them potentially to fall behind on the technological ladder.…”