This paper studies the effects of spatial concentration of innovation activity on local production of patents in the US. In doing so, we augment the standard knowledge production function with a structure that allows for spatial effects, accounting along with bilateral also for multilateral influences across states. Our findings corroborate with past evidence on the important role of state’s own R&D stock and human capital in producing new inventions. In addition, external knowledge, via spatial interactions, is also a purveyor of local innovation production. The effect is stronger when we consider spatial influences from all states, in particular from the most innovative ones, and to a lesser extent from close neighboring states. Finally, spillovers are more likely to occur between states with similar technological specialization, which share common technological knowledge and pour similar technological effort.
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For more specifications of weight matrices, see Anselin et al. (1996).
The potential endogeneity bias arises because, while some states’ innovation performance has an impact on state’s i innovation activity, state i’s activity may also have reverse impact on other states’ innovation activity.
Alternative error term structures are the spatial error component model, e j = λΣ i≠j w ij ξ i +υ j , and the spatial moving average model, e j = λΣ i≠j w ij e i +υ j , as discussed in Deltas and Karkalakos (2003).
The level of technological capability of a region is often proxied in the literature (Peri 2005) by the level of R&D activity and human capital (number of researchers). According to innovation-driven models of growth (Grossman and Helpman 1991; Aghion and Howitt 1997), R&D stimulates innovation and facilitates the imitation of others’ discoveries. Apart from contributing directly to invention, human capital also accounts for aspects of innovation not captured by the R&D sector, including ‘learning-by-doing’ and ‘on-the-job-training’ (Romer 1989; Redding 1996).
Structural proximity between two states is measured as in Jaffe (1986). We first classify each patent, according to their primary US Classification, in one of the 37 technology fields, as defined in Hall and Ziedonis (2001). Then, for each state, we create a patent profile by taking the vector of shares of patents issued in technology field, Sh i = (sh i1,sh i2, ... , sh i37, for a given year.
The database is available at: http://sites.google.com/site/patentdataproject.
Data extracted from the National Science Foundation database is given biannually. We use STATA’s interpolation methods to fill in the gaps.
Following the literature, we have tried different depreciation percentages, e.g., 15 %, and 20 %. The resulted R&D stocks are highly correlated.
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We are grateful to George Dellas and to an anonymous referee for useful comments. Kyriakos Drivas gratefully acknowledges financial support from the National Strategic Reference Framework No: SH1_4083. The usual disclaimer applies.
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Drivas, K., Economidou, C. & Karkalakos, S. Spatial Aspects of Innovation Activity in the US. J Knowl Econ 5, 464–480 (2014). https://doi.org/10.1007/s13132-014-0198-3
- Knowledge production