Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach

Dongwoo Kang () and Sandy Dall’erba ()
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Dongwoo Kang: Korea Labor Institute
Sandy Dall’erba: University of Illinois at Urbana-Champaign
Authors registered in the RePEc Author Service: Sandy Dall'erba ()

Journal of Geographical Systems, 2016, vol. 18, issue 2, No 2, 125-157

Abstract: Abstract Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888 ) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.

Keywords: Knowledge production function; Knowledge spillovers; Spatial heterogeneity; Mixed geographically weighted regression (MGWR) (search for similar items in EconPapers)
JEL-codes: C21 O31 R11 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10109-016-0228-8

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