TY - JOUR
T1 - Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function
T2 - a mixed GWR approach
AU - Kang, Dongwoo
AU - Dall’erba, Sandy
N1 - Funding Information:
This study was supported by the National Science Foundation Grant (SMA-1158172). Any opinions, findings and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - Knowledge production function
KW - Knowledge spillovers
KW - Mixed geographically weighted regression (MGWR)
KW - Spatial heterogeneity
UR - http://www.scopus.com/inward/record.url?scp=84961655007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961655007&partnerID=8YFLogxK
U2 - 10.1007/s10109-016-0228-8
DO - 10.1007/s10109-016-0228-8
M3 - Article
AN - SCOPUS:84961655007
SN - 1435-5930
VL - 18
SP - 125
EP - 157
JO - Journal of Geographical Systems
JF - Journal of Geographical Systems
IS - 2
ER -