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

Dongwoo Kang, Sandy Dall Erba

Research output: Contribution to journalArticle

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)125-157
Number of pages33
JournalJournal of Geographical Systems
Volume18
Issue number2
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

    Fingerprint

Keywords

  • Knowledge production function
  • Knowledge spillovers
  • Mixed geographically weighted regression (MGWR)
  • Spatial heterogeneity

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this