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

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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.

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Notes

  1. We do not model the spillovers of the remaining covariates because, to the best of our knowledge, there is no theoretical foundation to do so. We thank an anonymous referee for raising this point.

  2. We thank an anonymous reviewer for the suggestion of using the number of nearest neighbors chosen by the GWR methodology to set up the flexible spatial extent of local knowledge spillovers across counties.

  3. We do not estimate the distance decay parameter as several previous contributions have done (Burridge and Gordon 1981; Halleck Vega and Elhorst 2015; Pace et al. 1998) nor try to maximize the model’s likelihood based on pre-defined values (as in Kang and Dall’erba 2015) because it would generate different weighting schemes for the local versus distant spillovers thus making them difficult to compare. However, it is an interesting venue for future research as, to our knowledge, parameterizing the distance decay factor has never been done in a local framework like ours.

  4. We thank an anonymous reviewer for suggesting us to try out various kernel functions and distance cut-offs in the definition of the spatial extent of localized spillovers in order to find the best model specification.

  5. The GWR and MGWR calibration results with the other kernel functions are available upon request.

  6. The map of MGWR coefficients of local private knowledge spillovers based on 50 and 75 mile cut-offs is available upon request.

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Acknowledgments

We would like to thank the Editor-in-Chief Dr. Manfred M. Fischer and two anonymous reviewers for their helpful comments and suggestions.

Grant

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.

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  1. Korea Labor Institute, Sejong National Research Complex, 370, Sicheong-daero, Sejong-si, 30147, Korea

    Dongwoo Kang

  2. Department of Agricultural and Consumer Economics and Regional Economics Applications Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Sandy Dall’erba

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Correspondence to Dongwoo Kang.

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Kang, D., Dall’erba, S. Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach. J Geogr Syst 18, 125–157 (2016). https://doi.org/10.1007/s10109-016-0228-8

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