Abstract
Knowledge spillovers are critical for innovation and new value creation in an increasingly knowledge-intensive economy. The substantial scholarly attention on knowledge spillovers has shown that there is a rapid distance decay associated with knowledge spillovers and that there is a positive state border effect. We show that the effects of distance, technology proximity, and the state border effect on knowledge flows are dependent on the size of the regions (MSAs) involved in the knowledge flow. Not accounting for innovation size (innovative communities and social relationships) in the flow of knowledge across origin-destination regions results in aggregation bias in the parameter estimates. Knowledge spillovers are more localized for small innovation MSAs than for large ones. Distance is not as much of a resistance factor in knowledge flow for larger innovation metro areas compared with smaller regions. Spatial origin and destination effects due to technology compatibility of neighboring regions do not affect the knowledge flow among large innovation MSAs, but do have an effect when small MSAs interact with large MSAs.
Highlights
1.1 ObjectiveThe knowledge flow between citing and cited patents has been used extensively to test for knowledge spillovers since the availability of the NBER Patent Citations Data File (Hall et al, 2001)
These results demonstrate that when considering the effect of physical distance on knowledge transfers, metropolitan statistical area (MSA) innovation size must be taken into consideration
The results for the sub-samples split by number of patents confirm our hypothesis that the geographic and technology resistance factors are less for knowledge flows between pairs of metropolitan areas that are both large and greater for pairs of MSAs that are both small
Summary
The knowledge flow between citing and cited patents has been used extensively to test for knowledge spillovers since the availability of the NBER Patent Citations Data File (Hall et al, 2001). Papers that study knowledge flows between regions using patent citation data employing a spatial interaction regression model include Maurseth and Verspagen (2002), Singh et al (2010), Li (2014), Griffith et al (2007), LeSage et al (2007), Peri (2005), Hussler (2004), Fischer et al (2009), and Mukherji and Silberman (2013) We extend this literature by estimating knowledge flows separately for groups of metropolitan areas based on their innovative activity (alternatively called size) as measured by the number of patents issued to the area. Technology compatibility has a much stronger impact on knowledge flow for the origin-destination pairs that are small innovative regions compared with the origin-destination pairs that are large innovative metro areas. These results should be considered when formulating public policy to promote innovation and research
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