Quantifying Knowledge Spillovers Using Spatial Econometric Models

Abstract

This paper seeks to develop our understanding of the somewhat diffuse nature of technological externalities and space by associating a geographical dimension with the sectoral dimension. Using a panel data set containing French patents as well as private and public research expenditures by industry and region over the period from 1992 to 2000, this paper estimates a knowledge production function. The region-and industry-specific nature of the sample data allows us to empirically examine spatial spillovers associated with public and private research expenditures in own-and other-industry sectors for our sample of 94 French regions. We find that the largest direct and indirect effects are associated with private R&D activity that spills across industry boundaries. However, since Jacobs externalities decrease more drastically with distance than MAR externalities, our results also point to different optimal strategies for regional versus national officials.

Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity

“…It has become a stylized fact that empirical measures of regional knowledge K such as patent applications, educational attainment, expenditures or employment in research and development etc., exhibit spatial dependence (Autant-Bernard 2001, Autant-Bernard and LeSage 2010, Parent and LeSage 2008. That is, a choropleth map of these variables used to proxy regional knowledge would show systematic clustering of high and low values of regional knowledge measures in space.…”

Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?

“…Therefore, the study follows the method generally proclaimed in the literature by introducing the data of the spatial weight matrix to verify the spatial autocorrelation of the panel data. Spatial autocorrelation can be measured through the spatial autocorrelation index Moran's I [32][33][34]; this study adopts "Local Moran' s I". The calculation formula reads as follows:…”

Does intentional mean hierarchical? Knowledge flows and innovative performance of European regions

“…The first deals with the identification and study of innovation networks (Jaffe et al 1993;Audretsch and Feldman 1996;Maurseth and Verspagen 2002;Cowan and Jonard 2003;Usai 2000, 2009;Lissoni 2004, 2009;, 2011Hoekman et al 2009;Picci 2010;Cassi and Plunket 2012;Maggioni et al , 2013; the second exploits spatial econometric techniques to account for the existence of not directly measurable (or unmeasured) spillovers effects associated with the creation of new knowledge (Audretsch and Feldman 1996;Acs et al 2002;Fischer and Varga 2003;Bottazzi and Peri 2003;Greunz 2003;Bode 2004;Moreno et al 2005;LeSage and Pace 2009;Autant-Bernad and LeSage 2010;Usai 2011;Varga et al 2010). We build on previous works (Acs et al 2002;Cowan and Jonard 2004;) which assume that knowledge can be diffused and exchanged through unintentional diffusion patterns based on spatial contiguity or intentional relations based on a-spatial networks.…”