Knowledge, learning, and innovation are vital elements in facilitating economic development and growth. Technological change, which is a synonym for generating knowledge, the diffusion thereof, and subsequent application in the marketplace in the form of novel products and processes, i.e. innovations, has a strong effect on the collective wealth of regions and nations. Knowledge spillovers, which are unintended knowledge flows that take place among spatial (geography) and sectoral (industry) units of observation, provide a rationale for diverging growth rates among spatial units, well beyond what might be explained by variations in jurisdictional factor endowments, and thus are of particular interest in this context. Measuring and quantifying the creation and diffusion of knowledge has proven to be a challenging endeavor. One way to capture technical and economically valuable knowledge is by means of patent and patent citation analysis. Following this approach, and utilizing a novel patent database that has been specifically developed for this purpose, the present dissertation investigates the spatio-sectoral patterns of knowledge spillovers in the Canadian economy over the time period 1983 to 2007. The employed research methodology addresses existing limitations in this stream of research, and contributes to the continuing debate regarding the significance of sectoral specialization versus diversity, and local versus non-local knowledge spillovers as the main driver of knowledge formation processes leading to innovation at the sub-regional scale. The findings indicate that knowledge spillovers are localized, and furthermore, that this localization effect has increased over time for both spillovers within a particular industry, as well as between industry sectors. The analysis of micro-geographic industry specific spatio-sectoral knowledge formation processes, and the inquiry into local sectoral knowledge spillover patterns, outlines how regional evolutionary technology trajectories potentially shape the rate and direction of technological change, and consequently influence economic growth, at a particular place.

Knowledge, learning, and innovation are vital elements in facilitating economic development and growth. Technological change, which is a synonym for generating knowledge, the diffusion thereof, and subsequent application in the marketplace in the form of novel products and processes, i.e. innovations, has a strong effect on the collective wealth of regions and nations. Knowledge spillovers, which are unintended knowledge flows that take place among spatial (geography) and sectoral (industry) units of observation, provide a rationale for diverging growth rates among spatial units, well beyond what might be explained by variations in jurisdictional factor endowments, and thus are of particular interest in this context. Measuring and quantifying the creation and diffusion of knowledge has proven to be a challenging endeavor. One way to capture technical and economically valuable knowledge is by means of patent and patent citation analysis. Following this approach, and utilizing a novel patent database that has been specifically developed for this purpose, the present dissertation investigates the spatio-sectoral patterns of knowledge spillovers in the Canadian economy over the time period 1983 to 2007. The employed research methodology addresses existing limitations in this stream of research, and contributes to the continuing debate regarding the significance of sectoral specialization versus diversity, and local versus non-local knowledge spillovers as the main driver of knowledge formation processes leading to innovation at the sub-regional scale. The findings indicate that knowledge spillovers are localized, and furthermore, that this localization effect has increased over time for both spillovers within a particular industry, as well as between industry sectors. The analysis of micro-geographic industry specific spatio-sectoral knowledge formation processes, and the inquiry into local sectoral knowledge spillover patterns, outlines how regional evolutionary technology trajectories potentially shape the rate and direction of technological change, and consequently influence economic growth, at a particular place.

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.

The thesis examines impact of the institutional capital, which captures the ability to produce quality patents, on the number of applied patent applications after the adoption of new governmental system of research and development evaluation. For determination of the institutional capital I use similar approach as Turnovec (2005) in case of publications. The estimation of the institutional capital is based on the evaluation of patents received by publicly financed research institutions. The evaluation of patents is based on the analysis of patent family and patent citation data. The data are analyzed using methods of multi-criteria decision making. Further it is estimated that the institutional capital does not influence the number of applied patent applications.