We are in the process of updating the DRUM statistics and the number of downloads reported in DRUM records only reflects downloads from June 2014 to the present. The previous numbers have not been lost and we are in the process adding them to the total. Please contact firstname.lastname@example.org if you have any questions.
NETWORK MODELS OF REGIONAL INNOVATION CLUSTERS AND THEIR IMPACT ON ECONOMIC GROWTH
Dempwolf, Christopher Scott
MetadataShow full item record
This research uses social network analysis to develop models of regional innovation clusters using data from patent applications and other sources. These new models are more detailed than current industry cluster models, and they reveal actual and potential relationships among firms that industry cluster models cannot. The network models can identify specific clusters of firms with high potential for manufacturing job growth where business retention and expansion efforts may be targeted. They can also identify dense clusters of talent where innovation and entrepreneurial efforts may be targeted. Finally, this research measures relationships between network structure at the time of patent application and manufacturing job growth in subsequent years. This will permit the translation of a wide range of network-building activities into the ubiquitous "jobs created" metric. These new tools will help economic developers focus resources on high-yield activities, and measure the results of networking activities more effectively. There are three parts to this research. First, it evaluates the uses of social network analysis (SNA) in planning, reviewing the literature and empirical research where SNA has been used in planning related studies. Second, it presents the construction if innovation network models, covering methodology, data, results and direct applications of the network models themselves. Models are constructed for Pennsylvania between 1990 and 2007. The methodology presents a significant innovation in how networks and geography are modeled, embedding counties in the network as place nodes. The resulting network models more accurately reflect the complex and multiple relationships that firms and inventors have with each other and the locations where they interact. This approach makes it possible to evaluate relationships between innovation and economic growth at a smaller geographic level (counties) than previous research. Third, this research presents an econometric model that evaluates the influence of network structure on county-level manufacturing employment and value added. Network structure is measured in the year of patent application, with manufacturing employment and value added being measured annually for each subsequent year. Differences in network structure generally reflect differences in the level of social capital embedded in different parts of the network. I find that network structure influences manufacturing employment within three years (longer for medical devices and pharmaceuticals) but does not influence value added.