Seite - (000332) - in Knowledge and Networks

330 than other municipalities. Moreover, there is some indication that population size and a young population are positively correlated with being a learning hub. However, these findings are sensitive to the choice of hub measure. For instance, population size is not related to closeness centrality (Hub B) and average age of the municipal citizen is basically not related to betweenness centrality (Hub C). The other back- ground variables included in the analysis do not correlate with the hub measures. The main conclusion from Table 15.6 is that county seats are important. One way to further investigate this is to examine the E-I index for county seats (using the Girvan-Newman clusters as partitions). Table 15.7 shows the results. The mean E-I index for all municipalities is -.345, indicating that the ties of most municipalities are local (e.g., within county). By contrast, the average E-I index for county seats is .130. This means that in contrast with the localism of most municipalities, county seats have on balance more external than internal ties. However, there is also varia- tion among the county seats. For example, the municipalities of Nyköping and Falun are quite insular, whereas Malmö, Visby, and Örebro are quite cosmopolitan. Taken together, the evidence clearly suggests that county seats are acting as hubs in the learning network of Swedish municipalities. This conclusion is reinforced by looking at the network connecting county seats (Fig. 15.4; note that the figure roughly organizes the county seats geographically). Nyköping and Falun are iso- lates, but the rest of the county seats are linked together. Conclusion The purpose of this analysis was to better understand how a global learning network emerges from the local learning choices of autonomous Swedish municipalities. We found that the county is a basic structuring property of the global network. Municipalities learn from their near neighbors, especially from neighbors in the Table 15.5 Descriptive statistics: Swedish municipalities Mean Min Max Hub A (indegree × outdegree) 45.3 0.0 280.0 Hub B (integration × radiality) 49.9 0.0 70.4 Hub C (betweenness centrality) 907.5 0.0 11133.4 Log population (number of inhabitants) 2.9 0.9 6.7 Inhabitants/km2 135.0 0.2 4410.4 County seat (1 = Yes; 0 = No) 0.1 0 1 Unemployment rate (percent) 6.3 1.8 13.8 Tax base (SEK/citizen) 155,642.1 125,829.0 300,491.0 Population characteristics Mean age (in years) 42.8 36.3 48.5 College degree (percent of population) 13.0 6.6 43.8 C. Ansell et al.