Page - (000330) - in Knowledge and Networks

328 better than the others are not well articulated in the small-world literature. We there- fore approach hub identification ecumenically by looking for municipalities that score well on all three measures. To get a sense of how well hubs are spread out geographically, Fig. 15.3 depicts the percentage of municipalities over the 75th percentile on each hub measure within each county. The pattern is quite robust to the selection of measure, although there is some obvious variation. Västra Götland and Halland, two neighboring coun- ties in western Sweden, have a substantially larger share of cosmopolitan munici- palities than other counties. At the other extreme, we find Gotland and Kronoberg. In most other counties, around 10–30 % of the municipalities have a clear transit point character. Overall, the impression is that the hubs are fairly evenly distributed geographically, but that some counties depart from this general pattern. Above, in Fig. 15.2, we discovered a tight network in western Sweden consisting of the members of the Göteborg Region Association of Local Authorities. Many of the municipalities belonging to this association also score high on the three hub measures. If we once again use the 75th percentile to separate out more cosmopoli- tan cities, 54 % of the Associations’ members are hubs if we focus on the most basic hub measure (Hub A). Using the other two hub measures yields 85 % (Hub B) and 31 % (Hub C), respectively.10 At first glance, at least, this region has done an impres- sive job of promoting regional cooperation and diffusion of information. However, it is of course also possible that the association was formed around municipalities already having a lot of cooperation. If this is true, the causality runs in the opposite direction. In order to find out more about what characterizes hubs as transit points, we explore the correlation between the three hub measures introduced above and vari- ous municipal characteristics. A first idea is that county seats and larger or more urban municipalities could be important. Such cities might function as regional cen- ters where large companies, authorities, universities, and other organizations are located and where infrastructure is more developed. These features might increase the probability that knowledge diffuses through such municipalities. A second idea is that healthy fiscal conditions or a favorable economic climate might characterize the transit points. Cities like this are perhaps more innovative and outward-looking. Lastly, features of the local population could be important—perhaps transit points have a younger and more educated population? Table 15.5 provides descriptive statistics of the variables of interest. Log popula- tion, inhabitants/km2 and whether the municipality is a county seat or not are the variables designed to capture the importance of being a large and urban area. Economic climate and fiscal conditions are measured by the unemployment rate and the tax base (in Swedish crowns per inhabitant). Mean age in years and the percent- age of the population having a college degree are assumed to capture potentially important citizen characteristics. Table 15.5 displays large variation in all variables. To find out which factors are correlated with high scores on the 10 If hubs were randomly distributed, we would have expected 25 % of the hubs in the Göteborg Region Association of Local Authorities to be hubs. C. Ansell et al.