Careers and clusters: analyzing the career network dynamic of biotechnology clusters

Access to a pool of talented employees is an important element of entrepreneurial firms’ ability to build innovative capabilities. Through an empirical examination of two European biotechnology clusters – Cambridge, UK, and Munich, Germany – we investigate the degree to which macro-labor market institutions shape the micro-dynamics of career affiliation networks between scientific employees. Using bibliometric methods to trace careers and a series of social network analysis methods, we examine similarities and differences in career network dynamics across the two clusters. In particular, we investigate whether patterns of long-term employment within most German large firms, as opposed to more short-term employment in the United Kingdom, affects network structure, network performance and network composition in the two clusters. We show that contrary to the expectations of comparative institutional theory, network structures are grossly similar across the two clusters and, moreover, the performance of these networks as measured by ‘‘small-world’’ methods are similar; career affiliation networks in the two regions are formed through social interactions that appear largely unrelated to macro-institutional factors. Where the macro-institutional forces are effective is as a gatekeeper to network composition: the Cambridge network contains a roughly equal mix of scientists with recent industry and scientific experience, whereas the Munich network is populated almost entirely by academic scientists with no prior industrial experience. # 2004 Published by Elsevier B.V.